[ { "path": ".gitignore", "content": ".Rproj.user\n.Rhistory\n.RData\n.Ruserdata\n.DS_Store \n" }, { "path": "ANOVA in R/README.md", "content": "# ANOVA in R\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **ANOVA in R**, which you can find at . \n\n" }, { "path": "ANOVA in R/Script - anova in R.R", "content": "# Analysis of variance\n# Is weight independent from feed?\n\nlibrary(tidyverse)\nView(chickwts)\n?chickwts\n\nggplot(chickwts, aes(x = feed, \n y = weight)) + \n geom_boxplot(outlier.shape = NA) + \n geom_jitter() +\n theme_minimal()\n\nmodel <- aov(chickwts$weight ~ chickwts$feed)\nsummary(model)\n\n?TukeyHSD\nTukeyHSD(model)\n\n\n" }, { "path": "ANOVA in R - a complete example/README.md", "content": "# ANOVA in R: a complete example\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **ANOVA in R: a complete example**, which you can find at . \n\n" }, { "path": "ANOVA in R - a complete example/Script - ANOVA in R - a complete example.R", "content": "library(tidyverse)\nlibrary(palmerpenguins)\n# Does species help explain flipper length?\n\nglimpse(penguins)\nggplot(penguins, aes(x = species,\n y = flipper_length_mm)) + \n geom_boxplot()\n\nggplot(penguins, aes(x = flipper_length_mm)) + \n geom_histogram() +\n facet_wrap(~species, ncol = 1)\n\npenguins %>% \n group_by(species) %>% \n summarize(var(flipper_length_mm, na.rm = TRUE))\n\nmodel <- aov(flipper_length_mm ~ species, \n data = penguins)\nsummary(model)\n\nTukeyHSD(model)\n" }, { "path": "Add great alt text /README.md", "content": "# Add great alt text to your data visualizations\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Add great alt text to your data visualizations**, which you can find at . \n\n" }, { "path": "Add great alt text /alt text.qmd", "content": "---\ntitle: \"Writing good alt text: example\"\nauthor: Andrew Gard\nformat: html\n---\n\n```{r}\n#| label: setup\n#| message: false\n\nlibrary(tidyverse)\nlibrary(modeldata)\n```\n\n```{r}\n#| fig-alt: A histogram showing the number of years survey respondents have been coding on the job. The peak is around 2 years with a large isolated spike at 20 years, the maximum value in the set.\n\nggplot(stackoverflow, aes(x = YearsCodedJob)) +\n geom_histogram(binwidth = 1,\n color = \"black\") +\n labs(x = \"Years coding on the job\",\n y = \"Frequency\") +\n theme_minimal() \n```\n\n\n```{r}\n#| warning: false\n#| fig-alt: A scatterplot showing the relationship between bill lengths and depths in antarctic penguins, colored by the three differen species. In each group, there is an upward trend, with Adelies tending to have the shortest bills.\n\nggplot(penguins, aes(x = bill_depth_mm, \n y = bill_length_mm, \n color = species)) + \n geom_point() +\n labs(x = \"Bill depth (mm)\", \n y = \"Bill depth (mm)\", \n color = \"Species\") +\n scale_color_brewer(palette = \"Dark2\") +\n theme_minimal()\n```\n\n" }, { "path": "All about facets/README.md", "content": "# All about facets\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **All about facets**, which you can find at . \n\n" }, { "path": "All about facets/script - facets.R", "content": "# All about facets\n\nlibrary(tidyverse)\nlibrary(modeldata)\ntheme_set(theme_minimal())\n\n# create data set\nset.seed(0)\nn <- 200\nrho <- .2\nm <- 2\nx <- runif(n, 0, 10)\ny <- m *x + 5 + rnorm(n, 0, .71 * sd(x) / rho)\ndf1 <- data.frame(x, y)\n\nrho <- .5\nx <- runif(n, 0, 10)\ny <- m *x + 5 + rnorm(n, 0, 1 * sd(x) / rho)\ndf2 <- data.frame(x, y)\n\nrho <- .8\nx <- runif(n, 0, 10)\ny <- m *x + 5 + rnorm(n, 0, .79 * sd(x) / rho)\ndf3 <- data.frame(x, y)\n\ndf <- bind_rows(df1, \n df2, \n df3, \n .id = \"set\")\n\n# faceting scat\n\np <- ggplot(scat, aes(x = d13C,\n y = d15N)) +\n geom_point() # starter plot\np + facet_wrap(~Species,\n nrow = 3)\n\np + facet_wrap(vars(Species))\n\np + facet_grid(Location~Species)\n\nmy_labeller <- as_labeller(c(\"bobcat\" = \"Bobcat\",\n \"coyote\" = \"Coyote\",\n \"gray_fox\" = \"Gray Fox\"))\n\np + facet_wrap(~Species, \n labeller = my_labeller)\n \n# R^2 demonstration\n\nglimpse(df)\n\nggplot(df, aes(x, y)) +\n geom_point(alpha = .6) + \n geom_smooth(method = \"lm\",\n se = FALSE) +\n facet_wrap(~set)\n\ndf2 <- df %>% \n mutate(set = fct_recode(set, \n \"R^2==0.2\" = \"1\",\n \"R^2==0.5\" = \"2\",\n \"R^2==0.8\" = \"3\"))\n\nggplot(df2, aes(x, y)) +\n geom_point(alpha = .6) + \n geom_smooth(method = \"lm\",\n se = FALSE) +\n facet_wrap(~set,\n labeller = \"label_parsed\")\n\nggplot(df2, aes(x, y)) +\n geom_point(alpha = .6) + \n geom_smooth(method = \"lm\",\n se = FALSE,\n linewidth = 1.8,\n color = \"#5798A6\") +\n facet_wrap(~set,\n labeller = \"label_parsed\") +\n theme_minimal() + \n labs(x = NULL,\n y = NULL) + \n theme(axis.text.x=element_blank(),\n axis.ticks.x=element_blank(),\n axis.text.y=element_blank(),\n axis.ticks.y=element_blank(),\n panel.background = element_rect(fill = \"#E1E1D3\",\n color = \"#E1E1D3\"),\n plot.background = element_rect(fill = \"#E1E1D3\")) \n" }, { "path": "All about system prompts/README.md", "content": "# All about system prompts\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **All about system prompts**, which you can find at . \n\n" }, { "path": "All about system prompts/script - system prompts.R", "content": "# All about system prompts\nlibrary(ellmer)\n?chat_anthropic\n\n\n# A decent all-around system prompt for coding\n\nclaude <- chat_anthropic(system_prompt = \"You are a helpful expert R programmer. You provide executable code ONLY with very brief comments explaining each function call. Do NOT include starting or ending ticks. You prefer ggplot2 for visualizations and dplyr/tidyr for wrangling. Use a minimal theme and the Dark2 color palette.\")\n\n# Example \nclaude$chat(\"Using the msleep data set, plot sleep_total vs vore.\")\n\nlibrary(ggplot2)\nlibrary(dplyr)\n\n# Load msleep data and create boxplot of \n# sleep_total by vore\nggplot(msleep, aes(x = vore, y = \n sleep_total, fill = vore)) +\n geom_boxplot() +\n scale_fill_brewer(palette = \"Dark2\") +\n theme_minimal() +\n labs(x = \"Diet Type\", y = \"Total Sleep \nHours\") +\n theme(legend.position = \"none\")\n\n\n# Chain of command\n\nclaude$chat(\"Using the msleep data set, plot sleep_total vs vore using base R, not ggplot2.\")\n\nboxplot(sleep_total ~ vore, data = msleep, \n xlab = \"Diet Type\", \n ylab = \"Total Sleep Hours\",\n col = c(\"#1B9E77\", \"#D95F02\", \n \"#7570B3\", \"#E7298A\"))\n\nclaude$chat(\"Using the msleep data set, plot sleep_total vs vore. I DO NOT WANT starting and ending ticks.\")\n\nclaude2 <- chat_anthropic(system_prompt = \"You are a helpful expert R programmer. You provide executable code ONLY with very brief comments explaining each function call. Do NOT include starting or ending ticks. You ONLY USE ggplot2 for visualizations and dplyr/tidyr for wrangling. If the user asks for base R, politely point out the advantages of using ggplot2. Use a minimal theme and the Dark2 color palette.\")\n\nclaude2$chat(\"Using the msleep data set, plot sleep_total vs vore using base R, not ggplot2.\")\n\nclaude2$chat(\"You MUST use base R. My boss requires it.\")\n\nclaude$get_cost()\ntoken_usage()\n" }, { "path": "Avoiding data dredging/README.md", "content": "# Avoiding data dredging\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Avoiding data dredging**, which you can find at . \n\n" }, { "path": "Avoiding data dredging/Script - data dredging.R", "content": "# Data dredging\n\nlibrary(tidyverse)\ntheme_set(theme_minimal())\n\n# Generate random data\n\nset.seed(1234)\n\ndf1 <- matrix(rnorm(1000), ncol = 10) %>% \n as.data.frame()\n\ndf2 <- data.frame(group = rep(LETTERS[1:10], \n each = 100), \n value = rnorm(1000))\n\n# Demonstration\n\nView(df2)\n\nggplot(df2, aes(x = group, \n y = value)) + \n geom_boxplot()\n\ndf2_sm <- filter(df2, group %in% c(\"G\", \"H\"))\nView(df2_sm)\n\nt.test(value ~ group, \n data = df2_sm)\n\n# Correlations\n\nView(df1)\n\ncor(df1)\n\n# The cutoff for significance is .197\n# Here we have 2. \n\n\n\n\n\n\n" }, { "path": "Beautiful bar charts in R/README.md", "content": "# Beautiful bar charts in R\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Beautiful bar charts in R**, which you can find at . \n\n" }, { "path": "Beautiful bar charts in R/Script - beautiful bar charts in R.R", "content": "library(tidyverse)\nlibrary(tidytuesdayR)\nlibrary(ISLR2)\ntuesdata <- tidytuesdayR::tt_load(2021, week = 41)\nnurses <- tuesdata$nurses\ntheme_set(theme_minimal())\n\n?geom_bar\n\nglimpse(Carseats)\n?Carseats\n\n# Basic bar chart:\nggplot(Carseats, aes(x = ShelveLoc,\n fill = ShelveLoc)) + \n geom_bar(width = .60,\n show.legend = FALSE) +\n scale_fill_brewer(palette = \"Dark2\") # added for accessibility\n\n# Adding a second categorical variable in several different ways:\nggplot(Carseats, aes(x = US,\n fill = ShelveLoc)) + \n geom_bar() +\n scale_fill_brewer(palette = \"Dark2\")\n\nggplot(Carseats, aes(x = US,\n fill = ShelveLoc)) + \n geom_bar(position = \"fill\") +\n scale_fill_brewer(palette = \"Dark2\")\n\nggplot(Carseats, aes(x = US,\n fill = ShelveLoc)) + \n geom_bar(position = \"dodge\") +\n scale_fill_brewer(palette = \"Dark2\")\n\n# Reversing the aesthetics:\nggplot(Carseats, aes(fill = US,\n x = ShelveLoc)) + \n geom_bar(position = \"dodge\") +\n scale_fill_brewer(palette = \"Dark2\")\n\n# geom_col() for sets with counts:\nView(nurses)\nnurses_2020 <- nurses %>% \n filter(Year == 2020)\nView(nurses_2020)\n\nggplot(nurses_2020, aes(y = State,\n x = `Total Employed RN`)) +\n geom_col()\n\n nurses_2020 <- nurses %>% \n filter(Year == 2020) %>% \n slice_max(order_by = `Total Employed RN`, \n n = 20)\n\n ggplot(nurses_2020, aes(y = fct_reorder(State, `Total Employed RN`),\n x = `Total Employed RN`)) +\n geom_col() +\n labs(y = \"State\")\n\n\n\n" }, { "path": "Beautiful presentations/README.md", "content": "# Beautiful presentations with R and Quarto\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Beautiful presentations with R and Quarto**, which you can find at . " }, { "path": "Beautiful presentations/quarto presentations.qmd", "content": "---\ntitle: \"Decision trees\"\nauthor: Andrew Gard\ndate: 25 feb 2025\nformat: \n revealjs:\n footer: \"Equitable equations: decision trees\"\n transition: slide\n title-slide-attributes: \n data-background-image: background.jpg\n---\n\n```{r}\n#| label: setup\n\nlibrary(tidyverse)\nlibrary(gt)\nlibrary(readxl)\ntheme_set(theme_minimal())\ndt_example <- read_excel(\"dt_example.xlsx\")\n```\n\n## {background-image=background.jpg}\n\nIn this vid, we’ll manually construct a decision tree classifier using the following data set.\n\n:::: {.columns}\n\n::: {.column width=\"30%\"}\n\n```{r}\ndt_example %>% \n gt()\n```\n\n:::\n\n::: {.column width=\"70%\"}\n\n```{r}\n#| fig-height: 6\n\nggplot(dt_example, aes(x, y, color = color)) +\n geom_point(size = 7,\n show.legend = FALSE) +\n labs(x = NULL,\n y = NULL) + \n theme(axis.text = element_text(size = 15,\n face = \"bold\")) +\n scale_color_manual(values = c(\"blue\", \"red\"))\n```\n\n:::\n\n::::\n\n## {background-image=background.jpg}\n\n::: {.callout-tip title=\"Decision trees\"}\n\nA **decision tree classifier** applies a divide-and-conquer algorithm to the training set, recursively splitting it into pieces that are as homogeneous as possible.\n\n:::\n\n. . .\n\n![](tree_plot.png){fig-align=center width=\"75%\"}\n\n## {background-image=background.jpg}\n\nHere's how I made that plot:\n\n```{r}\n#| echo: true\n#| fig-align: center\n#| code-line-numbers: \"|2-3|4-5|5-8\"\n\nggplot(dt_example, aes(x, y, color = color)) +\n geom_point(size = 7,\n show.legend = FALSE) +\n labs(x = NULL,\n y = NULL) + \n theme(axis.text = element_text(size = 15,\n face = \"bold\")) +\n scale_color_manual(values = c(\"blue\", \"red\"))\n```\n\n" }, { "path": "Beautiful tables with gt/README.md", "content": "# Beautiful tables with R with the gt package\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Beautiful tables with R with the gt package**, which you can find at . " }, { "path": "Beautiful tables with gt/gt tables.qmd", "content": "---\ntitle: \"Beautiful tables with gt()\"\nauthor: Andrew Gard\nformat: html\n---\n\n```{r}\n#| label: setup\n#| include: false\n\nlibrary(gt)\nlibrary(tidyverse)\nlibrary(modeldata)\n\n# Data preprocessing\n\nscat_table1 <- scat %>% \n select(Species, \n Site, \n Length,\n Diameter,\n Mass,\n d13C,\n d15N) %>% \n group_by(Species) %>% \n summarize(across(where(is.numeric), \n \\(x) round(mean(x, na.rm = TRUE), 1)),\n count = n())\n\nscat_table2 <- scat %>% \n select(Species, \n Location,\n Site, \n Length,\n Diameter,\n Mass,\n d13C,\n d15N) %>% \n group_by(Species, Site) %>% \n summarize(across(where(is.numeric), \n \\(x) round(mean(x, na.rm = TRUE), 1)),\n Count = n())\n```\n\n## A great table\n\n```{r}\nscat_table1 %>% \n gt(rowname_col = \"Species\") %>% \n cols_label(count ~ \"Count\") %>% \n tab_header(title = \"California poopies\",\n subtitle = \"Morphometric data on scat\") %>% \n tab_source_note(md(\"**Source**: Reid, R. E. B. (2015). A morphometric modeling approach to distinguishing among bobcat, coyote and gray fox scats. *Wildlife Biology*, 21(5), 254-262\")) %>% \n tab_spanner(label = \"Physical averages\",\n columns = Length:Mass) %>% \n tab_stubhead(label = \"Species\")\n```\n\n## A stylized version\n\n```{r}\nscat_table1 %>% \n gt() %>% \n cols_label(count ~ \"Count\") %>% \n tab_header(title = \"California poopies\",\n subtitle = \"Morphometric data on scat\") %>% \n tab_source_note(md(\"**Source**: Reid, R. E. B. (2015). A morphometric modeling approach to distinguishing among bobcat, coyote and gray fox scats. *Wildlife Biology*, 21(5), 254-262\")) %>% \n tab_spanner(label = \"Physical averages\",\n columns = Length:Mass) %>% \n opt_stylize(style = 2, color = \"blue\")\n```\n\n# Grouped data\n\n```{r}\nscat_table2 %>% \n ungroup() %>% \n gt(groupname_col = \"Species\")\n```\n\n\n" }, { "path": "Better box plots with ggplot/README.md", "content": "# Better box plots in R with ggplot\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Better box plots in R with ggplot**, which you can find at . \n\n" }, { "path": "Better box plots with ggplot/Script - better box plots with ggplot.R", "content": "library(tidyverse)\nlibrary(modeldata)\n?geom_boxplot\n\nglimpse(msleep)\n\nggplot(msleep, aes(x = vore, \n y = sleep_total,\n fill = vore)) + \n geom_boxplot(show.legend = FALSE,\n outlier.shape = NA) +\n geom_jitter(width = .2)\n\n\ndata(penguins)\nglimpse(penguins)\n\nggplot(penguins, aes(x = species, \n y = bill_length_mm, \n fill = species)) +\n geom_violin(show.legend = FALSE) +\n geom_boxplot(width = .5)\n" }, { "path": "Bibliographies with R and Quarto/Quarto bibliographies.qmd", "content": "---\ntitle: \"Quarto bibliographies\"\nauthor: Andrew Gard\nformat: html\nbibliography: my-bib.bib\nnocite: |\n @*\ncsl: elsevier-with-titles.csl\n---\n\nMy reference: [@wickham2014tidy]" }, { "path": "Bibliographies with R and Quarto/README.md", "content": "# Bibliographies with R and Quarto\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Bibliographies with R and Quarto**, which you can find at . \n\n" }, { "path": "Bibliographies with R and Quarto/elsevier-with-titles.csl", "content": "\n\n" }, { "path": "Bibliographies with R and Quarto/my-bib.bib", "content": "%% This BibTeX bibliography file was created using BibDesk.\n%% https://bibdesk.sourceforge.io/\n\n%% Created for equitable_equations at 2024-03-24 13:12:36 -0500 \n\n\n%% Saved with string encoding Unicode (UTF-8) \n\n\n\n@article{Gard_2018,\n\tauthor = {Gard, Andrew},\n\tdate-added = {2024-03-24 13:12:02 -0500},\n\tdate-modified = {2024-03-24 13:12:30 -0500},\n\tdoi = {10.1080/00029890.2018.1465785},\n\tissn = {1930-0972},\n\tjournal = {The American Mathematical Monthly},\n\tmonth = aug,\n\tnumber = {7},\n\tpages = {602-611},\n\tpublisher = {Informa UK Limited},\n\ttitle = {The Wild Goose Chase Problem},\n\turl = {http://dx.doi.org/10.1080/00029890.2018.1465785},\n\tvolume = {125},\n\tyear = {2018},\n\tbdsk-file-1 = {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}}\n\n@article{wickham2014tidy,\n\tauthor = {Wickham, Hadley},\n\tdate-added = {2024-03-24 13:09:32 -0500},\n\tdate-modified = {2024-03-24 13:09:32 -0500},\n\tjournal = {Journal of statistical software},\n\tpages = {1--23},\n\ttitle = {Tidy data},\n\tvolume = {59},\n\tyear = {2014}}\n" }, { "path": "Binning variables in R/README.md", "content": "# Binning variables in R\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Binning variables in R**, which you can find at . \n\n" }, { "path": "Binning variables in R/script - binning variables.R", "content": "# binning quantitative variables in R\nlibrary(tidyverse)\nlibrary(readxl)\ntheme_set(theme_minimal())\n\ntitanic <- read_excel(\"titanic.xlsx\") \nView(titanic)\n\n?cut_number\n?cut # more general\n\ncut_number(titanic$age,\n n = 8) %>% \n table()\n\ntitanic <- titanic %>% \n drop_na(age)\n\ntitanic <- titanic %>% \n mutate(age_binned = cut_interval(age,\n n = 8))\n\ncount(titanic, age_binned)\n\ntitanic_summary <- titanic %>% \n group_by(age_binned) %>% \n summarize(survival_rate = mean(survived),\n count = n())\ntitanic_summary\n\nggplot(titanic_summary, aes(x = age_binned,\n y = survival_rate,\n size = count)) + \n geom_point()\n\n" }, { "path": "Bivariate continuous probability distributions/README.md", "content": "# Bivariate distributions of continuous random variables\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Bivariate distributions of continuous random variables**, which you can find at . \n\n" }, { "path": "Combining columns in R with unite()/README.md", "content": "# Combining columns in R with unite()\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Combining columns in R with unite()**, which you can find at . \n\n" }, { "path": "Combining columns in R with unite()/unite.R", "content": "library(tidyverse)\nlibrary(nycflights13)\n\n?unite\n\nglimpse(flights)\n\nflights_ee <- flights %>% \n unite(date, \n year, \n month, \n day)\nglimpse(flights_ee)\n\nflights_ee <- flights %>% \n unite(date, \n year, \n month, \n day, \n remove = FALSE)\n\nflights_ee <- flights %>% \n unite(date, \n year, \n month, \n day, \n remove = FALSE,\n sep = \"-\") %>% \n mutate(date = as.Date(date))\n\n\n\n" }, { "path": "Combining data sets with left_join/README.md", "content": "# Combining data sets with left_join\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Combining data sets with left_join**, which you can find at . \n\n" }, { "path": "Combining data sets with left_join/left_join.R", "content": "library(tidyverse)\nlibrary(babynames)\n?left_join\n\nbabynames\napplicants\n\nleft_join(babynames, appllicants)\n\n\n\n\n# Building the states data sets -------------------------------------------\n\nstate_geog <- data.frame(state = state.abb,\n area = state.area,\n center_long = state.center$x,\n center_lat = state.center$y)\nstate_reg = data.frame(name = state.name,\n abbr = state.abb,\n region = state.region)\n" }, { "path": "Combining observations with bind_rows()/README.md", "content": "# Combining observations with bind_rows\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Combining observations with bind_rows**, which you can find at . \n\n" }, { "path": "Combining observations with bind_rows()/bind_rows.R", "content": "# combining sets using bind_rows\nlibrary(tidyverse)\n?bind_rows\n\n# construct data sets \n\nmsleep1 <- data.frame(name = c(\"Cheetah\", \n \"Dog\"),\n genus = c(\"Acinonyx\",\n \"Canis\"))\nmsleep2 <- data.frame(name = c(\"Cow\",\n \"Goat\"),\n genus = c(\"Bos\",\n \"Capri\"))\nmsleep3 <- data.frame(Name = c(\"Red fox\",\n \"Mole rat\"),\n genus = c(\"Vulpes\",\n \"Spalax\"))\nmsleep1\nmsleep2\nmsleep3\n\nbind_rows(msleep1, msleep2)\nsleep_list <- list(msleep1, msleep2)\nsleep_list\n\nbind_rows(sleep_list)\nbind_rows(sleep_list,\n .id = \"researcher\") %>% \n mutate(researcher = fct_recode(researcher, \n \"George\" = \"1\",\n \"Sarah\" = \"2\"))\n\nbind_rows(msleep2, msleep3)\n\nmsleep3 %>% \n rename(\"name\" = \"Name\") %>% \n rbind(msleep2, \n msleep1)\n\nrbind(msleep2, msleep3)\n\n\n\n\n\n\n" }, { "path": "Computing normal probabilities examples/README.md", "content": "# Computing normal probabilities: examples\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Computing normal probabilities: examples**, which you can find at . \n\n" }, { "path": "Conditional probability distributions/README.md", "content": "# Conditional probability distributions\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Conditional probability distributions**, which you can find at . \n\n" }, { "path": "Contingency tables in R/README.md", "content": "# Contingency tables in R\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Contingency tables in R**, which you can find at . \n\n" }, { "path": "Contingency tables in R/Script - contingency tables.R", "content": "# Contingency tables\nlibrary(ISLR2)\n?table\n\nt <- table(Carseats$Urban, Carseats$ShelveLoc)\nstr(t)\ndim(t)\n\naddmargins(t)\ndim(addmargins(t))\n\nmarginSums(t)\nmarginSums(t, 1)\nmarginSums(t, 2)\n\nproportions(t)\nproportions(t, 1)\nproportions(t, 2)\n\ncounts <- c(1, 2, 3, 4)\nm <- matrix(counts, nrow = 2, byrow = TRUE)\nm\n\naddmargins(m)\n\n\n\n" }, { "path": "Controlling the family-wise error rate/README.md", "content": "# Controlling the family-wise error rate\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Controlling the family-wise error rate**, which you can find at . \n\n" }, { "path": "Controlling the family-wise error rate/Script - controlling the fwer.R", "content": "# Correcting for multiple testing\n?p.adjust\n\np <- c(0.005, 0.011, 0.025, 0.035, .045)\n\np.adjust(p, method = \"bonferroni\")\np.adjust(p, method = \"holm\")\n\n" }, { "path": "Creating columns with mutate/README.md", "content": "# Creating columns in R with mutate()\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Creating columns in R with mutate()**, which you can find at . \n\n" }, { "path": "Creating columns with mutate/script - mutate.R", "content": "library(tidyverse)\n?mutate\n\nIslands <- data.frame(Island = names(islands), \n Area = islands,\n row.names = NULL)\nView(Islands)\n\nIslands <- mutate(Islands, Area_km = Area * 2.59)\nIslands2 <- transmute(Islands, Area_km = Area * 2.59)\nView(Islands2)\n\nIslands <- Islands %>%\n mutate(Type = \"Island\",\n Number = 1:48,\n Rank = rank(Area), \n Previous = lag(Number),\n Next = lead(Number))\n" }, { "path": "Cross references in R and Quarto/README.md", "content": "# Cross references in R and Quarto\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Cross references in R and Quarto**, which you can find at . \n\n" }, { "path": "Cross references in R and Quarto/cross references.qmd", "content": "---\ntitle: \"Cross references\"\nauthor: \"Andrew Gard\"\nnumber-sections: true\nformat: html\n---\n\n```{r}\n#| label: setup\n#| include: false\n\nlibrary(tidyverse)\nlibrary(gt)\n```\n\n## Images {#sec-im}\n\n![My logo](rusty_logo.png){width=60% #fig-logo}\n\nReference: @fig-logo. We can reformat how this appears. For instance [-@fig-logo] or [Figgy @fig-logo]\n\n## Plots\n\n```{r}\n#| label: fig-msleep\n#| warning: false\n#| out-width: 60%\n\nqplot(msleep$sleep_total)\n```\n\nReference: @fig-msleep.\n\n## Tables\n\n```{r}\n#| label: tbl-msleep\n#| tbl-cap: Sleepy time\n#| echo: false\n\nmsleep %>% \n group_by(vore) %>% \n summarize(Count = n(), \n avg_sleep = mean(sleep_total,\n na.rm = TRUE)) %>% \n gt()\n```\n\nReference: @tbl-msleep.\n\n## Callouts\n\n::: {.callout-note #nte-mynote}\n\nCheck out this note!\n\n:::\n\nReference: @nte-mynote\n\n## Equations\n\n$$ e = mc^2 $$ {#eq-rel}\n\nReference: @eq-rel\n\n## Sections\n\nReference: @sec-im\n\n\n" }, { "path": "Data analysis - Billboard Hot 100/README.md", "content": "# Data analysis with R: the Billboard Hot 100\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Data analysis with R: the Billboard Hot 100**, which you can find at . \n\n" }, { "path": "Data analysis - Billboard Hot 100/Script - billboard_top_100.R", "content": "# billboard top 100\n# Tidy Tuesday Sept. 14, 2021\n\nlibrary(tidyverse)\nlibrary(tidytuesdayR)\ntuesdata <- tidytuesdayR::tt_load('2021-09-14')\nbillboard <- tuesdata$billboard\naudio <- tuesdata$audio_features\nView(billboard)\nView(audio)\n\n# Are number one jams more cheerful, \n# in general?\n\nbb1 <- billboard %>% \n mutate(no1 = (peak_position == 1),\n week_id = mdy(week_id)) %>% \n group_by(song_id) %>% \n arrange(week_position, .by_group = TRUE) %>% \n filter(!duplicated(song_id)) %>% \n ungroup() %>% \n select(song_id, \n no1)\n\nsongs <- bb1 %>% \n left_join(audio, \n by = \"song_id\")\n\nmean(is.na(songs$valence))\n\nmap_dbl(songs, \\(x) mean(is.na(x)))\n\nggplot(songs, aes(x = valence, \n y = no1)) + \n geom_boxplot() +\n theme_minimal()\n\nt.test(valence ~ no1, \n data = songs)\n\n# yes, no1 jams are more valent\n# however, the difference is small\n\n# How large an effect is this?\n# What is cohen's d?\n\ndf <- songs %>% \n group_by(no1) %>% \n summarize(avg_val = mean(valence, \n na.rm = TRUE),\n var_val = var(valence, \n na.rm = TRUE))\n\npooled_sd <- sqrt(sum(df$var_val) / 2)\n\ncohens_d <- diff(df$avg_val) / pooled_sd\ncohens_d\n\n" }, { "path": "Data analysis - US colleges/README.md", "content": "# Data analysis with R: US colleges\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Data analysis with R: US colleges**, which you can find at . \n\n" }, { "path": "Data analysis - US colleges/Script - analyzing college data.R", "content": "library(tidyverse)\nlibrary(ISLR2)\n\n# Do colleges with larger full-time enrollments\n# have lower graduation rates?\n# Is this different for public/private institutions?\n# If it different for more selective schools?\n\n?College\nglimpse(College)\n\n# Exploratory graphics\n\nggplot(College, aes(x = Grad.Rate)) +\n geom_histogram() # Note weird outlier\n\nsuspicious <- filter(College, Grad.Rate >= 100)\nView(suspicious)\n\nggplot(College, aes(x = log10(F.Undergrad),\n y = Grad.Rate)) + \n geom_point()\n\ncollege_sm <- College %>% \n mutate(log_full = log10(F.Undergrad)) %>% \n select(Grad.Rate,\n log_full,\n Private, \n Top25perc)\nView(college_sm)\n\n# Modeling\n\nggplot(College, aes(x = log10(F.Undergrad),\n y = Grad.Rate)) + \n geom_point() +\n geom_smooth(method = \"lm\")\n\nmodel_undergrad <- lm(Grad.Rate ~ log_full,\n data = college_sm)\nsummary(model_undergrad)\nplot(model_undergrad)\n\n# What about Private?\n\nggplot(College, aes(x = log10(F.Undergrad),\n y = Grad.Rate,\n color = Private)) + \n geom_point() +\n geom_smooth(method = \"lm\",\n se = FALSE) +\n scale_color_brewer(palette = \"Dark2\")\n\nmodel_private <- lm(Grad.Rate ~ Private + log_full,\n data = college_sm)\nsummary(model_private)\n\n# Interaction: private and F.Undergrad\n\nmodel_private_int <- lm(Grad.Rate ~ Private * log_full,\n data = college_sm)\nsummary(model_private_int)\nanova(model_private_int)\n\n# What about Top25perc?\n\nmodel_top <- lm(Grad.Rate ~ Private + \n log_full +\n Top25perc,\n data = college_sm)\nsummary(model_top)\nplot(model_top)\n\n\n\n# Inset plot from YouTube thumbnail\n\nggplot(College, aes(x = log10(F.Undergrad),\n y = Grad.Rate,\n color = Private)) + \n geom_point() +\n geom_smooth(method = \"lm\",\n se = FALSE) +\n labs(x = \"Full-time undergraduates (log scale)\",\n y = \"Graduation rate\",\n color = \"Private?\",\n title = \"Bigger schools, more degrees\") +\n scale_color_brewer(palette = \"Dark2\") +\n theme_minimal()\n\n\n\n\n" }, { "path": "Data analysis - age gaps and bechdel tests/README.md", "content": "# Data analysis with R: age gaps and Bechdel tests\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Data analysis with R: age gaps and Bechdel tests**, which you can find at . \n\n" }, { "path": "Data analysis - age gaps and bechdel tests/script - age gaps.R", "content": "# Question: is there a correlation between age\n# gaps and Bechdel ratings?\n# For this purpose: gap = man's age - woman's age\n\n# Setup\n\nlibrary(tidyverse)\ntheme_set(theme_minimal())\n\nage_gaps <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2023/2023-02-14/age_gaps.csv')\nbechdel <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2021/2021-03-09/raw_bechdel.csv')\n\n# Orientation\n\nView(bechdel)\nggplot(bechdel, \n aes(x = rating)) + \n geom_bar()\n\nView(age_gaps)\nggplot(age_gaps, \n aes(x = age_difference)) + \n geom_histogram()\n\n# Data prep\n\nage_gaps <- age_gaps %>% \n mutate(man_older = case_when(\n character_1_gender == \"man\" & character_2_gender == \"woman\" ~ \n actor_1_age - actor_2_age,\n character_2_gender == \"man\" & character_1_gender == \"woman\" ~ \n actor_2_age - actor_1_age\n ))\n\nggplot(age_gaps, \n aes(x = man_older)) + \n geom_histogram() \nmean(age_gaps$man_older,\n na.rm = TRUE)\n\n# Join the data\n\nmovies <- age_gaps %>% \n left_join(bechdel, \n by = c(\"movie_name\" = \"title\",\n \"release_year\" = \"year\")) \nage_gaps$movie_name[810]\n\n# 2017 Wonder woman has 3 bechdel ratings??\n\nbechdel %>% \n filter(title == \"Wonder Woman\")\n\nmovies <- movies %>% \n filter(!(id %in% c(9293,9294)))\n\n# Are there others?\n\nbechdel %>% \n count(title, \n year) %>% \n filter(n > 1) \n\n# Visualization: age gap and bechdel rating\n\nmovies <- movies %>% \n mutate(rating_cat = as.factor(rating)) %>% \n drop_na(man_older,\n rating)\n\nggplot(movies, aes(x = rating_cat, \n y = man_older)) + \n geom_boxplot(outlier.shape = NA) +\n geom_jitter(alpha = .2) + \n scale_x_discrete(labels = c(\"Fail\", \n \"No conversation\", \n \"Only about men\",\n \"Pass\")) +\n labs(x = \"Bechdel test result\",\n y = \"Male age gap\",\n title = \"Something to talk about\")\n\n# Correlation\n\ncor(movies$man_older,\n movies$rating)\n\ncor(movies$man_older,\n movies$rating,\n method = \"spearman\")\n\n# Testing \n\nmodel <- lm(man_older ~ rating_cat, \n data = movies)\nsummary(model)\n\n\n" }, { "path": "Data analysis - floristic quality/README.md", "content": "# Data analysis with R: floristic quality\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Data analysis with R: floristic quality**, which you can find at . \n\n" }, { "path": "Data analysis - floristic quality/Script - floristic quality data.R", "content": "# Student floristic quality data analysis\n# Question: were the C-values of the 4 transects different from one another?\n\nlibrary(tidyverse)\nlibrary(here)\nlibrary(readxl)\nlibrary(janitor)\n\nfile1 <- here(\"student floristic quality data\", \"lfc_fqa_team1.xlsx\")\nfile2 <- here(\"student floristic quality data\", \"lfc_fqa_team2.xlsx\")\nfile3 <- here(\"student floristic quality data\", \"lfc_fqa_team3.xlsx\")\n\nteam1 <- read_excel(file1)\nteam2 <- read_excel(file2)\nteam3 <- read_excel(file3)\n\nview(team1)\nview(team2)\nview(team3)\n\nfqa_data <- rbind(team1, team2, team3)\nview(fqa_data)\n\nfqa_data <- fqa_data |> \n filter(!is.na(species_name)) |> \n fill(team:plot_number) |> \n clean_names()\n\nfqa_data_tidier <- fqa_data |> \n mutate(c = case_when(c == \"NN\" ~ \"nn\",\n c == \"NA\" ~ NA_character_,\n TRUE ~ c\n ))\nview(fqa_data_tidier)\n\nfqa_data_tidier |> count(c)\n\nfqa_data_tidier <- fqa_data_tidier |> \n mutate(c = as.numeric(c)) |> \n filter(!is.na(c))\n\nggplot(fqa_data_tidier, aes(x = c)) + \n geom_bar() + \n scale_x_continuous(breaks = seq(from = 0, \n to = 10,\n by = 2)) +\n theme_minimal()\n\n# How do c-values compare across sites?\n\nfqa_data_tidier |> count(transect_name)\nfqa_data_tidier <- fqa_data_tidier |> \n mutate(transect_name = case_when(transect_name == \"Prarie\" ~ \"Prairie\",\n transect_name == \"Reverie Prairie\"~ \"Prairie\",\n transect_name == \"Savannah\" ~ \"Savanna\",\n transect_name == \"Shooting Star Ravine\" ~ \"Ravine\",\n transect_name == \"Shooting Star Savanna\" ~ \"Savanna\",\n transect_name == \"Wetland Restoration\" ~ \"Wetland\",\n transect_name == \"Wetlands\" ~ \"Wetland\",\n TRUE ~ .data$transect_name \n ))\n\nfqa_data_tidier |> group_by(transect_name) |> \n summarize(mean_c = mean(c),\n sd_c = sd(c),\n count = n()) |> \n arrange(-mean_c)\n\n# A visualization\n\nggplot(fqa_data_tidier, aes(x = transect_name, \n y = c)) +\n geom_boxplot() +\n geom_jitter(width = .2) + \n theme_minimal()\n\nmodel <- aov(c ~ transect_name, data = fqa_data_tidier)\nsummary(model)\n\nggplot(fqa_data_tidier, aes(x = transect_name, \n y = c)) +\n geom_jitter(width = .2) + \n theme_minimal()\n\n\n" }, { "path": "Data analysis - scooby doo/README.md", "content": "# Data analysis with R: Scooby Doo\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Data analysis with R: Scooby Doo**, which you can find at . \n\n" }, { "path": "Data analysis - scooby doo/script - analyzing scooby doo.R", "content": "# Scooby dooby doo!\n\nlibrary(tidyverse)\ntheme_set(theme_minimal())\n\nlibrary(tidytuesdayR)\ntuesdata <- tidytuesdayR::tt_load('2021-07-13')\nscooby <- tuesdata$scoobydoo\n\nglimpse(scooby)\n\nscooby_sm <- scooby |> \n select(series_name:format) |> \n mutate(imdb = as.double(imdb))\n\n# A preliminary visualization of imdb ratings over time\n\nggplot(scooby_sm, aes(x = date_aired,\n y = imdb,\n color = format)) +\n geom_point() +\n scale_color_brewer(palette = \"Dark2\")\n\n# Exploring format\n\ntable(scooby_sm$format)\n\ncrossover <- filter(scooby_sm, format == \"Crossover\")\nView(crossover)\n\n# Let's filter all non-TV series episodes\n\nscooby_sm <- scooby_sm |> \n filter(format != \"Crossover\",\n format != \"Movie\", \n format != \"Movie (Theatrical)\")\ntable(scooby_sm$format)\n\nView(scooby_sm)\n\n# Let's combine the segmented episodes for the purposes\n# of considering imdb ratings.\n\nsegmented <- scooby_sm |> \n filter(format == \"TV Series (segmented)\") |> \n group_by(date_aired) |> \n summarize(imdb = mean(imdb),\n network = unique(network),\n series_name = unique(series_name),\n total_runtime = sum(run_time))\nView(segmented)\n\nnon_seg <- scooby_sm |> \n filter(format != \"TV Series (segmented)\") |> \n select(date_aired,\n imdb,\n network,\n series_name,\n total_runtime = run_time)\n\nscooby_tidied <- rbind(non_seg,\n segmented)\nView(scooby_tidied)\n\n# A new ggplot:\n\nggplot(scooby_tidied, aes(x = date_aired,\n y = imdb,\n col = network)) +\n geom_point() +\n scale_color_brewer(palette = \"Dark2\")\n\ncw <- scooby_tidied |> \n filter(network == \"The CW\")\nView(cw)\n\n# Does network inform imdb? Is The CW really so bad?\n\nmodel <- aov(imdb ~ network, \n data = scooby_tidied)\nsummary(model)\n\nTukeyHSD(model)\n\n# Yep. The CW was bad.\n\n# Let's get an awesome bar chart for average imdb ratings\n# for the various networks. \n\nscooby_tidied |> \n group_by(network) |> \n summarize(mean_imdb = mean(imdb, na.rm = TRUE)) |> \n ggplot(aes(x = fct_reorder(network, -mean_imdb), \n y = mean_imdb,\n fill = network)) + \n geom_col() +\n geom_text(aes(label = round(mean_imdb, 1)),\n nudge_y = .3) +\n scale_fill_brewer(palette = \"Dark2\") +\n labs(x = \"Network\",\n y = \"Mean imdb rating\",\n title = \"Scooby dooby doo!\") +\n theme(legend.position = \"none\",\n axis.text.x = element_text(angle = 15))\n\n\n\n\n\n\n" }, { "path": "Data visualization in 36 minutes/README.md", "content": "# Data visualization with R in 36 minutes\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Data visualization with R in 36 minutes**, which you can find at . \n\n" }, { "path": "Data visualization in 36 minutes/Script - dataviz.R", "content": "library(tidyverse)\nlibrary(modeldata)\n?ggplot\n\n?crickets\nView(crickets)\n\n# The basics\n\nggplot(crickets, aes(x = temp, \n y = rate)) + \n geom_point() +\n labs(x = \"Temperature\",\n y = \"Chirp rate\",\n title = \"Cricket chirps\",\n caption = \"Source: McDonald (2009)\")\n\nggplot(crickets, aes(x = temp, \n y = rate,\n color = species)) + \n geom_point() +\n labs(x = \"Temperature\",\n y = \"Chirp rate\",\n color = \"Species\",\n title = \"Cricket chirps\",\n caption = \"Source: McDonald (2009)\") +\n scale_color_brewer(palette = \"Dark2\")\n\n# Modifiying basic properties of the plot\n\nggplot(crickets, aes(x = temp, \n y = rate)) + \n geom_point(color = \"red\",\n size = 2,\n alpha = .3,\n shape = \"square\") +\n labs(x = \"Temperature\",\n y = \"Chirp rate\",\n title = \"Cricket chirps\",\n caption = \"Source: McDonald (2009)\")\n\n# Learn more about the options for the geom_abline()\n# with ?geom_point\n\n# Adding another layer\n\n\nggplot(crickets, aes(x = temp, \n y = rate)) + \n geom_point() +\n geom_smooth(method = \"lm\",\n se = FALSE) +\n labs(x = \"Temperature\",\n y = \"Chirp rate\",\n title = \"Cricket chirps\",\n caption = \"Source: McDonald (2009)\")\n\nggplot(crickets, aes(x = temp, \n y = rate,\n color = species)) + \n geom_point() +\n geom_smooth(method = \"lm\",\n se = FALSE) +\n labs(x = \"Temperature\",\n y = \"Chirp rate\",\n color = \"Species\",\n title = \"Cricket chirps\",\n caption = \"Source: McDonald (2009)\") +\n scale_color_brewer(palette = \"Dark2\") \n\n# Other plots\n\nggplot(crickets, aes(x = rate)) + \n geom_histogram(bins = 15) # one quantitative variable\n\nggplot(crickets, aes(x = rate)) + \n geom_freqpoly(bins = 15)\n\nggplot(crickets, aes(x = species)) + \n geom_bar(color = \"black\",\n fill = \"lightblue\")\n\nggplot(crickets, aes(x = species, \n fill = species)) + \n geom_bar(show.legend = FALSE) +\n scale_fill_brewer(palette = \"Dark2\")\n\nggplot(crickets, aes(x = species, \n y = rate,\n color = species)) + \n geom_boxplot(show.legend = FALSE) +\n scale_color_brewer(palette = \"Dark2\") +\n theme_minimal()\n\n?theme_minimal()\n\n# faceting\n\n# not great:\nggplot(crickets, aes(x = rate, \n fill = species)) + \n geom_histogram(bins = 15) +\n scale_fill_brewer(palette = \"Dark2\")\n\nggplot(crickets, aes(x = rate,\n fill = species)) + \n geom_histogram(bins = 15,\n show.legend = FALSE) + \n facet_wrap(~species) +\n scale_fill_brewer(palette = \"Dark2\")\n?facet_wrap\n\nggplot(crickets, aes(x = rate,\n fill = species)) + \n geom_histogram(bins = 15,\n show.legend = FALSE) + \n facet_wrap(~species,\n ncol = 1) +\n scale_fill_brewer(palette = \"Dark2\") + \n theme_minimal()\n" }, { "path": "Data wrangling with R/README.md", "content": "# Data wrangling with R\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Data wrangling with R**, which you can find at . \n\n" }, { "path": "Data wrangling with R/script - data wrangling.R", "content": "# Data wrangling with tidyverse R\n# install.packages(\"tidyverse\") only need to do once\nlibrary(tidyverse)\n\n?diamonds\n\n# subset by row with filter() ---------------------------------------------\n\n?filter\n\ndiamonds_sm <- filter(diamonds, cut == \"Ideal\")\ndiamonds_sm <- filter(diamonds, price > 10000)\nView(diamonds_sm)\n\ndiamonds_sm <- filter(diamonds, \n cut == \"Ideal\",\n price > 10000)\n\ndiamonds_sm <- filter(diamonds, \n cut == \"Ideal\" |\n price > 10000)\n\n# filter for missing values using is.na or !is.na\n\n# subset by column with select() ------------------------------------------\n\n?select\n\ndiamonds_sm <- select(diamonds, color, cut)\nView(diamonds_sm)\n\ndiamonds_sm <- select(diamonds, 1:4)\n\ndiamonds_sm <- select(diamonds, starts_with(\"c\"))\ndiamonds_sm <- select(diamonds, contains(\"c\"))\n\ndiamonds_sm <- select(diamonds, \n price, \n everything())\n\ndiamonds_sm <- select(diamonds, -price)\n\n# using the pipe\n\ndiamonds_sm <- diamonds %>% \n select(-price)\n\n# reorder rows with arrange() ---------------------------------------------\n\ndiamonds_arr <- diamonds %>% \n arrange(color)\nView(diamonds_arr)\n\ndiamonds_arr <- diamonds %>% \n arrange(carat)\n\ndiamonds_arr <- diamonds %>% \n arrange(color, \n carat)\n\ndiamonds_arr <- diamonds %>% \n arrange(carat, \n color)\n\ndiamonds_arr <- diamonds %>% \n arrange(desc(carat))\nView(diamonds_arr)\n\nglimpse(diamonds_arr)\n\n# add or modify columns with mutate() -------------------------------------\n\ndiamonds_new <- diamonds %>% \n mutate(mass_g = .20 * carat)\nglimpse(diamonds_new)\n\ndiamonds_new <- diamonds %>% \n mutate(mass_g = .20 * carat,\n price_per_carat = price / carat)\nglimpse(diamonds_new)\n\ndiamonds_new <- diamonds %>% \n mutate(mass_g = .20 * carat,\n price_per_carat = price / carat,\n cut = tolower(cut))\nglimpse(diamonds_new)\n\ndiamonds_new <- diamonds %>% \n mutate(mass_g = .20 * carat,\n price_per_carat = price / carat,\n cut = tolower(cut),\n expensive_TF = price > 10000)\nglimpse(diamonds_new)\n\n# other smaller verbs -----------------------------------------------------\n\n?slice_max\n?bind_rows\n?left_join\n?rename\n?case_when\n\n# grouped summaries with group_by() and summarize() -----------------------\n\ndiamonds %>% \n group_by(cut) %>% \n summarize(mean(price))\n\ndiamonds %>% \n group_by(cut) %>% \n summarize(avg_price = mean(price),\n sd_price = sd(price))\n\ndiamonds %>% \n group_by(cut,\n color) %>% \n summarize(avg_price = mean(price),\n sd_price = sd(price),\n count = n())\n\ndiamonds %>% \n count(cut,\n color) # counts only. \n\ndiamonds %>% \n group_by(price > 10000) %>% \n summarize(avg_price = mean(price),\n sd_price = sd(price),\n count = n()) \n\ndiamonds %>% \n group_by(expensive = price > 10000) %>% \n summarize(avg_price = mean(price),\n sd_price = sd(price),\n count = n()) \n\n\n\n" }, { "path": "Deviance in logistic regression/README.md", "content": "# Deviance in logistic regression models\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Deviance in logistic regression models**, which you can find at . \n\n" }, { "path": "Deviance in logistic regression/script - deviance in log models.R", "content": "library(tidyverse)\n\niris <- iris %>% \n mutate(setosa = as.integer(Species == \"setosa\"))\n\nggplot(iris, aes(x = Sepal.Length,\n y = setosa)) + \n geom_jitter(height = .05,\n alpha = .4) +\n geom_smooth(method = \"glm\",\n se = FALSE,\n method.args = list(family = \"binomial\"))\n\nmodel <- glm(setosa ~ Sepal.Length,\n data = iris,\n family = \"binomial\")\nsummary(model)\n\n-2 * (50 * log(1/3) + 100 * log(2/3))\n\np_hat <- fitted.values(model, type = \"response\")\np_hat\n\n-2 * (sum(log(p_hat[1:50])) + sum(log(1 - p_hat[51:150])))\n" }, { "path": "Discrete bivariate distributions/README.md", "content": "# Discrete bivariate distributions\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Discrete bivariate distributions**, which you can find at . \n\n" }, { "path": "Du Bois challenge plate 25/README.md", "content": "# Dataviz with R: the Du Bois challenge\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, \n**Dataviz with R: the Du Bois challenge**, which you can find at . \n\n" }, { "path": "Du Bois challenge plate 25/camcorder_code.R", "content": "library(tidyverse)\nlibrary(camcorder)\n\ngg_record(\n \"dubois gif\",\n device = \"png\", \n width = 5, \n height = 6, \n units = \"in\", \n dpi = 300, \n bg = \"white\"\n)\n\ndata <- data.frame(value = c(1434975,\n 1322694,\n 1173624,\n 736170,\n 498532,\n 21186),\n color = c(\"#dc143c\",\n \"#e5e5e5\",\n \"#ffd700\",\n \"#7e7683\",\n \"#4682b4\",\n \"#ffc0cb\"),\n label = c(\"1899---$ 1,434,975 \",\n \"1895---$ 1,322,694 \",\n \"1890---$ 1,173,624 \",\n \"1885---$ 736,170 \",\n \"1880---$ 498,532 \",\n \"1875---$ 21,186 \"))\n\n\n# Bar charts --------------------------------------------------------------\n\n\n# Super basic\nggplot(data, aes(y = label,\n x = value,\n fill = label)) +\n geom_col(width = 1,\n orientation = \"y\")\n\n# Add color palette\nggplot(data, aes(y = label,\n x = value,\n fill = label)) +\n geom_col(width = 1,\n orientation = \"y\") +\n scale_fill_manual(values = data$color)\n\n# Fix ordering of labels\ndata <- data %>% \n mutate(label = fct_reorder(label, 1:6))\n\n# Add boundaries\nggplot(data, aes(y = label,\n x = value,\n fill = label)) +\n geom_col(width = 1,\n orientation = \"y\",\n linewidth = .1,\n color = \"black\") +\n scale_fill_manual(values = data$color)\n\n# Radial plot\nggplot(data, aes(y = label,\n x = value,\n fill = label)) +\n geom_col(width = 1,\n orientation = \"y\",\n linewidth = .1,\n color = \"black\") +\n scale_fill_manual(values = data$color) +\n coord_radial() +\n theme_void()\n\n\n\n# Angled bar chart --------------------------------------------------------\n\n\n\n# add columns to data set with coordinates of vertices\n\nm <- -6/(2*pi) # slope wrt theta\n\ndata <- data %>% \n mutate(theta = (value/max(value)) * (15/8) * (2*pi),\n y_ll = 20:25,\n y_ul = 21:26,\n y_ur = y_ul + m * theta,\n y_lr = y_ll + m * theta,\n x_ll = rep(0, times = 6),\n x_ul = rep(0, times = 6),\n x_ur = theta,\n x_lr = theta)\n\nbar_data <- data %>% \n pivot_longer(cols = contains(\"_\"),\n names_to = c(\".value\", \"vertex\"),\n names_sep = \"_\")\n\n# Super basic\nggplot(bar_data, \n aes(x = x, \n y = y, \n fill = label)) + \n geom_polygon()\n\n# Preview of radial version\nggplot(bar_data, \n aes(x, \n y, \n fill = label)) + \n geom_polygon() +\n coord_radial()\n\nggplot(bar_data, \n aes(x, \n y, \n fill = label)) + \n geom_polygon() +\n coord_radial(end = 15*pi/4) +\n theme_void() \n\n# fix colors and improve overall look\nggplot(bar_data, \n aes(x = x, \n y = y, \n fill = label)) + \n geom_polygon(color = \"black\",\n linewidth = .1) +\n scale_fill_manual(values = data$color)\n\n# Add labels\nggplot(bar_data, \n aes(x = x, \n y = y, \n fill = label)) + \n geom_polygon(color = \"black\",\n linewidth = .1) +\n geom_text(data = data,\n aes(x = 0,\n y = y_ll + 0.5,\n label = label),\n hjust = \"right\",\n size = 2) +\n scale_fill_manual(values = data$color) \n\n# preview radial version\nggplot(bar_data, \n aes(x = x, \n y = y, \n fill = label)) + \n geom_polygon(color = \"black\",\n linewidth = .1) +\n geom_text(data = data,\n aes(x = 0,\n y = y_ll + 0.5,\n label = label),\n hjust = \"right\",\n size = 2) +\n scale_fill_manual(values = data$color) +\n coord_radial(end = 15*pi/4) +\n theme_void()\n\n\n\n# Beautification ----------------------------------------------------------\n\n\n\n# need y-axis ranging from 0 to 26\nggplot(bar_data, \n aes(x = x, \n y = y, \n fill = label)) + \n geom_polygon(color = \"black\",\n linewidth = .1) +\n geom_text(data = data,\n aes(x = 0,\n y = y_ll + 0.5,\n label = label),\n hjust = \"right\",\n size = 2) +\n ylim(c(0, 26)) + \n scale_fill_manual(values = data$color) \n\n# radial version\nggplot(bar_data, \n aes(x = x, \n y = y, \n fill = label)) + \n geom_polygon(color = \"black\",\n linewidth = .1) +\n geom_text(data = data,\n aes(x = 0,\n y = y_ll + 0.5,\n label = label),\n hjust = \"right\",\n size = 2) +\n ylim(c(0, 26)) + \n scale_fill_manual(values = data$color) +\n coord_radial(end = 15*pi/4) +\n theme_void()\n\n# start plot on y-axis\nggplot(bar_data, \n aes(x = x, \n y = y, \n fill = label)) + \n geom_polygon(color = \"black\",\n linewidth = .1) +\n geom_text(data = data,\n aes(x = 0,\n y = y_ll + 0.5,\n label = label),\n hjust = \"right\",\n size = 2) +\n ylim(c(0, 26)) + \n scale_x_continuous(expand = c(0,0)) +\n scale_fill_manual(values = data$color) +\n coord_radial(start = 0,\n end = 15*pi/4) +\n theme_void()\n\n# Small rotation correction, remove legend\nggplot(bar_data, \n aes(x = x, \n y = y, \n fill = label)) + \n geom_polygon(color = \"black\",\n linewidth = .1,\n show.legend = FALSE) +\n geom_text(data = data,\n aes(x = 0,\n y = y_ll + 0.5,\n label = label),\n hjust = \"right\",\n size = 2) +\n ylim(c(0, 26)) + \n scale_x_continuous(expand = c(0,0)) +\n scale_fill_manual(values = data$color) +\n coord_radial(start = .02,\n end = 15*pi/4) +\n theme_void()\n\n# title and background color\n\nggplot(bar_data, \n aes(x = x, \n y = y, \n fill = label)) + \n geom_polygon(color = \"black\",\n linewidth = .1,\n show.legend = FALSE) +\n geom_text(data = data,\n aes(x = 0,\n y = y_ll + 0.5,\n label = label),\n hjust = \"right\",\n size = 2) +\n ylim(c(0, 26)) + \n scale_x_continuous(expand = c(0,0)) +\n scale_fill_manual(values = data$color) +\n coord_radial(start = .02,\n end = 15*pi/4) +\n labs(title = \"ASSESSED VALUE OF HOUSEHOLD AND KITCHEN FURNITURE \\n OWNED BY GEORGIA NEGROES\") +\n theme_void() +\n theme(plot.background = element_rect(fill = \"#f9f1e7\")) \n\n# center and bold title\nggplot(bar_data, \n aes(x = x, \n y = y, \n fill = label)) + \n geom_polygon(color = \"black\",\n linewidth = .1,\n show.legend = FALSE) +\n geom_text(data = data,\n aes(x = 0,\n y = y_ll + 0.5,\n label = label),\n hjust = \"right\",\n size = 2) +\n ylim(c(0, 26)) + \n scale_x_continuous(expand = c(0,0)) +\n scale_fill_manual(values = data$color) +\n coord_radial(start = .02,\n end = 15*pi/4) +\n labs(title = \"ASSESSED VALUE OF HOUSEHOLD AND KITCHEN FURNITURE \\n OWNED BY GEORGIA NEGROES\") +\n theme_void() +\n theme(plot.background = element_rect(fill = \"#f9f1e7\")) +\n theme(plot.title = element_text(hjust = 0.5,\n face = \"bold\",\n size = 9))\n\n# change font (final for now)\nggplot(bar_data, \n aes(x = x, \n y = y, \n fill = label)) + \n geom_polygon(color = \"black\",\n linewidth = .1,\n show.legend = FALSE) +\n geom_text(data = data,\n aes(x = 0,\n y = y_ll + 0.5,\n label = label),\n hjust = \"right\",\n size = 2) +\n ylim(c(0, 26)) + \n scale_x_continuous(expand = c(0,0)) +\n scale_fill_manual(values = data$color) +\n coord_radial(start = .02,\n end = 15*pi/4) +\n labs(title = \"ASSESSED VALUE OF HOUSEHOLD AND KITCHEN FURNITURE \\n OWNED BY GEORGIA NEGROES\") +\n theme_void() +\n theme(plot.background = element_rect(fill = \"#f9f1e7\")) +\n theme(plot.title = element_text(hjust = 0.5,\n face = \"bold\",\n size = 9),\n text = element_text(family = \"Charter\")) \n\n\ngg_playback(\n first_image_duration = 4,\n last_image_duration = 3,\n frame_duration = .6,\n image_resize = 800\n)\n" }, { "path": "Du Bois challenge plate 25/du bois script.R", "content": "# W.E.B. Du Bois data portraits plate 25\n# Ref: Battle-Baptiste and Rusert (book)\n# Ref: AJ Starks (style guide)\n\nlibrary(tidyverse)\n\ndata <- data.frame(value = c(1434975,\n 1322694,\n 1173624,\n 736170,\n 498532,\n 21186),\n color = c(\"#dc143c\",\n \"#e5e5e5\",\n \"#ffd700\",\n \"#7e7683\",\n \"#4682b4\",\n \"#ffc0cb\"),\n label = c(\"1899---$ 1,434,975 \",\n \"1895---$ 1,322,694 \",\n \"1890---$ 1,173,624 \",\n \"1885---$ 736,170 \",\n \"1880---$ 498,532 \",\n \"1875---$ 21,186 \"))\n\n\n# Bar charts --------------------------------------------------------------\n\n\n# Super basic\nggplot(data, aes(y = label,\n x = value,\n fill = label)) +\n geom_col(width = 1,\n orientation = \"y\")\n\n# Add color palette\nggplot(data, aes(y = label,\n x = value,\n fill = label)) +\n geom_col(width = 1,\n orientation = \"y\") +\n scale_fill_manual(values = data$color)\n\n# Fix ordering of labels\ndata <- data %>% \n mutate(label = fct_reorder(label, 1:6))\n\n# Add boundaries\nggplot(data, aes(y = label,\n x = value,\n fill = label)) +\n geom_col(width = 1,\n orientation = \"y\",\n linewidth = .1,\n color = \"black\") +\n scale_fill_manual(values = data$color)\n\n# Radial plot\nggplot(data, aes(y = label,\n x = value,\n fill = label)) +\n geom_col(width = 1,\n orientation = \"y\",\n linewidth = .1,\n color = \"black\") +\n scale_fill_manual(values = data$color) +\n coord_radial() +\n theme_void()\n\n\n\n# Angled bar chart --------------------------------------------------------\n\n\n\n# add columns to data set with coordinates of vertices\n\nm <- -6/(2*pi) # slope wrt theta\n\ndata <- data %>% \n mutate(theta = (value/max(value)) * (15/8) * (2*pi),\n y_ll = 20:25,\n y_ul = 21:26,\n y_ur = y_ul + m * theta,\n y_lr = y_ll + m * theta,\n x_ll = rep(0, times = 6),\n x_ul = rep(0, times = 6),\n x_ur = theta,\n x_lr = theta)\n\nbar_data <- data %>% \n pivot_longer(cols = contains(\"_\"),\n names_to = c(\".value\", \"vertex\"),\n names_sep = \"_\")\n\n# Super basic\nggplot(bar_data, \n aes(x = x, \n y = y, \n fill = label)) + \n geom_polygon()\n\n# Preview of radial version\nggplot(bar_data, \n aes(x, \n y, \n fill = label)) + \n geom_polygon() +\n coord_radial()\n\nggplot(bar_data, \n aes(x, \n y, \n fill = label)) + \n geom_polygon() +\n coord_radial(end = 15*pi/4) +\n theme_void()\n\n# fix colors and improve overall look\nggplot(bar_data, \n aes(x = x, \n y = y, \n fill = label)) + \n geom_polygon(color = \"black\",\n linewidth = .1) +\n scale_fill_manual(values = data$color)\n\n# Add labels\nggplot(bar_data, \n aes(x = x, \n y = y, \n fill = label)) + \n geom_polygon(color = \"black\",\n linewidth = .1) +\n geom_text(data = data,\n aes(x = 0,\n y = y_ll + 0.5,\n label = label),\n hjust = \"right\",\n size = 2) +\n scale_fill_manual(values = data$color) \n\n# preview radial version\nggplot(bar_data, \n aes(x = x, \n y = y, \n fill = label)) + \n geom_polygon(color = \"black\",\n linewidth = .1) +\n geom_text(data = data,\n aes(x = 0,\n y = y_ll + 0.5,\n label = label),\n hjust = \"right\",\n size = 2) +\n scale_fill_manual(values = data$color) +\n coord_radial(end = 15*pi/4) +\n theme_void()\n\n\n\n# Beautification ----------------------------------------------------------\n\n\n\n# need y-axis ranging from 0 to 26\nggplot(bar_data, \n aes(x = x, \n y = y, \n fill = label)) + \n geom_polygon(color = \"black\",\n linewidth = .1) +\n geom_text(data = data,\n aes(x = 0,\n y = y_ll + 0.5,\n label = label),\n hjust = \"right\",\n size = 2) +\n ylim(c(0, 26)) + \n scale_fill_manual(values = data$color) \n\n# radial version\nggplot(bar_data, \n aes(x = x, \n y = y, \n fill = label)) + \n geom_polygon(color = \"black\",\n linewidth = .1) +\n geom_text(data = data,\n aes(x = 0,\n y = y_ll + 0.5,\n label = label),\n hjust = \"right\",\n size = 2) +\n ylim(c(0, 26)) + \n scale_fill_manual(values = data$color) +\n coord_radial(end = 15*pi/4) +\n theme_void()\n\n# start plot on y-axis\nggplot(bar_data, \n aes(x = x, \n y = y, \n fill = label)) + \n geom_polygon(color = \"black\",\n linewidth = .1) +\n geom_text(data = data,\n aes(x = 0,\n y = y_ll + 0.5,\n label = label),\n hjust = \"right\",\n size = 2) +\n ylim(c(0, 26)) + \n scale_x_continuous(expand = c(0,0)) +\n scale_fill_manual(values = data$color) +\n coord_radial(start = 0,\n end = 15*pi/4) +\n theme_void()\n\n# Small rotation correction, remove legend\nggplot(bar_data, \n aes(x = x, \n y = y, \n fill = label)) + \n geom_polygon(color = \"black\",\n linewidth = .1,\n show.legend = FALSE) +\n geom_text(data = data,\n aes(x = 0,\n y = y_ll + 0.5,\n label = label),\n hjust = \"right\",\n size = 2) +\n ylim(c(0, 26)) + \n scale_x_continuous(expand = c(0,0)) +\n scale_fill_manual(values = data$color) +\n coord_radial(start = .02,\n end = 15*pi/4) +\n theme_void()\n\n# title and background color\n\nggplot(bar_data, \n aes(x = x, \n y = y, \n fill = label)) + \n geom_polygon(color = \"black\",\n linewidth = .1,\n show.legend = FALSE) +\n geom_text(data = data,\n aes(x = 0,\n y = y_ll + 0.5,\n label = label),\n hjust = \"right\",\n size = 2) +\n ylim(c(0, 26)) + \n scale_x_continuous(expand = c(0,0)) +\n scale_fill_manual(values = data$color) +\n coord_radial(start = .02,\n end = 15*pi/4) +\n labs(title = \"ASSESSED VALUE OF HOUSEHOLD AND KITCHEN FURNITURE \\n OWNED BY GEORGIA NEGROES\") +\n theme_void() +\n theme(plot.background = element_rect(fill = \"#f9f1e7\")) \n\n# center and bold title\nggplot(bar_data, \n aes(x = x, \n y = y, \n fill = label)) + \n geom_polygon(color = \"black\",\n linewidth = .1,\n show.legend = FALSE) +\n geom_text(data = data,\n aes(x = 0,\n y = y_ll + 0.5,\n label = label),\n hjust = \"right\",\n size = 2) +\n ylim(c(0, 26)) + \n scale_x_continuous(expand = c(0,0)) +\n scale_fill_manual(values = data$color) +\n coord_radial(start = .02,\n end = 15*pi/4) +\n labs(title = \"ASSESSED VALUE OF HOUSEHOLD AND KITCHEN FURNITURE \\n OWNED BY GEORGIA NEGROES\") +\n theme_void() +\n theme(plot.background = element_rect(fill = \"#f9f1e7\")) +\n theme(plot.title = element_text(hjust = 0.5,\n face = \"bold\",\n size = 9))\n\n# change font (final for now)\nggplot(bar_data, \n aes(x = x, \n y = y, \n fill = label)) + \n geom_polygon(color = \"black\",\n linewidth = .1,\n show.legend = FALSE) +\n geom_text(data = data,\n aes(x = 0,\n y = y_ll + 0.5,\n label = label),\n hjust = \"right\",\n size = 2) +\n ylim(c(0, 26)) + \n scale_x_continuous(expand = c(0,0)) +\n scale_fill_manual(values = data$color) +\n coord_radial(start = .02,\n end = 15*pi/4) +\n labs(title = \"ASSESSED VALUE OF HOUSEHOLD AND KITCHEN FURNITURE \\n OWNED BY GEORGIA NEGROES\") +\n theme_void() +\n theme(plot.background = element_rect(fill = \"#f9f1e7\")) +\n theme(plot.title = element_text(hjust = 0.5,\n face = \"bold\",\n size = 9),\n text = element_text(family = \"Charter\")) \n\n\n\n# For export --------------------------------------------------------------\n\n\nggplot(bar_data,\n aes(x = x,\n y = y,\n fill = label)) +\n geom_polygon(color = \"black\",\n linewidth = .1,\n show.legend = FALSE) +\n geom_text(data = data,\n aes(x = 0,\n y = y_ll + 0.5,\n label = label),\n hjust = \"right\",\n size = 10) +\n ylim(c(0, 26)) +\n scale_x_continuous(expand = c(0,0)) +\n scale_fill_manual(values = data$color) +\n coord_radial(start = .02,\n end = 15*pi/4) +\n labs(title = \"ASSESSED VALUE OF HOUSEHOLD AND KITCHEN FURNITURE \\n OWNED BY GEORGIA NEGROES\") +\n theme_void() +\n theme(plot.background = element_rect(fill = \"#f9f1e7\")) +\n theme(plot.title = element_text(hjust = 0.5,\n face = \"bold\",\n size = 50),\n text = element_text(family = \"Charter\"))\n\nggsave(\"dubois25_gard.png\",\n width = 22,\n height = 24,\n bg = \"white\")\n\n\n\n\n" }, { "path": "Essential R tools for regression modeling/README.md", "content": "# Essential R tools for regression modeling\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Essential R tools for regression modeling**, which you can find at . \n\n" }, { "path": "Essential R tools for regression modeling/broom.R", "content": "# See https://github.com/equitable-equations/youtube \n# for analysis of the College data set.\n\n# Preliminaries:\n\nlibrary(tidyverse)\nlibrary(ISLR2)\nlibrary(broom)\n\ncollege_sm <- College %>% \n mutate(log_full = log(F.Undergrad)) %>% \n select(Grad.Rate,\n log_full,\n Private,\n Top25perc)\n\n\n# Build the model:\n\nmodel_top <- lm(Grad.Rate ~ log_full + \n Private + \n Top25perc, \n data = college_sm)\n\n# Summarize the model:\n\nsummary(model_top)\n\n# Functions from broom:\n\ntidy(model_top)\n\nmodel_aug <- augment(model_top)\nView(model_aug)\n\nmodel_glance <- glance(model_top)\nView(model_glance)\n\n\n" }, { "path": "Gamma distribution/README.md", "content": "# The gamma distribution\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **The gamma distribution**, which you can find at . \n\n" }, { "path": "Gamma distribution in R/README.md", "content": "# The gamma distribution in R\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **The gamma distribution in R**, which you can find at . \n\n" }, { "path": "Gamma distribution in R/Script - the gamma distribution in R.R", "content": "1 - pgamma(60, 25, 1/3)\n1 - pgamma(60, 25, scale = 3)\n\nqgamma(.95, 5, 1/3)\n\ntimes <- rgamma(1000, 5, 1/3) \ntimes # your results may be different here.\n\nlibrary(tidyverse)\nqplot(times, bins = 25, xlab = \"Waiting times\") +\n geom_vline(xintercept = 27.46)\n" }, { "path": "Getting started with R/README.md", "content": "# Getting started with R\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Getting started with R**, which you can find at . \n\n" }, { "path": "Getting started with R/amy.csv", "content": "artist_name,artist_id,album_id,album_type,album_images,album_release_date,album_release_year,album_release_date_precision,danceability,energy,key,loudness,mode,speechiness,acousticness,instrumentalness,liveness,valence,tempo,track_id,analysis_url,time_signature,artists,available_markets,disc_number,duration_ms,explicit,track_href,is_local,track_name,track_preview_url,track_number,type,track_uri,external_urls.spotify,album_name,key_name,mode_name,key_mode\nAmy Winehouse,6Q192DXotxtaysaqNPy5yR,741JhzvIOudONDzRg6wuWJ,album,,2021-05-07,2021,day,0.782,0.506,9,-8.184,0,0.129,0.317,5.58e-5,0.979,0.748,91.075,7op4mx4y4ON4qT7pZnNM7u,https://api.spotify.com/v1/audio-analysis/7op4mx4y4ON4qT7pZnNM7u,4,,,1,230573,FALSE,https://api.spotify.com/v1/tracks/7op4mx4y4ON4qT7pZnNM7u,FALSE,Stronger Than Me - Live On Later... With Jools Holland / 2003,NA,1,track,spotify:track:7op4mx4y4ON4qT7pZnNM7u,https://open.spotify.com/track/7op4mx4y4ON4qT7pZnNM7u,At The BBC,A,minor,A minor\nAmy Winehouse,6Q192DXotxtaysaqNPy5yR,741JhzvIOudONDzRg6wuWJ,album,,2021-05-07,2021,day,0.443,0.584,1,-5.89,0,0.0595,0.444,0,0.978,0.316,107.168,4PnPQJmXamSFwxoamRuIae,https://api.spotify.com/v1/audio-analysis/4PnPQJmXamSFwxoamRuIae,4,,,1,230480,FALSE,https://api.spotify.com/v1/tracks/4PnPQJmXamSFwxoamRuIae,FALSE,Take The Box - Live From The Mercury Prize Awards / 2004,NA,2,track,spotify:track:4PnPQJmXamSFwxoamRuIae,https://open.spotify.com/track/4PnPQJmXamSFwxoamRuIae,At The BBC,C#,minor,C# minor\nAmy Winehouse,6Q192DXotxtaysaqNPy5yR,741JhzvIOudONDzRg6wuWJ,album,,2021-05-07,2021,day,0.454,0.326,5,-9.172,0,0.0384,0.721,0,0.981,0.553,102.492,1U0cCrP1EhDFlmB6CTRh5O,https://api.spotify.com/v1/audio-analysis/1U0cCrP1EhDFlmB6CTRh5O,3,,,1,198080,FALSE,https://api.spotify.com/v1/tracks/1U0cCrP1EhDFlmB6CTRh5O,FALSE,Teach Me Tonight - Live On Jools Holland Hootenanny / 2004,NA,3,track,spotify:track:1U0cCrP1EhDFlmB6CTRh5O,https://open.spotify.com/track/1U0cCrP1EhDFlmB6CTRh5O,At The BBC,F,minor,F minor\nAmy Winehouse,6Q192DXotxtaysaqNPy5yR,741JhzvIOudONDzRg6wuWJ,album,,2021-05-07,2021,day,0.509,0.759,0,-7.17,1,0.0982,0.292,4.05e-6,0.71,0.567,144.61,7dBVgA43mdpWAxeoc4nx2O,https://api.spotify.com/v1/audio-analysis/7dBVgA43mdpWAxeoc4nx2O,4,,,1,225080,FALSE,https://api.spotify.com/v1/tracks/7dBVgA43mdpWAxeoc4nx2O,FALSE,Rehab - Live On Later... With Jools Holland / 2006,NA,4,track,spotify:track:7dBVgA43mdpWAxeoc4nx2O,https://open.spotify.com/track/7dBVgA43mdpWAxeoc4nx2O,At The BBC,C,major,C major\nAmy Winehouse,6Q192DXotxtaysaqNPy5yR,741JhzvIOudONDzRg6wuWJ,album,,2021-05-07,2021,day,0.5,0.691,0,-10.17,1,0.317,0.915,0.00127,0.962,0.886,60.105,2RtYWELB3znKy86yd9VTfI,https://api.spotify.com/v1/audio-analysis/2RtYWELB3znKy86yd9VTfI,3,,,1,73693,FALSE,https://api.spotify.com/v1/tracks/2RtYWELB3znKy86yd9VTfI,FALSE,Tenderly - Live On Later... With Jools Holland / 2006,NA,5,track,spotify:track:2RtYWELB3znKy86yd9VTfI,https://open.spotify.com/track/2RtYWELB3znKy86yd9VTfI,At The BBC,C,major,C major\nAmy Winehouse,6Q192DXotxtaysaqNPy5yR,741JhzvIOudONDzRg6wuWJ,album,,2021-05-07,2021,day,0.458,0.857,1,-6.863,0,0.296,0.469,0,0.973,0.659,120.509,6p5rDpiFAwFqopcLP1cXIn,https://api.spotify.com/v1/audio-analysis/6p5rDpiFAwFqopcLP1cXIn,4,,,1,204506,FALSE,https://api.spotify.com/v1/tracks/6p5rDpiFAwFqopcLP1cXIn,FALSE,Tears Dry On Their Own - Live On Later... With Jools Holland / 2006,NA,6,track,spotify:track:6p5rDpiFAwFqopcLP1cXIn,https://open.spotify.com/track/6p5rDpiFAwFqopcLP1cXIn,At The BBC,C#,minor,C# minor\nAmy Winehouse,6Q192DXotxtaysaqNPy5yR,741JhzvIOudONDzRg6wuWJ,album,,2021-05-07,2021,day,0.663,0.871,10,-6.598,1,0.098,0.453,8.38e-4,0.973,0.926,135.228,11RwX2XrkrduAwdmug89ja,https://api.spotify.com/v1/audio-analysis/11RwX2XrkrduAwdmug89ja,4,,,1,199760,FALSE,https://api.spotify.com/v1/tracks/11RwX2XrkrduAwdmug89ja,FALSE,Monkey Man - Live On Jools Holland Hootenanny / 2006,NA,7,track,spotify:track:11RwX2XrkrduAwdmug89ja,https://open.spotify.com/track/11RwX2XrkrduAwdmug89ja,At The BBC,A#,major,A# major\nAmy Winehouse,6Q192DXotxtaysaqNPy5yR,741JhzvIOudONDzRg6wuWJ,album,,2021-05-07,2021,day,0.593,0.667,6,-7.266,0,0.0402,0.569,1.56e-5,0.974,0.63,113.781,5kB7BZY3dJJtQYiXkyesrD,https://api.spotify.com/v1/audio-analysis/5kB7BZY3dJJtQYiXkyesrD,4,,,1,234066,FALSE,https://api.spotify.com/v1/tracks/5kB7BZY3dJJtQYiXkyesrD,FALSE,I Heard It Through The Grapevine - Live On Jools Holland Hootenanny / 2006,NA,8,track,spotify:track:5kB7BZY3dJJtQYiXkyesrD,https://open.spotify.com/track/5kB7BZY3dJJtQYiXkyesrD,At The BBC,F#,minor,F# minor\nAmy Winehouse,6Q192DXotxtaysaqNPy5yR,741JhzvIOudONDzRg6wuWJ,album,,2021-05-07,2021,day,0.317,0.517,10,-7.921,1,0.0589,0.331,4.25e-5,0.984,0.342,99.59,3kt92YozPwnpnE5yQziJ60,https://api.spotify.com/v1/audio-analysis/3kt92YozPwnpnE5yQziJ60,4,,,1,276080,FALSE,https://api.spotify.com/v1/tracks/3kt92YozPwnpnE5yQziJ60,FALSE,Don't Go To Strangers - Live On Jools Holland Hootenanny / 2006,NA,9,track,spotify:track:3kt92YozPwnpnE5yQziJ60,https://open.spotify.com/track/3kt92YozPwnpnE5yQziJ60,At The BBC,A#,major,A# major\nAmy Winehouse,6Q192DXotxtaysaqNPy5yR,741JhzvIOudONDzRg6wuWJ,album,,2021-05-07,2021,day,0.398,0.155,0,-11.058,1,0.0417,0.945,9.51e-5,0.717,0.24,178.489,3dqHcjkKozhNqz6E4lK6bC,https://api.spotify.com/v1/audio-analysis/3dqHcjkKozhNqz6E4lK6bC,4,,,1,154893,FALSE,https://api.spotify.com/v1/tracks/3dqHcjkKozhNqz6E4lK6bC,FALSE,Love Is A Losing Game - Live From The Mercury Prize Awards / 2007,NA,10,track,spotify:track:3dqHcjkKozhNqz6E4lK6bC,https://open.spotify.com/track/3dqHcjkKozhNqz6E4lK6bC,At The BBC,C,major,C major\nAmy Winehouse,6Q192DXotxtaysaqNPy5yR,741JhzvIOudONDzRg6wuWJ,album,,2021-05-07,2021,day,0.556,0.696,5,-6.983,0,0.124,0.0954,6.19e-6,0.627,0.547,144.993,3NiKAPhnUU2OW9idi7xBRu,https://api.spotify.com/v1/audio-analysis/3NiKAPhnUU2OW9idi7xBRu,4,,,2,235266,FALSE,https://api.spotify.com/v1/tracks/3NiKAPhnUU2OW9idi7xBRu,FALSE,Know You Now - Live At The Leicester Summer Sundae / 2004,NA,1,track,spotify:track:3NiKAPhnUU2OW9idi7xBRu,https://open.spotify.com/track/3NiKAPhnUU2OW9idi7xBRu,At The BBC,F,minor,F minor\nAmy Winehouse,6Q192DXotxtaysaqNPy5yR,741JhzvIOudONDzRg6wuWJ,album,,2021-05-07,2021,day,0.423,0.533,2,-7.525,1,0.191,0.447,0,0.981,0.49,202.006,6qBNpIyUFTEG1CkODQsc9x,https://api.spotify.com/v1/audio-analysis/6qBNpIyUFTEG1CkODQsc9x,4,,,2,221560,FALSE,https://api.spotify.com/v1/tracks/6qBNpIyUFTEG1CkODQsc9x,FALSE,Fuck Me Pumps - Live From T In The Park / 2004,NA,2,track,spotify:track:6qBNpIyUFTEG1CkODQsc9x,https://open.spotify.com/track/6qBNpIyUFTEG1CkODQsc9x,At The BBC,D,major,D major\nAmy Winehouse,6Q192DXotxtaysaqNPy5yR,741JhzvIOudONDzRg6wuWJ,album,,2021-05-07,2021,day,0.574,0.7,10,-4.965,0,0.06,0.282,7.7e-6,0.686,0.728,98.053,2gFsfv8A295IgWxcf6ELNG,https://api.spotify.com/v1/audio-analysis/2gFsfv8A295IgWxcf6ELNG,4,,,2,277773,FALSE,https://api.spotify.com/v1/tracks/2gFsfv8A295IgWxcf6ELNG,FALSE,In My Bed - Live From T In The Park / 2004,NA,3,track,spotify:track:2gFsfv8A295IgWxcf6ELNG,https://open.spotify.com/track/2gFsfv8A295IgWxcf6ELNG,At The BBC,A#,minor,A# minor\nAmy Winehouse,6Q192DXotxtaysaqNPy5yR,741JhzvIOudONDzRg6wuWJ,album,,2021-05-07,2021,day,0.539,0.756,7,-5.194,1,0.248,0.242,0,0.905,0.571,99.986,14JgmDzPjbmaUY3pkQFdWX,https://api.spotify.com/v1/audio-analysis/14JgmDzPjbmaUY3pkQFdWX,4,,,2,221986,FALSE,https://api.spotify.com/v1/tracks/14JgmDzPjbmaUY3pkQFdWX,FALSE,October Song - Live From T In The Park / 2004,NA,4,track,spotify:track:14JgmDzPjbmaUY3pkQFdWX,https://open.spotify.com/track/14JgmDzPjbmaUY3pkQFdWX,At The BBC,G,major,G major\nAmy Winehouse,6Q192DXotxtaysaqNPy5yR,741JhzvIOudONDzRg6wuWJ,album,,2021-05-07,2021,day,0.591,0.635,5,-7.25,1,0.178,0.242,0,0.506,0.488,138.12,6T5wYCaa6wmaAsAPlDNPNv,https://api.spotify.com/v1/audio-analysis/6T5wYCaa6wmaAsAPlDNPNv,4,,,2,238040,FALSE,https://api.spotify.com/v1/tracks/6T5wYCaa6wmaAsAPlDNPNv,FALSE,\"Rehab - Live Pete Mitchell, BBC Radio Session / 2006\",NA,5,track,spotify:track:6T5wYCaa6wmaAsAPlDNPNv,https://open.spotify.com/track/6T5wYCaa6wmaAsAPlDNPNv,At The BBC,F,major,F major\nAmy Winehouse,6Q192DXotxtaysaqNPy5yR,741JhzvIOudONDzRg6wuWJ,album,,2021-05-07,2021,day,0.755,0.37,2,-6.787,0,0.0698,0.825,1.34e-4,0.0664,0.598,103.956,41kB3pCfmjTFEzUYsIWzI7,https://api.spotify.com/v1/audio-analysis/41kB3pCfmjTFEzUYsIWzI7,4,,,2,206906,FALSE,https://api.spotify.com/v1/tracks/41kB3pCfmjTFEzUYsIWzI7,FALSE,\"You Know I'm No Good - Live Jo Whiley, BBC Live Lounge Session / 2007\",NA,6,track,spotify:track:41kB3pCfmjTFEzUYsIWzI7,https://open.spotify.com/track/41kB3pCfmjTFEzUYsIWzI7,At The BBC,D,minor,D minor\nAmy Winehouse,6Q192DXotxtaysaqNPy5yR,741JhzvIOudONDzRg6wuWJ,album,,2021-05-07,2021,day,0.496,0.708,9,-6.844,1,0.0413,0.398,1.22e-4,0.881,0.648,104.12,2g6qGfcMKlv1KLn1Ia4Nw7,https://api.spotify.com/v1/audio-analysis/2g6qGfcMKlv1KLn1Ia4Nw7,4,,,2,197360,FALSE,https://api.spotify.com/v1/tracks/2g6qGfcMKlv1KLn1Ia4Nw7,FALSE,Just Friends - Live BBC Radio 2 Big Band Special / 2009,NA,7,track,spotify:track:2g6qGfcMKlv1KLn1Ia4Nw7,https://open.spotify.com/track/2g6qGfcMKlv1KLn1Ia4Nw7,At The BBC,A,major,A major\nAmy Winehouse,6Q192DXotxtaysaqNPy5yR,741JhzvIOudONDzRg6wuWJ,album,,2021-05-07,2021,day,0.508,0.506,0,-6.69,1,0.0482,0.516,2.74e-4,0.702,0.393,159.831,04oBXjVazNa2K9v7cwUTNj,https://api.spotify.com/v1/audio-analysis/04oBXjVazNa2K9v7cwUTNj,4,,,2,169746,FALSE,https://api.spotify.com/v1/tracks/04oBXjVazNa2K9v7cwUTNj,FALSE,Love Is A Losing Game - Live On Later... With Jools Holland / 2009,NA,8,track,spotify:track:04oBXjVazNa2K9v7cwUTNj,https://open.spotify.com/track/04oBXjVazNa2K9v7cwUTNj,At The BBC,C,major,C major\nAmy Winehouse,6Q192DXotxtaysaqNPy5yR,741JhzvIOudONDzRg6wuWJ,album,,2021-05-07,2021,day,0.611,0.587,1,-6.203,0,0.0333,0.433,4.51e-6,0.112,0.649,119.732,6GY7yKOI1ZTpXk6xSnjzUK,https://api.spotify.com/v1/audio-analysis/6GY7yKOI1ZTpXk6xSnjzUK,4,,,2,191186,FALSE,https://api.spotify.com/v1/tracks/6GY7yKOI1ZTpXk6xSnjzUK,FALSE,\"Tears Dry On Their Own - Live Jo Whiley, BBC Live Lounge Session / 2007\",NA,9,track,spotify:track:6GY7yKOI1ZTpXk6xSnjzUK,https://open.spotify.com/track/6GY7yKOI1ZTpXk6xSnjzUK,At The BBC,C#,minor,C# minor\nAmy Winehouse,6Q192DXotxtaysaqNPy5yR,741JhzvIOudONDzRg6wuWJ,album,,2021-05-07,2021,day,0.546,0.709,11,-6.557,1,0.0931,0.181,0,0.915,0.582,97.258,4vyL8RX03QfBRBs9dMUHxX,https://api.spotify.com/v1/audio-analysis/4vyL8RX03QfBRBs9dMUHxX,4,,,2,183693,FALSE,https://api.spotify.com/v1/tracks/4vyL8RX03QfBRBs9dMUHxX,FALSE,\"Best Friends, Right? - Live At The Leicester Summer Sundae / 2004\",NA,10,track,spotify:track:4vyL8RX03QfBRBs9dMUHxX,https://open.spotify.com/track/4vyL8RX03QfBRBs9dMUHxX,At The BBC,B,major,B major\nAmy Winehouse,6Q192DXotxtaysaqNPy5yR,741JhzvIOudONDzRg6wuWJ,album,,2021-05-07,2021,day,0.302,0.375,4,-6.187,0,0.0317,0.744,0.0507,0.73,0.0709,128.33,2vqISHPIeMcCS9P2JLQVBe,https://api.spotify.com/v1/audio-analysis/2vqISHPIeMcCS9P2JLQVBe,4,,,2,219026,FALSE,https://api.spotify.com/v1/tracks/2vqISHPIeMcCS9P2JLQVBe,FALSE,I Should Care - Live From The Stables / 2004,NA,11,track,spotify:track:2vqISHPIeMcCS9P2JLQVBe,https://open.spotify.com/track/2vqISHPIeMcCS9P2JLQVBe,At The BBC,E,minor,E minor\nAmy Winehouse,6Q192DXotxtaysaqNPy5yR,741JhzvIOudONDzRg6wuWJ,album,,2021-05-07,2021,day,0.338,0.706,3,-3.427,1,0.076,0.64,3.8e-6,0.941,0.579,182.973,52nsOnLZuRQSNJr7940Xyb,https://api.spotify.com/v1/audio-analysis/52nsOnLZuRQSNJr7940Xyb,4,,,2,154680,FALSE,https://api.spotify.com/v1/tracks/52nsOnLZuRQSNJr7940Xyb,FALSE,Lullaby Of Birdland - Live From The Stables / 2004,NA,12,track,spotify:track:52nsOnLZuRQSNJr7940Xyb,https://open.spotify.com/track/52nsOnLZuRQSNJr7940Xyb,At The BBC,D#,major,D# major\nAmy Winehouse,6Q192DXotxtaysaqNPy5yR,741JhzvIOudONDzRg6wuWJ,album,,2021-05-07,2021,day,0.757,0.302,5,-8.13,0,0.0375,0.739,2.08e-6,0.16,0.657,96.125,4ml8AFICctVaeDXoZyhp42,https://api.spotify.com/v1/audio-analysis/4ml8AFICctVaeDXoZyhp42,4,,,2,231066,FALSE,https://api.spotify.com/v1/tracks/4ml8AFICctVaeDXoZyhp42,FALSE,\"Valerie - Live Jo Whiley, BBC Live Lounge Session / 2007\",NA,13,track,spotify:track:4ml8AFICctVaeDXoZyhp42,https://open.spotify.com/track/4ml8AFICctVaeDXoZyhp42,At The BBC,F,minor,F minor\nAmy Winehouse,6Q192DXotxtaysaqNPy5yR,741JhzvIOudONDzRg6wuWJ,album,,2021-05-07,2021,day,0.401,0.425,2,-7.489,1,0.0327,0.778,0,0.119,0.301,171.525,1oUD1lrhU5MnAxrQvLvTdX,https://api.spotify.com/v1/audio-analysis/1oUD1lrhU5MnAxrQvLvTdX,3,,,2,153920,FALSE,https://api.spotify.com/v1/tracks/1oUD1lrhU5MnAxrQvLvTdX,FALSE,\"To Know Him Is To Love Him - Live Pete Mitchell, BBC Radio Session / 2006\",NA,14,track,spotify:track:1oUD1lrhU5MnAxrQvLvTdX,https://open.spotify.com/track/1oUD1lrhU5MnAxrQvLvTdX,At The BBC,D,major,D major\nAmy Winehouse,6Q192DXotxtaysaqNPy5yR,741JhzvIOudONDzRg6wuWJ,album,,2021-05-07,2021,day,0.471,0.775,1,-6.753,1,0.323,0.289,1.13e-4,0.918,0.607,75.012,0hyOh5ZiEZREY6Vq2aCbJu,https://api.spotify.com/v1/audio-analysis/0hyOh5ZiEZREY6Vq2aCbJu,4,,,3,193453,FALSE,https://api.spotify.com/v1/tracks/0hyOh5ZiEZREY6Vq2aCbJu,FALSE,Know You Now - Live At Porchester Hall / 2007,NA,1,track,spotify:track:0hyOh5ZiEZREY6Vq2aCbJu,https://open.spotify.com/track/0hyOh5ZiEZREY6Vq2aCbJu,At The BBC,C#,major,C# major\nAmy Winehouse,6Q192DXotxtaysaqNPy5yR,741JhzvIOudONDzRg6wuWJ,album,,2021-05-07,2021,day,0.451,0.771,1,-6.415,0,0.454,0.687,1.38e-5,0.776,0.545,121.284,6VYG3BMGYLDV0OR6sEXax8,https://api.spotify.com/v1/audio-analysis/6VYG3BMGYLDV0OR6sEXax8,4,,,3,207413,FALSE,https://api.spotify.com/v1/tracks/6VYG3BMGYLDV0OR6sEXax8,FALSE,Tears Dry On Their Own - Live At Porchester Hall / 2007,NA,2,track,spotify:track:6VYG3BMGYLDV0OR6sEXax8,https://open.spotify.com/track/6VYG3BMGYLDV0OR6sEXax8,At The BBC,C#,minor,C# minor\nAmy Winehouse,6Q192DXotxtaysaqNPy5yR,741JhzvIOudONDzRg6wuWJ,album,,2021-05-07,2021,day,0.555,0.718,9,-7.649,1,0.113,0.229,1.39e-4,0.892,0.476,111.377,1ZhAr4A06WiFRR99KUy6Cr,https://api.spotify.com/v1/audio-analysis/1ZhAr4A06WiFRR99KUy6Cr,4,,,3,255520,FALSE,https://api.spotify.com/v1/tracks/1ZhAr4A06WiFRR99KUy6Cr,FALSE,You Know I'm No Good - Live At Porchester Hall / 2007,NA,3,track,spotify:track:1ZhAr4A06WiFRR99KUy6Cr,https://open.spotify.com/track/1ZhAr4A06WiFRR99KUy6Cr,At The BBC,A,major,A major\nAmy Winehouse,6Q192DXotxtaysaqNPy5yR,741JhzvIOudONDzRg6wuWJ,album,,2021-05-07,2021,day,0.66,0.542,9,-8.506,1,0.0849,0.414,3.72e-4,0.677,0.531,95.94,10OcV3kj7y1mS2XproMP0L,https://api.spotify.com/v1/audio-analysis/10OcV3kj7y1mS2XproMP0L,4,,,3,192973,FALSE,https://api.spotify.com/v1/tracks/10OcV3kj7y1mS2XproMP0L,FALSE,Just Friends - Live At Porchester Hall / 2007,NA,4,track,spotify:track:10OcV3kj7y1mS2XproMP0L,https://open.spotify.com/track/10OcV3kj7y1mS2XproMP0L,At The BBC,A,major,A major\nAmy Winehouse,6Q192DXotxtaysaqNPy5yR,741JhzvIOudONDzRg6wuWJ,album,,2021-05-07,2021,day,0.546,0.732,11,-6.903,0,0.164,0.384,0,0.946,0.576,101.221,63GyGK1Xn6nolMAz0hOk7J,https://api.spotify.com/v1/audio-analysis/63GyGK1Xn6nolMAz0hOk7J,4,,,3,195240,FALSE,https://api.spotify.com/v1/tracks/63GyGK1Xn6nolMAz0hOk7J,FALSE,He Can Only Hold Her - Live At Porchester Hall / 2007,NA,5,track,spotify:track:63GyGK1Xn6nolMAz0hOk7J,https://open.spotify.com/track/63GyGK1Xn6nolMAz0hOk7J,At The BBC,B,minor,B minor\nAmy Winehouse,6Q192DXotxtaysaqNPy5yR,741JhzvIOudONDzRg6wuWJ,album,,2021-05-07,2021,day,0.407,0.454,9,-7.869,1,0.053,0.753,0,0.968,0.224,112.658,6jeGMGeUYfYSJPy1ucqbMC,https://api.spotify.com/v1/audio-analysis/6jeGMGeUYfYSJPy1ucqbMC,4,,,3,200573,FALSE,https://api.spotify.com/v1/tracks/6jeGMGeUYfYSJPy1ucqbMC,FALSE,I Heard Love Is Blind - Live At Porchester Hall / 2007,NA,6,track,spotify:track:6jeGMGeUYfYSJPy1ucqbMC,https://open.spotify.com/track/6jeGMGeUYfYSJPy1ucqbMC,At The BBC,A,major,A major\nAmy Winehouse,6Q192DXotxtaysaqNPy5yR,741JhzvIOudONDzRg6wuWJ,album,,2021-05-07,2021,day,0.42,0.79,0,-6.297,1,0.156,0.669,2.84e-5,0.967,0.474,152.139,6OZWdGJUyKiaApZhdcaaPa,https://api.spotify.com/v1/audio-analysis/6OZWdGJUyKiaApZhdcaaPa,4,,,3,218613,FALSE,https://api.spotify.com/v1/tracks/6OZWdGJUyKiaApZhdcaaPa,FALSE,Rehab - Live At Porchester Hall / 2007,NA,7,track,spotify:track:6OZWdGJUyKiaApZhdcaaPa,https://open.spotify.com/track/6OZWdGJUyKiaApZhdcaaPa,At The BBC,C,major,C major\nAmy Winehouse,6Q192DXotxtaysaqNPy5yR,741JhzvIOudONDzRg6wuWJ,album,,2021-05-07,2021,day,0.48,0.502,1,-7.21,0,0.0851,0.597,1.16e-6,0.93,0.363,113.677,3hxKhsgHf8ALzGBOTkvwId,https://api.spotify.com/v1/audio-analysis/3hxKhsgHf8ALzGBOTkvwId,4,,,3,210560,FALSE,https://api.spotify.com/v1/tracks/3hxKhsgHf8ALzGBOTkvwId,FALSE,Take The Box - Live At Porchester Hall / 2007,NA,8,track,spotify:track:3hxKhsgHf8ALzGBOTkvwId,https://open.spotify.com/track/3hxKhsgHf8ALzGBOTkvwId,At The BBC,C#,minor,C# minor\nAmy Winehouse,6Q192DXotxtaysaqNPy5yR,741JhzvIOudONDzRg6wuWJ,album,,2021-05-07,2021,day,0.449,0.439,5,-9.063,1,0.0677,0.878,0.00248,0.718,0.184,131.59,1QvZS74G7QMwNaooqXF5QQ,https://api.spotify.com/v1/audio-analysis/1QvZS74G7QMwNaooqXF5QQ,3,,,3,201706,FALSE,https://api.spotify.com/v1/tracks/1QvZS74G7QMwNaooqXF5QQ,FALSE,Some Unholy War - Live At Porchester Hall / 2007,NA,9,track,spotify:track:1QvZS74G7QMwNaooqXF5QQ,https://open.spotify.com/track/1QvZS74G7QMwNaooqXF5QQ,At The BBC,F,major,F major\nAmy Winehouse,6Q192DXotxtaysaqNPy5yR,741JhzvIOudONDzRg6wuWJ,album,,2021-05-07,2021,day,0.504,0.694,2,-7.154,1,0.255,0.432,0.00356,0.972,0.347,138.178,0jUfqdTFe6YacBEMpDjCef,https://api.spotify.com/v1/audio-analysis/0jUfqdTFe6YacBEMpDjCef,4,,,3,234840,FALSE,https://api.spotify.com/v1/tracks/0jUfqdTFe6YacBEMpDjCef,FALSE,Back To Black - Live At Porchester Hall / 2007,NA,10,track,spotify:track:0jUfqdTFe6YacBEMpDjCef,https://open.spotify.com/track/0jUfqdTFe6YacBEMpDjCef,At The BBC,D,major,D major\nAmy Winehouse,6Q192DXotxtaysaqNPy5yR,741JhzvIOudONDzRg6wuWJ,album,,2021-05-07,2021,day,0.461,0.705,5,-6.084,0,0.0825,0.539,1.5e-5,0.605,0.554,103.815,6xdXjusXkedyQKktEgH3D6,https://api.spotify.com/v1/audio-analysis/6xdXjusXkedyQKktEgH3D6,4,,,3,215826,FALSE,https://api.spotify.com/v1/tracks/6xdXjusXkedyQKktEgH3D6,FALSE,Valerie - Live At Porchester Hall / 2007,NA,11,track,spotify:track:6xdXjusXkedyQKktEgH3D6,https://open.spotify.com/track/6xdXjusXkedyQKktEgH3D6,At The BBC,F,minor,F minor\nAmy Winehouse,6Q192DXotxtaysaqNPy5yR,741JhzvIOudONDzRg6wuWJ,album,,2021-05-07,2021,day,0.48,0.763,8,-6.449,1,0.13,0.696,2.91e-5,0.969,0.465,109.107,4g3DjDIpi4pujMwQsKwBEP,https://api.spotify.com/v1/audio-analysis/4g3DjDIpi4pujMwQsKwBEP,4,,,3,178880,FALSE,https://api.spotify.com/v1/tracks/4g3DjDIpi4pujMwQsKwBEP,FALSE,Addicted - Live At Porchester Hall / 2007,NA,12,track,spotify:track:4g3DjDIpi4pujMwQsKwBEP,https://open.spotify.com/track/4g3DjDIpi4pujMwQsKwBEP,At The BBC,G#,major,G# major\nAmy Winehouse,6Q192DXotxtaysaqNPy5yR,741JhzvIOudONDzRg6wuWJ,album,,2021-05-07,2021,day,0.477,0.603,1,-6.786,0,0.0612,0.726,1.7e-4,0.95,0.428,88.302,4cks9W8cvxx0FnsdLSkqNj,https://api.spotify.com/v1/audio-analysis/4cks9W8cvxx0FnsdLSkqNj,4,,,3,161893,FALSE,https://api.spotify.com/v1/tracks/4cks9W8cvxx0FnsdLSkqNj,FALSE,Me & Mr Jones - Live At Porchester Hall / 2007,NA,13,track,spotify:track:4cks9W8cvxx0FnsdLSkqNj,https://open.spotify.com/track/4cks9W8cvxx0FnsdLSkqNj,At The BBC,C#,minor,C# minor\nAmy Winehouse,6Q192DXotxtaysaqNPy5yR,741JhzvIOudONDzRg6wuWJ,album,,2021-05-07,2021,day,0.345,0.891,5,-6.641,0,0.32,0.108,0,0.936,0.529,158.359,1FZxXiHj3oOYygBKO8cRcr,https://api.spotify.com/v1/audio-analysis/1FZxXiHj3oOYygBKO8cRcr,4,,,3,171493,FALSE,https://api.spotify.com/v1/tracks/1FZxXiHj3oOYygBKO8cRcr,FALSE,Monkey Man - Live At Porchester Hall / 2007,NA,14,track,spotify:track:1FZxXiHj3oOYygBKO8cRcr,https://open.spotify.com/track/1FZxXiHj3oOYygBKO8cRcr,At The BBC,F,minor,F minor\nAmy Winehouse,6Q192DXotxtaysaqNPy5yR,5Fb59VtYjJNTXPcNGpoDU3,album,,2015-10-30,2015,day,0.552,0.0227,3,-24.076,1,0.0585,0.993,0.826,0.108,0.053900000000000003,135.179,0ct0FgC4wIXBNXhxADiOYw,https://api.spotify.com/v1/audio-analysis/0ct0FgC4wIXBNXhxADiOYw,3,,,1,58226,FALSE,https://api.spotify.com/v1/tracks/0ct0FgC4wIXBNXhxADiOYw,FALSE,Opening - 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Winehouse,6Q192DXotxtaysaqNPy5yR,0RisvCe4jLWes0m89StnuZ,album,,2003,2003,year,0.539,0.809,10,-8.851,0,0.0683,0.114,1.02e-5,0.0715,0.724,95.631,4o2d25zcVbx5BEaHcKbtOI,https://api.spotify.com/v1/audio-analysis/4o2d25zcVbx5BEaHcKbtOI,4,,,1,317000,FALSE,https://api.spotify.com/v1/tracks/4o2d25zcVbx5BEaHcKbtOI,FALSE,In My Bed,NA,11,track,spotify:track:4o2d25zcVbx5BEaHcKbtOI,https://open.spotify.com/track/4o2d25zcVbx5BEaHcKbtOI,Frank (US Clean e-Version),A#,minor,A# minor\nAmy Winehouse,6Q192DXotxtaysaqNPy5yR,0RisvCe4jLWes0m89StnuZ,album,,2003,2003,year,0.633,0.5,1,-8.008,0,0.0387,0.0163,4.71e-6,0.112,0.157,115.822,0vlOooGJoEdbOGYt7ctoaF,https://api.spotify.com/v1/audio-analysis/0vlOooGJoEdbOGYt7ctoaF,4,,,1,200200,FALSE,https://api.spotify.com/v1/tracks/0vlOooGJoEdbOGYt7ctoaF,FALSE,Take The Box - Album Version (Edited),NA,12,track,spotify:track:0vlOooGJoEdbOGYt7ctoaF,https://open.spotify.com/track/0vlOooGJoEdbOGYt7ctoaF,Frank (US Clean e-Version),C#,minor,C# minor\nAmy Winehouse,6Q192DXotxtaysaqNPy5yR,0RisvCe4jLWes0m89StnuZ,album,,2003,2003,year,0.672,0.842,0,-7.673,1,0.256,0.0112,0.135,0.0381,0.694,147.093,1oP9MTgQc5qHhamMCt4sky,https://api.spotify.com/v1/audio-analysis/1oP9MTgQc5qHhamMCt4sky,3,,,1,205066,FALSE,https://api.spotify.com/v1/tracks/1oP9MTgQc5qHhamMCt4sky,FALSE,October Song,NA,13,track,spotify:track:1oP9MTgQc5qHhamMCt4sky,https://open.spotify.com/track/1oP9MTgQc5qHhamMCt4sky,Frank (US Clean e-Version),C,major,C major\nAmy Winehouse,6Q192DXotxtaysaqNPy5yR,0RisvCe4jLWes0m89StnuZ,album,,2003,2003,year,0.749,0.525,11,-7.041,0,0.065,0.35,8.9e-6,0.212,0.504,76.447,0q9SlVJ8Ygh01jmZNsbDa8,https://api.spotify.com/v1/audio-analysis/0q9SlVJ8Ygh01jmZNsbDa8,4,,,1,209306,FALSE,https://api.spotify.com/v1/tracks/0q9SlVJ8Ygh01jmZNsbDa8,FALSE,What Is It About Men,NA,14,track,spotify:track:0q9SlVJ8Ygh01jmZNsbDa8,https://open.spotify.com/track/0q9SlVJ8Ygh01jmZNsbDa8,Frank (US Clean e-Version),B,minor,B minor\nAmy Winehouse,6Q192DXotxtaysaqNPy5yR,0RisvCe4jLWes0m89StnuZ,album,,2003,2003,year,0.719,0.54,0,-8.718,1,0.242,0.0373,6.46e-5,0.0817,0.613,80.039,603FH2sLZj6p8zgl4UPkmu,https://api.spotify.com/v1/audio-analysis/603FH2sLZj6p8zgl4UPkmu,4,,,1,254600,FALSE,https://api.spotify.com/v1/tracks/603FH2sLZj6p8zgl4UPkmu,FALSE,Amy Amy Amy,NA,15,track,spotify:track:603FH2sLZj6p8zgl4UPkmu,https://open.spotify.com/track/603FH2sLZj6p8zgl4UPkmu,Frank (US Clean e-Version),C,major,C major\nAmy Winehouse,6Q192DXotxtaysaqNPy5yR,0RisvCe4jLWes0m89StnuZ,album,,2003,2003,year,0.655,0.557,7,-12.639,0,0.138,0.456,0.00271,0.0682,0.714,121.096,56FUhbh4zJYYWaZXRUoAN7,https://api.spotify.com/v1/audio-analysis/56FUhbh4zJYYWaZXRUoAN7,5,,,1,44093,FALSE,https://api.spotify.com/v1/tracks/56FUhbh4zJYYWaZXRUoAN7,FALSE,Outro,NA,16,track,spotify:track:56FUhbh4zJYYWaZXRUoAN7,https://open.spotify.com/track/56FUhbh4zJYYWaZXRUoAN7,Frank (US Clean e-Version),G,minor,G minor\nAmy Winehouse,6Q192DXotxtaysaqNPy5yR,0RisvCe4jLWes0m89StnuZ,album,,2003,2003,year,0.732,0.398,6,-11.108,1,0.274,0.216,1.49e-4,0.12,0.63,83.701,0s0YP92SuqVbaGoggFGJEd,https://api.spotify.com/v1/audio-analysis/0s0YP92SuqVbaGoggFGJEd,4,,,1,253946,FALSE,https://api.spotify.com/v1/tracks/0s0YP92SuqVbaGoggFGJEd,FALSE,Brother,NA,17,track,spotify:track:0s0YP92SuqVbaGoggFGJEd,https://open.spotify.com/track/0s0YP92SuqVbaGoggFGJEd,Frank (US Clean e-Version),F#,major,F# major\nAmy Winehouse,6Q192DXotxtaysaqNPy5yR,0RisvCe4jLWes0m89StnuZ,album,,2003,2003,year,0.587,0.519,5,-7.304,0,0.069,0.0141,0.0184,0.102,0.558,96.508,4bjmPbbudPDelbHzL8dxh2,https://api.spotify.com/v1/audio-analysis/4bjmPbbudPDelbHzL8dxh2,4,,,1,235933,FALSE,https://api.spotify.com/v1/tracks/4bjmPbbudPDelbHzL8dxh2,FALSE,Mr Magic (Through The Smoke),NA,18,track,spotify:track:4bjmPbbudPDelbHzL8dxh2,https://open.spotify.com/track/4bjmPbbudPDelbHzL8dxh2,Frank (US Clean e-Version),F,minor,F minor\n" }, { "path": "Getting started with simple linear regression/README.md", "content": "# Getting started with simple linear regression in R\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Getting started with simple linear regression in R**, which you can find at . \n\n" }, { "path": "Git branches and merges/README.md", "content": "# Git for R users: branches and merges\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Git for R users: branches and merges**, which you can find at . " }, { "path": "Git branches and merges/pivot longer.R", "content": "# pivot longer: billboard\n\nlibrary(tidyverse)\n\n# the pivot\nbillboard_long <- billboard %>% \n pivot_longer(cols = starts_with(\"wk\"),\n names_to = \"week\",\n values_to = \"rank\",\n names_prefix = \"wk\",\n values_drop_na = TRUE) %>% \n mutate(week = as.numeric(week))\nglimpse(billboard_long)\n\n# which tracks lasted at least 52 weeks?\nall_year <- billboard_long %>% \n count(track) %>% \n filter(n >= 52)\nall_year\n\n# filter for these tracks and add real date of chart\nbillboard_top4 <- billboard_long %>% \n filter(track %in% all_year$track) %>% \n mutate(Date = date.entered + 7 * week - 7)\n\n# the plot\nggplot(billboard_top4,\n aes(x = Date,\n y = rank,\n color = track)) +\n geom_line() + \n labs(y = \"Rank\",\n color = \"Song title\",\n title = \"Popular songs of 2000\") +\n scale_color_brewer(palette = \"Dark2\") +\n theme_minimal() \n\n " }, { "path": "Git branches and merges/pivot wider.R", "content": "# pivot wider: table2\n\n# load packages\nlibrary(tidyverse)\n\n# the problem\ntable2\n\n# the solution\ntable2 %>% \n pivot_wider(names_from = type,\n values_from = count)\n" }, { "path": "Git setup/README.md", "content": "# Git for R users: setup\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Git for R users: setup**, which you can find at . " }, { "path": "Git setup/git for R users.R", "content": "# First install git.\n# Mac: Enter `git config` in the terminal\n# Windows: https://gitforwindows.org/\n\n\n# Setup -------------------------------------------------------------------\n\nlibrary(usethis)\nuse_git_config(user.name = \"Your Name\", \n user.email = \"your@email.com\")\n" }, { "path": "Goodness of fit testing example/README.md", "content": "# Goodness of fit testing with R - example\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Goodness of fit testing with R - example**, which you can find at . \n\n" }, { "path": "Goodness of fit testing example/Script - goodness of fit example.R", "content": "# Goodness of fit testing example\n\nobs <- c(12, 31, 36, 21)\nexp_props <- dbinom(0:3, 3, .5)\nexp_props\nexp <- exp_props * 100\nexp\n\nchisq.test(obs, p = exp_props)\n\nchi_stat <- sum((obs - exp)^2 / exp)\nchi_stat\n\np <- 1 - pchisq(chi_stat, 3)\np\n" }, { "path": "Graphing functions/README.md", "content": "# Graphing functions in R using ggplot\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Graphing functions in R using ggplot**, which you can find at . \n\n" }, { "path": "Graphing functions/script - graphing functions.R", "content": "# Plotting functions with ggplot()\nlibrary(tidyverse)\n\nggplot() +\n xlim(c(-3, 3)) + \n geom_function(fun = dnorm)\n\nggplot() +\n xlim(c(-3, 3)) + \n geom_function(fun = dnorm, \n args = list(mean = .1, \n sd = 1.5)) +\n labs(y = NULL) +\n theme_bw()\n\nggplot() + \n xlim(c(-2, 2)) + \n geom_function(fun = function(x) x^2) +\n labs(y = NULL) +\n theme_bw()\n\nggplot() + \n xlim(c(-2, 2)) + \n geom_function(fun = function(x) x^2,\n aes(color = \"Second degree\")) +\n geom_function(fun = function(x) x^3,\n aes(color = \"Third degree\")) + \n labs(color = \"Degree\", \n y = NULL) +\n theme_bw()\n\nset.seed(1)\ndata <- rnorm(250)\ndf <- data.frame(data)\nwidth <- .2\narea <- width * length(data)\nggplot(df, aes(x = data)) +\n geom_histogram(binwidth = width, \n col = \"blue\", \n fill = \"lightblue\") + \n geom_function(fun = function(x) area * dnorm(x), \n col = \"black\") +\n theme_bw()\n\n\n" }, { "path": "How to add annotations/README.md", "content": "# How to add annotations to ggplots in R\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **How to add annotations to ggplots in R**, which you can find at . \n\n" }, { "path": "How to add annotations/Script - annotate.R", "content": "library(tidyverse)\ntheme_set(theme_minimal())\n\nset.seed(1)\nt <- slice_sample(trees, n = 5)\n\np <- ggplot(t, aes(x = Girth, y = Height)) + \n geom_point()\np\n\np + annotate(geom = \"text\",\n x = 16,\n y = 75.5,\n label = \"Outlier\")\n\np + annotate(geom = \"label\",\n x = 14, \n y = 71.5, \n label = \"Outlier\") +\n annotate(geom = \"segment\",\n x = 14, \n y = 72, \n xend = 16.1, \n yend = 76.5,\n arrow = arrow()) +\n annotate(geom = \"rect\",\n xmin = 8,\n xmax = 11.5, \n ymin = 64.5, \n ymax = 72.5,\n alpha = .1,\n color = \"darkgreen\", \n fill = \"green\")\n\n\n\n\n\n" }, { "path": "How to add color to R ggplots/README.md", "content": "# How to add colors to R ggplots\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **How to add colors to R ggplots**, which you can find at . \n\n" }, { "path": "How to add color to R ggplots/script - how to add color to R ggplots.R", "content": "# Better use of color in R\n\nlibrary(tidyverse)\ntheme_set(theme_minimal())\nview(mpg)\n\nggplot(mpg, aes(x = cty, y = hwy)) +\n geom_point(color = \"seashell\")\n\ncolors()\n\nggplot(mpg, aes(x = cty)) + \n geom_histogram(color = \"red\",\n fill = \"pink\")\n\nggplot(mpg, aes(x = cty, y = hwy)) +\n geom_point(color = \"#6e31d6\")\n\nggplot(mpg, aes(x = cty)) + \n geom_histogram(color = \"#be1529\",\n fill = \"#b6a960\")\n\nqplot(mpg$cty, \n geom = \"histogram\", \n fill = I(\"#be1529\"))\n\n\n\n" }, { "path": "How to create an other category/README.md", "content": "# How to create an \"other\" category in R\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **How to create an \"other\" category in R**, which you can find at . \n\n" }, { "path": "How to create an other category/lumping variables.R", "content": "# creating an 'other' category \n\nlibrary(tidyverse)\n?fct_lump\n\nquitting <- read_csv(\"quitting.csv\")\nView(quitting)\n\nggplot(quitting, aes(x = reason,\n fill = reason)) + \n geom_bar(show.legend = FALSE) +\n scale_fill_brewer(palette = \"Dark2\")\n\nquitting <- quitting %>% \n mutate(reason = fct_infreq(reason))\n\nfct_lump_n(quitting$reason, n = 4, other_level = \"other\")\n\nquitting <- quitting %>% \n mutate(reason_lumped = fct_lump_n(quitting$reason, \n n = 4, \n other_level = \"other\"))\n\nggplot(quitting, aes(x = reason_lumped,\n fill = reason_lumped)) + \n geom_bar(show.legend = FALSE) +\n scale_fill_brewer(palette = \"Dark2\")\n\nquitting <- quitting %>% \n mutate(reason_lumped = fct_lump_lowfreq(quitting$reason, \n other_level = \"other\"))\n\n\n" }, { "path": "How to create an other category/quitting.csv", "content": "employee_id,reason\n905892,bored\n902948,salary\n905732,no_advancement\n901026,bored\n901560,dislike_boss\n903671,dislike_boss\n903723,dislike_boss\n905577,salary\n905384,dislike_boss\n903936,no_advancement\n905239,salary\n902582,low_recognition\n905291,dislike_boss\n902746,low_recognition\n904765,dislike_boss\n902016,bored\n901308,low_recognition\n901250,low_recognition\n901887,bored\n904114,dislike_boss\n903222,dont_know\n903692,salary\n904530,no_advancement\n904732,salary\n902597,salary\n901212,low_recognition\n904563,dislike_boss\n905649,salary\n901500,no_advancement\n902087,salary\n905720,dislike_boss\n903760,no_advancement\n902160,dislike_boss\n901858,no_advancement\n904571,dislike_boss\n902070,low_recognition\n901014,salary\n901502,salary\n904926,health\n903094,no_advancement\n903540,no_advancement\n903438,dislike_boss\n903863,bored\n901978,low_recognition\n904151,dislike_boss\n905150,bored\n903283,no_advancement\n904664,health\n901609,no_advancement\n901489,salary\n903115,salary\n901655,salary\n905774,dislike_boss\n903021,salary\n905326,dislike_boss\n904701,dislike_boss\n902251,low_recognition\n903825,low_recognition\n904187,low_recognition\n903390,salary\n903677,salary\n901412,bored\n901202,bored\n901478,dislike_boss\n902545,unsafe_environment\n902036,salary\n903468,dont_know\n901138,low_recognition\n902258,bored\n902435,dislike_boss\n905013,dislike_boss\n905900,dislike_boss\n905102,salary\n905894,salary\n905813,salary\n902653,bored\n901426,low_recognition\n901862,bored\n904792,no_advancement\n904198,low_recognition\n905361,no_advancement\n902715,salary\n904436,dislike_boss\n901581,dislike_boss\n901575,no_advancement\n902816,dislike_boss\n904113,dislike_boss\n901224,low_recognition\n903955,dislike_boss\n904059,low_recognition\n901184,low_recognition\n903840,no_advancement\n903425,low_recognition\n905608,no_advancement\n905082,no_advancement\n901244,unsafe_environment\n902056,unsafe_environment\n904678,dislike_boss\n903887,dislike_boss\n904841,no_advancement\n905710,salary\n903927,low_recognition\n901449,dislike_boss\n901664,dislike_boss\n902669,no_advancement\n901613,low_recognition\n904312,no_advancement\n902089,bored\n902248,dislike_boss\n904714,no_advancement\n902797,no_advancement\n905835,no_advancement\n905588,no_advancement\n905595,salary\n903402,no_advancement\n901038,no_advancement\n904832,dislike_boss\n902551,dislike_boss\n904175,bored\n901720,salary\n905753,dislike_boss\n902960,low_recognition\n902431,salary\n901385,dislike_boss\n903954,low_recognition\n905459,salary\n902043,no_advancement\n903842,unsafe_environment\n901326,low_recognition\n905038,bored\n904799,salary\n902310,salary\n904329,salary\n901836,no_advancement\n902204,salary\n904564,salary\n904236,low_recognition\n902767,salary\n905037,no_advancement\n903295,dislike_boss\n901434,dislike_boss\n902692,no_advancement\n904315,no_advancement\n903186,unsafe_environment\n901837,salary\n904211,salary\n903489,bored\n905473,dislike_boss\n905702,bored\n905648,salary\n905629,salary\n904976,bored\n904877,salary\n901555,dislike_boss\n904321,dislike_boss\n904002,low_recognition\n905345,salary\n901305,no_advancement\n903434,salary\n901944,no_advancement\n903771,low_recognition\n905825,bored\n902278,low_recognition\n901748,low_recognition\n905285,salary\n901223,dislike_boss\n903506,low_recognition\n905219,dislike_boss\n904447,health\n905960,health\n904267,dislike_boss\n901218,bored\n901625,dont_know\n905359,dislike_boss\n903764,low_recognition\n903239,no_advancement\n905587,bored\n905104,dislike_boss\n904720,health\n901533,salary\n904021,dislike_boss\n904647,low_recognition\n902837,low_recognition\n902525,dislike_boss\n903388,low_recognition\n901400,no_advancement\n904278,low_recognition\n905779,salary\n904928,dislike_boss\n901033,no_advancement\n904727,health\n901115,bored\n903045,health\n904287,dislike_boss\n903750,bored\n901876,bored\n903373,salary\n901346,no_advancement\n904983,dislike_boss\n902301,no_advancement\n904754,no_advancement\n905177,salary\n901570,salary\n901854,no_advancement\n904579,bored\n905687,dislike_boss\n905991,no_advancement\n905994,dislike_boss\n902881,salary\n903205,no_advancement\n901067,dislike_boss\n905617,bored\n901822,bored\n901172,bored\n901390,salary\n903349,bored\n901504,salary\n905474,no_advancement\n904982,salary\n903288,low_recognition\n904443,low_recognition\n904552,no_advancement\n901934,dont_know\n902315,no_advancement\n904326,low_recognition\n905815,bored\n904935,unsafe_environment\n903877,salary\n903407,dislike_boss\n903437,dislike_boss\n901089,low_recognition\n903835,dislike_boss\n902622,no_advancement\n902513,bored\n905472,dislike_boss\n903599,salary\n903498,bored\n902688,health\n904509,no_advancement\n905976,no_advancement\n902953,low_recognition\n904386,dislike_boss\n905141,bored\n905343,bored\n902510,no_advancement\n901772,low_recognition\n904523,low_recognition\n904374,no_advancement\n904649,bored\n903144,dont_know\n901039,bored\n901040,no_advancement\n905238,dislike_boss\n901112,salary\n904889,lo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o_advancement\n902747,dislike_boss\n905936,dont_know\n901691,low_recognition\n905450,no_advancement\n905690,no_advancement\n905951,no_advancement\n903822,low_recognition\n902034,bored\n903886,dislike_boss\n904660,no_advancement\n902898,salary\n905314,salary\n904730,salary\n902447,no_advancement\n902456,bored\n904342,no_advancement\n902596,dislike_boss\n904057,bored\n901685,salary\n902988,low_recognition\n905534,bored\n903788,salary\n904325,bored\n904441,low_recognition\n903921,no_advancement\n905739,dislike_boss\n905733,salary\n901220,no_advancement\n902745,low_recognition\n904244,no_advancement\n905827,dislike_boss\n901497,low_recognition\n901557,low_recognition\n904142,bored\n902220,salary\n904587,low_recognition\n905552,dislike_boss\n903961,no_advancement\n904599,no_advancement\n901948,no_advancement\n904389,salary\n904099,dislike_boss\n904317,unsafe_environment\n904944,no_advancement\n902705,no_advancement\n901681,no_advancement\n904228,low_recognition\n902805,salary\n904038,low_recognition\n904366,low_recognition\n903448,bored\n903038,unsafe_environment\n904731,dislike_boss\n903002,low_recognition\n905199,dislike_boss\n903463,no_advancement\n903934,bored\n905449,dislike_boss\n904149,dislike_boss\n903011,low_recognition\n902212,dislike_boss\n905400,health\n905488,dislike_boss\n904764,salary\n902854,salary\n901459,salary\n902329,dislike_boss\n904415,low_recognition\n904378,dislike_boss\n902838,dislike_boss\n902452,low_recognition\n904197,salary\n904624,no_advancement\n902609,salary\n902235,no_advancement\n901462,dislike_boss\n901676,no_advancement\n903562,no_advancement\n903095,health\n902437,low_recognition\n901982,no_advancement\n903787,bored\n905746,low_recognition\n902602,dislike_boss\n901324,salary\n901262,dislike_boss\n904767,low_recognition\n904952,low_recognition\n902385,bored\n901272,dislike_boss\n905547,no_advancement\n903381,salary\n902751,salary\n902856,low_recognition\n905563,dislike_boss\n904617,no_advancement\n903185,dislike_boss\n904433,dislike_boss\n904031,no_advancement\n901234,low_recognition\n905303,salary\n905452,bored\n901466,no_advancement\n905111,unsafe_environment\n901035,dislike_boss\n901989,no_advancement\n905741,dislike_boss\n904088,dislike_boss\n902983,salary\n901129,dislike_boss\n901559,unsafe_environment\n903027,no_advancement\n903856,dislike_boss\n904814,salary\n902827,dislike_boss\n905358,salary\n902569,low_recognition\n901214,low_recognition\n904483,no_advancement\n902078,no_advancement\n903644,salary\n903377,no_advancement\n905854,dislike_boss\n903305,low_recognition\n903160,salary\n905684,salary\n902717,low_recognition\n901525,no_advancement\n904532,salary\n904828,salary\n901082,unsafe_environment\n904778,bored\n905122,no_advancement\n902228,no_advancement\n905311,bored\n901699,salary\n901760,bored\n904413,salary\n903418,dislike_boss\n902185,no_advancement\n905790,dislike_boss\n905019,salary\n905442,dislike_boss\n902691,salary\n901167,no_advancement\n901232,low_recognition\n904650,no_advancement\n902731,bored\n901614,dislike_boss\n902367,low_recognition\n901511,dislike_boss\n901433,dislike_boss\n903570,no_advancement\n901966,dislike_boss\n904380,bored\n903808,low_recognition\n902041,salary\n901880,salary\n901509,low_recognition\n902287,bored\n905972,salary\n903335,low_recognition\n902780,no_advancement\n903327,dislike_boss\n901563,salary\n905505,no_advancement\n903309,health\n903566,health\n905099,dislike_boss\n901395,dislike_boss\n901512,low_recognition\n905398,salary\n902388,low_recognition\n905268,bored\n905895,dislike_boss\n902023,low_recognition\n901897,dont_know\n904749,dislike_boss\n905777,salary\n905512,no_advancement\n903409,low_recognition\n904872,dislike_boss\n902919,low_recognition\n905078,salary\n904697,dislike_boss\n902512,no_advancement\n904055,salary\n901140,low_recognition\n901933,dislike_boss\n904899,low_recognition\n902130,health\n903073,salary\n903059,salary\n905945,no_advancement\n902599,unsafe_environment\n903435,no_advancement\n903436,bored\n904118,no_advancement\n903918,bored\n902592,salary\n903431,salary\n903607,no_advancement\n902273,salary\n905197,low_recognition\n904839,low_recognition\n901901,no_advancement\n903108,no_advancement\n904613,dislike_boss\n903257,health\n902263,no_advancement\n903485,salary\n903651,bored\n903747,low_recognition\n901148,salary\n902729,dislike_boss\n904203,salary\n901702,no_advancement\n904969,salary\n905869,dislike_boss\n903864,salary\n902906,bored\n905909,bored\n904728,dislike_boss\n902764,unsafe_environment\n902649,bored\n901268,health\n905200,salary\n902936,low_recognition\n904796,low_recognition\n903163,unsafe_environment\n903947,low_recognition\n903494,salary\n904695,salary\n902583,dislike_boss\n902115,low_recognition\n901572,dislike_boss\n903579,no_advancement\n902730,salary\n905984,dislike_boss\n903685,low_recognition\n901716,no_advancement\n902369,bored\n904851,no_advancement\n901976,no_advancement\n902426,dislike_boss\n903992,dislike_boss\n903663,no_advancement\n905175,no_advancement\n901037,bored\n901219,dislike_boss\n904355,low_recognition\n902566,no_advancement\n903920,salary\n902967,bored\n901169,low_recognition\n903304,dislike_boss\n905628,dislike_boss\n901333,dislike_boss\n902033,low_recognition\n902655,dislike_boss\n902423,bored\n904834,bored\n905529,dislike_boss\n904030,health\n903260,salary\n903796,bored\n903777,salary\n904788,low_recognition\n902094,salary\n905847,salary\n901890,salary\n904051,bored\n903428,low_recognition\n904488,salary\n902680,won_lottery\n902314,salary\n903024,health\n901712,dislike_boss\n904419,health\n905095,salary\n901021,bored\n902676,low_recognition\n904289,bored\n903150,salary\n902173,bored\n905293,low_recognition\n904699,dislike_boss\n904219,no_advancement\n901850,no_advancement\n905965,salary\n902830,low_recognition\n902567,no_advancement\n903147,salary\n902696,bored\n905124,no_advancement\n905447,low_recognition\n903052,dislike_boss\n903237,salary\n904445,no_advancement\n904172,low_recognition\n903298,bored\n905985,dislike_boss\n905356,health\n905481,low_recognition\n903290,low_recognition\n901740,salary\n903984,dislike_boss\n901745,low_recognition\n901246,salary\n904663,no_advancement\n903057,dislike_boss\n904430,no_advancement\n905723,no_advancement\n902450,dislike_boss\n904168,unsafe_environment\n904460,no_advancement\n901715,no_advancement\n904725,dislike_boss\n903169,low_recognition\n904780,no_advancement\n905506,dislike_boss\n902401,dislike_boss\n902788,low_recognition\n903369,salary\n905999,salary\n903098,low_recognition\n905852,salary\n904297,bored\n901475,no_advancement\n905510,dislike_boss\n902003,dislike_boss\n903400,low_recognition\n901198,salary\n901888,salary\n902993,salary\n903394,no_advancement\n905671,low_recognition\n903010,salary\n904965,low_recognition\n901225,no_advancement\n904183,health\n901096,low_recognition\n901447,salary\n902981,bored\n901841,no_advancement\n903362,no_advancement\n902578,unsafe_environment\n902077,dislike_boss\n905070,low_recognition\n901662,no_advancement\n904238,low_recognition\n902064,dislike_boss\n904800,low_recognition\n902458,low_recognition\n905407,no_advancement\n903783,no_advancement\n903041,dislike_boss\n905192,bored\n901378,salary\n905264,no_advancement\n902296,no_advancement\n902794,salary\n903529,no_advancement\n905032,dislike_boss\n905880,salary\n903909,no_advancement\n903367,low_recognition\n901523,salary\n904153,dont_know\n902232,salary\n905036,dislike_boss\n905174,dislike_boss\n902800,no_advancement\n903837,salary\n904126,no_advancement\n901162,no_advancement\n903444,no_advancement\n902025,dislike_boss\n903376,salary\n901971,no_advancement\n904542,dislike_boss\n904608,low_recognition\n905799,dislike_boss\n904164,dislike_boss\n903323,low_recognition\n902576,no_advancement\n904645,dislike_boss\n901313,dislike_boss\n902644,dislike_boss\n904574,dislike_boss\n902243,salary\n901376,salary\n901287,bored\n902772,no_advancement\n902844,low_recognition\n901397,no_advancement\n903136,dislike_boss\n901045,no_advancement\n902820,dislike_boss\n905996,dislike_boss\n901725,no_advancement\n901135,bored\n901531,unsafe_environment\n903535,no_advancement\n903891,no_advancement\n901569,dislike_boss\n905090,no_advancement\n904173,dislike_boss\n905026,unsafe_environment\n902022,salary\n905591,salary\n905461,low_recognition\n901159,dont_know\n904953,bored\n901992,no_advancement\n901731,salary\n902722,salary\n901380,health\n901363,bored\n905249,salary\n904133,low_recognition\n901771,low_recognition\n902405,health\n904352,no_advancement\n903843,dislike_boss\n901282,no_advancement\n901697,dislike_boss\n901456,salary\n902274,no_advancement\n901632,bored\n904942,salary\n901332,no_advancement\n904816,salary\n901243,salary\n904097,no_advancement\n901598,dislike_boss\n901970,salary\n904578,bored\n903994,dislike_boss\n901595,low_recognition\n904027,salary\n904655,dislike_boss\n903265,salary\n901391,low_recognition\n901191,dislike_boss\n905989,no_advancement\n902128,salary\n903187,salary\n904808,low_recognition\n902725,no_advancement\n902157,bored\n901727,bored\n903838,dislike_boss\n901669,low_recognition\n902897,salary\n902733,salary\n903470,dont_know\n902098,unsafe_environment\n904859,dislike_boss\n903012,dislike_boss\n902852,salary\n905454,no_advancement\n904529,dislike_boss\n902441,dislike_boss\n904431,no_advancement\n902843,dislike_boss\n903101,bored\n905098,no_advancement\n905115,salary\n903637,no_advancement\n901905,no_advancement\n904875,dislike_boss\n905006,salary\n905227,low_recognition\n904022,no_advancement\n904202,no_advancement\n903212,no_advancement\n901450,bored\n905228,no_advancement\n904668,low_recognition\n904442,bored\n902075,dislike_boss\n905637,dislike_boss\n904679,bored\n903789,no_advancement\n902487,no_advancement\n902834,no_advancement\n904639,bored\n903648,low_recognition\n902952,no_advancement\n904438,dislike_boss\n903396,salary\n903857,dislike_boss\n903772,no_advancement\n904033,dislike_boss\n901455,low_recognition\n904502,dislike_boss\n902667,no_advancement\n905236,low_recognition\n904218,salary\n905982,low_recognition\n905568,no_advancement\n902021,low_recognition\n904103,no_advancement\n904087,salary\n902796,bored\n905769,no_advancement\n901667,no_advancement\n903366,unsafe_environment\n902165,no_advancement\n901795,salary\n904951,no_advancement\n903803,dislike_boss\n903636,low_recognition\n902433,dislike_boss\n902494,low_recognition\n905532,no_advancement\n901600,dislike_boss\n903622,low_recognition\n903911,low_recognition\n905990,no_advancement\n904912,salary\n905211,no_advancement\n905803,no_advancement\n902129,salary\n904251,bored\n903988,no_advancement\n904346,salary\n904159,salary\n905308,low_recognition\n902348,salary\n903471,health\n903785,low_recognition\n905448,dislike_boss\n903829,low_recognition\n902658,no_advancement\n902579,low_recognition\n903145,salary\n903334,salary\n905031,bored\n901240,bored\n902359,dislike_boss\n903490,no_advancement\n905523,no_advancement\n901870,no_advancement\n902821,salary\n902163,dislike_boss\n901375,unsafe_environment\n901263,no_advancement\n903707,no_advancement\n905963,no_advancement\n903672,no_advancement\n904376,salary\n903584,dont_know\n902662,low_recognition\n902443,bored\n902383,dislike_boss\n901518,low_recognition\n902152,health\n901766,low_recognition\n901820,low_recognition\n904043,no_advancement\n901873,no_advancement\n903322,dislike_boss\n903742,salary\n901825,low_recognition\n904169,salary\n905992,dislike_boss\n905522,salary\n904074,no_advancement\n901847,bored\n901062,no_advancement\n902244,no_advancement\n901275,low_recognition\n903476,bored\n903900,dislike_boss\n903503,bored\n901049,no_advancement\n905785,low_recognition\n903618,dislike_boss\n902973,dislike_boss\n904998,salary\n905632,salary\n901739,dislike_boss\n901325,salary\n902531,dislike_boss\n904751,no_advancement\n902954,low_recognition\n901924,no_advancement\n901079,salary\n901947,bored\n901777,salary\n902224,dislike_boss\n905831,salary\n901528,no_advancement\n901337,dislike_boss\n904437,low_recognition\n903412,no_advancement\n902132,dislike_boss\n901361,salary\n903966,no_advancement\n901111,dislike_boss\n905612,low_recognition\n902336,salary\n903643,dislike_boss\n901125,salary\n904209,no_advancement\n901213,bored\n903879,low_recognition\n902175,dislike_boss\n903567,dislike_boss\n903986,low_recognition\n" }, { "path": "How to make a pride flag/README.md", "content": "# How to make a pride flag with R\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **How to make a pride flag with R**, which you can find at . \n\n" }, { "path": "How to make a pride flag/script - pride flag.R", "content": "library(tidyverse)\n\ncolors <- c(\"#770088\",\n \"#004CFF\",\n \"#028121\",\n \"#FFEE00\",\n \"#FF8D00\",\n \"#E50000\")\n# Or just use \"red\", \"orange\", etc. \n\ncats <- letters[1:6]\ncats\n\ndf <- tibble(cats)\ndf\n\nggplot(df, aes(y = cats, \n fill = cats)) +\n geom_bar(show.legend = FALSE,\n width = 1) +\n scale_fill_manual(values = colors) +\n theme_void()\n\n# Can you make a progress flag?\n# Hint: geom_polygon\n\n\n\n" }, { "path": "How to purrr/README.md", "content": "# How to purrr\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **How to purrr**, which you can find at . " }, { "path": "How to purrr/script - purrr.R", "content": "library(tidyverse)\n\nmap(1:5, sqrt)\n\nmap_dbl(1:5, sqrt)\nsqrt(1:5)\n\nsilly_list <- list(\"foo\",\n 1:5,\n faithful,\n TRUE)\nclass(silly_list)\n\nmap_chr(silly_list, class)\n\nfaithful_chr <- faithful %>% \n map_df(as.character)\nglimpse(faithful_chr)\n\nfaithful_chr <- faithful %>% \n map_chr(as.character) # error\n\nmin_to_sec <- function(x){\n x * 60\n}\n\nfaithful_sec <- faithful %>% \n map_df(min_to_sec)\n\nglimpse(faithful_sec)\n\nfaithful_sec <- faithful %>% \n map_df(function(x) x * 60)\n\nfaithful_sec <- faithful %>% \n map_df(\\(x) x * 60)\n\n# additional arguments\n\nmeans <- c(1, 10, 20, 50)\n\nmap(means, rnorm, n = 5)\nmap(means, rnorm, nm = 5) # error\n\nmap(means, \\(m) rnorm(5, m))\n\n# multiple iterations\n\nsds <- c(.1, 1, 3, 5)\n\nmap2(means, \n sds,\n \\(m, s) rnorm(5, m, s)) # iterate over 2 vectors\n\n# If you want all combinations, use\n?expand_grid\nexpand_grid(means, sds)\n\nnums <- 1:4\n\npmap(list(nums, means, sds), rnorm)\n\n\n" }, { "path": "How to put a project onto GitHub/README.md", "content": "# How to put a project onto GitHub\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **How to put a project onto GitHub**, which you can find at . " }, { "path": "How to put a project onto GitHub/github setup.R", "content": "# GitHub setup\n# First, sign up for an account at www.github.com\nlibrary(usethis)\n\n# Connect RStudio to your GitHub\ncreate_github_token()\ngitcreds::gitcreds_set()\n\n# Put a project onto GitHub\nuse_github()\n\n# Create a readme\nuse_readme_md()\n\n\n" }, { "path": "How to show a ggplot to AI/README.md", "content": "# How to show a ggplot to an AI assistant\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **How to show a ggplot to an AI assistant**, which you can find at . \n\n" }, { "path": "How to show a ggplot to AI/script - ggplot with AI.R", "content": "library(tidyverse)\nlibrary(ellmer)\n\nglimpse(msleep)\n\nggplot(msleep, aes(x = bodywt, \n y = brainwt)) + \n geom_point()\n\n?chat_anthropic\n\nclaude <- chat_anthropic()\n\n?content_image_plot\n\nclaude$chat(\"Provide suggestions for improving this plot based on dataziv best practice\",\n content_image_plot()) \n\nclaude$chat(\"write r code to implement suggestions 1, 2, 4, and 6. Label the most extreme outlier only as African Elephant. The data set is msleep.\")\n\nggplot(msleep, aes(x = bodywt, y = \n brainwt)) +\n # Use log10 scales for both axes\n scale_x_log10(labels = scales::comma) +\n scale_y_log10(labels = scales::comma) +\n # Add points\n geom_point(alpha = 0.7) +\n # Label the African Elephant point only\n geom_text(data = msleep %>% filter(name \n == \"African elephant\"), \n aes(label = name), \n hjust = -0.2, \n vjust = 0.5,\n size = 3.5) +\n # Add title and proper axis labels\n labs(\n title = \"Relationship Between Brain \nWeight and Body Weight in Mammals\",\n x = \"Body Weight (kg)\",\n y = \"Brain Weight (kg)\",\n caption = \"Data source: msleep dataset\nfrom ggplot2 package\"\n ) +\n # Use a clean theme\n theme_minimal() +\n # Additional theme customization\n theme(\n plot.title = element_text(face = \n \"bold\", size = 14),\n axis.title = element_text(size = 12),\n plot.caption = element_text(hjust = 0,\n face = \"italic\")\n )\n\nlive_browser(claude)\n\n# Load libraries\nlibrary(ggplot2)\nlibrary(dplyr)\n\n# Create improved scatter plot\nggplot(msleep, aes(x = bodywt, y = brainwt)) +\n # Use log scales\n scale_x_log10(labels = scales::comma) +\n scale_y_log10(labels = scales::comma) +\n # Add points\n geom_point(alpha = 0.7) +\n # Label African Elephant only\n geom_text(data = msleep %>% filter(name == \"African elephant\"), \n aes(label = name), \n hjust = -0.2, \n vjust = 0.5,\n size = 3.5) +\n # Add title and axis labels\n labs(\n title = \"Relationship Between Brain Weight and Body Weight in Mammals\",\n x = \"Body Weight (kg)\",\n y = \"Brain Weight (kg)\",\n caption = \"Data source: msleep dataset\"\n ) +\n # Apply theme\n theme_minimal() +\n # Customize appearance\n theme(\n plot.title = element_text(face = \"bold\", size = 14),\n axis.title = element_text(size = 12),\n plot.caption = element_text(hjust = 0, face = \"italic\")\n )\n\nclaude$get_cost()\n\n" }, { "path": "Hypothesis testing in R/README.md", "content": "# Hypothesis testing in R\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Hypothesis testing in R**, which you can find at . \n\n" }, { "path": "Hypothesis testing in R/Script - hypothesis testing in R.R", "content": "library(tidyverse)\n?airquality\nView(airquality)\n\nqplot(airquality$Wind, geom = \"histogram\")\nt.test(airquality$Wind, mu = 9)\n\nqplot(airquality$Solar.R, geom = \"histogram\")\nt.test(airquality$Solar.R, \n mu = 175, \n alternative = \"greater\")\n" }, { "path": "If-else statements in R/README.md", "content": "# If-else statements in R\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **If-else statements in R**, which you can find at . " }, { "path": "If-else statements in R/Script - ifelse.R", "content": "# if-else statements in R\nlibrary(tidyverse)\n\n# the basics --------------------------------------------------------------\n\nmy_word <- \"hello there!\"\nif (nchar(my_word) <= 5) {\n print(\"It's a short string\")\n} else if (nchar(my_word) <= 8){\n print(\"It's a mid-length string\")\n} else {\n print(\"It's a long string\")\n}\n\n# a handy shortcut --------------------------------------------------------\n\nx <- 4\ny <- if (x < 5) 0 else 10\n\n?ifelse\n\ny <- ifelse(x < 5, 0, 10)\ny\n\n# practical example -------------------------------------------------------\n\n# import the data (it's on my GitHub)\nplant_updates <- read_csv(\"If-else statements in R/plant_updates.csv\")\nView(plant_updates)\n\nplants_new <- plant_updates %>% \n mutate(name_best = ifelse(!is.na(name_2017), \n name_2017,\n scientific_name),\n c_best = ifelse(!is.na(c_2017),\n c_2017, \n c))\nView(plants_new)\n\nskimr::skim(plants_new)\n\n" }, { "path": "If-else statements in R/plant_updates.csv", "content": "scientific_name,c,name_2017,c_2017\nAcanthopanax sieboldianus,0,Eleutherococcus sieboldianus,0\nActaea rubra ssp. arguta,10,Actaea rubra,10\nActaea rubra ssp. rubra,10,Actaea rubra,10\nAgalinis paupercula var. borealis,9,Agalinis paupercula,9\nAgalinis paupercula var. paupercula,9,Agalinis paupercula,9\nAgastache scrophulariaefolia,5,Agastache scrophulariifolia,5\nAlcea rosea,0,Althaea rosea,0\nAllium ?stulosum,0,Allium fistulosum,0\nAllium canadense var. canadense,3,Allium canadense,3\nAllium canadense var. lavandulare,9,Allium canadense var. lavendulare,9\nAllium mutabile,0,Allium canadense var. lavendulare,9\nAlnus incana,8,Alnus rugosa,8\nAmpelamus albidus,0,Ampelamus laevis,0\nAmphicarpaea bracteata var. bracteata,5,Amphicarpaea bracteata,5\nAmsinckia spectabilis,0,Amsinckia menziesii,0\nAnchusa of?cinalis,0,Anchusa officinalis,0\nAndromeda polifolia var. latifolia,10,Andromeda glaucophylla,10\nAnemone patens var. multi?da,10,Pulsatilla patens subsp. multifida,10\nAnemone quinquefolia var. bifolia,7,Anemone quinquefolia,7\nAnemone quinquefolia var. quinquefolia,7,Anemone quinquefolia,7\nAntennaria howellii,3,NA,3\nAntennaria parlinii ssp. fallax,3,Antennaria parlinii subsp. fallax,3\nAntennaria parlinii ssp. parlinii,3,Antennaria parlinii,3\nAnthoxanthum hirtum,10,Hierochloe hirta,10\nAnticlea elegans,10,Anticlea elegans var. glauca,10\nApocynum androsaemifolium var. androsaemifolium,5,Apocynum androsaemifolium,5\nApocynum androsaemifolium var. incanum,5,Apocynum androsaemifolium,5\nApocynum cannabinum,2,Apocynum cannabinum var. glaberrimum,2\nApocynum x ?oribundum,4,Apocynum x floribundum,4\nAralia chinensis,0,Aralia elata,0\nArctostaphylos uva-ursi ssp. coactilis,10,Arctostaphylos uva-ursi var. coactilis,10\nArgemone albi?ora,0,Argemone albiflora,0\nAristida intermedia,5,Aristida necopina,3\nAristida longespica,1,Aristida longispica,1\nAristida longespica var. geniculata,1,Aristida necopina,3\nAristida longespica var. longespica,1,Aristida longispica,1\nArtemisia campestris ssp. caudata,5,Artemisia campestris subsp. caudata,5\nArtemisia ludoviciana var. gnaphalodes,0,Artemisia ludoviciana,0\nArtemisia ludoviciana var. ludoviciana,0,Artemisia ludoviciana,0\nArtemisia vulgaris var. glabra,0,Artemisia vulgaris,0\nArtemisia vulgaris var. latiloba,0,Artemisia ludoviciana,0\nArtemisia vulgaris var. vulgaris,0,Artemisia vulgaris,0\nAruncus dioicus var. pubescens,10,Aruncus dioicus,10\nAsclepias longifolia,10,Asclepias hirtella,10\nAsclepias tuberosa ssp. interior,8,Asclepias tuberosa,8\nAsclepias viridi?ora,10,Asclepias viridiflora,10\nAthyrium filix-femina ssp. angustum,8,Athyrium filix-femina,8\nAtriplex glabriuscula,0,Atriplex patula,0\nAureolaria ?ava var. macrantha,9,Aureolaria flava,10\nAureolaria grandi?ora var. pulchra,10,Aureolaria grandiflora var. pulchra,10\nAureolaria pedicularia var. ambigens,7,Aureolaria pedicularia,7\nAzolla cristata,5,Azolla caroliniana,5\nBaptisia alba var. macrophylla,8,Baptisia lactea,8\nBaptisia bracteata,10,Baptisia leucophaea,10\nBetula pumila var. glabra,10,Betula pumila,10\nBetula pumila var. glandulifera,10,Betula pumila,10\nBetula pumila var. pumila,10,Betula pumila,10\nBidens beckii,10,Megalodonta beckii,10\nBidens trichosperma,9,Bidens trichosperma,8\nBidens tripartita,3,NA,NA\nBoechera shortii,6,Boechera dentata,8\nBoltonia asteroides var. asteroides,8,Boltonia asteroides,8\nBoltonia asteroides var. latisquama,8,Boltonia asteroides,8\nBoltonia asteroides var. recognita,8,Boltonia asteroides,8\nBorago of?cinalis,0,Borago officinalis,0\nBothriochloa laguroides,0,Bothriochloa laguroides subsp. torreyana,0\nBromus arvensis,0,Bromus japonicus,0\nBromus hordeaceus ssp. hordeaceus,0,Bromus hordaceus subsp. hordaceus,0\nBromus hordeaceus ssp. pseudothominei,0,Bromus hordaceus subsp. pseudothominei,0\nBromus pubescens,5,Bromus pubescens,10\nCakile edentula ssp. edentula var. edentula,0,Cakile edentula,0\nCakile edentula ssp. edentula var. lacustris,7,Cakile lacustris,7\nCalamagrostis stricta,8,Calamagrostis stricta subsp. inexpansa,8\nCalamovilfa longifolia,8,Calamovilfa longifolia var. magna,8\nCallirhoe digitata,0,Callirhoe alcaeoides,0\nCalystegia hederacea,0,Calystegia pubescens,0\nCalystegia sepium ssp. americana,1,Calystegia sepium,1\nCalystegia sepium ssp. angulata,1,Calystegia sepium subsp. angulata,1\nCalystegia sepium ssp. sepium,1,Calystegia sepium,1\nCalystegia silvatica ssp. fraterniflorus,1,Calystegia silvatica subsp. fraterniflora,1\nCardamine concatenata,5,Dentaria laciniata,5\nCardamine diphylla,10,Dentaria diphylla,10\nCardaria pubescens var. elongata,0,Lepidium appelianum,0\nCarex abdita,7,Carex umbellata,7\nCarex atlantica ssp. atlantica,10,Carex atlantica,10\nCarex atlantica ssp. capillacea,10,Carex atlantica subsp. capillacea,10\nCarex canescens ssp. canescens,10,Carex canescens,10\nCarex canescens ssp. disjuncta,10,Carex canescens,10\nCarex crinita var. brevicrinis,7,Carex crinita,7\nCarex crinita var. crinita,7,Carex crinita,7\nCarex debilis var. debilis,10,Carex debilis,10\nCarex echinodes,10,Carex echinodes,5\nCarex emmonsii,9,Carex albicans var. emmonsii,5\nCarex heliophila,5,Carex inops subsp. heliophila,10\nCarex laxi?ora,8,Carex laxiflora,8\nCarex nigromarginata,6,Carex albicans var. emmonsii,5\nCarex vaginata,0,Carex tetanica,7\nCarex x subimpressa,4,NA,NA\nCarpinus caroliniana ssp. virginiana,8,Carpinus caroliniana subsp. virginiana,8\nCastilleja sessili?ora,10,Castilleja sessiliflora,10\nCeanothus americanus,6,Ceanothus americanus,8\nCeanothus americanus var. americanus,6,Ceanothus americanus,8\nCeanothus americanus var. pitcheri,6,Ceanothus americanus,8\nCentaurea stoebe ssp. micranthos,0,Centaurea stoebe subsp. micranthos,0\nChaenorrhinum minus,0,Chaenorhinum minus,0\nChenopodium leptophyllum,5,Chenopodium pallescens,10\nChimaphila umbellata ssp. cisatlantica,10,Chimaphila umbellata subsp. cisatlantica,10\nChrysopsis camporum,10,Heterotheca camporum,10\nChrysopsis camporum var. glandulissima,0,Heterotheca camporum var. glandulissima,0\nChrysopsis villosa,8,Heterotheca villosa,8\nChrysopsis villosa var. foliosa,0,Heterotheca villosa var. foliosa,0\nCirsium altissimum,6,Cirsium altissimum,8\nCirsium pumilum var. hillii,10,Cirsium hillii,10\nClematis terni?ora,0,Clematis terniflora,0\nCleome serrulata,0,Peritoma serrulata,0\nClinopodium arkansanum,10,Calamintha arkansana,10\nCommelina erecta,10,Commelina erecta var. deamiana,10\nCorallorhiza trifida,10,Corallorhiza trifida var. verna,10\nCoreopsis grandi?ora,0,Coreopsis grandiflora,0\nCoreopsis grandiflora var. grandiflora,0,Coreopsis grandiflora,0\nCorispermum welshii,2,NA,NA\nCornus alba,5,Cornus sericea,5\nCorydalis sempervirens,10,Capnoides sempervirens,10\nCotoneaster acutifolia,0,Cotoneaster lucidus,0\nCotoneaster multiflora,0,Cotoneaster magnificus,0\nCrataegus ?abellata,5,Crataegus macrosperma,5\nCrataegus intricata,5,Crataegus chrysocarpa,4\nCrataegus pedicellata,5,Crataegus coccinea,5\nCrataegus punctata var. aurea,4,Crataegus punctata,4\nCrataegus punctata var. punctata,4,Crataegus punctata,4\nCuscuta compacta,10,Cuscuta gronovii,5\nCuscuta gronovii var. gronovii,5,Cuscuta gronovii,5\nCuscuta gronovii var. latifolia,4,Cuscuta gronovii,5\nCynoglossum of?cinale,0,Cynoglossum officinale,0\nCynoglossum virginianum,10,Cynoglossum boreale,10\nCyperus esculentus,0,Cyperus esculentus var. leptostachyus,0\nCyperus flavescens,9,Cyperus flavescens,10\nCyperus houghtonii,2,Cyperus houghtonii,4\nCyperus lupulinus,5,Cyperus lupulinus,4\nCypripedium parviflorum,10,NA,10\nCypripedium parviflorum var. parviflorum,10,Cypripedium parviflorum var. pubescens,10\nCypripedium x andrewsii var. andrewsii,10,Cypripedium x andrewsii,10\nCypripedium x andrewsii var. favillianum,10,Cypripedium x andrewsii,10\nCyrtorhyncha cymbalaria,1,Halerpestes cymbalaria,1\nCystopteris bulbifera,8,Cystopteris bulbifera,10\nDatura stramonium var. stramonium,0,Datura stramonium,0\nDatura stramonium var. tatula,0,Datura stramonium,0\nDeschampsia ?exuosa,10,Avenella flexuousa,0\nDeschampsia caespitosa,10,Deschampsia cespitosa,10\nDescurainia pinnata ssp. brachycarpa,0,Descurainia pinnata var. brachycarpa,0\nDesmodium cuspidatum var. cuspidatum,10,Desmodium cuspidatum,10\nDesmodium glutinosum,5,Hylodesmum glutinosum,7\nDesmodium marilandicum,10,Desmodium marilandicum,9\nDesmodium nudi?orum,9,Hylodesmum nudiflorum,10\nDiarrhena americana,10,Diarrhena obovata,10\nDichanthelium acuminatum,4,NA,NA\nDichanthelium oligosanthes,4,Dichanthelium oligosanthes,9\nDichanthelium ovale var. addisonii,10,Dichanthelium commonsianum,10\nDichanthelium portoricense,8,Dichanthelium columbianum,8\nDichanthelium sphaerocarpon var. isophyllum,8,Dichanthelium polyanthes,10\nDichanthelium sphaerocarpon var. sphaerocarpon,8,Dichanthelium sphaerocarpon,7\nDigitaria ?liformis,10,Digitaria filiformis,10\nDiphasiastrum complanatum,4,Diphasiastrum digitatum,4\nDiplachne fusca,0,Leptochloa fascicularis,0\nDiplazium pycnocarpon,10,Homalosorus pycnocarpos,10\nDistichlis spicata var. stricta,0,Distichlis stricta,0\nDoellingeria umbellata var. pubens,8,Doellingeria umbellata,8\nDoellingeria umbellata var. umbellata,8,Doellingeria umbellata,8\nDraba reptans var. reptans,4,Draba reptans,4\nDraba reptans var. stellifera,4,Draba reptans,4\nDraba verna,0,Erophila verna,0\nDryopteris ?lix-mas,10,Dryopteris filix-mas,10\nEchinochloa colona,0,Echinochloa colonum,0\nEchinodorus berteroi,4,Echinodorus berteroi var. lanceolatus,4\nEleocharis palustris,1,Eleocharis palustris,7\nEleocharis tenuis,8,Eleocharis elliptica,10\nElyhordium x macounii,0,Elyhordium macounii,0\nElyhordium x montanense,0,Elyhordium montanense,0\nElymus curvatus,4,Elmyus submuticus,4\nElymus hystrix,5,Hystrix patula,5\nElymus hystrix var. bigeloviana,5,Hystrix patula,5\nElymus hystrix var. hystrix,5,Hystrix patula,5\nElymus lanceolatus,0,Elytrigia dasystachya,0\nElymus lanceolatus ssp. psammophilus,0,Elytrigia dasystachya subsp. psammophila,0\nElymus repens,0,Elytrigia repens,0\nElymus trachycaulus,8,Roegneria trachycaula,8\nElymus trachycaulus ssp. subsecundus,8,Roegneria subsecunda,8\nElytrigia elongata,0,Thinopyrum ponticum,0\nEragrostis mexicana,0,Eragrostis pectinacea,0\nEragrostis reptans,0,Eragrostis hypnoides,5\nEragrostis trichodes var. pilifera,0,Eragrostis trichodes,0\nEragrostis trichodes var. trichodes,0,Eragrostis trichodes,0\nErianthus ravennae,0,Saccharum ravennae,0\nErigeron canadensis,0,Conyza canadensis,0\nEriophorum vaginatum var. spissum,10,Eriophorum vaginatum,10\nErysimum hieracifolium,0,Erysimum hieraciifolium,0\nEschscholtzia californica,0,Eschscholzia californica,0\nEuonymus hederaceus,0,Euonymus fortunei,0\nEuphorbia esula,0,Euphorbia virgata,0\nEuphorbia heterophylla,0,Euphorbia cyathophora,0\nFestuca ?liformis,0,Festuca filiformis,0\nFestuca ovina,0,Festuca trachyphylla,0\nFestuca saximontana,6,Festuca saximontana,7\nFicaria verna ssp. fragilis,0,Ficaria verna subsp. fertilis,0\nFragaria vesca ssp. americana,8,Fragaria vesca subsp. americana,8\nFroelichia ?oridana,0,Froelichia floridana,0\nFumaria of?cinalis,0,Fumaria officinalis,0\nGalium circaezans,5,NA,NA\nGalium mollugo,0,Galium album,0\nGalium pedemontanum,0,Cruciata pedemontana,0\nGaura ?lipes,0,Stenosiphon linifolius,0\nGaura biennis,2,Gaura biennis,0\nGentiana andrewsii var. andrewsii,9,Gentiana andrewsii,9\nGentiana septem?da,0,Gentiana septemfida,0\nGentianella quinquefolia ssp. occidentalis,8,Gentiana quinqufolia subsp. occidentalis,8\nGeum laciniatum var. laciniatum,3,Geum laciniatum,3\nGeum laciniatum var. trichocarpum,3,Geum laciniatum,3\nGymnocladus dioicus,8,Gymnocladus dioicus,5\nHelanthium tenellum,10,Helanthium parvulum,10\nHelianthemum bicknellii,8,Crocanthemum bicknellii,8\nHelianthemum canadense,7,Crocanthemum canadense,7\nHelianthus hirsutus var. hirsutus,5,Helianthus hirsutus,7\nHelianthus hirsutus var. trachyphyllus,5,Helianthus hirsutus,7\nHelianthus maximilianii,0,Helianthus maximiliani,0\nHelianthus pauci?orus,9,Helianthus pauciflorus,9\nHelianthus subrhomboideus,9,Helianthus pauciflorus,9\nHelianthus x laeti?orus,5,Helianthus x laetiflorus,5\nHeliopsis helianthoides var. helianthoides,7,Heliopsis helianthoides,7\nHeliopsis helianthoides var. scabra,7,Heliopsis helianthoides,7\nHemerocallis lilio-asphodelus,0,Hemerocallis lilioasphodelus,0\nHeserostipa comata,0,Hesperostipa comata,0\nHeserostipa spartea,8,Hesperostipa spartea,8\nHibiscus moscheutos,7,Hibiscus palustris,7\nHieracium scabrum var. intonsum,7,Hieracium scabrum,7\nHieracium scabrum var. scabrum,7,Hieracium scabrum,7\nHordeum marinum,0,Hordeum marinum subsp. gussoneanum,0\nHordeum vulgare var. trifurcatum,0,Hordeum vulgare,0\nHordeum vulgare var. vulgare,0,Hordeum vulgare,0\nHumulus lupulus,3,Humulus lupulus var. pubescens,3\nHylotelephium x erythrostictum,0,Hylotelephium erythrostictum,0\nHypericum fraseri,8,Triadenum fraseri,8\nHypericum virginicum,10,Triadenum virginicum,10\nImpatiens baifourii,0,Impatiens balfourii,0\nImpatiens capensis,3,Impatiens capensis,4\nIonactis linariifolius,10,Ionactis linariifolia,10\nIpomoea pandurata,5,Ipomoea pandurata,4\nIris ?avescens,0,Iris flavescens,0\nIris x germanica,0,Iris germanica,0\nJuncus alpinoarticulatus ssp. americanus,8,Juncus alpinoarticulatus,8\nJuncus balticus ssp. littoralis,2,Juncus balticus,2\nJuncus effusus ssp. solutus,5,Juncus effusus,5\nJuniperus communis var. communis,8,Juniperis communis,8\nJuniperus communis var. depressa,8,Juniperis communis,8\nKallstroemia parvi?ora,0,Kallstroemia parviflora,0\nKochia scoparia,0,Bassia scoparia,0\nLamium galeobdolon,0,Lamiastrum galeobdolon,0\nLathyrus japonicus var. maritimus,10,Lathyrus japonicus var. glaber,10\nLathyrus palustris,7,Lathyrus palustris,8\nLechea pulchella,7,Lechea pulchella,8\nLeptoloma cognatum,1,Leptoloma cognata,1\nLespedeza intermedia,8,Lespedeza violacea,10\nLiatris aspera var. aspera,8,Liatris aspera,8\nLinaria genistifolia ssp. dalmatica,0,Linaria dalmatica,0\nLindernia dubia var. anagallidea,4,Lindernia anagallidea,4\nLindernia dubia var. dubia,4,Lindernia dubia,4\nLinnaea borealis ssp. longiflora,10,Linnaea borealis subsp. longiflora,10\nLiparis lilifolia,4,Liparis liliifolia,4\nLithospermum of?cinale,0,Lithospermum officinale,0\nLobelia spicata var. hirtella,4,Lobelia spicata,4\nLobelia spicata var. spicata,4,Lobelia spicata,4\nLolium perenne ssp. multiflorum,0,Lolium multiflorum,0\nLolium perenne ssp. perenne,0,Lolium perenne,0\nLonicera x minuti?ora,0,Lonicera x minutiflora,0\nLotus unifoliolatus,0,Acmispon americanus,0\nLudwigia palustris,3,Ludwigia palustris var. americana,3\nLudwigia peploides ssp. glabrescens,0,Jussiaea repens glabrescens,0\nLudwigia sphaerocarpa,10,Ludwigia sphaerocarpa var. deamii,10\nLythrum hyssopifolium,0,Lythrum hyssopifolia,0\nMahonia repens,0,Mahonia aquifolium,0\nMaianthemum canadense,7,Maianthemum canadense var. interius,7\nMaianthemum racemosum,5,Smilacina racemosa,5\nMaianthemum stellatum,5,Smilacina stellata,5\nMaianthemum trifolium,10,Smilacina trifolia,10\nMalcolmia africana,0,Malcolma africana,0\nMalus toringa,0,Malus toringo,0\nMalus x soulardii,0,Malus ioensis,4\nMalva rotundifolia,0,Malva neglecta,0\nMedicago sativa ssp. falcata,0,Medicago falcata,0\nMedicago sativa ssp. sativa,0,Medicago sativa,0\nMelampyrum lineare var. latifolium,10,Melampyrum lineare,10\nMelica mutica,10,NA,NA\nMentha arvensis,5,Mentha canadensis,5\nMentha x citrata,0,Mentha aquatica var. citrata,0\nMimulus glabratus,10,Mimulus glabratus var. jamesii,10\nMiscanthus sacchari?orus,0,Miscanthus sacchariflorus,0\nMiscanthus sinensis,0,NA,0\nMonarda fistulosa ssp. fistulosa,4,Monarda fistulosa,4\nMonarda fistulosa ssp. fistulosa var. mollis,4,Monarda fistulosa,4\nMonotropa hypopithys,10,Hypopitys monotropa,10\nMuhlenbergia glabri?oris,10,Muhlenbergia glabrifloris,10\nMuhlenbergia tenui?ora,10,Muhlenbergia tenuiflora,10\nMuscari racemosum,0,Muscari neglectum,0\nNabalus albus,5,Prenanthes alba,5\nNabalus altissimus,7,Prenanthes altissima,7\nNabalus asperus,10,Prenanthes aspera,10\nNabalus crepidineus,9,Prenanthes crepinidea,9\nNabalus racemosus,10,Prenanthes racemosa,10\nNajas guadalupensis ssp. guadalupensis,2,Najas guadalupensis,2\nNajas guadalupensis ssp. olivacea,2,Najas guadalupensis,2\nNarcissus pseudo-narcissus,0,Narcissus pseudonarcissus,0\nNicotiana longi?ora,0,Nicotiana longiflora,0\nNymphaea odorata,5,NA,NA\nNymphaea odorata ssp. odorata,5,Nymphaea odorata,7\nNymphaea odorata ssp. tuberosa,5,Nymphaea odorata subsp. tuberosa,5\nOenothera oakesiana,0,Oenothera oakesiana,2\nOpuntia humifusa,5,Opuntia cespitosa,4\nOryzopsis pungens,10,Piptatherum pungens,10\nOryzopsis racemosa,10,Piptatherum racemosum,10\nOsmunda cinnamomeum,8,Osmundastrum cinnamomeum,8\nPanicum dichotomiflorum ssp. puritanorum,2,Panicum dichotomiflorum subsp. puritanorum,10\nPaspalum repens,10,Paspalum fluitans,10\nPaspalum setaceum,3,NA,3\nPenstemon grandi?orus,0,Penstemon grandiflorus,0\nPenstemon tubae?orus,0,Penstemon tubaeflorus,0\nPersicaria amphibia,4,Persicaria amphibia var. stipulacea,4\nPersicaria arifolia,9,Tracaulon arifolium var. pubescens,9\nPersicaria sagittata,7,Tracaulon sagittatum,7\nPersicaria virginiana,4,Antenoron virginianum,4\nPhedimus aizoon,0,NA,0\nPhemeranthus rugospermum,10,Phemeranthus rugospermus,10\nPhiladelphus ?oridus,0,Philadelphus x polyanthus,0\nPhiladelphus inodorus,0,NA,0\nPhlox bi?da,8,Phlox bifida,8\nPhlox glaberrima ssp. interior,9,Phlox glaberrima subsp. interior,9\nPhlox maculata ssp. maculata,10,Phlox maculata,10\nPhlox maculata ssp. pyramidalis,10,Phlox maculata,10\nPhlox pilosa ssp. fulgida,8,Phlox pilosa var. fulgida,8\nPhlox pilosa ssp. pilosa,8,Phlox pilosa var. pilosa,8\nPhragmites australis ssp. americanus,3,Phragmites americanus,3\nPhragmites australis ssp. australis,0,Phragmites australis,0\nPhysalis heterophylla var. ambigua,3,Physalis heterophylla,3\nPhysalis heterophylla var. heterophylla,3,Physalis heterophylla,3\nPhysalis lanceolata,0,NA,0\nPhysocarpus opulifolius var. intermedius,5,Physocarpus opulifolius,5\nPhysocarpus opulifolius var. opulifolius,5,Physocarpus opulifolius,5\nPhysostegia angustifolia,0,Physostegia praemorsa,10\nPhysostegia virginiana ssp. praemorsa,4,Physostegia praemorsa,10\nPhysostegia virginiana ssp. virginiana,4,Physostegia virginiana,4\nPlagiobothrys hispidulus,0,Plagiobothrys scouleri,0\nPlatanthera huronensis,10,Platanthera aquilonis,10\nPoa wol?i,8,Poa wolfii,8\nPoinsettia dentata,0,Euphorbia dentata,0\nPoinsettia dentata var. cuphosperma,0,NA,0\nPoinsettia dentata var. dentata,0,Euphorbia dentata,0\nPolanisia trachysperma,0,Polanisia dodecandra subsp. trachysperma,0\nPolygala ambigua,10,Polygala verticillata var. ambigua,10\nPolygala polygama,9,Polygala polygama var. obtusata,9\nPolygala senega,8,Polygala senega,10\nPolygala verticillata,7,Polygala verticillata var. isocycla,7\nPolygala verticillata var. verticillata,7,Polygala verticillata var. isocycla,7\nPolygonum ramosissimum ssp. prolificum,0,Polygonatum prolificum,0\nPolygonum ramosissimum ssp. ramosissimum,1,Polygonatum ramosissimum,1\nPopulus nigra var. italica,0,Populus nigra cult. italica,0\nPopulus x canescens,0,Populus canescens,0\nPotamogeton x hagstroemii,8,Potamogeton x hagstroemii,10\nPotamogeton x haynesii,10,NA,NA\nPotentilla indica,0,Duchesnea indica,0\nPotentilla intermedia,0,Potentilla inclinata,0\nPotentilla pensylvanica var. bipinnati?da,10,Potentilla bipinnatifida,10\nPoterium sanguisorba,0,Poterium sanguisorba var. polygamum,0\nPrionopsis ciliata,0,Haplopappus ciliatus,0\nProboscidea louisianica,0,Proboscidea louisiana,0\nProserpinaca palustris var. crebra,6,Proserpinaca palustris,6\nProserpinaca palustris var. palustris,6,Proserpinaca palustris,6\nPrunella vulgaris ssp. lanceolata,1,Prunella vulgaris var. lanceolata,1\nPrunella vulgaris ssp. vulgaris,0,Prunella vulgaris,0\nPrunus subhirtella,0,Prunus sargentii,0\nPsoralidium tenui?orum,10,Psoralidium tenuiflorum,10\nPsoralidium tenui?orum var. ?oribundum,10,Psoralidium tenuiflorum,10\nPsoralidium tenui?orum var. tenui?orum,10,Psoralidium tenuiflorum,10\nPtelea trifoliata ssp. trifoliata,4,Ptelea trifoliata,4\nPtelea trifoliata ssp. trifoliata var. mollis,6,Ptelea trifoliata var. mollis,6\nPueraria montana,0,Pueraria montana var. lobata,0\nPyrola asarifolia,10,Pyrola asarifolia var. purpurea,10\nPyrus betulaefolia,0,Pyrus betulifolia,0\nQuercus muhlenbergii,8,Quercus muehlenbergii,8\nRanunculus gmelinii,10,Ranunculus gmelinii var. hookeri,10\nRanunculus hispidus var. caricetorum,10,Ranunculus caricetorum,10\nRanunculus hispidus var. hispidus,8,Ranunculus hispidus,8\nRanunculus hispidus var. nitidus,5,Ranunculus septentrionalis,5\nRanunculus testiculatus,0,Ceratocephala testiculata,0\nRhamnus lanceolata,10,Rhamnus lanceolata,9\nRhamnus lanceolata ssp. glabrata,10,Rhamnus lanceolata,9\nRhamnus lanceolata ssp. lanceolata,10,Rhamnus lanceolata,9\nRhododendron groenlandicum,10,Ledum groenlandicum,10\nRhus aromatica var. aromatica,9,Rhus aromatica,9\nRhus hirta,1,Rhus typhina,1\nRhus hirta cult. laciniata,0,Rhus typhina cult. laciniatum,0\nRhynchospora globularis,10,Rhynchospora recognita,10\nRibes aureum var. villosum,0,Ribes odoratum,0\nRobinia luxurians,0,Robinia meomexicana,0\nRorippa palustris,4,NA,NA\nRorippa palustris ssp. hispida,3,Rorippa palustris var. hispida,3\nRorippa palustris ssp. palustris,2,Rorippa palustris var. fernaldiana,2\nRosa cinnamomea,0,Rosa majalis,0\nRosa micrantha,0,Rosa rubiginosa,0\nRosa virginiana,0,NA,5\nRosa x rudiuscula,5,Rosa x medioccidentis,5\nRubus armeniacus,0,Rubus bifrons,0\nRubus bellobatus,0,NA,NA\nRubus idaeus ssp. idaeus,0,Rubus idaeus,0\nRubus idaeus ssp. strigosus,3,Rubus strigosus,1\nRubus parviflorus,0,NA,0\nRubus pergratus,2,Rubus avipes,2\nRubus setosus,10,NA,NA\nRudbeckia fulgida var. sullivantii,6,Rudbeckia sullivantii,10\nRudbeckia grandi?ora,0,Rudbeckia grandiflora,0\nRumex britannica,8,Rumex orbiculatus,8\nRumex conglomeratus,0,NA,NA\nSagittaria calycina,0,Lophotocarpus calycinus,0\nSalicornia maritima,0,Salicornia rubra,0\nSalix cordata,10,NA,NA\nSalix humilis var. humilis,6,Salix humilis,6\nSalix humilis var. tristis,6,Salix humilis,6\nSalix x pendulina,0,NA,NA\nSalix x sepulcralis,0,NA,NA\nSalvia azurea var. grandi?ora,0,Salvia azurea var. grandiflora,0\nSalvia re?exa,0,Salvia reflexa,0\nSalvia sylvestris,0,Salvia x sylvestris,0\nSambucus nigra ssp. canadensis,4,Sambucus canadensis,4\nSambucus nigra ssp. canadensis cult. acutiloba,0,Sambucus canadensis cult. acutiloba,0\nSambucus nigra ssp. nigra,0,Sambucus nigra,0\nSambucus racemosa,10,Sambucus racemosa subsp. pubens,10\nSanicula canadensis var. canadensis,5,Sanicula canadensis,5\nSatureja acinos,0,Acinos arvensis,0\nSaxifraga pensylvanica,10,Micranthes pensylvanica,10\nScheuchzeria palustris ssp. americana,10,Scheuchzeria palustris subsp. americana,10\nSchoenoplectus fluviatilis,4,Bolboschoenus fluviatilis,3\nSchoenoplectus hallii,10,Schoenoplectiella hallii,5\nSchoenoplectus maritimus,0,Bolboschoenus maritimus,0\nSchoenoplectus purshianus,5,Schoenoplectiella purshiana,5\nSchoenoplectus smithii,10,Schoenoplectiella smithii,10\nSchrankia nuttallii,0,Mimosa quadrivalvis var. nuttallii,0\nScilla non-scripta,0,Hyacinthoides non-scripta,0\nScirpus atrocinctus,6,Scirpus cyperinus,6\nScleria pauciflora var. caroliniana,10,Scleria pauciflora,10\nScleria pauciflora var. pauciflora,10,Scleria pauciflora,10\nScutellaria elliptica,10,Scutellaria elliptica var. hirsuta,10\nScutellaria parvula,7,Scutellaria parvula,8\nSedum alboroseum,0,Hylotelephium erythrostictum,0\nSedum re?exum,0,Sedum rupestre,0\nSelaginella apoda,10,Selaginella eclipes,10\nSenecio hieraciifolius,0,Erechtites hieraciifolius,0\nSenecio jacobaea,0,Jacobaea vulgaris,0\nSenecio suaveolens,10,Hasteola suaveolens,10\nSenna obtusifolia,0,Senna tora,0\nSetaria parviflora,0,Setaria parviflora,5\nSetaria viridis var. viridis,0,Setaria viridis,0\nSibara virginica,0,Planodes virginicum,0\nSibbaldia tridentata,10,Sibbaldiopsis tridentata,10\nSilene nocti?ora,0,Silene noctiflora,0\nSilphium integrifolium var. integrifolium,5,Silphium integrifolium,5\nSilphium integrifolium var. laeve,5,Silphium speciosum,0\nSilphium laciniatum var. laciniatum,5,Silphium laciniatum,5\nSilphium laciniatum var. robinsonii,5,Silphium laciniatum,5\nSisymbrium officinale var. leiocarpum,0,Sisymbrium officinale,0\nSisymbrium officinale var.officinale,0,Sisymbrium officinale,0\nSisyrinchium montanum var. crebrum,8,Sisyrinchium montanum,10\nSisyrinchium montanum var. montanum,8,Sisyrinchium montanum,10\nSmallanthus uvedalius,10,Smallanthus uvedalia,10\nSmilax lasioneuron,5,Smilax lasioneura,5\nSolanum americanum,0,Solanum ptychanthum,NA\nSolanum dulcamara var. dulcamara,0,Solanum dulcamara,0\nSolanum dulcamara var. villosissimum,0,Solanum dulcamara,0\nSolanum tri?orum,0,Solidago triflorum,0\nSolidago canadensis var. canadensis,1,Solidago canadensis,1\nSolidago canadensis var. hargeri,1,Solidago canadensis,1\nSolidago glaberrima,7,Solidago missouriensis,9\nSolidago ohioensis,10,Oligoneuron ohiense,10\nSolidago ptarmicoides,10,Oligoneuron album,10\nSolidago riddellii,8,Oligoneuron riddellii,8\nSolidago rigida,3,Oligoneuron rigidum,3\nSolidago rigida ssp. humilis,3,Solidago deamii,10\nSolidago rigida ssp. rigida,3,Oligoneuron rigidum,3\nSolidago rigidiuscula,7,Solidago speciosa,8\nSolidago sempervirens var. mexicana,0,Solidago sempervirens,0\nSolidago sempervirens var. sempervirens,0,Solidago sempervirens,0\nSolidago simplex ssp. randii var. gillmanii,10,Solidago deamii,10\nSonchus arvensis ssp. arvensis,0,Sonchus arvensis,NA\nSonchus arvensis ssp. uliginosus,0,Sonchus uliginosa,0\nSorbus americana,10,Sorbus decora,10\nSorghum bicolor ssp. drummondii,0,Sorghum x drummondii,0\nSorghum bicolor ssp. verticilliflorum,0,Sorghum bicolor,0\nSparganium emersum,10,Sparganium emersum,8\nSpirodela polyrhiza,4,Spirodella polyrhiza,4\nStachys palustris,0,Stachys pilosa,5\nStachys tenuifolia,5,Stachys tenuifolia,6\nStuckenia filiformis ssp. occidentalis,0,Stuckenia filiformis subsp. occidentalis,0\nSullivantia sullivantii,10,Sullivantia renifolia,10\nSymphoricarpos albus var. albus,10,Symphoricarpos albus,10\nSymphyotrichum ciliatum,0,Brachyactis ciliata,0\nSymphyotrichum lanceolatum ssp. lanceolatum var. interior,3,Symphyotrichum lanceolatum var. interior,3\nSymphyotrichum lanceolatum ssp. lanceolatum var. lanceolatum,3,Symphyotrichum lanceolatum,3\nSymphyotrichum praealtum var. angustior,7,Symphyotrichum praealtum,7\nSymphyotrichum praealtum var. praealtum,7,Symphyotrichum praealtum,7\nSymphyotrichum praealtum var. subasperum,7,Symphyotrichum praealtum,7\nSymphytum of?cinale,0,Symphytum officinale,0\nSyringa pubescens ssp. patens,0,Syringa pubescens subsp. patula,0\nTephrosia virginiana var. holosericea,8,Tephrosia virginiana,8\nTephrosia virginiana var. virginiana,8,Tephrosia virginiana,8\nTeucrium canadense var. canadense,3,Teucrium canadense,3\nThalictrum thalictroides,7,Anemonella thalictroides,7\nThaspium trifoliatum var. ?avum,8,Thaspium trifoliatum,8\nThaspium trifoliatum var. trifoliatum,8,Thaspium trifoliatum,8\nThelypteris palustris var. pubescens,7,Thelypteris palustris,7\nThymus serpyllum,0,Thymus pulegioides,0\nTomanthera auriculata,8,Agalinis auriculata,8\nToxicodendron radicans ssp. negundo,2,Toxicodendron radicans,2\nToxicodendron radicans ssp. radicans,2,Toxicodendron radicans,2\nTrichophorum caespitosum,10,Trichophorum cespitosum,10\nTridens ?avus,0,Tridens flavus,1\nTrifolium re?exum var. glabrum,10,Trifolium reflexum var. glabrum,10\nTrillium grandi?orum,9,Trillium grandiflorum,9\nTriosteum aurantiacum,4,Triosetum aurantiacum,5\nTriosteum illinoense,4,Triosteum aurantiacum var. illinoense,5\nTriosteum perfoliatum,5,Triosteum perfoliatum,4\nTripleurospermum maritimum ssp. inodora,0,Tripleurospermum inodorum,0\nUlmus x notha,0,Ulmus x intermedia,0\nUrtica dioica ssp. gracilis,1,Urtica gracilis,1\nVaccinium macrocarpon,10,Oxycoccus macrocarpos,10\nVaccinium oxycoccos,10,Oxycoccus palustrus,10\nValeriana of?cinalis,0,Valeriana officinalis,0\nValerianella chenopodifolia,8,Valerianella chenopodiifolia,8\nVeratrum virginicum,10,Melanthium virginicum,10\nVerbascum virgatum,0,NA,NA\nVerbena bipinnatifida,0,Glandularia bipinnatifida,0\nVerbena x deamii,0,Verbena x perriana,0\nVeronica anagallis-aquatica,9,Veronica anagallis-aquatica,0\nVeronica hederaefolia,0,Veronica hederifolia,0\nVeronica peregrina ssp. peregrina,0,Veronica peregrina var. peregrina,0\nVeronica peregrina ssp. xalapensis,3,Veronica peregrina var. xalapensis,3\nVeronica teucrium,0,Veronica austriaca subsp. teucrium,0\nViburnum nudum var. cassinoides,10,Viburnum cassinoides,10\nViburnum opulus var. americanum,10,Viburnum trilobum,10\nViburnum opulus var. opulus,0,Viburnum opulus,0\nViburnum ra?nesquianum,8,Viburnum rafinesquianum,8\nVicia sativa ssp. nigra,0,Vicia angustifolia,0\nViola ?mbriatula,6,Viola sagittata,5\nViola bicolor,0,Viola rafinesquii,0\nViola blanda,10,Viola blanda var. palustriformis,10\nViola blanda var. blanda,10,Viola blanda var. palustriformis,10\nViola lanceolata ssp. lanceolata,7,Viola lanceolata,7\nViola lanceolata ssp. vittata,7,Viola lanceolata,7\nViola macloskeyi ssp. pallens,10,Viola pallens,10\nViola pedata,9,Viola pedata var. lineariloba,9\nViola septentrionalis,3,Viola sororia,NA\nVitis aestivalis var. aestivalis,5,Vitis aestivalis,5\nVitis aestivalis var. bicolor,5,Vitis aestivalis,5\nVulpia octoflora var. octoflora,1,Vulpia octoflora,1\nVulpia octoflora var. tenella,1,Vulpia octoflora,1\nXanthium strumarium,0,NA,NA\nYucca smalliana,0,Yucca flaccida,0\n" }, { "path": "Imputing missing bikeshare data/README.md", "content": "# Imputing missing bikeshare data with R\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Imputing missing bikeshare data with R**, which you can find at . " }, { "path": "Imputing missing bikeshare data/bikeshare missing data.R", "content": "# Missing values in the Bikeshare data set\n\nlibrary(tidyverse)\nlibrary(ISLR2)\nlibrary(broom)\n\n?Bikeshare\nglimpse(Bikeshare)\n\n365 * 24 # 8760 hours in a year, 115 missing rows\nsum(Bikeshare$bikers == 0) # none\n\n# Add missing rows to Bikeshare\n\nbikes_new <- expand_grid(day = 1:365,\n hr = as.factor(0:23)) %>% \n left_join(Bikeshare,\n by = join_by(day,\n hr))\n\n# Impute missing values as appropriate\n\nbikes_imputed <- bikes_new %>% \n mutate(bikers = replace_na(bikers, 0),\n temp = ifelse(is.na(temp),\n (lead(temp) + lag(temp))/2,\n temp),\n temp = ifelse(is.na(temp),\n lead(temp),\n temp),\n temp = ifelse(is.na(temp),\n lag(temp),\n temp),\n weathersit = case_when(!is.na(weathersit) ~ weathersit,\n TRUE ~ lag(weathersit)),\n weathersit = case_when(!is.na(weathersit) ~ weathersit,\n TRUE ~ lead(weathersit)),\n workingday = mean(workingday, na.rm = TRUE),\n .by = day)\n\nsum(is.na(bikes_imputed$temp))\n\n# Compare poisson models\n\nmodel_old <- glm(bikers ~ temp + workingday + weathersit,\n data = Bikeshare, \n family = \"poisson\")\n\nmodel_new <- glm(bikers ~ temp + workingday + weathersit,\n data = bikes_imputed, \n family = \"poisson\")\n\ntidy(model_old)\ntidy(model_new)\n\n# One test value\n\nnew_data <- data.frame(temp = .5,\n workingday = 0,\n weathersit = \"clear\")\n\npredict(model_old, \n new_data,\n type = \"response\")\n\npredict(model_new, \n new_data,\n type = \"response\")\n\n" }, { "path": "Integrate AI with GitHub Copilot/README.md", "content": "# Integrate AI into your R workflows with GitHub Copilot\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Integrate AI into your R workflows with GitHub Copilot**, which you can find at . \n\n" }, { "path": "Integrate AI with GitHub Copilot/script - integrating AI.R", "content": "library(tidyverse)\n\nggplot(penguins,\n aes(x = bill_len, \n y = body_mass, \n color = species)) +\n geom_point() +\n labs(title = \"Penguin Bill Dimensions\",\n x = \"Bill Length (mm)\",\n y = \"Bill Depth (mm)\") +\n theme_minimal()\nglimpse(penguins)\n\n# a histogram showing body mass for penguins colored by species\n# use an accessible color palette and the default position\n\nggplot(penguins, \n aes(x = body_mass, \n fill = species)) +\n geom_histogram(position = \"identity\", \n bins = 30, \n alpha = 0.7) +\n labs(title = \"Penguin Body Mass Distribution\",\n x = \"Body Mass (g)\",\n y = \"Count\") +\n scale_fill_brewer(palette = \"Set1\") +\n theme_minimal()\n\n# load the scooby data set\n\nlibrary(readxl)\nscooby <- read_excel(\"scooby.xlsx\")\nglimpse(scooby)\n\n# a plot showing imdb ratings vs network for scooby\n\nggplot(scooby, \n aes(x = network, \n y = imdb, \n color = network)) +\n geom_boxplot() +\n labs(title = \"IMDB Ratings by Network\",\n x = \"Network\",\n y = \"IMDB Rating\") +\n scale_color_brewer(palette = \"Set2\") +\n theme_minimal() +\n theme(axis.text.x = element_text(angle = 45, hjust = 1))\n\n# a data analysis for scooby considering ratings and the gender of the unmasker\n\n\n" }, { "path": "Latex for R users/LaTeX for R users.qmd", "content": "---\ntitle: \"LaTeX for R users\"\nformat: \n latex:\n include-in-header: \n - text: \\usepackage{physics}\n---\n \n## Using math & scientific notation in your docs\n\nInline: $\\alpha$\n\nIndented:\n$$\\hat{y}_i = \\beta_0 + \\sum_{j=1}^p \\beta_jx_{ji}$$\nFunctions: $\\sin x,\\ \\log_2{x},\\hspace{.5cm} \\sqrt[3]{x^2+1}$\n\nFractions: $\\frac{p}{q}$\n$$\\left[\\frac{\\$5}{\\left(x+\\frac{1}{y}\\right)}\\right\\}$$\nChanging faces: $\\mathbb{R}, \\mathcal{C}$. Also $\\frac{\\text{ordinary stuff}}{2}$\n\nA symbol from the physics package: $\\pdv{x}$\n\n```{r}\nlibrary(tidyverse)\nggplot(mtcars, aes(x = mpg)) + \n geom_histogram()\n```\n\nAligned equations:\n\n\\begin{align*}\n(x + y)^2 &= x^2 + 2xy + y^2\\\\\n &\\ne x^2 + y^2\n\\end{align*}\n\n\n" }, { "path": "Latex for R users/README.md", "content": "# LaTeX for R users\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **LaTeX for R users**, which you can find at . " }, { "path": "Learn R in 39 minutes/README.md", "content": "# Learn R in 39 minutes\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Learn R in 39 minutes**, which you can find at . " }, { "path": "Learn R in 39 minutes/script - R in 39.R", "content": "library(tidyverse)\n\ndata()\nView(mpg)\n?mpg\n?mean\n\nglimpse(mpg)\n\n?filter\nfilter(mpg, cty >= 20)\nmpg_efficient <- filter(mpg, cty >= 20)\nView(mpg_efficient)\n\nmpg_ford <- filter(mpg, manufacturer == \"ford\")\nView(mpg_ford)\n\nmpg_metric <- mutate(mpg, cty_metric = .425144 * cty)\nglimpse(mpg_metric)\n\nmpg_metric <- mpg %>% \n mutate(cty_metric = .425144 * cty)\n\nView(mpg)\n\nmpg %>% \n group_by(class) %>% \n summarize(mean(cty),\n median(cty))\n\n# Data viz with ggplot2\n\nggplot(mpg, aes(x = cty)) +\n geom_histogram() +\n labs(x = \"City mileage\")\n\nggplot(mpg, aes(x = cty)) +\n geom_freqpoly() +\n labs(x = \"City mileage\")\n\nggplot(mpg, aes(x = cty)) +\n geom_histogram() +\n geom_freqpoly() +\n labs(x = \"City mileage\")\n\nggplot(mpg, aes(x = cty, \n y = hwy)) + \n geom_point() +\n geom_smooth(method = \"lm\")\n\nggplot(mpg, aes(x = cty, \n y = hwy,\n color = class)) + \n geom_point() +\n scale_color_brewer(palette = \"Dark2\")\n\n\n\n\n\n\n\n" }, { "path": "Linear modeling using categorical variables/README.md", "content": "# Linear modeling in R using categorical variables - parallel slopes models\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Linear modeling in R using categorical variables - parallel slopes models**, which you can find at . " }, { "path": "Linear modeling using categorical variables/Script - parallel slopes models.R", "content": "# multiple regression with a categorical explainer\nlibrary(tidyverse)\nlibrary(tidymodels)\ndata(crickets)\nView(crickets)\n\nggplot(crickets, aes(x = temp, y = rate, col = species)) + \n geom_point() +\n geom_smooth(method = \"lm\", se = FALSE)\n\nmodel_additive <- lm(rate ~ temp + species, data = crickets)\nsummary(model_additive)\n\nggplot(crickets, aes(x = temp, y = rate, col = species)) + \n geom_point() +\n geom_abline(aes(intercept = -7.210906, \n slope = 3.602753, \n col = \"O. exclamationis\")) +\n geom_abline(aes(intercept = -7.210906 - 10.06529, \n slope = 3.602753, \n col = \"O. niveus\")) \n\nmodel_interactive <- lm(rate ~ temp * species, data = crickets)\nsummary(model_interactive)\n\nggplot(crickets, aes(x = temp, y = rate, col = species)) + \n geom_point() +\n geom_abline(aes(slope = 3.7514, \n intercept = -11.0408, \n col = \"O. exclamationis\")) +\n geom_abline(aes(slope = 3.7514 - .2340, \n intercept = -11.0408- 4.3484, \n col = \"O. niveus\")) +\n geom_smooth(method = \"lm\", se = F)\n\n" }, { "path": "Locating files with here()/README.md", "content": "# Locating files in R with here()\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Locating files in R with here()**, which you can find at . \n\n" }, { "path": "Locating files with here()/Script - here.R", "content": "# You should be working in a project called sample_project that includes a\n# folder called data containing the Scooby.xlsx data set.\n\nlibrary(here)\nlibrary(readxl)\n\nfile <- here(\"data\", \"Scooby.xlsx\") \nscoobs <- readexcel(file)\n\n\n# Data source: \n# https://www.kaggle.com/datasets/williamschooleman/scoobydoo-complete\n " }, { "path": "Log-normal/README.md", "content": "# The log-normal distribution\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **The log-normal distribution**, which you can find at . \n\n" }, { "path": "Log-normal in R/README.md", "content": "# Log-normal calculations using R\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Log-normal calculations using R**, which you can find at . \n\n" }, { "path": "Log-normal in R/script - lognormal in R.R", "content": "# Log-normal problems\nlibrary(tidyverse)\n\n# Problem 1\nplnorm(1000, 6.1, 1.0) - plnorm(500, 6.1, 1.0)\n\n# Problem 2\nqlnorm(.99, 6.1, 1.0)\n\n# Problem 3\nfresh <- rlnorm(100, 6.1, 1.0)\nfresh\n\nqplot(fresh, geom = \"histogram\")\n\n\n" }, { "path": "Logistic regression example/README.md", "content": "# Logistic regression with R: example\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Logistic regression with R: example**, which you can find at . \n\n" }, { "path": "Logistic regression example/logistic regression in R example.R", "content": "# Logistic regression in R: example\nlibrary(tidymodels)\n\n# Prep data\n\niris <- iris %>% \n mutate(setosa = as.integer(Species == \"setosa\"))\nView(iris)\n\n# Split data\n\nset.seed(2)\nsplit <- initial_split(iris, \n prop = .80, \n strata = setosa) \niris_train <- training(split)\niris_test <- testing(split)\nView(iris_train)\n\n# Visualize the data\n\nggplot(iris_train, aes(x = Sepal.Length,\n y = setosa)) +\n geom_jitter(height = .05, \n alpha = .5) +\n geom_smooth(method = \"glm\",\n method.args = list(family = \"binomial\"),\n se = FALSE) +\n theme_minimal()\n\n# Build that model\n\nmodel <- glm(setosa ~ Sepal.Length,\n data = iris_train,\n family = \"binomial\")\nsummary(model)\n\n# evaluate the model on the testing set\n\niris_test <- iris_test %>% \n mutate(setosa_prob = predict(model,\n iris_test,\n type = \"response\"),\n setosa_pred = ifelse(setosa_prob > .5, 1, 0))\nView(iris_test)\n\nt <- table(iris_test$setosa,\n iris_test$setosa_pred)\nsum(diag(t)) / sum(t) # 83.3% accuracy\n\n8/10 # TPR or sensitivity\n2/10 # FNR\n17/20 # TNR or specificity\n3/20 # FPR\n\n\n\n" }, { "path": "Logistic regression in R/README.md", "content": "# Logistic regression in R\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Logistic regression in R**, which you can find at . " }, { "path": "Logistic regression in R/admissions.csv", "content": 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.5,1\n2.1,0\n2.7,1\n3.5,1\n2,0\n2.1,0\n3.1,0\n3.3,1\n2.7,0\n3,1\n2.8,0\n2.4,0\n3.9,0\n3.7,1\n3.3,1\n2.4,0\n2.9,1\n2.2,0\n2.3,0\n2.4,0\n3.3,1\n3.3,0\n2.8,0\n2.8,0\n2.8,1\n3.4,1\n2.6,0\n2.2,0\n2.1,0\n4,1\n2.1,0\n3.7,1\n3,1\n2.1,1\n2.4,0\n2.1,0\n3.5,1\n3.9,1\n3.6,0\n3.9,1\n4,1\n3,0\n3.5,1\n2.2,0\n2,0\n2.9,0\n2,0\n2.2,0\n3.8,1\n2.4,0\n2.6,0\n3.5,1\n2.6,1\n2.4,0\n2.7,0\n3,1\n2.4,0\n3.1,0\n3.6,0\n2.7,1\n2.1,0\n2,0\n2.6,1\n3.6,1\n3.3,1\n2.3,0\n2.6,0\n2.7,0\n2.1,0\n2.9,0\n3,0\n2,0\n2.2,0\n4,1\n2.3,0\n3.6,1\n2.2,0\n3.6,1\n2.8,0\n4,1\n3.1,1\n3.8,1\n2.3,0\n2.5,0\n2.2,0\n3.2,0\n4,1\n2,0\n2.7,0\n2.3,0\n2.3,0\n3.9,1\n3.9,1\n2.2,0\n3.9,1\n2.8,0\n3,1\n3.2,1\n3.1,1\n4,1\n2.8,0\n3.1,0\n2.7,1\n3.5,1\n2.2,0\n3.6,1\n3.1,1\n2,0\n2.7,0\n3.1,1\n3.2,0\n3.6,1\n2.1,0\n3.7,1\n3.2,1\n2.8,1\n3.2,0\n2.5,0\n2.4,1\n2,0\n3.4,1\n3,0\n3.2,1\n3.7,1\n3.9,1\n3.8,1\n3.3,1\n2.3,0\n2.2,0\n2.9,0\n3.8,1\n2.4,0\n3.6,1\n2.5,0\n3.7,1\n3.9,1\n3.4,1\n3.7,1\n4,1\n2,0\n2.4,0\n2.3,0\n3.6,1\n3.2,1\n3.8,1\n3.5,1\n2,0\n2.4,0\n3,1\n3.7,1\n3.5,0\n3.4,1\n3.3,0\n4,1\n3,1\n2.9,0\n3.1,0\n3.7,1\n3.7,0\n2.2,0\n3.6,1\n2.9,0\n3.6,1\n4,1\n2.6,0\n3.2,0\n2.8,0\n2.3,0\n3.5,0\n3.2,1\n3,1\n2.4,0\n4,1\n2.6,0\n2,0\n2.4,0\n4,1\n3,0\n2.3,0\n2.5,0\n2.4,0\n2.6,0\n3,0\n2.9,0\n2.8,0\n2,0\n2.5,0\n2.8,1\n2.9,0\n2.7,0\n3.4,1\n3.2,0\n3,1\n3.2,0\n3.2,1\n3.5,1\n3.2,1\n2.7,1\n3.9,1\n3,1\n3.3,1\n2.8,0\n2.5,0\n4,1\n3.9,1\n3.5,1\n2.5,0\n3.2,1\n2.3,0\n4,1\n3.9,1\n2.4,0\n3.2,1\n4,1\n3.8,1\n3.8,1\n3.1,1\n2.1,0\n3.2,0\n2.3,0\n2.7,1\n3.2,1\n4,1\n2.7,1\n3.4,1\n2.1,0\n2.9,0\n3.1,1\n2.6,0\n3.7,1\n2.6,0\n3.8,1\n3.9,1\n3.9,1\n3.9,0\n3,0\n2.5,1\n2.3,0\n2.4,0\n2.2,0\n3.4,1\n3.6,1\n3.6,1\n2.7,0\n2,1\n3.8,1\n3.2,1\n3.4,0\n3.7,1\n3,1\n2.2,1\n3,0\n2,0\n2.6,0\n3.2,1\n3.8,1\n2.1,0\n4,1\n2.7,0\n2.8,0\n2.2,0\n3.1,0\n3.7,1\n3.8,1\n2.7,1\n2.2,0\n2.7,1\n2.6,0\n3.2,1\n2.6,0\n3.8,1\n2.9,1\n3.4,1\n3,1\n2.1,0\n2,0\n2.5,0\n3.2,1\n2.9,0\n3,1\n3.7,1\n3,1\n3.4,1\n2.1,0\n3.7,1\n2.7,0\n2.8,0\n2.6,0\n3.9,1\n3.9,1\n3.2,1\n2,0\n2.9,1\n3.6,1\n3.8,0\n2.4,0\n4,1\n3.8,1\n3.3,1\n3.6,1\n2.7,1\n3.3,1\n3.6,1\n3.1,0\n3.3,0\n3.7,1\n3.5,1\n2.5,0\n3.9,1\n3.8,1\n3.7,1\n3.4,1\n3.1,1\n2.8,0\n3.2,1\n3.8,1\n3.5,1\n2.6,1\n4,1\n2.1,0\n2.2,0\n3.9,1\n4,1\n3.3,1\n2.1,0\n2,0\n2.3,0\n3.5,0\n2.8,0\n3.4,1\n3.6,1\n4,1\n3.3,1\n3.7,1\n2.6,0\n2.6,0\n3.6,1\n3.3,1\n3.1,1\n2.7,1\n2.4,0\n3.3,1\n3.7,1\n2.5,0\n3.4,1\n3.5,0\n3.4,1\n2.3,0\n3.5,1\n2.5,0\n3.8,1\n2.4,0\n3.4,1\n4,1\n2.1,0\n2,0\n2.6,1\n3.1,0\n3.9,1\n3.6,1\n2.6,0\n3,0\n2.3,0\n3.5,1\n3.8,1\n3.5,1\n2,0\n3,1\n3.8,1\n3,1\n2.9,0\n2.2,0\n2.5,0\n3.9,1\n2.5,0\n3.3,1\n4,1\n2,0\n3.9,1\n2.3,0\n3.4,1\n3.5,1\n3.1,1\n2.4,1\n3.2,1\n3.8,1\n3.9,1\n2.1,0\n4,1\n3,1\n2.1,0\n2.4,0\n2.7,0\n3.3,1\n2.5,0\n3.6,1\n2.3,0\n2.1,0\n3.6,1\n3.5,1\n3.2,1\n3.2,0\n3.3,1\n3.6,1\n2.6,0\n2.9,0\n3.9,1\n3.6,1\n3.9,1\n2.4,0\n3.2,0\n3.3,0\n2.2,0\n2.8,0\n3.8,1\n2,0\n3,1\n2.1,0\n2.3,0\n2,0\n3.4,1\n3.7,1\n3,0\n2.4,0\n2.1,0\n3.4,0\n2.2,0\n3.2,1\n3.9,1\n3.4,1\n2.8,1\n2,0\n2.1,0\n2.8,0\n3.1,1\n3.5,1\n3.2,1\n3.7,1\n3.7,1\n2.2,0\n3.2,1\n3.2,1\n2.1,0\n3.8,1\n2.4,0\n2.3,0\n3.9,1\n3,1\n2.1,0\n3.8,1\n2.8,0\n2.1,0\n2.2,0\n2.9,0\n3.1,0\n4,1\n2.6,0\n3,1\n2.3,0\n3.2,1\n3.7,1\n3.9,1\n3.7,1\n3.2,1\n2.6,0\n3.4,1\n3.9,1\n2.1,0\n4,1\n2.8,0\n3.2,1\n3.5,1\n3.1,1\n3.5,1\n2.8,0\n2.4,0\n3.6,1\n2.5,0\n2.2,0\n3.5,1\n2.4,0\n3.9,1\n3.1,1\n2.4,0\n3.1,1\n2.9,0\n2.1,0\n3.6,1\n3.2,1\n3.7,1\n2.8,0\n2.8,0\n3.2,1\n2.9,0\n2.1,0\n3.4,1\n3.9,1\n2.6,0\n3.2,1\n4,1\n2.8,1\n3.2,1\n2.5,0\n3,1\n3.5,1\n2.3,0\n3.2,0\n2.7,0\n2.2,0\n3.7,0\n3.5,1\n3.7,1\n2.8,0\n2.4,0\n2,0\n3.5,1\n2.2,0\n2.2,0\n3.3,1\n3.4,1\n3.9,1\n2.2,0\n3.7,1\n3.5,1\n2.7,0\n2.8,1\n3.1,1\n2.5,0\n2.6,0\n2,0\n3.8,1\n2.7,0\n3.7,1\n2.8,0\n2.5,0\n2.4,0\n2.5,0\n3.3,1\n3.4,1\n2.9,1\n2.7,1\n3.9,1\n3.8,1\n2.9,1\n2.8,1\n2.1,0\n2.3,0\n4,1\n2.3,0\n3.7,1\n3.8,1\n3.7,1\n3.3,1\n3.7,1\n2.1,0\n3.2,1\n2.1,0\n3.8,1\n2.9,0\n3.7,1\n2.4,0\n2.6,0\n3.5,1\n2.7,1\n2.7,1\n2.5,0\n2.7,1\n2.7,1\n3.7,1\n2.3,0\n3.6,1\n2.6,1\n2.1,0\n3.8,1\n2.1,0\n2.4,0\n2.6,0\n2.6,0\n2.1,0\n3.6,1\n2.3,0\n3.2,1\n2.7,0\n3.1,1\n2,0\n2.8,0\n2.6,0\n3.1,1\n2.8,0\n2.5,0\n3.6,1\n3.6,1\n2.8,0\n2.5,0\n2.7,0\n3.5,1\n3.6,1\n3.3,1\n3.8,1\n3,0\n3.1,1\n2.7,0\n3.7,1\n3.4,1\n2,0\n3.9,1\n3.6,1\n2.7,1\n2.7,1\n3.4,1\n2.7,1\n2.6,0\n" }, { "path": "Logistic regression in R/script - logistic regression in R.R", "content": "library(tidyverse)\ntheme_set(theme_minimal())\nadmissions <- read_csv(\"admissions.csv\")\nView(admissions)\n\nggplot(admissions, aes(x = gpa, \n y = admitted)) +\n geom_jitter(height = .05, \n alpha = .1)\n\nmodel <- glm(admitted ~ gpa, \n data = admissions,\n family = \"binomial\")\nsummary(model)\n\nggplot(admissions, aes(x = gpa, \n y = admitted)) +\n geom_jitter(height = .05, \n alpha = .1) +\n geom_smooth(method = \"glm\",\n method.args = list(family = \"binomial\"),\n se = FALSE)\n\nadm_sum <- admissions %>% \n group_by(gpa) %>% \n summarize(prop_adm = mean(admitted),\n count = n())\nadm_sum\n\nggplot(adm_sum, aes(x = gpa, \n y = prop_adm)) +\n geom_point()\n\nmodel2 <- glm(prop_adm ~ gpa, \n family = \"binomial\",\n data = adm_sum, \n weights = count)\nsummary(model2)\n\n\nggplot(adm_sum, aes(x = gpa, \n y = prop_adm,\n weight = count)) +\n geom_point() +\n geom_smooth(method = \"glm\",\n se = FALSE,\n method.args = list(family = \"binomial\"))\n\n\n\n\n\n" }, { "path": "Maximum likelihood example/README.md", "content": "# Maximum likelihood estimation - example\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Maximum likelihood estimation - example**, which you can find at . \n\n" }, { "path": "Multiple linear regression in R/README.md", "content": "# Multiple linear regression in R\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Multiple linear regression in R**, which you can find at . " }, { "path": "Multiple linear regression in R/Script - multiple regression in R.R", "content": "# Multiple regression in R\nlibrary(tidyverse)\nlibrary(GGally)\nlibrary(broom)\n\n# The data ----------------------------------------------------------------\n\nset.seed(2)\nx1 <- runif(60, 100, 300)\nx2 <- runif(60, 20, 120)\ny <- 20 + 4*x1 - 3*x2 + rnorm(60, 0, 40)\ndf <- tibble(x1, x2, y)\n\nnew_x1 <- seq(from = 120,\n to = 300,\n length.out = 20)\nnew_x2 <- runif(20, 30, 110)\nnew_data <- tibble(x1 = new_x1,\n x2 = new_x2)\n\n# Preliminaries -----------------------------------------------------------\n\ndf\nggpairs(df)\n\n# The model ---------------------------------------------------------------\n\nmodel <- lm(y ~ x1 + x2, data = df)\nsummary(model)\nplot(model) #for diagnostics\n\n# Predictions -------------------------------------------------------------\n\nnew_data\nfitted <- predict(model, new_data)\nnew_data$pred <- fitted\nnew_data\n\n\n\n\n\n\n" }, { "path": "Multiple linear regression with interaction in R/README.md", "content": "# Multiple linear regression with interaction in R\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Multiple linear regression with interaction in R**, which you can find at . \n\n" }, { "path": "Multiple linear regression with interaction in R/Script - multiple linear regression with interaction.R", "content": "library(tidyverse)\nlibrary(modeldata)\ntheme_set(theme_minimal())\ndata(penguins)\npenguins_sm <- filter(penguins, species != \"Adelie\")\n\nglimpse(penguins_sm)\n\nggplot(penguins_sm, aes(x = flipper_length_mm,\n y = body_mass_g,\n col = species)) + \n geom_point() +\n geom_smooth(method = \"lm\", se = FALSE)\n\nmodel <- lm(body_mass_g ~ flipper_length_mm * species,\n data = penguins_sm)\nsummary(model)\n\nggplot(penguins_sm, aes(x = flipper_length_mm,\n y = body_mass_g,\n col = species)) + \n geom_point() +\n geom_abline(aes(intercept = -3037.196,\n slope = 34.573,\n col = \"Chinstrap\"))+\n geom_abline(aes(intercept = -3037.196-3750.085,\n slope = 34.573 + 20.049,\n col = \"Gentoo\"))\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n# A better plot ----------------------------------------------------\n\npenguin_lines <- penguins_sm %>% \n group_by(species) %>% \n summarise(minimum = min(flipper_length_mm, na.rm = TRUE),\n maximum = max(flipper_length_mm, na.rm = TRUE)) %>% \n pivot_longer(cols = 2:3, \n names_to = \"min_max\", \n values_to = \"flipper_length_mm\")\npenguin_lines <- penguin_lines %>% \n mutate(fitted = predict(model, penguin_lines)) %>% \n pivot_wider(names_from = min_max,\n values_from = c(flipper_length_mm,\n fitted)) \n\nggplot(penguins_sm, aes(flipper_length_mm, \n body_mass_g,\n col = species)) + \n geom_point() +\n geom_segment(data = penguin_lines, \n aes(x = flipper_length_mm_minimum,\n y = fitted_minimum,\n xend = flipper_length_mm_maximum,\n yend = fitted_maximum,\n col = species),\n size = 1)\n" }, { "path": "Multiple regression in R - example/README.md", "content": "# Multiple regression in R: example\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Multiple regression in R: example**, which you can find at . \n\nThe `parenthood` data set comes from Danielle Navarro's excellent book, *Learning Statistics with R*, linked [here](https://learningstatisticswithr.com/). Check it out!" }, { "path": "Multiple regression in R - example/Script - multiple regression in R - example.R", "content": "library(tidyverse)\nlibrary(broom)\nlibrary(GGally)\n\n# Download the LSR data sets from https://learningstatisticswithr.com/. \n# The following command likely won't work on your machine. Replace with the\n# file's full path or import directly using the RStudio file browser.\n# load(\"parenthood.Rdata\") \n\nparenthood <- select(parenthood, 1:3)\nglimpse(parenthood)\n\nggpairs(parenthood)\nmodel <- lm(dan.grump ~ ., \n data = parenthood)\nsummary(model)\n\nmodel_baby <- lm(dan.grump ~ baby.sleep,\n data = parenthood)\nsummary(model_baby)\n\nmodel_dan <- lm(dan.grump ~ dan.sleep, \n data = parenthood)\nsummary(model_dan)\n\nglance(model)\nglance(model_baby)\nglance(model_dan)\n" }, { "path": "Mutating data frames with case_when/README.md", "content": "# Mutating data frames with case_when\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Mutating data frames with case_when**, which you can find at . " }, { "path": "Mutating data frames with case_when/Script - case_when.R", "content": "# Assigning letter grades using case_when\nlibrary(tidyverse)\ngradebook <- read_csv(\"Mutating data frames with case_when/gradebook.csv\")\nView(gradebook)\n\n?case_when\n# generalizes ifelse()\n\ngradebook_new <- gradebook %>% \n mutate(grade = case_when(score >= 90 ~ \"A\",\n score >= 80 ~ \"B\",\n score >= 70 ~ \"C\",\n score >= 60 ~ \"D\",\n .default = \"F\"))\nview(gradebook_new)\n" }, { "path": "Mutating data frames with case_when/gradebook.csv", "content": "name,score\nstudent 1,80\nstudent 2,66\nstudent 3,72\nstudent 4,75\nstudent 5,74\nstudent 6,71\nstudent 7,77\nstudent 8,49\nstudent 9,66\nstudent 10,84\nstudent 11,99\nstudent 12,89\nstudent 13,95\nstudent 14,62\nstudent 15,18\nstudent 16,83\nstudent 17,96\nstudent 18,75\nstudent 19,71\nstudent 20,68\nstudent 21,87\nstudent 22,83\nstudent 23,77\nstudent 24,85\nstudent 25,77\nstudent 26,83\nstudent 27,45\nstudent 28,82\nstudent 29,78\nstudent 30,80\nstudent 31,80\nstudent 32,83\nstudent 33,96\nstudent 34,93\n" }, { "path": "Nested data frames and multiple models/README.md", "content": "# Nested data frames and multiple models in R\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Nested data frames and multiple models in R**, which you can find at . " }, { "path": "Nested data frames and multiple models/Script - multiple models.R", "content": "# Nested data frames, multiple models\n\nlibrary(tidymodels)\n\nglimpse(penguins)\n\npenguins_nested <- penguins |> \n group_by(species) |> \n nest()\n\nView(penguins_nested)\nView(penguins_nested[[1]])\n\npenguins_lm <- function(df){\n lm(bill_length_mm ~ bill_depth_mm, \n data = df)\n}\n\npenguins_nested <- penguins_nested |> \n mutate(model = map(data, penguins_lm), \n model_tidy = map(model, tidy),\n model_glance = map(model, glance))\n\nsummary(penguins_nested$model[[1]])\nView(penguins_nested$model_glance[[1]])\n\npenguins_m <- penguins_nested |> \n unnest(model_tidy)\nView(penguins_m)\n\npenguins_g <- penguins_nested |> \n unnest(model_glance)\nView(penguins_g)\n\n# Let's get a plot showing the linear models in the 3 groups.\n\npenguins_wide <- penguins_m |> \n pivot_wider(names_from = term,\n values_from = estimate:model_glance)\nView(penguins_wide)\n\nggplot() +\n geom_point(data = penguins, \n aes(x = bill_depth_mm,\n y = bill_length_mm, \n color = species)) + \n geom_abline(data = penguins_wide, \n aes(slope = estimate_bill_depth_mm,\n intercept = `estimate_(Intercept)`,\n color = species)) +\n scale_color_brewer(palette = \"Dark2\")\n\n\n" }, { "path": "Normal distribution in R/README.md", "content": "# The normal distribution in R\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **The normal distribution in R**, which you can find at . \n\n" }, { "path": "Normal distribution in R/Script - normal distribution in R.R", "content": "# The normal distribution in R\nlibrary(tidyverse)\n\n# Problem 1\npnorm(200, 192.9, 7.1) # less than 200 \n1 - pnorm(200, 192.9, 7.1) # more than 200\n\n# Problem 2\nqnorm(.9, 192.9, 7.1)\n\n# Problem 3\nflippers <- rnorm(400, 192.9, 7.1)\nqplot(flippers, col = I(\"black\")) # plots will differ slightly due to random variation\n" }, { "path": "Not-so-simple linear regression/README.md", "content": "# Not-so-simple linear regression with R\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Not-so-simple linear regression with R**, which you can find at . \n\n" }, { "path": "Not-so-simple linear regression/simple linear regression.R", "content": "# Simple linear regression - deep dive\n\nlibrary(tidymodels)\ntheme_set(theme_minimal())\nset.seed(0)\n\nView(airquality)\n\n# split data\n\naq_split <- initial_split(airquality)\naq_train <- training(aq_split)\naq_test <- testing(aq_split)\n\n# visualize the data\n\nggplot(aq_train, aes(x = Wind,\n y = Temp)) + \n geom_point() +\n geom_smooth(method = \"lm\",\n se = FALSE)\n\n# building the model\n\nmodel <- lm(Temp ~ Wind, \n data = aq_train)\nnames(model)\n\nmodel$coefficients\nmodel$residuals\nmodel$fitted.values\n\nsummary(model)\nfitted.values(model)\nconfint(model, level = .90)\n\nplot(model)\n\n# the broom package\n\ntidy(model)\nglance(model)\naq_aug <- augment(model)\nView(aq_aug)\n\n# model performance\n\npredict(model, \n aq_test,\n interval = \"prediction\")\n\nsqrt(mean((aq_test$Temp - predict(model, aq_test))^2))\n\n\n\n\n\n\n" }, { "path": "Organizing bar charts by frequency/README.md", "content": "# Arranging ggplot bar charts by frequency\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Arranging ggplot bar charts by frequency**, which you can find at . \n\n" }, { "path": "Organizing bar charts by frequency/Script - organizing bar charts by frequency.R", "content": "library(tidyverse)\n?fct_infreq\n\nglimpse(gss_cat)\nggplot(gss_cat, aes(x = marital)) +\n geom_bar() +\n theme_minimal()\n\nlevels(gss_cat$marital)\n\nggplot(gss_cat, aes(x = fct_infreq(marital),\n fill = fct_infreq(marital))) +\n geom_bar() +\n labs(x = \"Marital status\") +\n scale_fill_brewer(palette = \"Set2\") +\n theme_minimal() +\n theme(legend.position = \"none\")\n" }, { "path": "Overfitting in machine learning/README.md", "content": "# Overfitting in machine learning\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Overfitting in machine learning**, which you can find at . \n\n" }, { "path": "Overfitting in machine learning/overfitting demonstration.R", "content": "# Overfitting demonstration\n\nlibrary(tidyverse)\n\n# Create the data ---------------------------------------------------------\n\nset.seed(1)\nx <- runif(1000, 0, 10)\ny <- 6 - .5 * x + rnorm(1000, sd = 2)\ndf <- data.frame(x, y)\n\nset.seed(0)\ndf_sample <- df %>% \n slice_sample(n = 25)\n\n# Visualization -----------------------------------------------------------\n\nggplot(df, aes(x, y)) + \n geom_point(color = \"lightblue\") + \n geom_abline(slope = -.5, \n intercept = 6,\n color = \"blue\",\n linewidth = 1)\n\nggplot(df, aes(x, y)) + \n geom_point(color = \"lightblue\") + \n geom_abline(slope = -.5, \n intercept = 6,\n color = \"blue\",\n linewidth = 1) +\n geom_point(data = df_sample,\n color = \"black\") +\n geom_smooth(data = df_sample,\n se = FALSE,\n color = \"black\")\n\nggplot(df, aes(x, y)) + \n geom_point(color = \"lightblue\") + \n geom_abline(slope = -.5, \n intercept = 6,\n color = \"blue\",\n linewidth = 1) +\n geom_point(data = df_sample,\n color = \"black\") +\n geom_smooth(data = df_sample,\n se = FALSE,\n method = \"lm\",\n color = \"black\")\n\n# Model building ----------------------------------------------------------\n\nmodel_linear <- lm(y ~ x, \n data = df_sample)\nmodel_loess <- loess(y ~ x, \n data = df_sample)\n\nsummary(model_linear)\nsummary(model_loess)\n\n# Model evaluation --------------------------------------------------------\n\ndf_resid <- df %>% \n mutate(linear_resid = df$y - predict(model_linear, df),\n loess_resid = df$y - predict(model_loess, df)) %>% \n drop_na()\nView(df_resid)\n\nrss_linear <- sum(df_resid$linear_resid^2)\nrss_loess <- sum(df_resid$loess_resid^2)\n\nrss_linear\nrss_loess\n" }, { "path": "Percentiles in R/README.md", "content": "# Percentiles and quantiles in R\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Percentiles and quantiles in R**, which you can find at . \n\n" }, { "path": "Percentiles in R/script - percentiles.R", "content": "library(tidyverse)\n\nggplot(faithful, aes(x = waiting)) + \n geom_histogram(color = \"black\") +\n labs(x = \"Waiting time\")\n\nquantile(faithful$waiting, .35)\nquantile(faithful$waiting, c(.35, .70, .95))\n\nsummary(faithful$waiting)\n\necdf(faithful$waiting)(79)\n" }, { "path": "Percentiles of continuous distributions/README.md", "content": "# Percentiles of continuous distributions\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Percentiles of continuous distributions**, which you can find at . \n\n" }, { "path": "Pie charts with R/README.md", "content": "# Pie charts with R\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Pie charts with R**, which you can find at . \n\n" }, { "path": "Pie charts with R/pie charts.R", "content": "# Pie charts in R\n\nlibrary(tidyverse)\nlibrary(modeldata)\nView(penguins)\n\n# Bar chart for comparison\n\nggplot(penguins, aes(x = species,\n fill = species)) +\n geom_bar(show.legend = FALSE) +\n scale_fill_brewer(palette = \"Dark2\") +\n labs(x = \"Species\",\n y = NULL) + \n theme_minimal() \n\n# Obtain counts for penguin species\n\npen_sum <- penguins %>% \n count(species)\npen_sum\n\n# A stacked bar chart\n\nggplot(pen_sum, aes(x = \"\",\n y = n, \n fill = species)) + \n geom_col() +\n scale_fill_brewer(palette = \"Dark2\") +\n labs(fill = \"Species\",\n x = NULL,\n y = NULL) +\n theme_minimal()\n\n# A basic pie chart\n\nggplot(pen_sum, aes(x = \"\",\n y = n, \n fill = species)) + \n geom_col() +\n coord_polar(\"y\") +\n scale_fill_brewer(palette = \"Dark2\") +\n labs(fill = \"Species\",\n x = NULL,\n y = NULL) +\n theme_void()\n\n# Adding labels\n\npen_sum <- penguins %>% \n count(species) %>% \n arrange(desc(species)) %>% \n mutate(frac = round(n / sum(n), 2),\n percent = paste0(frac * 100, \"%\"),\n ypos = cumsum(n) - .5*n)\npen_sum\n\nggplot(pen_sum, aes(x = \"\",\n y = n,\n fill = species)) + \n geom_col() +\n geom_text(aes(label = percent, \n y = ypos)) +\n scale_fill_brewer(palette = \"Dark2\") +\n labs(fill = \"Species\") +\n theme_void()\n\nggplot(pen_sum, aes(x = \"\",\n y = n,\n fill = species)) + \n geom_col() +\n geom_text(aes(label = percent, \n y = ypos)) + \n coord_polar(\"y\") +\n scale_fill_brewer(palette = \"Dark2\") +\n labs(fill = \"Species\") +\n theme_void()\n" }, { "path": "Poisson regression/README.md", "content": "# Poisson regression\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Poisson regression**, which you can find at . \n\n" }, { "path": "Poisson regression in R/README.md", "content": "# Poisson regression in R\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Poisson regression in R**, which you can find at . \n\n" }, { "path": "Poisson regression in R/script - poisson in R.R", "content": "library(tidyverse)\nlibrary(ISLR2)\ntheme_set(theme_minimal())\n\n?Bikeshare\nglimpse(Bikeshare)\n\nggplot(Bikeshare, aes(y = bikers, \n x = temp)) +\n geom_point(alpha = .1) + \n facet_wrap(~weathersit) \n\nggplot(Bikeshare, aes(y = bikers, \n x = temp)) +\n geom_point(alpha = .1) + \n facet_wrap(~workingday) \n\nmodel <- glm(bikers ~ temp + workingday + weathersit,\n data = Bikeshare,\n family = \"poisson\")\nsummary(model)\n\nggplot(Bikeshare, aes(y = bikers, \n x = temp,\n color = weathersit)) +\n geom_point(alpha = .05) + \n geom_smooth(method = \"glm\",\n se = FALSE,\n method.args = list(family = \"poisson\")) +\n facet_wrap(~workingday) +\n scale_color_brewer(palette = \"Dark2\")\n\n365 * 24 # hours in a year.\n\n\n" }, { "path": "Prediction intervals for linear models/README.md", "content": "# Prediction intervals for linear models in R\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Prediction intervals for linear models in R**, which you can find at . " }, { "path": "Prediction intervals for linear models/Script - prediction intervals.R", "content": "# Prediction intervals for linear models\nlibrary(tidyverse)\nlibrary(modeldata)\n?predict \ndata(penguins)\n\nadelie <- penguins %>% \n filter(species == \"Adelie\",\n !is.na(bill_length_mm))\n\nView(adelie)\n\nggplot(adelie, aes(x = bill_depth_mm,\n y = bill_length_mm)) +\n geom_point() +\n geom_smooth(method = \"lm\",\n level = .99)\n\nmodel <- lm(bill_length_mm ~ bill_depth_mm, \n data = adelie)\nplot(model)\n\nadelie_pred <- predict(model, \n interval = \"prediction\")\n\nadelie_new <- cbind(adelie, \n adelie_pred)\nView(adelie_new)\n\nggplot(adelie_new, aes(x = bill_depth_mm)) + \n geom_point(aes(y = bill_length_mm)) +\n geom_line(aes(y = fit),\n col = \"blue\",\n size = .8) +\n geom_line(aes(y = upr),\n linetype = \"dashed\",\n col = \"blue\",\n size = .8) +\n geom_line(aes(y = lwr),\n linetype = \"dashed\",\n col = \"blue\", \n size = .8) # note 'linewidth' is now used instead of 'size'\n\nadelie_pred <- predict(model, \n interval = \"prediction\",\n level = .8)\nadelie_pred <- cbind(adelie, adelie_pred)\n\naugment(model)\n\n" }, { "path": "R Markdown/R Markdown practice.Rmd", "content": "---\ntitle: \"RMarkdown practice\"\nauthor: \"Andrew Gard\"\ndate: \"`r Sys.Date()`\"\noutput: html_document\n---\n\n```{r setup, include=FALSE}\nknitr::opts_chunk$set(echo = TRUE)\nlibrary(tidyverse)\nlibrary(palmerpenguins)\nlibrary(knitr)\n```\n\n# Introduction\n\nHi everybody! This is an RMarkdown document. You can do *italics* and **bold**, and include a [link](https://www.youtube.com/c/EquitableEquations).\n\n# R Code\n\n```{r our first chunk, eval=FALSE}\nvalues <- rnorm(5)\nvalues\n```\n\nWe can also use echo to show or not show the code and results to show or not show the output. \n\n# More text tools\n\n## subheadings\n\nUnordered lists:\n\n- first item\n\n- second item \n\nOrdered lists:\n\n1. first\n\n2. second\n\nWe can insert an image like this: ![alt text](rusty_logo.png){width=\"20%\"}\n\nWe can add citations^[footnotes]\n\nWe can use math mode $\\alpha \\beta$ and so on.\n\nFinally, we can make nice tables. \n\n```{r}\npenguins_sm <- head(penguins)\nkable(penguins_sm) # from the knitr package\n```\n\nSee the RMarkdown cheat sheet and reference guide (help -> cheat sheets) for more!\n\n\n\n\n\n" }, { "path": "R Markdown/R-Markdown-practice.html", "content": "\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nRMarkdown practice\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
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RMarkdown practice

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Andrew Gard

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2022-08-20

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Introduction

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Hi everybody! This is an RMarkdown document. You can do\nitalics and bold, and include a link.

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R Code

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values <- rnorm(5)\nvalues
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We can also use echo to show or not show the code and results to show\nor not show the output.

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More text tools

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subheadings

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Unordered lists:

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  • first item

    • \n

  • second item

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Ordered lists:

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  1. first

    2. \n

  2. second

    3. \n
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We can insert an image like this: \"alt

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We can add citations1

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We can use math mode \\(\\alpha\n\\beta\\) and so on.

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Finally, we can make nice tables.

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penguins_sm <- head(penguins)\nkable(penguins_sm) # from the knitr package
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speciesislandbill_length_mmbill_depth_mmflipper_length_mmbody_mass_gsexyear
AdelieTorgersen39.118.71813750male2007
AdelieTorgersen39.517.41863800female2007
AdelieTorgersen40.318.01953250female2007
AdelieTorgersenNANANANANA2007
AdelieTorgersen36.719.31933450female2007
AdelieTorgersen39.320.61903650male2007
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See the RMarkdown cheat sheet and reference guide (help -> cheat\nsheets) for more!

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  1. footnotes↩︎

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\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n" }, { "path": "R Markdown/README.md", "content": "# What is R Markdown?\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **What is R Markdown?**, which you can find at . \n\n" }, { "path": "README.md", "content": "# Equitable equations on YouTube\n[]()\n\nHi everybody! In this repo, you'll find files to supplement the vids on my YouTube channel, www.youtube.com/c/EquitableEquations, where I usually post new content every second Monday. I'm working to document older vids as well, but this will take time. Thanks for visiting!\n\n" }, { "path": "Regression analysis - backward selection/README.md", "content": "# Regression analysis in R: backward selection\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Regression analysis in R: backward selection**, which you can find at . \n\n" }, { "path": "Regression analysis - backward selection/Slides - bss.R", "content": "library(tidyverse)\nlibrary(broom)\nlibrary(GGally)\n\n# Data from Regression Analysis by Example\n# by Chatterjee and Hadi (set P060)\n\nperformance <- read_csv(\"performance.csv\")\nView(performance)\n\nggpairs(performance)\n\n# I'll use the t-statistic to decide which\n# variable to throw away at each step.\n# I'll keep going as long as I have a t-statistic\n# under 1. \n\nmodel_6 <- lm(overall_rating ~ ., \n data = performance)\nsummary(model_6)\n\nmodel_5 <- update(model_6,\n . ~ . -not_overly_critical)\nsummary(model_5)\n\nmodel_4 <- update(model_5,\n . ~ . - raises_for_performance)\nsummary(model_4)\n\nmodel_3 <- update(model_4, \n . ~ . -no_special_privileges)\nsummary(model_3)\n\n# residual plot\n\nperf_aug <- augment(model_3)\nView(perf_aug)\n\nggplot(perf_aug, aes(x = .fitted,\n y = .resid)) + \n geom_point() +\n geom_hline(yintercept = 0)\n\n# How does AIC compare between these models?\n\nmodel_list <- list(model_6, \n model_5, \n model_4, \n model_3)\n\nlapply(model_list, \n extractAIC)\n\n\n" }, { "path": "Regression analysis - backward selection/performance.csv", "content": "overall_rating,handles_employee_complaints,no_special_privileges,space_for_learning,raises_for_performance,not_overly_critical,rate_of_advancement\n43,51,30,39,61,92,45\n63,64,51,54,63,73,47\n71,70,68,69,76,86,48\n61,63,45,47,54,84,35\n81,78,56,66,71,83,47\n43,55,49,44,54,49,34\n58,67,42,56,66,68,35\n71,75,50,55,70,66,41\n72,82,72,67,71,83,31\n67,61,45,47,62,80,41\n64,53,53,58,58,67,34\n67,60,47,39,59,74,41\n69,62,57,42,55,63,25\n68,83,83,45,59,77,35\n77,77,54,72,79,77,46\n81,90,50,72,60,54,36\n74,85,64,69,79,79,63\n65,60,65,75,55,80,60\n65,70,46,57,75,85,46\n50,58,68,54,64,78,52\n50,40,33,34,43,64,33\n64,61,52,62,66,80,41\n53,66,52,50,63,80,37\n40,37,42,58,50,57,49\n63,54,42,48,66,75,33\n66,77,66,63,88,76,72\n78,75,58,74,80,78,49\n48,57,44,45,51,83,38\n85,85,71,71,77,74,55\n82,82,39,59,64,78,39\n" }, { "path": "Repositioning ggplot legends/README.md", "content": "# Repositioning ggplot legends in R\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Repositioning ggplot legends in R**, which you can find at . \n\n" }, { "path": "Repositioning ggplot legends/Script - repositioning ggplot legends.R", "content": "library(tidyverse)\ntheme_set(theme_minimal())\n\nggplot(diamonds, aes(x = cut, fill = cut)) +\n geom_bar() +\n theme(legend.position = \"top\")\n\nggplot(diamonds, aes(x = cut, fill = cut)) +\n geom_bar() +\n theme(legend.position = \"none\")\n\nggplot(diamonds, aes(x = cut, fill = cut)) +\n geom_bar() +\n theme(legend.position = c(0, 1),\n legend.justification = c(0, 1))\n" }, { "path": "Return of the starwars data set/README.md", "content": "# Return of the starwars data set\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Return of the starwars data set**, which you can find at . \n\n" }, { "path": "Return of the starwars data set/script - starwars.R", "content": "# Unnesting Starwars\nlibrary(tidyverse)\ntheme_set(theme_minimal())\nView(starwars)\nglimpse(starwars)\n\nstarwars_sm <- starwars %>% \n select(name, \n films, \n vehicles,\n starships)\n\nsw_films <- starwars_sm %>% \n unnest(films)\nView(sw_films)\n\nsw_films %>% \n count(name) %>% \n arrange(-n)\n\nsw_films %>% \n count(name) %>% \n filter(n >= 4) %>%\n mutate(name = fct_reorder(name, n)) %>% \n ggplot(aes(y = name,\n x = n)) + \n geom_col()\n\n?unnest\nsw_ships <- starwars_sm %>% \n unnest(starships, keep_empty = TRUE)\nView(sw_ships)\n\nsw_long <- starwars_sm %>% \n unnest(c(starships, vehicles)) # error, vectors aren't the same length\n\nsw_long <- starwars_sm %>%\n unnest(starships) %>% \n unnest(vehicles)\nView(sw_long)\n" }, { "path": "Scatterplots and regression lines in R/README.md", "content": "# Scatterplots and regression lines in R\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Scatterplots and regression lines in R**, which you can find at . \n\n" }, { "path": "Scatterplots and regression lines in R/Script - scatterplots and regression lines.R", "content": "# scatterplots in R\n\nlibrary(tidyverse)\nlibrary(modeldata)\n\nView(penguins)\n\nggplot(penguins, aes(x = flipper_length_mm,\n y = bill_length_mm)) + \n geom_point(size = 2,\n color = \"red\",\n shape = \"square\",\n alpha = .5) +\n geom_smooth(method = \"lm\",\n se = FALSE,\n color = \"pink\") # not ideal color choices\n\nggplot(penguins, aes(x = flipper_length_mm,\n y = bill_length_mm)) + \n geom_point() +\n geom_smooth(method = \"lm\",\n se = FALSE)\n\nlm(bill_length_mm ~ flipper_length_mm,\n data = penguins)\n\nggplot(penguins, aes(x = flipper_length_mm,\n y = bill_length_mm,\n color = island)) + \n geom_point() +\n scale_color_brewer(palette = \"Dark2\")\n\nggplot(penguins, aes(x = flipper_length_mm,\n y = bill_length_mm,\n color = species)) + \n geom_point() +\n scale_color_brewer(palette = \"Dark2\")\n\ntable(penguins$species,\n penguins$island)\n\nggplot(penguins, aes(x = flipper_length_mm,\n y = bill_length_mm,\n color = species)) + \n geom_point() +\n geom_smooth(method = \"lm\",\n se = FALSE) +\n scale_color_brewer(palette = \"Dark2\")\n\nggplot(penguins, aes(x = flipper_length_mm,\n y = bill_length_mm,\n color = species)) + \n geom_point() +\n geom_smooth(method = \"lm\",\n se = FALSE) +\n scale_color_brewer(palette = \"Dark2\") +\n labs(x = \"Flipper length\",\n y = \"Bill length\",\n color = \"Species\",\n title = \"Penguin measurements\") +\n theme_minimal()\n\n\n" }, { "path": "Sensitivity, specificity, precision, recall/README.md", "content": "# Sensitivity, specificity, precision, recall\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Sensitivity, specificity, precision, recall**, which you can find at . \n\n" }, { "path": "Simple linear regression with R (intro)/README.md", "content": "# Simple linear regression with R (introductory)\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Simple linear regression with R (introductory)**, which you can find at . \n\n" }, { "path": "Simpson's paradox/README.md", "content": "# Simpson's paradox\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Simpson's paradox**, which you can find at . " }, { "path": "Simpson's paradox/batting.csv", "content": "year,average,group\n1969,0.292,A\n1965,0.28,A\n1970,0.284,A\n1969,0.288,A\n1967,0.279,A\n1961,0.286,A\n1963,0.276,A\n1965,0.295,A\n1966,0.282,A\n1973,0.3,A\n1968,0.284,A\n1963,0.278,A\n1962,0.284,A\n1965,0.285,A\n1965,0.275,A\n1964,0.291,A\n1966,0.279,A\n1963,0.286,A\n1967,0.289,A\n1962,0.284,A\n1965,0.284,A\n1967,0.287,A\n1966,0.279,A\n1968,0.282,A\n1965,0.279,A\n1967,0.296,A\n1969,0.296,A\n1963,0.286,A\n1962,0.276,A\n1966,0.285,A\n1966,0.28,B\n1965,0.268,B\n1966,0.275,B\n1965,0.264,B\n1969,0.277,B\n1971,0.268,B\n1970,0.266,B\n1966,0.265,B\n1971,0.268,B\n1966,0.266,B\n1972,0.279,B\n1969,0.273,B\n1966,0.269,B\n1965,0.263,B\n1963,0.263,B\n1964,0.265,B\n1962,0.262,B\n1971,0.271,B\n1970,0.276,B\n1966,0.264,B\n1968,0.281,B\n1966,0.263,B\n1975,0.275,B\n1965,0.284,B\n1967,0.277,B\n1968,0.27,B\n1969,0.267,B\n1967,0.275,B\n1960,0.267,B\n1963,0.267,B\n1975,0.27,C\n1973,0.262,C\n1969,0.287,C\n1973,0.281,C\n1969,0.271,C\n1974,0.273,C\n1967,0.257,C\n1975,0.275,C\n1974,0.264,C\n1974,0.257,C\n1973,0.283,C\n1974,0.258,C\n1970,0.262,C\n1973,0.26,C\n1976,0.259,C\n1978,0.274,C\n1974,0.253,C\n1973,0.264,C\n1969,0.259,C\n1967,0.25,C\n1970,0.263,C\n1979,0.266,C\n1977,0.263,C\n1972,0.274,C\n1971,0.27,C\n1971,0.264,C\n1978,0.27,C\n1972,0.259,C\n1979,0.268,C\n1976,0.278,C\n1979,0.269,C\n1969,0.265,C\n1973,0.284,C\n1969,0.257,C\n1976,0.276,C\n1974,0.282,C\n1972,0.256,C\n1980,0.27,C\n1974,0.263,C\n1978,0.264,C\n" }, { "path": "Simpson's paradox/script - simpsons paradox.R", "content": "# Simulate the data -------------------------------------------------------\n# (or download from GitHub and load directly)\n\nset.seed(0)\n\nx <- c(rnorm(30, 65, 10),\n rnorm(30, 70, 10),\n rnorm(40, 90, 15))\ny <- c(rnorm(30, 75, 10),\n rnorm(30, 50, 10),\n rnorm(40, 35, 15)) + .3 * x\ngroup <- c(rep(\"A\", 30),\n rep(\"B\", 30), \n rep(\"C\", 40))\n\nxnew <- round(((x - 47) / (112 - 47)) * 20 + 1960)\nynew <- round(((y - 35) / (119 - 35)) * 50 + 250) / 1000\n\nbatting <- data.frame(year = xnew, average = ynew, group)\n\n# Simpson's paradox -------------------------------------------------------\n\nlibrary(tidyverse)\nView(batting)\n\nggplot(batting, aes(x = year, \n y = average)) +\n geom_point() +\n geom_smooth(method = \"lm\",\n se = FALSE) +\n scale_y_continuous(labels = scales::label_number(accuracy = .001)) +\n theme_minimal()\n\nggplot(batting, aes(x = year, \n y = average,\n color = group)) +\n geom_point() +\n geom_smooth(method = \"lm\",\n se = FALSE) +\n scale_y_continuous(labels = scales::label_number(accuracy = .001)) +\n scale_color_brewer(palette = \"Dark2\") +\n theme_minimal() \n\n# Common example ----------------------------------------------------------\n\nggplot(iris, aes(x = Sepal.Width, \n y = Sepal.Length)) + \n geom_point() +\n geom_smooth(method = \"lm\",\n se = FALSE)\n\nggplot(iris, aes(x = Sepal.Width, \n y = Sepal.Length,\n color = Species)) + \n geom_point() +\n scale_color_brewer(palette = \"Dark2\") +\n geom_smooth(method = \"lm\",\n se = FALSE)\n\n\n\n\n\n\n" }, { "path": "Splitting columns in R with separate()/README.md", "content": "# Splitting columns in R with separate()\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Splitting columns in R with separate()**, which you can find at . \n" }, { "path": "Splitting columns in R with separate()/script - splitting columns with separate.R", "content": "# Notes for separate script\nlibrary(tidyverse)\n?separate\n\nView(smiths)\n\nseparate(smiths,\n col = subject,\n into = c(\"first\", \"last\"))\n\nseparate(smiths,\n col = subject,\n into = c(\"first\", \"last\"),\n remove = FALSE)\n\nseparate(smiths,\n col = subject,\n into = c(\"first\", \"last\"),\n sep = \" \")\n\nseparate(smiths,\n col = subject,\n into = c(\"first\", NA))\n\nseparate(smiths,\n col = height,\n into = c(\"m\", \"cm\"),\n convert = TRUE)\n" }, { "path": "Splitting data frames with slice()/README.md", "content": "# Splitting data frames in R\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Splitting data frames in R**, which you can find at . \n\n" }, { "path": "Splitting data frames with slice()/Script - splitting data sets.R", "content": "# Slicing a data set\n\nlibrary(tidyverse)\n?slice\nstarwars\n\nslice(starwars, 4:8) # only keep rows 4-8\nslice(starwars, c(-4, -5)) # omit rows 4 and 5\n\nslice_tail(starwars, n = 4) # keep the last 4 rows\nslice_tail(starwars, prop = .20) # keep the last 20% of rows\n\nstarwars %>% slice_max(order_by = mass,\n n = 4) # keep the row with top 4 values of mass\nstarwars %>% slice_max(order_by = mass,\n n = 4, \n with_ties = FALSE) # gives exactly 4 rows despite ties\n\ntest <- starwars %>% \n slice_sample(prop = .20) # keep 20% at random\ntest\n\ntraining <- starwars %>%\n anti_join(test)\ntraining\n" }, { "path": "Statistical power and sample size/README.md", "content": "# Statistical power and sample size\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Statistical power and sample size**, which you can find at . \n\n" }, { "path": "Teaching an AI to webscrape/README.md", "content": "# Teaching an AI to webscrape using the ellmer package\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Teaching an AI to webscrape using the ellmer package**, which you can find at . \n\n" }, { "path": "Teaching an AI to webscrape/ellmer tools.R", "content": "# tool calling with Claude\n\nlibrary(tidyverse)\nlibrary(ellmer)\nlibrary(rvest)\n\ntop_movie <- function(){\n url <- \"https://www.boxofficemojo.com/daily/2025/?view=year\"\n top_movie_df <- read_html(url) %>% \n html_table() %>% \n pluck(1) %>% \n slice_head(n = 1)\n top_movie_df\n}\n\ntop_movie()\n\ntop_movie_newest <- tool(\n .fun = top_movie,\n .name = \"top_movie\",\n .description = \"Returns the most recently recorded top movie of the day as listed on boxofficemojo.com.\"\n )\n\nchat <- chat_anthropic(system_prompt = \n \"you are a terse R assistant\")\n\nchat$register_tool(top_movie_newest)\n\nchat$chat(\"what is the most recent top movie?\")\n\nchat$chat(\"what was the top movie on christmas of 2022?\")\n\n\ntop_movies_year <- function(year){\n url <- paste0(\"https://www.boxofficemojo.com/daily/\", year)\n top_movie_df <- read_html(url) %>% \n html_table() %>% \n pluck(1) %>% \n janitor::clean_names()\n top_movie_df\n}\n\ndf <- top_movies_year(2023)\nglimpse(df)\n\ntop_movies_year <- tool(\n .fun = top_movies_year,\n .name = \"top_movies_year\",\n .description = \"Returns the top-grossing movies by day for the specified year. Data from boxofficemojo.com.\",\n year = type_integer(\"the year of the date requested\")\n)\n\nchat$register_tool(top_movies_year)\n\nchat$chat(\"what was the top movie on christmas of 2022?\")\n\nchat$chat(\"how long was it the top-grossing movie?\")\n\nchat$chat(\"also look at 2023\")\n\nchat$chat(\"give R code to create a single data set showing this movie's performance\")\n\nchat$chat(\"the code should download the data and combine the sets, not create the data from scratch\")\n\nchat$get_cost()\n\n" }, { "path": "The poisson distribution/README.md", "content": "# The poisson distribution\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **The poisson distribution**, which you can find at . " }, { "path": "Tidy data/README.md", "content": "# What is tidy data?\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **What is Tidy data?**, which you can find at . \n\n" }, { "path": "Tidy tuesday analysis - Ravelry yarn/README.md", "content": "# Tidy Tuesday analysis: Ravelry yarn\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Tidy Tuesday analysis: Ravelry yarn**, which you can find at . \n\n" }, { "path": "Tidy tuesday analysis - Ravelry yarn/yarn script.R", "content": "library(tidyverse)\nlibrary(tidytuesdayR)\ntheme_set(theme_minimal())\n\ntuesdata <- tidytuesdayR::tt_load(2022, week = 41)\nyarn <- tuesdata$yarn\n\nView(yarn)\n\n# How are NAs distributed?\n\nmean(is.na(yarn$max_gauge))\n\nyarn_na_counts <- yarn |> \n summarize(across(everything(), \n \\(x) mean(is.na(x)))) \nView(yarn_na_counts)\n\n# Big idea: can we impute missing NA values for \n# min_gauge using grams and yardage?\n\n# some weirdness:\nsummary(yarn$yardage)\n# Note the min. \n\nweird <- filter(yarn, yardage == 0)\nView(weird)\n\n# Let's replace Kmart Sayelle's yardage with 240\n\nyarn[yarn$permalink == \"kmart-sayelle\", \"yardage\"] <- 240\n\n# There are also zeros for grams.\n# For brevity, we're going to just filter these out.\n\nyarn_sm <- yarn |> \n filter(grams > 0,\n yardage > 0) |> \n mutate(dens = grams / yardage,\n gauge_std = min_gauge / gauge_divisor)\n\nView(yarn_sm)\n\n# How do dens and gauge_std relate in this smaller set?\n\nggplot(yarn_sm, aes(x = log10(dens), \n y = log10(gauge_std))) + \n geom_point(alpha = .1)\n\n# let's remove the zeroes from gauge_std as well. \n\nyarn_sm <- yarn_sm |> \n filter(gauge_std > 0)\n\nggplot(yarn_sm, aes(x = log10(dens), \n y = log10(gauge_std))) + \n geom_point(alpha = .1) +\n geom_smooth(method = \"lm\")\n\n# Let's get the actual model now:\n\nmodel <- lm(log10(gauge_std) ~ log10(dens),\n data = yarn_sm)\nsummary(model)\n\n# The model says that\n# gauge_std = 10 ^ (.495 - .514 * log10(dens) + error)\n\nyarn_new <- yarn |> \n mutate(gauge_std_imputed = 10 ^ (.495 - .514 * log10(grams / yardage)))\n# Note there are some problematics values here. Zeros and infinities. \n\nggplot(yarn_new, aes(x = log10(min_gauge / gauge_divisor), \n y = log10(gauge_std_imputed))) + \n geom_point(alpha = .1)\n\n" }, { "path": "Tidying data with pivot_longer/README.md", "content": "# Tidying data with pivot_longer\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Tidying data with pivot_longer**, which you can find at . \n\n" }, { "path": "Tidying data with pivot_longer/Script - pivot_longer.R", "content": "# pivot_longer\nlibrary(tidyverse)\n\nBody_temps <- read_excel(\"03 - Body Temperatures.xlsx\") # read in data\n\n?pivot_longer\n\ntemps <- pivot_longer(Body_temps, \n cols = 3:6,\n names_to = \"Observation\",\n values_to = \"Temperature\")\nView(temps)\n\n# using starts_with to select columns\ntemps <- pivot_longer(Body_temps, \n cols = starts_with(\"Day\"),\n names_to = \"Observation\",\n values_to = \"Temperature\")\nView(temps)\n\n# drop rows without temperature observations\ntemps <- pivot_longer(Body_temps, \n cols = starts_with(\"Day\"),\n names_to = \"Observation\",\n values_to = \"Temperature\",\n values_drop_na = TRUE)\n\nggplot(temps, aes(x = Observation, \n y = Temperature,\n color = Observation)) +\n geom_boxplot()\n\n# breaking up the Observation columns\n\ntemps <- pivot_longer(Body_temps, \n cols = starts_with(\"Day\"),\n names_to = c(\"Day\", \"Time\"),\n names_sep = \"-\",\n values_to = \"Temperature\",\n values_drop_na = TRUE)\nView(temps)\n\nggplot(temps, aes(x = \"\", \n y = Temperature)) +\n geom_boxplot() + \n facet_grid(Day ~ Time) +\n labs(x = \"\")\n\n" }, { "path": "Tidying data with pivot_wider/FQA.csv", "content": "variable,value\nAssessment,Coyne Plot 4\nDate,1/1/12\nSite,Coyne Prairie\nFQA DB Region:,Missouri\nFQA DB Publication Year:,2015\nPractitioner:,Justin Thomas\nPrivate/Public:,Public\nTotal Mean C:,4.2\nNative Mean C:,4.3\nTotal FQI:,38.5\nNative FQI:,38.7\nAdjusted FQI:,42.2\n% C value 0:,7.1\n% C value 1-3:,31\n% C value 4-6:,50\n% C value 7-10:,11.9\nNative Tree Mean C:,4\nNative Shrub Mean C:,3.5\nNative Herbaceous Mean C:,4.4\nTotal Species:,84\nNative Species:,81\nNon-native Species:,3\nMean Wetness:,2.7\nNative Mean Wetness:,2.7\nTree:,1\nShrub:,2\nVine:,0\nForb:,47\nGrass:,26\nSedge:,8\nRush:,0\nFern:,0\nBryophyte:,0\nAnnual:,17\nPerennial:,66\nBiennial:,1\nNative Annual:,17\nNative Perennial:,64\n" }, { "path": "Tidying data with pivot_wider/README.md", "content": "# Tidying data in R with pivot_wider\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Tidying data in R with pivot_wider**, which you can find at . \n\n" }, { "path": "Tidying data with pivot_wider/Script - pivot_wider.R", "content": "# tidying data with pivot_wider\nlibrary(tidyverse)\n?pivot_wider\n\nFQA <- read_csv(\"FQA.csv\")\nView(FQA)\n\nFQA_wide <- pivot_wider(FQA, \n names_from = variable,\n values_from = value)\n\nlibrary(GLMsData)\ndata(blocks)\n\nblocks_wide <- blocks %>%\n pivot_wider(names_from = Shape,\n values_from = c(Number, Time))\nView(blocks_wide)\n\nblocks_wide <- blocks_wide %>%\n mutate(Difference = Number_Cube - Number_Cylinder)\n\nggplot(blocks_wide, aes(Difference)) +\n geom_histogram(binwidth = 1,\n color = \"black\") +\n theme_minimal()\n\nblocks_wide <- blocks %>%\n pivot_wider(names_from = c(Trial, Shape),\n values_from = c(Number, Time))\nView(blocks_wide)\n\nblocks_wide <- blocks %>%\n pivot_wider(names_from = c(Trial, Shape),\n values_from = c(Number, Time),\n names_glue = \"Trial.{Trial}_{Shape}_{.value}\")\n\n" }, { "path": "Type I and type II errors/README.md", "content": "# Type I and type II errors in significance tests\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Type I and type II errors in significance tests**, which you can find at . \n\n" }, { "path": "Understand ANOVA in 8 minutes/README.md", "content": "# Understand ANOVA in 8 minutes\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Understand ANOVA in 8 minutes**, which you can find at . \n\n" }, { "path": "Understanding R functions/README.md", "content": "# Understanding the structure of R functions\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Understanding the structure of R functions**, which you can find at . \n\n" }, { "path": "Understanding R functions/script - undedrstanding R functions.R", "content": "# understanding R functions\n\n# from last vid:\n\nkeep_top_spec <- function(vals, cutoff = mean(vals)) {\n # returns only elements of vals above cutoff \n vals[vals > cutoff] \n}\n\nformals(keep_top_spec)\nbody(keep_top_spec)\n\nView(keep_top_spec)\n\nView(which)\nbody(which)\n?which\n\nbody(is.na)\ntypeof(is.na)\n\ntypeof(keep_top_spec)\ntypeof(which)\n\njust_y <- function(){\n y <- 4\n y\n}\n\njust_y()\ny\n\ny <- 9\n\njust_y()\ny\n\nsimpler_y <- function(){\n y\n}\n\nsimpler_y()\n\ny <- 10\nsimpler_y()\n\nadd_one <- function(){\n if (!exists(\"u\")){\n u <- 0 \n } else {\n u <- u + 1\n }\n u\n}\n\nadd_one()\n\n\n\n" }, { "path": "Understanding geom_smooth/README.md", "content": "# Understanding geom_smooth's default loess method\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Understanding geom_smooth's default loess method**, which you can find at . \n\n" }, { "path": "Understanding geom_smooth/script - geom_smooth.R", "content": "# Understanding geom_smooth\n\nlibrary(tidyverse)\nlibrary(broom)\ntheme_set(theme_minimal())\n\nggplot(mtcars, aes(x = wt, \n y = mpg)) + \n geom_point() + \n geom_smooth(se = FALSE)\n\n?loess\n\ncars_model <- loess(mpg ~ wt, \n data = mtcars,\n span = .5)\nsummary(cars_model)\n\nmt_aug <- augment(cars_model)\nView(mt_aug)\n\nggplot(mt_aug) +\n geom_point(aes(x = wt, \n y = mpg)) +\n geom_line(aes(x = wt, \n y = .fitted))\n\nggplot(mtcars, aes(x = wt, \n y = mpg)) + \n geom_point() + \n geom_smooth(se = FALSE,\n span = 10000)\n\nggplot(mtcars, aes(x = wt, \n y = mpg)) + \n geom_point() + \n geom_smooth(se = FALSE,\n method = \"lm\",\n formula = y ~ poly(x, 2))\n\n\n\n" }, { "path": "Understanding the chi-squared distribution/README.md", "content": "# Understanding the chi-squared distribution\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Understanding the chi-squared distribution**, which you can find at . \n\n" }, { "path": "Uniform distribution/README.md", "content": "# The uniform distribution\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **The uniform distribution**, which you can find at . \n\n" }, { "path": "Using Quarto with R and Python/Quarto demo.qmd", "content": "---\ntitle: \"Quarto demo\"\nauthor: \"Andrew Gard\"\ndate: \"25 Jan 2024\"\nformat: \n html:\n theme: lux\n toc: true\n toc-title: \"It's my thing!\"\n fig-align: left\n pdf: default\neditor: visual\neditor_options: \n chunk_output_type: console\nexecute: \n echo: true\n---\n\n## Quarto\n\nQuarto enables you to weave together content and executable code into a finished document. To learn more about Quarto see .\n\nWe can do *italics*, bulleted lists\n\n- first\n\n- second\n\nand many other things.\n\n![My logo](rusty_logo.png){fig-alt=\"A chi-squared distribution with rusty colors\" fig-align=\"left\" width=\"262\"}\n\n## Running Code\n\nWhen you click the **Render** button a document will be generated that includes both content and the output of embedded code. You can embed code like this:\n\n```{r}\n#| label: load packages\n#| echo: true\n#| output: false\n\nlibrary(tidyverse)\n```\n\n## Include a plot\n\n```{r}\n#| label: fig-diamonds\n#| fig-cap: \"A bar chart of diamond cuts\"\n#| fig-alt: |\n#| \"more descriptive stuff here\"\n#| out-width: 50%\n#| code-fold: true\n\nggplot(diamonds, aes(x = cut)) + \n geom_bar()\n```\n\n## Using LaTeX\n\nWe can put formulas inline like this: $\\alpha$ We indent like this\n\n$$ \\pi \\in \\mathbb{R}$$ Google for more info and specific symbols.\n\n## Using python\n\n```{python}\nx, y = 4, 7\nmtcars_py = r.mtcars\nprint(mtcars_py)\n```\n\n```{r}\nlibrary(reticulate)\nlibrary(gt)\nmtcars_new <- py$mtcars_py\nhead(mtcars_new) %>% gt()\n```\n" }, { "path": "Using Quarto with R and Python/README.md", "content": "# Using Quarto with R and Python\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Using Quarto with R and Python**, which you can find at . " }, { "path": "What are order statistics?/README.md", "content": "# What are order statistics?\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **What are order statistics?**, which you can find at . \n\n" }, { "path": "What is Anscombe's quartet?/README.md", "content": "# What is Anscombe's quartet?\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **What is Anscombe's quartet?** which you can find at . \n\n" }, { "path": "What is Anscombe's quartet?/Script - Anscombe.R", "content": "# The Anscombe quartet\n\nlibrary(tidyverse)\ntheme_set(theme_minimal())\n\nView(anscombe)\n\n# working column-wise:\n\nsapply(anscombe, sd)\nsummarize(anscombe, \n across(everything(), sd))\n\n# plotting the first set:\n\nggplot(anscombe, aes(x = x1, y = y1)) + \n geom_point() +\n geom_smooth(method = \"lm\",\n se = FALSE)\n\n# pivoting:\n# note: a similar command is also found in the pivot_longer help file.\n\nlonger <- anscombe |> \n pivot_longer(cols = everything(),\n names_pattern = \"(.)(.)\",\n names_to = c(\".value\", \"set\")\n ) \nview(longer)\n\n# plots and summary stats\n\nggplot(longer, aes(x = x, \n y = y,\n color = set)) + \n geom_point() + \n geom_smooth(method = \"lm\",\n se = FALSE) +\n facet_wrap(~set) +\n scale_color_brewer(palette = \"Dark2\")\n\nlonger |> \n group_by(set) |> \n summarize(mean_x = mean(x), \n mean_y = mean(y), \n sd_x = sd(x),\n sd_y = sd(y), \n cor = cor(x, y))\n \n\n\n\n\n\n\n\n" }, { "path": "What is quarto?/README.md", "content": "# What is Quarto?\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **What is Quarto?**, which you can find at . \n\n" }, { "path": "What is quarto?/what is quarto.qmd", "content": "---\ntitle: \"Getting started with Quarto\"\nformat: html\neditor: visual\n---\n\n## Quarto\n\nQuarto enables you to weave together content and executable code into a finished document. To learn more about Quarto see . Hi everybody!\n\n## Running Code\n\nWhen you click the **Render** button a document will be generated that includes both content and the output of embedded code. You can embed code like this:\n\n```{r}\n1 + 1\n```\n\nYou can add options to executable code like this\n\n```{r}\n#| echo: false\n2 * 2\n```\n\nThe `echo: false` option disables the printing of code (only output is displayed).\n\nWe can make our own code chunks with the green \"c\" button:\n\n```{r}\nlibrary(tidyverse)\nlibrary(readxl)\nerykah <- read_excel(\"erykah.xlsx\")\nglimpse(erykah)\n```\n\nA few Quarto best-practices:\n\n- don't include interactive commands like `View` and `install.packages` into quarto docs.\n\n- Always make your docs self-contained.\n" }, { "path": "What is the central limit theorem?/README.md", "content": "# What is the central limit theorem?\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **What is the central limit theorem?**, which you can find at . \n\n" }, { "path": "Working with lists in R/README.md", "content": "# Working with lists in R\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Lists in R**, which you can find at . \n\n" }, { "path": "Working with lists in R/Script - lists in R.R", "content": "\n# Lists in R --------------------------------------------------------------\nlibrary(tidyverse)\n\nnums <- 2:6\nwords <- \"hello world!\"\nlogicals <- c(TRUE, FALSE)\n\nl1 <- list(nums, words, logicals)\nl1\nstr(l1)\n\nl1[1]\nstr(l1[1])\n\nl1[[1]]\n\nl2 <- list(a = 1:10, b = \"hi\", c = letters)\nl2\nl2$a\n\nl3 <- list(letters, l1, msleep)\nstr(l3)\n\nmammals_list <- msleep %>% \n group_by(vore) %>% \n group_split()\nstr(mammals_list)\n" }, { "path": "Write your own R functions/README.md", "content": "# Write your own R functions\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Write your own R functions**, which you can find at . \n\n" }, { "path": "Write your own R functions/Script - functions.R", "content": "# User-defined functions\n\nlibrary(modeldata)\nView(penguins)\n\n# A basic example\n\nkeep_top <- function(values){\n values[values > mean(values,\n na.rm = TRUE)]\n}\n\nkeep_top(penguins$bill_length_mm)\n\n# A second argument with a default value\n\nkeep_top_spec <- function(values, cutoff){\n values[values > cutoff]\n}\n\nkeep_top_spec(penguins$bill_length_mm, cutoff = 40)\n\nkeep_top_spec <- function(values, cutoff = 45){\n values[values > cutoff]\n}\n\nkeep_top_spec(penguins$bill_length_mm, cutoff = 40)\nkeep_top_spec(penguins$bill_length_mm)\n\n# Errors & warnings\n\nkeep_top(penguins$species) # ugly warning and output\n\nkeep_top <- function(values){\n if (!is.numeric(values)){\n stop(\"Input vector must be numeric.\",\n call. = FALSE)\n }\n values[values > mean(values,\n na.rm = TRUE)]\n}\n\nkeep_top(penguins$species) \n\nkeep_top <- function(values){\n if (!is.numeric(values)){\n warning(\"Input vector must be numeric.\",\n call. = FALSE)\n return(values)\n }\n values[values > mean(values,\n na.rm = TRUE)]\n}\n\nkeep_top(penguins$species)\n\n# passing arguments to function as a list\n\nargs <- list(penguins$bill_length_mm,\n cutoff = 50)\n\nkeep_top_spec(args) # won't work\n\ndo.call(keep_top_spec, args)\n\n\n\n" }, { "path": "Writing prompts using interpolate()/README.md", "content": "# Writing prompts using `interpolate()`\n[]()\n\nHi everybody! In this folder, you'll find materials supporting my vid, **Writing prompts using `interpolate()`**, which you can find at . \n\n" }, { "path": "Writing prompts using interpolate()/interpolate.R", "content": "library(ellmer)\n?interpolate\n\n# Simple prompting --------------------------------------------------------\n\nchat <- chat_openai(\"You are a friendly but terse \n statistics mentor. Give answers at\n an advanced undergraduate level.\",\n model = \"gpt-5-nano\")\n\nprompt <- \"Briefly explain the central limit theorem.\"\nchat$chat(prompt)\n\n\n# Interpolation -----------------------------------------------------------\n\nprompt_open <- \"Briefly explain {{ topic }}.\"\ninterpolate(prompt_open, \n topic = \"the central limit theorem\")\n\nprompt_open_g <- \"Briefly explain {topic}.\"\nglue::glue(prompt_open_g, \n topic = \"the central limit theorem\")\n\nmy_topics <- c(\"the central limit theorem\",\n \"maximum likelihood estimation\")\ninterpolate(prompt_open, \n topic = my_topics)\n\n\n# Functions ---------------------------------------------------------------\n\nstats_help <- function(topic){\n chat <- \n chat_openai(\"You are a friendly but terse \n statistics mentor. Give answers at\n an advanced undergraduate level.\",\n model = \"gpt-5-nano\") \n chat$chat(interpolate(\"Briefly explain \n {{ topic }}.\"),\n echo = \"none\")\n }\n\nstats_help(\"central limit theorem\")\n\nlapply(my_topics, stats_help)\n" }, { "path": "youtube.Rproj", "content": "Version: 1.0\nProjectId: 6d3f8929-1ad4-4ed7-a37e-f5c04529d548\n\nRestoreWorkspace: Default\nSaveWorkspace: Default\nAlwaysSaveHistory: Default\n\nEnableCodeIndexing: Yes\nUseSpacesForTab: Yes\nNumSpacesForTab: 2\nEncoding: UTF-8\n\nRnwWeave: Sweave\nLaTeX: pdfLaTeX\n" } ]