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Repository: liangliangzhuang/R_example
Branch: master
Commit: 14a9ba56c52e
Files: 607
Total size: 154.8 MB
Directory structure:
gitextract_2nr_zcnv/
├── 2020年/
│ ├── 2020.07.13Network_igraph/
│ │ ├── edge.csv
│ │ ├── graph.r
│ │ └── vertices.csv
│ ├── 2020.07.14China_map/
│ │ ├── bou2_4p.dbf
│ │ ├── bou2_4p.shp
│ │ ├── bou2_4p.shx
│ │ ├── china_map.r
│ │ └── data_dt.csv
│ ├── 2020.07.15World_map/
│ │ ├── Country_Data.csv
│ │ ├── cores.r
│ │ └── world_data.csv
│ ├── 2020.07.22Movielense/
│ │ ├── ch11-task.Rmd
│ │ └── ch11-task.log
│ ├── 2020.07.22坐标轴截断画图/
│ │ ├── code_truncation .R
│ │ ├── code_truncation .Rmd
│ │ └── code_truncation-.docx
│ ├── 2020.07.24饼图与圆环图/
│ │ └── pie.rmd
│ ├── 2020.07.29温大招生/
│ │ └── code_ggimage.r
│ ├── 2020.08.14amazon/
│ │ ├── RECOM.Rmd
│ │ ├── code.Rmd
│ │ └── 亚马逊产品推荐算法.xmind
│ ├── 2020.08.15分面/
│ │ ├── .R数据科学.pdf.icloud
│ │ ├── facet.log
│ │ └── facet.rmd
│ ├── 2020.08.23好玩的图/
│ │ ├── fun.r
│ │ └── readme.txt
│ ├── 2020.08.25马赛克/
│ │ ├── 马赛克.html
│ │ └── 马赛克.rmd
│ ├── 2020.08.26混合多个图形/
│ │ ├── 混合图.html
│ │ └── 混合图.rmd
│ ├── 2020.09.15reticulate/
│ │ └── reticulate.rmd
│ ├── 2020.09.27tidyverse数据清洗/
│ │ └── Tidy_data.rmd
│ ├── 2020.10.09散点图系列一/
│ │ ├── scater_plot1.html
│ │ └── scater_plot1.rmd
│ ├── 2020.10.27散点图系列二/
│ │ ├── HighDensity_Scatter_Data.csv
│ │ ├── scater_plot2.html
│ │ └── scater_plot2.rmd
│ ├── 2020.10.30ggpubr/
│ │ └── ggpubr.rmd
│ ├── 2020.11.05气泡图/
│ │ ├── 1.txt
│ │ ├── Bubble_plot.html
│ │ └── Bubble_plot.rmd
│ ├── 2020.11.14gghalves/
│ │ ├── gghalves.html
│ │ └── gghalves.rmd
│ ├── 2020.11.16flexdashboard/
│ │ ├── flexdashboard.md
│ │ ├── test.html
│ │ ├── test.rmd
│ │ ├── 例子/
│ │ │ └── 09_rbokeh-iris-dataset/
│ │ │ ├── dashboard-pandoc2.0.3.html
│ │ │ └── dashboard.Rmd
│ │ ├── 教程.html
│ │ ├── 教程.rmd
│ │ └── 链接.txt
│ ├── 2020.11.22三维散点图/
│ │ ├── 3d_scatter.html
│ │ ├── 3d_scatter.rmd
│ │ └── ThreeD_Scatter_Data.csv
│ ├── 2020.11.22数据处理ntile()/
│ │ └── 数据处理数据按从小到大分成n类.md
│ ├── 2020.11.28等高线/
│ │ ├── .RData
│ │ ├── .Rhistory
│ │ ├── 等高线.html
│ │ ├── 等高线.r
│ │ ├── 等高线.rmd
│ │ └── 等高线.txt
│ ├── 2020.12.05日历/
│ │ ├── .My_calendar7(1).pdf.icloud
│ │ ├── 日历_EasyShu.rmd
│ │ ├── 日历教程.md
│ │ ├── 日历教程.rmd
│ │ └── 背景/
│ │ ├── .1.png.icloud
│ │ ├── .2.png.icloud
│ │ └── .3.jpg.icloud
│ ├── 2020.12.07瀑布图/
│ │ ├── Facting_Data.csv
│ │ └── 瀑布图.rmd
│ ├── 2020.12.21esquisse包/
│ │ └── esquisse包.md
│ ├── 2020.12.24Mandalas/
│ │ └── Mandalas/
│ │ ├── .Rproj.user/
│ │ │ ├── 9B97F8EE/
│ │ │ │ └── sources/
│ │ │ │ ├── prop/
│ │ │ │ │ ├── 34FB05E2
│ │ │ │ │ ├── 5C967418
│ │ │ │ │ ├── B38F6FEB
│ │ │ │ │ ├── D25723BC
│ │ │ │ │ └── INDEX
│ │ │ │ └── s-75E8F375/
│ │ │ │ ├── 17CC9D9C
│ │ │ │ ├── 17CC9D9C-contents
│ │ │ │ ├── 20B6EE7D
│ │ │ │ ├── 20B6EE7D-contents
│ │ │ │ ├── 28FAC3B5
│ │ │ │ ├── 28FAC3B5-contents
│ │ │ │ ├── 4326A9CE
│ │ │ │ ├── 4326A9CE-contents
│ │ │ │ ├── C7D0C74B
│ │ │ │ ├── C7D0C74B-contents
│ │ │ │ └── lock_file
│ │ │ └── shared/
│ │ │ └── notebooks/
│ │ │ ├── 40CC0FD6-Mandalas/
│ │ │ │ └── 1/
│ │ │ │ ├── 9B97F8EE87974FA4/
│ │ │ │ │ └── chunks.json
│ │ │ │ └── s/
│ │ │ │ ├── cct2o5lt4yw4d/
│ │ │ │ │ └── 000011.csv
│ │ │ │ └── chunks.json
│ │ │ ├── 540673E1-峰峦图/
│ │ │ │ └── 1/
│ │ │ │ ├── 9B97F8EE75E8F375/
│ │ │ │ │ └── chunks.json
│ │ │ │ ├── 9B97F8EE87974FA4/
│ │ │ │ │ └── chunks.json
│ │ │ │ └── s/
│ │ │ │ └── chunks.json
│ │ │ ├── patch-chunk-names
│ │ │ └── paths
│ │ ├── Mandalas.Rproj
│ │ ├── Mandalas.html
│ │ └── Mandalas.rmd
│ ├── 2020.12.24圣诞节/
│ │ ├── 1.json
│ │ └── marry.R
│ ├── 2020.12.25峰峦图/
│ │ └── 峰峦图.rmd
│ ├── 2020.12.30ggvis/
│ │ ├── ggvis.html
│ │ ├── ggvis.md
│ │ └── ggvis.rmd
│ └── 2020年R数据科学系列/
│ ├── 2020.08.23几何对象/
│ │ └── 几何对象.md
│ ├── 2020.08.23速查表/
│ │ ├── 让微信排版变 Nice.html
│ │ └── 速查表.html
│ ├── 2020.09.27tidy data/
│ │ └── Tidy_data.rmd
│ └── 《R数据科学》源代码/
│ ├── DESCRIPTION
│ ├── EDA.Rmd
│ ├── LICENSE
│ ├── README.md
│ ├── _bookdown.yml
│ ├── _common.R
│ ├── _output.yaml
│ ├── communicate-plots.Rmd
│ ├── communicate.Rmd
│ ├── contribs.txt
│ ├── contribute.rmd
│ ├── datetimes.Rmd
│ ├── explore.Rmd
│ ├── factors.Rmd
│ ├── figures.R
│ ├── functions.Rmd
│ ├── hierarchy.Rmd
│ ├── import.Rmd
│ ├── index.rmd
│ ├── intro.Rmd
│ ├── issues.json
│ ├── iteration.Rmd
│ ├── model-assess.Rmd
│ ├── model-basics.Rmd
│ ├── model-building.Rmd
│ ├── model-many.Rmd
│ ├── model.Rmd
│ ├── pipes.Rmd
│ ├── program.Rmd
│ ├── r4ds.Rproj
│ ├── r4ds.css
│ ├── relational-data.Rmd
│ ├── rmarkdown-formats.Rmd
│ ├── rmarkdown-workflow.Rmd
│ ├── rmarkdown.Rmd
│ ├── strings.Rmd
│ ├── tibble.Rmd
│ ├── transform.Rmd
│ ├── vectors.Rmd
│ ├── visualize.Rmd
│ ├── workflow-basics.Rmd
│ ├── workflow-projects.Rmd
│ ├── workflow-scripts.Rmd
│ └── wrangle.Rmd
├── 2021年/
│ ├── 2021.01.31主成分结果可视化/
│ │ ├── pac_visual.log
│ │ └── pac_visual.rmd
│ ├── 2021.02.21克利夫兰点图系列/
│ │ ├── DotPlots_Data.csv
│ │ ├── cleveland's .Rmd
│ │ └── cleveland-s-.log
│ └── 2021.02.28常用主题风格/
│ ├── .R可视乎｜ggplot常用主题风格汇总.pdf.icloud
│ └── R可视乎｜ggplot常用主题风格汇总.md
├── 2022年/
│ ├── 2022.01.14 如何使用 ggplot2 绘制双轴分离图？/
│ │ └── 双轴分离.R
│ ├── 2022.01.28 如何绘制省市级地图？/
│ │ ├── df_China4.csv
│ │ ├── 各区县经营效率平均值.csv
│ │ ├── 温州地图绘制.r
│ │ ├── 温州市.json
│ │ └── 绘制浙江省地图.R
│ ├── 2022.02.08读者投稿｜绘制一系列黑白印刷风格图表/
│ │ └── acchist.r
│ ├── 2022.03.14绘制混合密度函数图以及添加分位数线/
│ │ └── mix-quantile.r
│ ├── 2022.04.08R 案例｜绘制不同分布的 QQ 图/
│ │ └── qqplot.r
│ ├── 2022.05.15老板让你复现一个图片，你会使用什么软件？/
│ │ └── example.r
│ ├── 2022.08.08 ggplot 分面的细节调整汇总/
│ │ └── ggplot_facet.r
│ ├── 2022.09.09 如何在分面中添加数学表达式标签？ /
│ │ └── add_math_label.r
│ ├── 2022.09.24 中国地图绘制/
│ │ ├── .RData
│ │ ├── .Rhistory
│ │ └── .Rproj.user/
│ │ ├── 161F88A0/
│ │ │ ├── pcs/
│ │ │ │ ├── files-pane.pper
│ │ │ │ ├── packages-pane.pper
│ │ │ │ ├── source-pane.pper
│ │ │ │ ├── windowlayoutstate.pper
│ │ │ │ └── workbench-pane.pper
│ │ │ ├── rmd-outputs
│ │ │ ├── saved_source_markers
│ │ │ └── sources/
│ │ │ └── prop/
│ │ │ ├── 212A8ABB
│ │ │ ├── 21BA30B6
│ │ │ ├── 702BC91B
│ │ │ ├── 9F335933
│ │ │ ├── D8AA2E2E
│ │ │ └── INDEX
│ │ └── shared/
│ │ └── notebooks/
│ │ ├── patch-chunk-names
│ │ └── paths
│ ├── 2022.10.12使用 ggplot2 绘制单个和多个省份地图/
│ │ ├── china_shp/
│ │ │ ├── CHN_adm0.cpg
│ │ │ ├── CHN_adm0.csv
│ │ │ ├── CHN_adm0.dbf
│ │ │ ├── CHN_adm0.prj
│ │ │ ├── CHN_adm0.shp
│ │ │ ├── CHN_adm0.shx
│ │ │ ├── CHN_adm1.cpg
│ │ │ ├── CHN_adm1.csv
│ │ │ ├── CHN_adm1.dbf
│ │ │ ├── CHN_adm1.prj
│ │ │ ├── CHN_adm1.shp
│ │ │ ├── CHN_adm1.shx
│ │ │ ├── CHN_adm2.cpg
│ │ │ ├── CHN_adm2.csv
│ │ │ ├── CHN_adm2.dbf
│ │ │ ├── CHN_adm2.prj
│ │ │ ├── CHN_adm2.shp
│ │ │ ├── CHN_adm2.shx
│ │ │ ├── CHN_adm3.cpg
│ │ │ ├── CHN_adm3.csv
│ │ │ ├── CHN_adm3.dbf
│ │ │ ├── CHN_adm3.prj
│ │ │ ├── CHN_adm3.shp
│ │ │ ├── CHN_adm3.shx
│ │ │ ├── license.txt
│ │ │ └── my_file.txt
│ │ ├── colour.csv
│ │ ├── map.Rproj
│ │ ├── province.csv
│ │ ├── 南海.geojson
│ │ ├── 地图十段线.R
│ │ ├── 测试数据.xlsx
│ │ └── 省份地图.R
│ ├── 2022.11.07 常用 7 大类图形可视化汇总——ggplot2包/
│ │ ├── .RData
│ │ ├── .Rhistory
│ │ ├── .Rproj.user/
│ │ │ ├── 161F88A0/
│ │ │ │ ├── build_options
│ │ │ │ ├── build_options 2
│ │ │ │ ├── pcs/
│ │ │ │ │ ├── .files-pane 2.pper.icloud
│ │ │ │ │ ├── .packages-pane 2.pper.icloud
│ │ │ │ │ ├── .source-pane 2.pper.icloud
│ │ │ │ │ ├── .windowlayoutstate 2.pper.icloud
│ │ │ │ │ ├── .workbench-pane 2.pper.icloud
│ │ │ │ │ ├── files-pane.pper
│ │ │ │ │ ├── packages-pane.pper
│ │ │ │ │ ├── source-pane.pper
│ │ │ │ │ ├── windowlayoutstate.pper
│ │ │ │ │ └── workbench-pane.pper
│ │ │ │ ├── rmd-outputs
│ │ │ │ ├── saved_source_markers
│ │ │ │ └── sources/
│ │ │ │ ├── per/
│ │ │ │ │ ├── t/
│ │ │ │ │ │ ├── CD5422DD
│ │ │ │ │ │ ├── CD5422DD 2
│ │ │ │ │ │ ├── CD5422DD-contents
│ │ │ │ │ │ └── CD5422DD-contents 2
│ │ │ │ │ └── u/
│ │ │ │ │ ├── 86D463FB
│ │ │ │ │ ├── 86D463FB 2
│ │ │ │ │ ├── 86D463FB-contents
│ │ │ │ │ └── 86D463FB-contents 2
│ │ │ │ └── prop/
│ │ │ │ ├── 6244B089
│ │ │ │ ├── 6244B089 2
│ │ │ │ ├── 66C45745
│ │ │ │ ├── 66C45745 2
│ │ │ │ ├── DCED28DF
│ │ │ │ ├── DCED28DF 2
│ │ │ │ ├── INDEX
│ │ │ │ └── INDEX 2
│ │ │ ├── C6560784/
│ │ │ │ ├── pcs/
│ │ │ │ │ ├── .files-pane 2.pper.icloud
│ │ │ │ │ ├── .source-pane 2.pper.icloud
│ │ │ │ │ ├── .windowlayoutstate 2.pper.icloud
│ │ │ │ │ ├── .workbench-pane 2.pper.icloud
│ │ │ │ │ ├── files-pane.pper
│ │ │ │ │ ├── source-pane.pper
│ │ │ │ │ ├── windowlayoutstate.pper
│ │ │ │ │ └── workbench-pane.pper
│ │ │ │ ├── rmd-outputs
│ │ │ │ ├── saved_source_markers
│ │ │ │ └── sources/
│ │ │ │ ├── per/
│ │ │ │ │ └── t/
│ │ │ │ │ ├── C36CFFC0
│ │ │ │ │ ├── C36CFFC0 2
│ │ │ │ │ ├── C36CFFC0-contents
│ │ │ │ │ └── C36CFFC0-contents 2
│ │ │ │ └── prop/
│ │ │ │ ├── F9089F45
│ │ │ │ ├── F9089F45 2
│ │ │ │ ├── INDEX
│ │ │ │ └── INDEX 2
│ │ │ └── shared/
│ │ │ └── notebooks/
│ │ │ ├── CED44E14-50pictures_ggplot/
│ │ │ │ └── 1/
│ │ │ │ ├── C656078445C2030A/
│ │ │ │ │ ├── chunks 2.json
│ │ │ │ │ └── chunks.json
│ │ │ │ └── s/
│ │ │ │ ├── cg7cix78cerjz/
│ │ │ │ │ ├── .00000f 2.metadata.icloud
│ │ │ │ │ ├── .00000f 2.rdf.icloud
│ │ │ │ │ ├── 00000f.metadata
│ │ │ │ │ └── 00000f.rdf
│ │ │ │ ├── chunks 2.json
│ │ │ │ ├── chunks.json
│ │ │ │ └── csetup_chunk/
│ │ │ │ ├── .00000f 2.csv.icloud
│ │ │ │ └── 00000f.csv
│ │ │ ├── patch-chunk-names
│ │ │ ├── patch-chunk-names 2
│ │ │ ├── paths
│ │ │ └── paths 2
│ │ ├── 50pictures_ggplot.Rproj
│ │ ├── 50pictures_ggplot.html
│ │ ├── 50pictures_ggplot.rmd
│ │ ├── midwest.csv
│ │ └── readme.txt
│ ├── 2022.11.15 R绘图案例｜基于分面的折线图绘制/
│ │ ├── .RData
│ │ ├── .Rhistory
│ │ ├── .Rproj.user/
│ │ │ ├── C6560784/
│ │ │ │ ├── pcs/
│ │ │ │ │ ├── files-pane.pper
│ │ │ │ │ ├── packages-pane.pper
│ │ │ │ │ ├── source-pane.pper
│ │ │ │ │ ├── windowlayoutstate.pper
│ │ │ │ │ └── workbench-pane.pper
│ │ │ │ ├── rmd-outputs
│ │ │ │ ├── saved_source_markers
│ │ │ │ └── sources/
│ │ │ │ ├── prop/
│ │ │ │ │ ├── 3664F9C7
│ │ │ │ │ ├── A35D258F
│ │ │ │ │ ├── E600AE88
│ │ │ │ │ └── INDEX
│ │ │ │ └── session-417D82C1/
│ │ │ │ ├── 3B4320B1
│ │ │ │ ├── 3B4320B1-contents
│ │ │ │ └── lock_file
│ │ │ └── shared/
│ │ │ └── notebooks/
│ │ │ ├── patch-chunk-names
│ │ │ └── paths
│ │ ├── data_city.csv
│ │ ├── line_point.R
│ │ └── plot.Rproj
│ ├── 2022.11.19 分面中添加不同的直线/
│ │ ├── facet-line.r
│ │ ├── plot.Rproj
│ │ └── test.xlsx
│ ├── 2022.11.29 R绘图案例｜基于分面的面积图绘制/
│ │ ├── .RData
│ │ ├── .Rhistory
│ │ ├── .Rproj.user/
│ │ │ ├── C6560784/
│ │ │ │ ├── jobs/
│ │ │ │ │ └── 7897C193-output.json
│ │ │ │ ├── pcs/
│ │ │ │ │ ├── files-pane.pper
│ │ │ │ │ ├── source-pane.pper
│ │ │ │ │ ├── windowlayoutstate.pper
│ │ │ │ │ └── workbench-pane.pper
│ │ │ │ ├── rmd-outputs
│ │ │ │ ├── saved_source_markers
│ │ │ │ └── sources/
│ │ │ │ ├── prop/
│ │ │ │ │ ├── 0508C42F
│ │ │ │ │ ├── 0D0D59B5
│ │ │ │ │ ├── 19008281
│ │ │ │ │ ├── A62E60CA
│ │ │ │ │ └── INDEX
│ │ │ │ ├── session-23478791/
│ │ │ │ │ ├── 0BC0007C
│ │ │ │ │ ├── 0BC0007C-contents
│ │ │ │ │ ├── AF358D71-contents
│ │ │ │ │ ├── EB6BB15A
│ │ │ │ │ ├── EB6BB15A-contents
│ │ │ │ │ └── lock_file
│ │ │ │ └── session-51C83C65/
│ │ │ │ ├── 8752579D
│ │ │ │ ├── 8752579D-contents
│ │ │ │ └── lock_file
│ │ │ └── shared/
│ │ │ └── notebooks/
│ │ │ ├── patch-chunk-names
│ │ │ └── paths
│ │ ├── R-plot.Rproj
│ │ ├── rev_plot.R
│ │ └── test.xlsx
│ └── 2022.12.31 2023年日历大派送/
│ ├── .RData
│ ├── .Rhistory
│ ├── .Rproj.user/
│ │ ├── C6560784/
│ │ │ ├── jobs/
│ │ │ │ └── E803742C-output.json
│ │ │ ├── pcs/
│ │ │ │ ├── files-pane.pper
│ │ │ │ ├── source-pane.pper
│ │ │ │ ├── windowlayoutstate.pper
│ │ │ │ └── workbench-pane.pper
│ │ │ ├── rmd-outputs
│ │ │ ├── saved_source_markers
│ │ │ └── sources/
│ │ │ ├── per/
│ │ │ │ └── t/
│ │ │ │ ├── 13B3FE41
│ │ │ │ ├── 13B3FE41-contents
│ │ │ │ ├── 873D867D
│ │ │ │ └── 873D867D-contents
│ │ │ └── prop/
│ │ │ ├── 26BD393F
│ │ │ ├── 8A1E9267
│ │ │ ├── F0AB5896
│ │ │ └── INDEX
│ │ └── shared/
│ │ └── notebooks/
│ │ ├── patch-chunk-names
│ │ └── paths
│ ├── version1.R
│ ├── version2.R
│ └── 未命名.Rproj
├── 2023年/
│ ├── 2022.08.31 分面+双轴/
│ │ ├── .RData
│ │ ├── .Rhistory
│ │ ├── .Rproj.user/
│ │ │ ├── C6560784/
│ │ │ │ ├── pcs/
│ │ │ │ │ ├── files-pane.pper
│ │ │ │ │ ├── source-pane.pper
│ │ │ │ │ ├── windowlayoutstate.pper
│ │ │ │ │ └── workbench-pane.pper
│ │ │ │ ├── rmd-outputs
│ │ │ │ ├── saved_source_markers
│ │ │ │ └── sources/
│ │ │ │ ├── per/
│ │ │ │ │ └── t/
│ │ │ │ │ ├── EA22C3C6
│ │ │ │ │ └── EA22C3C6-contents
│ │ │ │ └── prop/
│ │ │ │ ├── D4B371E0
│ │ │ │ ├── F02804FC
│ │ │ │ └── INDEX
│ │ │ └── shared/
│ │ │ └── notebooks/
│ │ │ ├── patch-chunk-names
│ │ │ └── paths
│ │ ├── Add secondary axis.R
│ │ └── df.csv
│ ├── 2023.03.07 高亮柱状图/
│ │ └── 高亮柱状图.R
│ ├── 2023.03.08 基于 ggridges 绘制剩余使用寿命密度图/
│ │ └── rul_ggbridges.R
│ ├── 2023.03.09基于 ggdensity 包的等高线绘制/
│ │ └── 等高线绘制.R
│ ├── 2023.03.16使用 ggTimeSeries 包构建日历图/
│ │ ├── timeseries.R
│ │ ├── 日历图.html
│ │ ├── 日历图.qmd
│ │ └── 日历图_files/
│ │ └── libs/
│ │ ├── bootstrap/
│ │ │ └── bootstrap-icons.css
│ │ └── quarto-html/
│ │ ├── quarto-syntax-highlighting.css
│ │ ├── quarto.js
│ │ └── tippy.css
│ ├── 2023.04.01 基于 R 语言的科研论文绘图技巧汇总/
│ │ ├── .RData
│ │ ├── .Rhistory
│ │ ├── .Rproj.user/
│ │ │ ├── C6560784/
│ │ │ │ ├── pcs/
│ │ │ │ │ ├── files-pane.pper
│ │ │ │ │ ├── packages-pane.pper
│ │ │ │ │ ├── source-pane.pper
│ │ │ │ │ ├── windowlayoutstate.pper
│ │ │ │ │ └── workbench-pane.pper
│ │ │ │ ├── rmd-outputs
│ │ │ │ ├── saved_source_markers
│ │ │ │ └── sources/
│ │ │ │ ├── per/
│ │ │ │ │ └── t/
│ │ │ │ │ ├── 7696DA26
│ │ │ │ │ ├── 7696DA26-contents
│ │ │ │ │ ├── 83B6CCD0
│ │ │ │ │ ├── 83B6CCD0-contents
│ │ │ │ │ ├── A32C5A91
│ │ │ │ │ ├── A32C5A91-contents
│ │ │ │ │ ├── E41305B6
│ │ │ │ │ └── E41305B6-contents
│ │ │ │ └── prop/
│ │ │ │ ├── 31AEA8EB
│ │ │ │ ├── 51574A35
│ │ │ │ ├── 53B7DAA0
│ │ │ │ ├── 5916C35B
│ │ │ │ ├── 5B81A5B2
│ │ │ │ ├── 75B7761D
│ │ │ │ ├── 86D189DF
│ │ │ │ ├── ABCABC27
│ │ │ │ ├── BCA3F795
│ │ │ │ ├── C757E81D
│ │ │ │ ├── E86E28FA
│ │ │ │ └── INDEX
│ │ │ └── shared/
│ │ │ └── notebooks/
│ │ │ ├── 18DAA490-A/
│ │ │ │ └── 1/
│ │ │ │ └── s/
│ │ │ │ └── chunks.json
│ │ │ ├── patch-chunk-names
│ │ │ └── paths
│ │ ├── LICENSE
│ │ ├── Panel_C.R
│ │ ├── Panel_D.R
│ │ ├── Panel_E.R
│ │ ├── Panel_F.R
│ │ ├── README.md
│ │ ├── data_B.csv
│ │ ├── data_Cmu_E10.5.csv
│ │ ├── data_Cmu_E11.5.csv
│ │ ├── data_Cmu_E8.5.csv
│ │ ├── data_Cmu_E9.5.csv
│ │ ├── data_Cwt_E10.5.csv
│ │ ├── data_Cwt_E11.5.csv
│ │ ├── data_Cwt_E8.5.csv
│ │ ├── data_Cwt_E9.5.csv
│ │ ├── data_D1.csv
│ │ ├── data_D2.csv
│ │ ├── data_Ea.csv
│ │ ├── data_Eb.csv
│ │ ├── data_Ec.csv
│ │ ├── data_F.csv
│ │ ├── figure_example.R
│ │ └── figure_example.Rproj
│ ├── 2023.04.12 Rmarkdown重复性报告/
│ │ ├── .RData
│ │ ├── .Rhistory
│ │ ├── .Rproj.user/
│ │ │ ├── C6560784/
│ │ │ │ ├── pcs/
│ │ │ │ │ ├── files-pane.pper
│ │ │ │ │ ├── source-pane.pper
│ │ │ │ │ ├── windowlayoutstate.pper
│ │ │ │ │ └── workbench-pane.pper
│ │ │ │ ├── quarto-crossref/
│ │ │ │ │ ├── AA033108
│ │ │ │ │ └── INDEX
│ │ │ │ ├── rmd-outputs
│ │ │ │ ├── saved_source_markers
│ │ │ │ └── sources/
│ │ │ │ ├── per/
│ │ │ │ │ ├── t/
│ │ │ │ │ │ ├── 24D937C1
│ │ │ │ │ │ ├── 24D937C1-contents
│ │ │ │ │ │ ├── 47C21D39
│ │ │ │ │ │ └── 47C21D39-contents
│ │ │ │ │ └── u/
│ │ │ │ │ ├── F684F954
│ │ │ │ │ └── F684F954-contents
│ │ │ │ └── prop/
│ │ │ │ ├── 07CCB8CA
│ │ │ │ ├── 55F7E8FD
│ │ │ │ ├── 5A14198B
│ │ │ │ ├── 998B2979
│ │ │ │ ├── CA24840E
│ │ │ │ ├── DD741787
│ │ │ │ ├── EAE6CDE4
│ │ │ │ ├── FD4997C5
│ │ │ │ └── INDEX
│ │ │ └── shared/
│ │ │ └── notebooks/
│ │ │ ├── 72C89A46-中文/
│ │ │ │ └── 1/
│ │ │ │ ├── C65607844eefea2c/
│ │ │ │ │ └── chunks.json
│ │ │ │ ├── C6560784b90da11a/
│ │ │ │ │ └── chunks.json
│ │ │ │ ├── C6560784f87258bb/
│ │ │ │ │ └── chunks.json
│ │ │ │ └── s/
│ │ │ │ ├── chunks.json
│ │ │ │ ├── cq8zbuk6wx2qd/
│ │ │ │ │ ├── 00000f.metadata
│ │ │ │ │ └── 00000f.rdf
│ │ │ │ └── csetup_chunk/
│ │ │ │ └── 00000f.csv
│ │ │ ├── patch-chunk-names
│ │ │ └── paths
│ │ ├── Project.Rproj
│ │ ├── data/
│ │ │ ├── A中学.csv
│ │ │ ├── B中学.csv
│ │ │ └── C中学.csv
│ │ ├── exploratory_A中学.log
│ │ ├── exploratory_B中学.log
│ │ ├── libs/
│ │ │ ├── header-attrs-2.20/
│ │ │ │ └── header-attrs.js
│ │ │ └── remark-css-0.0.1/
│ │ │ └── default.css
│ │ ├── test.R
│ │ ├── zh-CN.css
│ │ └── 中文.Rmd
│ ├── 2023.05.04 如何一步步提高图形 B 格？以 ggplot 绘图为例/
│ │ ├── .Rhistory
│ │ └── line.R
│ ├── 2023.05.08个性化树状图/
│ │ └── ggparty.R
│ ├── 2023.05.13 高级棒棒图/
│ │ └── 棒棒图.R
│ ├── 2023.06.29 旭日图/
│ │ ├── .Rproj.user/
│ │ │ ├── C6560784/
│ │ │ │ ├── jobs/
│ │ │ │ │ └── 4152B7D6-output.json
│ │ │ │ └── sources/
│ │ │ │ ├── prop/
│ │ │ │ │ ├── 1E4518E3
│ │ │ │ │ ├── 5E635AFA
│ │ │ │ │ └── INDEX
│ │ │ │ └── session-ef7e8bd6/
│ │ │ │ ├── 3A928347-contents
│ │ │ │ ├── F749EB52
│ │ │ │ ├── F749EB52-contents
│ │ │ │ └── lock_file
│ │ │ └── shared/
│ │ │ └── notebooks/
│ │ │ ├── patch-chunk-names
│ │ │ └── paths
│ │ ├── d.csv
│ │ ├── 旭日图.R
│ │ ├── 未命名.Rproj
│ │ └── 未命名.html
│ ├── 2023.08.05 局部细节放大图/
│ │ └── 局部细节放大图.R
│ ├── 2023.08.24 绘制足球数据/
│ │ └── ggsoccer.R
│ ├── 2023.10.08 灯芯柱状图/
│ │ ├── .RData
│ │ ├── .Rhistory
│ │ ├── .Rproj.user/
│ │ │ ├── C6560784/
│ │ │ │ ├── pcs/
│ │ │ │ │ ├── debug-breakpoints.pper
│ │ │ │ │ ├── files-pane.pper
│ │ │ │ │ ├── source-pane.pper
│ │ │ │ │ ├── windowlayoutstate.pper
│ │ │ │ │ └── workbench-pane.pper
│ │ │ │ ├── persistent-state
│ │ │ │ ├── rmd-outputs
│ │ │ │ ├── saved_source_markers
│ │ │ │ └── sources/
│ │ │ │ ├── prop/
│ │ │ │ │ ├── 00943EE4
│ │ │ │ │ ├── 59990D2B
│ │ │ │ │ └── INDEX
│ │ │ │ └── session-6c0d0c41/
│ │ │ │ ├── 4BEAD114
│ │ │ │ ├── 4BEAD114-contents
│ │ │ │ └── lock_file
│ │ │ └── shared/
│ │ │ └── notebooks/
│ │ │ ├── patch-chunk-names
│ │ │ └── paths
│ │ ├── animal_rescues.txt
│ │ ├── df_animals_sum.csv
│ │ ├── 未命名.Rproj
│ │ └── 灯芯柱状图.r
│ ├── 2023.12.02 分面中添加不同表格/
│ │ ├── .RData
│ │ ├── .Rhistory
│ │ ├── .Rproj.user/
│ │ │ ├── C6560784/
│ │ │ │ ├── pcs/
│ │ │ │ │ ├── files-pane.pper
│ │ │ │ │ ├── source-pane.pper
│ │ │ │ │ ├── windowlayoutstate.pper
│ │ │ │ │ └── workbench-pane.pper
│ │ │ │ ├── rmd-outputs
│ │ │ │ ├── saved_source_markers
│ │ │ │ └── sources/
│ │ │ │ ├── per/
│ │ │ │ │ └── t/
│ │ │ │ │ ├── 23FB0CA3
│ │ │ │ │ └── 23FB0CA3-contents
│ │ │ │ └── prop/
│ │ │ │ ├── 276F3DD0
│ │ │ │ └── INDEX
│ │ │ └── shared/
│ │ │ └── notebooks/
│ │ │ └── patch-chunk-names
│ │ ├── project.Rproj
│ │ └── 分面中添加不同表格.R
│ ├── 2023.12.16 分面中添加拟合曲线/
│ │ ├── .Rproj.user/
│ │ │ ├── C6560784/
│ │ │ │ ├── pcs/
│ │ │ │ │ ├── files-pane.pper
│ │ │ │ │ ├── source-pane.pper
│ │ │ │ │ ├── windowlayoutstate.pper
│ │ │ │ │ └── workbench-pane.pper
│ │ │ │ └── sources/
│ │ │ │ ├── prop/
│ │ │ │ │ ├── DAA25768
│ │ │ │ │ └── INDEX
│ │ │ │ └── session-427bca66/
│ │ │ │ ├── AB1928BC
│ │ │ │ ├── AB1928BC-contents
│ │ │ │ ├── C6EFA56A-contents
│ │ │ │ └── lock_file
│ │ │ └── shared/
│ │ │ └── notebooks/
│ │ │ └── patch-chunk-names
│ │ ├── data_fit.RData
│ │ ├── project.Rproj
│ │ ├── true_data.RData
│ │ └── 分面中添加拟合曲线.R
│ └── 合并图形+共享图例/
│ └── 合并图形-共享图例.R
├── 2024年/
│ ├── 2024.02.07 多分类的条形图并标记数字/
│ │ ├── .RData
│ │ ├── .Rhistory
│ │ ├── .Rproj.user/
│ │ │ ├── C6560784/
│ │ │ │ ├── pcs/
│ │ │ │ │ ├── files-pane.pper
│ │ │ │ │ ├── source-pane.pper
│ │ │ │ │ ├── windowlayoutstate.pper
│ │ │ │ │ └── workbench-pane.pper
│ │ │ │ ├── rmd-outputs
│ │ │ │ ├── saved_source_markers
│ │ │ │ └── sources/
│ │ │ │ ├── per/
│ │ │ │ │ └── t/
│ │ │ │ │ ├── AF66B3A8
│ │ │ │ │ └── AF66B3A8-contents
│ │ │ │ └── prop/
│ │ │ │ ├── 0418CBEF
│ │ │ │ ├── 048F8B4D
│ │ │ │ ├── 1A4EC317
│ │ │ │ └── INDEX
│ │ │ └── shared/
│ │ │ └── notebooks/
│ │ │ ├── patch-chunk-names
│ │ │ └── paths
│ │ ├── project.Rproj
│ │ └── 多分类条形图并标记数字.R
│ ├── 2024.02.12 TiKZ绘图/
│ │ ├── tutorial.aux
│ │ ├── tutorial.log
│ │ └── tutorial.tex
│ └── 2024.02.19 柱状图+分面/
│ ├── .Rproj.user/
│ │ ├── C6560784/
│ │ │ ├── pcs/
│ │ │ │ ├── files-pane.pper
│ │ │ │ ├── source-pane.pper
│ │ │ │ ├── windowlayoutstate.pper
│ │ │ │ └── workbench-pane.pper
│ │ │ └── sources/
│ │ │ ├── prop/
│ │ │ │ ├── 23624D4E
│ │ │ │ └── INDEX
│ │ │ └── session-fd2b58b4/
│ │ │ ├── 1FF7DD4B
│ │ │ ├── 1FF7DD4B-contents
│ │ │ └── lock_file
│ │ └── shared/
│ │ └── notebooks/
│ │ ├── patch-chunk-names
│ │ └── paths
│ ├── demo_dat.csv
│ ├── project.Rproj
│ └── 柱状图+分面.R
├── README.md
├── 公众号资料/
│ ├── nice主题.docx
│ ├── ~$创意大气述职报告.pptx
│ ├── 封面制作模板.pptx
│ ├── 年度总结/
│ │ └── 2020年公众号文章汇总.md
│ ├── 数据科学模板.docx
│ └── 资料获取.md
└── 资料分享.md
================================================
FILE CONTENTS
================================================
================================================
FILE: 2020年/2020.07.13Network_igraph/edge.csv
================================================
V1,V2
24,25
1,23
13,18
23,24
14,25
9,14
14,26
16,22
1,16
2,23
18,32
9,17
24,31
7,8
2,21
23,32
7,35
19,27
7,23
1,19
3,17
10,11
15,30
10,20
15,18
11,34
16,20
10,33
6,34
12,21
4,29
29,34
5,30
13,28
5,36
================================================
FILE: 2020年/2020.07.13Network_igraph/graph.r
================================================
library(igraph)
#1)导入边数据和节点数据=======
setwd("")
edges <- read.table('edge.csv', header=T, sep=',') #导入边数据，里面可以包含每个边的频次数据或权重
vertices <- read.table('vertices.csv', header=T, sep=',') #导入节点数据，可以包含属性数据，如分类
edges ;vertices
#2)导入数据后，要转化成图数据才能用R作图，不同数据格式用不同方式=======
graph <- graph_from_data_frame(edges, directed = F, vertices=vertices) #directed = TRUE表示有方向,如果不需要点数据，可以设置vertices=NULL
#生成方式1（没有颜色分类）：======
igraph.options(vertex.size=3, vertex.label=NA, edge.arrow.size=0.5)
V(graph)$color <- colrs[V(graph)$color]
plot(graph,
layout=layout.reingold.tilford(graph,circular=T), #layout.fruchterman.reingold表示弹簧式发散的布局，
#其他还有环形布局layout.circle，分层布局layout.reingold.tilford，中心向外发散layout.reingold.tilford(graph,circular=T) ，核心布局layout_as_star，大型网络可视化layout_with_drl
vertex.size=5, #节点大小
vertex.shape='circle', #节点不带边框none,,圆形边框circle,方块形rectangle
vertex.color="lightgreen",#设置颜色，其他如red,blue,cyan,yellow等
vertex.label=vertices$name, #NULL表示不设置，为默认状态，即默认显示数据中点的名称，可以是中文。如果是NA则表示不显示任何点信息
vertex.label.cex=0.8, #节点字体大小
vertex.label.color='black', #节点字体颜色,red
vertex.label.dist=0.4, #标签和节点位置错开
edge.arrow.size=0,#连线的箭头的大小,若为0即为无向图，当然有些数据格式不支持有向图
edge.width = 0.5, #连接线宽度
edge.label=NA, #不显示连接线标签，默认为频次
edge.color="gray") #连线颜色
#生成方式2（有颜色分类）：==========
#set.seed() #生成随机数，这样图的布局就会可重复，而不是每次生成的时候都变
#l<-layout.fruchterman.reingold(graph) #设置图的布局方式为弹簧式发散的布局
l = layout.reingold.tilford(graph,circular=T)
#具体修改过程
V(graph)$size <- 8 #节点大小与点中心度成正比，中心度即与该点相连的点的总数
colrs <- c('#0096ff', "lightblue", "azure3","firebrick1")
V(graph)$color <- colrs[vertices$color] #根据类型设置颜色,按照类型分组
V(graph)$label.color <- 'black' #设置节点标记的颜色
V(graph)$label <- V(graph)$name
#E(graph)$width <- E(graph)$fre #根据频次列设置边宽度
#E(graph)$label <- E(graph)$fre #根据频次列设置边标签
E(graph)$arrow.size=0.3 #设置箭头大小
#生成图
plot(graph, layout=l)
================================================
FILE: 2020年/2020.07.13Network_igraph/vertices.csv
================================================
id,name,color
1,,1
2,,1
3,,2
4,ҵ,3
5,ͳƵʵ,1
6,ְҵķչ,3
7,,4
8,һ,4
9,,2
10,ʵ,3
11,¡,3
12,ݷ,1
13,ԪͳƷ,1
14,,2
15,˼ͷ,1
16,ߵȴ(),1
17,ߵȴ(һ),2
18,ѧ,1
19,ͳƷ뽨ģ,1
20,˼,3
21,ʱз,1
22,ʵ,1
23,ͳ,1
24,ѧ(),2
25,ѧ(),2
26,ѧ(һ),2
27,ͳƷд,1
28,ͳƽģ,1
29,,3
30,ͳƶӦ,1
31,ͳѧ,2
32,Ӧ,1
33,й˼,3
34,йִʷҪ,3
35,רҵ,4
36,רҵʵϰ,1
================================================
FILE: 2020年/2020.07.14China_map/china_map.r
================================================
#9.1中国疫情图============
#（https://my.oschina.net/u/2306127/blog/473842）
library(mapdata)
library(maptools)
library(ggplot2)
library(plyr)
china_map = readShapePoly("bou2_4p.shp")#导入shp格式的中国地图
x<-china_map@data
xs<-data.frame(x,id=seq(0:924)-1)#地图中共计有925个地域信息
china_map1<-fortify(china_map) #转化为数据框
china_map_data<-join(china_map1,xs,type="full")#基于id进行连接
a = data.frame(unique(china_map@data$NAME))
#准备数据
mydata<-read.csv("data_dt.csv",header=T,as.is=T)
china_data <- join(china_map_data, mydata, type="full")#基于NAME字段进行连接，NAME字段来自于地图文件中
#3、绘制地图
## 版本一(无省份名称的当日各省确认人数)
ggplot(china_data, aes(x = long, y = lat, group = group, fill = people)) +
geom_polygon(colour="grey40") +
scale_fill_gradient(low="white",high="steelblue") +#指定渐变填充色，可使用RGB
theme( #清除不需要的元素
panel.grid = element_blank(),
panel.background = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank(),
axis.title = element_blank(),
legend.position = c(0.2,0.3)
)
## 版本二（有省份名称的当日各省确认人数）
midpos <- function(x) mean(range(x,na.rm=TRUE)) #取形状内的平均坐标
centres <- ddply(china_data,.(NAME),colwise(midpos,.(long,lat)))
ggplot(china_data,aes(long,lat))+ #此处语法与前面不同，参考ggplot2一书P85
geom_polygon(aes(group=group,fill=people),colour="black")+
scale_fill_gradient(low="white",high="steelblue") +
geom_text(aes(label=NAME),data=centres) +
theme(
panel.grid = element_blank(),
panel.background = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank(),
axis.title = element_blank()
)
================================================
FILE: 2020年/2020.07.14China_map/data_dt.csv
================================================
NAME,ratio,people,province
ʡ,68135,1405,
㶫ʡ,1637,732,㶫
ʡ,1276,667,
㽭ʡ,1269,572,㽭
ʡ,1019,532,
ʡ,991,510,
ʡ,947,452,
ʡ,932,340,
,905,322,
ɽʡ,792,297,ɽ
Ϻ,707,270,Ϻ
ʡ,654,242,
Ĵʡ,589,205,Ĵ
,582,201,
ʡ,363,176,
ӱʡ,349,166,ӱ
ʡ,320,154,
׳,254,103,
ɹ,238,93,ɹ
ʡ,198,84,
ɽʡ,198,77,ɽ
ʡ,185,54,
ʡ,171,49,
ʡ,163,45,
ʡ,155,45,
ʡ,154,42,
ʡ,147,40,
½ά,76,27,½
Ļ,75,9,
ຣʡ,18,7,ຣ
,1,5,
ر,1197,1,
̨ʡ,447,100,̨
================================================
FILE: 2020年/2020.07.15World_map/Country_Data.csv
================================================
"Country ",Scale
Aruba,
Afghanistan,14337184953
Angola,122686093
Anguilla,
Albania,52348966
Finland,
Andorra,
United Arab Emirates,1420302
Argentina,4793871
Armenia,118083985
American Samoa,1706789
Antarctica,
Australia,
French Southern and Antarctic Lands,
Antigua,1024000
Barbuda,
Austria,
Azerbaijan,99645874
Burundi,71626580
Belgium,4143675
Benin,72834739
Burkina Faso,291773051
Bangladesh,657712016
Bulgaria,40346336
Bahrain,22984534
Bahamas,5530627
Bosnia and Herzegovina,113820206
Saint Barthelemy,
Belarus,33366555
Belize,19013177
Bermuda,
Bolivia,40456906
Brazil,47003710
Barbados,1195377
Brunei,336196
Bhutan,827607
Botswana,108084913
Central African Republic,163301353
Canada,25171012
Switzerland,1182463
Chile,9166231
China,109468893
Ivory Coast,230099050
Cameroon,107714146
Democratic Republic of the Congo,774104905
Republic of Congo,9313958
Cook Islands,
Colombia,1008396303
Comoros,1162798
Cape Verde,
Costa Rica,23807023
Cuba,19674492
Curacao,
Cayman Islands,
Cyprus,5878004
Czech Republic,12429517
Germany,627585
Djibouti,43437611
Dominica,958000
Denmark,
Dominican Republic,105256853
Algeria,21999637
Ecuador,66630571
Egypt,285844459
Eritrea,1113076
Canary Islands,
Spain,5227911
Estonia,12470092
Ethiopia,1421649545
Fiji,4602905
Falkland Islands,
Reunion,
Mayotte,
French Guiana,
Martinique,
Guadeloupe,
France,1464862
Faroe Islands,
Micronesia,302553791
Gabon,13844417
UK,244658
Georgia,495691015
Guernsey,
Ghana,307723748
Guinea,62034795
Gambia,4398110
Guinea-Bissau,3299809
Equatorial Guinea,335973
Greece,1347158
Grenada,991550
Greenland,
Guatemala,304130054
Guam,
Guyana,18365479
Heard Island,
Honduras,197701659
Croatia,14439801
Haiti,702753017
Hungary,32842750
Indonesia,425241264
Isle of Man,
India,261495537
Cocos Islands,
Christmas Island,
Chagos Archipelago,
Ireland,7389871
Iran,2064968
Iraq,817392816
Iceland,
Israel,6249503135
Italy,2758463
San Marino,
Jamaica,63406681
Jersey,
Jordan,2229268225
Japan,5583196
Siachen Glacier,
Kazakhstan,284994227
Kenya,1757291419
Kyrgyzstan,142055012
Cambodia,215972560
Kiribati,
Nevis,
Saint Kitts,
South Korea,2457582
Kosovo,133600340
Kuwait,92372
Laos,50370856
Lebanon,639875848
Liberia,456462263
Libya,70061408
Saint Lucia,
Liechtenstein,
Sri Lanka,81459320
Lesotho,108025026
Lithuania,18158581
Luxembourg,
Latvia,9766796
Saint Martin,
Morocco,170856475
Monaco,
Moldova,186466454
Madagascar,125564678
Maldives,4842109
Mexico,557646557
Marshall Islands,128170833
Macedonia,56725720
Mali,361809062
Malta,1602646
Myanmar,225938871
Montenegro,9798308
Mongolia,30992965
Northern Mariana Islands,
Mozambique,777992540
Mauritania,41288351
Montserrat,
Mauritius,2569586
Malawi,440212898
Malaysia,25398180
Namibia,231306565
New Caledonia,
Niger,217698383
Norfolk Island,
Nigeria,1165516940
Nicaragua,94025520
Niue,
Bonaire,
Sint Eustatius,
Saba,
Netherlands,80365
Norway,
Nepal,169303579
Nauru,
New Zealand,7154
Oman,26659268
Pakistan,1972822305
Panama,24522344
Pitcairn Islands,
Peru,304607830
Philippines,728003613
Palau,28769020
Papua New Guinea,14424130
Poland,93171533
Puerto Rico,
North Korea,3997348
Madeira Islands,
Azores,
Portugal,916654
Paraguay,36856601
Palestine,884730488
French Polynesia,
Qatar,3098
Romania,76362825
Russia,297186633
Rwanda,343709226
Western Sahara,
Saudi Arabia,713501
Sudan,412033512
South Sudan,1480315209
Senegal,389694501
Singapore,1040834
South Sandwich Islands,
South Georgia,
Saint Helena,
Ascension Island,
Solomon Islands,2817980
Sierra Leone,53212837
El Salvador,153467672
Somalia,699889670
Saint Pierre and Miquelon,
Serbia,75712324
Sao Tome and Principe,933472
Suriname,1453709
Slovakia,4589332
Slovenia,7086955
Sweden,45798
Swaziland,91046517
Sint Maarten,
Seychelles,552879
Syria,1581881739
Turks and Caicos Islands,
Chad,245554164
Togo,7650651
Thailand,132772482
Tajikistan,132816030
Turkmenistan,17402234
Timor-Leste,72044958
Tonga,3221962
Trinidad,
Tobago,
Tunisia,137888152
Turkey,190530825
Taiwan,2722516
Tanzania,1187669230
Uganda,1058084960
Ukraine,500475016
Uruguay,2884931
USA,
Uzbekistan,104348366
Vatican,
Grenadines,
Saint Vincent,
Venezuela,14047441
Virgin Islands,
Vietnam,240210318
Vanuatu,5726365
Wallis and Futuna,
Samoa,
Yemen,485596030
South Africa,1072163281
Zambia,1011659748
Zimbabwe,357528936
================================================
FILE: 2020年/2020.07.15World_map/cores.r
================================================
#========================================================================
#============================= Task =====================================
#========================================================================
setwd("C:/Users/DELL/Desktop/wechat/2020.07.15World_map")
# 世界疫情图==========
library(maps)
library(ggplot2)
library(RColorBrewer)
library(plyr)
colormap<-c(rev(brewer.pal(9,"Greens")[c(4,6)]), brewer.pal(9,"YlOrRd")[c(3,4,5,6,7,8,9)])
mydata1<-read.csv("Country_Data.csv",stringsAsFactors=FALSE)#这个是全球数据
names(mydata1)=c("Country","Scale") #重新命名
mydata2 = read.csv("world_data.csv",header=TRUE) #我们的数据（疫情）
head(mydata2)
#将两个表格匹配
mydata <- join(mydata1, mydata2, type="full")
head(mydata)
#把ratio参数设置成分类型，以便于好绘制
mydata$fan<-cut(mydata$ratio,
breaks=c(min(mydata$million,na.rm=TRUE),
0,1000,5000,10000,50000,200000,500000,2000000,
max(mydata$ratio,na.rm=TRUE)),
labels=c(" <=0","0~1000","1000~5000","5000~10000","10000~50000","50000~200000",
"200000~500000","500000~2000000"," >=2000000"),
order=TRUE)
#定义地图用全球的
world_map <- map_data("world")
#绘图
ggplot()+
geom_map(data=mydata,aes(map_id=Country,fill=fan),map=world_map)+
geom_path(data=world_map,aes(x=long,y=lat,group=group),colour="black",size=.2)+
scale_y_continuous(breaks=(-3:3)*30) +
scale_x_continuous(breaks=(-6:6)*30) +
scale_fill_manual(name="Ratio",values= colormap,na.value="grey75")+
guides(fill=guide_legend(reverse=TRUE)) +
theme_minimal()
================================================
FILE: 2020年/2020.07.15World_map/world_data.csv
================================================
Country,long,lat,ratio
Andorra,1.601554,42.546245,855
United Arab Emirates,53.847818,23.424076,NA
Afghanistan,67.709953,33.93911,30175
Antigua and Barbuda,-61.796428,17.060816,26
Anguilla,-63.068615,18.220554,3
Albania,20.168331,41.153332,2192
Armenia,45.038189,40.069099,22488
Netherlands Antilles,-69.060087,12.226079,NA
Angola,17.873887,-11.202692,189
Antarctica,-0.071389,-75.250973,NA
Argentina,-63.616672,-38.416097,49851
American Samoa,-170.132217,-14.270972,NA
Austria,14.550072,47.516231,17477
Australia,133.775136,-25.274398,7558
Aruba,-69.968338,12.52111,101
Azerbaijan,47.576927,40.143105,14852
Bosnia and Herzegovina,17.679076,43.915886,NA
Barbados,-59.543198,13.193887,97
Bangladesh,90.356331,23.684994,126606
Belgium,4.469936,50.503887,61007
Burkina Faso,-1.561593,12.238333,NA
Bulgaria,25.48583,42.733883,4242
Bahrain,50.637772,25.930414,23570
Burundi,29.918886,-3.373056,144
Benin,2.315834,9.30769,1017
Bermuda,-64.75737,32.321384,141
Brunei,114.727669,4.535277,141
Bolivia,-63.588653,-16.290154,28631
Brazil,-51.92528,-14.235004,1193609
Bahamas,-77.39628,25.03428,104
Bhutan,90.433601,27.514162,70
Bouvet Island,3.413194,-54.423199,NA
Botswana,24.684866,-22.328474,89
Belarus,27.953389,53.709807,60382
Belize,-88.49765,17.189877,23
Canada,-106.346771,56.130366,102242
Cocos [Keeling] Islands,96.870956,-12.164165,NA
Congo [DRC],21.758664,-4.038333,NA
Central African Republic,20.939444,6.611111,NA
Congo [Republic],15.827659,-0.228021,NA
Switzerland,8.227512,46.818188,31428
C?te d'Ivoire,-5.54708,7.539989,NA
Cook Islands,-159.777671,-21.236736,NA
Chile,-71.542969,-35.675147,254416
Cameroon,12.354722,7.369722,12592
China,104.195397,35.86166,85260
Colombia,-74.297333,4.570868,77113
Costa Rica,-83.753428,9.748917,2515
Cuba,-77.781167,21.521757,2319
Cape Verde,-24.013197,16.002082,NA
Christmas Island,105.690449,-10.447525,NA
Cyprus,33.429859,35.126413,991
Czech Republic,15.472962,49.817492,NA
Germany,10.451526,51.165691,193987
Djibouti,42.590275,11.825138,4630
Denmark,9.501785,56.26392,12636
Dominica,-61.370976,15.414999,NA
Dominican Republic,-70.162651,18.735693,NA
Algeria,1.659626,28.033886,12248
Ecuador,-78.183406,-1.831239,51643
Estonia,25.013607,58.595272,1984
Egypt,30.802498,26.820553,59561
Western Sahara,-12.885834,24.215527,9
Eritrea,39.782334,15.179384,143
Spain,-3.74922,40.463667,247086
Ethiopia,40.489673,9.145,5034
Finland,25.748151,61.92411,7172
Fiji,179.414413,-16.578193,18
Falkland Islands [Islas Malvinas],-59.523613,-51.796253,NA
Micronesia,150.550812,7.425554,NA
Faroe Islands,-6.911806,61.892635,187
France,2.213749,46.227638,161348
Gabon,11.609444,-0.803689,4956
United Kingdom,-3.435973,55.378051,306862
Grenada,-61.604171,12.262776,23
Georgia,43.356892,42.315407,917
French Guiana,-53.125782,3.933889,2827
Guernsey,-2.585278,49.465691,252
Ghana,-1.023194,7.946527,15013
Gibraltar,-5.345374,36.137741,176
Greenland,-42.604303,71.706936,13
Gambia,-15.310139,13.443182,42
Guinea,-9.696645,9.945587,5174
Guadeloupe,-62.067641,16.995971,174
Equatorial Guinea,10.267895,1.650801,NA
Greece,21.824312,39.074208,3310
South Georgia and the South Sandwich Islands,-36.587909,-54.429579,NA
Guatemala,-90.230759,15.783471,14819
Guam,144.793731,13.444304,226
Guinea-Bissau,-15.180413,11.803749,1556
Guyana,-58.93018,4.860416,206
Gaza Strip,34.308825,31.354676,NA
Hong Kong,114.109497,22.396428,NA
Heard Island and McDonald Islands,73.504158,-53.08181,NA
Honduras,-86.241905,15.199999,14571
Croatia,15.2,45.1,2483
Haiti,-72.285215,18.971187,5429
Hungary,19.503304,47.162494,4123
Indonesia,113.921327,-0.789275,51087
Ireland,-8.24389,53.41291,25396
Israel,34.851612,31.046051,22139
Isle of Man,-4.548056,54.236107,NA
India,78.96288,20.593684,473634
British Indian Ocean Territory,71.876519,-6.343194,NA
Iraq,43.679291,33.223191,39139
Iran,53.688046,32.427908,215096
Iceland,-19.020835,64.963051,1824
Italy,12.56738,41.87194,239410
Jersey,-2.13125,49.214439,308
Jamaica,-77.297508,18.109581,678
Jordan,36.238414,30.585164,1071
Japan,138.252924,36.204824,18212
Kenya,37.906193,-0.023559,5384
Kyrgyzstan,74.766098,41.20438,3954
Cambodia,104.990963,12.565679,130
Kiribati,-168.734039,-3.370417,NA
Comoros,43.872219,-11.875001,265
Saint Kitts and Nevis,-62.782998,17.357822,15
North Korea,127.510093,40.339852,NA
South Korea,127.766922,35.907757,NA
Kuwait,47.481766,29.31166,42788
Cayman Islands,-80.566956,19.513469,NA
Kazakhstan,66.923684,48.019573,19285
Laos,102.495496,19.85627,19
Lebanon,35.862285,33.854721,1644
Saint Lucia,-60.978893,13.909444,NA
Liechtenstein,9.555373,47.166,86
Sri Lanka,80.771797,7.873054,NA
Liberia,-9.429499,6.428055,662
Lesotho,28.233608,-29.609988,17
Lithuania,23.881275,55.169438,1806
Luxembourg,6.129583,49.815273,4140
Latvia,24.603189,56.879635,1111
Libya,17.228331,26.3351,670
Morocco,-7.09262,31.791702,11279
Monaco,7.412841,43.750298,101
Moldova,28.369885,47.411631,15078
Montenegro,19.37439,42.708678,389
Madagascar,46.869107,-18.766947,1829
Marshall Islands,171.184478,7.131474,NA
Macedonia [FYROM],21.745275,41.608635,NA
Mali,-3.996166,17.570692,2005
Myanmar [Burma],95.956223,21.913965,NA
Mongolia,103.846656,46.862496,213
Macau,113.543873,22.198745,NA
Northern Mariana Islands,145.38469,17.33083,31
Martinique,-61.024174,14.641528,236
Mauritania,-10.940835,21.00789,3519
Montserrat,-62.187366,16.742498,11
Malta,14.375416,35.937496,668
Mauritius,57.552152,-20.348404,341
Maldives,73.22068,3.202778,2261
Malawi,34.301525,-13.254308,941
Mexico,-102.552784,23.634501,196847
Malaysia,101.975766,4.210484,8600
Mozambique,35.529562,-18.665695,762
Namibia,18.49041,-22.95764,76
New Caledonia,165.618042,-20.904305,21
Niger,8.081666,17.607789,1051
Norfolk Island,167.954712,-29.040835,NA
Nigeria,8.675277,9.081999,22020
Nicaragua,-85.207229,12.865416,2170
Netherlands,5.291266,52.132633,49914
Norway,8.468946,60.472024,8788
Nepal,84.124008,28.394857,11162
Nauru,166.931503,-0.522778,NA
Niue,-169.867233,-19.054445,NA
New Zealand,174.885971,-40.900557,1519
Oman,55.923255,21.512583,34902
Panama,-80.782127,8.537981,28030
Peru,-75.015152,-9.189967,264689
French Polynesia,-149.406843,-17.679742,60
Papua New Guinea,143.95555,-6.314993,NA
Philippines,121.774017,12.879721,33069
Pakistan,69.345116,30.375321,192970
Poland,19.145136,51.919438,33119
Saint Pierre and Miquelon,-56.27111,46.941936,NA
Pitcairn Islands,-127.439308,-24.703615,NA
Puerto Rico,-66.590149,18.220833,6877
Palestinian Territories,35.233154,31.952162,NA
Portugal,-8.224454,39.399872,40415
Palau,134.58252,7.51498,NA
Paraguay,-58.443832,-23.442503,1528
Qatar,51.183884,25.354826,91838
Runion,55.536384,-21.115141,NA
Romania,24.96676,45.943161,NA
Serbia,21.005859,44.016521,13372
Russia,105.318756,61.52401,613994
Rwanda,29.873888,-1.940278,830
Saudi Arabia,45.079162,23.885942,170639
Solomon Islands,160.156194,-9.64571,NA
Seychelles,55.491977,-4.679574,11
Sudan,30.217636,12.862807,8889
Sweden,18.643501,60.128161,62324
Singapore,103.819836,1.352083,42736
Saint Helena,-10.030696,-24.143474,NA
Slovenia,14.995463,46.151241,1547
Svalbard and Jan Mayen,23.670272,77.553604,NA
Slovakia,19.699024,48.669026,1630
Sierra Leone,-11.779889,8.460555,NA
San Marino,12.457777,43.94236,NA
Senegal,-14.452362,14.497401,6233
Somalia,46.199616,5.152149,2835
Suriname,-56.027783,3.919305,357
S?o Tom and Prncipe,6.613081,0.18636,NA
El Salvador,-88.89653,13.794185,5336
Syria,38.996815,34.802075,231
Swaziland,31.465866,-26.522503,690
Turks and Caicos Islands,-71.797928,21.694025,NA
Chad,18.732207,15.454166,860
French Southern Territories,69.348557,-49.280366,NA
Togo,0.824782,8.619543,583
Thailand,100.992541,15.870032,3158
Tajikistan,71.276093,38.861034,5630
Tokelau,-171.855881,-8.967363,NA
Timor-Leste,125.727539,-8.874217,NA
Turkmenistan,59.556278,38.969719,NA
Tunisia,9.537499,33.886917,1160
Tonga,-175.198242,-21.178986,NA
Turkey,35.243322,38.963745,191657
Trinidad and Tobago,-61.222503,10.691803,123
Tuvalu,177.64933,-7.109535,NA
Taiwan,120.960515,23.69781,NA
Tanzania,34.888822,-6.369028,509
Ukraine,31.16558,48.379433,40008
Uganda,32.290275,1.373333,805
U.S. Minor Outlying Islands,NA,NA,NA
United States,-95.712891,37.09024,NA
Uruguay,-55.765835,-32.522779,902
Uzbekistan,64.585262,41.377491,6990
Vatican City,12.453389,41.902916,NA
Saint Vincent and the Grenadines,-61.287228,12.984305,29
Venezuela,-66.58973,6.42375,4366
British Virgin Islands,-64.639968,18.420695,NA
U.S. Virgin Islands,-64.896335,18.335765,NA
Vietnam,108.277199,14.058324,NA
Vanuatu,166.959158,-15.376706,NA
Wallis and Futuna,-177.156097,-13.768752,NA
Samoa,-172.104629,-13.759029,NA
Kosovo,20.902977,42.602636,2216
Yemen,48.516388,15.552727,1015
Mayotte,45.166244,-12.8275,2467
South Africa,22.937506,-30.559482,111796
Zambia,27.849332,-13.133897,1497
Zimbabwe,29.154857,-19.015438,530
USA,NA,NA,2462554
Bosnia,NA,NA,3676
Burkina faso,NA,NA,934
Burma,NA,NA,293
Bvi,NA,NA,8
Cape verde,NA,NA,999
Cayman islands,NA,NA,195
Congo (Bu),NA,NA,1087
Congo (DRC),NA,NA,6213
Curacao,NA,NA,20
Czech,NA,NA,10780
Diamond Princess,NA,NA,712
Dominic,NA,NA,18
Dominican,NA,NA,28631
East timor,NA,NA,24
Equatorial guinea,NA,NA,1664
Falkland islands,NA,NA,13
Isle,NA,NA,336
Ivory coast,NA,NA,8164
Korea,NA,NA,12563
Lucia,NA,NA,19
Netherlands Caribbean,NA,NA,7
Non -,NA,NA,3099
North Macedonia,NA,NA,5445
Palestinian,NA,NA,1328
Papua new guinea,NA,NA,9
Reunion,NA,NA,426
Romanian,NA,NA,25286
Saint Barthelemi,NA,NA,6
Saint-Pierre and Miquelon Islands,NA,NA,1
San marino,NA,NA,698
Sao Tome and Principe,NA,NA,710
Sierra leone,NA,NA,1354
South Sudan,NA,NA,1942
Sri lanka,NA,NA,2007
"St. Martin, France",NA,NA,117
"St. Martin, Netherlands",NA,NA,77
Tci,NA,NA,14
U.A.E,NA,NA,46563
Usvi,NA,NA,76
Vatican,NA,NA,12
Viet Nam,NA,NA,352
================================================
FILE: 2020年/2020.07.22Movielense/ch11-task.Rmd
================================================
---
title: "第11章作业MovieLense数据集分析"
author:
documentclass: ctexart
output:
word_document: default
html_document: default
pdf_document: default
rticles::ctex:
fig_caption: yes
number_sections: yes
toc: yes
classoption: "hyperref,"
---
# 前言
R的recommenderlab包可以实现协同过滤算法。这个包中有许多关于推荐算法建立、处理及可视化的函数。选用recommenderlab包中内置的MovieLense数据集进行分析，该数据集收集了网站MovieLens（movielens.umn.edu）从1997年9月19日到1998年4月22日的数据，包括943名用户对1664部电影的评分。
```{r message=FALSE, warning=FALSE}
library(recommenderlab)
library(ggplot2)
```
# 数据处理与数据探索性分析
```{r message=FALSE, warning=FALSE}
data(MovieLense)
image(MovieLense)
# 获取评分
ratings.movie <- data.frame(ratings = getRatings(MovieLense))
summary(ratings.movie$ratings)
ggplot(ratings.movie, aes(x = ratings)) +
geom_histogram(fill = "beige", color = "black",
binwidth = 1, alpha = 0.7) + xlab("rating") + ylab("count")
```
利用`summary()`获取评分数据，可知最大值为5，最小值为1，平均值为3.53。并将其柱状图进行绘制，如下所示。
## 数据标准化
在进行数据分析前，利用`normalize()`我们将数据进行标准化，并进行绘制。
```{r message=FALSE, warning=FALSE}
ratings.movie1 <- data.frame(ratings =
getRatings(normalize(MovieLense, method = "Z-score")))
summary(ratings.movie1$ratings)
ggplot(ratings.movie1, aes(x = ratings)) +
geom_histogram(fill = "beige", color = "black",
alpha = 0.7) + xlab("rating") + ylab("count")
```
## 用户的电影点评数
我们还对用户的电影点评数进行描述性分析，具体结果如下所示。
```{r message=FALSE, warning=FALSE}
movie.count <- data.frame(count = rowCounts(MovieLense))
ggplot(movie.count, aes(x = count)) +
geom_histogram(fill = "beige", color = "black",
alpha = 0.7) + xlab("counts of users") + ylab("counts of movies rated")
rating.mean <- data.frame(rating = colMeans(MovieLense))
ggplot(rating.mean, aes(x = rating)) +
geom_histogram(fill = "beige", color = "black",
alpha = 0.7) + xlab("rating") + ylab("counts of movies ")
```
# 建立推荐模型与模型评估
对于realRatingMatrix有六种方法：IBCF(基于物品的推荐)、UBCF（基于用户的推荐）、SVD（矩阵因子化）、PCA（主成分分析）、 RANDOM（随机推荐）、POPULAR（基于流行度的推荐）。
模型评估主要使用：recommenderlab包中自带的评估方案，对应的函数是evaluationScheme，能够设置采用n-fold交叉验证还是简单的training/train分开验证，本文采用后一种方法，即将数据集简单分为training和test，在training训练模型，然后在test上评估。接下来我们使用三种不同技术进行构建推荐系统，并利用评估方案比较三种技术的好坏。
```{r message=FALSE, warning=FALSE}
library(recommenderlab)
data(MovieLense)
scheme <- evaluationScheme(MovieLense, method = "split",
train = 0.9, k = 1, given = 10, goodRating = 4)
algorithms <- list(popular = list(name = "POPULAR",
param = list(normalize = "Z-score")),
ubcf = list(name = "UBCF", param = list(normalize = "Z-score",
method = "Cosine",nn = 25, minRating = 3)),
ibcf = list(name = "IBCF", param = list(normalize = "Z-score")))
results <- evaluate(scheme, algorithms, n = c(1, 3, 5, 10, 15, 20))
plot(results, annotate = 1:3, legend = "topleft") #ROC
plot(results, "prec/rec", annotate = 3)#precision-recall
```
```{r}
# 按照评价方案建立推荐模型
model.popular <- Recommender(getData(scheme, "train"), method = "POPULAR")
model.ibcf <- Recommender(getData(scheme, "train"), method = "IBCF")
model.ubcf <- Recommender(getData(scheme, "train"), method = "UBCF")
# 对推荐模型进行预测
predict.popular <- predict(model.popular, getData(scheme, "known"), type = "ratings")
predict.ibcf <- predict(model.ibcf, getData(scheme, "known"), type = "ratings")
predict.ubcf <- predict(model.ubcf, getData(scheme, "known"), type = "ratings")
# 做误差的计算
predict.err <- rbind(calcPredictionAccuracy(predict.popular,
getData(scheme, "unknown")),calcPredictionAccuracy(predict.ubcf, getData(scheme,
"unknown")), calcPredictionAccuracy(predict.ibcf,getData(scheme, "unknown")))
rownames(predict.err) <- c("POPULAR", "UBCF", "IBCF")
predict.err
```
通过结果我们可以看到：基于流行度推荐系统对于本案例数据的效果最好，RMSE，MSE，MAE都是三者中的最小值。其次是基于用户的推荐，最后是基于项目协同过滤。
# 参考资料{-}
1. [Recommenderlab包实现电影评分预测(R语言)
](https://blog.csdn.net/seuponder/article/details/21040917)
2. [R语言：recommenderlab包的总结与应用案例](https://www.cnblogs.com/yjd_hycf_space/p/6702764.html)
3. [recommender system handbook](https://www.amazon.com/Recommender-Systems-Handbook-Francesco-Ricci/dp/1489976361)
4. [Item-Based Collaborative Filtering Recommendation Algorithms](http://www.ra.ethz.ch/CDstore/www10/papers/pdf/p519.pdf)
5. [recommenderlab: A Framework for Developing and Testing Recommendation Algorithms](https://cran.r-project.org/web/packages/recommenderlab/vignettes/recommenderlab.pdf)
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================================================
FILE: 2020年/2020.07.22坐标轴截断画图/code_truncation .R
================================================
## [ggplot坐标轴截断](https://www.jianshu.com/p/0e4fa8849479)
library(ggplot2)
set.seed(2019-01-19)
d <- data.frame(
x = 1:20,
y = c(rnorm(5) + 4, rnorm(5) + 20, rnorm(5) + 5, rnorm(5) + 22)
)
ggplot(d, aes(x, y)) + geom_col()
library(dplyr)
breaks = c(7, 17)
d$.type <- NA
d$.type[d$y < breaks[1]] = "small"
d$.type[d$y > breaks[2]] = "big"
d <- filter(d, .type == 'big') %>%
mutate(.type = "small", y = breaks[1]) %>%
bind_rows(d)
mymin = function(y) ifelse(y <= breaks[1], 0, breaks[2])
p <- ggplot(d, aes(x, y)) +
geom_rect(aes(xmin = x - .4, xmax = x + .4, ymin = mymin(y), ymax = y)) +
facet_grid(.type ~ ., scales = "free") +
theme(strip.text=element_blank())
p
## [R语言作图——坐标轴截断画图](http://blog.sina.com.cn/s/blog_6a4ee1ad0102x5at.html)
library(plotrix)
w <- c(75, 64.4, 47.3, 66.9, 456, 80.6, 70, 55.8, 57.9, 561, 58.6, 61.2, 50.3, 54.6, 57.8)
x <- c(1:15)
gap.barplot(w,gap=c(90,420),xaxlab=x,ytics=c(50,70,450,500),col=rainbow(15),xlab ="mumbers", ylab = "height", main="test image")
axis.break(2,90,breakcol="snow",style="gap")##去掉中间的那两道横线；
axis.break(2,90*(1+0.02),breakcol="black",style="slash")##在左侧Y轴把gap位置换成slash；
axis.break(4,90*(1+0.02),breakcol="black",style="slash")##在右侧Y轴把gap位置换成slash；
### 案例
twogrp<-c(rnorm(10)+4,rnorm(10)+20)
gap.barplot(twogrp,gap=c(8,16),xlab="Index",ytics=c(3,6,17,20),
ylab="Group values",main="Barplot with gap")
gap.barplot(twogrp,gap=c(8,16),xlab="Index",ytics=c(3,6,17,20),
ylab="Group values",horiz=TRUE,main="Horizontal barplot with gap")
================================================
FILE: 2020年/2020.07.22坐标轴截断画图/code_truncation .Rmd
================================================
---
title: "R中坐标轴截断的不同实现方式"
author:
- 庄闪闪
documentclass: ctexart
output:
word_document: default
html_document: default
pdf_document: default
rticles::ctex:
fig_caption: yes
number_sections: yes
toc: yes
classoption: "hyperref,"
---
# plotrix包
[R语言作图——坐标轴截断画图](http://blog.sina.com.cn/s/blog_6a4ee1ad0102x5at.html)
利用gap.barplot()进进行绘制，将gap参数设置为90，420进行y轴截断，可加入参数axis.break()对截断形状进行修改
```{r}
library(plotrix)
w <- c(75, 64.4, 47.3, 66.9, 456, 80.6, 70, 55.8, 57.9, 561, 58.6, 61.2, 50.3, 54.6, 57.8)
x <- c(1:15)
gap.barplot(w,gap=c(90,420),xaxlab=x,ytics=c(50,70,450,500),col=rainbow(15),xlab ="mumbers", ylab = "height", main="test image")
axis.break(2,90,breakcol="snow",style="gap")##去掉中间的那两道横线；
axis.break(2,90*(1+0.02),breakcol="black",style="slash")##在左侧Y轴把gap位置换成slash；
axis.break(4,90*(1+0.02),breakcol="black",style="slash")##在右侧Y轴把gap位置换成slash；
```
- 其他案例
```{r}
twogrp<-c(rnorm(10)+4,rnorm(10)+20)
gap.barplot(twogrp,gap=c(8,16),xlab="Index",ytics=c(3,6,17,20),
ylab="Group values",main="Barplot with gap")
gap.barplot(twogrp,gap=c(8,16),xlab="Index",ytics=c(3,6,17,20),
ylab="Group values",horiz=TRUE,main="Horizontal barplot with gap")
```
# ggplot包
[ggplot坐标轴截断](https://www.jianshu.com/p/0e4fa8849479)
```{r message=FALSE, warning=FALSE}
library(ggplot2)
set.seed(123)
d <- data.frame(
x = 1:20,
y = c(rnorm(5) + 4, rnorm(5) + 20, rnorm(5) + 5, rnorm(5) + 22)
)
ggplot(d, aes(x, y)) + geom_col()
library(dplyr)
breaks = c(7, 17)
d$type <- NA
d$type[d$y < breaks[1]] = "small"
d$type[d$y > breaks[2]] = "big"
d <- filter(d, type == 'big') %>%
mutate(type = "small", y = breaks[1]) %>%
bind_rows(d)
mymin = function(y) ifelse(y <= breaks[1], 0, breaks[2])
p <- ggplot(d, aes(x, y)) +
geom_rect(aes(xmin = x - .4, xmax = x + .4, ymin = mymin(y), ymax = y)) +
facet_grid(type ~ ., scales = "free") +
theme(strip.text=element_blank())#去除text
p
```
================================================
FILE: 2020年/2020.07.24饼图与圆环图/pie.rmd
================================================
---
title: "饼状图"
date: 2020-08-05
author:
- 庄闪闪
documentclass: ctexart
output:
word_document: default
pdf_document: default
html_document: default
classoption: "hyperref,"
---
# 饼图
饼图（pie chart）被广泛地应用于各个领域，用于表示不同分类的占比情况，通过弧度大小来对比各种分类。饼图通过将一个圆饼按照分类的占比划分成多个切片，整个圆饼代表数据的总量，每个切片（圆弧）表示该分类占总体的比例，所有切片（圆弧）的加和等于100%。
## graphics绘制饼图
```{r message=FALSE, warning=FALSE}
library(RColorBrewer)
library(dplyr)
library(graphics)
library(ggplot2)
```
```{r}
sessionInfo(
)
```
init.angle可设定饼图的初始角度，labels可添加标签。颜色用了brewer.pal函数，第一个参数为个数，第二个参数为名字，这里用的是BrBG，具体可help一下。
```{r message=FALSE, warning=FALSE}
df <- data.frame(value = c(24.20,30.90,12.50,12.30,8.10,12.10),
group = c('LVS','SJM','MCE','Galaxy','MGM','Wynn'))
df <-arrange(df,value)
labs <- paste0(df$group," \n(", round(df$value/sum(df$value)*100,2), "%)") #标签
lab <- paste0(round(df$value/sum(df$value)*100,2), "%") #标签
pie(df$value,labels=labs, init.angle=90,col = brewer.pal(nrow(df), "BrBG"),
border="black")
pie(df$value,labels=lab, init.angle=90,col = brewer.pal(nrow(df), "Blues"),
border="black")
```
## ggplot2包绘制
使用R中ggplot2包的geom_bar()函数绘制堆积柱形图，然后将直角坐标系转换成极坐标系，
就可以显示为饼图，但还是需要使用geom_text()函数添加数据标签。注意的是：ymax，ymin也需要自己计算得到。
```{r message=FALSE, warning=FALSE}
df$fraction = df$value / sum(df$value)
df$ymax = cumsum(df$fraction)
df$ymin = c(0, head(df$ymax, n = -1))
ggplot(data = df, aes(fill = group, ymax = ymax, ymin = ymin, xmax = 4, xmin = 3)) +
geom_rect(show.legend = F,alpha=0.8) +
scale_fill_brewer(palette = 'Set3')+
coord_polar(theta = "y") +
labs(x = "", y = "", title = "",fill='地区') +
theme_light() +
theme(panel.grid=element_blank()) + ## 去掉白色外框
theme(axis.text=element_blank()) + ## 把图旁边的标签去掉
theme(axis.ticks=element_blank()) + ## 去掉左上角的坐标刻度线
theme(panel.border=element_blank()) + ## 去掉最外层的正方形边框
geom_text(aes(x = 3.5, y = ((ymin+ymax)/2),label = labs) ,size=3.6)
```
但是可以看到：由于缺乏饼图与数据标签之间的引导线，总感觉美观度不够，所以推荐使用graphics 包的pie()函数绘制饼图。
# 圆环图
## ggplot绘制圆环图
在刚才的gglpot绘制饼图的基础上，我们只要再加一条代码即可完成：xlim(c(0, 5))，即将x轴范围控制在0-5。
```{r}
df$fraction = df$value / sum(df$value)
df$ymax = cumsum(df$fraction)
df$ymin = c(0, head(df$ymax, n = -1))
ggplot(data = df, aes(fill = group, ymax = ymax, ymin = ymin, xmax = 4, xmin = 3)) +
geom_rect(show.legend = F,alpha=0.8) +
scale_fill_brewer(palette = 'Set3')+
coord_polar(theta = "y") +
labs(x = "", y = "", title = "",fill='地区') +
xlim(c(0, 5)) +
theme_light() +
theme(panel.grid=element_blank()) + ## 去掉白色外框
theme(axis.text=element_blank()) + ## 把图旁边的标签去掉
theme(axis.ticks=element_blank()) + ## 去掉左上角的坐标刻度线
theme(panel.border=element_blank()) + ## 去掉最外层的正方形边框
geom_text(aes(x = 3.5, y = ((ymin+ymax)/2),label = labs) ,size=3.6)
```
# 复合饼图系列
散点复合饼图（compound scatter and pie chart）可以展示三个数据变量的信息：(x, y, P)，其中x
和y 决定气泡在直角坐标系中的位置，P 表示饼图的数据信息，决定饼图中各个类别的占比情况，
如图(a)所示。
气泡复合饼图（compound bubble and pie chart）可以展示四个数据变量的信息：(x, y, z, P)，其中
x 和y 决定气泡在直角坐标系中的位置，z 决定气泡的大小，P 表示饼图的数据信息，决定饼图中各
个类别的占比情况，如图(b)所示。
```{r}
library(ggplot2)
library(scatterpie)
library(RColorBrewer)
crime <- read.csv("C:/Users/DELL/Desktop/我的书籍/R语言数据可视化之美/第7章 局部整体型图表/crimeRatesByState2005.tsv",header = TRUE, sep = "\t", stringsAsFactors = F)
radius <- sqrt(crime$population / pi)
Max_radius<-max(radius)
Bubble_Scale<-0.1
crime$radius <- Bubble_Scale * radius/Max_radius
mydata<-crime[,c(2,4,3,5:8)] #数据集构造
Col_Mean<-apply(mydata,2,mean)
Col_Sort<-sort(Col_Mean,index.return=TRUE,decreasing = TRUE)
mydata<-mydata[,Col_Sort$ix]
x<-(mydata$murder-min(mydata$murder))/(max(mydata$murder)-min(mydata$murder))+0.00001
y<-(mydata$Robbery-min(mydata$Robbery))/(max(mydata$Robbery)-min(mydata$Robbery))+0.00001
xlabel<-seq(0,10,2)
xbreak<-(xlabel-min(mydata$murder))/(max(mydata$murder)-min(mydata$murder))+0.00001
ylabel<-seq(0,260,50)
ybreak<-(ylabel-min(mydata$Robbery))/(max(mydata$Robbery)-min(mydata$Robbery))+0.00001
mydata2<-data.frame(x,y,radius=crime$radius)
mydata2<-cbind(mydata2,mydata)
Legnd_label<-colnames(mydata2)[4:10]
colnames(mydata2)[4:10]<-LETTERS[1:7]
```
## 散点复合饼图系列(a)
```{r}
ggplot() +
geom_scatterpie(aes(x=x, y=y,r=0.05), data=mydata2, cols=colnames(mydata2)[4:10],alpha=0.9,size=0.1) +
scale_fill_manual(values=colorRampPalette(brewer.pal(7, "Set2"))(7),labels=Legnd_label)+
#geom_scatterpie_legend(mydata2$radius, x=0.1, y=0.95, n=5,labeller=function(x) round((x* Max_radius/ Bubble_Scale)^2*pi))+
#geom_scatterpie_legend(mydata2$radius, x=0.009758116, y=0.090868067, n=4,labeller=function(x) round((x* Max_radius/ Bubble_Scale)^2*pi))+
scale_x_continuous(breaks=xbreak, labels=xlabel)+
scale_y_continuous(breaks=ybreak, labels=ylabel)+
xlab("murder")+
ylab("Robbery")+
coord_fixed()+
theme(
axis.title=element_text(size=15,face="plain",color="black"),
axis.text = element_text(size=13,face="plain",color="black"),
legend.title=element_text(size=15,face="plain",color="black"),
legend.text = element_text(size=14,face="plain",color="black")
)
```
## 散点复合饼图系列(b)
```{r}
ggplot() +
geom_scatterpie(aes(x=x, y=y,r=radius), data=mydata2, cols=colnames(mydata2)[4:10],alpha=0.9,size=0.25) +
scale_fill_manual(values=colorRampPalette(brewer.pal(7, "Set2"))(7),labels=Legnd_label)+
geom_scatterpie_legend(mydata2$radius, x=0.1, y=0.95, n=5,
labeller=function(x) round((x* Max_radius/ Bubble_Scale)^2*pi))+
#geom_scatterpie_legend(mydata2$radius, x=0.009758116, y=0.090868067, n=4,labeller=function(x) round((x* Max_radius/ Bubble_Scale)^2*pi))+
scale_x_continuous(breaks=xbreak, labels=xlabel)+
scale_y_continuous(breaks=ybreak, labels=ylabel)+
xlab("murder")+
ylab("Robbery")+
coord_fixed()+
theme(
axis.title=element_text(size=15,face="plain",color="black"),
axis.text = element_text(size=13,face="plain",color="black"),
legend.title=element_text(size=15,face="plain",color="black"),
legend.text = element_text(size=14,face="plain",color="black")
)
```
参考资料
https://zhuanlan.zhihu.com/p/69617844
================================================
FILE: 2020年/2020.07.29温大招生/code_ggimage.r
================================================
require(magick)
require(ggplot2)
require(ggplotify)
require(shadowtext)
require(ggimage)
windows()
x = image_read("C:/Users/DELL/Desktop/bing/3.jpg")
p = as.ggplot(x)
#图上嵌图
smu = "http://www.wzu.edu.cn/dfiles/9987/template/default/newzhuzhan/wenda/images/logo.jpg"
#smu = "C:/Users/DELL/Desktop/bing/2.jpg"
p <- p + geom_rect(xmin=.2, xmax=.8, ymin=.4,
ymax=.6, fill='steelblue', alpha=.5) +
geom_image(x=.5, y=.5, image=smu, size=.4)
#寄语
msg = "填志愿一定要遵从本心\n第一眼看到哪个，就报哪个！"
p + geom_shadowtext(
x=.5, y=.8, label=msg,
size=10, color='firebrick')
#参考https://mp.weixin.qq.com/s?__biz=MzI5NjUyNzkxMg==&mid=2247489599&idx=1&sn=e689331bc5500222ec7d0c1c61942362&chksm=ec43a978db34206ed932e7e086db00f707e5cb9f9aa4e38254e40c3ef642dee5693a03532994&mpshare=1&scene=1&srcid=07293Jcp2dA2gLL6If2GGh8s&sharer_sharetime=1596004011941&sharer_shareid=ee38888b33e1d0070e96aeb454518587&key=6614a0a10b7b6719e9a2b6aaf9b5a70639efbf3c7c8e8e92b20a32c4badbf01c713092a60b32600156388a8c91a282772e89bcfe2a7eb0236746b8e10cee021cae0a3722f18222f708988e462583107f&ascene=1&uin=OTk1MTUyNzI2&devicetype=Windows+10+x64&version=62090529&lang=zh_CN&exportkey=A%2FUKJTXVcz2E27Ve0FcZuIk%3D&pass_ticket=GHX0j6fsfiEATjqcMrcVQQYSihtF3L6yDim2tm78a1XP0v2qucpofrFRF8%2Bz4zjt
================================================
FILE: 2020年/2020.08.14amazon/RECOM.Rmd
================================================
---
title: "亚马逊产品的推荐算法"
author:
documentclass: ctexart
always_allow_html: true
output:
word_document: default
pdf_document: default
html_document: default
classoption: "hyperref,"
---
# 前言
R的recommenderlab包有许多关于推荐算法建立、处理及可视化的函数。上一次也利用这个包对Movielisence进行了分析，但是这个数据集来源于包本身。本文对于一个实际数据进行分析，该数据集来源于亚马逊网站，我们的目标是利用recommenderlab包构建相应的推荐系统，利用用户对产品的打分，做到给用户个性化推荐，包括
1. 构建多个不同方法的推荐系统，并进行比较，选取最优推荐系统。
2. 给出每个用户Top3的产品推荐。
3. 对于某个产品，预测出用户的评分情况。
# 数据处理与数据探索性分析
```{r message=FALSE, warning=FALSE}
library(recommenderlab)
library(reshape)
```
## 数据处理
选取有用数据，包括：用户名，产品名称,打分情况构建新的数据集。并删除含有缺失值的行，最后数据仅剩下34621行。
```{r message=FALSE, warning=FALSE}
data = read.csv("C:/Users/DELL/Desktop/2020.08.12亚马逊/data.csv",header = T)
data = data[,c(21,3,15)] #userid,product,rating
#删除na的行
data = na.omit(data)
dim(data)
names(data) = c('V1','V2','V3')
unique(data$V2)
```
## 数据探索性分析
利用`summary()`获取评分数据，可知最大值为5，最小值为1，平均值为4.58。并将其柱状图进行绘制，如下所示。
```{r message=FALSE, warning=FALSE}
summary(data[, 3])
barplot(prop.table(table(data[, 3])),col="skyblue",
main="各评分分数占比情况",xlab="rating",ylab="proportion")
length(unique(data[, 2]))
factor(unique(data[, 2]))
barplot(table(data[, 2]),col="skyblue",
main="",xlab="Product",ylab="Frequent")
names(table(data[, 2]))
fre = as.numeric(table(data[, 2]))
type = unique(data[, 2])
levels(type)[28]<-'B01E6AO69U'
fre = fre[fre!=0]
library(tidyverse)
product <- tibble(
type = factor(unique(data[, 2])),
freq = fre
)
p = ggplot(data = product, mapping = aes(
x = fct_reorder(type, desc(freq)),
y = freq ))
p + geom_col() +
coord_flip()
plot(as.numeric(table(data[, 2])),)
```
## 数据格式构造
构造新的数据类型`realRatingMatrix`，以便更好的分析。生成一个以v1为行，v2为列的矩阵，使用v3进行填充。最后生成26762 x 39稀疏矩阵。
```{r message=FALSE, warning=FALSE}
mydata <- cast(data,V1~V2,value="V3",fun.aggregate=mean)
#生成一个以v1为行，v2为列的矩阵，使用v3进行填充
mydata <- mydata[,-1] #第一列数字为序列，可以删除
class(mydata)
class(mydata)<-"data.frame" #只选取data.frame
mydata<-as.matrix(mydata)
mydata<-as(mydata,"realRatingMatrix")
mydata
```
# 模型评估与构建最优模型
- 对于realRatingMatrix有六种方法：IBCF(基于物品的推荐)、UBCF（基于用户的推荐）、SVD（矩阵因子化）、PCA（主成分分析）、 RANDOM（随机推荐）、POPULAR（基于流行度的推荐）。
## 模型评估
主要使用：`recommenderlab`包中自带的评估方案，对应的函数是`evaluationScheme`，能够设置采用`n-fold`交叉验证还是简单的`training/train`分开验证，本文采用后一种方法，即将数据集简单分为`training`和`test`，在`training`训练模型，然后在`test`上评估。接下来我们使用三种不同技术进行构建推荐系统，并利用评估方案比较三种技术的好坏。
- 在此我们比较三种方法的结果：IBCF(基于物品的推荐)，RANDOM（随机推荐）,POPULAR（基于流行度的推荐）。结果如下
```{r message=FALSE, warning=FALSE}
scheme <- evaluationScheme(mydata, method = "split",
train = 0.9, k = 1, given = 1, goodRating = 4)
algorithms <- list(popular = list(name = "POPULAR",
param = list(normalize = "Z-score")),random =
list(name = "RANDOM",param = list(normalize = "Z-score", method = "Cosine",nn = 25, minRating = 3)),
ibcf = list(name = "IBCF", param = list(normalize = "Z-score")))
results <- evaluate(scheme, algorithms, n = c(1, 3, 5, 10, 15, 20))
plot(results, annotate = 1:3, legend = "topleft") #ROC
plot(results, "prec/rec", annotate = 3)#precision-recall
```
- 按照评价方案建立推荐模型
```{r message=FALSE, warning=FALSE}
# 按照评价方案建立推荐模型
model.popular <- Recommender(getData(scheme, "train"), method = "POPULAR")
model.ibcf <- Recommender(getData(scheme, "train"), method = "IBCF")
model.random <- Recommender(getData(scheme, "train"), method = "RANDOM")
```
- 对推荐模型进行预测
```{r message=FALSE, warning=FALSE}
predict.popular <- predict(model.popular, getData(scheme, "known"),
type = "ratings")
predict.ibcf <- predict(model.ibcf, getData(scheme, "known"),
type = "ratings")
predict.random <- predict(model.random, getData(scheme, "known"),
type = "ratings")
```
- 做误差的计算
```{r message=FALSE, warning=FALSE}
predict.err <- rbind(calcPredictionAccuracy(predict.popular,
getData(scheme, "unknown")),calcPredictionAccuracy(predict.random,
getData(scheme,"unknown")),
calcPredictionAccuracy(predict.ibcf,getData(scheme, "unknown")))
rownames(predict.err) <- c("POPULAR", "RANDOM", "IBCF")
predict.err
```
通过结果我们可以看到：三种方法的比较**基于随机推荐系统**对于本案例数据的效果最好，RMSE，MSE，MAE都是三者中的最小值。其次是基于物品的推荐，最后是基于流行度过滤。
## 构建最优模型
利用以上结果，我们构建最优模型：**基于随机推荐系统**。首先先对系数矩阵的行列名进行定义。
```{r message=FALSE, warning=FALSE}
colnames(mydata)<-paste0("asins",1:dim(mydata)[2],sep="")
mydata.model <- Recommender(mydata[1:dim(mydata)[1]], method = "RANDOM")
```
数据处理完毕，接来下是进行预测，可以显示三个用户的Top3推荐列表.
### TopN推荐
给出users201，202，203每人前三个产品的推荐。
```{r message=FALSE, warning=FALSE}
##TopN推荐
mydata.predict1 <- predict(mydata.model,mydata[201:203], n = 3)
#n指数量
as(mydata.predict1,"list")
```
### 用户对产品的评分预测
给出前三个users对前6个产品的评分预测。
```{r message=FALSE, warning=FALSE}
mydata.predict2 <- predict(mydata.model, mydata[201:403], type = "ratings")
mydata.predict2
a = as(mydata.predict2, "matrix")[1:3, 1:6]
knitr::kable(a)
```
# 参考资料{-}
1. [基于协同过滤算法的电影推荐系统](https://blog.csdn.net/weixin_44035441/article/details/90728889)
2. [R语言：recommenderlab包的总结与应用案例](https://www.cnblogs.com/yjd_hycf_space/p/6702764.html)
3. [recommenderlab: A Framework for Developing and Testing Recommendation Algorithms](https://cran.r-project.org/web/packages/recommenderlab/vignettes/recommenderlab.pdf)
================================================
FILE: 2020年/2020.08.14amazon/code.Rmd
================================================
---
title: "亚马逊产品的推荐算法"
author:
documentclass: ctexart
always_allow_html: true
output:
word_document: default
pdf_document: default
html_document: default
classoption: "hyperref,"
---
# 前言
R的recommenderlab包有许多关于推荐算法建立、处理及可视化的函数。上一次也利用这个包对Movielisence进行了分析，但是这个数据集来源于包本身。本文对于一个实际数据进行分析，该数据集来源于亚马逊网站，我们的目标是利用recommenderlab包构建相应的推荐系统，利用用户对产品的打分，做到给用户个性化推荐，包括
1. 构建多个不同方法的推荐系统，并进行比较，选取最优推荐系统。
2. 给出每个用户Top3的产品推荐。
3. 对于某个产品，预测出用户的评分情况。
# 数据处理与数据探索性分析
```{r message=FALSE, warning=FALSE}
library(recommenderlab)
library(reshape)
```
## 数据处理
选取有用数据，包括：用户名，产品名称,打分情况构建新的数据集。并删除含有缺失值的行，最后数据仅剩下34621行。
```{r message=FALSE, warning=FALSE}
data = read.csv("C:/Users/DELL/Desktop/2020.08.12亚马逊/data.csv",header = T)
data = data[,c(21,3,15)] #userid,product,rating
#删除na的行
data = na.omit(data)
dim(data)
names(data) = c('V1','V2','V3')
unique(data$V2)
```
## 数据探索性分析
利用`summary()`获取评分数据，可知最大值为5，最小值为1，平均值为4.58。并将其柱状图进行绘制，如下所示。
```{r message=FALSE, warning=FALSE}
summary(data[, 3])
barplot(prop.table(table(data[, 3])),col="skyblue",
main="各评分分数占比情况",xlab="rating",ylab="proportion")
length(unique(data[, 2]))
unique(data[, 2])
barplot(table(data[, 2]),col="skyblue",
main="",xlab="Product",ylab="Frequent")
names(table(data[, 2]))
plot(as.numeric(table(data[, 2])),)
```
## 数据格式构造
构造新的数据类型`realRatingMatrix`，以便更好的分析。生成一个以v1为行，v2为列的矩阵，使用v3进行填充。最后生成26762 x 39稀疏矩阵。
```{r message=FALSE, warning=FALSE}
mydata <- cast(data,V1~V2,value="V3",fun.aggregate=mean)
#生成一个以v1为行，v2为列的矩阵，使用v3进行填充
mydata <- mydata[,-1] #第一列数字为序列，可以删除
class(mydata)
class(mydata)<-"data.frame" #只选取data.frame
mydata<-as.matrix(mydata)
mydata<-as(mydata,"realRatingMatrix")
mydata
```
# 模型评估与构建最优模型
- 对于realRatingMatrix有六种方法：IBCF(基于物品的推荐)、UBCF（基于用户的推荐）、SVD（矩阵因子化）、PCA（主成分分析）、 RANDOM（随机推荐）、POPULAR（基于流行度的推荐）。
## 模型评估
主要使用：`recommenderlab`包中自带的评估方案，对应的函数是`evaluationScheme`，能够设置采用`n-fold`交叉验证还是简单的`training/train`分开验证，本文采用后一种方法，即将数据集简单分为`training`和`test`，在`training`训练模型，然后在`test`上评估。接下来我们使用三种不同技术进行构建推荐系统，并利用评估方案比较三种技术的好坏。
- 在此我们比较三种方法的结果：IBCF(基于物品的推荐)，RANDOM（随机推荐）,POPULAR（基于流行度的推荐）。结果如下
```{r message=FALSE, warning=FALSE}
scheme <- evaluationScheme(mydata, method = "split",
train = 0.9, k = 1, given = 1, goodRating = 4)
algorithms <- list(popular = list(name = "POPULAR",
param = list(normalize = "Z-score")),random =
list(name = "RANDOM",param = list(normalize = "Z-score", method = "Cosine",nn = 25, minRating = 3)),
ibcf = list(name = "IBCF", param = list(normalize = "Z-score")))
results <- evaluate(scheme, algorithms, n = c(1, 3, 5, 10, 15, 20))
plot(results, annotate = 1:3, legend = "topleft") #ROC
plot(results, "prec/rec", annotate = 3)#precision-recall
```
- 按照评价方案建立推荐模型
```{r message=FALSE, warning=FALSE}
# 按照评价方案建立推荐模型
model.popular <- Recommender(getData(scheme, "train"), method = "POPULAR")
model.ibcf <- Recommender(getData(scheme, "train"), method = "IBCF")
model.random <- Recommender(getData(scheme, "train"), method = "RANDOM")
```
- 对推荐模型进行预测
```{r message=FALSE, warning=FALSE}
predict.popular <- predict(model.popular, getData(scheme, "known"),
type = "ratings")
predict.ibcf <- predict(model.ibcf, getData(scheme, "known"),
type = "ratings")
predict.random <- predict(model.random, getData(scheme, "known"),
type = "ratings")
```
- 做误差的计算
```{r message=FALSE, warning=FALSE}
predict.err <- rbind(calcPredictionAccuracy(predict.popular,
getData(scheme, "unknown")),calcPredictionAccuracy(predict.random,
getData(scheme,"unknown")),
calcPredictionAccuracy(predict.ibcf,getData(scheme, "unknown")))
rownames(predict.err) <- c("POPULAR", "RANDOM", "IBCF")
predict.err
```
通过结果我们可以看到：三种方法的比较**基于随机推荐系统**对于本案例数据的效果最好，RMSE，MSE，MAE都是三者中的最小值。其次是基于物品的推荐，最后是基于流行度过滤。
## 构建最优模型
利用以上结果，我们构建最优模型：**基于随机推荐系统**。首先先对系数矩阵的行列名进行定义。
```{r message=FALSE, warning=FALSE}
colnames(mydata)<-paste0("asins",1:dim(mydata)[2],sep="")
mydata.model <- Recommender(mydata[1:dim(mydata)[1]], method = "RANDOM")
```
数据处理完毕，接来下是进行预测，可以显示三个用户的Top3推荐列表.
### TopN推荐
给出users201，202，203每人前三个产品的推荐。
```{r message=FALSE, warning=FALSE}
##TopN推荐
mydata.predict1 <- predict(mydata.model,mydata[201:203], n = 3)
#n指数量
as(mydata.predict1,"list")
```
### 用户对产品的评分预测
给出前三个users对前6个产品的评分预测。
```{r message=FALSE, warning=FALSE}
mydata.predict2 <- predict(mydata.model, mydata[201:403], type = "ratings")
mydata.predict2
a = as(mydata.predict2, "matrix")[1:3, 1:6]
knitr::kable(a)
```
# 参考资料{-}
1. [基于协同过滤算法的电影推荐系统](https://blog.csdn.net/weixin_44035441/article/details/90728889)
2. [R语言：recommenderlab包的总结与应用案例](https://www.cnblogs.com/yjd_hycf_space/p/6702764.html)
3. [recommenderlab: A Framework for Developing and Testing Recommendation Algorithms](https://cran.r-project.org/web/packages/recommenderlab/vignettes/recommenderlab.pdf)
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================================================
FILE: 2020年/2020.08.15分面/facet.rmd
================================================
---
title: "分面|一页多图"
date: 2020-08-15
author:
- 庄闪闪
documentclass: ctexart
output:
rticles::ctex:
fig_caption: yes
number_sections: yes
toc: yes
classoption: "hyperref,"
---
# 前言
双变量数据可视化可能对于我们比较简单， 但是如果变量是三个或者更多，怎么在一幅图一起显示呢？今天我们就来讨论这个问题，解决方案有两种。
# 使用图形属性
**使用图形属性**，比如说：散点图点的形状/透明度/颜色用第三个属性表示。
```{r}
library(ggplot2)
head(mpg)
#散点图的点的形状表示第三个属性（离散）
ggplot(data=mpg)+
geom_point(mapping = aes(x=displ,y=cty,shape=as.factor(cyl)),size=2,color='skyblue')
#散点图点的透明度表示第三个属性
ggplot(data=mpg)+
geom_point(mapping = aes(x=displ,y=cty,alpha=cyl),size=5,color='purple')
```
`geom_point()`中可以改变的参数alpha，colour，fill，group，shape，size，stroke（边缘的厚度）。所以我们还可以通过其他参数来引进更多的属性，但是越多图就显得越复杂。看下面这个图，但是可读性不是很高。
```{r}
ggplot(data=mpg)+
geom_point(mapping = aes(x=displ,y=cty,shape=drv,color=fl,alpha=cyl), size=2)
```
# 分面
我们可以将图片按照第三个属性进行**分面**处理。ggplot2的分面有两种方式，分别使用 facet_wrap 或 facet_grid 函数。
## facet_wrap()
当想通过单个变量进行分面，则可以使用函数`facet_wrap()`其第一个参数是一个公式，创建公式的方式是在~符号后面加一个变量名，并且该变量应该是离散的。facet_wrap的参数如下
```{r eval=FALSE, include=TRUE}
facet_wrap(facets, nrow = NULL, ncol = NULL, scales = "fixed",
shrink = TRUE, as.table = TRUE, drop = TRUE)
```
facets：分面参数如 ~cut，表示用 cut 变量进行数据分类
nrow：绘制图形的行数
ncol：绘制图形的列数，一般nrow/ncol只设定一个即可
scales：坐标刻度的范围，可以设定四种类型。fixed
表示所有小图均使用统一坐标范围；free表示每个小图按照各自数据范围自由调整坐标刻度范围；free_x为自由调整x轴刻度范围；free_y为自由调整y轴刻度范围。
shrinks：也和坐标轴刻度有关，如果为TRUE（默认值）则按统计后的数据调整刻度范围，否则按统计前的数据设定坐标。
as.table：和小图排列顺序有关的选项。如果为TRUE（默认）则按表格方式排列，即最大值（指分组level值）排在表格最后即右下角，否则排在左上角。
drop：是否丢弃没有数据的分组，如果为TRUE（默认），则空数据组不绘图。
strip.position： 条子位置，默认为"top"，可改为bottom", "left", "right"
具体例子如下：
x轴是displ，y轴是hwy，用class（离散，7个分类）进行分面。
```{r}
ggplot(data=mpg)+
geom_point(mapping = aes(x=displ,y=hwy))+
facet_wrap(~class,nrow = 2)
```
## facet_grid()
如果想通过两个变量对图进行分面，则使用`facet_grid()`。这个函数第一个参数也是公式，但该公式包含由~隔开的两个变量。
```{r eval=FALSE, include=TRUE}
facet_grid(facets, margins = FALSE, scales = "fixed", space = "fixed", shrink = TRUE,
labeller = "label_value", as.table = TRUE, drop = TRUE)
```
和facet_wrap比较，除不用设置ncol和nrow外（facets公式已经包含）外还有几个参数不同：
margins：这不是设定图形边界的参数。它是指用于分面的包含每个变量元素所有数据的数据组。很好用的参数！
具体例子如下：用drv与cyl变量进行分面，x轴方向是cyl，y轴方向是drv的值。注意的是俩都是分类型变量。
```{r}
ggplot(data=mpg)+
geom_point(mapping = aes(x=displ,y=hwy))+
facet_grid(drv~cyl)
```
# 思考及拓展
1. 如果使用连续变量进行分面，得到的图会非常的多，每个数值分一次面，可读性很差，不建议使用该方法。
2. 使用`facet_grid(drv~cyl)`生成的图中，空白单元的意义说明drv与cyl在该单元没有关系。以下代码可以看出两者之间的关系。
```{r}
ggplot(data=mpg)+
geom_point(mapping = aes(drv,cyl))
```
3. facet_grid()可以转换为facet_wrap图，只需改为facet_grid(drv~.)或facet_grid(.~cyl)。
```{r}
ggplot(data=mpg)+
geom_point(mapping = aes(x=displ,y=hwy))+
facet_grid(drv~.)
ggplot(data=mpg)+
geom_point(mapping = aes(x=displ,y=hwy))+
facet_grid(.~cyl)
```
4.要在每个面板中重复相同的数据，只需构造一个不包含faceting变量的数据框架。
```{r}
ggplot(mpg, aes(displ, hwy)) +
geom_point(data = transform(mpg, class = NULL), colour = "grey85") +
geom_point(color='purple') +
facet_wrap(~class)
```
5. 去除条子框以及改变条子位置
加入参数：strip.position = "top"（默认），可改为其他（见上面参数详解）并加入theme将strip.placement="outside"就可以去除条子的框了
```{r}
ggplot(economics_long, aes(date, value)) +
geom_line() +
facet_wrap(vars(variable), scales = "free_y", nrow = 2, strip.position = "top") +
theme(strip.background = element_blank(), strip.placement = "outside")
```
# 参考资料{-}
[ggplot2作图详解4：分面（faceting）](https://blog.csdn.net/u014801157/article/details/24372507)
[R数据科学](R数据科学.pdf)
================================================
FILE: 2020年/2020.08.23好玩的图/fun.r
================================================
# 有趣的图=====
# 1.画爱心蛋糕======
require(grid)
require(gridExtra)
require(yyplot)
library(devtools)
#install_github("GuangchuangYu/yyplot")
require(ggplot2)
t <- seq(0,2*pi, by=0.2)
x <- 16*sin(t)^3
y <- 13*cos(t) - 5*cos(2*t) - 2*cos(3*t) - cos(4*t)
d <- data.frame(x=x, y=y)
ggplot(d, aes(x, y)) + geom_cake(size=0.1)
# 2.构建自己的二维码，只要输入链接即可==========
require(ggplot2)
require(ggimage)
require(yyplot)
ggqrcode("https://mp.weixin.qq.com/mp/profile_ext?action=home&__biz=MzI1NjUwMjQxMQ==&scene=124#wechat_redirect") #自己公众号
#表白用=======
ggqrcode('I miss you!')
d <- data.frame(x=1, y=1,
img="C:/Users/DELL/Desktop/avatar.jpg")
p <- ggplot(d, aes(x,y)) + geom_image(aes(image=img), size=Inf) + theme_void()
p2 <- ggqrcode("http://mp.weixin.qq.com/s/oLgpTGdQgcka-OD757_3lA", "blue", alpha=.8)
p + geom_subview(p2, width=Inf, height=Inf, x=1, y=1)
pg <- ggqrcode("https://mp.weixin.qq.com/s/tposUJYrPuRnptXyNX03mA")
d <- data.frame(x=15, y=15,
img="C:/Users/DELL/Desktop/avatar.jpg")
pg + geom_image(aes(x,y, image=img), data=d, size=.2)
geom_emoji("heart_eyes" )
ggplot() + geom_emoji("duck") + theme_void()
require(ggplot2)
require(magick)
require(purrr)
x <- search_emoji("heart")
plot_heart=function(x) {
p = ggplot() + geom_emoji(x)
o = paste0(x, ".png")
ggsave(o, p, width=5, height=5)
o
}
x %>% map(plot_heart) %>%
map(image_read) %>%
image_join() %>%
image_animate(fps=1) %>%
image_write("heart.gif")
================================================
FILE: 2020年/2020.08.23好玩的图/readme.txt
================================================
================================================
FILE: 2020年/2020.08.25马赛克/马赛克.html
================================================
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o/jNPY4W82Os88wiJ34K4tdAIQjAOQkx8YArcM2PaAOjSZBL8uolzAJFFvGDXd8ej67P2AvKpUkOYghcnK7zl300RBcsExwzJ/hbrd7GuYBwhgAIYtbTx/3+d4klJ3gtKCQnGIz9InYZEzqG8EkjSzNavCB/cXYlcQshhyMsZrI6PYLWc3lOG/vlA4rHr/3uTFD3r38/r+3fMKOke9W4oJ9G566u7au84CpOz/ct5R99wF7W6dIYjjnawrHIAh3hlungFOWgXoyzVKbHOr1eD19Il6vISsrrU8kSzbY+0QMGpdjgYh60zDTHJKHoyP4404pw27zB4o1o62gq+BLL299am8j+zv774zj995/dgTOZsOfWr3rnTWPj2h8qGbo1/M//kYYvmxfms7TtPrM54E7ns4vwBw0rFy/aNJjRRVTet31OgCBPABhongUDOCAzuE0h6gnxChToCJ1ulB0iH0jeqvscFBZotflk+hMQ5oJDqhrC/l//FxmAUlGYeK5Z6Jl5MDec2yJQdc+l5ViNduL1avoZ805eGll04jy6COKheT8S+U6kQwdw+lW6nPpXF4qtEoBziwAye3mMnRLkqlPRLqZdQlsKxTcLghkqhzjrLL5M+WgUwldSkjbL1HPLrCf51d8MHbv66zu/mcGl5Kz0YNZ0+mcf759kbEB29qGGrZiYWop2b2R9fYqnKnlWOVzqXqgNfQIB5LtRr8fQLLT7CyT0ZLaL2K0WFzU5e0TcfmojkckcgvcyhJ4pNlr8Bd63VyEhIbiGhfIBFGTq8R9lqcWB2Dl1G79Rn/9i8n08OU3L/760UX2E369YuvqVUPrI9VryFR8CXc5V/rYefbW7svv/YNdxUHv/OnFVQ1V8yse2Dde0UcAIY/zU4L0sA1FEQg3jJT0jVAJFBlqbOOrALk1dCOmkuHNF+mpaKOYunHhldNAlZhEyFGpz4R20C+c47Vmu+6gqXo9lewuq5TfXrLnZORk9Ink5JjAlNwvYvJBoF8E5N8qd9nN3jrmj7mOx8OPLDXqolpgwv0zZkpuzaeTynf+vWjNvnr22b+bsfDJR7+e+cL6dQ1bXlu3CDvOWfHIMytnrhJPHt7x4L7eg/48+8C5U0euLuu/f8ozr1xteHTRssdGru8V3kwfeHTMsN937/zksLEzFdlO5NQpNsMLWdAtnJlizzQYAAQu26AljUvWZbEQlyuJi1Ymcr8Iaal2jjKNg5qJ9Ctqx02jMyDFKHJw8TpUIvjHKhXZQlZ0/Iwe1eO++6/RVHpg2mv/uPbBuguPMtfKLU+tuXfjkIFraEVzg2tlMuZg6O57/vXBP1C3kZ3H9od2PPV81RMVE/aNAy3HEcaokRS34Ta+LAA8XotzQMRiizkRDVfN87X0JXae6NzkVR6Znehb6J8XL+Y3IKovXMjn0oEDMrkmmc2iXu9yGm0DIkab6hgTZklwj/T6FDccpXsmn6Rjlxv+knyrTFMR8+U/cF9+DiRwh/UCiChwdeXD58cDhSwsRjeikNNcTo83/0AtP2DDKLywji1nhxSezMTjgo9eVHOy3LBbJgIQ0OsEsToiIFRHrIjI4wHOlfxEz6a4ZOTXTLq9eTjdTofW1bEH6up+g5GIBDhGEr2BkRNVlMZTa/P3HKVyrMMKrF3H/KPYUAWjlGsXaRnXrxTIhrJwqp/bMtnphFYWIdgGoLWtddqASGuPzdA7YhNaqFZLvVJSEa48LZwUd4YSN4mJ+aq/ctSSXgtmD6gf2emV91/9KNj38bHd9l3PX0tq19dMnzFw3OSsgsWjj+zqPXn0w4On3e9nZ+NJLYFZ1yqkQ2ITFEM5zzwyA+1KLJ1kVwpAjsvSTgx3S+rQQeiisxv5Ky+9kGbnqUmllmSFEhOP6/G4ug6C2nJQUPdSt0td36R1IFMgbsUalrqlQAbw4KK1v1BwIH/udKqm8NCQbeMHP2LUtVk3rv7Fb4712N3Tt/DeaWvZt3+8wA7swe6Y/5cvjv3I1rHJn+AyhLM44ODVn14/7bBUDpq/hpxb8c388XfdM+rU3veu+Tws17Pv7O79aFvzMnvxc3aaHRq8sAZX4jgUsP7CfvYntoNhGYquJiAAAKJNPAIyWLjk0ojFqENR0SwqyILNaiG9I0bRYhFECoKD518xh6iplZYz+5W8H0OIlBsz/tURB6IHmnaT7itJORvb6A94cnbjGZYvHrnSg0zENwfPGTGddQIKJwCEo9xyW8ALGdA7nO0UUg1Wn89iEGQLjwd01iRrUlXEarWAxVcVsTjAWxUBevt4QnM9/gxBMbluwe4SAjxpj/mcgN0ef3cCt2IAhVVLsR/7+TIjjZjU9PTeY1ew4I9/Ovhn8cCeI/Nf9BnK2Pk3/kZ7TF00+6HoquhndauXPAGAMIdb09Oqr8gOu6jFpbdQb5IDekccglHi/HK2DL+4emRymUNIE3+Ro3WokKfbtNP37Cs0/7rxjQ0X2Cvs2Rex/NNLuysbxBB7lX3FPmdvl64rwyU44QusOVSzuj8AUTgmDuEc04FdsYcWQQ8COJyiuSoiUsFSFREct4ppwc9rSBlA+ZuAPZTBx2Az2Uo2CY/hIHysic/1z59PI/dU5CtWz+aJB9gi9gKmYebVKZgHgMq89Bc+r1GJWSSDAQXQoWAyS/reEUlCQsTeEUKRr3B03DZmUZBwxy/6S/MZmh+dTYZHt5OF4oH1LKc+eilhJj0UhpMlAKQ6pAbjTRPxSW45Q0CbAac3asPzwaNfrY9LTuyi2ilOhUvnI8SSohNapUJK7wiAaDLZe0dMgujtHRGdt4+8/HaphRyV9+rq5lT1xe9nfPc0a2IrDuKQL//9bve3DrL/so/Qj0kbVrGXCYuWZWXjUhzzD7xn/+D6GvYau8Q+Ze8H8LUY7WK6yuVQ2KdHBJ0giCCaTTraO6LTiQaJoshJV81RgnG/Qbydi5f/DYnpjc2ssZGSRrI3Ws1z7dXkYQC8NoLNxfFqVpwaNht1OotVT4GzFDJj9GrpGI15+JJiPpxLMg0v6dVv9AONx9jclFWuR6fyFGvI0TNxvRC+UjHmnkjBViRGg4Ix0Yn6RGzLWkgJZRVRDKHw1TvRrzc2NpL1J6JN5M0l0dc5snnk4+jCBF0QIT1soQCCJCMFzgtw3EBXxTekkO0+0aio0pV/bIp9V+KIgpPrUZJOFCUev/JSmsuNBjuVjDK1gKQgp2DnLbuZlRjwuJUAn2MY4nce4COtZjadZSsCntbhh6zRomMm0bbpo+bh4oGrVQLPOume7Uev/BCXo1IDsUG7sFsvcaytVpDB7jBS2aqjKCdypaUI4xPzabNJKZdj+WvNn+tsW4/RVB2xkGeEk582NR/nE3ZMwaxy2guAqFp99FZ5bu+IXqDW3hHqvLVNiOltBiTmueJRtpW9oZgjHIE9sBOOujo9+v1/fvn5h/9Eeb77LHuYa+94HIt1bArbxs6yU1iIuRjEAnYqZp+E8erqdUBRONnA+c75DE6XQaiKGAySLDuqIjKVEtavhpXmSgW/mlplYChutYXx7Ay7tLsRZ5PWUePGL949euKoYPr7t1HOh2jK6mdXrVC5wHaoXLBCCp+Zp8MeAIEa+OqmZtns6x0xC7KTL2yZM+MtlRs3J6I2pViG8q258sX7OOxndrH0tpz5ki3rzuqxivyf/DnN+WMCN1SGs8yIxKS3y0aDQdYTwePVm8EMVRGzmVDK5UepkSi6cntnp2Ku8ktw20SOf5bGNm4BcRXyGdhfcfkJ9jQ7/VXTzl2vfEZGRLeJB94/zf4+LjqZjFi9cuWqJwDVHIFw29ha4V6a0wSQ5BSFrGxTGvV4uH30CFSfoEoJiY4mt0CGlozy8D+o5jgx+6jmBbwy4BEI+9d3rHnZ0I/GN+7usnL1ey+xM389WLx/1+INHRbWXfoDLjz+6Z07su+YN73vyIFFvd959sV3qtf2nfFA35F3FQw8AoDgABCGcv7JvJ7iABSRUp1epgK3CYLmFeJ5qGYSi7k3IEsbWYFQyQrE9PWqJzjM14yPj2OHrLDdhgYZZafDrqOCmQ8UpzGUuFzsLkUnVHMYs4uij/2F/cJfFxrfee3ld8QDzf2vsC8wo5nuaa44+Mabh+ghQAAA4XW1/pMcNqJgMuooCJQqiPLlrxWvQhjgF8//SgXTwej3O6M/NmF1x8zWHdVaFh/5uU3bnwXkmg1yXz6aT6km+QwpyW6LRdQn2Q0U9TGTotqUGOKqNclWAjJldKcyenwSZ0h8cyc75y5CT3v2xU42u+nL9p6UYpSa0Nne7yy+1EQ/7PaW6/dbm0N88llHNx18ic5qnrv59RXv0YUK93QAQr1q9QNhhyCJ3ORLiskXFJMvtDT5KhocAz63Yu7rj/PIY0oTXmKdjuAkfHg/60QWROeQZnI4+gq5M9oX4lybrUY5GWGrIBJRpnoDiChTUeOcJmE+qKL+GCJdcNEhlrSb+Q6T8+R887zoCZJPFyv1ZQBBscZ6pWKmQyqDLKBgMIoCNwcUdUrMcuuKmVot8AvlzU6qi9roq82/0LSFwoaNC69OAIQGdoRMVnSRY2mRUFAYoxcJlTDIOdBSfeJRD5nMSvEEu4B+dkS6svyKX6HWC0A+i1c2Kd5c2XRy3h0mgYbo/4spg/KNEDuCzdrMFFACSacHOUgFevPMXj5rMb9CfMoLfOrSA+KF5b9KyigFJCgExOMgQVJYD1TWiQQEwrO+G5rpVFUTC3DfaPxsA1vG9pEg3dQ8jnwV9QJea2Zv0k3XKtUKsJLHIlEqwBgjmU/LQUfRp9mbCwCxTjhHHZIf9OA8AILRID2BkJ+s1ZoxwDW1OMStBHU83G1fm5MZ0+4QzhUdK3f33F8MRKk50lPCUEXzoVc4K1NnTEvz+Rw6yqMpYkzrFSFGI7jd1ooIt4LJFRHRA24o/98LVH4tX7NllapJZ7zS6LZn8QVeLKsVKjrQrxv43GPPvUychyc/VveH0F3HR77xCrNs/mPDWy89tOWB3js3Y1+b1GPe7Jq5dxTuORZ11TZuHC3LD00fOhwI7OVWtVZygRPSeVUt0+D1Wq2mVGqiGX4zmNwOu8HOhccRljzgqoiArYV5DSXF1SDB1sddEk825YBijeRQiVcrvHAqyJ5Pv/3+k0l/7GwKzGzQ6Wa811i/qXFjfb0wlJ1jP/DXxwMGLpdcbNHcsTuWvv7ll29fOPPJXwAQpnMOLxWGxbIaK6VuPU3ySmaOmQ0cHDPPzVmNGM9qlJ1DHgNzu6hmOGTcZXYV9f8d8HTbUOn8QrbvuW11Tz3swiw0oRPvyPQu96Sywe9+2mlNGRBlVqGU88fB+dM97E+VvGCx2CV7ht/htgIgmqhez9mjt1FnRYR6bscerSYTkLTqvTcUDPLPA6osi+JOiG7ST//n2W+/++TCTLMsNCxmTzdu3Ny4evOmNS9gNlr5647tA/rh0V+/mfny+4Gv3r54+i+fxLF0cN44IRk6hdOTDF4jpdzqtkrxGit4uRskyaUyyqIw6paZQyiRZQ632++JsUuivNbh53Kb+x/2JYp/e/+7qFl8eecf/zBk65bfb7WQLstc2AZl1GMH9v3fJxx/p2pttp/+c/eGrS8oUksFoBYpHVxK3cVlMjkJ4UaSuj0GvhQMgKIsVkScspUqq0GtY98IAxWmOZS1p2QNgeJSXkPW3DX3mE+zrxreeANH3lObN6LH8KHopW83l9G3+3TugmsDC9PnPNkLgEKQuYQCzplcKIVu8HC4a56vQ5YpvYtY4ESnSHIzW6Vn+Qzd72xlLbYWV0R0nXpFDJm6XKvOqvPk5pJekVxrm/JekTY2T7teEU9KnHUa+zj/8pXd+rzbxD1uragaVBdAqDC+jaAUkrJv/OXKcGMXmJOnbhQXF/F3QsHJVnf87VhB3sSqoa/te5X9jf3r7FdPzMgtC/ccNOnTtwb3ZPb6ZWdOPLzh7amPD50/4z8/1T4uVE5ICkzt9ewxXYdBbfPqVx54ddvqMauTndXFnYfmBnY+2PS66ypEhs2ZFOn5IO08/ZFvfn4cEPYCCD24nnuUzM5i0nFz7dF7vEkWvcMhVEQcNgOA3q0Y7xjlCatesVT2mALbtRUfM1P06cfm/+GZhgadoWD/jBMnyJuLfn/kk+jrfHXnDOow4N5XP4gWAxDYDoDjxAtAwcr9tZ3PJCDa7Ga5MmImVlQ04/3EwqZSIqAJJVQc3NDQ1CG3TceObXI7CJWYU1Zc0qFDaSkAubaKudSxTZAEd4Q9TqPRrNP5kj22yognrLcC1z6ISzW5xSTOhATTljhb3v2det7Zv/eNGZnLt9g16B6h+aqNHZHv0yaP8TSV89QGJTzetxgMRqNOEkSdYHeYAGw2nY7KRje1xiKGfD5zeUyFyuJsRTUiQi0bdclYkzcER73JeuD5E2zOnB07dKSgy2icydpGlxLpQTZOcjW/XTo9NjcO5nNT4GQCoiASQHfca2tMVBjHYVRo6SRfJQGoCAfcdruDiz+gdwRo66xWHrfb4RPMPm5p0302p1UPDkUPuCLEt534Igi1bHVIVIgEzfAqepHh1bRDypryyOa1DVNmblnVsDhFl79rIuIAXcHhmYdfJicWLNj3cnSLcv/zx9HjQmV99dDDg8e8+heuMZq2cnxdUBBOApeiri69x23S22xcWW02g/V2ytpSV72Jmrp7m4JG6NDUt95RNPXwJ+q8d0XUSWM2dhSfU9EknsU6wSyDnOwzeLgds1GbYvxvmcVylSHFilGFxE4PYRT74fKaf/wOTZcvobX5lZ3PPffii88/10Cy2I/swyeR/AFNmMfeZ1f/8rfzH545p1j5vdyW1apU+6E8nOEzCrKsS3foHJkBwQhWq7siYrXprboUaHXDzMdZ0GLBqpaeO2hPAhMUr62Y+gRHrThpU8Niry7c+PBf/+f7yzvryabGFc8+6xowcMRg1kUqqh9azT5h/1GcNr14+GTWl29fevfUeYVXHNNSlVexqMKW6qHJyT6bL8OfnOK1pqalecxOp8wtv80MFRHz/+Y2VT5yJ1l63Ul6r3vQ0njtQyL9GzaIW15cvXnjnI8uf/fJ57P0SQsajObpM/d9mHXp3YunT59birloRDO2a6z/9T38eEzFCzE9okGOpw1ywy6zXm8wEF4DsZrB4FYtg03rc2nRkaE5IY15ZEfvjt4eRQtfaahz6rrsFoaZNlk/fTbaJFSenDQjlrnS6XyW1twOtIplrqLzeuZaEfHYJKq/rj/5t8pdueG5kbsG25Hfpq50+j/e/+tjA/bXzF82+dmN88r/evSPL3Z6ftEjj7Yds+J13jSzsaHnpjbt7h4Uvrdr2aAH+yzaXLm4R1W3O7p2KO71FCCkX/uG7BQrwKPWJlwu3jPioEKS1+C0OXtFLGGbVeaCkj1xU3kqIVjV5ONWqo52xVGXhtxKNuHyEMcdA5NSJuSy17ZurRiBXdlrw2vN8lyzHQeQZdU9/83mRWePngiAsIOvrjKhElx8fh86ZZPJ4DS4PSaz2aZzWdVV7TFqEbMS/4daVmW0rJcrhBY127EvX9TPNNQl6UP7Z7zztlAZLeMO6GMSvnpozV2Dj54hp7RcjgiVau+HAQ0ms6hHK6jhiJZl+NX0NFTicIYQt7ER+76ptuiMte/tYyP4oI/8o0cx9iPtrx6K5UpSgI/Winsblz4lNc3rsZipYBZ0yQ7ubnTuxCyYK7c2A1U2Z2Rlk8LhUHSq1BmbsoRPKeSfcBbp2qSdPsY+3jNxsk5nLHCcaHqjg0snBF7dzc6QBZ3OvHR/dK5QyUaz6j5l+4tJbXTp7trW9eRvHClACAIIOpXGzLBdFiVAUWlxQZ3RLaD1pnQ4ngmjmhUfYgteQT9m/JktwFVH2Cn27hFSQLxsGO6IfhU9jUdYD0AgfL1LfHw3z/sVMqnHK5jB7OBLO0UHfIJCVam1GRJo46KKOdrSUrLvuwFOnfnuS/tYTsWfl/StKu2xq3cXzuCVn9wf+pn87mrGy5vtC03HtkAsZ6YPCZW3yJl7RUQr6npF0P2/5cz0oeZ/ksHR0+TL6D5y31Q6eN685sPxrixetlPl5/YlJxu9AFbZRbmnpqlpTq09K3F7TdV/bpXcPJZTfEtxCddDvj7d3EK4ZLfHjedrpx794PFH58/49MClCxdM44aRZaRxE+aPjywnw0Zg4ebdS6Xj7NzZoCl4FhAvMxuZrfluorSo0RSABN+tlHzx8nKeJv3cDAiV7Ijaw5Oq4OwWDQ4H8UFqqsXiE2laujso0QScEzYFFXSDxYr7U7DPVNCV5Dj2pcRw4eKhDx+Z/9jjp45OnvHwVFIePIvB49LSPRvZ+yPvJcsjvOq5cRenZNg4zJn2qEvdpyXVQg6tAS/XAzu1JvkcpuoIdVglCaojEuTngS3pjfw38rSkOlOZT8nQVNOmbD9lKoU5HFg8t2TMUz2mRrqPyi95omTcisrHK/sMJSfuLFn/UKvsVinhsvqH/RkZSeoOPFuKdcJwrcuYCALV8343AGpSu4xtNPOWXcZcCQNO1/Xt0PNKk/Gszp3Ly0IVZPfVC2Lfxb3C5ZVhQDjK7fd5dVemazjNozNTahCARxo62irVJxKnwUz4SzDKgg+07k9ljt9sw2apra1KOJCldLR6NAOuqD89OWHNwpPHcdniPisKChY+tHv7My8sX/FdifTO+xlov4LNXXfvoH7vstCH5z462QkQypUYSDzBpV4Zzk5y6s3mZI+dGD1OMS3dlORL6h/R+3xOcNr6RpxJIPa5uRWkRdPQzZ6Nm29lf5Lfinl2ypuduEqQxqONXTatnD0HG9jQblU05erVU2+99f/EEzUL+/1uGTs397MxS+7YtDz/xwtzsfO+U4psZqMkeIVtnHNByAibW0GmBSxtctLd7iwZeNSYn1gJchaVBku9il8r9co82Ja9clCxDnKwNLs0IXQ6VLV4+OLx8+eOq7t/UVXVgmF14+YuGrN42MKqeVtnzHh627QZW8mHj01aNmxh794Lhz059ZEFD/CHvfj7JZN+N2XbM1Onbd8BiscDEJT9Fw8MDrdzWGSj0WYS9URPTS6LW/YmGSwW2So5HBScbqsz3UmsTqvThG7JlATlWg+33RHrzL7lpjuGUOGj1uaovjBEKnH2HjYCJfY6dmGv72BvYGd+ARu7j1wgZ5vZ3Ma57Ec08RslQBKsgaxUVYkkUR726QUqUDlmFjgmiYqtbgjFLYRiI5p/YebmnxVpXPuF1kupUABdeGdcdiE4pdy0Dj5fmkmCgNS13E07lbRqK/n1/mCviN+tt/WK6OGGznh/s4t9I39VVFmLztSUlwuwZdCiRC2l/Kk33lG0dHD/qprTbw5/ZmTxqMV9Z8yYvelw/cCqjf/+6K9P9H9t4KLl7R+cvmJR99W/f6Ggbs3LPQbRnMF1WW0mD5q1NDW4IJjSKdy5prTH+klDl+fctXrZxm5rs9r27dWuY8e8oqHTRvWb0MVZPfnuKWXOMUCwWLTQ8eKH6u5TWpiTanKAI8lnpW495N90QCAhzctKeI/FxVnZpaXZWcU4pzgrq7Q0K6tYnFrUrl1RYUFBYfwOQGEM7xzvEdt5hxKeSwWDXmrNT0936a1esbSDZAKH1ZRuIuCwOYjJYXKk5AWcoRQByhNPBdhblgFRMxHuG90bnN2obu8KDjc3eYHM1py5DiFU2NqhNXTQOXMWz10weE77sRWvffDZq0880vHB5vXv4PB3les1tv2D02z76xP2YNvdezD3pT3s7N497JOXhMCeTTu3t/2dq9X3n575qfMjIXZI/Q7b/u6brOGD0zj0rT+wD/+wB3P2xr8GQKCCushU8W1OdzqUhlt5pRQDokeJazP8rQwGh88D1EYJNTvSOakf3feGku9qVGpqG4xTV8ojfbXWGSt18iYUtdZJXEnDlt0/edPztWvHjM+btnB+HauecmLUlAeov2bk6HHjJkhCcGFoRIcJs1jnI2OaCgRBqd8NhFraSI+CBGbICTupxI21YNTrBbMkWKwmUYegHGS5WbPRiyhjVuw2EAfPVEriM1kjLsUhtexzTK9lO0kQ1/dk29mzvXB9yo23qh9EHfeDXhAhJWwiKKAki0J1RCSQr20nattixUJOXfM71Bv9Hhc+CdeuaV3LRAIbAAjXdUoX16r7wqGgF3iOLui5Zpn1JodXKu1gsnFoi9Pi0DmtjnQHAR63E4fT4bythikCCP22ZKVVoUS+hp0Bqm51Fnr+L2UjHz5YPXLwfRNx36B+l3eeXrwWxYbNVy/8n+pGrtwd7tNtSfXsNFaLo9jTdPZ89ub/pXB47YrkEiRpzW3r+oJ09UfBJLnmAoG5dBi5LJ5U83Z/2GIGp7L7nGwzHPNQhS3J7yWaAKe27LkytvA6c/fPn39g4Oqa+fun195VPX3qwLunC2vmH9i/oGZlTdOCgdOm3l0zdZoiv/GASic8yQYLAMhwBiA6Q93NqCLLub9OUmpcstOLaHGCwAsItnQvZqjyadHEUVx6cz+0JMt+sjy645vIQH91edGont0XbPj9msiaPXiIVI2/NHhk35IePbMLh0yeP6V6/ZPPA4KflKlzBqAsnGkVRaCONIPUOstxn/MhJ+nrRKMzxUmcTl2yP92s88eVhKvIfTe2KDHRmKtlyd/2PpPpA3vsPbRzw4w1sz/8snbmA6Or7+w+pUPP8mXDl2wVvqx+wJu//YmVHWb32L5q0oAeXXrkBYa2LZl5056LnkfvwhP6xD0X5YAIN3pyAOvaT85494494cnCD133dnN3O1oEqNZDegiV4IHicLJoMOhs4HS6dC6+LeC2ulLMRKks6LWkMWHX6XqfaELKyMnTOhsGs13PNCxJNkz+Z/0Qg6GhAeewK698pKaNLwyr2caOScrsU1mzMEJygRWCYYcgIoBopDa7TidSq4jaQa/8RJkG7MortqVTEvILI6Z9PL1rzacn//ov0pY1S3t/raYhx5WrKDBA2ED6Yh0dqvitsEECMJuofkCEQsyAJOqq2jzatUOseZR82L1nz+7xMwlZzIVNAOBQIge7xQhgUfrILXa7jtog/71CzQq3qDNoZYbSkOzBpo31obZtOw24a8BDQx4ubWIXRk7UT9S1Kckrtu+bHgSEvqQKP1d3kPleHwFKDSZuX2mGBGlK3sc5EGO7FpnEzw8MXLlQ8pQsvpNv4K4ld9471NP2/hFAoDt1kaPi26q3zgo7lONnEnBvHfMfbr3iP964r4XTTjgzJSYsWHJ0V/3qF3eu3/B8lN07fsKwYRMeGCZM3nHw8LPP7T+w/TH+b/YjjwCBau4hdsY9BF+ZRr1AgMrEoJdu5R/4fBhELEUxdqM72c5aTGef1+IQVnvjPTGxCb3wfhzek01IufGW24c+AOIZzq8gnCYLACAbHrsGKMNHNDV6EPR/osTBA8ziYuCw7Tjs+ThseQz2CwV2Ou3PYeV9xMZBVchkAMkvnuAQM34FFf4CxEZ9KD5qXmxUIBBiM2mNMBxSoY3Sba1zpQWwlbVVwCXk5EIqmmhqKj93lzEgkm2zG3tH7IEWecP9w+9rGZ4ohslCYnXDUm9MGF2J0ihbnJBfkf59Rs7q4vv9Y9X1ozq9+dbRTwPhSMnYbk2zOnXtXqqkXKHH1tZM7NOvw5ip2e0XjzjcWDEhMjB/yIz70jFvcU/eGRvmVKrdoPJ0bltbq9R1v/YaDgTdn4hNzIa84ltA1MLCGETS7SCOQSAGkdoSIv86xGsg3HKMrOsQE6CUQxiaKGmtgtyAkWIwIMNxKIN5QK4xAIk3MIIVnNA/fAdPM+wIOhPaRNEtuvROycm7kHm7iMHM7wabASUqOtByowkglmHm5an5G8bOiYau9y/SAF7vYVQ2zqR5UUeUXdxLDtMT0SMkNXqR9Lhag0cfURpetbZG/AvZr2jRHOZSOkc5ztkqzrMIAf55rM9N5VmbON8PqhxBs8aRmyFqoTwG4b4dxLFrV2MQyS0hsq5DTACHylWC/hhXgUA+gFip9id54Z5wod3t1glmAKcgCUk+rogS11erXC6/JJ+WL8jcIsuyoNfbqiJ6Kri17tNEXW55EDWhHZV7uVhLarxnM5QhVqpNqbM3bcJ9eBf+bn/07S9xNlt4lIyKtaWSunqyntWxHSQcba5nhhhNYrmqS+3jurSmJdWx7jiVLwUx3sKsmLb5bgdRi4YYhP92EMegKQaR3RIiX4PgeGy65RhZ1yEmwMdxnW4b5z7CQrQJJmEDGMEX1st6ino0mXXgy0+0x2rMHLeOu0ewbTh8BHua7RiLw9m2MThS2DCa/3fbaLyfPTsaR+CIsWwrAOXzv877434CJ6RAQFkZnnRvmsAPExtcAA6rqFMCF0+a32f2945YHTpRoDazQHnjnES1lrm3+Fq4+YgL/ygm0lglwc7fxSoM1BZEj3qKzovZ1zsLv1479tEH9ykddGe2jnx04rGmh6Mjpu/9zy/NwbFk68SdWpPhmOUDNr2FDyl9dMMXV699l61D26bmvgOVZjp2ZRN9qTc7xVdOrI9LlUxpXLoVMfk7Nb7fDFELp2MQKbeDOAZzYhAZLSGyrkNMgA3xlRNMtEfCbHWUTvF5CmKjOFSQeO/frHjvH9+pMOtFUbKDBB6vWeALiC8fs96sl2LdkZoVarkRrHVH8v9lCDcaJGexM+zzQ42NZ9GHnuYrO3mL5LvvUdvFy4zXWq/B6ei/V+5Y9yQAqv0oW6R0aK94ppxcMTUAXpMJUu25YkGhw5Hbrl12RaQd5LrV3S5tj+vm0xpaZCBL2vZIQjWCo6Q2/2lnOTKUqE/1UYJv5ZAOKb36Lxv32p+OTCrfUnn27ofnjujZq094yVz2TcPf/v7+58IPi6dX3OnPyC0L3b917LZdPTcF8w/0mVQxcHZN+cTisqHF1YMuXO0r7Nv3562c52pXkOTnPL8TACXovgLUVWlXOH6L57V56vN2t3t+7FP1eajFc/Gz689fe+UW3xc/vP58whegruiOKsCNGRZehzj+cwyiTQwCqAIhKbtXOVDENWdkOJQLre3tedlIaF+WlJTe3ghi5y4pbYNtKyK+AqGgV6RD66BdECyZQU+xzqKriLgsNtBaO9R97viBxZsNL1corarUot3Jy/+qHSkOv7bLFExMz5TiAMaaVIb/wg7NmPnUc0VVb4+a/3xO8a6Hj/0reqcOO967tWbwurHswpy73lz03Mt7Jg1ZtfPpwzvoK7OWGon8BOY/+yddrEUqp/ie+4eMYP/9+yRWGwjyVpav5k5sXH9/5MVNo2XdQ6Sw4ektO5V1zXc4lW4kzreeMU+JFaqnVDtxVIn1ikl8vyqRVppEbn5e21993vp2z4/9rD7PafGcS1R7PsEQk1d7TaLX/gqAo9URXolZHHYXKGOgqI3xIgApTICovZYRgzDHIa79iUMMSoA4xl6IQTg0iG84RDrHQ4OYwA4CqBbHZ9d89VRlx1zyq6euqsJ5fsnUqhXwYN5jsTttkj7YRp9eETFSj91nsfLIR0+9LqSttY3QmLJw6/3b430QyITiIlAqxdlBMcj/lHpUk+6gRVqnV4kwil39+e/sK5T/9sUYXdkp9n3vr4YN77ll3OW+pzc8v7NpC3vppe0vPUtC7Ev2FzR/cQmlWcInr25+cGHXgtrefZ6cNHMlm8b+taaRbXjh4Aku21jXgbraqmOrzaLyJC1RNqNUrt0Vk/1HquySb/e8drD6PPN2z4+p45Ngi+d8fu35a9/f4vtcJtrzCSkx3Wh3fS2Ph2YhR9gJVO1CD4WTPAaDTSACKjsZTifKZjMqJ/QQ8tX1yhOfG8nPjUN6iccXE96Pp8ejezqVFHXsFCrqot3J8iefZP/q3KW8Y1m4nPwYfwOUY3tEGCUsjvv7PvxEa3orl8vQ6iZn76u47uxt1M+b2Kjnf3P2ZWVxBdGcfXw7QXSpTl4Si1SnX6L2X2yaUjNt+Dw0Xd40o6Z25NzmV4rxTJ9pvAljfYjl95r63Iuxboyetf0XbEBQGjL6zuy7cMOvu8aRRcWffLRjTHRO6DzXjNjutSq5e2KSf0PVDI8mmZuf107VNOfWz4851OeBFs+5ZLXnE/yxtZarrfrYDqw6wr2xGWIjpKsAWu+I2t+VyXex0jOkFJfNZpfsrQMOsKeYPHqqT+NdjB7q5euvRZPnb3oYUWsXUUomXo/W9JUVbx7J4HugOKR748Sz333/yd8fMwk63mSElTs38OYRzF9LmyID2Efsvwpjn83sV86KdcDaFQ1NOXQi58u3ce/ZMxo1nF6Nmgn7Y/TmxejV+puEyuv9TaJArLfsb+Iw6gkU6UvxFLggHe4Ot0uSrE5nKpjtqZKY4bc6eDxpBaOR51hGGj+Vwg8UUAc4b5zk4det2ia1fWVJO2TlvZF9aafq7NnSl1EYN4y9zJ7BYRgeN5RaonxdR8+Rfs09fmXXEH+ecs89LqzDiTgeF3ljSZmwlZ1m55QTGn6hNi32qy1yujAU0iAXCmBQuG26zkI8nqx8t7tVlk4oDOW1Mbbh0RHvSCKixdiunWg32pIyxcyKCIieFj7YoVjVRAeseV9R9a0q5rdyvYktTFkxnyvWs/Nzup6pu8B+ROnrBae6djz2+InL0aAOq4Y/e8+QDVf9G154buPm5xvWCb3mrjKRjN+7vp4xEwtQh3q8Y+a0KbPYz19MYDO5tw1mkLIPz3985rOPP/10x9NP7wBEE68Q7pH8YFF6wGWwWXmN0KJs3CSfKkwsE/Igzx1QzhIE0DR3nLfB89CcmUMWLuFF2u+WPJGTu3C+t3TBoiIAgpP5iG2lhdp+kEMyxSpMejflw753u9KSrHUfcfpp29njxj46a8zY3z3YPRTq3rmsqJu4b9TM2lGjps8c3qFLlw78AkQdn+k78TN1N5wPn+Szg2gC/nKrZc73En4mKLYb3o4vKU6BwvQ0olRTQpJEXXkDB/TOLAxZRpmn39tucP/KjIL21tHmqcL5rLZZnbvMquO3Tl1n1aldEci5Ff/FEyCCePMvngykw+K/eMIh5f8VUtYgffQ49lB7+R0HUNTpQenhP6WBBkscHEs5y+QZ1WF29yx63DMUTVyicNM3RdTpRZly061Rq55Od5RisXIk/bGKDPGARzmLjqmfcouq/e4LkcAKAEQZizSpY1khOWwS0KwXbHbQUZP2M1+x3pUgbyrhA/vjeGG9tcNjs9M6maNnb2B4FnXTeR1Tw7TF6DZldL0ZRcHuMIs2WRn9LW10DWe/ei9JQJ4ELUkjOsxJ7m6+QYbnXvbTY2Ow6D6FHh/7lTTBZZSVLOtqB8g4iCCHzeZK+dC1Y38ymWJ3vb5SBnteXszG7cAfyXB6EYzgPBD/URrIP3Wr6u+OqQ9OmDF94qRp5JtZj/9u9sx5C/icym8TiHvgB8gGOwAEwU4c/M4nELJA1RaoJelK5ZPTbBAIlYikk0WuCInpvPM3e2CJ+16ASv2UpGqjUBAIkMRRWhRNSeqtK6QAyGYBkJXxUyYgEkE7ZYLxAQJIVjbPWkkXx4+ZIJRzr1gnnuT0TQ2Xp3rTPZ5kI5Hl5NZ2wZDslYJtjN4kb/+ILklMTUvtHyFp1rT0tPw0qqdJaUlpzsxM6BvJlJ0W3iDhg5ZN3bwwdMsfKruRW2ZQbuRlt9evdcorVpPyolGwuJT/dUDsCHUKOz4AWfRHQvA065Z1snHLxtW7/oddaNewgZANO4LY+n9OPN+rQSxmD80rC7ed1/Rm9/puaEacl3tH9TwUsfXIpYPVzprl6o4iBXdYT0AUtDAtYc3y+EuJtrjkUwGEVlI650ylKvE+5ABA/HNTwuf9lc+BgItUcf0/AgZwQedwuks0ypTyaYjSqY+iqLe60l3E5aIWOZ1mxPuV70toergeGwR4g0v8V2eKi0otVJZJ05xV7GHcsHQO+0ESk9LSjDup6913x/KzVKdeX9THFGzb1v5TDDfpQ45bECoJ9+43cBcf0nCXXr/F8/43notvxJ6rVEnqc1TWG05X9cp+AAQRKWiHl2Knck80KgqljCAC4Aq1QvJpPHP6XaxCImp1FiUv6pwAUXstt2Ud9NrbHGJCAsQx9ufEKktsFtJBzroOMYF9EK/V+GK1mv8PflNJUQAAAAABAAAAARmahXJJOF8PPPUACQgAAAAAAMk1MYsAAAAAyehMTPua/dUJoghiAAAACQACAAAAAAAAeAFjYGRg4Oj9u4KBgXPN71n/qjkXAUVQwU0Ap6sHhAB4AW2SA6wYQRRF786+2d3atm3b9ldQ27atsG6D2mFt2zaC2ra2d/YbSU7u6C3OG7mIowAgGQFlKIBldiXM1CVQQRZiurMEffRtDLVOYqbqhBBSS/ohgnt9rG+ooxYiTOXDMvUBGbnWixwgPUgnUoLMJCOj5n1IP3Oe1ImajzZpD0YOtxzG6rSALoOzOiUm6ps4K8NJPs6vc/4cZ1UBv4u85FoRnHWr4azjkRqYKFej8hP3eqCfDER61uyT44DbBzlkBTwZD8h8/sMabOD3ZmFWkAiUs5f4f2SFNZfv6iTPscW+jOHynEzEcLULuaQbivCdW5SDNcrx50uFYLzFHYotZl1umvNM1tgNWX+V/3gdebi3ThTgVEMWKYci4kHZhxBie3TYx3rHbGr+Pdo7x4dIHTKe5DFn+O/j+W2VnE3ooW6isf0LIUENvZs1gf/LHojJwdpplCP5gn/5gi26FoYa19ZVFOJ6Sxuoz/q2Ti20IKVJdnqvYJwnhfPH/2f6YHoQF30aZaK9J8T026RxH5fA/WPW/8IW4zkpnIfoFLifGB86v0ffm5nbyRs5iaHR3hNBD0HSfTzoPugRM+hdN0x052KoHLBS0tdgpidAiEesDsgWYO73RWQz2LWIwjqnMe/uYISQtlbyf2NlT9Q9PoBcBnrO6I5ELoMeyHkNnIXGdv809H/DXNOTeAEc0jWMJFcQxvFnto/5LjEvHrdbmh2Kji9aPL4839TcKPNAa6mlZUyOmZk6lzbPJ3bo56//Cz+Vaqqrat5rY8x7xnzxl3nvo+27jFnz8c/mI9Nmh2XBdMsilrBitsnD9rI8aiN5DI/jSftC9mIf9pMfIB4kHiI+hWfQY5aPAYYYYYwpcyfpMMX0aZzBWZzDeVygchGXcBlX8ApexWt4HW/gLbzNbnfwLt7DJ/p0TX4+Uucji1hCnY/U+cijVB7D46jzkb3Yh/3kB4gHiYeIT+EZ9JjlY4AhRhhjytxJOkwxfRpncBbncB4XqFzEJVzGFbyCV/EaXscbeAtvs9sdvIv3cjmftWavuWs2mg6byt3ooIsFOyx77Kos2kiWsIK/UVPDOjawiQmO4CgdxnAcJzClz2PVbNKsy2ZzvoncjQ66qE2kNpHaRJawgr9RU8M6NrCJCY6gNpFjOI4TmNIn36TNfGSH5RrssKtyN+59b410iF0sUFO0l2UJtY/8jU9rWMcGNjHBEUypf0z8mm7vZLvZaC/LzdhmV2XBvpBF25IlLJOvEFfRI+NjgCFGGGNK5Rs6Z7Ij/45yNzro4m9Ywzo2sIkJjuBj2ZnvLDdjGxntLLWzLGGZfIW4ih4ZHwMMMcIYUyq1s8xkl97bH0y3JkZyM36j/+58rvTQxwBDjDDGNzyVyX35Ccjd6KCLv2EN69jAJiY4go/lfr05F+Ua7CCzGx10sYA9tiWLxCWs2BfyN+Ia1rGBTUxwBEfpMIbjOIEpfdjHvGaTd9LJb0duRp2S1O1I3Y4sYZl8hbiKHhkfAwwxwhhTKt/QOZPfmY3//Ss3Y5tNpTpL9ZQeGR8DDDHCGN/wbCbdfHO5GbW51OZSm8sSlslXiKvokfExwBAjjDGlUpvLTBY0K5KbiDcT672SbXZY6k7lbnTQxQI1h+1FeZTKY3gcT2KvTWUf9pMZIB4kHiI+xcQzxGfpfA7P4wW8yG4eT/kYYIgRxvgb9TWsYwObmOAITlI/xf7TOIOzOIfzuEDlIi7hMq7gFbyK1/A63sBbeJtvdwfv4j28zyaP8QmVL/imL/ENJ5PJHt3RqtyMbbYlPfQxwBAjjPEN9ZksqkMqN6PuV7bZy7LDtuRudNDFwzx1FI/hcTzJp73Yh/3kB4gHiYeIT+EZ9JjlY4AhRhjjb1TWsI4NbGKCIzjJlCmcxhmcxTmcxwVcxCVcxhW8glfxGl7HG3gLbzPxDt7Fe/gY/+egvq0YCAEoCNa1n+KVyTUl3Q0uIhoe+3DnRfV7nXGOc5zjHOc4xznOcY5znOMc5zjHOc5xjnOc4xznOMc5znGOc5zjHOc4xznOcY5znOMc5zjHOc5xjnOc4xznOMc5znGOc5zjHOc4xznOcY5znOM8XZouTZemS1OAKcAUYAowBZgCTAHm3x31O7p3vNf5c1iXeBkEAQDFcbsJX0IqFBwK7tyEgkPC3R0K7hrXzsIhePPK/7c77jPM1yxSPua0WmuDzNcuNmuLtmq7sbyfsUu7De/xu9fvvvDNfN3ioN9j5pq0ximd1hmd1TmlX7iky7qiq7qmG3pgXYd6pMd6oqd6pud6oZd6pdd6p/f6oI/6pC/KSxvf9F0/1LFl1naRcwwzrAu7AHNarbW6oEu6rCu6qmu6ob9Y7xu+kbfHH1ZopCk25RVrhXKn4LCO6KiOGfvpd+R3is15xXmVWKGRptgaysQKpUwc1hEdVcpEysTI7xTbKHMcKzTSFDtCmVihkab4z0FdI0QQBAEUbRz6XLh3Lc7VcI/WN54IuxXFS97oH58+MBoclE1usbHHW77wlW985wcHHHLEMSecsUuPXMNRqfzib3pcllj5xd+0lSVW5nNIL3nF6389h+Y5NG3Thja0oQ1taEMb2tCGNrQn+QwjrcwxM93gJre4Y89mvsdb3vGeD3zkE5/5wle+8Z0fHHDIEceccMaOX67wNz3747gObCQAQhCKdjlRzBVD5be7rwAmfOMQsUvPLj279OzSYBks49Ibl97In/HCuNDGO+NOW6qlWqqlWqqlWqqlWqqYUkwpphTzifnEfII92IM92IM92IM92IM92IM92I/D4/A4PA6Pw+PwODwOj8M/f7kaaDXQyt7K3mqglcCVwNVAq4FWA60GWglZCVkJWQlZCVkJWQlZDbQyqhpoNdAPh3NAwCAAwwDM+7b2sg8kCjIO4zAO4zAO4zAO4zAO4zAO4zAO4zAO4zAO4zAO4zAO47AO67AO67AO67AO67AO67AO67AO67AO67AO67AO67AO63AO53AO53AO53AO53AO53AO53AO53AO53AO53AO53AO5xCHOMQhDnGIQxziEIc4xCEOcYhDHOIQhzjEIQ5xiEMd6lCHOtShDnWoQx3qUIc61KEOdahDHepQhzrUoQ6/h+P6RpIjiKEoyOPvCARUoK9LctP5ZqXTop7q/6H/0H+4P9yfPz82bdm2Y9ee/T355bS3/divDW9reFtDb4beDL0ZejP0ZujN0JuhN0Nvht4MvRl6M/Rm6M3w1of3PVnJSlaykpWsZCUrWclKVrKSlaxkJStZySpWsYpVrGIVq1jFKlaxilWsYhWrWMUqVrGa1axmNatZzWpWs5rVrGY1q1nNalazmtWsYQ1rWMMa1rCGNaxhDWtYwxrWsIY1rGENa1nLWtaylrWsZS1rWcta1rKWtaxlLWtZyzrWsY51rGMd61jHOtaxjnWsYx3rWMc61rEeTf1o6kdTP/84rpMqCKAYhmH8Cfy2JjuLCPiYPDH1Y+rH1I+pH1M/pn5M/Zh6FEZhFEZhFEZhFEZhFEZhFFZhFVZhFVZhFVZhFVZhFVbhFE7hFE7hFE7hFE7hFE7hFCKgCChPHQFlc7I52ZxsTgQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQti5bl63L1mXrsnXZuggoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCyt5GQBFQBPTlwD7OEIaBKAxSOrmJVZa2TsJcwJ6r0/+9sBOGnTDshOF+DndyXG7k7vfh9+n35fft978Thp2wKuqqqKtarmq58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<section class="page-header">
<h1 class="title toc-ignore project-name">马赛克图</h1>
<h4 class="author project-author">庄亮亮</h4>
<h4 class="date project-date">2020/11/10</h4>
</section>
<section class="main-content">
<p>记得安装prettydoc包，html模板在该包渲染而成。</p>
<div id="前言" class="section level2">
<h2>1.前言</h2>
<p><strong>马赛克图</strong>（mosaic plot），显示分类数据中一对变量之间的关系，原理类似双向的100%堆叠式条形图，但其中所有条形在数值/标尺轴上具有相等长度，并会被划分成段。可以通过这两个变量来检测类别与其子类别之间的关系。</p>
<div id="主要优点" class="section level3">
<h3>主要优点</h3>
<p>马赛克图能按行或按列展示多个类别的比较关系。</p>
</div>
<div id="主要缺点" class="section level3">
<h3>主要缺点</h3>
<p>难以阅读，特别是当含有大量分段的时候。此外，我们也很难准确地对每个分段进行比较，因为它们并非沿着共同基线排列在一起。</p>
</div>
<div id="适用" class="section level3">
<h3>适用</h3>
<p>马赛克图比较适合提供数据概览。</p>
</div>
<div id="注意" class="section level3">
<h3>注意</h3>
<p><strong>非坐标轴非均匀的马赛克图</strong>也是统计学领域标准的马赛克图，一个非均匀的马赛克图包含以下构成元素：①非均匀的分类坐标轴；②面积、颜色均有含义的矩形块；③图例。对于非均匀的马赛克图，关注的数据维度非常多，一般的用户很难直观理解，在多数情况下可以被拆解成多个不同的图表，以下我们会对其进行绘制。</p>
</div>
</div>
<div id="数据介绍" class="section level2">
<h2>2.数据介绍</h2>
<p>数据构建代码来源《R数据可视化之美》，任意拟定一个数据框。并用melt()函数将数据转化成以下结果：</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb1-1" title="1"><span class="kw">library</span>(ggplot2)</a>
<a class="sourceLine" id="cb1-2" title="2"><span class="kw">library</span>(RColorBrewer)</a>
<a class="sourceLine" id="cb1-3" title="3"><span class="kw">library</span>(reshape2) <span class="co">#提供melt()函数</span></a>
<a class="sourceLine" id="cb1-4" title="4"><span class="kw">library</span>(plyr) <span class="co">#提供ddply()函数,join()函数</span></a>
<a class="sourceLine" id="cb1-5" title="5"></a>
<a class="sourceLine" id="cb1-6" title="6">df <-<span class="st"> </span><span class="kw">data.frame</span>(<span class="dt">segment =</span> <span class="kw">c</span>(<span class="st">"A"</span>, <span class="st">"B"</span>, <span class="st">"C"</span>,<span class="st">"D"</span>),<span class="dt">Alpha =</span> <span class="kw">c</span>(<span class="dv">2400</span> ,<span class="dv">1200</span>, <span class="dv">600</span> ,<span class="dv">250</span>), </a>
<a class="sourceLine" id="cb1-7" title="7"> <span class="dt">Beta =</span> <span class="kw">c</span>(<span class="dv">1000</span> ,<span class="dv">900</span>, <span class="dv">600</span>, <span class="dv">250</span>),</a>
<a class="sourceLine" id="cb1-8" title="8"> <span class="dt">Gamma =</span> <span class="kw">c</span>(<span class="dv">400</span>, <span class="dv">600</span> ,<span class="dv">400</span>, <span class="dv">250</span>), </a>
<a class="sourceLine" id="cb1-9" title="9"> <span class="dt">Delta =</span> <span class="kw">c</span>(<span class="dv">200</span>, <span class="dv">300</span> ,<span class="dv">400</span>, <span class="dv">250</span>))</a>
<a class="sourceLine" id="cb1-10" title="10"></a>
<a class="sourceLine" id="cb1-11" title="11">melt_df<-<span class="kw">melt</span>(df,<span class="dt">id=</span><span class="st">"segment"</span>)</a>
<a class="sourceLine" id="cb1-12" title="12"><span class="kw">str</span>(melt_df)</a></code></pre></div>
<pre><code>## 'data.frame': 16 obs. of 3 variables:
## $ segment : chr "A" "B" "C" "D" ...
## $ variable: Factor w/ 4 levels "Alpha","Beta",..: 1 1 1 1 2 2 2 2 3 3 ...
## $ value : num 2400 1200 600 250 1000 900 600 250 400 600 ...</code></pre>
<p>计算出每行的最大，最小值，并计算每行各数的百分比。ddply()对data.frame分组计算，并利用join()函数进行两个表格连接。</p>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb3-1" title="1">segpct<-<span class="kw">rowSums</span>(df[,<span class="dv">2</span><span class="op">:</span><span class="kw">ncol</span>(df)])</a>
<a class="sourceLine" id="cb3-2" title="2"><span class="cf">for</span> (i <span class="cf">in</span> <span class="dv">1</span><span class="op">:</span><span class="kw">nrow</span>(df)){</a>
<a class="sourceLine" id="cb3-3" title="3"> <span class="cf">for</span> (j <span class="cf">in</span> <span class="dv">2</span><span class="op">:</span><span class="kw">ncol</span>(df)){</a>
<a class="sourceLine" id="cb3-4" title="4"> df[i,j]<-df[i,j]<span class="op">/</span>segpct[i]<span class="op">*</span><span class="dv">100</span> <span class="co">#将数字转换成百分比</span></a>
<a class="sourceLine" id="cb3-5" title="5"> }</a>
<a class="sourceLine" id="cb3-6" title="6">}</a>
<a class="sourceLine" id="cb3-7" title="7"></a>
<a class="sourceLine" id="cb3-8" title="8">segpct<-segpct<span class="op">/</span><span class="kw">sum</span>(segpct)<span class="op">*</span><span class="dv">100</span></a>
<a class="sourceLine" id="cb3-9" title="9">df<span class="op">$</span>xmax <-<span class="st"> </span><span class="kw">cumsum</span>(segpct)</a>
<a class="sourceLine" id="cb3-10" title="10">df<span class="op">$</span>xmin <-<span class="st"> </span>(df<span class="op">$</span>xmax <span class="op">-</span><span class="st"> </span>segpct)</a>
<a class="sourceLine" id="cb3-11" t
gitextract_2nr_zcnv/ ├── 2020年/ │ ├── 2020.07.13Network_igraph/ │ │ ├── edge.csv │ │ ├── graph.r │ │ └── vertices.csv │ ├── 2020.07.14China_map/ │ │ ├── bou2_4p.dbf │ │ ├── bou2_4p.shp │ │ ├── bou2_4p.shx │ │ ├── china_map.r │ │ └── data_dt.csv │ ├── 2020.07.15World_map/ │ │ ├── Country_Data.csv │ │ ├── cores.r │ │ └── world_data.csv │ ├── 2020.07.22Movielense/ │ │ ├── ch11-task.Rmd │ │ └── ch11-task.log │ ├── 2020.07.22坐标轴截断画图/ │ │ ├── code_truncation .R │ │ ├── code_truncation .Rmd │ │ └── code_truncation-.docx │ ├── 2020.07.24饼图与圆环图/ │ │ └── pie.rmd │ ├── 2020.07.29温大招生/ │ │ └── code_ggimage.r │ ├── 2020.08.14amazon/ │ │ ├── RECOM.Rmd │ │ ├── code.Rmd │ │ └── 亚马逊产品推荐算法.xmind │ ├── 2020.08.15分面/ │ │ ├── .R数据科学.pdf.icloud │ │ ├── facet.log │ │ └── facet.rmd │ ├── 2020.08.23好玩的图/ │ │ ├── fun.r │ │ └── readme.txt │ ├── 2020.08.25马赛克/ │ │ ├── 马赛克.html │ │ └── 马赛克.rmd │ ├── 2020.08.26混合多个图形/ │ │ ├── 混合图.html │ │ └── 混合图.rmd │ ├── 2020.09.15reticulate/ │ │ └── reticulate.rmd │ ├── 2020.09.27tidyverse数据清洗/ │ │ └── Tidy_data.rmd │ ├── 2020.10.09散点图系列一/ │ │ ├── scater_plot1.html │ │ └── scater_plot1.rmd │ ├── 2020.10.27散点图系列二/ │ │ ├── HighDensity_Scatter_Data.csv │ │ ├── scater_plot2.html │ │ └── scater_plot2.rmd │ ├── 2020.10.30ggpubr/ │ │ └── ggpubr.rmd │ ├── 2020.11.05气泡图/ │ │ ├── 1.txt │ │ ├── Bubble_plot.html │ │ └── Bubble_plot.rmd │ ├── 2020.11.14gghalves/ │ │ ├── gghalves.html │ │ └── gghalves.rmd │ ├── 2020.11.16flexdashboard/ │ │ ├── flexdashboard.md │ │ ├── test.html │ │ ├── test.rmd │ │ ├── 例子/ │ │ │ └── 09_rbokeh-iris-dataset/ │ │ │ ├── dashboard-pandoc2.0.3.html │ │ │ └── dashboard.Rmd │ │ ├── 教程.html │ │ ├── 教程.rmd │ │ └── 链接.txt │ ├── 2020.11.22三维散点图/ │ │ ├── 3d_scatter.html │ │ ├── 3d_scatter.rmd │ │ └── ThreeD_Scatter_Data.csv │ ├── 2020.11.22数据处理ntile()/ │ │ └── 数据处理数据按从小到大分成n类.md │ ├── 2020.11.28等高线/ │ │ ├── .RData │ │ ├── .Rhistory │ │ ├── 等高线.html │ │ ├── 等高线.r │ │ ├── 等高线.rmd │ │ └── 等高线.txt │ ├── 2020.12.05日历/ │ │ ├── .My_calendar7(1).pdf.icloud │ │ ├── 日历_EasyShu.rmd │ │ ├── 日历教程.md │ │ ├── 日历教程.rmd │ │ └── 背景/ │ │ ├── .1.png.icloud │ │ ├── .2.png.icloud │ │ └── .3.jpg.icloud │ ├── 2020.12.07瀑布图/ │ │ ├── Facting_Data.csv │ │ └── 瀑布图.rmd │ ├── 2020.12.21esquisse包/ │ │ └── esquisse包.md │ ├── 2020.12.24Mandalas/ │ │ └── Mandalas/ │ │ ├── .Rproj.user/ │ │ │ ├── 9B97F8EE/ │ │ │ │ └── sources/ │ │ │ │ ├── prop/ │ │ │ │ │ ├── 34FB05E2 │ │ │ │ │ ├── 5C967418 │ │ │ │ │ ├── B38F6FEB │ │ │ │ │ ├── D25723BC │ │ │ │ │ └── INDEX │ │ │ │ └── s-75E8F375/ │ │ │ │ ├── 17CC9D9C │ │ │ │ ├── 17CC9D9C-contents │ │ │ │ ├── 20B6EE7D │ │ │ │ ├── 20B6EE7D-contents │ │ │ │ ├── 28FAC3B5 │ │ │ │ ├── 28FAC3B5-contents │ │ │ │ ├── 4326A9CE │ │ │ │ ├── 4326A9CE-contents │ │ │ │ ├── C7D0C74B │ │ │ │ ├── C7D0C74B-contents │ │ │ │ └── lock_file │ │ │ └── shared/ │ │ │ └── notebooks/ │ │ │ ├── 40CC0FD6-Mandalas/ │ │ │ │ └── 1/ │ │ │ │ ├── 9B97F8EE87974FA4/ │ │ │ │ │ └── chunks.json │ │ │ │ └── s/ │ │ │ │ ├── cct2o5lt4yw4d/ │ │ │ │ │ └── 000011.csv │ │ │ │ └── chunks.json │ │ │ ├── 540673E1-峰峦图/ │ │ │ │ └── 1/ │ │ │ │ ├── 9B97F8EE75E8F375/ │ │ │ │ │ └── chunks.json │ │ │ │ ├── 9B97F8EE87974FA4/ │ │ │ │ │ └── chunks.json │ │ │ │ └── s/ │ │ │ │ └── chunks.json │ │ │ ├── patch-chunk-names │ │ │ └── paths │ │ ├── Mandalas.Rproj │ │ ├── Mandalas.html │ │ └── Mandalas.rmd │ ├── 2020.12.24圣诞节/ │ │ ├── 1.json │ │ └── marry.R │ ├── 2020.12.25峰峦图/ │ │ └── 峰峦图.rmd │ ├── 2020.12.30ggvis/ │ │ ├── ggvis.html │ │ ├── ggvis.md │ │ └── ggvis.rmd │ └── 2020年R数据科学系列/ │ ├── 2020.08.23几何对象/ │ │ └── 几何对象.md │ ├── 2020.08.23速查表/ │ │ ├── 让微信排版变 Nice.html │ │ └── 速查表.html │ ├── 2020.09.27tidy data/ │ │ └── Tidy_data.rmd │ └── 《R数据科学》源代码/ │ ├── DESCRIPTION │ ├── EDA.Rmd │ ├── LICENSE │ ├── README.md │ ├── _bookdown.yml │ ├── _common.R │ ├── _output.yaml │ ├── communicate-plots.Rmd │ ├── communicate.Rmd │ ├── contribs.txt │ ├── contribute.rmd │ ├── datetimes.Rmd │ ├── explore.Rmd │ ├── factors.Rmd │ ├── figures.R │ ├── functions.Rmd │ ├── hierarchy.Rmd │ ├── import.Rmd │ ├── index.rmd │ ├── intro.Rmd │ ├── issues.json │ ├── iteration.Rmd │ ├── model-assess.Rmd │ ├── model-basics.Rmd │ ├── model-building.Rmd │ ├── model-many.Rmd │ ├── model.Rmd │ ├── pipes.Rmd │ ├── program.Rmd │ ├── r4ds.Rproj │ ├── r4ds.css │ ├── relational-data.Rmd │ ├── rmarkdown-formats.Rmd │ ├── rmarkdown-workflow.Rmd │ ├── rmarkdown.Rmd │ ├── strings.Rmd │ ├── tibble.Rmd │ ├── transform.Rmd │ ├── vectors.Rmd │ ├── visualize.Rmd │ ├── workflow-basics.Rmd │ ├── workflow-projects.Rmd │ ├── workflow-scripts.Rmd │ └── wrangle.Rmd ├── 2021年/ │ ├── 2021.01.31主成分结果可视化/ │ │ ├── pac_visual.log │ │ └── pac_visual.rmd │ ├── 2021.02.21克利夫兰点图系列/ │ │ ├── DotPlots_Data.csv │ │ ├── cleveland's .Rmd │ │ └── cleveland-s-.log │ └── 2021.02.28常用主题风格/ │ ├── .R可视乎｜ggplot常用主题风格汇总.pdf.icloud │ └── R可视乎｜ggplot常用主题风格汇总.md ├── 2022年/ │ ├── 2022.01.14 如何使用 ggplot2 绘制双轴分离图？/ │ │ └── 双轴分离.R │ ├── 2022.01.28 如何绘制省市级地图？/ │ │ ├── df_China4.csv │ │ ├── 各区县经营效率平均值.csv │ │ ├── 温州地图绘制.r │ │ ├── 温州市.json │ │ └── 绘制浙江省地图.R │ ├── 2022.02.08读者投稿｜绘制一系列黑白印刷风格图表/ │ │ └── acchist.r │ ├── 2022.03.14绘制混合密度函数图以及添加分位数线/ │ │ └── mix-quantile.r │ ├── 2022.04.08R 案例｜绘制不同分布的 QQ 图/ │ │ └── qqplot.r │ ├── 2022.05.15老板让你复现一个图片，你会使用什么软件？/ │ │ └── example.r │ ├── 2022.08.08 ggplot 分面的细节调整汇总/ │ │ └── ggplot_facet.r │ ├── 2022.09.09 如何在分面中添加数学表达式标签？ / │ │ └── add_math_label.r │ ├── 2022.09.24 中国地图绘制/ │ │ ├── .RData │ │ ├── .Rhistory │ │ └── .Rproj.user/ │ │ ├── 161F88A0/ │ │ │ ├── pcs/ │ │ │ │ ├── files-pane.pper │ │ │ │ ├── packages-pane.pper │ │ │ │ ├── source-pane.pper │ │ │ │ ├── windowlayoutstate.pper │ │ │ │ └── workbench-pane.pper │ │ │ ├── rmd-outputs │ │ │ ├── saved_source_markers │ │ │ └── sources/ │ │ │ └── prop/ │ │ │ ├── 212A8ABB │ │ │ ├── 21BA30B6 │ │ │ ├── 702BC91B │ │ │ ├── 9F335933 │ │ │ ├── D8AA2E2E │ │ │ └── INDEX │ │ └── shared/ │ │ └── notebooks/ │ │ ├── patch-chunk-names │ │ └── paths │ ├── 2022.10.12使用 ggplot2 绘制单个和多个省份地图/ │ │ ├── china_shp/ │ │ │ ├── CHN_adm0.cpg │ │ │ ├── CHN_adm0.csv │ │ │ ├── CHN_adm0.dbf │ │ │ ├── CHN_adm0.prj │ │ │ ├── CHN_adm0.shp │ │ │ ├── CHN_adm0.shx │ │ │ ├── CHN_adm1.cpg │ │ │ ├── CHN_adm1.csv │ │ │ ├── CHN_adm1.dbf │ │ │ ├── CHN_adm1.prj │ │ │ ├── CHN_adm1.shp │ │ │ ├── CHN_adm1.shx │ │ │ ├── CHN_adm2.cpg │ │ │ ├── CHN_adm2.csv │ │ │ ├── CHN_adm2.dbf │ │ │ ├── CHN_adm2.prj │ │ │ ├── CHN_adm2.shp │ │ │ ├── CHN_adm2.shx │ │ │ ├── CHN_adm3.cpg │ │ │ ├── CHN_adm3.csv │ │ │ ├── CHN_adm3.dbf │ │ │ ├── CHN_adm3.prj │ │ │ ├── CHN_adm3.shp │ │ │ ├── CHN_adm3.shx │ │ │ ├── license.txt │ │ │ └── my_file.txt │ │ ├── colour.csv │ │ ├── map.Rproj │ │ ├── province.csv │ │ ├── 南海.geojson │ │ ├── 地图十段线.R │ │ ├── 测试数据.xlsx │ │ └── 省份地图.R │ ├── 2022.11.07 常用 7 大类图形可视化汇总——ggplot2包/ │ │ ├── .RData │ │ ├── .Rhistory │ │ ├── .Rproj.user/ │ │ │ ├── 161F88A0/ │ │ │ │ ├── build_options │ │ │ │ ├── build_options 2 │ │ │ │ ├── pcs/ │ │ │ │ │ ├── .files-pane 2.pper.icloud │ │ │ │ │ ├── .packages-pane 2.pper.icloud │ │ │ │ │ ├── .source-pane 2.pper.icloud │ │ │ │ │ ├── .windowlayoutstate 2.pper.icloud │ │ │ │ │ ├── .workbench-pane 2.pper.icloud │ │ │ │ │ ├── files-pane.pper │ │ │ │ │ ├── packages-pane.pper │ │ │ │ │ ├── source-pane.pper │ │ │ │ │ ├── windowlayoutstate.pper │ │ │ │ │ └── workbench-pane.pper │ │ │ │ ├── rmd-outputs │ │ │ │ ├── saved_source_markers │ │ │ │ └── sources/ │ │ │ │ ├── per/ │ │ │ │ │ ├── t/ │ │ │ │ │ │ ├── CD5422DD │ │ │ │ │ │ ├── CD5422DD 2 │ │ │ │ │ │ ├── CD5422DD-contents │ │ │ │ │ │ └── CD5422DD-contents 2 │ │ │ │ │ └── u/ │ │ │ │ │ ├── 86D463FB │ │ │ │ │ ├── 86D463FB 2 │ │ │ │ │ ├── 86D463FB-contents │ │ │ │ │ └── 86D463FB-contents 2 │ │ │ │ └── prop/ │ │ │ │ ├── 6244B089 │ │ │ │ ├── 6244B089 2 │ │ │ │ ├── 66C45745 │ │ │ │ ├── 66C45745 2 │ │ │ │ ├── DCED28DF │ │ │ │ ├── DCED28DF 2 │ │ │ │ ├── INDEX │ │ │ │ └── INDEX 2 │ │ │ ├── C6560784/ │ │ │ │ ├── pcs/ │ │ │ │ │ ├── .files-pane 2.pper.icloud │ │ │ │ │ ├── .source-pane 2.pper.icloud │ │ │ │ │ ├── .windowlayoutstate 2.pper.icloud │ │ │ │ │ ├── .workbench-pane 2.pper.icloud │ │ │ │ │ ├── files-pane.pper │ │ │ │ │ ├── source-pane.pper │ │ │ │ │ ├── windowlayoutstate.pper │ │ │ │ │ └── workbench-pane.pper │ │ │ │ ├── rmd-outputs │ │ │ │ ├── saved_source_markers │ │ │ │ └── sources/ │ │ │ │ ├── per/ │ │ │ │ │ └── t/ │ │ │ │ │ ├── C36CFFC0 │ │ │ │ │ ├── C36CFFC0 2 │ │ │ │ │ ├── C36CFFC0-contents │ │ │ │ │ └── C36CFFC0-contents 2 │ │ │ │ └── prop/ │ │ │ │ ├── F9089F45 │ │ │ │ ├── F9089F45 2 │ │ │ │ ├── INDEX │ │ │ │ └── INDEX 2 │ │ │ └── shared/ │ │ │ └── notebooks/ │ │ │ ├── CED44E14-50pictures_ggplot/ │ │ │ │ └── 1/ │ │ │ │ ├── C656078445C2030A/ │ │ │ │ │ ├── chunks 2.json │ │ │ │ │ └── chunks.json │ │ │ │ └── s/ │ │ │ │ ├── cg7cix78cerjz/ │ │ │ │ │ ├── .00000f 2.metadata.icloud │ │ │ │ │ ├── .00000f 2.rdf.icloud │ │ │ │ │ ├── 00000f.metadata │ │ │ │ │ └── 00000f.rdf │ │ │ │ ├── chunks 2.json │ │ │ │ ├── chunks.json │ │ │ │ └── csetup_chunk/ │ │ │ │ ├── .00000f 2.csv.icloud │ │ │ │ └── 00000f.csv │ │ │ ├── patch-chunk-names │ │ │ ├── patch-chunk-names 2 │ │ │ ├── paths │ │ │ └── paths 2 │ │ ├── 50pictures_ggplot.Rproj │ │ ├── 50pictures_ggplot.html │ │ ├── 50pictures_ggplot.rmd │ │ ├── midwest.csv │ │ └── readme.txt │ ├── 2022.11.15 R绘图案例｜基于分面的折线图绘制/ │ │ ├── .RData │ │ ├── .Rhistory │ │ ├── .Rproj.user/ │ │ │ ├── C6560784/ │ │ │ │ ├── pcs/ │ │ │ │ │ ├── files-pane.pper │ │ │ │ │ ├── packages-pane.pper │ │ │ │ │ ├── source-pane.pper │ │ │ │ │ ├── windowlayoutstate.pper │ │ │ │ │ └── workbench-pane.pper │ │ │ │ ├── rmd-outputs │ │ │ │ ├── saved_source_markers │ │ │ │ └── sources/ │ │ │ │ ├── prop/ │ │ │ │ │ ├── 3664F9C7 │ │ │ │ │ ├── A35D258F │ │ │ │ │ ├── E600AE88 │ │ │ │ │ └── INDEX │ │ │ │ └── session-417D82C1/ │ │ │ │ ├── 3B4320B1 │ │ │ │ ├── 3B4320B1-contents │ │ │ │ └── lock_file │ │ │ └── shared/ │ │ │ └── notebooks/ │ │ │ ├── patch-chunk-names │ │ │ └── paths │ │ ├── data_city.csv │ │ ├── line_point.R │ │ └── plot.Rproj │ ├── 2022.11.19 分面中添加不同的直线/ │ │ ├── facet-line.r │ │ ├── plot.Rproj │ │ └── test.xlsx │ ├── 2022.11.29 R绘图案例｜基于分面的面积图绘制/ │ │ ├── .RData │ │ ├── .Rhistory │ │ ├── .Rproj.user/ │ │ │ ├── C6560784/ │ │ │ │ ├── jobs/ │ │ │ │ │ └── 7897C193-output.json │ │ │ │ ├── pcs/ │ │ │ │ │ ├── files-pane.pper │ │ │ │ │ ├── source-pane.pper │ │ │ │ │ ├── windowlayoutstate.pper │ │ │ │ │ └── workbench-pane.pper │ │ │ │ ├── rmd-outputs │ │ │ │ ├── saved_source_markers │ │ │ │ └── sources/ │ │ │ │ ├── prop/ │ │ │ │ │ ├── 0508C42F │ │ │ │ │ ├── 0D0D59B5 │ │ │ │ │ ├── 19008281 │ │ │ │ │ ├── A62E60CA │ │ │ │ │ └── INDEX │ │ │ │ ├── session-23478791/ │ │ │ │ │ ├── 0BC0007C │ │ │ │ │ ├── 0BC0007C-contents │ │ │ │ │ ├── AF358D71-contents │ │ │ │ │ ├── EB6BB15A │ │ │ │ │ ├── EB6BB15A-contents │ │ │ │ │ └── lock_file │ │ │ │ └── session-51C83C65/ │ │ │ │ ├── 8752579D │ │ │ │ ├── 8752579D-contents │ │ │ │ └── lock_file │ │ │ └── shared/ │ │ │ └── notebooks/ │ │ │ ├── patch-chunk-names │ │ │ └── paths │ │ ├── R-plot.Rproj │ │ ├── rev_plot.R │ │ └── test.xlsx │ └── 2022.12.31 2023年日历大派送/ │ ├── .RData │ ├── .Rhistory │ ├── .Rproj.user/ │ │ ├── C6560784/ │ │ │ ├── jobs/ │ │ │ │ └── E803742C-output.json │ │ │ ├── pcs/ │ │ │ │ ├── files-pane.pper │ │ │ │ ├── source-pane.pper │ │ │ │ ├── windowlayoutstate.pper │ │ │ │ └── workbench-pane.pper │ │ │ ├── rmd-outputs │ │ │ ├── saved_source_markers │ │ │ └── sources/ │ │ │ ├── per/ │ │ │ │ └── t/ │ │ │ │ ├── 13B3FE41 │ │ │ │ ├── 13B3FE41-contents │ │ │ │ ├── 873D867D │ │ │ │ └── 873D867D-contents │ │ │ └── prop/ │ │ │ ├── 26BD393F │ │ │ ├── 8A1E9267 │ │ │ ├── F0AB5896 │ │ │ └── INDEX │ │ └── shared/ │ │ └── notebooks/ │ │ ├── patch-chunk-names │ │ └── paths │ ├── version1.R │ ├── version2.R │ └── 未命名.Rproj ├── 2023年/ │ ├── 2022.08.31 分面+双轴/ │ │ ├── .RData │ │ ├── .Rhistory │ │ ├── .Rproj.user/ │ │ │ ├── C6560784/ │ │ │ │ ├── pcs/ │ │ │ │ │ ├── files-pane.pper │ │ │ │ │ ├── source-pane.pper │ │ │ │ │ ├── windowlayoutstate.pper │ │ │ │ │ └── workbench-pane.pper │ │ │ │ ├── rmd-outputs │ │ │ │ ├── saved_source_markers │ │ │ │ └── sources/ │ │ │ │ ├── per/ │ │ │ │ │ └── t/ │ │ │ │ │ ├── EA22C3C6 │ │ │ │ │ └── EA22C3C6-contents │ │ │ │ └── prop/ │ │ │ │ ├── D4B371E0 │ │ │ │ ├── F02804FC │ │ │ │ └── INDEX │ │ │ └── shared/ │ │ │ └── notebooks/ │ │ │ ├── patch-chunk-names │ │ │ └── paths │ │ ├── Add secondary axis.R │ │ └── df.csv │ ├── 2023.03.07 高亮柱状图/ │ │ └── 高亮柱状图.R │ ├── 2023.03.08 基于 ggridges 绘制剩余使用寿命密度图/ │ │ └── rul_ggbridges.R │ ├── 2023.03.09基于 ggdensity 包的等高线绘制/ │ │ └── 等高线绘制.R │ ├── 2023.03.16使用 ggTimeSeries 包构建日历图/ │ │ ├── timeseries.R │ │ ├── 日历图.html │ │ ├── 日历图.qmd │ │ └── 日历图_files/ │ │ └── libs/ │ │ ├── bootstrap/ │ │ │ └── bootstrap-icons.css │ │ └── quarto-html/ │ │ ├── quarto-syntax-highlighting.css │ │ ├── quarto.js │ │ └── tippy.css │ ├── 2023.04.01 基于 R 语言的科研论文绘图技巧汇总/ │ │ ├── .RData │ │ ├── .Rhistory │ │ ├── .Rproj.user/ │ │ │ ├── C6560784/ │ │ │ │ ├── pcs/ │ │ │ │ │ ├── files-pane.pper │ │ │ │ │ ├── packages-pane.pper │ │ │ │ │ ├── source-pane.pper │ │ │ │ │ ├── windowlayoutstate.pper │ │ │ │ │ └── workbench-pane.pper │ │ │ │ ├── rmd-outputs │ │ │ │ ├── saved_source_markers │ │ │ │ └── sources/ │ │ │ │ ├── per/ │ │ │ │ │ └── t/ │ │ │ │ │ ├── 7696DA26 │ │ │ │ │ ├── 7696DA26-contents │ │ │ │ │ ├── 83B6CCD0 │ │ │ │ │ ├── 83B6CCD0-contents │ │ │ │ │ ├── A32C5A91 │ │ │ │ │ ├── A32C5A91-contents │ │ │ │ │ ├── E41305B6 │ │ │ │ │ └── E41305B6-contents │ │ │ │ └── prop/ │ │ │ │ ├── 31AEA8EB │ │ │ │ ├── 51574A35 │ │ │ │ ├── 53B7DAA0 │ │ │ │ ├── 5916C35B │ │ │ │ ├── 5B81A5B2 │ │ │ │ ├── 75B7761D │ │ │ │ ├── 86D189DF │ │ │ │ ├── ABCABC27 │ │ │ │ ├── BCA3F795 │ │ │ │ ├── C757E81D │ │ │ │ ├── E86E28FA │ │ │ │ └── INDEX │ │ │ └── shared/ │ │ │ └── notebooks/ │ │ │ ├── 18DAA490-A/ │ │ │ │ └── 1/ │ │ │ │ └── s/ │ │ │ │ └── chunks.json │ │ │ ├── patch-chunk-names │ │ │ └── paths │ │ ├── LICENSE │ │ ├── Panel_C.R │ │ ├── Panel_D.R │ │ ├── Panel_E.R │ │ ├── Panel_F.R │ │ ├── README.md │ │ ├── data_B.csv │ │ ├── data_Cmu_E10.5.csv │ │ ├── data_Cmu_E11.5.csv │ │ ├── data_Cmu_E8.5.csv │ │ ├── data_Cmu_E9.5.csv │ │ ├── data_Cwt_E10.5.csv │ │ ├── data_Cwt_E11.5.csv │ │ ├── data_Cwt_E8.5.csv │ │ ├── data_Cwt_E9.5.csv │ │ ├── data_D1.csv │ │ ├── data_D2.csv │ │ ├── data_Ea.csv │ │ ├── data_Eb.csv │ │ ├── data_Ec.csv │ │ ├── data_F.csv │ │ ├── figure_example.R │ │ └── figure_example.Rproj │ ├── 2023.04.12 Rmarkdown重复性报告/ │ │ ├── .RData │ │ ├── .Rhistory │ │ ├── .Rproj.user/ │ │ │ ├── C6560784/ │ │ │ │ ├── pcs/ │ │ │ │ │ ├── files-pane.pper │ │ │ │ │ ├── source-pane.pper │ │ │ │ │ ├── windowlayoutstate.pper │ │ │ │ │ └── workbench-pane.pper │ │ │ │ ├── quarto-crossref/ │ │ │ │ │ ├── AA033108 │ │ │ │ │ └── INDEX │ │ │ │ ├── rmd-outputs │ │ │ │ ├── saved_source_markers │ │ │ │ └── sources/ │ │ │ │ ├── per/ │ │ │ │ │ ├── t/ │ │ │ │ │ │ ├── 24D937C1 │ │ │ │ │ │ ├── 24D937C1-contents │ │ │ │ │ │ ├── 47C21D39 │ │ │ │ │ │ └── 47C21D39-contents │ │ │ │ │ └── u/ │ │ │ │ │ ├── F684F954 │ │ │ │ │ └── F684F954-contents │ │ │ │ └── prop/ │ │ │ │ ├── 07CCB8CA │ │ │ │ ├── 55F7E8FD │ │ │ │ ├── 5A14198B │ │ │ │ ├── 998B2979 │ │ │ │ ├── CA24840E │ │ │ │ ├── DD741787 │ │ │ │ ├── EAE6CDE4 │ │ │ │ ├── FD4997C5 │ │ │ │ └── INDEX │ │ │ └── shared/ │ │ │ └── notebooks/ │ │ │ ├── 72C89A46-中文/ │ │ │ │ └── 1/ │ │ │ │ ├── C65607844eefea2c/ │ │ │ │ │ └── chunks.json │ │ │ │ ├── C6560784b90da11a/ │ │ │ │ │ └── chunks.json │ │ │ │ ├── C6560784f87258bb/ │ │ │ │ │ └── chunks.json │ │ │ │ └── s/ │ │ │ │ ├── chunks.json │ │ │ │ ├── cq8zbuk6wx2qd/ │ │ │ │ │ ├── 00000f.metadata │ │ │ │ │ └── 00000f.rdf │ │ │ │ └── csetup_chunk/ │ │ │ │ └── 00000f.csv │ │ │ ├── patch-chunk-names │ │ │ └── paths │ │ ├── Project.Rproj │ │ ├── data/ │ │ │ ├── A中学.csv │ │ │ ├── B中学.csv │ │ │ └── C中学.csv │ │ ├── exploratory_A中学.log │ │ ├── exploratory_B中学.log │ │ ├── libs/ │ │ │ ├── header-attrs-2.20/ │ │ │ │ └── header-attrs.js │ │ │ └── remark-css-0.0.1/ │ │ │ └── default.css │ │ ├── test.R │ │ ├── zh-CN.css │ │ └── 中文.Rmd │ ├── 2023.05.04 如何一步步提高图形 B 格？以 ggplot 绘图为例/ │ │ ├── .Rhistory │ │ └── line.R │ ├── 2023.05.08个性化树状图/ │ │ └── ggparty.R │ ├── 2023.05.13 高级棒棒图/ │ │ └── 棒棒图.R │ ├── 2023.06.29 旭日图/ │ │ ├── .Rproj.user/ │ │ │ ├── C6560784/ │ │ │ │ ├── jobs/ │ │ │ │ │ └── 4152B7D6-output.json │ │ │ │ └── sources/ │ │ │ │ ├── prop/ │ │ │ │ │ ├── 1E4518E3 │ │ │ │ │ ├── 5E635AFA │ │ │ │ │ └── INDEX │ │ │ │ └── session-ef7e8bd6/ │ │ │ │ ├── 3A928347-contents │ │ │ │ ├── F749EB52 │ │ │ │ ├── F749EB52-contents │ │ │ │ └── lock_file │ │ │ └── shared/ │ │ │ └── notebooks/ │ │ │ ├── patch-chunk-names │ │ │ └── paths │ │ ├── d.csv │ │ ├── 旭日图.R │ │ ├── 未命名.Rproj │ │ └── 未命名.html │ ├── 2023.08.05 局部细节放大图/ │ │ └── 局部细节放大图.R │ ├── 2023.08.24 绘制足球数据/ │ │ └── ggsoccer.R │ ├── 2023.10.08 灯芯柱状图/ │ │ ├── .RData │ │ ├── .Rhistory │ │ ├── .Rproj.user/ │ │ │ ├── C6560784/ │ │ │ │ ├── pcs/ │ │ │ │ │ ├── debug-breakpoints.pper │ │ │ │ │ ├── files-pane.pper │ │ │ │ │ ├── source-pane.pper │ │ │ │ │ ├── windowlayoutstate.pper │ │ │ │ │ └── workbench-pane.pper │ │ │ │ ├── persistent-state │ │ │ │ ├── rmd-outputs │ │ │ │ ├── saved_source_markers │ │ │ │ └── sources/ │ │ │ │ ├── prop/ │ │ │ │ │ ├── 00943EE4 │ │ │ │ │ ├── 59990D2B │ │ │ │ │ └── INDEX │ │ │ │ └── session-6c0d0c41/ │ │ │ │ ├── 4BEAD114 │ │ │ │ ├── 4BEAD114-contents │ │ │ │ └── lock_file │ │ │ └── shared/ │ │ │ └── notebooks/ │ │ │ ├── patch-chunk-names │ │ │ └── paths │ │ ├── animal_rescues.txt │ │ ├── df_animals_sum.csv │ │ ├── 未命名.Rproj │ │ └── 灯芯柱状图.r │ ├── 2023.12.02 分面中添加不同表格/ │ │ ├── .RData │ │ ├── .Rhistory │ │ ├── .Rproj.user/ │ │ │ ├── C6560784/ │ │ │ │ ├── pcs/ │ │ │ │ │ ├── files-pane.pper │ │ │ │ │ ├── source-pane.pper │ │ │ │ │ ├── windowlayoutstate.pper │ │ │ │ │ └── workbench-pane.pper │ │ │ │ ├── rmd-outputs │ │ │ │ ├── saved_source_markers │ │ │ │ └── sources/ │ │ │ │ ├── per/ │ │ │ │ │ └── t/ │ │ │ │ │ ├── 23FB0CA3 │ │ │ │ │ └── 23FB0CA3-contents │ │ │ │ └── prop/ │ │ │ │ ├── 276F3DD0 │ │ │ │ └── INDEX │ │ │ └── shared/ │ │ │ └── notebooks/ │ │ │ └── patch-chunk-names │ │ ├── project.Rproj │ │ └── 分面中添加不同表格.R │ ├── 2023.12.16 分面中添加拟合曲线/ │ │ ├── .Rproj.user/ │ │ │ ├── C6560784/ │ │ │ │ ├── pcs/ │ │ │ │ │ ├── files-pane.pper │ │ │ │ │ ├── source-pane.pper │ │ │ │ │ ├── windowlayoutstate.pper │ │ │ │ │ └── workbench-pane.pper │ │ │ │ └── sources/ │ │ │ │ ├── prop/ │ │ │ │ │ ├── DAA25768 │ │ │ │ │ └── INDEX │ │ │ │ └── session-427bca66/ │ │ │ │ ├── AB1928BC │ │ │ │ ├── AB1928BC-contents │ │ │ │ ├── C6EFA56A-contents │ │ │ │ └── lock_file │ │ │ └── shared/ │ │ │ └── notebooks/ │ │ │ └── patch-chunk-names │ │ ├── data_fit.RData │ │ ├── project.Rproj │ │ ├── true_data.RData │ │ └── 分面中添加拟合曲线.R │ └── 合并图形+共享图例/ │ └── 合并图形-共享图例.R ├── 2024年/ │ ├── 2024.02.07 多分类的条形图并标记数字/ │ │ ├── .RData │ │ ├── .Rhistory │ │ ├── .Rproj.user/ │ │ │ ├── C6560784/ │ │ │ │ ├── pcs/ │ │ │ │ │ ├── files-pane.pper │ │ │ │ │ ├── source-pane.pper │ │ │ │ │ ├── windowlayoutstate.pper │ │ │ │ │ └── workbench-pane.pper │ │ │ │ ├── rmd-outputs │ │ │ │ ├── saved_source_markers │ │ │ │ └── sources/ │ │ │ │ ├── per/ │ │ │ │ │ └── t/ │ │ │ │ │ ├── AF66B3A8 │ │ │ │ │ └── AF66B3A8-contents │ │ │ │ └── prop/ │ │ │ │ ├── 0418CBEF │ │ │ │ ├── 048F8B4D │ │ │ │ ├── 1A4EC317 │ │ │ │ └── INDEX │ │ │ └── shared/ │ │ │ └── notebooks/ │ │ │ ├── patch-chunk-names │ │ │ └── paths │ │ ├── project.Rproj │ │ └── 多分类条形图并标记数字.R │ ├── 2024.02.12 TiKZ绘图/ │ │ ├── tutorial.aux │ │ ├── tutorial.log │ │ └── tutorial.tex │ └── 2024.02.19 柱状图+分面/ │ ├── .Rproj.user/ │ │ ├── C6560784/ │ │ │ ├── pcs/ │ │ │ │ ├── files-pane.pper │ │ │ │ ├── source-pane.pper │ │ │ │ ├── windowlayoutstate.pper │ │ │ │ └── workbench-pane.pper │ │ │ └── sources/ │ │ │ ├── prop/ │ │ │ │ ├── 23624D4E │ │ │ │ └── INDEX │ │ │ └── session-fd2b58b4/ │ │ │ ├── 1FF7DD4B │ │ │ ├── 1FF7DD4B-contents │ │ │ └── lock_file │ │ └── shared/ │ │ └── notebooks/ │ │ ├── patch-chunk-names │ │ └── paths │ ├── demo_dat.csv │ ├── project.Rproj │ └── 柱状图+分面.R ├── README.md ├── 公众号资料/ │ ├── nice主题.docx │ ├── ~$创意大气述职报告.pptx │ ├── 封面制作模板.pptx │ ├── 年度总结/ │ │ └── 2020年公众号文章汇总.md │ ├── 数据科学模板.docx │ └── 资料获取.md └── 资料分享.md
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FILE: 2023年/2023.03.16使用 ggTimeSeries 包构建日历图/日历图_files/libs/quarto-html/quarto.js
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"path": "2020年/2020.12.24圣诞节/marry.R",
"chars": 7909,
"preview": "# https://www.data-imaginist.com/2016/Data-driven-x-mas-card/\n\nlibrary(jsonlite)\nlibrary(ggplot2)\n\ngreet <- fromJSON(rea"
},
{
"path": "2020年/2020.12.25峰峦图/峰峦图.rmd",
"chars": 5862,
"preview": "---\ntitle: \"峰峦图\"\nauthor:\n - 庄闪闪\ndocumentclass: ctexart\nkeywords:\n - 中文\n - R Markdown\noutput:\n rticles::ctex:\n fig"
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"chars": 926477,
"preview": "<!DOCTYPE html>\n\n<html>\n\n<head>\n\n<meta charset=\"utf-8\" />\n<meta name=\"generator\" content=\"pandoc\" />\n<meta http-equiv=\"X"
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"path": "2020年/2020.12.30ggvis/ggvis.md",
"chars": 5096,
"preview": "## 简介\n[ggvis](http://ggvis.rstudio.com \"ggvis github\")是R的一个数据可视化包，它可以：\n\n- 使用与ggplot2类似的语法描述数据图形；\n\n- 创建丰富的交互式图形，在本地Rstudi"
},
{
"path": "2020年/2020.12.30ggvis/ggvis.rmd",
"chars": 3069,
"preview": "---\ntitle: \"ggvis包\"\nauthor: \"庄闪闪\"\ndate: \"`r Sys.Date()`\"\noutput:\n prettydoc::html_pretty:\n theme: cayman\n highlig"
},
{
"path": "2020年/2020年R数据科学系列/2020.08.23几何对象/几何对象.md",
"chars": 4151,
"preview": "## 前言\n\n本最近打算把《R数据科学》过一遍，并且把课后习题都做一下。先从第一章开始吧，快速把ggplot过一下。第一章目录如下：![](https://mmbiz.qpic.cn/mmbiz_png/MIcgkkEyTHjzHjPmjO"
},
{
"path": "2020年/2020年R数据科学系列/2020.08.23速查表/让微信排版变 Nice.html",
"chars": 70959,
"preview": "<!DOCTYPE html>\n<!-- saved from url=(0023)https://www.mdnice.com/ -->\n<html lang=\"en\"><head><meta http-equiv=\"Content-Ty"
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"path": "2020年/2020年R数据科学系列/2020.08.23速查表/速查表.html",
"chars": 68283,
"preview": "<!DOCTYPE html>\n<!-- saved from url=(0023)https://www.mdnice.com/ -->\n<html lang=\"en\"><head><meta http-equiv=\"Content-Ty"
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"path": "2020年/2020年R数据科学系列/2020.09.27tidy data/Tidy_data.rmd",
"chars": 5673,
"preview": "---\ntitle: \"[R数据科学]Tidy data案例\"\nauthor:\n - 庄亮亮\ndocumentclass: ctexart\nalways_allow_html: true\noutput:\n rticles::ctex:\n"
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{
"path": "2020年/2020年R数据科学系列/《R数据科学》源代码/DESCRIPTION",
"chars": 565,
"preview": "Package: r4ds\nTitle: R for data science.\nVersion: 0.1\nAuthors@R: c(\n person(\"Hadley\", \"Wickham\", , \"hadley@rstudio.com\""
},
{
"path": "2020年/2020年R数据科学系列/《R数据科学》源代码/EDA.Rmd",
"chars": 32395,
"preview": "# Exploratory Data Analysis\n\n## Introduction\n\nThis chapter will show you how to use visualisation and transformation to "
},
{
"path": "2020年/2020年R数据科学系列/《R数据科学》源代码/LICENSE",
"chars": 309,
"preview": "This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivs 3.0 United States License. To view a"
},
{
"path": "2020年/2020年R数据科学系列/《R数据科学》源代码/README.md",
"chars": 376,
"preview": "# R for Data Science\n\nThis is code and text behind the [R for Data Science](http://r4ds.had.co.nz)\nbook. \n\nThe R package"
},
{
"path": "2020年/2020年R数据科学系列/《R数据科学》源代码/_bookdown.yml",
"chars": 681,
"preview": "new_session: yes\n\nrmd_files: [\n \"index.rmd\",\n \"intro.Rmd\",\n\n \"explore.Rmd\",\n \"visualize.Rmd\",\n \"workflow-basics.Rmd"
},
{
"path": "2020年/2020年R数据科学系列/《R数据科学》源代码/_common.R",
"chars": 277,
"preview": "set.seed(1014)\noptions(digits = 3)\n\nknitr::opts_chunk$set(\n comment = \"#>\",\n collapse = TRUE,\n cache = TRUE,\n out.wi"
},
{
"path": "2020年/2020年R数据科学系列/《R数据科学》源代码/_output.yaml",
"chars": 315,
"preview": "bookdown::gitbook:\n config:\n toc:\n collapse: section\n before: |\n <li><strong><a href=\"./\">R for Dat"
},
{
"path": "2020年/2020年R数据科学系列/《R数据科学》源代码/communicate-plots.Rmd",
"chars": 29840,
"preview": "# Graphics for communication\n\n## Introduction\n\nIn [exploratory data analysis], you learned how to use plots as tools for"
},
{
"path": "2020年/2020年R数据科学系列/《R数据科学》源代码/communicate.Rmd",
"chars": 1807,
"preview": "# (PART) Communicate {-}\n\n# Introduction {#communicate-intro}\n\nSo far, you've learned the tools to get your data into R,"
},
{
"path": "2020年/2020年R数据科学系列/《R数据科学》源代码/contribs.txt",
"chars": 1909,
"preview": " 625\thadley\n 93\tGarrett\n 77\tHadley Wickham\n 50\tS'busiso Mkhondwane\n 21\tbehrman\n 11\tBrett Klamer\n 10\t"
},
{
"path": "2020年/2020年R数据科学系列/《R数据科学》源代码/contribute.rmd",
"chars": 935,
"preview": "# Contributing\n\nThis book has been developed in the open, and it wouldn't be nearly as good \nwithout your contributions."
},
{
"path": "2020年/2020年R数据科学系列/《R数据科学》源代码/datetimes.Rmd",
"chars": 20849,
"preview": "# Dates and times\n\n## Introduction\n\nThis chapter will show you how to work with dates and times in R. At first glance, d"
},
{
"path": "2020年/2020年R数据科学系列/《R数据科学》源代码/explore.Rmd",
"chars": 2069,
"preview": "# (PART) Explore {-}\n\n# Introduction {#explore-intro}\n\nThe goal of the first part of this book is to get you up to speed"
},
{
"path": "2020年/2020年R数据科学系列/《R数据科学》源代码/factors.Rmd",
"chars": 6971,
"preview": "# Factors\n\n## Introduction\n\nIn R, factors are used to work with categorical variables, variables that have a fixed and k"
},
{
"path": "2020年/2020年R数据科学系列/《R数据科学》源代码/figures.R",
"chars": 419,
"preview": "library(stringr)\nlibrary(purrr)\n\nchapters <- dir(\"_bookdown_files\", full.names = TRUE, pattern = \"_files$\")\n\nfigures <- "
},
{
"path": "2020年/2020年R数据科学系列/《R数据科学》源代码/functions.Rmd",
"chars": 30808,
"preview": "# Functions\n\n## Introduction \n\nOne of the best ways to improve your reach as a data scientist is to write functions. Fun"
},
{
"path": "2020年/2020年R数据科学系列/《R数据科学》源代码/hierarchy.Rmd",
"chars": 7688,
"preview": "# Hierarchical data {#hierarchy}\n\n## Introduction\n\nThis chapter belongs in [wrangle](#wrangle-intro): it will give you a"
},
{
"path": "2020年/2020年R数据科学系列/《R数据科学》源代码/import.Rmd",
"chars": 26937,
"preview": "# Data import\n\n## Introduction\n\nWorking with data provided by R packages is a great way to learn the tools of data scien"
},
{
"path": "2020年/2020年R数据科学系列/《R数据科学》源代码/index.rmd",
"chars": 2346,
"preview": "---\nknit: \"bookdown::render_book\"\ntitle: \"R for Data Science\"\nauthor: [\"Garrett Grolemund\", \"Hadley Wickham\"]\ndescriptio"
},
{
"path": "2020年/2020年R数据科学系列/《R数据科学》源代码/intro.Rmd",
"chars": 22752,
"preview": "# Introduction\n\nData science is an exciting discipline that allows you to turn raw data into understanding, insight, and"
},
{
"path": "2020年/2020年R数据科学系列/《R数据科学》源代码/issues.json",
"chars": 16092,
"preview": "[\n {\n \"url\": \"https://api.github.com/repos/hadley/r4ds/issues/11\",\n \"labels_url\": \"https://api.github.com/repos/h"
},
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"path": "2020年/2020年R数据科学系列/《R数据科学》源代码/iteration.Rmd",
"chars": 34592,
"preview": "# Iteration\n\n## Introduction\n\nIn [functions], we talked about how important it is to reduce duplication in your code by "
},
{
"path": "2020年/2020年R数据科学系列/《R数据科学》源代码/model-assess.Rmd",
"chars": 12624,
"preview": "# Model assessment\n\nIn this chapter, you'll turn the tools of multiple models towards model assessment: learning how the"
},
{
"path": "2020年/2020年R数据科学系列/《R数据科学》源代码/model-basics.Rmd",
"chars": 32808,
"preview": "# Model basics\n\n## Introduction\n\nThe goal of a model is to provide a simple low-dimensional summary of a dataset. In the"
},
{
"path": "2020年/2020年R数据科学系列/《R数据科学》源代码/model-building.Rmd",
"chars": 21644,
"preview": "# Model building\n\n## Introduction\n\nIn the previous chapter you learned how linear models work, and learned some basic to"
},
{
"path": "2020年/2020年R数据科学系列/《R数据科学》源代码/model-many.Rmd",
"chars": 13189,
"preview": "# Many models\n\n## Introduction\n\nIn this chapter you're going to learn three powerful ideas that help you to work with la"
},
{
"path": "2020年/2020年R数据科学系列/《R数据科学》源代码/model.Rmd",
"chars": 4878,
"preview": "# (PART) Model {-}\n\n# Introduction {#model-intro}\n\nNow that you are equipped with powerful programming tools we can fina"
},
{
"path": "2020年/2020年R数据科学系列/《R数据科学》源代码/pipes.Rmd",
"chars": 10953,
"preview": "# Pipes\n\n## Introduction\n\nPipes are a powerful tool for clearly expressing a sequence of multiple operations. So far, yo"
},
{
"path": "2020年/2020年R数据科学系列/《R数据科学》源代码/program.Rmd",
"chars": 4651,
"preview": "# (PART) Program {-}\n\n# Introduction {#program-intro}\n\nIn this part of the book, you'll improve your programming skills."
},
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"preview": "Version: 1.0\n\nRestoreWorkspace: Default\nSaveWorkspace: Default\nAlwaysSaveHistory: Default\n\nEnableCodeIndexing: Yes\nUseSp"
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"path": "2020年/2020年R数据科学系列/《R数据科学》源代码/relational-data.Rmd",
"chars": 25732,
"preview": "# Relational data\n\n## Introduction\n\nIt's rare that a data analysis involves only a single table of data. Typically you h"
},
{
"path": "2020年/2020年R数据科学系列/《R数据科学》源代码/rmarkdown-formats.Rmd",
"chars": 13053,
"preview": "# R Markdown formats\n\n## Introduction\n\nSo far you've seen R Markdown used to produce HTML documents. This chapter gives "
},
{
"path": "2020年/2020年R数据科学系列/《R数据科学》源代码/rmarkdown-workflow.Rmd",
"chars": 3984,
"preview": "# R Markdown workflow\n\nEarlier, we discussed a basic workflow for capturing your R code where you work interactively in"
},
{
"path": "2020年/2020年R数据科学系列/《R数据科学》源代码/rmarkdown.Rmd",
"chars": 24605,
"preview": "# R Markdown\n\n## Introduction\n\nR Markdown provides an unified authoring framework for data science, combining your code,"
},
{
"path": "2020年/2020年R数据科学系列/《R数据科学》源代码/strings.Rmd",
"chars": 39517,
"preview": "# Strings\n\n## Introduction\n\nThis chapter introduces you to string manipulation in R. You'll learn the basics of how stri"
},
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"path": "2020年/2020年R数据科学系列/《R数据科学》源代码/tibble.Rmd",
"chars": 7068,
"preview": "# Tibbles\n\n## Introduction\n\nThroughout this book we work with \"tibbles\" instead of R's traditional `data.frame`. Tibbles"
},
{
"path": "2020年/2020年R数据科学系列/《R数据科学》源代码/transform.Rmd",
"chars": 36512,
"preview": "# Data transformation {#transform}\n\n## Introduction\n\nVisualisation is an important tool for insight generation, but it i"
},
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"path": "2020年/2020年R数据科学系列/《R数据科学》源代码/vectors.Rmd",
"chars": 26020,
"preview": "# Vectors\n\n## Introduction\n\nSo far this book has focussed on tibbles and packages that work with them. But as you start "
},
{
"path": "2020年/2020年R数据科学系列/《R数据科学》源代码/visualize.Rmd",
"chars": 39374,
"preview": "# Data visualisation\n\n## Introduction\n\n> \"The simple graph has brought more information to the data analyst’s mind \n> th"
},
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"path": "2020年/2020年R数据科学系列/《R数据科学》源代码/workflow-basics.Rmd",
"chars": 5752,
"preview": "# Workflow: basics\n\nYou now have some experience running R code. I didn't give you many details, but you've obviously fi"
},
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"path": "2020年/2020年R数据科学系列/《R数据科学》源代码/workflow-projects.Rmd",
"chars": 7649,
"preview": "# Workflow: projects\n\nOne day you will need to quit R, go do something else and return to your analysis the next day. On"
},
{
"path": "2020年/2020年R数据科学系列/《R数据科学》源代码/workflow-scripts.Rmd",
"chars": 3551,
"preview": "# Workflow: scripts\n\nSo far you've been using the console to run code. That's a great place to start, but you'll find it"
},
{
"path": "2020年/2020年R数据科学系列/《R数据科学》源代码/wrangle.Rmd",
"chars": 1605,
"preview": "# (PART) Wrangle {-}\n\n# Introduction {#wrangle-intro}\n\nIn this part of the book, you'll learn about data wrangling, the "
},
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"path": "2021年/2021.01.31主成分结果可视化/pac_visual.rmd",
"chars": 3945,
"preview": "---\ntitle: \"主成分分析结果可视化\"\nauthor:\n - 庄闪闪\ndocumentclass: ctexart\nkeywords:\n - 中文\n - R Markdown\noutput:\n rticles::ctex:\n"
},
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"path": "2021年/2021.02.21克利夫兰点图系列/DotPlots_Data.csv",
"chars": 592,
"preview": "City,Female,Male\nAcapulco,2565.51,2595.8\nBellingham,453.36,539.27\nBeverly Hills,5050.46,5269.71\nBremerton,5269.89,5705\nC"
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"path": "2021年/2021.02.21克利夫兰点图系列/cleveland's .Rmd",
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"preview": "---\ntitle: \"R可视乎｜克利夫兰点图系列\"\nauthor:\n - 庄亮亮\ndocumentclass: ctexart\nkeywords:\n - 中文\n - R Markdown\noutput:\n rticles::cte"
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"path": "2021年/2021.02.28常用主题风格/R可视乎｜ggplot常用主题风格汇总.md",
"chars": 3105,
"preview": "\n\n借助`theme()`函数，可以自定义ggplot2图表的任何部分。 幸运的是，可以使用大量的预构建主题，仅用一行代码即可获得良好的样式。小编汇总了常用几个包的主题风格以供参考，以后可以根据论文的风格选择内置的一些主题。\n\n## 1.具"
},
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"path": "2022年/2022.01.14 如何使用 ggplot2 绘制双轴分离图？/双轴分离.R",
"chars": 1452,
"preview": "# 代码解释见推文：https://mp.weixin.qq.com/s/QiMHA10X8nGtK5iOH4armQ\n# 作者：庄亮亮/庄闪闪 \n# 公众号：《庄闪闪的R语言手册》\n\nlibrary(ggplot2)\nlibrary(dp"
},
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"path": "2022年/2022.01.28 如何绘制省市级地图？/各区县经营效率平均值.csv",
"chars": 1834,
"preview": ",2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020\r\n龙湾区,0.726381633,0.721543426,0.753039566,0.728987862,0.7170"
},
{
"path": "2022年/2022.01.28 如何绘制省市级地图？/温州地图绘制.r",
"chars": 1288,
"preview": "#===============================================================\n#======================== 温州市地图绘制 ====================="
},
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"path": "2022年/2022.01.28 如何绘制省市级地图？/温州市.json",
"chars": 104064,
"preview": "{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"properties\":{\"adcode\":330302,\"name\":\"鹿城区\",\"center\":[120.67423"
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"path": "2022年/2022.01.28 如何绘制省市级地图？/绘制浙江省地图.R",
"chars": 825,
"preview": "# 代码解释见推文:https://mp.weixin.qq.com/s/qCNTvFCgaNBH_HJQ_U7uPg\n# 作者：庄亮亮/庄闪闪 \n# 公众号：《庄闪闪的R语言手册》\n\n# 绘制浙江省地图 ===========\nlibra"
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"path": "2022年/2022.02.08读者投稿｜绘制一系列黑白印刷风格图表/acchist.r",
"chars": 5676,
"preview": "# 本篇推文由西南财经大学经济学院杜云晗投稿，由闪闪排版整理。以下是正文内容：\n# 代码解释见推文：https://mp.weixin.qq.com/s/f47_ujT2diu5CO0yUxKTbw\n# 公众号：《庄闪闪的R语言手册》\n\n#"
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"path": "2022年/2022.03.14绘制混合密度函数图以及添加分位数线/mix-quantile.r",
"chars": 1347,
"preview": "# 代码解释见推文：https://mp.weixin.qq.com/s/bQ_aefTxk32x30Avfzv5kw\n# 作者：庄亮亮/庄闪闪 \n# 公众号：《庄闪闪的R语言手册》\n\n\n\n# =====绘制混合密度函数图以及添加分位数线="
},
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"path": "2022年/2022.04.08R 案例｜绘制不同分布的 QQ 图/qqplot.r",
"chars": 2449,
"preview": "# 代码解释见推文：https://mp.weixin.qq.com/s/ncorK41sntA4iBJWBCdVkA\n# 作者：庄亮亮/庄闪闪 \n# 公众号：《庄闪闪的R语言手册》\n# 参考博客：An Introduction to qq"
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"path": "2022年/2022.05.15老板让你复现一个图片，你会使用什么软件？/example.r",
"chars": 2453,
"preview": "# 代码解释见推文：https://mp.weixin.qq.com/s/V8H-U719PjCgsn1lVY_PFg\n# 作者：庄亮亮/庄闪闪 \n# 公众号：《庄闪闪的R语言手册》\n\n\n# 数据产生\nset.seed(1) #确保不同机子"
},
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"path": "2022年/2022.08.08 ggplot 分面的细节调整汇总/ggplot_facet.r",
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"preview": "# 代码解释见推文:https://mp.weixin.qq.com/s/fzcGJugs0VxJpM6nb25hLA\n# 作者：庄亮亮/庄闪闪 \n# 公众号：《庄闪闪的R语言手册》\n\n# 生成数据\n\n# 导入包\nlibrary(ggplo"
},
{
"path": "2022年/2022.09.09 如何在分面中添加数学表达式标签？ /add_math_label.r",
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"preview": "# 代码解释见推文：https://mp.weixin.qq.com/s/Dbx9J_flkTtyi7LtRAdRPQ\n# 作者：庄亮亮/庄闪闪 \n# 公众号：《庄闪闪的R语言手册》\n\n# 构建模拟数据\nlibrary(ggplot2)\nl"
},
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// ... and 407 more files (download for full content)
About this extraction
This page contains the full source code of the liangliangzhuang/R_example GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 607 files (154.8 MB), approximately 5.1M tokens, and a symbol index with 16 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.