Repository: jschoeley/tricolore Branch: master Commit: 2e722f37844f Files: 55 Total size: 171.8 KB Directory structure: gitextract_ffmohb3v/ ├── .Rbuildignore ├── .github/ │ ├── .gitignore │ └── workflows/ │ └── R-CMD-check.yaml ├── .gitignore ├── CODE_OF_CONDUCT.md ├── CONTRIBUTING.md ├── CRAN-SUBMISSION ├── DESCRIPTION ├── LICENSE ├── NAMESPACE ├── NEWS.md ├── R/ │ ├── tricolore.R │ └── zzz.R ├── README.Rmd ├── README.md ├── cran-comments.md ├── data/ │ ├── euro_basemap.RData │ └── euro_example.RData ├── data-raw/ │ ├── euro_basemap.R │ ├── euro_basemap.RData │ ├── euro_example.R │ └── euro_example.RData ├── inst/ │ ├── CITATION │ └── shiny/ │ └── app.R ├── man/ │ ├── BasicKey.Rd │ ├── BreaksAndLabels.Rd │ ├── Centre.Rd │ ├── ColorKeySextant.Rd │ ├── ColorKeyTricolore.Rd │ ├── ColorMapSextant.Rd │ ├── ColorMapTricolore.Rd │ ├── DemoTricolore.Rd │ ├── GeometricMean.Rd │ ├── Pertube.Rd │ ├── PowerScale.Rd │ ├── TernaryCenterGrid.Rd │ ├── TernaryDistance.Rd │ ├── TernaryLimits.Rd │ ├── TernaryMeshCentroids.Rd │ ├── TernaryMeshVertices.Rd │ ├── TernaryNearest.Rd │ ├── TernarySextantVertices.Rd │ ├── TernarySurroundingSextant.Rd │ ├── Tricolore.Rd │ ├── TricoloreSextant.Rd │ ├── ValidateMainArguments.Rd │ ├── ValidateParametersShared.Rd │ ├── ValidateParametersTricolore.Rd │ ├── ValidateParametersTricoloreSextant.Rd │ ├── euro_basemap.Rd │ └── euro_example.Rd ├── tests/ │ ├── testthat/ │ │ └── test-global.R │ └── testthat.R └── vignettes/ ├── choropleth_maps_with_tricolore.R └── choropleth_maps_with_tricolore.Rmd ================================================ FILE CONTENTS ================================================ ================================================ FILE: .Rbuildignore ================================================ ^.*\.Rproj$ ^\.Rproj\.user$ priv README_files README.md README.R data-raw TODO.txt examples ^cran-comments\.md$ CODE_OF_CONDUCT.md LICENSE CONTRIBUTING.md ^CRAN-RELEASE$ ^\.github$ ^CRAN-SUBMISSION$ ================================================ FILE: .github/.gitignore ================================================ *.html ================================================ FILE: .github/workflows/R-CMD-check.yaml ================================================ # Workflow derived from https://github.com/r-lib/actions/tree/v2/examples # Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help on: push: branches: [main, master, devel] pull_request: branches: [main, master, devel] name: R-CMD-check jobs: R-CMD-check: runs-on: ${{ matrix.config.os }} name: ${{ matrix.config.os }} (${{ matrix.config.r }}) strategy: fail-fast: false matrix: config: - {os: macos-latest, r: 'release'} - {os: windows-latest, r: 'release'} - {os: ubuntu-latest, r: 'devel', http-user-agent: 'release'} - {os: ubuntu-latest, r: 'release'} - {os: ubuntu-latest, r: 'oldrel-1'} env: GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} R_KEEP_PKG_SOURCE: yes steps: - uses: actions/checkout@v3 - uses: r-lib/actions/setup-pandoc@v2 - uses: r-lib/actions/setup-r@v2 with: r-version: ${{ matrix.config.r }} http-user-agent: ${{ matrix.config.http-user-agent }} use-public-rspm: true - uses: r-lib/actions/setup-r-dependencies@v2 with: extra-packages: any::rcmdcheck needs: check - uses: r-lib/actions/check-r-package@v2 with: upload-snapshots: true args: 'c("--no-manual", "--as-cran")' ================================================ FILE: .gitignore ================================================ # General --------------------------------------------------------------------- priv # R specific ------------------------------------------------------------------ # History files .Rhistory .Rapp.history # Example code in package build process *-Ex.R # RStudio files .Rproj.user/ .Rproj.user *.Rproj # produced vignettes vignettes/*.html vignettes/*.pdf # OAuth2 token, see https://github.com/hadley/httr/releases/tag/v0.3 .httr-oauth # cached Rmarkdown files *_cache # Rpubs rsconnect inst/doc ================================================ FILE: CODE_OF_CONDUCT.md ================================================ # Contributor Covenant Code of Conduct ## Our Pledge In the interest of fostering an open and welcoming environment, we as contributors and maintainers pledge to making participation in our project and our community a harassment-free experience for everyone, regardless of age, body size, disability, ethnicity, sex characteristics, gender identity and expression, level of experience, education, socio-economic status, nationality, personal appearance, race, religion, or sexual identity and orientation. ## Our Standards Examples of behavior that contributes to creating a positive environment include: * Using welcoming and inclusive language * Being respectful of differing viewpoints and experiences * Gracefully accepting constructive criticism * Focusing on what is best for the community * Showing empathy towards other community members Examples of unacceptable behavior by participants include: * The use of sexualized language or imagery and unwelcome sexual attention or advances * Trolling, insulting/derogatory comments, and personal or political attacks * Public or private harassment * Publishing others' private information, such as a physical or electronic address, without explicit permission * Other conduct which could reasonably be considered inappropriate in a professional setting ## Our Responsibilities Project maintainers are responsible for clarifying the standards of acceptable behavior and are expected to take appropriate and fair corrective action in response to any instances of unacceptable behavior. Project maintainers have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, or to ban temporarily or permanently any contributor for other behaviors that they deem inappropriate, threatening, offensive, or harmful. ## Scope This Code of Conduct applies both within project spaces and in public spaces when an individual is representing the project or its community. Examples of representing a project or community include using an official project e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event. Representation of a project may be further defined and clarified by project maintainers. ## Enforcement Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by contacting the project team at jschoeley@gmail.com. All complaints will be reviewed and investigated and will result in a response that is deemed necessary and appropriate to the circumstances. The project team is obligated to maintain confidentiality with regard to the reporter of an incident. Further details of specific enforcement policies may be posted separately. Project maintainers who do not follow or enforce the Code of Conduct in good faith may face temporary or permanent repercussions as determined by other members of the project's leadership. ## Attribution This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html [homepage]: https://www.contributor-covenant.org For answers to common questions about this code of conduct, see https://www.contributor-covenant.org/faq ================================================ FILE: CONTRIBUTING.md ================================================ Contributing to `tricolore` --------------------------- *This guide is adapted from the `devtools` template* The goal of this guide is to help you contribute to `tricolore` as quickly and as easily possible. The guide is divided into two main pieces: 1. Filing a bug report or feature request in an issue. 2. Suggesting a change via a pull request. ## Issues Before you file an issue: 1. Check that you're using the latest version of `tricolore`. It's quite possible that the problem you're experiencing has already been fixed. 2. Check that the issue belongs in `tricolore`. Much functionality now lives in separate packages (e.g. `ggtern`). 3. Spend a few minutes looking at the existing issues. It's possible that your issue has already been filed. But it's almost always better to open a new issue instead of commenting on an existing issue. The only exception is that you are confident that your issue is identical to an existing problem, and your contribution will help us better understand the general case. It's generally a bad idea to comment on a closed issue or a commit. Those comments don't show up in the issue tracker and are easily misplaced. When filing an issue, the most important thing is to include a minimal reproducible example so that we can quickly verify the problem, and then figure out how to fix it. There are three things you need to include to make your example reproducible: required packages, data, code. 1. **Packages** should be loaded at the top of the script, so it's easy to see which ones the example needs. 2. The easiest way to include **data** is to use `dput()` to generate the R code to recreate it. For example, to recreate the `mtcars` dataset in R, I'd perform the following steps: 1. Run `dput(mtcars)` in R 2. Copy the output 3. In my reproducible script, type `mtcars <- ` then paste. But even better is if you can create a `data.frame()` with just a handful of rows and columns that still illustrates the problem. 3. Spend a little bit of time ensuring that your **code** is easy for others to read: * make sure you've used spaces and your variable names are concise, but informative * use comments to indicate where your problem lies * do your best to remove everything that is not related to the problem. The shorter your code is, the easier it is to understand. * Learn a little [markdown][markdown] so you can correctly format your issue. The most important thing is to surround your code with ```` ``` R ```` and ```` ``` ```` so it's syntax highlighted (which makes it easier to read). 4. Check that you've actually made a reproducible example by using the [reprex package](https://github.com/jennybc/reprex). ## Pull requests * Your pull request will be easiest for us to read if you use a common style: . Please pay particular attention to whitespace. * You should always add a bullet point to `NEWS.md` motivating the change. It should look like "This is what changed (@yourusername, #issuenumber)". Please don't add headings like "bug fix" or "new features" - these are added during the release process. * If you propose a new feature, write a test for it. * If you're adding new parameters or a new function, you'll also need to document them with [roxygen2](http://r-pkgs.had.co.nz/man.html). Make sure to re-run `devtools::document()` on the code before submitting. A pull request is a process, and unless you're a practised contributor it's unlikely that your pull request will be accepted as is. Typically the process looks like this: 1. You submit the pull request. 2. We review at a high-level and determine if this is something that we want to include in the package. If not, we'll close the pull request and suggest an alternative home for your code. 3. We'll take a closer look at the code and give you feedback. 4. You respond to our feedback, update the pull request and add a comment like "PTAL" (please take a look). Adding the comment is important, otherwise we don't get any notification that your pull request is ready for review. Don't worry if your pull request isn't perfect. It's a learning process and we'll be happy to help you out. [markdown]: https://help.github.com/articles/basic-writing-and-formatting-syntax/ ================================================ FILE: CRAN-SUBMISSION ================================================ Version: 1.2.4 Date: 2024-05-14 13:32:45 UTC SHA: c4f25b8a52e7e6ca54bf876796e1e0f9b4432a9e ================================================ FILE: DESCRIPTION ================================================ Package: tricolore Type: Package Title: A Flexible Color Scale for Ternary Compositions Version: 1.2.6 Authors@R: c( person( "Jonas", "Schöley", email = "jschoeley@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-3340-8518") ), person( "Ilya", "Kashnitsky", role = c("aut"), comment = c(ORCID = "0000-0003-1835-8687") )) Description: Compositional data consisting of three-parts can be color mapped with a ternary color scale. Such a scale is provided by the tricolore packages with options for discrete and continuous colors, mean-centering and scaling. See Jonas Schöley (2021) "The centered ternary balance scheme. A technique to visualize surfaces of unbalanced three-part compositions" , Jonas Schöley, Frans Willekens (2017) "Visualizing compositional data on the Lexis surface" , and Ilya Kashnitsky, Jonas Schöley (2018) "Regional population structures at a glance" . License: GPL-3 URL: https://github.com/jschoeley/tricolore Encoding: UTF-8 LazyData: true Depends: R (>= 4.0) Imports: grDevices, ggplot2 (>= 4.0.0), ggtern (>= 4.0.0), rlang (>= 1.1.0), shiny, assertthat RoxygenNote: 7.3.3 Suggests: testthat, knitr, rmarkdown, sf, leaflet, httpuv, dplyr VignetteBuilder: knitr ================================================ FILE: LICENSE ================================================ ### GNU GENERAL PUBLIC LICENSE Version 3, 29 June 2007 Copyright (C) 2007 Free Software Foundation, Inc. Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed. ### Preamble The GNU General Public License is a free, copyleft license for software and other kinds of works. The licenses for most software and other practical works are designed to take away your freedom to share and change the works. By contrast, the GNU General Public License is intended to guarantee your freedom to share and change all versions of a program--to make sure it remains free software for all its users. We, the Free Software Foundation, use the GNU General Public License for most of our software; it applies also to any other work released this way by its authors. You can apply it to your programs, too. When we speak of free software, we are referring to freedom, not price. Our General Public Licenses are designed to make sure that you have the freedom to distribute copies of free software (and charge for them if you wish), that you receive source code or can get it if you want it, that you can change the software or use pieces of it in new free programs, and that you know you can do these things. To protect your rights, we need to prevent others from denying you these rights or asking you to surrender the rights. Therefore, you have certain responsibilities if you distribute copies of the software, or if you modify it: responsibilities to respect the freedom of others. For example, if you distribute copies of such a program, whether gratis or for a fee, you must pass on to the recipients the same freedoms that you received. You must make sure that they, too, receive or can get the source code. And you must show them these terms so they know their rights. Developers that use the GNU GPL protect your rights with two steps: (1) assert copyright on the software, and (2) offer you this License giving you legal permission to copy, distribute and/or modify it. For the developers' and authors' protection, the GPL clearly explains that there is no warranty for this free software. For both users' and authors' sake, the GPL requires that modified versions be marked as changed, so that their problems will not be attributed erroneously to authors of previous versions. Some devices are designed to deny users access to install or run modified versions of the software inside them, although the manufacturer can do so. This is fundamentally incompatible with the aim of protecting users' freedom to change the software. The systematic pattern of such abuse occurs in the area of products for individuals to use, which is precisely where it is most unacceptable. Therefore, we have designed this version of the GPL to prohibit the practice for those products. If such problems arise substantially in other domains, we stand ready to extend this provision to those domains in future versions of the GPL, as needed to protect the freedom of users. Finally, every program is threatened constantly by software patents. States should not allow patents to restrict development and use of software on general-purpose computers, but in those that do, we wish to avoid the special danger that patents applied to a free program could make it effectively proprietary. To prevent this, the GPL assures that patents cannot be used to render the program non-free. The precise terms and conditions for copying, distribution and modification follow. ### TERMS AND CONDITIONS #### 0. Definitions. "This License" refers to version 3 of the GNU General Public License. "Copyright" also means copyright-like laws that apply to other kinds of works, such as semiconductor masks. "The Program" refers to any copyrightable work licensed under this License. Each licensee is addressed as "you". "Licensees" and "recipients" may be individuals or organizations. To "modify" a work means to copy from or adapt all or part of the work in a fashion requiring copyright permission, other than the making of an exact copy. The resulting work is called a "modified version" of the earlier work or a work "based on" the earlier work. A "covered work" means either the unmodified Program or a work based on the Program. To "propagate" a work means to do anything with it that, without permission, would make you directly or secondarily liable for infringement under applicable copyright law, except executing it on a computer or modifying a private copy. Propagation includes copying, distribution (with or without modification), making available to the public, and in some countries other activities as well. To "convey" a work means any kind of propagation that enables other parties to make or receive copies. Mere interaction with a user through a computer network, with no transfer of a copy, is not conveying. An interactive user interface displays "Appropriate Legal Notices" to the extent that it includes a convenient and prominently visible feature that (1) displays an appropriate copyright notice, and (2) tells the user that there is no warranty for the work (except to the extent that warranties are provided), that licensees may convey the work under this License, and how to view a copy of this License. If the interface presents a list of user commands or options, such as a menu, a prominent item in the list meets this criterion. #### 1. Source Code. The "source code" for a work means the preferred form of the work for making modifications to it. "Object code" means any non-source form of a work. A "Standard Interface" means an interface that either is an official standard defined by a recognized standards body, or, in the case of interfaces specified for a particular programming language, one that is widely used among developers working in that language. The "System Libraries" of an executable work include anything, other than the work as a whole, that (a) is included in the normal form of packaging a Major Component, but which is not part of that Major Component, and (b) serves only to enable use of the work with that Major Component, or to implement a Standard Interface for which an implementation is available to the public in source code form. A "Major Component", in this context, means a major essential component (kernel, window system, and so on) of the specific operating system (if any) on which the executable work runs, or a compiler used to produce the work, or an object code interpreter used to run it. The "Corresponding Source" for a work in object code form means all the source code needed to generate, install, and (for an executable work) run the object code and to modify the work, including scripts to control those activities. However, it does not include the work's System Libraries, or general-purpose tools or generally available free programs which are used unmodified in performing those activities but which are not part of the work. For example, Corresponding Source includes interface definition files associated with source files for the work, and the source code for shared libraries and dynamically linked subprograms that the work is specifically designed to require, such as by intimate data communication or control flow between those subprograms and other parts of the work. The Corresponding Source need not include anything that users can regenerate automatically from other parts of the Corresponding Source. The Corresponding Source for a work in source code form is that same work. #### 2. Basic Permissions. All rights granted under this License are granted for the term of copyright on the Program, and are irrevocable provided the stated conditions are met. This License explicitly affirms your unlimited permission to run the unmodified Program. The output from running a covered work is covered by this License only if the output, given its content, constitutes a covered work. This License acknowledges your rights of fair use or other equivalent, as provided by copyright law. You may make, run and propagate covered works that you do not convey, without conditions so long as your license otherwise remains in force. You may convey covered works to others for the sole purpose of having them make modifications exclusively for you, or provide you with facilities for running those works, provided that you comply with the terms of this License in conveying all material for which you do not control copyright. Those thus making or running the covered works for you must do so exclusively on your behalf, under your direction and control, on terms that prohibit them from making any copies of your copyrighted material outside their relationship with you. Conveying under any other circumstances is permitted solely under the conditions stated below. Sublicensing is not allowed; section 10 makes it unnecessary. #### 3. Protecting Users' Legal Rights From Anti-Circumvention Law. No covered work shall be deemed part of an effective technological measure under any applicable law fulfilling obligations under article 11 of the WIPO copyright treaty adopted on 20 December 1996, or similar laws prohibiting or restricting circumvention of such measures. When you convey a covered work, you waive any legal power to forbid circumvention of technological measures to the extent such circumvention is effected by exercising rights under this License with respect to the covered work, and you disclaim any intention to limit operation or modification of the work as a means of enforcing, against the work's users, your or third parties' legal rights to forbid circumvention of technological measures. #### 4. Conveying Verbatim Copies. You may convey verbatim copies of the Program's source code as you receive it, in any medium, provided that you conspicuously and appropriately publish on each copy an appropriate copyright notice; keep intact all notices stating that this License and any non-permissive terms added in accord with section 7 apply to the code; keep intact all notices of the absence of any warranty; and give all recipients a copy of this License along with the Program. You may charge any price or no price for each copy that you convey, and you may offer support or warranty protection for a fee. #### 5. Conveying Modified Source Versions. You may convey a work based on the Program, or the modifications to produce it from the Program, in the form of source code under the terms of section 4, provided that you also meet all of these conditions: - a) The work must carry prominent notices stating that you modified it, and giving a relevant date. - b) The work must carry prominent notices stating that it is released under this License and any conditions added under section 7. This requirement modifies the requirement in section 4 to "keep intact all notices". - c) You must license the entire work, as a whole, under this License to anyone who comes into possession of a copy. This License will therefore apply, along with any applicable section 7 additional terms, to the whole of the work, and all its parts, regardless of how they are packaged. This License gives no permission to license the work in any other way, but it does not invalidate such permission if you have separately received it. - d) If the work has interactive user interfaces, each must display Appropriate Legal Notices; however, if the Program has interactive interfaces that do not display Appropriate Legal Notices, your work need not make them do so. A compilation of a covered work with other separate and independent works, which are not by their nature extensions of the covered work, and which are not combined with it such as to form a larger program, in or on a volume of a storage or distribution medium, is called an "aggregate" if the compilation and its resulting copyright are not used to limit the access or legal rights of the compilation's users beyond what the individual works permit. Inclusion of a covered work in an aggregate does not cause this License to apply to the other parts of the aggregate. #### 6. Conveying Non-Source Forms. You may convey a covered work in object code form under the terms of sections 4 and 5, provided that you also convey the machine-readable Corresponding Source under the terms of this License, in one of these ways: - a) Convey the object code in, or embodied in, a physical product (including a physical distribution medium), accompanied by the Corresponding Source fixed on a durable physical medium customarily used for software interchange. - b) Convey the object code in, or embodied in, a physical product (including a physical distribution medium), accompanied by a written offer, valid for at least three years and valid for as long as you offer spare parts or customer support for that product model, to give anyone who possesses the object code either (1) a copy of the Corresponding Source for all the software in the product that is covered by this License, on a durable physical medium customarily used for software interchange, for a price no more than your reasonable cost of physically performing this conveying of source, or (2) access to copy the Corresponding Source from a network server at no charge. - c) Convey individual copies of the object code with a copy of the written offer to provide the Corresponding Source. This alternative is allowed only occasionally and noncommercially, and only if you received the object code with such an offer, in accord with subsection 6b. - d) Convey the object code by offering access from a designated place (gratis or for a charge), and offer equivalent access to the Corresponding Source in the same way through the same place at no further charge. You need not require recipients to copy the Corresponding Source along with the object code. If the place to copy the object code is a network server, the Corresponding Source may be on a different server (operated by you or a third party) that supports equivalent copying facilities, provided you maintain clear directions next to the object code saying where to find the Corresponding Source. Regardless of what server hosts the Corresponding Source, you remain obligated to ensure that it is available for as long as needed to satisfy these requirements. - e) Convey the object code using peer-to-peer transmission, provided you inform other peers where the object code and Corresponding Source of the work are being offered to the general public at no charge under subsection 6d. A separable portion of the object code, whose source code is excluded from the Corresponding Source as a System Library, need not be included in conveying the object code work. A "User Product" is either (1) a "consumer product", which means any tangible personal property which is normally used for personal, family, or household purposes, or (2) anything designed or sold for incorporation into a dwelling. In determining whether a product is a consumer product, doubtful cases shall be resolved in favor of coverage. For a particular product received by a particular user, "normally used" refers to a typical or common use of that class of product, regardless of the status of the particular user or of the way in which the particular user actually uses, or expects or is expected to use, the product. A product is a consumer product regardless of whether the product has substantial commercial, industrial or non-consumer uses, unless such uses represent the only significant mode of use of the product. "Installation Information" for a User Product means any methods, procedures, authorization keys, or other information required to install and execute modified versions of a covered work in that User Product from a modified version of its Corresponding Source. The information must suffice to ensure that the continued functioning of the modified object code is in no case prevented or interfered with solely because modification has been made. If you convey an object code work under this section in, or with, or specifically for use in, a User Product, and the conveying occurs as part of a transaction in which the right of possession and use of the User Product is transferred to the recipient in perpetuity or for a fixed term (regardless of how the transaction is characterized), the Corresponding Source conveyed under this section must be accompanied by the Installation Information. But this requirement does not apply if neither you nor any third party retains the ability to install modified object code on the User Product (for example, the work has been installed in ROM). The requirement to provide Installation Information does not include a requirement to continue to provide support service, warranty, or updates for a work that has been modified or installed by the recipient, or for the User Product in which it has been modified or installed. Access to a network may be denied when the modification itself materially and adversely affects the operation of the network or violates the rules and protocols for communication across the network. Corresponding Source conveyed, and Installation Information provided, in accord with this section must be in a format that is publicly documented (and with an implementation available to the public in source code form), and must require no special password or key for unpacking, reading or copying. #### 7. Additional Terms. "Additional permissions" are terms that supplement the terms of this License by making exceptions from one or more of its conditions. Additional permissions that are applicable to the entire Program shall be treated as though they were included in this License, to the extent that they are valid under applicable law. If additional permissions apply only to part of the Program, that part may be used separately under those permissions, but the entire Program remains governed by this License without regard to the additional permissions. When you convey a copy of a covered work, you may at your option remove any additional permissions from that copy, or from any part of it. (Additional permissions may be written to require their own removal in certain cases when you modify the work.) You may place additional permissions on material, added by you to a covered work, for which you have or can give appropriate copyright permission. Notwithstanding any other provision of this License, for material you add to a covered work, you may (if authorized by the copyright holders of that material) supplement the terms of this License with terms: - a) Disclaiming warranty or limiting liability differently from the terms of sections 15 and 16 of this License; or - b) Requiring preservation of specified reasonable legal notices or author attributions in that material or in the Appropriate Legal Notices displayed by works containing it; or - c) Prohibiting misrepresentation of the origin of that material, or requiring that modified versions of such material be marked in reasonable ways as different from the original version; or - d) Limiting the use for publicity purposes of names of licensors or authors of the material; or - e) Declining to grant rights under trademark law for use of some trade names, trademarks, or service marks; or - f) Requiring indemnification of licensors and authors of that material by anyone who conveys the material (or modified versions of it) with contractual assumptions of liability to the recipient, for any liability that these contractual assumptions directly impose on those licensors and authors. All other non-permissive additional terms are considered "further restrictions" within the meaning of section 10. If the Program as you received it, or any part of it, contains a notice stating that it is governed by this License along with a term that is a further restriction, you may remove that term. If a license document contains a further restriction but permits relicensing or conveying under this License, you may add to a covered work material governed by the terms of that license document, provided that the further restriction does not survive such relicensing or conveying. If you add terms to a covered work in accord with this section, you must place, in the relevant source files, a statement of the additional terms that apply to those files, or a notice indicating where to find the applicable terms. Additional terms, permissive or non-permissive, may be stated in the form of a separately written license, or stated as exceptions; the above requirements apply either way. #### 8. Termination. You may not propagate or modify a covered work except as expressly provided under this License. Any attempt otherwise to propagate or modify it is void, and will automatically terminate your rights under this License (including any patent licenses granted under the third paragraph of section 11). However, if you cease all violation of this License, then your license from a particular copyright holder is reinstated (a) provisionally, unless and until the copyright holder explicitly and finally terminates your license, and (b) permanently, if the copyright holder fails to notify you of the violation by some reasonable means prior to 60 days after the cessation. Moreover, your license from a particular copyright holder is reinstated permanently if the copyright holder notifies you of the violation by some reasonable means, this is the first time you have received notice of violation of this License (for any work) from that copyright holder, and you cure the violation prior to 30 days after your receipt of the notice. Termination of your rights under this section does not terminate the licenses of parties who have received copies or rights from you under this License. If your rights have been terminated and not permanently reinstated, you do not qualify to receive new licenses for the same material under section 10. #### 9. Acceptance Not Required for Having Copies. You are not required to accept this License in order to receive or run a copy of the Program. Ancillary propagation of a covered work occurring solely as a consequence of using peer-to-peer transmission to receive a copy likewise does not require acceptance. However, nothing other than this License grants you permission to propagate or modify any covered work. These actions infringe copyright if you do not accept this License. Therefore, by modifying or propagating a covered work, you indicate your acceptance of this License to do so. #### 10. Automatic Licensing of Downstream Recipients. Each time you convey a covered work, the recipient automatically receives a license from the original licensors, to run, modify and propagate that work, subject to this License. You are not responsible for enforcing compliance by third parties with this License. An "entity transaction" is a transaction transferring control of an organization, or substantially all assets of one, or subdividing an organization, or merging organizations. If propagation of a covered work results from an entity transaction, each party to that transaction who receives a copy of the work also receives whatever licenses to the work the party's predecessor in interest had or could give under the previous paragraph, plus a right to possession of the Corresponding Source of the work from the predecessor in interest, if the predecessor has it or can get it with reasonable efforts. You may not impose any further restrictions on the exercise of the rights granted or affirmed under this License. For example, you may not impose a license fee, royalty, or other charge for exercise of rights granted under this License, and you may not initiate litigation (including a cross-claim or counterclaim in a lawsuit) alleging that any patent claim is infringed by making, using, selling, offering for sale, or importing the Program or any portion of it. #### 11. Patents. A "contributor" is a copyright holder who authorizes use under this License of the Program or a work on which the Program is based. The work thus licensed is called the contributor's "contributor version". A contributor's "essential patent claims" are all patent claims owned or controlled by the contributor, whether already acquired or hereafter acquired, that would be infringed by some manner, permitted by this License, of making, using, or selling its contributor version, but do not include claims that would be infringed only as a consequence of further modification of the contributor version. For purposes of this definition, "control" includes the right to grant patent sublicenses in a manner consistent with the requirements of this License. Each contributor grants you a non-exclusive, worldwide, royalty-free patent license under the contributor's essential patent claims, to make, use, sell, offer for sale, import and otherwise run, modify and propagate the contents of its contributor version. In the following three paragraphs, a "patent license" is any express agreement or commitment, however denominated, not to enforce a patent (such as an express permission to practice a patent or covenant not to sue for patent infringement). To "grant" such a patent license to a party means to make such an agreement or commitment not to enforce a patent against the party. If you convey a covered work, knowingly relying on a patent license, and the Corresponding Source of the work is not available for anyone to copy, free of charge and under the terms of this License, through a publicly available network server or other readily accessible means, then you must either (1) cause the Corresponding Source to be so available, or (2) arrange to deprive yourself of the benefit of the patent license for this particular work, or (3) arrange, in a manner consistent with the requirements of this License, to extend the patent license to downstream recipients. "Knowingly relying" means you have actual knowledge that, but for the patent license, your conveying the covered work in a country, or your recipient's use of the covered work in a country, would infringe one or more identifiable patents in that country that you have reason to believe are valid. If, pursuant to or in connection with a single transaction or arrangement, you convey, or propagate by procuring conveyance of, a covered work, and grant a patent license to some of the parties receiving the covered work authorizing them to use, propagate, modify or convey a specific copy of the covered work, then the patent license you grant is automatically extended to all recipients of the covered work and works based on it. A patent license is "discriminatory" if it does not include within the scope of its coverage, prohibits the exercise of, or is conditioned on the non-exercise of one or more of the rights that are specifically granted under this License. You may not convey a covered work if you are a party to an arrangement with a third party that is in the business of distributing software, under which you make payment to the third party based on the extent of your activity of conveying the work, and under which the third party grants, to any of the parties who would receive the covered work from you, a discriminatory patent license (a) in connection with copies of the covered work conveyed by you (or copies made from those copies), or (b) primarily for and in connection with specific products or compilations that contain the covered work, unless you entered into that arrangement, or that patent license was granted, prior to 28 March 2007. Nothing in this License shall be construed as excluding or limiting any implied license or other defenses to infringement that may otherwise be available to you under applicable patent law. #### 12. No Surrender of Others' Freedom. If conditions are imposed on you (whether by court order, agreement or otherwise) that contradict the conditions of this License, they do not excuse you from the conditions of this License. If you cannot convey a covered work so as to satisfy simultaneously your obligations under this License and any other pertinent obligations, then as a consequence you may not convey it at all. For example, if you agree to terms that obligate you to collect a royalty for further conveying from those to whom you convey the Program, the only way you could satisfy both those terms and this License would be to refrain entirely from conveying the Program. #### 13. Use with the GNU Affero General Public License. Notwithstanding any other provision of this License, you have permission to link or combine any covered work with a work licensed under version 3 of the GNU Affero General Public License into a single combined work, and to convey the resulting work. The terms of this License will continue to apply to the part which is the covered work, but the special requirements of the GNU Affero General Public License, section 13, concerning interaction through a network will apply to the combination as such. #### 14. Revised Versions of this License. The Free Software Foundation may publish revised and/or new versions of the GNU General Public License from time to time. Such new versions will be similar in spirit to the present version, but may differ in detail to address new problems or concerns. Each version is given a distinguishing version number. If the Program specifies that a certain numbered version of the GNU General Public License "or any later version" applies to it, you have the option of following the terms and conditions either of that numbered version or of any later version published by the Free Software Foundation. If the Program does not specify a version number of the GNU General Public License, you may choose any version ever published by the Free Software Foundation. If the Program specifies that a proxy can decide which future versions of the GNU General Public License can be used, that proxy's public statement of acceptance of a version permanently authorizes you to choose that version for the Program. Later license versions may give you additional or different permissions. However, no additional obligations are imposed on any author or copyright holder as a result of your choosing to follow a later version. #### 15. Disclaimer of Warranty. THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING, REPAIR OR CORRECTION. #### 16. Limitation of Liability. IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. #### 17. Interpretation of Sections 15 and 16. If the disclaimer of warranty and limitation of liability provided above cannot be given local legal effect according to their terms, reviewing courts shall apply local law that most closely approximates an absolute waiver of all civil liability in connection with the Program, unless a warranty or assumption of liability accompanies a copy of the Program in return for a fee. END OF TERMS AND CONDITIONS ### How to Apply These Terms to Your New Programs If you develop a new program, and you want it to be of the greatest possible use to the public, the best way to achieve this is to make it free software which everyone can redistribute and change under these terms. To do so, attach the following notices to the program. It is safest to attach them to the start of each source file to most effectively state the exclusion of warranty; and each file should have at least the "copyright" line and a pointer to where the full notice is found. Copyright (C) This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see . Also add information on how to contact you by electronic and paper mail. If the program does terminal interaction, make it output a short notice like this when it starts in an interactive mode: Copyright (C) This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. This is free software, and you are welcome to redistribute it under certain conditions; type `show c' for details. The hypothetical commands \`show w' and \`show c' should show the appropriate parts of the General Public License. Of course, your program's commands might be different; for a GUI interface, you would use an "about box". You should also get your employer (if you work as a programmer) or school, if any, to sign a "copyright disclaimer" for the program, if necessary. For more information on this, and how to apply and follow the GNU GPL, see . The GNU General Public License does not permit incorporating your program into proprietary programs. If your program is a subroutine library, you may consider it more useful to permit linking proprietary applications with the library. If this is what you want to do, use the GNU Lesser General Public License instead of this License. But first, please read . ================================================ FILE: NAMESPACE ================================================ # Generated by roxygen2: do not edit by hand export(DemoTricolore) export(Tricolore) export(TricoloreSextant) importFrom(assertthat,assert_that) importFrom(assertthat,is.flag) importFrom(assertthat,is.number) importFrom(assertthat,is.scalar) importFrom(assertthat,is.string) importFrom(ggplot2,element_text) importFrom(ggplot2,labs) importFrom(ggplot2,layer) importFrom(ggplot2,scale_color_identity) importFrom(ggplot2,scale_fill_identity) importFrom(ggplot2,theme) importFrom(ggtern,aes) importFrom(ggtern,geom_Lline) importFrom(ggtern,geom_Rline) importFrom(ggtern,geom_Tline) importFrom(ggtern,geom_mask) importFrom(ggtern,ggtern) importFrom(ggtern,scale_L_continuous) importFrom(ggtern,scale_R_continuous) importFrom(ggtern,scale_T_continuous) importFrom(ggtern,theme_classic) importFrom(grDevices,hcl) importFrom(grDevices,hsv) importFrom(rlang,.data) ================================================ FILE: NEWS.md ================================================ # tricolore 1.2.6 * establish compatibility with ggplot/ggtern 4.0.0 # tricolore 1.2.5 * re-export `euro_basemap.RData` to fix (@clementviolet, #24) # tricolore 1.2.4 * establish compatibility with ggplot/ggtern 3.4.2 * update deprecated ggplot code * update outdated crs spec in example data * add Schöley (2021) reference to vignette # tricolore 1.2.3 * add startup message and citation information * establish compatibility with ggplot2 3.3.4/3.3.5 (@hhmacedo, #13) # tricolore 1.2.2 * establish compatibility with ggplot/ggtern 3.3.0 * remove 'caption' labels from example plots as it causes rendering bug # tricolore 1.2.1 * establish compatibility with ggplot/ggtern 3.2.0 * allow TricoloreDemo() to run as stand-alone shiny-app (i.e. on shinyapps server) # tricolore 1.2.0 * allow for discrete re-centered scales * add new discrete scales TricoloreSextant * reorder Tricolore*() arguments * rename Tricolore*() list output to `rgb` and `key` * add new features to shiny app # tricolore 1.1.1 * make TernaryLimits() internal # tricolore 1.1.0 * change defaults * make defaults dynamic * remove alpha part from rgb codes # tricolore 1.0.8 * add legend crop option * update README * add dependencies to travis recipe # tricolore 1.0.7 * add dependencies to travis recipe # tricolore 1.0.6 * provide example data as sf data frame * use sf in the examples * add choropleth maps with tricolore vignette featuring leaflets # tricolore 1.0.5 * add option for percent-point difference labeling in ternary legend * add tests # tricolore 1.0.4 * establish compatibility with ggplot/ggtern 3.0.0 # tricolore 1.0.3 * Initial CRAN release ================================================ FILE: R/tricolore.R ================================================ # Misc -------------------------------------------------------------------- # from nnet::which.is.max() MaxIndex <- function (x) { y <- seq_along(x)[x == max(x)] if (length(y) > 1L) { sample(y, 1L) } else { y } } #' Validate Main Arguments #' #' Validate main arguments of tricolore function. #' #' @param df Data frame of compositions. #' @param p1 Column name for variable in df giving first proportion #' of ternary composition (string). #' @param p2 Column name for variable in df giving second proportion #' of ternary composition (string. #' @param p3 Column name for variable in df giving third proportion #' of ternary composition (string). #' #' @importFrom assertthat assert_that is.string #' #' @keywords internal ValidateMainArguments <- function (df, p1, p2, p3) { # missing arguments assert_that(!missing(df), !missing(p1), !missing(p2), !missing(p3), msg = 'main argument missing') # compositional data is data frame assert_that(is.data.frame(df)) # variable names as strings assert_that(is.string(p1), is.string(p2), is.string(p3)) # missing variables in data frame assert_that(p1 %in% names(df), msg = paste('variable', p1 ,'not found in df')) assert_that(p2 %in% names(df), msg = paste('variable', p2 ,'not found in df')) assert_that(p3 %in% names(df), msg = paste('variable', p3 ,'not found in df')) # compositional data is numeric assert_that(is.numeric(df[[p1]]), msg = paste('variable', p1 ,'is not numeric')) assert_that(is.numeric(df[[p2]]), msg = paste('variable', p2 ,'is not numeric')) assert_that(is.numeric(df[[p3]]), msg = paste('variable', p3 ,'is not numeric')) # compositional data is not negative assert_that(!any(df[[p1]] < 0, na.rm = TRUE), msg = paste('variable', p1 ,'contains negative values')) assert_that(!any(df[[p2]] < 0, na.rm = TRUE), msg = paste('variable', p2 ,'contains negative values')) assert_that(!any(df[[p3]] < 0, na.rm = TRUE), msg = paste('variable', p3 ,'contains negative values')) # NA, Inf, NaN are allowed and are expected to return NA as color } #' Validate Shared Parameters #' #' Validate parameters shared across tricolore functions. #' #' @param pars A named list of parameters. #' #' @importFrom assertthat assert_that is.scalar is.flag #' #' @keywords internal ValidateParametersShared <- function (pars) { with(pars, { # center either NA or three element numeric vector # with sum 1 and elements > 0 assert_that((is.scalar(center) && is.na(center)) || (length(center) == 3L && all(is.numeric(center)) && sum(center) == 1 && all(center != 0)), msg = 'center must be either NA or a three element numeric vector with sum == 1 and all element > 0.') # flags assert_that(is.flag(legend), is.flag(show_data), is.flag(show_center), is.flag(crop)) # character options assert_that(is.scalar(label_as), is.character(label_as), label_as %in% c('pct', 'pct_diff'), msg = 'label_as must be either "pct" or "pct_diff".') }) } #' Validate Tricolore Parameters #' #' Validate parameters of Tricolore function. #' #' @param pars A named list of parameters. #' #' @importFrom assertthat assert_that is.number is.scalar #' #' @keywords internal ValidateParametersTricolore <- function (pars) { # a modified version of assertthat::is.count that regards # infinite values as counts is.count2 <- function (x) { if (length(x) != 1) return(FALSE) integerish <- is.integer(x) || (is.numeric(x) && (x == trunc(x))) if (!integerish) return(FALSE) x > 0 } with(pars, { # breaks is count scalar > 1 (can't use is.count() because # it throws an error when encountering infinite values) assert_that(is.scalar(breaks), is.count2(breaks), breaks > 1) # hue is numeric scalar in range [0, 1] assert_that(is.number(hue), hue >= 0 && hue <= 1) # chroma is numeric scalar in range [0, 1] assert_that(is.number(chroma), chroma >= 0 && chroma <= 1) # lightness is numeric scalar in range [0, 1] assert_that(is.number(lightness), lightness >= 0 && lightness <= 1) # contrast is numeric scalar in range [0, 1] assert_that(is.number(contrast), contrast >= 0 && contrast <= 1) # spread is positive numeric scalar assert_that(is.number(spread), spread > 0, is.finite(spread)) }) ValidateParametersShared(pars) } #' Validate TricoloreSextant Parameters #' #' Validate parameters of TricoloreSextant function. #' #' @param pars A named list of parameters. #' #' @importFrom assertthat assert_that is.number is.scalar #' #' @keywords internal ValidateParametersTricoloreSextant <- function (pars) { with(pars, { assert_that(is.character(values), length(values) == 6) }) ValidateParametersShared(pars) } # Compositional Data Analysis --------------------------------------------- #' Geometric Mean #' #' Calculate the geometric mean for a numeric vector. #' #' @param x Numeric vector. #' @param na.rm Should NAs be removed? (default=TRUE) #' @param zero.rm Should zeros be removed? (default=TRUE) #' #' @return The geometric mean as numeric scalar. #' #' @examples #' # NOTE: only intended for internal use and not part of the API #' tricolore:::GeometricMean(0:100) #' tricolore:::GeometricMean(0:100, zero.rm = FALSE) #' #' @keywords internal GeometricMean <- function (x, na.rm = TRUE, zero.rm = TRUE) { # the geometric mean can't really deal with elements equal to 0 # this option removes 0 elements from the vector if (zero.rm) { x <- x[x!=0] } return(exp(mean(log(x), na.rm = na.rm))) } #' Compositional Centre #' #' Calculate the centre of a compositional data set. #' #' @param P n by m matrix of compositions [p1, ..., pm]_i for i=1,...,n. #' #' @return The centre of P as an m element numeric vector. #' #' @examples #' # NOTE: only intended for internal use and not part of the API #' P <- prop.table(matrix(runif(300), 100), margin = 1) #' tricolore:::Centre(P) #' #' @references #' Von Eynatten, H., Pawlowsky-Glahn, V., & Egozcue, J. J. (2002). #' Understanding perturbation on the simplex: A simple method to better #' visualize and interpret compositional data in ternary diagrams. #' Mathematical Geology, 34(3), 249-257. #' #' Pawlowsky-Glahn, V., Egozcue, J. J., & Tolosana-Delgado, R. (2007). Lecture #' Notes on Compositional Data Analysis. Retrieved from #' https://dugi-doc.udg.edu/bitstream/handle/10256/297/CoDa-book.pdf #' #' @keywords internal Centre <- function (P) { # calculate the geometric mean for each element of the composition g <- apply(P, MARGIN = 2, FUN = GeometricMean) # the closed vector of geometric means is the mean (centre) # of the compositional data set return(g/sum(g)) } #' Compositional Pertubation #' #' Pertubate a compositional data set by a compositional vector. #' #' @param P n by m matrix of compositions [p1, ..., pm]_i for i=1,...,n. #' @param c Compositional pertubation vector [c1, ..., cm]. #' #' @return n by m matrix of pertubated compositions. #' #' @examples #' # NOTE: only intended for internal use and not part of the API #' P <- prop.table(matrix(runif(12), 4), margin = 1) #' cP <- tricolore:::Pertube(P, 1/tricolore:::Centre(P)) #' tricolore:::Centre(cP) #' #' @references #' Von Eynatten, H., Pawlowsky-Glahn, V., & Egozcue, J. J. (2002). #' Understanding perturbation on the simplex: A simple method to better #' visualize and interpret compositional data in ternary diagrams. #' Mathematical Geology, 34(3), 249-257. #' #' Pawlowsky-Glahn, V., Egozcue, J. J., & Tolosana-Delgado, R. (2007). Lecture #' Notes on Compositional Data Analysis. Retrieved from #' https://dugi-doc.udg.edu/bitstream/handle/10256/297/CoDa-book.pdf #' #' @keywords internal Pertube <- function (P, c = rep(1/3, 3)) { return(prop.table(t(t(P)*c), margin = 1)) } #' Compositional Powering #' #' Raise a compositional data-set to a given power. #' #' @param P n by m matrix of compositions [p1, ..., pm]_i for i=1,...,n. #' @param scale Power scalar. #' #' @return n by m numeric matrix of powered compositions. #' #' @examples #' # NOTE: only intended for internal use and not part of the API #' P <- prop.table(matrix(runif(12), 4), margin = 1) #' tricolore:::PowerScale(P, 2) #' #' @references #' Pawlowsky-Glahn, V., Egozcue, J. J., & Tolosana-Delgado, R. (2007). Lecture #' Notes on Compositional Data Analysis. Retrieved from #' https://dugi-doc.udg.edu/bitstream/handle/10256/297/CoDa-book.pdf #' #' @keywords internal PowerScale <- function (P, scale = 1) { return(prop.table(P^scale, margin = 1)) } # Ternary Geometry -------------------------------------------------------- # T(K=k^2): Equilateral triangle subdivided into K equilateral sub-triangles. # Each side of T is divided into k intervals of equal length. # (p1,p2,p3): Barycentric coordinates wrt. T(K). # id: One-dimensional index of sub-triangles in T(K). # # p2 id index # /\ 9 # / \ 6 7 8 # /____\ 1 2 3 4 5 # p1 p3 #' Centroid Coordinates of Sub-Triangles in Segmented Equilateral Triangle #' #' Segment an equilateral triangle into k^2 equilateral sub-triangles and return #' the barycentric centroid coordinates of each sub-triangle. #' #' @param k Number of rows in the segmented equilateral triangle. #' #' @return A numeric matrix of with index and barycentric centroid coordinates #' of regions id=1,...,k^2. #' #' @references #' S. H. Derakhshan and C. V. Deutsch (2009): A Color Scale for Ternary Mixtures. #' #' @examples #' # NOTE: only intended for internal use and not part of the API #' tricolore:::TernaryMeshCentroids(1) #' tricolore:::TernaryMeshCentroids(2) #' tricolore:::TernaryMeshCentroids(3) #' #' @keywords internal TernaryMeshCentroids <- function (k) { # total number of centroids and centroid id K = k^2; id = 1:K # centroid coordinates as function of K and id g <- floor(sqrt(K-id)); gsq <- g^2 c1 <- (((-K + id + g*(g+2) + 1) %% 2) - 3*gsq - 3*id + 3*K + 1) / (6*k) c2 <- -(((-K + gsq + id + 2*g + 1) %% 2) + 3*g - 3*k + 1) / (3*k) c3 <- (((-K + gsq + id + 2*g + 1) %% 2) + 3*gsq + 6*g + 3*id - 3*K + 1) / (6*k) return(cbind(id = id, p1 = c1, p2 = c2, p3 = c3)) } #' Vertex Coordinates of Sub-Triangles in Segmented Equilateral Triangle #' #' Given the barycentric centroid coordinates of the sub-triangles in an #' equilateral triangle subdivided into k^2 equilateral sub-triangles, return #' the barycentric vertex coordinates of each sub-triangle. #' #' @param C n by 4 matrix of barycentric centroid coordinates of n=k^2 #' sub-triangles. Column order: id, p1, p2, p3 with id=1,...,k^2. #' #' @return A numeric matrix with index, vertex id and barycentric vertex #' coordinates for each of the k^2 sub-triangles. #' #' @examples #' # NOTE: only intended for internal use and not part of the API #' k = 2 #' C <- tricolore:::TernaryMeshCentroids(k) #' tricolore:::TernaryMeshVertices(C) #' #' @references #' S. H. Derakhshan and C. V. Deutsch (2009): A Color Scale for Ternary Mixtures. #' #' @keywords internal TernaryMeshVertices <- function (C) { k <- sqrt(nrow(C)) j <- k - floor(sqrt(k^2-C[,1])) i <- C[,1] - (j-1)*(2*k-j+1) term1 <- ((-1)^(i %% 2) * 2) / (3*k) term2 <- ((-1)^(i %% 2)) / (3*k) v1 <- cbind(C[,2] - term1, C[,3] + term2, C[,4] + term2) v2 <- cbind(C[,2] + term2, C[,3] - term1, C[,4] + term2) v3 <- cbind(C[,2] + term2, C[,3] + term2, C[,4] - term1) V <- cbind(C[,1], rep(1:3, each = nrow(C)), rbind(v1, v2, v3)) colnames(V) <- c('id', 'vertex', 'p1', 'p2', 'p3') return(V) } #' Distance Between Points in Ternary Coordinates #' #' The distances between ternary coordinate p and a set of ternary coordinates C. #' #' @param p A vector of ternary coordinates [p1, p2, p3]. #' @param C n by 3 matrix of ternary coordinates [p1, p2, p3](i) for i=1,...,n. #' #' @return A numeric vector of distances between coordinate p and all #' coordinates in C. #' #' @references #' https://en.wikipedia.org/wiki/Barycentric_coordinate_system#Distance_between_points #' #' @examples #' # NOTE: only intended for internal use and not part of the API #' p <- c(0.5, 0.2, 0.3) #' C <- prop.table(matrix(runif(3*10), ncol = 3), 1) #' tricolore:::TernaryDistance(p, C) #' #' @keywords internal TernaryDistance <- function(p, C) { Q <- t(p-t(C)) return(-Q[,2]*Q[,3]-Q[,3]*Q[,1]-Q[,1]*Q[,2]) } #' For Ternary Coordinates P Return the Nearest Coordinate in Set C #' #' @param P,C n by 3 matrix of ternary coordinates [p1, p2, p3](i) for #' i=1,...,n. n may be different for P and C. #' #' @return n by 3 matrix of ternary coordinates in C. #' #' @examples #' # NOTE: only intended for internal use and not part of the API #' P <- prop.table(matrix(runif(9), ncol = 3), 1) #' C <- tricolore:::TernaryMeshCentroids(2)[,-1] #' tricolore:::TernaryNearest(P, C) #' #' @keywords internal TernaryNearest <- function (P, C) { id <- apply(P, 1, function (x) MaxIndex(-TernaryDistance(x, C))) return(C[id,]) } #' Return Ternary Gridlines Centered Around Some Composition #' #' @param center The center of the grid. #' A vector of ternary coordinates [p1, p2, p3]. #' @param spacing The spacing of the grid in percent-point increments. #' A numeric > 0. #' #' @return A list of lists. #' #' @examples #' # NOTE: only intended for internal use and not part of the API #' tricolore:::TernaryCenterGrid(c(1/6, 2/6, 3/6), 10) #' #' @keywords internal TernaryCenterGrid <- function (center, spacing) { # -1 to 1 by spacing/100 with 0 point div_seq <- seq(0, 1, spacing/100) div_seq <- c(-rev(div_seq), div_seq[-1]) # proportion difference from center for all three ternary axes. # keep only possible values div_seq <- list( p1 = div_seq[div_seq >= -center[1] & div_seq <= 1-center[1]], p2 = div_seq[div_seq >= -center[2] & div_seq <= 1-center[2]], p3 = div_seq[div_seq >= -center[3] & div_seq <= 1-center[3]] ) # percent-point difference from center composition labels <- lapply(div_seq, function(x) formatC(x*100, flag = '+')) # label center point as percent share center_pct <- paste0(formatC(center*100, digits = 1, format = 'f'), '%') labels[['p1']][labels[['p1']] == '-0'] <- center_pct[1] labels[['p2']][labels[['p2']] == '-0'] <- center_pct[2] labels[['p3']][labels[['p3']] == '-0'] <- center_pct[3] # breaks in ternary coordinates breaks <- list( p1 = div_seq[['p1']] + center[1], p2 = div_seq[['p2']] + center[2], p3 = div_seq[['p3']] + center[3] ) return(list(breaks = breaks, labels = labels)) } #' Return the Limits of Ternary Coordinates #' #' @param P n by 3 matrix of ternary coordinates [p1, p2, p3](i) for #' i=1,...,n. #' @param na.rm Should NAs be removed? (default=TRUE) #' #' @return A 2 by 3 matrix of lower and upper limits for p1, p2 and p3. #' #' @examples #' # NOTE: only intended for internal use and not part of the API #' P <- prop.table(matrix(runif(9), ncol = 3), 1) #' tricolore:::TernaryLimits(P) #' #' @keywords internal TernaryLimits <- function (P, na.rm = TRUE) { limits <- matrix(NA, nrow = 2, ncol = 3, dimnames = list(c('lower', 'upper'), c('p1', 'p2', 'p3'))) limits[1,] <- apply(P, 2, min, na.rm = na.rm) limits[2,] <- c(1 - (limits[1,2] + limits[1,3]), 1 - (limits[1,1] + limits[1,3]), 1 - (limits[1,1] + limits[1,2])) return(limits) } #' Vertex Coordinates of Sextants in Equilateral Triangle #' #' Given a barycentric center coordinate return the vertex coordinates of the #' of the sextant regions. #' #' @param center The sextant center. #' A vector of ternary coordinates [p1, p2, p3]. #' #' @return Index, vertex id and barycentric vertex coordinates for each of the #' 6 sextants. #' #' @examples #' # NOTE: only intended for internal use and not part of the API #' tricolore:::TernarySextantVertices(rep(1/3, 3)) #' #' @keywords internal TernarySextantVertices <- function (center) { # define corner points p1 = c(1, 0, 0); p2 = c(0, 1, 0); p3 = c(0, 0, 1) a1 <- c(center[1], 1-center[1], 0); a2 <- c(center[1], 0, 1-center[1]) b1 <- c(0, center[2], 1-center[2]); b2 <- c(1-center[2], center[2], 0) c1 <- c(1-center[3], 0, center[3]); c2 <- c(0, 1-center[3], center[3]) # ternary sextant vertices V <- cbind( id = c(rep(1, 5), rep(2, 4), rep(3, 5), rep(4, 4), rep(5, 5), rep(6, 4)), vertex = rep(c(1:5, 1:4), 3), matrix( c(center, c1, p1, b2, center, # 1 center, b2, a1, center, # 2 center, a1, p2, c2, center, # 3 center, c2, b1, center, # 4 center, b1, p3, a2, center, # 5 center, a2, c1, center), # 6 ncol = 3, nrow = 27, byrow = TRUE, dimnames = list(NULL, c('p1', 'p2', 'p3')) ) ) return(V) } #' Return Surrounding Sextant of Barycentric Coordinates #' #' Given barycentric coordinates return the id of the surrounding sextant. #' #' @param P n by 3 matrix of ternary coordinates [p1, p2, p3](i) for #' i=1,...,n. #' @param center The sextant center. #' A vector of ternary coordinates [p1, p2, p3]. #' #' @return An n element character vector of sextant id's 1 to 6. #' #' @examples #' # NOTE: only intended for internal use and not part of the API #' P <- prop.table(matrix(runif(9), ncol = 3), 1) #' tricolore:::TernarySurroundingSextant(P, rep(1/3, 3)) #' #' @keywords internal TernarySurroundingSextant <- function (P, center) { # six cases, six sextants, NA if at center or NA in input is_larger <- t(t(P) > center) id <- apply(is_larger, 1, function (x) { y <- NA if (identical(x, c(TRUE, FALSE, FALSE))) y <- 1 if (identical(x, c(TRUE, TRUE, FALSE))) y <- 2 if (identical(x, c(FALSE, TRUE, FALSE))) y <- 3 if (identical(x, c(FALSE, TRUE, TRUE))) y <- 4 if (identical(x, c(FALSE, FALSE, TRUE))) y <- 5 if (identical(x, c(TRUE, FALSE, TRUE))) y <- 6 y }) return(id) } # Ternary Color Maps ------------------------------------------------------ #' CIE-Lch Mixture of Ternary Composition #' #' Return the ternary balance scheme colors for a matrix of ternary compositions. #' #' @param P n by 3 matrix of ternary compositions [p1, p2, p3](i) for #' i=1, ..., n. #' @param center Ternary coordinates of the grey-point. #' @param breaks Number of breaks in the discrete color scale. An integer >1. #' Values above 99 imply no discretization. #' @param h_ Primary hue of the first ternary element in angular degrees [0, 360]. #' @param c_ Maximum possible chroma of mixed colors [0, 200]. #' @param l_ Lightness of mixed colors [0, 100]. #' @param contrast Lightness contrast of the color scale [0, 1). #' @param spread Spread of the color scale around center > 0. #' #' @return An n row data frame giving, for each row of the input P, the input #' proportions [p1, p2, p3], parameters of the color mixture (h, c, l) and the #' hex-rgb string of the mixed colors (rgb). #' #' @examples #' # NOTE: only intended for internal use and not part of the API #' P <- prop.table(matrix(runif(9), ncol = 3), 1) #' tricolore:::ColorMapTricolore(P, center = rep(1/3, 3), breaks = 4, #' h_ = 80, c_ = 140, l_ = 80, #' contrast = 0.4, spread = 1) #' #' @importFrom grDevices hcl hsv #' #' @keywords internal ColorMapTricolore <- function (P, center, breaks, h_, c_, l_, contrast, spread) { ### Discretize ### # closing (copy of closed, non-transformed input data for output) P <- P_notrans <- prop.table(P, margin = 1) # discretize to nearest ternary mesh centroid # don't discretize if breaks > 99 to avoid expensive calculations # which don't make much of a difference in output if (breaks < 100) { P <- TernaryNearest(P, TernaryMeshCentroids(breaks)[,-1]) } ### Center and scale ### # centering P <- Pertube(P, 1/center) # scaling P <- PowerScale(P, spread) ### Colorize ### # calculate the chroma matrix C by scaling the row proportions # of the input matrix P by the maximum chroma parameter. C <- P*c_ # generate primary colors starting with a hue value in [0, 360) and then # picking two equidistant points on the circumference of the color wheel. # input hue in degrees, all further calculations in radians. phi <- (h_*0.0174 + c(0, 2.09, 4.19)) %% 6.28 # the complex matrix Z represents each case (i) and group (j=1,2,3) specific # color in complex polar form with hue as angle and chroma as radius. Z <- matrix(complex(argument = phi, modulus = c(t(C))), ncol = 3, byrow = TRUE) # adding up the rows gives the CIE-Lab (cartesian) coordinates # of the convex color mixture in complex form. z <- rowSums(Z) # convert the cartesian CIE-Lab coordinates to polar CIE-Luv coordinates # and add lightness level. M <- cbind(h = (Arg(z)*57.3)%%360, c = Mod(z), l = l_) # decrease lightness and chroma towards the center of the color scale cfactor <- M[,2]*contrast/c_ + 1-contrast M[,3] <- cfactor*M[,3] M[,2] <- cfactor*M[,2] # convert the complex representation of the color mixture to # hex-srgb representation via the hcl (CIE-Luv) color space rgb <- hcl(h = M[,1], c = M[,2], l = M[,3], alpha = 1, fixup = TRUE) # remove alpha information rgb <- substr(rgb, 1, 7) ### Output ### # non-transformed compositions, hcl values of mixtures and rgb code result <- data.frame(P_notrans, M[,1], M[,2], M[,3], rgb, row.names = NULL, check.rows = FALSE, check.names = FALSE, stringsAsFactors = FALSE) colnames(result) <- c('p1', 'p2', 'p3', 'h', 'c', 'l', 'rgb') return(result) } #' Sextant Encoding of Ternary Composition #' #' Return the sextant scheme colors for a matrix of ternary compositions. #' #' @param P n by 3 matrix of ternary compositions [p1, p2, p3](i) for #' i=1, ..., n. #' @param center Ternary coordinates of the sextant meeting point. #' @param values 6 element character vector of rgb-codes. #' #' @return An n row data frame giving, for each row of the input P, the input #' proportions [p1, p2, p3], sextant id (sextant) and the hex-rgb string of the #' mixed colors (rgb). #' #' @examples #' # NOTE: only intended for internal use and not part of the API #' P <- prop.table(matrix(runif(9), ncol = 3), 1) #' tricolore:::ColorMapSextant(P, c(1/3, 1/3, 1/3), #' c('#01A0C6', '#B8B3D8', '#F11D8C', '#FFB3B3', #' '#FFFF00', '#B3DCC3')) #' @keywords internal ColorMapSextant <- function (P, center, values) { # close composition P <- prop.table(P, margin = 1) # assign points to sextants and corresponding color codes sextant <- TernarySurroundingSextant(P, center) rgb <- values[sextant] # non-transformed compositions, sextant id and hexsrgb code result <- data.frame(P, sextant, rgb, row.names = NULL, check.rows = FALSE, check.names = FALSE, stringsAsFactors = FALSE) colnames(result) <- c('p1', 'p2', 'p3', 'sextant', 'rgb') return(result) } # Ternary Color Keys ------------------------------------------------------ #' Breaks and Labels for Ternary Color Key #' #' Return various types of breaks and labels for ternary color keys. #' #' @param type An integer 1, 2, or 3. #' @param center Ternary coordinates of the grey-point. #' @param breaks Number of breaks in the discrete color scale. An integer >1. #' Values above 99 imply no discretization. #' #' @return A list of lists containing breaks and labels for each of the 3 #' ternary axes. #' #' @examples #' # NOTE: only intended for internal use and not part of the API #' tricolore:::BreaksAndLabels(1, breaks = 3) #' tricolore:::BreaksAndLabels(2) #' tricolore:::BreaksAndLabels(3, center = c(1/3, 1/3, 1/3)) #' #' @keywords internal BreaksAndLabels <- function (type, center = NULL, breaks = NULL) { brk_lab <- switch(type, list(breaks = list(p1 = seq(0, 1, length.out = breaks+1), p2 = seq(0, 1, length.out = breaks+1), p3 = seq(0, 1, length.out = breaks+1)), labels = list(p1 = round(seq(0, 1, length.out = breaks+1)*100, 1), p2 = round(seq(0, 1, length.out = breaks+1)*100, 1), p3 = round(seq(0, 1, length.out = breaks+1)*100, 1))), list(breaks = list(p1 = c(0.25, 0.5, 0.75), p2 = c(0.25, 0.5, 0.75), p3 = c(0.25, 0.5, 0.75)), labels = list(p1 = c('25', '50', '75'), p2 = c('25', '50', '75'), p3 = c('25', '50', '75'))), TernaryCenterGrid(center = center, spacing = 10) ) return(brk_lab) } #' Template for Ternary Key #' #' Return various types of breaks and labels for ternary color keys. #' #' @param legend_surface A data frame with numeric 'id', 'p1', 'p2', 'p3' and #' character column 'rgb'. #' @param limits A 2 by 3 matrix of lower and upper limits for p1, p2 and p3. #' @param brklab Breaks and labels as returned by \code{\link{BreaksAndLabels}}. #' @param show_center Should the center be marked on the legend? (logical) #' @param center Ternary coordinates of the grey-point. #' @param lwd A numeric scalar giving the linewidth of the legend surface #' polygons. #' #' @return A ggtern grob. #' #' @importFrom ggplot2 scale_color_identity #' scale_fill_identity element_text theme layer #' @importFrom ggtern ggtern aes geom_mask #' scale_L_continuous scale_R_continuous scale_T_continuous #' geom_Lline geom_Tline geom_Rline theme_classic #' @importFrom rlang .data #' #' @keywords internal BasicKey <- function(legend_surface, limits, brklab, show_center, center, lwd) { key <- # basic legend ggtern(legend_surface) + layer( geom = 'polygon', stat = 'identity', position = 'identity', mapping = aes( x = .data[['p1']], y = .data[['p2']], z = .data[['p3']], group = .data[['id']], fill = .data[['rgb']], color = .data[['rgb']] ), params = list(lwd = lwd), check.aes = FALSE, check.param = FALSE ) + geom_mask() + # rgb color input scale_color_identity(guide = 'none') + scale_fill_identity(guide = 'none') + # theme theme_classic() + theme(tern.axis.title.L = element_text(hjust = 0.2, vjust = 1, angle = -60), tern.axis.title.R = element_text(hjust = 0.8, vjust = 0.6, angle = 60)) + # grid and labels list( list( scale_L_continuous( limits = limits[,1], breaks = brklab[['breaks']][['p1']], labels = brklab[['labels']][['p1']] ), scale_T_continuous( limits = limits[,2], breaks = brklab[['breaks']][['p2']], labels = brklab[['labels']][['p2']] ), scale_R_continuous( limits = limits[,3], breaks = brklab[['breaks']][['p3']], labels = brklab[['labels']][['p3']] ) ), if (show_center) { list( geom_Lline(Lintercept = center[1], color = 'black', alpha = 0.5), geom_Tline(Tintercept = center[2], color = 'black', alpha = 0.5), geom_Rline(Rintercept = center[3], color = 'black', alpha = 0.5) ) } ) return(key) } #' Ternary Balance Scheme Legend #' #' Plot a ternary balance scheme legend. #' #' @inheritParams ColorMapTricolore #' @param label_as "pct" for percent-share labels or "pct_diff" for #' percent-point-difference from center labels. #' @param show_center Should the center be marked on the legend? (logical) #' @param limits A 2 by 3 matrix of lower and upper limits for p1, p2 and p3. #' #' @return A ggtern grob. #' #' @examples #' # NOTE: only intended for internal use and not part of the API #' tricolore:::ColorKeyTricolore(center = rep(1/3, 3), breaks = 4, #' h_ = 80, c_ = 140, l_ = 80, #' contrast = 0.4, spread = 1, #' label_as = "pct", show_center = FALSE) #' #' @keywords internal ColorKeyTricolore <- function (center, breaks, h_, c_, l_, contrast, spread, label_as, show_center, limits = matrix(0:1, nrow = 2, ncol = 3)) { ### Create and colorize legend surface ### # don't allow more than 99^2 different colors/regions in the legend if (breaks > 99) { breaks = 100 } # calculate ternary vertex coordinates and # fill color for each sub-triangle C <- TernaryMeshCentroids(breaks) V <- TernaryMeshVertices(C) rgb <- ColorMapTricolore(P = C[,-1], center, breaks = 100, h_, c_, l_, contrast, spread)[['rgb']] legend_surface <- data.frame(V, rgb = rep(rgb, 3), row.names = NULL, check.rows = FALSE, check.names = FALSE, stringsAsFactors = FALSE) ### Breaks and labels ### if (label_as == 'pct' && breaks <= 10) { brklab <- BreaksAndLabels(1, center, breaks) } if (label_as == 'pct' && breaks > 10) { brklab <- BreaksAndLabels(2, center, breaks) } if (label_as == 'pct_diff') { brklab <- BreaksAndLabels(3, center, breaks) } ### Plot key ### return(BasicKey(legend_surface, limits, brklab, show_center, center, lwd = 1)) } #' Sextant Scheme Legend #' #' Plot a sextant scheme legend. #' #' @inheritParams ColorMapSextant #' @param label_as "pct" for percent-share labels or "pct_diff" for #' percent-point-difference from center labels. #' @param show_center Should the center be marked on the legend? (logical) #' @param limits A 2 by 3 matrix of lower and upper limits for p1, p2 and p3. #' #' @return A ggtern grob. #' #' @examples #' # NOTE: only intended for internal use and not part of the API #' tricolore:::ColorKeySextant(center = prop.table(runif(3)), #' values = c('#01A0C6', '#B8B3D8', '#F11D8C', #' '#FFB3B3', '#FFFF00', '#B3DCC3'), #' label_as = 'pct_diff', show_center = TRUE) #' #' @keywords internal ColorKeySextant <- function (center, values, label_as, show_center, limits = matrix(0:1, nrow = 2, ncol = 3)) { ### Create and colorize legend surface ### # calculate ternary vertex coordinates and # fill color for each sub-triangle V <- TernarySextantVertices(center) rgb <- rep(values, c(5, 4, 5, 4, 5, 4)) legend_surface <- data.frame(V, rgb = rgb, row.names = NULL, check.rows = FALSE, check.names = FALSE, stringsAsFactors = FALSE) ### Breaks and labels ### if (label_as == 'pct') { brklab <- BreaksAndLabels(2, center) } if (label_as == 'pct_diff') { brklab <- BreaksAndLabels(3, center) } ### Plot key ### return(BasicKey(legend_surface, limits, brklab, show_center, center, lwd = 0)) } # User functions ---------------------------------------------------------- #' Ternary Balance Color Scale #' #' Color-code three-part compositions with a ternary balance color scale and #' return a color key. #' #' @param df Data frame of compositional data. #' @param p1 Column name for variable in df giving first proportion #' of ternary composition (string). #' @param p2 Column name for variable in df giving second proportion #' of ternary composition (string). #' @param p3 Column name for variable in df giving third proportion #' of ternary composition (string). #' @param center Ternary coordinates of the color scale center. #' (default = 1/3,1/3,1/3). NA puts center over the compositional #' mean of the data. #' @param breaks Number of per-axis breaks in the discrete color scale. #' An integer >1. Values above 99 imply no discretization. #' @param hue Primary hue of the first ternary element (0 to 1). #' @param chroma Maximum possible chroma of mixed colors (0 to 1). #' @param lightness Lightness of mixed colors (0 to 1). #' @param contrast Lightness contrast of the color scale (0 to 1). #' @param spread The spread of the color scale. Choose values > 1 to focus the #' color scale on the center. #' @param legend Should a legend be returned along with the colors? (default=TRUE) #' @param show_data Should the data be shown on the legend? (default=TRUE) #' @param show_center Should the center be shown on the legend? #' (default=FALSE if center is at c(1/3, 1/3, 1/3), otherwise TRUE) #' @param label_as "pct" for percent-share labels or "pct_diff" for #' percent-point-difference from center labels. #' (default='pct' if center is at c(1/3, 1/3, 1/3), otherwise 'pct_diff') #' @param crop Should the legend be cropped to the data? (default=FALSE) #' @param input_validation Should the function arguments be validated? (default=TRUE) #' #' @return #' * legend=FALSE: A vector of rgbs hex-codes representing the ternary balance #' scheme colors. #' * legend=TRUE: A list with elements "rgb" and "key". #' #' @examples #' P <- as.data.frame(prop.table(matrix(runif(3^6), ncol = 3), 1)) #' Tricolore(P, 'V1', 'V2', 'V3') #' #' @importFrom ggplot2 labs layer #' @importFrom ggtern aes #' @importFrom rlang .data #' #' @md #' #' @export Tricolore <- function (df, p1, p2, p3, center = rep(1/3, 3), breaks = ifelse(identical(center, rep(1/3, 3)), 4, Inf), hue = 0.2, chroma = 0.7, lightness = 0.8, contrast = 0.4, spread = 1, legend = TRUE, show_data = TRUE, show_center = ifelse(identical(center, rep(1/3, 3)), FALSE, TRUE), label_as = ifelse(identical(center, rep(1/3, 3)), 'pct', 'pct_diff'), crop = FALSE, input_validation = TRUE) { # validation of main input arguments if (input_validation) { ValidateMainArguments(df, p1, p2, p3) ValidateParametersTricolore( list(breaks = breaks, hue = hue, chroma = chroma, lightness = lightness, contrast = contrast, center = center, spread = spread, legend = legend, show_data = show_data, show_center = show_center, label_as = label_as, crop = crop) ) } # construct 3 column matrix of proportions P <- cbind(df[[p1]], df[[p2]], df[[p3]]) # ensure data is closed P <- prop.table(P, 1) # center color-scale over data's centre if center==NA if ( is.na(center[1]) ) { center = Centre(P) } # derive the color mixture # the magic numbers rescale the [0,1] color-specification to the # cylindrical-coordinates format required by ColorMapTricolore() mixture <- ColorMapTricolore(P, center, breaks, hue*360, chroma*200, lightness*100, contrast, spread) # if specified, return a legend along with the srgb color mixtures... if (legend) { # crop legend to to data range if crop==TRUE if (crop) { limits <- TernaryLimits(P, na.rm = TRUE) # else use full range } else { limits <- matrix(0:1, nrow = 2, ncol = 3) } key <- ColorKeyTricolore(center, breaks, hue*360, chroma*200, lightness*100, contrast, spread, label_as, show_center, limits) + list( # labels take names from input variables labs(x = p1, y = p2, z = p3), if (show_data) { layer( geom = 'point', stat = 'identity', position = 'identity', mapping = aes(x = .data[['p1']], y = .data[['p2']], z = .data[['p3']]), params = list(color = 'black', shape = 16, size = 0.5, alpha = 0.5), data = mixture, check.aes = FALSE, check.param = FALSE ) } ) result <- list(rgb = mixture[['rgb']], key = key) # ... else just return a vector of hexsrgb codes of the mixed colors } else { result <- mixture[['rgb']] } return(result) } #' Ternary Sextant Color Scale #' #' Color-code three-part compositions with a ternary sextant color scale and #' return a color key. #' #' @param df Data frame of compositional data. #' @param p1 Column name for variable in df giving first proportion #' of ternary composition (string). #' @param p2 Column name for variable in df giving second proportion #' of ternary composition (string). #' @param p3 Column name for variable in df giving third proportion #' of ternary composition (string). #' @param center Ternary coordinates of the color scale center. #' (default = 1/3,1/3,1/3). NA puts center over the compositional #' mean of the data. #' @param values 6 element character vector of rgb-codes. #' @param legend Should a legend be returned along with the colors? (default=TRUE) #' @param show_data Should the data be shown on the legend? (default=TRUE) #' @param show_center Should the center be shown on the legend? #' (default=FALSE if center is at c(1/3, 1/3, 1/3), otherwise TRUE) #' @param label_as "pct" for percent-share labels or "pct_diff" for #' percent-point-difference from center labels. #' (default='pct' if center is at c(1/3, 1/3, 1/3), otherwise 'pct_diff') #' @param crop Should the legend be cropped to the data? (default=FALSE) #' @param input_validation Should the function arguments be validated? (default=TRUE) #' #' @return #' * legend=FALSE: A vector of rgbs hex-codes representing the ternary balance #' scheme colors. #' * legend=TRUE: A list with elements "rgb" and "key". #' #' @examples #' P <- as.data.frame(prop.table(matrix(runif(3^6), ncol = 3), 1)) #' TricoloreSextant(P, 'V1', 'V2', 'V3') #' #' @importFrom ggplot2 labs layer #' @importFrom ggtern aes #' @importFrom rlang .data #' #' @md #' #' @export TricoloreSextant <- function (df, p1, p2, p3, center = rep(1/3, 3), values = c("#FFFF00", "#B3DCC3", "#01A0C6", "#B8B3D8", "#F11D8C", "#FFB3B3"), legend = TRUE, show_data = TRUE, show_center = TRUE, label_as = ifelse(identical(center, rep(1/3, 3)), 'pct', 'pct_diff'), crop = FALSE, input_validation = TRUE) { # validation of main input arguments if (input_validation) { ValidateMainArguments(df, p1, p2, p3) ValidateParametersTricoloreSextant( list(values = values, center = center, legend = legend, show_data = show_data, show_center = show_center, label_as = label_as, crop = crop) ) } # construct 3 column matrix of proportions P <- cbind(df[[p1]], df[[p2]], df[[p3]]) # ensure data is closed P <- prop.table(P, 1) # center color-scale over data's centre if center==NA if ( is.na(center[1]) ) { center = Centre(P) } # derive the color mixture mixture <- ColorMapSextant(P, center, values) # if specified, return a legend along with the srgb color mixtures... if (legend) { # crop legend to to data range if crop==TRUE if (crop) { limits <- TernaryLimits(P, na.rm = TRUE) # else use full range } else { limits <- matrix(0:1, nrow = 2, ncol = 3) } key <- ColorKeySextant(center, values, label_as, show_center, limits) + list( # labels take names from input variables labs(x = p1, y = p2, z = p3), if (show_data) { layer( geom = 'point', stat = 'identity', position = 'identity', mapping = aes(x = .data[['p1']], y = .data[['p2']], z = .data[['p3']]), params = list(color = 'black', shape = 16, size = 0.5, alpha = 0.5), data = mixture, check.aes = FALSE, check.param = FALSE ) } ) result <- list(rgb = mixture[['rgb']], key = key) # ... else just return a vector of hexsrgb codes of the mixed colors } else { result <- mixture[['rgb']] } return(result) } #' Interactive Tricolore Demonstration #' #' An interactive demonstration of the tricolore color scale inspired by the #' colorbrewer2.org application. Helps in picking the right color scale for your #' data. #' #' @return Opens a shiny app session. #' #' @export DemoTricolore <- function () { app_dir <- system.file('shiny', package = 'tricolore') if (app_dir == '') { stop("Could not find example directory. Try re-installing 'tricolore'.", call. = FALSE) } shiny::runApp(app_dir, display.mode = 'normal') } # Data -------------------------------------------------------------------- #' Flat Map of European Continent #' #' A ggplot object rendering a flat background map of the European continent. #' #' @source #' Derived from geodata provided by the Natural Earth project. #' \url{https://www.naturalearthdata.com/} 'euro_basemap' #' NUTS-2 Level Geodata and Compositional Data for Europe #' #' A simple-features dataframe containing the NUTS-2 level polygons of European #' regions along with regional compositional data on education and labor-force. #' #' @format #' A data frame with 312 rows and 9 variables: #' \describe{ #' \item{id}{NUTS-2 code.} #' \item{name}{Name of NUTS-2 region.} #' \item{ed_0to2}{Share of population with highest attained education "lower secondary or less".} #' \item{ed_3to4}{Share of population with highest attained education "upper secondary".} #' \item{ed_5to8}{Share of population with highest attained education "tertiary".} #' \item{lf_pri}{Share of labor-force in primary sector.} #' \item{lf_sec}{Share of labor-force in secondary sector.} #' \item{lf_ter}{Share of labor-force in tertiary sector.} #' \item{geometry}{Polygon outlines for regions in sf package format.} #' } #' #' @details #' Variables starting with "ed" refer to the relative share of population ages #' 25 to 64 by educational attainment in the European NUTS-2 regions 2016. #' #' Variables starting with "lf" refer to the relative share of workers by #' labor-force sector in the European NUTS-2 regions 2016. The original NACE #' (rev. 2) codes have been recoded into the three sectors "primary" (A), #' "secondary" (B-E & F) and "tertiary" (all other NACE codes). #' #' @source #' Derived from Eurostats European Geodata. #' (c) EuroGeographics for the administrative boundaries. #' \url{https://gisco-services.ec.europa.eu/distribution/v2/nuts/nuts-2016-files.html} #' #' Education data derived from Eurostats table "edat_lfse_04". #' #' Labor-force data derived from Eurostats table "lfst_r_lfe2en2". 'euro_example' ================================================ FILE: R/zzz.R ================================================ .onAttach <- function(...) { packageStartupMessage('Please cite tricolore. See citation("tricolore").') } ================================================ FILE: README.Rmd ================================================ --- title: "tricolore. A flexible color scale for ternary compositions" output: github_document --- ```{r echo=FALSE} knitr::opts_chunk$set(warning=FALSE, message=FALSE, fig.width = 12, fig.height = 12) ``` Jonas Schöley [![ORCID](https://info.orcid.org/wp-content/uploads/2019/11/orcid_16x16.png)](https://orcid.org/0000-0002-3340-8518) [jschoeley.com](https://www.jschoeley.com/) · Ilya Kashnitsky [![ORCID](https://info.orcid.org/wp-content/uploads/2019/11/orcid_16x16.png)](https://orcid.org/0000-0003-1835-8687) [ikashnitsky.phd](https://ikashnitsky.phd/me.html) [![CRAN_Version](https://www.r-pkg.org/badges/version/tricolore)](https://cran.r-project.org/package=tricolore) ![GitHub Actions R-CMD-check](https://github.com/jschoeley/tricolore/actions/workflows/R-CMD-check.yaml/badge.svg) [![License: GPL v3](https://img.shields.io/badge/License-GPL%20v3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0) What is *tricolore*? -------------------- `tricolore` is an R library providing a flexible color scale for the visualization of three-part (ternary) compositions. Its main functionality is to color-code any ternary composition as a mixture of three primary colors and to draw a suitable color-key. `tricolore` flexibly adapts to different visualization challenges via - *discrete* and *continuous* color support, - support for unbalanced compositional data via *centering*, - support for data with very narrow range via *scaling*, - *hue*, *chroma* and *lightness* options. ![](README_files/teaser.png) Getting Started --------------- ```{r eval=FALSE} install.packages('tricolore') library(tricolore); DemoTricolore() ``` The `Tricolore()` function expects a dataframe of three-part compositions, color-codes the compositions and returns a list with elements `rgb` and `key`. The first list element is a vector of rgb codes for the color-coded compositions, the latter element gives a plot of the color key. Here's a minimal example using simulated data. ```{r message=FALSE, fig.cap='A ternary color key with the color-coded compositional data visible as points.'} library(tricolore) # simulate 243 ternary compositions P <- as.data.frame(prop.table(matrix(runif(3^6), ncol = 3), 1)) # color-code each composition and return a corresponding color key colors_and_legend <- Tricolore(P, 'V1', 'V2', 'V3') # the color-coded compositions head(colors_and_legend$rgb) colors_and_legend$key ``` You can familiarize yourself with the various options of `tricolore` by running `DemoTricolore()`. Ternary choropleth maps ----------------------- Here I demonstrate how to create a choropleth map of the regional distribution of education attainment in Europe 2016 using `ggplot2`. The data set `euro_example` contains the administrative boundaries for the European NUTS-2 regions in the column `geometry`. This data can be used to plot a choropleth map of Europe using the `sf` package. Each region is represented by a single row. The name of a region is given by the variable `name` while the respective [NUTS-2](https://en.wikipedia.org/wiki/Nomenclature_of_Territorial_Units_for_Statistics) geocode is given by the variable `id`. For each region some compositional statistics are available: Variables starting with `ed` refer to the relative share of population ages 25 to 64 by educational attainment in 2016 and variables starting with `lf` refer to the relative share of workers by labor-force sector in the European NUTS-2 regions 2016. **1. Using the `Tricolore()` function, color-code each educational composition in the `euro_example` data set and add the resulting vector of hex-srgb colors as a new variable to the dataframe. Store the color key separately.** ```{r} # color-code the data set and generate a color-key tric_educ <- Tricolore(euro_example, p1 = 'ed_0to2', p2 = 'ed_3to4', p3 = 'ed_5to8') ``` `tric` contains both a vector of color-coded compositions (`tric$rgb`) and the corresponding color key (`tric$key`). We add the vector of colors to the map-data. ```{r} # add the vector of colors to the `euro_example` data euro_example$educ_rgb <- tric_educ$rgb ``` **2. Using `ggplot2` and the joined color-coded education data and geodata, plot a ternary choropleth map of education attainment in the European regions. Add the color key to the map.** The secret ingredient is `scale_fill_identity()` to make sure that each region is colored according to the value in the `educ_rgb` variable of `euro_example`. ```{r} library(ggplot2) plot_educ <- # using data sf data `euro_example`... ggplot(euro_example) + # ...draw a choropleth map geom_sf(aes(fill = educ_rgb, geometry = geometry), size = 0.1) + # ...and color each region according to the color-code # in the variable `educ_rgb` scale_fill_identity() plot_educ ``` Using `annotation_custom()` and `ggplotGrob` we can add the color key produced by `Tricolore()` to the map. Internally, the color key is produced with the [`ggtern`](http://www.ggtern.com/) package. In order for it to render correctly we need to load `ggtern` *after* loading `ggplot2`. Don't worry, the `ggplot2` functions still work. ```{r} library(ggtern) plot_educ + annotation_custom( ggplotGrob(tric_educ$key), xmin = 55e5, xmax = 75e5, ymin = 8e5, ymax = 80e5 ) ``` Because the color key behaves just like a `ggplot2` plot we can change it to our liking. ```{r} plot_educ <- plot_educ + annotation_custom( ggplotGrob( tric_educ$key + labs(L = '0-2', T = '3-4', R = '5-8')), xmin = 55e5, xmax = 75e5, ymin = 8e5, ymax = 80e5 ) plot_educ ``` Some final touches... ```{r} plot_educ + theme_void() + coord_sf(datum = NA) + labs(title = 'European inequalities in educational attainment', subtitle = 'Regional distribution of ISCED education levels for people aged 25-64 in 2016.') ``` Continuous vs. discrete colors ------------------------------ By default `tricolore` uses a discrete colors scale with 16 colors. This can be changed via the `breaks` parameter. A value of `Inf` gives a continuous color scale... ```{r} # color-code the data set and generate a color-key tric_educ_disc <- Tricolore(euro_example, p1 = 'ed_0to2', p2 = 'ed_3to4', p3 = 'ed_5to8', breaks = Inf) euro_example$educ_rgb_disc <- tric_educ_disc$rgb ggplot(euro_example) + geom_sf(aes(fill = educ_rgb_disc, geometry = geometry), size = 0.1) + scale_fill_identity() + annotation_custom( ggplotGrob( tric_educ_disc$key + labs(L = '0-2', T = '3-4', R = '5-8')), xmin = 55e5, xmax = 75e5, ymin = 8e5, ymax = 80e5 ) + theme_void() + coord_sf(datum = NA) + labs(title = 'European inequalities in educational attainment', subtitle = 'Regional distribution of ISCED education levels for people aged 25-64 in 2016.') ``` ...and a `breaks = 2` gives a discrete color scale with $2^2=4$ colors, highlighting the regions with an absolute majority of any part of the composition. ```{r} # color-code the data set and generate a color-key tric_educ_disc <- Tricolore(euro_example, p1 = 'ed_0to2', p2 = 'ed_3to4', p3 = 'ed_5to8', breaks = 2) euro_example$educ_rgb_disc <- tric_educ_disc$rgb ggplot(euro_example) + geom_sf(aes(fill = educ_rgb_disc, geometry = geometry), size = 0.1) + scale_fill_identity() + annotation_custom( ggplotGrob( tric_educ_disc$key + labs(L = '0-2', T = '3-4', R = '5-8')), xmin = 55e5, xmax = 75e5, ymin = 8e5, ymax = 80e5 ) + theme_void() + coord_sf(datum = NA) + labs(title = 'European inequalities in educational attainment', subtitle = 'Regional distribution of ISCED education levels for people aged 25-64 in 2016.') ``` Ternary centering ----------------- While the ternary balance scheme allows for dense yet clear visualizations of *well spread out* ternary compositions the technique is less informative when used with highly *unbalanced data*. The map below shows the regional labor force composition in Europe as of 2016 in nearly monochromatic colors, the different shades of blue signifying a working population which is concentrated in the tertiary (services) sector. Regions in Turkey and Eastern Europe show a somewhat higher concentration of workers in the primary (production) sector but overall the data shows little variation with regards to the *visual reference point*, i.e. the greypoint marking perfectly balanced proportions. ```{r} tric_lf_non_centered <- Tricolore(euro_example, breaks = Inf, 'lf_pri', 'lf_sec', 'lf_ter') euro_example$rgb_lf_non_centered <- tric_lf_non_centered$rgb ggplot(euro_example) + geom_sf(aes(fill = rgb_lf_non_centered, geometry = geometry), size = 0.1) + scale_fill_identity() + annotation_custom( ggplotGrob(tric_lf_non_centered$key + labs(L = '% Primary', T = '% Secondary', R = '% Tertiary')), xmin = 55e5, xmax = 75e5, ymin = 8e5, ymax = 80e5 ) + theme_void() + coord_sf(datum = NA) + labs(title = 'European inequalities in labor force composition', subtitle = 'Regional distribution of labor force across the three sectors in 2016.') ``` A remedy for analyzing data which shows little variation in relation to some reference point is to *change the point of reference*. The map below yet again shows the European regional labor force composition in 2016 but the color scale has been altered so that its greypoint -- the visual point of reference -- is positioned at the European annual average. Consequently the colors now show direction and magnitude of the deviation from the European average labor force composition. Pink, Green and Blue hues show a higher than average share of workers in the primary, secondary and tertiary sector respectively. The saturation of the colors show the magnitude of that deviation with perfect grey marking a region that has a labor force composition equal to the European average, i.e. the reference point. Centering the color scale over the labor-force composition of the average European NUTS-2 region shows various patterns of deviations from the average. Metropolitan regions (Hamburg, Stockholm, Paris, Madrid) have a higher than average share of tertiary workers. Large parts of France are quite grey, indicating a labor-force composition close to the average, while Eastern Europe, the south of Spain and Italy have a higher than average share of workers active in the primary sector. ```{r} tric_lf_centered <- Tricolore(euro_example, 'lf_pri', 'lf_sec', 'lf_ter', center = NA, crop = FALSE) euro_example$rgb_lf_centered <- tric_lf_centered$rgb ggplot(euro_example) + geom_sf(aes(fill = rgb_lf_centered, geometry = geometry), size = 0.1) + scale_fill_identity() + annotation_custom( ggplotGrob( tric_lf_centered$key + labs(L = '% Primary', T = '% Secondary', R = '% Tertiary')), xmin = 55e5, xmax = 75e5, ymin = 8e5, ymax = 80e5 ) + theme_void() + coord_sf(datum = NA) + labs(title = 'European inequalities in labor force composition', subtitle = 'Regional distribution of labor force across the three sectors in 2016.') ``` Contributing ------------ This software is an academic project. We welcome any issues and pull requests. Please report any bugs you find by submitting an issue on github.com/jschoeley/tricolore/issues. If you wish to contribute, please submit a pull request following the guidelines stated in [CONTRIBUTING.md](https://github.com/jschoeley/tricolore/blob/devel/CONTRIBUTING.md). ================================================ FILE: README.md ================================================ tricolore. A flexible color scale for ternary compositions ================ Jonas Schöley [![ORCID](https://info.orcid.org/wp-content/uploads/2019/11/orcid_16x16.png)](https://orcid.org/0000-0002-3340-8518) [jschoeley.com](https://www.jschoeley.com/) · Ilya Kashnitsky [![ORCID](https://info.orcid.org/wp-content/uploads/2019/11/orcid_16x16.png)](https://orcid.org/0000-0003-1835-8687) [ikashnitsky.phd](https://ikashnitsky.phd/me.html) [![CRAN_Version](https://www.r-pkg.org/badges/version/tricolore)](https://cran.r-project.org/package=tricolore) ![GitHub Actions R-CMD-check](https://github.com/jschoeley/tricolore/actions/workflows/R-CMD-check.yaml/badge.svg) [![License: GPL v3](https://img.shields.io/badge/License-GPL%20v3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0) ## What is *tricolore*? `tricolore` is an R library providing a flexible color scale for the visualization of three-part (ternary) compositions. Its main functionality is to color-code any ternary composition as a mixture of three primary colors and to draw a suitable color-key. `tricolore` flexibly adapts to different visualization challenges via - *discrete* and *continuous* color support, - support for unbalanced compositional data via *centering*, - support for data with very narrow range via *scaling*, - *hue*, *chroma* and *lightness* options. ![](README_files/teaser.png) ## Getting Started ``` r install.packages('tricolore') library(tricolore); DemoTricolore() ``` The `Tricolore()` function expects a dataframe of three-part compositions, color-codes the compositions and returns a list with elements `rgb` and `key`. The first list element is a vector of rgb codes for the color-coded compositions, the latter element gives a plot of the color key. Here’s a minimal example using simulated data. ``` r library(tricolore) # simulate 243 ternary compositions P <- as.data.frame(prop.table(matrix(runif(3^6), ncol = 3), 1)) # color-code each composition and return a corresponding color key colors_and_legend <- Tricolore(P, 'V1', 'V2', 'V3') # the color-coded compositions head(colors_and_legend$rgb) ``` ## [1] "#727272" "#4AA0BB" "#6E8E72" "#BC8C67" "#37A789" "#A48AC6" ``` r colors_and_legend$key ```
A ternary color key with the color-coded compositional data visible as points.
You can familiarize yourself with the various options of `tricolore` by running `DemoTricolore()`. ## Ternary choropleth maps Here I demonstrate how to create a choropleth map of the regional distribution of education attainment in Europe 2016 using `ggplot2`. The data set `euro_example` contains the administrative boundaries for the European NUTS-2 regions in the column `geometry`. This data can be used to plot a choropleth map of Europe using the `sf` package. Each region is represented by a single row. The name of a region is given by the variable `name` while the respective [NUTS-2](https://en.wikipedia.org/wiki/Nomenclature_of_Territorial_Units_for_Statistics) geocode is given by the variable `id`. For each region some compositional statistics are available: Variables starting with `ed` refer to the relative share of population ages 25 to 64 by educational attainment in 2016 and variables starting with `lf` refer to the relative share of workers by labor-force sector in the European NUTS-2 regions 2016. **1. Using the `Tricolore()` function, color-code each educational composition in the `euro_example` data set and add the resulting vector of hex-srgb colors as a new variable to the dataframe. Store the color key separately.** ``` r # color-code the data set and generate a color-key tric_educ <- Tricolore(euro_example, p1 = 'ed_0to2', p2 = 'ed_3to4', p3 = 'ed_5to8') ``` `tric` contains both a vector of color-coded compositions (`tric$rgb`) and the corresponding color key (`tric$key`). We add the vector of colors to the map-data. ``` r # add the vector of colors to the `euro_example` data euro_example$educ_rgb <- tric_educ$rgb ``` **2. Using `ggplot2` and the joined color-coded education data and geodata, plot a ternary choropleth map of education attainment in the European regions. Add the color key to the map.** The secret ingredient is `scale_fill_identity()` to make sure that each region is colored according to the value in the `educ_rgb` variable of `euro_example`. ``` r library(ggplot2) plot_educ <- # using data sf data `euro_example`... ggplot(euro_example) + # ...draw a choropleth map geom_sf(aes(fill = educ_rgb, geometry = geometry), size = 0.1) + # ...and color each region according to the color-code # in the variable `educ_rgb` scale_fill_identity() plot_educ ``` ![](README_files/figure-gfm/unnamed-chunk-6-1.png) Using `annotation_custom()` and `ggplotGrob` we can add the color key produced by `Tricolore()` to the map. Internally, the color key is produced with the [`ggtern`](http://www.ggtern.com/) package. In order for it to render correctly we need to load `ggtern` *after* loading `ggplot2`. Don’t worry, the `ggplot2` functions still work. ``` r library(ggtern) plot_educ + annotation_custom( ggplotGrob(tric_educ$key), xmin = 55e5, xmax = 75e5, ymin = 8e5, ymax = 80e5 ) ``` ![](README_files/figure-gfm/unnamed-chunk-7-1.png) Because the color key behaves just like a `ggplot2` plot we can change it to our liking. ``` r plot_educ <- plot_educ + annotation_custom( ggplotGrob( tric_educ$key + labs(L = '0-2', T = '3-4', R = '5-8')), xmin = 55e5, xmax = 75e5, ymin = 8e5, ymax = 80e5 ) plot_educ ``` ![](README_files/figure-gfm/unnamed-chunk-8-1.png) Some final touches… ``` r plot_educ + theme_void() + coord_sf(datum = NA) + labs(title = 'European inequalities in educational attainment', subtitle = 'Regional distribution of ISCED education levels for people aged 25-64 in 2016.') ``` ![](README_files/figure-gfm/unnamed-chunk-9-1.png) ## Continuous vs. discrete colors By default `tricolore` uses a discrete colors scale with 16 colors. This can be changed via the `breaks` parameter. A value of `Inf` gives a continuous color scale… ``` r # color-code the data set and generate a color-key tric_educ_disc <- Tricolore(euro_example, p1 = 'ed_0to2', p2 = 'ed_3to4', p3 = 'ed_5to8', breaks = Inf) euro_example$educ_rgb_disc <- tric_educ_disc$rgb ggplot(euro_example) + geom_sf(aes(fill = educ_rgb_disc, geometry = geometry), size = 0.1) + scale_fill_identity() + annotation_custom( ggplotGrob( tric_educ_disc$key + labs(L = '0-2', T = '3-4', R = '5-8')), xmin = 55e5, xmax = 75e5, ymin = 8e5, ymax = 80e5 ) + theme_void() + coord_sf(datum = NA) + labs(title = 'European inequalities in educational attainment', subtitle = 'Regional distribution of ISCED education levels for people aged 25-64 in 2016.') ``` ![](README_files/figure-gfm/unnamed-chunk-10-1.png) …and a `breaks = 2` gives a discrete color scale with $2^2=4$ colors, highlighting the regions with an absolute majority of any part of the composition. ``` r # color-code the data set and generate a color-key tric_educ_disc <- Tricolore(euro_example, p1 = 'ed_0to2', p2 = 'ed_3to4', p3 = 'ed_5to8', breaks = 2) euro_example$educ_rgb_disc <- tric_educ_disc$rgb ggplot(euro_example) + geom_sf(aes(fill = educ_rgb_disc, geometry = geometry), size = 0.1) + scale_fill_identity() + annotation_custom( ggplotGrob( tric_educ_disc$key + labs(L = '0-2', T = '3-4', R = '5-8')), xmin = 55e5, xmax = 75e5, ymin = 8e5, ymax = 80e5 ) + theme_void() + coord_sf(datum = NA) + labs(title = 'European inequalities in educational attainment', subtitle = 'Regional distribution of ISCED education levels for people aged 25-64 in 2016.') ``` ![](README_files/figure-gfm/unnamed-chunk-11-1.png) ## Ternary centering While the ternary balance scheme allows for dense yet clear visualizations of *well spread out* ternary compositions the technique is less informative when used with highly *unbalanced data*. The map below shows the regional labor force composition in Europe as of 2016 in nearly monochromatic colors, the different shades of blue signifying a working population which is concentrated in the tertiary (services) sector. Regions in Turkey and Eastern Europe show a somewhat higher concentration of workers in the primary (production) sector but overall the data shows little variation with regards to the *visual reference point*, i.e. the greypoint marking perfectly balanced proportions. ``` r tric_lf_non_centered <- Tricolore(euro_example, breaks = Inf, 'lf_pri', 'lf_sec', 'lf_ter') euro_example$rgb_lf_non_centered <- tric_lf_non_centered$rgb ggplot(euro_example) + geom_sf(aes(fill = rgb_lf_non_centered, geometry = geometry), size = 0.1) + scale_fill_identity() + annotation_custom( ggplotGrob(tric_lf_non_centered$key + labs(L = '% Primary', T = '% Secondary', R = '% Tertiary')), xmin = 55e5, xmax = 75e5, ymin = 8e5, ymax = 80e5 ) + theme_void() + coord_sf(datum = NA) + labs(title = 'European inequalities in labor force composition', subtitle = 'Regional distribution of labor force across the three sectors in 2016.') ``` ![](README_files/figure-gfm/unnamed-chunk-12-1.png) A remedy for analyzing data which shows little variation in relation to some reference point is to *change the point of reference*. The map below yet again shows the European regional labor force composition in 2016 but the color scale has been altered so that its greypoint – the visual point of reference – is positioned at the European annual average. Consequently the colors now show direction and magnitude of the deviation from the European average labor force composition. Pink, Green and Blue hues show a higher than average share of workers in the primary, secondary and tertiary sector respectively. The saturation of the colors show the magnitude of that deviation with perfect grey marking a region that has a labor force composition equal to the European average, i.e. the reference point. Centering the color scale over the labor-force composition of the average European NUTS-2 region shows various patterns of deviations from the average. Metropolitan regions (Hamburg, Stockholm, Paris, Madrid) have a higher than average share of tertiary workers. Large parts of France are quite grey, indicating a labor-force composition close to the average, while Eastern Europe, the south of Spain and Italy have a higher than average share of workers active in the primary sector. ``` r tric_lf_centered <- Tricolore(euro_example, 'lf_pri', 'lf_sec', 'lf_ter', center = NA, crop = FALSE) euro_example$rgb_lf_centered <- tric_lf_centered$rgb ggplot(euro_example) + geom_sf(aes(fill = rgb_lf_centered, geometry = geometry), size = 0.1) + scale_fill_identity() + annotation_custom( ggplotGrob( tric_lf_centered$key + labs(L = '% Primary', T = '% Secondary', R = '% Tertiary')), xmin = 55e5, xmax = 75e5, ymin = 8e5, ymax = 80e5 ) + theme_void() + coord_sf(datum = NA) + labs(title = 'European inequalities in labor force composition', subtitle = 'Regional distribution of labor force across the three sectors in 2016.') ``` ![](README_files/figure-gfm/unnamed-chunk-13-1.png) ## Contributing This software is an academic project. We welcome any issues and pull requests. Please report any bugs you find by submitting an issue on github.com/jschoeley/tricolore/issues. If you wish to contribute, please submit a pull request following the guidelines stated in [CONTRIBUTING.md](https://github.com/jschoeley/tricolore/blob/devel/CONTRIBUTING.md). ================================================ FILE: cran-comments.md ================================================ This submission fixes CRAN check ERRORs which arose due to the 4.0.0 update of the ggtern import and were ultimately related to the ggplot 4.0.0 version update. ## Test environments * Linux Mint 21.3, R 4.5.2 * macOS 15.7.2, R 4.5.2 * Microsoft Windows Server 2025 10.0.26100, R 4.5.2 * Ubuntu 24.04.3, R devel * Ubuntu 24.04.3, R 4.5.2 * Ubuntu 24.04.3, R 4.4.3 ## R CMD check results > 0 errors ✔ | 0 warnings ✔ | 0 notes ✔ ## Test results > [ FAIL 0 | WARN 0 | SKIP 0 | PASS 39 ] ## CRAN maintainer comments - FIXED invalid URLs > Found the following (possibly) invalid URLs: > https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units/ > > Please fix and resubmit. > > Best, > Uwe Ligges ================================================ FILE: data-raw/euro_basemap.R ================================================ #'--- #' title: A flat and simplified map of Europe #' author: Jonas Schöley #' date: 2018-08-28 #'--- library(tidyverse) library(sf) library(rnaturalearth) eura_sf <- # download geospatial data for European, Asian and African countries ne_countries(continent = c('europe', 'asia', 'africa'), returnclass = 'sf', scale = 50) %>% # project to crs 3035 st_transform(crs = 3035) %>% # merge into single polygon st_union(by_feature = FALSE) %>% st_crop(xmin = 25e5, xmax = 75e5, ymin = 13.5e5, ymax = 54.5e5) # draw a basemap of Europe euro_basemap <- ggplot(eura_sf) + geom_sf(color = NA, fill = 'grey90') + coord_sf(expand = FALSE, datum = NA) + theme_void() + theme(panel.border = element_rect(fill = NA, color = 'grey90', linewidth = 1)) save(euro_basemap, file = './data-raw/euro_basemap.RData', compress = 'xz') ================================================ FILE: data-raw/euro_example.R ================================================ #'--- #' title: Geodata for European NUTS-2 regions with added variables #' author: Jonas Schöley #' date: 2019-07-19 #'--- # Init -------------------------------------------------------------------- library(tidyverse) library(stringi) library(sf) library(rmapshaper) library(eurostat) # European NUTS-2 geodata ------------------------------------------------- # download geodata on nuts-2 regions euro_geo_nuts2 <- get_eurostat_geospatial(output_class = 'sf', resolution = '60', nuts_level = 2, year = 2016) %>% # exclude some regions which don't report # the statistics we're interested in filter(!(str_detect(geo, '^AL') | str_detect(geo, '^LI') | geo == 'FI20')) %>% # project to crs 3035 st_transform(crs = 3035) %>% # pseudo-buffer regions to avoid self-intersection errors st_buffer(0) %>% # crop to Europe st_crop(xmin = 25e5, xmax = 75e5, ymin = 13.5e5, ymax = 54.5e5) %>% # simplify to save space ms_simplify(keep = 0.05, keep_shapes = TRUE) %>% # transliterate non-ASCII characters in region names # (so that CRAN-check stops complaining) mutate( name = stri_trans_general(NUTS_NAME, id = 'any-latin; latin-ascii') ) %>% # select nuts id, region name and geometry columns select(id, name, geometry) # Download data on European educational composition ----------------------- # download data on education composition by NUTS-2 level for Europe educ <- get_eurostat('edat_lfse_04') # select data for 2016 and calculate shares euro_education <- educ %>% mutate(year = lubridate::year(time), id = as.character(geo)) %>% # year 2016, total population, nuts 2 levels filter(year == 2016, str_length(geo) == 4, isced11 %in% c('ED0-2', 'ED3_4', 'ED5-8'), sex == 'T') %>% mutate(values = values/100) %>% spread(isced11, values) %>% select(id, ed_0to2 = `ED0-2`, ed_3to4 = `ED3_4`, ed_5to8 = `ED5-8`) %>% drop_na() # Download data on European labor-force composition ----------------------- # download data on labor-force composition by NUTS-2 level for Europe lf <- get_eurostat("lfst_r_lfe2en2") # select data for 2016, recode to ternary sectors and calculate shares euro_sectors <- lf %>% # recode time as year and geo as character mutate( year = as.integer(lubridate::year(time)), geo = as.character(geo) ) %>% # subset to total age, year 2016 and NUTS-2 regions filter( age == 'Y_GE15', str_length(geo) == 4, year == 2016 ) %>% # if a sector wasn't reported, assume no one worked there # (this is motivated by the "missing" agricultural workers in innner london) complete(nace_r2, geo, year, fill = list(values = 0)) %>% # recode into three sectors mutate( sector = recode(as.character(nace_r2), `A` = 'primary', `B-E` = 'secondary', `F` = 'secondary'), sector = ifelse(!sector %in% c('primary', 'secondary', 'TOTAL'), 'tertiary', sector) ) %>% group_by(year, geo, sector) %>% summarise(N = sum(values, na.rm = TRUE)) %>% ungroup() %>% # calculate shares on total spread(sector, N) %>% mutate_at(vars(primary, secondary, tertiary), .funs = ~ ./TOTAL) %>% # simplify select(id = geo, lf_pri = primary, lf_sec = secondary, lf_ter = tertiary) %>% drop_na() # Join compositional data with geodata ------------------------------------ euro_example <- euro_geo_nuts2 %>% left_join(euro_education, 'id') %>% left_join(euro_sectors, 'id') %>% arrange(id) save( euro_example, file = './data-raw/euro_example.RData', compress = 'xz', version = 2 ) # Test -------------------------------------------------------------------- # library(leaflet) # foo <- tricolore::Tricolore(euro_example, # p1 = 'lf_pri', p2 = 'lf_sec', p3 = 'lf_ter', # center = NA, hue = 0.2) # euro_example %>% # st_transform(crs = 4326) %>% # leaflet() %>% # addProviderTiles(providers$Esri.WorldTerrain) %>% # addPolygons(color = str_sub(foo$rgb, 1, 7), # weight = 1, smoothFactor = 0.1, # fillColor = str_sub(foo$rgb, 1, 7), # fillOpacity = 1, # popup = # paste0( # euro_example$id, euro_example$name, '
', # 'Primary: ', # formatC(euro_example$lf_pri*100, # digits = 1, format = 'f'), '%
', # ' Secondary: ', # formatC(euro_example$lf_sec*100, # digits = 1, format = 'f'), '%
', # ' Tertiary: ', # formatC(euro_example$lf_ter*100, # digits = 1, format = 'f'), '%
' # ) # ) # foo <- tricolore::Tricolore(euro_example, # p1 = 'ed_0to2', p2 = 'ed_3to4', p3 = 'ed_5to8', hue = 0.2) # euro_example %>% # st_transform(crs = 4326) %>% # leaflet() %>% # addProviderTiles(providers$Esri.WorldTerrain) %>% # addPolygons(color = str_sub(foo$rgb, 1, 7), # weight = 1, smoothFactor = 0.1, # fillColor = str_sub(foo$rgb, 1, 7), # fillOpacity = 1, # popup = # paste0( # euro_example$id, euro_example$name, '
', # 'Primary: ', # formatC(euro_example$ed_0to2*100, # digits = 1, format = 'f'), '%
', # ' Secondary: ', # formatC(euro_example$ed_3to4*100, # digits = 1, format = 'f'), '%
', # ' Tertiary: ', # formatC(euro_example$ed_5to8*100, # digits = 1, format = 'f'), '%
' # ) # ) ================================================ FILE: inst/CITATION ================================================ citHeader('To cite tricolore in publications, please use:') bibentry( bibtype = 'Article', author = c(person('Jonas', 'Schöley', role = c('aut', 'cre')), person('Ilya', 'Kashnitsky', role = 'aut')), title = 'tricolore. A flexible color scale for ternary compositions', journal = 'CRAN', year = '2025', note = 'Version 1.2.6', url = 'https://cran.r-project.org/package=tricolore', textVersion = 'J. Schöley and I. Kashnitsky (2024). tricolore: A flexible color scale for ternary compositions. Version 1.2.4. CRAN. URL https://cran.r-project.org/package=tricolore' ) bibentry( bibtype = 'Article', author = person('Jonas', 'Schöley'), title = 'The centered ternary balance scheme. A technique to visualize surfaces of unbalanced three-part compositions', journal = 'Demographic Research', year = '2021', month = 'mar', pages = '443--458', volume = '44', doi = '10.4054/DemRes.2021.44.19', textVersion = 'J. Schöley (2021). The centered ternary balance scheme: A technique to visualize surfaces of unbalanced three-part compositions. Demographic Research, Vol. 44, p. 443-458. DOI 10.4054/DemRes.2021.44.19' ) ================================================ FILE: inst/shiny/app.R ================================================ library(shiny) library(sf) library(ggtern) library(tricolore) # UI ---------------------------------------------------------------------- ui <- fluidPage( titlePanel(title = 'Tricolore: A flexible color scale for ternary compositions'), sidebarLayout( # INPUT sidebarPanel(width = 3, radioButtons(inputId = 'data', label = 'Data', inline = TRUE, choices = list('Labour force' = 'lf', 'Education' = 'educ'), selected = 'educ'), radioButtons(inputId = 'type', label = 'Type', inline = TRUE, choices = list('Default' = 'tricolore', 'Sextant' = 'sextant'), selected = 'tricolore'), conditionalPanel( condition = 'input.type == "tricolore"', sliderInput(inputId = 'hue', label = 'Hue', ticks = FALSE, min = 0, max = 1, step = 0.1, value = 0.2), sliderInput(inputId = 'chroma', label = 'Chroma', ticks = FALSE, min = 0, max = 1, step = 0.1, value = 0.7), sliderInput(inputId = 'lightness', label = 'Lightness', ticks = FALSE, min = 0, max = 1, step = 0.1, value = 0.8), sliderInput(inputId = 'contrast', label = 'Contrast', ticks = FALSE, min = 0, max = 1, step = 0.1, value = 0.4), sliderInput(inputId = 'spread', label = 'Spread', min = 0.5, max = 2, step = 0.1, value = 1, ticks = FALSE), checkboxInput(inputId = 'discrete', label = 'Discrete', value = FALSE), conditionalPanel( condition = 'input.discrete', sliderInput(inputId = 'breaks', label = 'Breaks', ticks = FALSE, min = 2, max = 20, step = 1, value = 4) )), checkboxInput(inputId = 'center', label = 'Mean centering', value = FALSE), checkboxInput(inputId = 'show_center', label = 'Show center', value = FALSE), checkboxInput(inputId = 'show_data', label = 'Show data', value = TRUE), checkboxInput(inputId = 'crop', label = 'Crop legend', value = FALSE), radioButtons(inputId = 'label_as', label = 'Label as', choices = list('percent-share' = 'pct', 'pct-pt. difference' = 'pct_diff'), selected = 'pct'), verbatimTextOutput(outputId = 'call') ), # OUTPUT mainPanel(plotOutput(outputId = 'example')) ) ) # Server ------------------------------------------------------------------ server <- function(input, output) { output$call <- renderText({ paste0( if (input$type == 'tricolore') 'Tricolore(', if (input$type == 'sextant') 'TricoloreSextant(', "euro_example, ", if (input$data == 'educ') "p1 = 'ed_0to2', p2 = 'ed_3to4', p3 = 'ed_5to8'", if (input$data == 'lf') "p1 = 'lf_pri', p2 = 'lf_sec', p3 = 'lf_ter'", ', center = ', ifelse(input$center, 'NA', 'rep(1/3,3)'), if (input$type == 'tricolore') { paste0( ', breaks = ', ifelse(input$discrete, input$breaks, 'Inf'), ', hue = ', input$hue, ', chroma = ', input$chroma, ', lightness = ', input$lightness, ', contrast = ', input$contrast, ', spread = ', input$spread ) }, ', legend = TRUE', ', show_data = ', input$show_data, ', show_center = ', input$show_center, ', label_as = "', input$label_as, '"', ', crop = ', input$crop, ')' ) }) output$example <- renderPlot(res = 120, width = 1000, height = 800, { if (input$data == 'educ') { p1 = 'ed_0to2'; p2 = 'ed_3to4'; p3 = 'ed_5to8' title = 'Composition of education levels in European regions 2016\n' } if (input$data == 'lf') { p1 = 'lf_pri'; p2 = 'lf_sec'; p3 = 'lf_ter' title = 'Labor force composition in European regions 2016\n' } if (input$type == 'tricolore') { # mix color, generate legend mixed <- Tricolore(euro_example, p1 = p1, p2 = p2, p3 = p3, center = if (input$center) NA else rep(1/3,3), breaks = ifelse(input$discrete, input$breaks, Inf), hue = input$hue, chroma = input$chroma, lightness = input$lightness, contrast = input$contrast, spread = input$spread, show_data = input$show_data, show_center = input$show_center, label_as = input$label_as, crop = input$crop, legend = TRUE) } if (input$type == 'sextant') { # mix color, generate legend mixed <- TricoloreSextant(euro_example, p1 = p1, p2 = p2, p3 = p3, center = if (input$center) NA else rep(1/3,3), show_data = input$show_data, show_center = input$show_center, label_as = input$label_as, crop = input$crop, legend = TRUE) } # customize legend lgnd <- mixed[['key']] + labs(x = 'Primary', y = 'Secondary', z = 'Tertiary', subtitle = paste0( title, ifelse(input$center, 'Colors show deviation from average composition\n', 'Colors show deviations from balanced composition\n'), 'Data by eurostat' ) ) + theme( plot.background = element_blank(), plot.subtitle = element_text(size = 8), panel.background = element_blank(), tern.plot.background = element_blank(), tern.panel.background = element_blank(), ) # merge data and map euro_example$rgb <- mixed[['rgb']] # generate map euro_map <- euro_basemap + geom_sf(aes(fill = rgb, geometry = geometry), color = NA, data = euro_example) + annotation_custom(ggplotGrob(lgnd), xmin = 54e5, xmax = 74e5, ymin = 8e5, ymax = 80e5) + scale_fill_identity() + coord_sf(expand = FALSE, datum = NA, default = TRUE) print(euro_map) }) } shinyApp(ui, server) ================================================ FILE: man/BasicKey.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/tricolore.R \name{BasicKey} \alias{BasicKey} \title{Template for Ternary Key} \usage{ BasicKey(legend_surface, limits, brklab, show_center, center, lwd) } \arguments{ \item{legend_surface}{A data frame with numeric 'id', 'p1', 'p2', 'p3' and character column 'rgb'.} \item{limits}{A 2 by 3 matrix of lower and upper limits for p1, p2 and p3.} \item{brklab}{Breaks and labels as returned by \code{\link{BreaksAndLabels}}.} \item{show_center}{Should the center be marked on the legend? (logical)} \item{center}{Ternary coordinates of the grey-point.} \item{lwd}{A numeric scalar giving the linewidth of the legend surface polygons.} } \value{ A ggtern grob. } \description{ Return various types of breaks and labels for ternary color keys. } \keyword{internal} ================================================ FILE: man/BreaksAndLabels.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/tricolore.R \name{BreaksAndLabels} \alias{BreaksAndLabels} \title{Breaks and Labels for Ternary Color Key} \usage{ BreaksAndLabels(type, center = NULL, breaks = NULL) } \arguments{ \item{type}{An integer 1, 2, or 3.} \item{center}{Ternary coordinates of the grey-point.} \item{breaks}{Number of breaks in the discrete color scale. An integer >1. Values above 99 imply no discretization.} } \value{ A list of lists containing breaks and labels for each of the 3 ternary axes. } \description{ Return various types of breaks and labels for ternary color keys. } \examples{ # NOTE: only intended for internal use and not part of the API tricolore:::BreaksAndLabels(1, breaks = 3) tricolore:::BreaksAndLabels(2) tricolore:::BreaksAndLabels(3, center = c(1/3, 1/3, 1/3)) } \keyword{internal} ================================================ FILE: man/Centre.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/tricolore.R \name{Centre} \alias{Centre} \title{Compositional Centre} \usage{ Centre(P) } \arguments{ \item{P}{n by m matrix of compositions [p1, ..., pm]_i for i=1,...,n.} } \value{ The centre of P as an m element numeric vector. } \description{ Calculate the centre of a compositional data set. } \examples{ # NOTE: only intended for internal use and not part of the API P <- prop.table(matrix(runif(300), 100), margin = 1) tricolore:::Centre(P) } \references{ Von Eynatten, H., Pawlowsky-Glahn, V., & Egozcue, J. J. (2002). Understanding perturbation on the simplex: A simple method to better visualize and interpret compositional data in ternary diagrams. Mathematical Geology, 34(3), 249-257. Pawlowsky-Glahn, V., Egozcue, J. J., & Tolosana-Delgado, R. (2007). Lecture Notes on Compositional Data Analysis. Retrieved from https://dugi-doc.udg.edu/bitstream/handle/10256/297/CoDa-book.pdf } \keyword{internal} ================================================ FILE: man/ColorKeySextant.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/tricolore.R \name{ColorKeySextant} \alias{ColorKeySextant} \title{Sextant Scheme Legend} \usage{ ColorKeySextant( center, values, label_as, show_center, limits = matrix(0:1, nrow = 2, ncol = 3) ) } \arguments{ \item{center}{Ternary coordinates of the sextant meeting point.} \item{values}{6 element character vector of rgb-codes.} \item{label_as}{"pct" for percent-share labels or "pct_diff" for percent-point-difference from center labels.} \item{show_center}{Should the center be marked on the legend? (logical)} \item{limits}{A 2 by 3 matrix of lower and upper limits for p1, p2 and p3.} } \value{ A ggtern grob. } \description{ Plot a sextant scheme legend. } \examples{ # NOTE: only intended for internal use and not part of the API tricolore:::ColorKeySextant(center = prop.table(runif(3)), values = c('#01A0C6', '#B8B3D8', '#F11D8C', '#FFB3B3', '#FFFF00', '#B3DCC3'), label_as = 'pct_diff', show_center = TRUE) } \keyword{internal} ================================================ FILE: man/ColorKeyTricolore.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/tricolore.R \name{ColorKeyTricolore} \alias{ColorKeyTricolore} \title{Ternary Balance Scheme Legend} \usage{ ColorKeyTricolore( center, breaks, h_, c_, l_, contrast, spread, label_as, show_center, limits = matrix(0:1, nrow = 2, ncol = 3) ) } \arguments{ \item{center}{Ternary coordinates of the grey-point.} \item{breaks}{Number of breaks in the discrete color scale. An integer >1. Values above 99 imply no discretization.} \item{h_}{Primary hue of the first ternary element in angular degrees [0, 360].} \item{c_}{Maximum possible chroma of mixed colors [0, 200].} \item{l_}{Lightness of mixed colors [0, 100].} \item{contrast}{Lightness contrast of the color scale [0, 1).} \item{spread}{Spread of the color scale around center > 0.} \item{label_as}{"pct" for percent-share labels or "pct_diff" for percent-point-difference from center labels.} \item{show_center}{Should the center be marked on the legend? (logical)} \item{limits}{A 2 by 3 matrix of lower and upper limits for p1, p2 and p3.} } \value{ A ggtern grob. } \description{ Plot a ternary balance scheme legend. } \examples{ # NOTE: only intended for internal use and not part of the API tricolore:::ColorKeyTricolore(center = rep(1/3, 3), breaks = 4, h_ = 80, c_ = 140, l_ = 80, contrast = 0.4, spread = 1, label_as = "pct", show_center = FALSE) } \keyword{internal} ================================================ FILE: man/ColorMapSextant.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/tricolore.R \name{ColorMapSextant} \alias{ColorMapSextant} \title{Sextant Encoding of Ternary Composition} \usage{ ColorMapSextant(P, center, values) } \arguments{ \item{P}{n by 3 matrix of ternary compositions [p1, p2, p3](i) for i=1, ..., n.} \item{center}{Ternary coordinates of the sextant meeting point.} \item{values}{6 element character vector of rgb-codes.} } \value{ An n row data frame giving, for each row of the input P, the input proportions [p1, p2, p3], sextant id (sextant) and the hex-rgb string of the mixed colors (rgb). } \description{ Return the sextant scheme colors for a matrix of ternary compositions. } \examples{ # NOTE: only intended for internal use and not part of the API P <- prop.table(matrix(runif(9), ncol = 3), 1) tricolore:::ColorMapSextant(P, c(1/3, 1/3, 1/3), c('#01A0C6', '#B8B3D8', '#F11D8C', '#FFB3B3', '#FFFF00', '#B3DCC3')) } \keyword{internal} ================================================ FILE: man/ColorMapTricolore.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/tricolore.R \name{ColorMapTricolore} \alias{ColorMapTricolore} \title{CIE-Lch Mixture of Ternary Composition} \usage{ ColorMapTricolore(P, center, breaks, h_, c_, l_, contrast, spread) } \arguments{ \item{P}{n by 3 matrix of ternary compositions [p1, p2, p3](i) for i=1, ..., n.} \item{center}{Ternary coordinates of the grey-point.} \item{breaks}{Number of breaks in the discrete color scale. An integer >1. Values above 99 imply no discretization.} \item{h_}{Primary hue of the first ternary element in angular degrees [0, 360].} \item{c_}{Maximum possible chroma of mixed colors [0, 200].} \item{l_}{Lightness of mixed colors [0, 100].} \item{contrast}{Lightness contrast of the color scale [0, 1).} \item{spread}{Spread of the color scale around center > 0.} } \value{ An n row data frame giving, for each row of the input P, the input proportions [p1, p2, p3], parameters of the color mixture (h, c, l) and the hex-rgb string of the mixed colors (rgb). } \description{ Return the ternary balance scheme colors for a matrix of ternary compositions. } \examples{ # NOTE: only intended for internal use and not part of the API P <- prop.table(matrix(runif(9), ncol = 3), 1) tricolore:::ColorMapTricolore(P, center = rep(1/3, 3), breaks = 4, h_ = 80, c_ = 140, l_ = 80, contrast = 0.4, spread = 1) } \keyword{internal} ================================================ FILE: man/DemoTricolore.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/tricolore.R \name{DemoTricolore} \alias{DemoTricolore} \title{Interactive Tricolore Demonstration} \usage{ DemoTricolore() } \value{ Opens a shiny app session. } \description{ An interactive demonstration of the tricolore color scale inspired by the colorbrewer2.org application. Helps in picking the right color scale for your data. } ================================================ FILE: man/GeometricMean.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/tricolore.R \name{GeometricMean} \alias{GeometricMean} \title{Geometric Mean} \usage{ GeometricMean(x, na.rm = TRUE, zero.rm = TRUE) } \arguments{ \item{x}{Numeric vector.} \item{na.rm}{Should NAs be removed? (default=TRUE)} \item{zero.rm}{Should zeros be removed? (default=TRUE)} } \value{ The geometric mean as numeric scalar. } \description{ Calculate the geometric mean for a numeric vector. } \examples{ # NOTE: only intended for internal use and not part of the API tricolore:::GeometricMean(0:100) tricolore:::GeometricMean(0:100, zero.rm = FALSE) } \keyword{internal} ================================================ FILE: man/Pertube.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/tricolore.R \name{Pertube} \alias{Pertube} \title{Compositional Pertubation} \usage{ Pertube(P, c = rep(1/3, 3)) } \arguments{ \item{P}{n by m matrix of compositions [p1, ..., pm]_i for i=1,...,n.} \item{c}{Compositional pertubation vector [c1, ..., cm].} } \value{ n by m matrix of pertubated compositions. } \description{ Pertubate a compositional data set by a compositional vector. } \examples{ # NOTE: only intended for internal use and not part of the API P <- prop.table(matrix(runif(12), 4), margin = 1) cP <- tricolore:::Pertube(P, 1/tricolore:::Centre(P)) tricolore:::Centre(cP) } \references{ Von Eynatten, H., Pawlowsky-Glahn, V., & Egozcue, J. J. (2002). Understanding perturbation on the simplex: A simple method to better visualize and interpret compositional data in ternary diagrams. Mathematical Geology, 34(3), 249-257. Pawlowsky-Glahn, V., Egozcue, J. J., & Tolosana-Delgado, R. (2007). Lecture Notes on Compositional Data Analysis. Retrieved from https://dugi-doc.udg.edu/bitstream/handle/10256/297/CoDa-book.pdf } \keyword{internal} ================================================ FILE: man/PowerScale.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/tricolore.R \name{PowerScale} \alias{PowerScale} \title{Compositional Powering} \usage{ PowerScale(P, scale = 1) } \arguments{ \item{P}{n by m matrix of compositions [p1, ..., pm]_i for i=1,...,n.} \item{scale}{Power scalar.} } \value{ n by m numeric matrix of powered compositions. } \description{ Raise a compositional data-set to a given power. } \examples{ # NOTE: only intended for internal use and not part of the API P <- prop.table(matrix(runif(12), 4), margin = 1) tricolore:::PowerScale(P, 2) } \references{ Pawlowsky-Glahn, V., Egozcue, J. J., & Tolosana-Delgado, R. (2007). Lecture Notes on Compositional Data Analysis. Retrieved from https://dugi-doc.udg.edu/bitstream/handle/10256/297/CoDa-book.pdf } \keyword{internal} ================================================ FILE: man/TernaryCenterGrid.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/tricolore.R \name{TernaryCenterGrid} \alias{TernaryCenterGrid} \title{Return Ternary Gridlines Centered Around Some Composition} \usage{ TernaryCenterGrid(center, spacing) } \arguments{ \item{center}{The center of the grid. A vector of ternary coordinates [p1, p2, p3].} \item{spacing}{The spacing of the grid in percent-point increments. A numeric > 0.} } \value{ A list of lists. } \description{ Return Ternary Gridlines Centered Around Some Composition } \examples{ # NOTE: only intended for internal use and not part of the API tricolore:::TernaryCenterGrid(c(1/6, 2/6, 3/6), 10) } \keyword{internal} ================================================ FILE: man/TernaryDistance.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/tricolore.R \name{TernaryDistance} \alias{TernaryDistance} \title{Distance Between Points in Ternary Coordinates} \usage{ TernaryDistance(p, C) } \arguments{ \item{p}{A vector of ternary coordinates [p1, p2, p3].} \item{C}{n by 3 matrix of ternary coordinates [p1, p2, p3](i) for i=1,...,n.} } \value{ A numeric vector of distances between coordinate p and all coordinates in C. } \description{ The distances between ternary coordinate p and a set of ternary coordinates C. } \examples{ # NOTE: only intended for internal use and not part of the API p <- c(0.5, 0.2, 0.3) C <- prop.table(matrix(runif(3*10), ncol = 3), 1) tricolore:::TernaryDistance(p, C) } \references{ https://en.wikipedia.org/wiki/Barycentric_coordinate_system#Distance_between_points } \keyword{internal} ================================================ FILE: man/TernaryLimits.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/tricolore.R \name{TernaryLimits} \alias{TernaryLimits} \title{Return the Limits of Ternary Coordinates} \usage{ TernaryLimits(P, na.rm = TRUE) } \arguments{ \item{P}{n by 3 matrix of ternary coordinates [p1, p2, p3](i) for i=1,...,n.} \item{na.rm}{Should NAs be removed? (default=TRUE)} } \value{ A 2 by 3 matrix of lower and upper limits for p1, p2 and p3. } \description{ Return the Limits of Ternary Coordinates } \examples{ # NOTE: only intended for internal use and not part of the API P <- prop.table(matrix(runif(9), ncol = 3), 1) tricolore:::TernaryLimits(P) } \keyword{internal} ================================================ FILE: man/TernaryMeshCentroids.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/tricolore.R \name{TernaryMeshCentroids} \alias{TernaryMeshCentroids} \title{Centroid Coordinates of Sub-Triangles in Segmented Equilateral Triangle} \usage{ TernaryMeshCentroids(k) } \arguments{ \item{k}{Number of rows in the segmented equilateral triangle.} } \value{ A numeric matrix of with index and barycentric centroid coordinates of regions id=1,...,k^2. } \description{ Segment an equilateral triangle into k^2 equilateral sub-triangles and return the barycentric centroid coordinates of each sub-triangle. } \examples{ # NOTE: only intended for internal use and not part of the API tricolore:::TernaryMeshCentroids(1) tricolore:::TernaryMeshCentroids(2) tricolore:::TernaryMeshCentroids(3) } \references{ S. H. Derakhshan and C. V. Deutsch (2009): A Color Scale for Ternary Mixtures. } \keyword{internal} ================================================ FILE: man/TernaryMeshVertices.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/tricolore.R \name{TernaryMeshVertices} \alias{TernaryMeshVertices} \title{Vertex Coordinates of Sub-Triangles in Segmented Equilateral Triangle} \usage{ TernaryMeshVertices(C) } \arguments{ \item{C}{n by 4 matrix of barycentric centroid coordinates of n=k^2 sub-triangles. Column order: id, p1, p2, p3 with id=1,...,k^2.} } \value{ A numeric matrix with index, vertex id and barycentric vertex coordinates for each of the k^2 sub-triangles. } \description{ Given the barycentric centroid coordinates of the sub-triangles in an equilateral triangle subdivided into k^2 equilateral sub-triangles, return the barycentric vertex coordinates of each sub-triangle. } \examples{ # NOTE: only intended for internal use and not part of the API k = 2 C <- tricolore:::TernaryMeshCentroids(k) tricolore:::TernaryMeshVertices(C) } \references{ S. H. Derakhshan and C. V. Deutsch (2009): A Color Scale for Ternary Mixtures. } \keyword{internal} ================================================ FILE: man/TernaryNearest.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/tricolore.R \name{TernaryNearest} \alias{TernaryNearest} \title{For Ternary Coordinates P Return the Nearest Coordinate in Set C} \usage{ TernaryNearest(P, C) } \arguments{ \item{P, C}{n by 3 matrix of ternary coordinates [p1, p2, p3](i) for i=1,...,n. n may be different for P and C.} } \value{ n by 3 matrix of ternary coordinates in C. } \description{ For Ternary Coordinates P Return the Nearest Coordinate in Set C } \examples{ # NOTE: only intended for internal use and not part of the API P <- prop.table(matrix(runif(9), ncol = 3), 1) C <- tricolore:::TernaryMeshCentroids(2)[,-1] tricolore:::TernaryNearest(P, C) } \keyword{internal} ================================================ FILE: man/TernarySextantVertices.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/tricolore.R \name{TernarySextantVertices} \alias{TernarySextantVertices} \title{Vertex Coordinates of Sextants in Equilateral Triangle} \usage{ TernarySextantVertices(center) } \arguments{ \item{center}{The sextant center. A vector of ternary coordinates [p1, p2, p3].} } \value{ Index, vertex id and barycentric vertex coordinates for each of the 6 sextants. } \description{ Given a barycentric center coordinate return the vertex coordinates of the of the sextant regions. } \examples{ # NOTE: only intended for internal use and not part of the API tricolore:::TernarySextantVertices(rep(1/3, 3)) } \keyword{internal} ================================================ FILE: man/TernarySurroundingSextant.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/tricolore.R \name{TernarySurroundingSextant} \alias{TernarySurroundingSextant} \title{Return Surrounding Sextant of Barycentric Coordinates} \usage{ TernarySurroundingSextant(P, center) } \arguments{ \item{P}{n by 3 matrix of ternary coordinates [p1, p2, p3](i) for i=1,...,n.} \item{center}{The sextant center. A vector of ternary coordinates [p1, p2, p3].} } \value{ An n element character vector of sextant id's 1 to 6. } \description{ Given barycentric coordinates return the id of the surrounding sextant. } \examples{ # NOTE: only intended for internal use and not part of the API P <- prop.table(matrix(runif(9), ncol = 3), 1) tricolore:::TernarySurroundingSextant(P, rep(1/3, 3)) } \keyword{internal} ================================================ FILE: man/Tricolore.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/tricolore.R \name{Tricolore} \alias{Tricolore} \title{Ternary Balance Color Scale} \usage{ Tricolore( df, p1, p2, p3, center = rep(1/3, 3), breaks = ifelse(identical(center, rep(1/3, 3)), 4, Inf), hue = 0.2, chroma = 0.7, lightness = 0.8, contrast = 0.4, spread = 1, legend = TRUE, show_data = TRUE, show_center = ifelse(identical(center, rep(1/3, 3)), FALSE, TRUE), label_as = ifelse(identical(center, rep(1/3, 3)), "pct", "pct_diff"), crop = FALSE, input_validation = TRUE ) } \arguments{ \item{df}{Data frame of compositional data.} \item{p1}{Column name for variable in df giving first proportion of ternary composition (string).} \item{p2}{Column name for variable in df giving second proportion of ternary composition (string).} \item{p3}{Column name for variable in df giving third proportion of ternary composition (string).} \item{center}{Ternary coordinates of the color scale center. (default = 1/3,1/3,1/3). NA puts center over the compositional mean of the data.} \item{breaks}{Number of per-axis breaks in the discrete color scale. An integer >1. Values above 99 imply no discretization.} \item{hue}{Primary hue of the first ternary element (0 to 1).} \item{chroma}{Maximum possible chroma of mixed colors (0 to 1).} \item{lightness}{Lightness of mixed colors (0 to 1).} \item{contrast}{Lightness contrast of the color scale (0 to 1).} \item{spread}{The spread of the color scale. Choose values > 1 to focus the color scale on the center.} \item{legend}{Should a legend be returned along with the colors? (default=TRUE)} \item{show_data}{Should the data be shown on the legend? (default=TRUE)} \item{show_center}{Should the center be shown on the legend? (default=FALSE if center is at c(1/3, 1/3, 1/3), otherwise TRUE)} \item{label_as}{"pct" for percent-share labels or "pct_diff" for percent-point-difference from center labels. (default='pct' if center is at c(1/3, 1/3, 1/3), otherwise 'pct_diff')} \item{crop}{Should the legend be cropped to the data? (default=FALSE)} \item{input_validation}{Should the function arguments be validated? (default=TRUE)} } \value{ \itemize{ \item legend=FALSE: A vector of rgbs hex-codes representing the ternary balance scheme colors. \item legend=TRUE: A list with elements "rgb" and "key". } } \description{ Color-code three-part compositions with a ternary balance color scale and return a color key. } \examples{ P <- as.data.frame(prop.table(matrix(runif(3^6), ncol = 3), 1)) Tricolore(P, 'V1', 'V2', 'V3') } ================================================ FILE: man/TricoloreSextant.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/tricolore.R \name{TricoloreSextant} \alias{TricoloreSextant} \title{Ternary Sextant Color Scale} \usage{ TricoloreSextant( df, p1, p2, p3, center = rep(1/3, 3), values = c("#FFFF00", "#B3DCC3", "#01A0C6", "#B8B3D8", "#F11D8C", "#FFB3B3"), legend = TRUE, show_data = TRUE, show_center = TRUE, label_as = ifelse(identical(center, rep(1/3, 3)), "pct", "pct_diff"), crop = FALSE, input_validation = TRUE ) } \arguments{ \item{df}{Data frame of compositional data.} \item{p1}{Column name for variable in df giving first proportion of ternary composition (string).} \item{p2}{Column name for variable in df giving second proportion of ternary composition (string).} \item{p3}{Column name for variable in df giving third proportion of ternary composition (string).} \item{center}{Ternary coordinates of the color scale center. (default = 1/3,1/3,1/3). NA puts center over the compositional mean of the data.} \item{values}{6 element character vector of rgb-codes.} \item{legend}{Should a legend be returned along with the colors? (default=TRUE)} \item{show_data}{Should the data be shown on the legend? (default=TRUE)} \item{show_center}{Should the center be shown on the legend? (default=FALSE if center is at c(1/3, 1/3, 1/3), otherwise TRUE)} \item{label_as}{"pct" for percent-share labels or "pct_diff" for percent-point-difference from center labels. (default='pct' if center is at c(1/3, 1/3, 1/3), otherwise 'pct_diff')} \item{crop}{Should the legend be cropped to the data? (default=FALSE)} \item{input_validation}{Should the function arguments be validated? (default=TRUE)} } \value{ \itemize{ \item legend=FALSE: A vector of rgbs hex-codes representing the ternary balance scheme colors. \item legend=TRUE: A list with elements "rgb" and "key". } } \description{ Color-code three-part compositions with a ternary sextant color scale and return a color key. } \examples{ P <- as.data.frame(prop.table(matrix(runif(3^6), ncol = 3), 1)) TricoloreSextant(P, 'V1', 'V2', 'V3') } ================================================ FILE: man/ValidateMainArguments.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/tricolore.R \name{ValidateMainArguments} \alias{ValidateMainArguments} \title{Validate Main Arguments} \usage{ ValidateMainArguments(df, p1, p2, p3) } \arguments{ \item{df}{Data frame of compositions.} \item{p1}{Column name for variable in df giving first proportion of ternary composition (string).} \item{p2}{Column name for variable in df giving second proportion of ternary composition (string.} \item{p3}{Column name for variable in df giving third proportion of ternary composition (string).} } \description{ Validate main arguments of tricolore function. } \keyword{internal} ================================================ FILE: man/ValidateParametersShared.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/tricolore.R \name{ValidateParametersShared} \alias{ValidateParametersShared} \title{Validate Shared Parameters} \usage{ ValidateParametersShared(pars) } \arguments{ \item{pars}{A named list of parameters.} } \description{ Validate parameters shared across tricolore functions. } \keyword{internal} ================================================ FILE: man/ValidateParametersTricolore.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/tricolore.R \name{ValidateParametersTricolore} \alias{ValidateParametersTricolore} \title{Validate Tricolore Parameters} \usage{ ValidateParametersTricolore(pars) } \arguments{ \item{pars}{A named list of parameters.} } \description{ Validate parameters of Tricolore function. } \keyword{internal} ================================================ FILE: man/ValidateParametersTricoloreSextant.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/tricolore.R \name{ValidateParametersTricoloreSextant} \alias{ValidateParametersTricoloreSextant} \title{Validate TricoloreSextant Parameters} \usage{ ValidateParametersTricoloreSextant(pars) } \arguments{ \item{pars}{A named list of parameters.} } \description{ Validate parameters of TricoloreSextant function. } \keyword{internal} ================================================ FILE: man/euro_basemap.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/tricolore.R \docType{data} \name{euro_basemap} \alias{euro_basemap} \title{Flat Map of European Continent} \format{ An object of class \code{ggplot} (inherits from \code{ggplot2::ggplot}, \code{ggplot2::gg}, \code{S7_object}, \code{gg}) of length 1. } \source{ Derived from geodata provided by the Natural Earth project. \url{https://www.naturalearthdata.com/} } \usage{ euro_basemap } \description{ A ggplot object rendering a flat background map of the European continent. } \keyword{datasets} ================================================ FILE: man/euro_example.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/tricolore.R \docType{data} \name{euro_example} \alias{euro_example} \title{NUTS-2 Level Geodata and Compositional Data for Europe} \format{ A data frame with 312 rows and 9 variables: \describe{ \item{id}{NUTS-2 code.} \item{name}{Name of NUTS-2 region.} \item{ed_0to2}{Share of population with highest attained education "lower secondary or less".} \item{ed_3to4}{Share of population with highest attained education "upper secondary".} \item{ed_5to8}{Share of population with highest attained education "tertiary".} \item{lf_pri}{Share of labor-force in primary sector.} \item{lf_sec}{Share of labor-force in secondary sector.} \item{lf_ter}{Share of labor-force in tertiary sector.} \item{geometry}{Polygon outlines for regions in sf package format.} } } \source{ Derived from Eurostats European Geodata. (c) EuroGeographics for the administrative boundaries. \url{https://gisco-services.ec.europa.eu/distribution/v2/nuts/nuts-2016-files.html} Education data derived from Eurostats table "edat_lfse_04". Labor-force data derived from Eurostats table "lfst_r_lfe2en2". } \usage{ euro_example } \description{ A simple-features dataframe containing the NUTS-2 level polygons of European regions along with regional compositional data on education and labor-force. } \details{ Variables starting with "ed" refer to the relative share of population ages 25 to 64 by educational attainment in the European NUTS-2 regions 2016. Variables starting with "lf" refer to the relative share of workers by labor-force sector in the European NUTS-2 regions 2016. The original NACE (rev. 2) codes have been recoded into the three sectors "primary" (A), "secondary" (B-E & F) and "tertiary" (all other NACE codes). } \keyword{datasets} ================================================ FILE: tests/testthat/test-global.R ================================================ context('test-global.R') test_that('GeometricMean() works', { expect_equal(GeometricMean(0:4), exp(mean(log(1:4)))) expect_equal(GeometricMean(0:4, zero.rm = FALSE), 0) expect_equal(GeometricMean(c(NA, 0:4), na.rm = TRUE, zero.rm = FALSE), 0) expect_equal(GeometricMean(c(NA, 0:4), na.rm = FALSE, zero.rm = FALSE), as.numeric(NA)) expect_equal(GeometricMean(0:4, na.rm = FALSE, zero.rm = TRUE), exp(mean(log(1:4)))) expect_equal(GeometricMean(c(NA, 0:4), na.rm = FALSE, zero.rm = TRUE), as.numeric(NA)) expect_equal(GeometricMean(0, zero.rm = TRUE), NaN) }) test_that('Centre() works', { P <- prop.table(matrix(runif(300), 100), margin = 1) expect_equal(prop.table(apply(t(t(P)/Centre(P)), 2, GeometricMean)), rep(1/3, 3)) expect_equal(NROW(Centre(P)), 3) expect_equal(NCOL(Centre(P)), 1) }) test_that('Pertube() works', { P <- prop.table(matrix(runif(300), 100), margin = 1) expect_equal(Pertube(P, rep(1/3, 3)), P) expect_equal(Centre(Pertube(P, 1/Centre(P))), rep(1/3, 3)) expect_equal(NROW(Pertube(P, rep(1/3, 3))), 100) expect_equal(NCOL(Pertube(P, rep(1/3, 3))), 3) }) test_that('TernaryMeshCentroids() works', { k = sample(2:100, size = 1) expect_equal(NROW(TernaryMeshCentroids(k)), k^2) expect_equal(TernaryMeshCentroids(k)[,'id'], 1:k^2) expect_equal(rowSums(TernaryMeshCentroids(k)[,2:4]), rep(1, k^2)) expect_equivalent(prop.table(apply(TernaryMeshCentroids(k)[,2:4], 2, GeometricMean)), rep(1/3, 3)) }) test_that('Argument checks work', { P <- as.data.frame(prop.table(matrix(runif(300), 100), margin = 1)) # missing main arguments expect_error(Tricolore(p1 = 'V1', p2 = 'V2', p3 = 'V3'), 'main argument missing') expect_error(Tricolore(P, p2 = 'V2', p3 = 'V3'), 'main argument missing') expect_error(Tricolore(P, p1 = 'V1', p3 = 'V3'), 'main argument missing') expect_error(Tricolore(P, p1 = 'V1', p2 = 'V2'), 'main argument missing') expect_error(Tricolore(P, p1 = 'Foo1', p2 = 'V2', p3 = 'V3'), 'Foo1 not found') expect_error(Tricolore(P, p1 = 'V1', p2 = 'Foo2', p3 = 'V3'), 'Foo2 not found') expect_error(Tricolore(P, p1 = 'V1', p2 = 'V2', p3 = 'Foo3'), 'Foo3 not found') # type checks for main arguments expect_error(Tricolore(as.matrix(P), p1 = 'V1', p2 = 'V2', p3 = 'V3'), 'df is not a data frame') expect_error(Tricolore(P, p1 = 1, p2 = 2, p3 = 3), 'not a string') expect_error(Tricolore(data.frame(V1 = as.character(P$V1), V2 = P$V2, V3 = P$V3), p1 = 'V1', p2 = 'V2', p3 = 'V3'), 'variable V1 is not numeric') expect_error(Tricolore(data.frame(V1 = P$V1, V2 = as.character(P$V2), V3 = P$V3), p1 = 'V1', p2 = 'V2', p3 = 'V3'), 'variable V2 is not numeric') expect_error(Tricolore(data.frame(V1 = P$V1, V2 = P$V2, V3 = as.character(P$V3)), p1 = 'V1', p2 = 'V2', p3 = 'V3'), 'variable V3 is not numeric') expect_error(Tricolore(data.frame(V1 = -P$V1, V2 = P$V2, V3 = P$V3), p1 = 'V1', p2 = 'V2', p3 = 'V3'), 'variable V1 contains negative values') expect_error(Tricolore(data.frame(V1 = P$V1, V2 = -P$V2, V3 = P$V3), p1 = 'V1', p2 = 'V2', p3 = 'V3'), 'variable V2 contains negative values') expect_error(Tricolore(data.frame(V1 = P$V1, V2 = P$V2, V3 = -P$V3), p1 = 'V1', p2 = 'V2', p3 = 'V3'), 'variable V3 contains negative values') }) # NA, Inf, NaN are allowed and are expected to return NA as color test_that('NA, Inf, NaNs in input return NA in output', { P <- data.frame(a = c(1, NA), b = c(0, 0.5), c = c(0, 0.2)) tric <- Tricolore(P, 'a', 'b', 'c', breaks = Inf) expect_equal(tric$rgb, c('#F0C500', NA)) expect_true(all(c('gg', 'ggplot') %in% class(tric$key))) P <- data.frame(a = c(1, Inf), b = c(0, 0.5), c = c(0, 0.2)) tric <- Tricolore(P, 'a', 'b', 'c', breaks = Inf) expect_equal(tric$rgb, c('#F0C500', NA)) expect_true(all(c('gg', 'ggplot') %in% class(tric$key))) P <- data.frame(a = c(1, NaN), b = c(0, 0.5), c = c(0, 0.2)) tric <- Tricolore(P, 'a', 'b', 'c', breaks = Inf) expect_equal(tric$rgb, c('#F0C500', NA)) expect_true(all(c('gg', 'ggplot') %in% class(tric$key))) }) ================================================ FILE: tests/testthat.R ================================================ library(testthat) library(tricolore) test_check('tricolore') ================================================ FILE: vignettes/choropleth_maps_with_tricolore.R ================================================ ## ----setup, include = FALSE--------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, tidy = FALSE, comment = "#>", fig.width = 6, fig.height = 6 ) ## ----------------------------------------------------------------------------- library(tricolore) ## ----------------------------------------------------------------------------- # color-code the data set and generate a color-key tric <- Tricolore(euro_example, p1 = 'ed_0to2', p2 = 'ed_3to4', p3 = 'ed_5to8') ## ----------------------------------------------------------------------------- # add the vector of colors to the `euro_example` data euro_example$rgb <- tric$rgb ## ----------------------------------------------------------------------------- library(ggplot2) plot_educ <- # using sf dataframe `euro_example`... ggplot(euro_example) + # ...draw a polygon for each region... geom_sf(aes(fill = rgb, geometry = geometry), size = 0.1) + # ...and color each region according to the color code in the variable `rgb` scale_fill_identity() plot_educ ## ----------------------------------------------------------------------------- library(ggtern) plot_educ + annotation_custom( ggplotGrob(tric$key), xmin = 55e5, xmax = 75e5, ymin = 8e5, ymax = 80e5 ) ## ----------------------------------------------------------------------------- plot_educ <- plot_educ + annotation_custom( ggplotGrob(tric$key + theme(plot.background = element_rect(fill = NA, color = NA)) + labs(L = '0-2', T = '3-4', R = '5-8')), xmin = 55e5, xmax = 75e5, ymin = 8e5, ymax = 80e5 ) plot_educ ## ----------------------------------------------------------------------------- plot_educ + theme_void() + coord_sf(datum = NA) + labs( title = 'European inequalities in educational attainment', subtitle = 'Regional distribution of ISCED education levels for people aged 25-64 in 2016.' ) ## ----------------------------------------------------------------------------- # color-code the data set and generate a color-key tric <- Tricolore(euro_example, p1 = 'ed_0to2', p2 = 'ed_3to4', p3 = 'ed_5to8', breaks = Inf) # add the vector of colors to the `euro_example` data euro_example$rgb <- tric$rgb ## ----------------------------------------------------------------------------- library(sf) library(leaflet) euro_example %>% st_transform(crs = 4326) %>% leaflet() %>% addPolygons(smoothFactor = 0.1, weight = 0, fillColor = euro_example$rgb, fillOpacity = 1) ## ----------------------------------------------------------------------------- euro_example %>% st_transform(crs = 4326) %>% leaflet() %>% addProviderTiles(providers$Esri.WorldTerrain) %>% addPolygons(smoothFactor = 0.1, weight = 0, fillColor = euro_example$rgb, fillOpacity = 1, popup = paste0( '', euro_example$name, '
', 'Primary: ', formatC(euro_example$ed_0to2*100, digits = 1, format = 'f'), '%
', 'Secondary: ', formatC(euro_example$ed_3to4*100, digits = 1, format = 'f'), '%
', 'Tertiary: ', formatC(euro_example$ed_5to8*100, digits = 1, format = 'f'), '%
' ) ) ## ----------------------------------------------------------------------------- makePlotURI <- function(expr, width, height, ...) { pngFile <- shiny::plotPNG(function() { expr }, width = width, height = height, ...) on.exit(unlink(pngFile)) base64 <- httpuv::rawToBase64(readBin(pngFile, raw(1), file.size(pngFile))) paste0("data:image/png;base64,", base64) } legend_symbol <- makePlotURI({ print(tric$key + theme(plot.background = element_rect(fill = NA, color = NA)) + labs(L = '0-2', T = '3-4', R = '5-8')) }, 200, 200, bg = "transparent") df <- data.frame( lng = 30, lat = 70, plot = legend_symbol, stringsAsFactors = FALSE ) euro_example %>% st_transform(crs = 4326) %>% leaflet() %>% addProviderTiles(providers$Esri.WorldGrayCanvas) %>% addPolygons(smoothFactor = 0.1, weight = 0, fillColor = euro_example$rgb, fillOpacity = 1, popup = paste0( '', euro_example$name, '
', 'Primary: ', formatC(euro_example$ed_0to2*100, digits = 1, format = 'f'), '%
', 'Secondary: ', formatC(euro_example$ed_3to4*100, digits = 1, format = 'f'), '%
', 'Tertiary: ', formatC(euro_example$ed_5to8*100, digits = 1, format = 'f'), '%
' ) ) %>% addMarkers(data = df, icon = ~icons(plot)) ================================================ FILE: vignettes/choropleth_maps_with_tricolore.Rmd ================================================ --- title: "Choropleth maps with tricolore" author: "Jonas Schöley" date: "`r Sys.Date()`" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{Choropleth maps with tricolore} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} %\VignetteDepends{shiny, sf, leaflet, tricolore, dplyr, ggplot2, ggtern, httpuv} --- ```{r setup, include = FALSE} knitr::opts_chunk$set( collapse = TRUE, tidy = FALSE, comment = "#>", fig.width = 6, fig.height = 6 ) ``` Here I demonstrate how to use the `tricolore` library to generate ternary choropleth maps using both `ggplot2` and `leaflet`. The data -------- ```{r} library(tricolore) ``` The data set `euro_example` contains the administrative boundaries for the European NUTS-2 regions in the column `geometry`. This data can be used to plot a choropleth map of Europe using the `sf` package. Each region is represented by a single row. The name of a region is given by the variable `name` while the respective [NUTS-2](https://en.wikipedia.org/wiki/Nomenclature_of_Territorial_Units_for_Statistics) geocode is given by the variable `id`. For each region some compositional statistics are available: Variables starting with `ed` refer to the relative share of population ages 25 to 64 by educational attainment in 2016 and variables starting with `lf` refer to the relative share of workers by labor-force sector in the European NUTS-2 regions 2016. Take the first row of the data set as an example: in the Austrian region of "Burgenland" (`id` = `AT11`) 16.5% of the population aged 25--64 had attained an education of "Lower secondary or less" (`ed_0to2`), 55.7% attained "upper secondary" education (`ed_3to4`), and 27.9% attained "tertiary" education. In the very same region 4.4% of the labor-force works in the primary sector, 26.8% in the secondary and 68.2% in the tertiary sector. The education and labor-force compositions are *ternary*, i.e. made up from three elements, and therefore can be color-coded as the weighted mixture of three primary colors, each primary mapped to one of the three elements. Such a color scale is called a *ternary balance scheme*^[See for example Dorling (2012) and Brewer (1994).]. This is what `tricolore` does. `ggplot2` for ternary choropleth maps ------------------------------------- Here I show how to create a choropleth map of the regional distribution of education attainment in Europe 2016 using `ggplot2`. **1. Using the `Tricolore()` function, color-code each educational composition in the `euro_example` data set and add the resulting vector of hex-srgb colors as a new variable to the data frame. Store the color key separately.** ```{r} # color-code the data set and generate a color-key tric <- Tricolore(euro_example, p1 = 'ed_0to2', p2 = 'ed_3to4', p3 = 'ed_5to8') ``` `tric` contains both a vector of color-coded compositions (`tric$rgb`) and the corresponding color key (`tric$key`). We add the vector of colors to the map-data. ```{r} # add the vector of colors to the `euro_example` data euro_example$rgb <- tric$rgb ``` **2. Using `ggplot2` and the joined color-coded education data and geodata, plot a ternary choropleth map of education attainment in the European regions. Add the color key to the map.** The secret ingredient is `scale_fill_identity()` to make sure that each region is colored according to the value in the `rgb` variable of `euro_educ_map`. ```{r} library(ggplot2) plot_educ <- # using sf dataframe `euro_example`... ggplot(euro_example) + # ...draw a polygon for each region... geom_sf(aes(fill = rgb, geometry = geometry), size = 0.1) + # ...and color each region according to the color code in the variable `rgb` scale_fill_identity() plot_educ ``` Using `annotation_custom()` and `ggplotGrob` we can add the color key produced by `Tricolore()` to the map. Internally, the color key is produced with the [`ggtern`](https://CRAN.R-project.org/package=ggtern) package. In order for it to render correctly we need to load `ggtern` *after* loading `ggplot2`. Don't worry, the `ggplot2` functions still work. ```{r} library(ggtern) plot_educ + annotation_custom( ggplotGrob(tric$key), xmin = 55e5, xmax = 75e5, ymin = 8e5, ymax = 80e5 ) ``` Because the color key behaves just like a `ggplot2` plot we can change it to our liking. ```{r} plot_educ <- plot_educ + annotation_custom( ggplotGrob(tric$key + theme(plot.background = element_rect(fill = NA, color = NA)) + labs(L = '0-2', T = '3-4', R = '5-8')), xmin = 55e5, xmax = 75e5, ymin = 8e5, ymax = 80e5 ) plot_educ ``` Some final touches... ```{r} plot_educ + theme_void() + coord_sf(datum = NA) + labs( title = 'European inequalities in educational attainment', subtitle = 'Regional distribution of ISCED education levels for people aged 25-64 in 2016.' ) ``` `leaflet` for ternary choropleth maps ------------------------------------- The `ggplot2` example above is easily adapted to `leaflet`. This time I use a continuous color scale. ```{r} # color-code the data set and generate a color-key tric <- Tricolore(euro_example, p1 = 'ed_0to2', p2 = 'ed_3to4', p3 = 'ed_5to8', breaks = Inf) # add the vector of colors to the `euro_example` data euro_example$rgb <- tric$rgb ``` `leaflet` requires geodata in spherical coordinates (longitude-latitude format). Therefore I reproject the data to a [suitable crs](https://spatialreference.org/ref/epsg/4326/) using the `sf` package. ```{r} library(sf) library(leaflet) euro_example %>% st_transform(crs = 4326) %>% leaflet() %>% addPolygons(smoothFactor = 0.1, weight = 0, fillColor = euro_example$rgb, fillOpacity = 1) ``` Adding a background map gives geographical context to the map. I also add a mouse pop-up of the actual data. ```{r} euro_example %>% st_transform(crs = 4326) %>% leaflet() %>% addProviderTiles(providers$Esri.WorldTerrain) %>% addPolygons(smoothFactor = 0.1, weight = 0, fillColor = euro_example$rgb, fillOpacity = 1, popup = paste0( '', euro_example$name, '
', 'Primary: ', formatC(euro_example$ed_0to2*100, digits = 1, format = 'f'), '%
', 'Secondary: ', formatC(euro_example$ed_3to4*100, digits = 1, format = 'f'), '%
', 'Tertiary: ', formatC(euro_example$ed_5to8*100, digits = 1, format = 'f'), '%
' ) ) ``` Adding the legend to the leaflet map requires a bit of a [hack](https://github.com/rstudio/leaflet/issues/51#issuecomment-213108125). ```{r} makePlotURI <- function(expr, width, height, ...) { pngFile <- shiny::plotPNG(function() { expr }, width = width, height = height, ...) on.exit(unlink(pngFile)) base64 <- httpuv::rawToBase64(readBin(pngFile, raw(1), file.size(pngFile))) paste0("data:image/png;base64,", base64) } legend_symbol <- makePlotURI({ print(tric$key + theme(plot.background = element_rect(fill = NA, color = NA)) + labs(L = '0-2', T = '3-4', R = '5-8')) }, 200, 200, bg = "transparent") df <- data.frame( lng = 30, lat = 70, plot = legend_symbol, stringsAsFactors = FALSE ) euro_example %>% st_transform(crs = 4326) %>% leaflet() %>% addProviderTiles(providers$Esri.WorldGrayCanvas) %>% addPolygons(smoothFactor = 0.1, weight = 0, fillColor = euro_example$rgb, fillOpacity = 1, popup = paste0( '', euro_example$name, '
', 'Primary: ', formatC(euro_example$ed_0to2*100, digits = 1, format = 'f'), '%
', 'Secondary: ', formatC(euro_example$ed_3to4*100, digits = 1, format = 'f'), '%
', 'Tertiary: ', formatC(euro_example$ed_5to8*100, digits = 1, format = 'f'), '%
' ) ) %>% addMarkers(data = df, icon = ~icons(plot)) ``` Literature ---------- Brewer, C. A. (1994). Color Use Guidelines for Mapping and Visualization. In A. M. MacEachren & D. R. F. Taylor (Eds.), Visualization in Modern Cartography (pp. 123–147). Oxford, UK: Pergamon. Dorling, D. (2012). The Visualization of Spatial Social Structure. Chichester, UK: Wiley. Schöley, J. (2021). The centered ternary balance scheme: A technique to visualize surfaces of unbalanced three-part compositions. Demographic Research (44).