├── .github ├── .gitignore ├── ISSUE_TEMPLATE │ └── issue_template.md ├── workflows │ ├── pkgdown.yaml │ └── R-CMD-check.yaml ├── SUPPORT.md └── CODE_OF_CONDUCT.md ├── LICENSE ├── NAMESPACE ├── data ├── box_plots.rda ├── box_plots_long.rda ├── datasaurus_dozen.rda ├── simpsons_paradox.rda ├── datasaurus_dozen_wide.rda ├── simpsons_paradox_wide.rda ├── twelve_from_slant_long.rda ├── twelve_from_slant_wide.rda ├── twelve_from_slant_alternate_long.rda └── twelve_from_slant_alternate_wide.rda ├── tests ├── testthat.R └── testthat │ ├── test-rawfiles.R │ └── test-datasets.R ├── _pkgdown.yml ├── images ├── datasauRus.pdf ├── datasauRusPRINT.png └── datasauRusSCREEN.png ├── man ├── figures │ ├── logo.png │ └── datasets-1.png ├── datasaurus_dozen.Rd ├── twelve_from_slant_long.Rd ├── twelve_from_slant_alternate_long.Rd ├── simpsons_paradox.Rd ├── simpsons_paradox_wide.Rd ├── box_plots_long.Rd ├── box_plots.Rd ├── twelve_from_slant_wide.Rd ├── twelve_from_slant_alternate_wide.Rd └── datasaurus_dozen_wide.Rd ├── CRAN-SUBMISSION ├── cran-comments.md ├── pkgdown └── favicon │ ├── favicon.ico │ ├── favicon-16x16.png │ ├── favicon-32x32.png │ ├── apple-touch-icon.png │ ├── apple-touch-icon-120x120.png │ ├── apple-touch-icon-152x152.png │ ├── apple-touch-icon-180x180.png │ ├── apple-touch-icon-60x60.png │ └── apple-touch-icon-76x76.png ├── .lintr ├── .Rbuildignore ├── inst ├── examples │ ├── twelve_from_slant_wide.R │ ├── datasaurus_dozen_wide.R │ ├── simpsons_paradox_wide.R │ ├── twelve_from_slant_alternate_wide.R │ ├── simpsons_paradox.R │ ├── datasaurus_dozen.R │ ├── datasauRus.R │ ├── twelve_from_slant_long.R │ ├── twelve_from_slant_alternate_long.R │ └── box_plots.R └── extdata │ ├── SimpsonsParadox-Wide.tsv │ ├── SimpsonsParadox-Long.tsv │ ├── DatasaurusDozen-Wide.tsv │ └── TwelveFromSlant-Wide.tsv ├── NEWS.md ├── Datasaurus.Rproj ├── data-raw └── convert-data.R ├── .gitignore ├── LICENSE.md ├── DESCRIPTION ├── vignettes └── Datasaurus.Rmd ├── README.md ├── README.Rmd └── R └── datasaurus-package.R /.github/.gitignore: -------------------------------------------------------------------------------- 1 | *.html 2 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | YEAR: 2022 2 | COPYRIGHT HOLDER: datasauRus authors 3 | -------------------------------------------------------------------------------- /NAMESPACE: -------------------------------------------------------------------------------- 1 | # Generated by roxygen2: do not edit by hand 2 | 3 | -------------------------------------------------------------------------------- /data/box_plots.rda: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jumpingrivers/datasauRus/HEAD/data/box_plots.rda -------------------------------------------------------------------------------- /tests/testthat.R: -------------------------------------------------------------------------------- 1 | library(testthat) 2 | library(datasauRus) 3 | 4 | test_check("datasauRus") 5 | -------------------------------------------------------------------------------- /_pkgdown.yml: -------------------------------------------------------------------------------- 1 | url: https://jumpingrivers.github.io/datasauRus 2 | template: 3 | bootstrap: 5 4 | 5 | -------------------------------------------------------------------------------- /images/datasauRus.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jumpingrivers/datasauRus/HEAD/images/datasauRus.pdf -------------------------------------------------------------------------------- /man/figures/logo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jumpingrivers/datasauRus/HEAD/man/figures/logo.png 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-------------------------------------------------------------------------------- https://raw.githubusercontent.com/jumpingrivers/datasauRus/HEAD/images/datasauRusPRINT.png -------------------------------------------------------------------------------- /man/figures/datasets-1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jumpingrivers/datasauRus/HEAD/man/figures/datasets-1.png -------------------------------------------------------------------------------- /CRAN-SUBMISSION: -------------------------------------------------------------------------------- 1 | Version: 0.1.8 2 | Date: 2024-02-26 17:30:25 UTC 3 | SHA: 33c09bcdec20cec355b4bfa415276c6100a17433 4 | -------------------------------------------------------------------------------- /cran-comments.md: -------------------------------------------------------------------------------- 1 | ## R CMD check results 2 | 3 | 0 errors | 0 warnings | 1 note 4 | 5 | * Changing maintainer 6 | -------------------------------------------------------------------------------- /images/datasauRusSCREEN.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jumpingrivers/datasauRus/HEAD/images/datasauRusSCREEN.png -------------------------------------------------------------------------------- /pkgdown/favicon/favicon.ico: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jumpingrivers/datasauRus/HEAD/pkgdown/favicon/favicon.ico -------------------------------------------------------------------------------- /data/datasaurus_dozen_wide.rda: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jumpingrivers/datasauRus/HEAD/data/datasaurus_dozen_wide.rda -------------------------------------------------------------------------------- /data/simpsons_paradox_wide.rda: 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https://raw.githubusercontent.com/jumpingrivers/datasauRus/HEAD/pkgdown/favicon/apple-touch-icon-76x76.png -------------------------------------------------------------------------------- /tests/testthat/test-rawfiles.R: -------------------------------------------------------------------------------- 1 | context("Raw files") 2 | 3 | test_that("extdata exists", { 4 | files = list.files(system.file("extdata", package = "datasauRus")) 5 | expect_equal(length(files), 10) 6 | }) 7 | -------------------------------------------------------------------------------- /.lintr: -------------------------------------------------------------------------------- 1 | linters: linters_with_defaults( 2 | assignment_linter = NULL, 3 | indentation_linter = NULL, 4 | commented_code_linter = NULL, 5 | object_length_linter = NULL, 6 | line_length_linter(100), 7 | cyclocomp_linter(complexity_limit = 15), 8 | undesirable_operator_linter = undesirable_operator_linter( 9 | op = list("<-" = "Please use '=' for assignment"))) 10 | -------------------------------------------------------------------------------- /.Rbuildignore: -------------------------------------------------------------------------------- 1 | ^\.git$ 2 | ^\.github$ 3 | ^CODE_OF_CONDUCT\.md$ 4 | ^.*\.Rproj$ 5 | ^\.Rproj\.user$ 6 | ^\.travis\.yml$ 7 | ^CONDUCT\.md$ 8 | ^codecov\.yml$ 9 | ^README\.Rmd$ 10 | ^README-.*\.png$ 11 | ^docs$ 12 | ^data-raw$ 13 | ^cran-comments\.md$ 14 | ^CONTRIBUTING\.md$ 15 | ^_pkgdown\.yml$ 16 | ^images$ 17 | ^codemeta\.json$ 18 | ^pkgdown$ 19 | ^.lintr$ 20 | ^CRAN-SUBMISSION$ 21 | ^LICENSE\.md$ 22 | -------------------------------------------------------------------------------- /inst/examples/twelve_from_slant_wide.R: -------------------------------------------------------------------------------- 1 | #save current settings 2 | state = par("mar", "mfrow") 3 | 4 | # plot 5 | par(mfrow = c(4, 3), mar = c(1, 3, 3, 1)) 6 | 7 | nms = names(twelve_from_slant_wide) 8 | for (i in seq(1, 23, by = 2)) { 9 | nm = substr(nms[i], 1, nchar(nms[i]) - 2) 10 | plot(twelve_from_slant_wide[[nms[i]]], 11 | twelve_from_slant_wide[[nms[i + 1]]], 12 | xlab = "", ylab = "", main = nm) 13 | } 14 | 15 | #reset settings 16 | par(state) 17 | -------------------------------------------------------------------------------- /inst/examples/datasaurus_dozen_wide.R: -------------------------------------------------------------------------------- 1 | # Save current settings 2 | state = par("mar", "mfrow") 3 | 4 | # Base R Plots 5 | par(mfrow = c(5, 3), mar = c(1, 3, 3, 1)) 6 | 7 | nms = names(datasaurus_dozen_wide) 8 | for (i in seq(1, 25, by = 2)) { 9 | nm = substr(nms[i], 1, nchar(nms[i]) - 2) 10 | plot(datasaurus_dozen_wide[[nms[i]]], 11 | datasaurus_dozen_wide[[nms[i + 1]]], 12 | xlab = "", ylab = "", main = nm) 13 | } 14 | 15 | #reset settings 16 | par(state) 17 | -------------------------------------------------------------------------------- /NEWS.md: -------------------------------------------------------------------------------- 1 | # datasauRus 0.1.9 2 | * Spring clean: linting 3 | 4 | # datasauRus 0.1.8 5 | 6 | * Update maintainer 7 | 8 | # datasauRus 0.1.7 9 | 10 | * Add long boxplots data 11 | 12 | # datasauRus 0.1.6 13 | 14 | * No functional changes; just code styling and linting 15 | 16 | # datasauRus 0.1.5 17 | 18 | * Update maintainer 19 | 20 | # datasauRus 0.1.4 21 | 22 | * Updated datasets (@jmatejka, #18) 23 | 24 | # datasauRus 0.1.2 25 | 26 | * First release, contains datasaurus datasets 27 | 28 | -------------------------------------------------------------------------------- /inst/examples/simpsons_paradox_wide.R: -------------------------------------------------------------------------------- 1 | #save current settings 2 | state = par("mar", "mfrow") 3 | 4 | par(mfrow = c(1, 2)) 5 | 6 | plot(simpsons_paradox_wide[["simpson_1_x"]], 7 | simpsons_paradox_wide[["simpson_1_y"]], 8 | xlab = "x", ylab = "y", main = "Simpson's Paradox 1") 9 | 10 | plot(simpsons_paradox_wide[["simpson_2_x"]], 11 | simpsons_paradox_wide[["simpson_2_y"]], 12 | xlab = "x", ylab = "y", main = "Simpson's Paradox 2") 13 | 14 | #reset settings 15 | par(state) 16 | -------------------------------------------------------------------------------- /inst/examples/twelve_from_slant_alternate_wide.R: -------------------------------------------------------------------------------- 1 | #save current settings 2 | state = par("mar", "mfrow") 3 | 4 | # plot 5 | par(mfrow = c(4, 3), mar = c(1, 3, 3, 1)) 6 | 7 | nms = names(twelve_from_slant_alternate_wide) 8 | for (i in seq(1, 23, by = 2)) { 9 | nm = substr(nms[i], 1, nchar(nms[i]) - 2) 10 | plot(twelve_from_slant_alternate_wide[[nms[i]]], 11 | twelve_from_slant_alternate_wide[[nms[i + 1]]], 12 | xlab = "", ylab = "", main = nm) 13 | } 14 | 15 | #reset settings 16 | par(state) 17 | -------------------------------------------------------------------------------- /Datasaurus.Rproj: -------------------------------------------------------------------------------- 1 | Version: 1.0 2 | ProjectId: 16075796-80fe-42ac-87e0-61c1c1ac3f33 3 | 4 | RestoreWorkspace: No 5 | SaveWorkspace: No 6 | AlwaysSaveHistory: Default 7 | 8 | EnableCodeIndexing: Yes 9 | UseSpacesForTab: Yes 10 | NumSpacesForTab: 2 11 | Encoding: UTF-8 12 | 13 | RnwWeave: knitr 14 | LaTeX: pdfLaTeX 15 | 16 | AutoAppendNewline: Yes 17 | StripTrailingWhitespace: Yes 18 | 19 | BuildType: Package 20 | PackageUseDevtools: Yes 21 | PackageInstallArgs: --no-multiarch --with-keep.source 22 | PackageRoxygenize: rd,collate,namespace 23 | -------------------------------------------------------------------------------- /inst/examples/simpsons_paradox.R: -------------------------------------------------------------------------------- 1 | if (require(ggplot2)) { 2 | ggplot(simpsons_paradox, aes(x = x, y = y, colour = dataset)) + 3 | geom_point() + 4 | theme(legend.position = "none") + 5 | facet_wrap(~dataset, ncol = 3) 6 | } 7 | 8 | # Base R Plots 9 | state = par("mfrow") 10 | 11 | par(mfrow = c(1, 2)) 12 | 13 | sets = unique(datasaurus_dozen$dataset) 14 | 15 | for (i in 1:2) { 16 | df = simpsons_paradox[simpsons_paradox$dataset == paste0("simpson_", i), ] 17 | plot(df$x, df$y, pch = 16, xlab = "", ylab = "") 18 | title(paste0("Simpson\'s Paradox ", i)) 19 | } 20 | 21 | par(state) 22 | -------------------------------------------------------------------------------- /inst/examples/datasaurus_dozen.R: -------------------------------------------------------------------------------- 1 | if (require(ggplot2)) { 2 | ggplot(datasaurus_dozen, aes(x = x, y = y, colour = dataset)) + 3 | geom_point() + 4 | theme_void() + 5 | theme(legend.position = "none") + 6 | facet_wrap(~dataset, ncol = 3) 7 | } 8 | 9 | # Base R plots 10 | state = par("mar", "mfrow") 11 | 12 | # plot 13 | par(mfrow = c(5, 3), mar = c(1, 2, 2, 1)) 14 | 15 | sets = sort(unique(datasaurus_dozen$dataset)) 16 | 17 | for (s in sets) { 18 | df = datasaurus_dozen[datasaurus_dozen$dataset == s, ] 19 | plot(df$x, df$y, pch = 16) 20 | title(s) 21 | } 22 | 23 | #reset settings 24 | par(state) 25 | -------------------------------------------------------------------------------- /inst/examples/datasauRus.R: -------------------------------------------------------------------------------- 1 | if (require(ggplot2)) { 2 | library(ggplot2) 3 | library(datasauRus) 4 | ggplot(datasaurus_dozen, aes(x = x, y = y, colour = dataset)) + 5 | geom_point() + 6 | theme_void() + 7 | theme(legend.position = "none") + 8 | facet_wrap(~ dataset, ncol = 3) 9 | } 10 | 11 | # Base R Plots 12 | state = par("mfrow", "mar") 13 | 14 | par(mfrow = c(5, 3), mar = c(2, 2, 2, 2)) 15 | 16 | sets = unique(datasaurus_dozen$dataset) 17 | 18 | for (s in sets) { 19 | df = datasaurus_dozen[datasaurus_dozen$dataset == s, ] 20 | plot(df$x, df$y, pch = 16) 21 | title(s) 22 | } 23 | 24 | par(state) 25 | -------------------------------------------------------------------------------- /inst/examples/twelve_from_slant_long.R: -------------------------------------------------------------------------------- 1 | if (require(ggplot2)) { 2 | ggplot(twelve_from_slant_long, aes(x = x, y = y, colour = dataset)) + 3 | geom_point() + 4 | theme_void() + 5 | theme(legend.position = "none") + 6 | facet_wrap(~dataset, ncol = 3) 7 | } 8 | 9 | # Base R Plots 10 | state = par("mfrow", "mar") 11 | 12 | par(mfrow = c(4, 3), mar = c(3, 2, 2, 2)) 13 | 14 | sets = sort(unique(twelve_from_slant_long$dataset)) 15 | 16 | for (s in sets) { 17 | df = twelve_from_slant_long[twelve_from_slant_long$dataset == s, ] 18 | plot(df$x, df$y, pch = 16, xlab = "", ylab = "") 19 | title(s) 20 | } 21 | 22 | par(state) 23 | -------------------------------------------------------------------------------- /.github/ISSUE_TEMPLATE/issue_template.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: Bug report or feature request 3 | about: Describe a bug you've seen or make a case for a new feature 4 | --- 5 | 6 | Please briefly describe your problem and what output you expect. If you have a question, please don't use this form. Instead, ask on or . 7 | 8 | Please include a minimal reproducible example (AKA a reprex). If you've never heard of a [reprex](http://reprex.tidyverse.org/) before, start by reading . 9 | 10 | Brief description of the problem 11 | 12 | ```r 13 | # insert reprex here 14 | ``` 15 | -------------------------------------------------------------------------------- /inst/examples/twelve_from_slant_alternate_long.R: -------------------------------------------------------------------------------- 1 | if (require(ggplot2)) { 2 | ggplot(twelve_from_slant_alternate_long, aes(x = x, y = y, colour = dataset)) + 3 | geom_point() + 4 | theme_void() + 5 | theme(legend.position = "none") + 6 | facet_wrap(~dataset, ncol = 3) 7 | } 8 | 9 | # Base R Plots 10 | state = par("mfrow", "mar") 11 | 12 | par(mfrow = c(4, 3), mar = c(2, 2, 2, 2)) 13 | 14 | sets = sort(unique(twelve_from_slant_alternate_long$dataset)) 15 | 16 | for (s in sets) { 17 | df = twelve_from_slant_alternate_long[twelve_from_slant_alternate_long$dataset == s, ] 18 | plot(df$x, df$y, pch = 16, xlab = "", ylab = "") 19 | title(s) 20 | } 21 | 22 | par(state) 23 | -------------------------------------------------------------------------------- /inst/examples/box_plots.R: -------------------------------------------------------------------------------- 1 | summary(box_plots) 2 | 3 | ## base plot 4 | 5 | #save current settings 6 | state = par("mar", "mfrow") 7 | 8 | par(mfrow = c(5, 2), mar = c(1, 2, 2, 1)) 9 | 10 | nms = names(box_plots) 11 | 12 | for (i in 1:5) { 13 | nm = nms[i] 14 | hist(box_plots[[nms[i]]], 15 | breaks = 100, 16 | main = nm) 17 | boxplot(box_plots[[nms[i]]], 18 | horizontal = TRUE) 19 | } 20 | 21 | 22 | #reset settings 23 | par(state) 24 | 25 | ## ggplot 26 | if (require(ggplot2)) { 27 | ggplot(box_plots, aes(x = left)) + 28 | geom_density() 29 | ggplot(box_plots, aes(x = lines)) + 30 | geom_density() 31 | ggplot(box_plots, aes(x = normal)) + 32 | geom_density() 33 | ggplot(box_plots, aes(x = right)) + 34 | geom_density() 35 | ggplot(box_plots, aes(x = split)) + 36 | geom_density() 37 | } 38 | -------------------------------------------------------------------------------- /data-raw/convert-data.R: -------------------------------------------------------------------------------- 1 | # here is how I converted 2 | files = list.files(path = "./inst/extdata", pattern = "*.tsv", full.names = TRUE) 3 | 4 | purrr::walk(files, function(x) { 5 | nm = gsub("\\.tsv", "", basename(x)) 6 | nm = gsub("-", "_", nm) 7 | 8 | # make it slither 9 | nm = gsub("^_+", "", tolower(gsub("([A-Z])", "_\\1", nm))) 10 | nm = gsub("\\_\\_", "\\_", nm) 11 | if (grepl("wide", nm)) { 12 | header1 = scan(x, nlines = 1, what = character()) 13 | header1 = paste(header1, c("x", "y"), sep = "_") 14 | .dat = readr::read_tsv(x, col_names = header1, skip = 2) 15 | } else { 16 | .dat = readr::read_tsv(x) 17 | } 18 | assign(nm, .dat) 19 | save(list = nm, file = paste0("data/", nm, ".rda")) 20 | }) 21 | 22 | # remove box_plots rownames 23 | box_plots = readr::read_tsv("./inst/extdata/BoxPlots.tsv")[, 2:6] 24 | save(box_plots, file = "data/box_plots.rda") 25 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # History files 2 | .Rhistory 3 | .Rapp.history 4 | 5 | # Session Data files 6 | .RData 7 | .RDataTmp 8 | 9 | # User-specific files 10 | .Ruserdata 11 | 12 | # Example code in package build process 13 | *-Ex.R 14 | 15 | # Output files from R CMD build 16 | /*.tar.gz 17 | 18 | # Output files from R CMD check 19 | /*.Rcheck/ 20 | 21 | # RStudio files 22 | .Rproj.user/ 23 | 24 | # produced vignettes 25 | vignettes/*.html 26 | vignettes/*.pdf 27 | 28 | # OAuth2 token, see https://github.com/hadley/httr/releases/tag/v0.3 29 | .httr-oauth 30 | 31 | # knitr and R markdown default cache directories 32 | *_cache/ 33 | /cache/ 34 | 35 | # Temporary files created by R markdown 36 | *.utf8.md 37 | *.knit.md 38 | 39 | # R Environment Variables 40 | .Renviron 41 | 42 | # pkgdown site 43 | docs/ 44 | 45 | # translation temp files 46 | po/*~ 47 | 48 | # RStudio Connect folder 49 | rsconnect/ 50 | 51 | inst/doc 52 | docs 53 | -------------------------------------------------------------------------------- /LICENSE.md: -------------------------------------------------------------------------------- 1 | # MIT License 2 | 3 | Copyright (c) 2022 datasauRus authors 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /.github/workflows/pkgdown.yaml: -------------------------------------------------------------------------------- 1 | # Workflow derived from https://github.com/r-lib/actions/tree/v2/examples 2 | # Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help 3 | on: 4 | push: 5 | branches: [main, master] 6 | pull_request: 7 | branches: [main, master] 8 | release: 9 | types: [published] 10 | workflow_dispatch: 11 | 12 | name: pkgdown 13 | 14 | jobs: 15 | pkgdown: 16 | runs-on: ubuntu-latest 17 | # Only restrict concurrency for non-PR jobs 18 | concurrency: 19 | group: pkgdown-${{ github.event_name != 'pull_request' || github.run_id }} 20 | env: 21 | GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} 22 | permissions: 23 | contents: write 24 | steps: 25 | - uses: actions/checkout@v4 26 | 27 | - uses: r-lib/actions/setup-pandoc@v2 28 | 29 | - uses: r-lib/actions/setup-r@v2 30 | with: 31 | use-public-rspm: true 32 | 33 | - uses: r-lib/actions/setup-r-dependencies@v2 34 | with: 35 | extra-packages: any::pkgdown, local::. 36 | needs: website 37 | 38 | - name: Build site 39 | run: pkgdown::build_site_github_pages(new_process = FALSE, install = FALSE) 40 | shell: Rscript {0} 41 | 42 | - name: Deploy to GitHub pages 🚀 43 | if: github.event_name != 'pull_request' 44 | uses: JamesIves/github-pages-deploy-action@v4.5.0 45 | with: 46 | clean: false 47 | branch: gh-pages 48 | folder: docs 49 | -------------------------------------------------------------------------------- /tests/testthat/test-datasets.R: -------------------------------------------------------------------------------- 1 | context("datasets") 2 | 3 | datashapetests = function(df, ncols, nrows, uniquecol = NULL, nuniques = NULL) { 4 | testthat::expect_equal(ncol(df), ncols) 5 | testthat::expect_equal(nrow(df), nrows) 6 | if (!is.null(uniquecol)) { 7 | testthat::expect_equal(nrow(unique(df[uniquecol])), nuniques) 8 | } 9 | } 10 | 11 | test_that("box_plots is correctly shaped", { 12 | datashapetests(box_plots, 5, 2484) 13 | }) 14 | 15 | test_that("datasaurus_dozen is correctly shaped", { 16 | datashapetests(datasaurus_dozen, 3, 1846, "dataset", 13) 17 | }) 18 | 19 | test_that("datasaurus_dozen_wide is correctly shaped", { 20 | datashapetests(datasaurus_dozen_wide, 26, 142) 21 | }) 22 | 23 | test_that("simpsons_paradox is correctly shaped", { 24 | datashapetests(simpsons_paradox, 3, 444, "dataset", 2) 25 | }) 26 | 27 | test_that("simpsons_paradox_wide is correctly shaped", { 28 | datashapetests(simpsons_paradox_wide, 4, 222) 29 | }) 30 | 31 | test_that("twelve_from_slant_long is correctly shaped", { 32 | datashapetests(twelve_from_slant_long, 3, 2184, "dataset", 12) 33 | }) 34 | 35 | test_that("twelve_from_slant_wide is correctly shaped", { 36 | datashapetests(twelve_from_slant_wide, 24, 182) 37 | }) 38 | 39 | test_that("twelve_from_slant_alternate_long is correctly shaped", { 40 | datashapetests(twelve_from_slant_alternate_long, 3, 2184, "dataset", 12) 41 | }) 42 | 43 | test_that("twelve_from_slant_alternate_wide is correctly shaped", { 44 | datashapetests(twelve_from_slant_alternate_wide, 24, 182) 45 | }) 46 | -------------------------------------------------------------------------------- /.github/workflows/R-CMD-check.yaml: -------------------------------------------------------------------------------- 1 | # Workflow derived from https://github.com/r-lib/actions/tree/v2/examples 2 | # Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help 3 | on: 4 | push: 5 | branches: [main, master] 6 | pull_request: 7 | branches: [main, master] 8 | 9 | name: R-CMD-check 10 | 11 | jobs: 12 | R-CMD-check: 13 | runs-on: ${{ matrix.config.os }} 14 | 15 | name: ${{ matrix.config.os }} (${{ matrix.config.r }}) 16 | 17 | strategy: 18 | fail-fast: false 19 | matrix: 20 | config: 21 | - {os: macos-latest, r: 'release'} 22 | - {os: windows-latest, r: 'release'} 23 | - {os: ubuntu-latest, r: 'devel', http-user-agent: 'release'} 24 | - {os: ubuntu-latest, r: 'release'} 25 | - {os: ubuntu-latest, r: 'oldrel-1'} 26 | 27 | env: 28 | GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} 29 | R_KEEP_PKG_SOURCE: yes 30 | 31 | steps: 32 | - uses: actions/checkout@v4 33 | 34 | - uses: r-lib/actions/setup-pandoc@v2 35 | 36 | - uses: r-lib/actions/setup-r@v2 37 | with: 38 | r-version: ${{ matrix.config.r }} 39 | http-user-agent: ${{ matrix.config.http-user-agent }} 40 | use-public-rspm: true 41 | 42 | - uses: r-lib/actions/setup-r-dependencies@v2 43 | with: 44 | extra-packages: any::rcmdcheck 45 | needs: check 46 | 47 | - uses: r-lib/actions/check-r-package@v2 48 | with: 49 | upload-snapshots: true 50 | build_args: 'c("--no-manual","--compact-vignettes=gs+qpdf")' 51 | -------------------------------------------------------------------------------- /man/datasaurus_dozen.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/datasaurus-package.R 3 | \docType{data} 4 | \name{datasaurus_dozen} 5 | \alias{datasaurus_dozen} 6 | \title{Datasaurus Dozen data} 7 | \format{ 8 | A data frame with 1846 rows and 3 variables: 9 | \itemize{ 10 | \item \strong{dataset}: indicates which dataset the data are from 11 | \item \strong{x}: x-values 12 | \item \strong{y}: y-values 13 | } 14 | } 15 | \usage{ 16 | datasaurus_dozen 17 | } 18 | \description{ 19 | A dataset demonstrating the utility of visualization. 20 | These 12 datasets are equal in standard measures: mean, 21 | standard deviation, and Pearson's correlation. 22 | } 23 | \examples{ 24 | if (require(ggplot2)) { 25 | ggplot(datasaurus_dozen, aes(x = x, y = y, colour = dataset)) + 26 | geom_point() + 27 | theme_void() + 28 | theme(legend.position = "none") + 29 | facet_wrap(~dataset, ncol = 3) 30 | } 31 | 32 | # Base R plots 33 | state = par("mar", "mfrow") 34 | 35 | # plot 36 | par(mfrow = c(5, 3), mar = c(1, 2, 2, 1)) 37 | 38 | sets = sort(unique(datasaurus_dozen$dataset)) 39 | 40 | for (s in sets) { 41 | df = datasaurus_dozen[datasaurus_dozen$dataset == s, ] 42 | plot(df$x, df$y, pch = 16) 43 | title(s) 44 | } 45 | 46 | #reset settings 47 | par(state) 48 | } 49 | \references{ 50 | Matejka, J., & Fitzmaurice, G. (2017). 51 | Same Stats, Different Graphs: Generating Datasets with 52 | Varied Appearance and Identical Statistics through Simulated 53 | Annealing. \emph{CHI 2017 Conference proceedings: ACM SIGCHI 54 | Conference on Human Factors in Computing Systems.} 55 | Retrieved from \url{https://www.research.autodesk.com/publications/same-stats-different-graphs/}. #nolint 56 | } 57 | \keyword{datasets} 58 | -------------------------------------------------------------------------------- /man/twelve_from_slant_long.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/datasaurus-package.R 3 | \docType{data} 4 | \name{twelve_from_slant_long} 5 | \alias{twelve_from_slant_long} 6 | \title{Twelve From Slant (long) data} 7 | \format{ 8 | A data frame with 2184 rows and 3 variables: 9 | \itemize{ 10 | \item \strong{dataset}: the dataset the data are from 11 | \item \strong{x}: x-values 12 | \item \strong{y}: y-values 13 | } 14 | } 15 | \usage{ 16 | twelve_from_slant_long 17 | } 18 | \description{ 19 | A dataset demonstrating the utility of visualization. 20 | These 12 datasets are equal in standard measures: mean, 21 | standard deviation, and Pearson's correlation. 22 | } 23 | \examples{ 24 | if (require(ggplot2)) { 25 | ggplot(twelve_from_slant_long, aes(x = x, y = y, colour = dataset)) + 26 | geom_point() + 27 | theme_void() + 28 | theme(legend.position = "none") + 29 | facet_wrap(~dataset, ncol = 3) 30 | } 31 | 32 | # Base R Plots 33 | state = par("mfrow", "mar") 34 | 35 | par(mfrow = c(4, 3), mar = c(3, 2, 2, 2)) 36 | 37 | sets = sort(unique(twelve_from_slant_long$dataset)) 38 | 39 | for (s in sets) { 40 | df = twelve_from_slant_long[twelve_from_slant_long$dataset == s, ] 41 | plot(df$x, df$y, pch = 16, xlab = "", ylab = "") 42 | title(s) 43 | } 44 | 45 | par(state) 46 | } 47 | \references{ 48 | Matejka, J., & Fitzmaurice, G. (2017). 49 | Same Stats, Different Graphs: Generating Datasets with 50 | Varied Appearance and Identical Statistics through Simulated 51 | Annealing. \emph{CHI 2017 Conference proceedings: ACM SIGCHI 52 | Conference on Human Factors in Computing Systems.} 53 | Retrieved from \url{https://www.research.autodesk.com/publications/same-stats-different-graphs/}. #nolint 54 | } 55 | \keyword{datasets} 56 | -------------------------------------------------------------------------------- /man/twelve_from_slant_alternate_long.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/datasaurus-package.R 3 | \docType{data} 4 | \name{twelve_from_slant_alternate_long} 5 | \alias{twelve_from_slant_alternate_long} 6 | \title{Twelve From Slant Alternate (long) data} 7 | \format{ 8 | A data frame with 2184 rows and 3 variables: 9 | \itemize{ 10 | \item \strong{dataset}: the dataset the data are from 11 | \item \strong{x}: x-values 12 | \item \strong{y}: y-values 13 | } 14 | } 15 | \usage{ 16 | twelve_from_slant_alternate_long 17 | } 18 | \description{ 19 | A dataset demonstrating the utility of visualization. 20 | These 12 datasets are equal in non-parametric measures: median, 21 | interquartile range, and Spearman's rank correlation. 22 | } 23 | \examples{ 24 | if (require(ggplot2)) { 25 | ggplot(twelve_from_slant_alternate_long, aes(x = x, y = y, colour = dataset)) + 26 | geom_point() + 27 | theme_void() + 28 | theme(legend.position = "none") + 29 | facet_wrap(~dataset, ncol = 3) 30 | } 31 | 32 | # Base R Plots 33 | state = par("mfrow", "mar") 34 | 35 | par(mfrow = c(4, 3), mar = c(2, 2, 2, 2)) 36 | 37 | sets = sort(unique(twelve_from_slant_alternate_long$dataset)) 38 | 39 | for (s in sets) { 40 | df = twelve_from_slant_alternate_long[twelve_from_slant_alternate_long$dataset == s, ] 41 | plot(df$x, df$y, pch = 16, xlab = "", ylab = "") 42 | title(s) 43 | } 44 | 45 | par(state) 46 | } 47 | \references{ 48 | Matejka, J., & Fitzmaurice, G. (2017). 49 | Same Stats, Different Graphs: Generating Datasets with 50 | Varied Appearance and Identical Statistics through Simulated 51 | Annealing. \emph{CHI 2017 Conference proceedings: ACM SIGCHI 52 | Conference on Human Factors in Computing Systems.} 53 | Retrieved from \url{https://www.research.autodesk.com/publications/same-stats-different-graphs/}. #nolint 54 | } 55 | \keyword{datasets} 56 | -------------------------------------------------------------------------------- /man/simpsons_paradox.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/datasaurus-package.R 3 | \docType{data} 4 | \name{simpsons_paradox} 5 | \alias{simpsons_paradox} 6 | \title{Simpsons Paradox data} 7 | \format{ 8 | A data frame with 444 rows and 3 variables: 9 | \itemize{ 10 | \item \strong{dataset}: indicates which of the two datasets the data are from, \code{simpson_1} or \code{simpson_2} 11 | \item \strong{x}: x-values 12 | \item \strong{y}: y-values 13 | } 14 | } 15 | \usage{ 16 | simpsons_paradox 17 | } 18 | \description{ 19 | A dataset demonstrating Simpson's Paradox with a strongly positively 20 | correlated dataset (\code{simpson_1}) 21 | and a dataset with the same positive correlation as \code{simpson_1}, 22 | but where individual groups have a 23 | strong negative correlation (\code{simpson_2}). 24 | } 25 | \examples{ 26 | if (require(ggplot2)) { 27 | ggplot(simpsons_paradox, aes(x = x, y = y, colour = dataset)) + 28 | geom_point() + 29 | theme(legend.position = "none") + 30 | facet_wrap(~dataset, ncol = 3) 31 | } 32 | 33 | # Base R Plots 34 | state = par("mfrow") 35 | 36 | par(mfrow = c(1, 2)) 37 | 38 | sets = unique(datasaurus_dozen$dataset) 39 | 40 | for (i in 1:2) { 41 | df = simpsons_paradox[simpsons_paradox$dataset == paste0("simpson_", i), ] 42 | plot(df$x, df$y, pch = 16, xlab = "", ylab = "") 43 | title(paste0("Simpson\'s Paradox ", i)) 44 | } 45 | 46 | par(state) 47 | } 48 | \references{ 49 | Matejka, J., & Fitzmaurice, G. (2017). 50 | Same Stats, Different Graphs: Generating Datasets with 51 | Varied Appearance and Identical Statistics through Simulated 52 | Annealing. \emph{CHI 2017 Conference proceedings: ACM SIGCHI 53 | Conference on Human Factors in Computing Systems.} 54 | Retrieved from \url{https://www.research.autodesk.com/publications/same-stats-different-graphs/}. #nolint 55 | } 56 | \keyword{datasets} 57 | -------------------------------------------------------------------------------- /man/simpsons_paradox_wide.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/datasaurus-package.R 3 | \docType{data} 4 | \name{simpsons_paradox_wide} 5 | \alias{simpsons_paradox_wide} 6 | \title{Simpsons Paradox (wide) data} 7 | \format{ 8 | A data frame with 222 rows and 4 variables: 9 | \itemize{ 10 | \item \strong{simpson_1_x}: x-values from the \code{simpson_1} dataset 11 | \item \strong{simpson_1_y}: y-values from the \code{simpson_1} dataset 12 | \item \strong{simpson_2_x}: x-values from the \code{simpson_2} dataset 13 | \item \strong{simpson_2_y}: y-values from the \code{simpson_2} dataset 14 | } 15 | } 16 | \usage{ 17 | simpsons_paradox_wide 18 | } 19 | \description{ 20 | A dataset demonstrating Simpson's Paradox with a 21 | strongly positively correlated dataset (\code{simpson_1}) 22 | and a dataset with the same positive correlation as \code{simpson_1}, 23 | but where individual groups have a 24 | strong negative correlation (\code{simpson_2}). 25 | } 26 | \examples{ 27 | #save current settings 28 | state = par("mar", "mfrow") 29 | 30 | par(mfrow = c(1, 2)) 31 | 32 | plot(simpsons_paradox_wide[["simpson_1_x"]], 33 | simpsons_paradox_wide[["simpson_1_y"]], 34 | xlab = "x", ylab = "y", main = "Simpson's Paradox 1") 35 | 36 | plot(simpsons_paradox_wide[["simpson_2_x"]], 37 | simpsons_paradox_wide[["simpson_2_y"]], 38 | xlab = "x", ylab = "y", main = "Simpson's Paradox 2") 39 | 40 | #reset settings 41 | par(state) 42 | } 43 | \references{ 44 | Matejka, J., & Fitzmaurice, G. (2017). 45 | Same Stats, Different Graphs: Generating Datasets with 46 | Varied Appearance and Identical Statistics through Simulated 47 | Annealing. \emph{CHI 2017 Conference proceedings: ACM SIGCHI 48 | Conference on Human Factors in Computing Systems.} 49 | Retrieved from \url{https://www.research.autodesk.com/publications/same-stats-different-graphs/}.#nolint 50 | } 51 | \keyword{datasets} 52 | -------------------------------------------------------------------------------- /man/box_plots_long.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/datasaurus-package.R 3 | \docType{data} 4 | \name{box_plots_long} 5 | \alias{box_plots_long} 6 | \title{Box plot data} 7 | \format{ 8 | A data frame with 12420 rows and 2 variables: 9 | \itemize{ 10 | \item \strong{Plot}: either the left, lines, normal, right or split boxplot 11 | \item \strong{Values}: the corresponding values from each dataset 12 | } 13 | } 14 | \usage{ 15 | box_plots_long 16 | } 17 | \description{ 18 | This dataset is the box plot data produced by Matjeka & 19 | Fitzmaurice to demonstrate applicability of their process. 20 | } 21 | \examples{ 22 | summary(box_plots) 23 | 24 | ## base plot 25 | 26 | #save current settings 27 | state = par("mar", "mfrow") 28 | 29 | par(mfrow = c(5, 2), mar = c(1, 2, 2, 1)) 30 | 31 | nms = names(box_plots) 32 | 33 | for (i in 1:5) { 34 | nm = nms[i] 35 | hist(box_plots[[nms[i]]], 36 | breaks = 100, 37 | main = nm) 38 | boxplot(box_plots[[nms[i]]], 39 | horizontal = TRUE) 40 | } 41 | 42 | 43 | #reset settings 44 | par(state) 45 | 46 | ## ggplot 47 | if (require(ggplot2)) { 48 | ggplot(box_plots, aes(x = left)) + 49 | geom_density() 50 | ggplot(box_plots, aes(x = lines)) + 51 | geom_density() 52 | ggplot(box_plots, aes(x = normal)) + 53 | geom_density() 54 | ggplot(box_plots, aes(x = right)) + 55 | geom_density() 56 | ggplot(box_plots, aes(x = split)) + 57 | geom_density() 58 | } 59 | } 60 | \references{ 61 | Matejka, J., & Fitzmaurice, G. (2017). 62 | Same Stats, Different Graphs: Generating Datasets with 63 | Varied Appearance and Identical Statistics through Simulated 64 | Annealing. \emph{CHI 2017 Conference proceedings: ACM SIGCHI 65 | Conference on Human Factors in Computing Systems.} 66 | Retrieved from \url{https://www.research.autodesk.com/publications/same-stats-different-graphs/}. #nolint 67 | } 68 | \keyword{datasets} 69 | -------------------------------------------------------------------------------- /man/box_plots.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/datasaurus-package.R 3 | \docType{data} 4 | \name{box_plots} 5 | \alias{box_plots} 6 | \title{Box plot data} 7 | \format{ 8 | A data frame with 2484 rows and 5 variables: 9 | \itemize{ 10 | \item \strong{left}: data pulled to the left 11 | \item \strong{lines}: data with arbitrary spikes along a range 12 | \item \strong{normal}: normally distributed data 13 | \item \strong{right}: data pulled to the right 14 | \item \strong{split}: split data 15 | } 16 | } 17 | \usage{ 18 | box_plots 19 | } 20 | \description{ 21 | This dataset is the box plot data produced by Matjeka & 22 | Fitzmaurice to demonstrate applicability of their process. 23 | } 24 | \examples{ 25 | summary(box_plots) 26 | 27 | ## base plot 28 | 29 | #save current settings 30 | state = par("mar", "mfrow") 31 | 32 | par(mfrow = c(5, 2), mar = c(1, 2, 2, 1)) 33 | 34 | nms = names(box_plots) 35 | 36 | for (i in 1:5) { 37 | nm = nms[i] 38 | hist(box_plots[[nms[i]]], 39 | breaks = 100, 40 | main = nm) 41 | boxplot(box_plots[[nms[i]]], 42 | horizontal = TRUE) 43 | } 44 | 45 | 46 | #reset settings 47 | par(state) 48 | 49 | ## ggplot 50 | if (require(ggplot2)) { 51 | ggplot(box_plots, aes(x = left)) + 52 | geom_density() 53 | ggplot(box_plots, aes(x = lines)) + 54 | geom_density() 55 | ggplot(box_plots, aes(x = normal)) + 56 | geom_density() 57 | ggplot(box_plots, aes(x = right)) + 58 | geom_density() 59 | ggplot(box_plots, aes(x = split)) + 60 | geom_density() 61 | } 62 | } 63 | \references{ 64 | Matejka, J., & Fitzmaurice, G. (2017). 65 | Same Stats, Different Graphs: Generating Datasets with 66 | Varied Appearance and Identical Statistics through Simulated 67 | Annealing. \emph{CHI 2017 Conference proceedings: ACM SIGCHI 68 | Conference on Human Factors in Computing Systems.} 69 | Retrieved from \url{https://www.research.autodesk.com/publications/same-stats-different-graphs/}. #nolint 70 | } 71 | \keyword{datasets} 72 | -------------------------------------------------------------------------------- /DESCRIPTION: -------------------------------------------------------------------------------- 1 | Package: datasauRus 2 | Title: Datasets from the Datasaurus Dozen 3 | Version: 0.1.9 4 | Authors@R: c( 5 | person("Colin", "Gillespie", , "colin@jumpingrivers.com", role = c("cre", "aut")), 6 | person("Steph", "Locke", , "stephanie.g.locke@gmail.com", role = "aut"), 7 | person("Alberto", "Cairo", , "alberto.cairo@gmail.com", role = "dtc"), 8 | person("Rhian", "Davies", , "statsrhian@gmail.com", role = "aut"), 9 | person("Justin", "Matejka", , "Justin.Matejka@autodesk.com", role = "dtc"), 10 | person("George", "Fitzmaurice", , "George.Fitzmaurice@autodesk.com", role = "dtc"), 11 | person("Lucy", "D'Agostino McGowan", , "ld.mcgowan@vanderbilt.edu", role = "aut"), 12 | person("Richard", "Cotton", , "richierocks@gmail.com", role = "ctb"), 13 | person("Tim", "Book", , "TimothyKBook@gmail.com", role = "ctb"), 14 | person("Jumping Rivers", role = "fnd") 15 | ) 16 | Description: The Datasaurus Dozen is a set of datasets with the same 17 | summary statistics. They retain the same summary statistics despite 18 | having radically different distributions. The datasets represent a 19 | larger and quirkier object lesson that is typically taught via 20 | Anscombe's Quartet (available in the 'datasets' package). Anscombe's 21 | Quartet contains four very different distributions with the same 22 | summary statistics and as such highlights the value of visualisation 23 | in understanding data, over and above summary statistics. As well as 24 | being an engaging variant on the Quartet, the data is generated in a 25 | novel way. The simulated annealing process used to derive datasets 26 | from the original Datasaurus is detailed in "Same Stats, Different 27 | Graphs: Generating Datasets with Varied Appearance and Identical 28 | Statistics through Simulated Annealing" . 29 | License: MIT + file LICENSE 30 | URL: https://github.com/jumpingrivers/datasauRus, 31 | https://jumpingrivers.github.io/datasauRus/ 32 | BugReports: https://github.com/jumpingrivers/datasauRus/issues 33 | Depends: 34 | R (>= 3.5.0) 35 | Suggests: 36 | dplyr, 37 | ggplot2, 38 | knitr, 39 | rmarkdown, 40 | spelling, 41 | testthat 42 | VignetteBuilder: 43 | knitr 44 | Encoding: UTF-8 45 | Language: en-US 46 | LazyData: true 47 | Roxygen: list(markdown = TRUE) 48 | RoxygenNote: 7.3.2 49 | -------------------------------------------------------------------------------- /vignettes/Datasaurus.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "The Datasaurus data package" 3 | date: "`r Sys.Date()`" 4 | output: rmarkdown::html_vignette 5 | vignette: > 6 | %\VignetteIndexEntry{The Datasaurus data package} 7 | %\VignetteEngine{knitr::rmarkdown} 8 | %\VignetteEncoding{UTF-8} 9 | --- 10 | 11 | This package wraps the awesome Datasaurus Dozen dataset, which contains [13](https://www.phrases.org.uk/meanings/Bakers-dozen.html) sets of x-y data. Each sub-dataset has five statistics that are (almost) the same in each case. (These are the mean of x, mean of y, standard deviation of x, standard deviation of y, and Pearson correlation between x and y). However, scatter plots reveal that each sub-dataset looks very different. The dataset is intended to be used to teach students that it is important to plot their own datasets, rather than relying only on statistics. 12 | 13 | The Datasaurus was created by Alberto Cairo. Datasaurus shows us why visualisation is important, not just summary statistics. 14 | 15 | He's been subsequently made even more famous in the paper [Same Stats, Different Graphs: Generating Datasets with Varied Appearance and Identical Statistics through Simulated Annealing](https://www.research.autodesk.com/publications/same-stats-different-graphs/) by Justin Matejka and George Fitzmaurice. 16 | 17 | In the paper, Justin and George simulate a variety of datasets that the same summary statistics to the Datasaurus but have very different distributions. 18 | 19 | This package looks to make these datasets available for use as an advanced [Anscombe's Quartet](https://en.wikipedia.org/wiki/Anscombe%27s_quartet), available in R as `anscombe`. 20 | 21 | ## Usage 22 | 23 | To see that statistics are (almost) the same for each sub-dataset, you can use `dplyr`. 24 | 25 | ```{r} 26 | library(datasauRus) 27 | if (requireNamespace("dplyr")) { 28 | suppressPackageStartupMessages(library(dplyr)) 29 | datasaurus_dozen %>% 30 | group_by(dataset) %>% 31 | summarize( 32 | mean_x = mean(x), 33 | mean_y = mean(y), 34 | std_dev_x = sd(x), 35 | std_dev_y = sd(y), 36 | corr_x_y = cor(x, y) 37 | ) 38 | } 39 | ``` 40 | 41 | To see that each sub-dataset looks very different, you can draw scatter plots. 42 | 43 | ```{r fig.height=12, fig.width=9} 44 | if (requireNamespace("ggplot2")) { 45 | library(ggplot2) 46 | ggplot(datasaurus_dozen, aes(x = x, y = y, colour = dataset)) + 47 | geom_point() + 48 | theme_void() + 49 | theme(legend.position = "none") + 50 | facet_wrap(~dataset, ncol = 3) 51 | } 52 | ``` 53 | -------------------------------------------------------------------------------- /.github/SUPPORT.md: -------------------------------------------------------------------------------- 1 | # Getting help with datasauRus 2 | 3 | Thanks for using datasauRus! 4 | Before filing an issue, there are a few places to explore and pieces to put together to make the process as smooth as possible. 5 | 6 | ## Make a reprex 7 | 8 | Start by making a minimal **repr**oducible **ex**ample using the [reprex](https://reprex.tidyverse.org/) package. 9 | If you haven't heard of or used reprex before, you're in for a treat! 10 | Seriously, reprex will make all of your R-question-asking endeavors easier (which is a pretty insane ROI for the five to ten minutes it'll take you to learn what it's all about). 11 | For additional reprex pointers, check out the [Get help!](https://www.tidyverse.org/help/) section of the tidyverse site. 12 | 13 | ## Where to ask? 14 | 15 | Armed with your reprex, the next step is to figure out [where to ask](https://www.tidyverse.org/help/#where-to-ask). 16 | 17 | * If it's a question: start with [community.rstudio.com](https://community.rstudio.com/), and/or StackOverflow. There are more people there to answer questions. 18 | 19 | * If it's a bug: you're in the right place, [file an issue](https://github.com/jumpingrivers/datasauRus/issues/new). 20 | 21 | * If you're not sure: let the community help you figure it out! 22 | If your problem _is_ a bug or a feature request, you can easily return here and report it. 23 | 24 | Before opening a new issue, be sure to [search issues and pull requests](https://github.com/jumpingrivers/datasauRus/issues) to make sure the bug hasn't been reported and/or already fixed in the development version. 25 | By default, the search will be pre-populated with `is:issue is:open`. 26 | You can [edit the qualifiers](https://help.github.com/articles/searching-issues-and-pull-requests/) (e.g. `is:pr`, `is:closed`) as needed. 27 | For example, you'd simply remove `is:open` to search _all_ issues in the repo, open or closed. 28 | 29 | ## What happens next? 30 | 31 | To be as efficient as possible, development of tidyverse packages tends to be very bursty, so you shouldn't worry if you don't get an immediate response. 32 | Typically we don't look at a repo until a sufficient quantity of issues accumulates, then there’s a burst of intense activity as we focus our efforts. 33 | That makes development more efficient because it avoids expensive context switching between problems, at the cost of taking longer to get back to you. 34 | This process makes a good reprex particularly important because it might be multiple months between your initial report and when we start working on it. 35 | If we can’t reproduce the bug, we can’t fix it! 36 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | # datasauRus 5 | 6 | 7 | 8 | [![Lifecycle: 9 | stable](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://lifecycle.r-lib.org/articles/stages.html#stable) 10 | [![CRAN 11 | status](https://www.r-pkg.org/badges/version/datasauRus)](https://CRAN.R-project.org/package=datasauRus) 12 | [![R-CMD-check](https://github.com/jumpingrivers/datasauRus/workflows/R-CMD-check/badge.svg)](https://github.com/jumpingrivers/datasauRus/actions) 13 | 14 | 15 | This package wraps the awesome Datasaurus Dozen datasets. The Datasaurus 16 | Dozen show us why visualisation is important – summary statistics can be 17 | the same but distributions can be very different. In short, this package 18 | gives a fun alternative to [Anscombe’s 19 | Quartet](https://en.wikipedia.org/wiki/Anscombe%27s_quartet), available 20 | in R as `anscombe`. 21 | 22 | The original Datasaurus was created by Alberto Cairo. The other Dozen 23 | were generated using simulated annealing and the process is described in 24 | the paper “Same Stats, Different Graphs: Generating Datasets with Varied 25 | Appearance and Identical Statistics through Simulated Annealing” by 26 | Justin Matejka and George Fitzmaurice ([open access materials including 27 | manuscript and 28 | code](https://www.research.autodesk.com/publications/same-stats-different-graphs/), 29 | [official paper](https://doi.org/10.1145/3025453.3025912)). 30 | 31 | In the paper, Justin and George simulate a variety of datasets that the 32 | same summary statistics to the Datasaurus but have very different 33 | distributions. 34 | 35 | Sequential dinosaur gif 36 | 37 | ## Install 38 | 39 | The latest stable version is available on CRAN 40 | 41 | ``` r 42 | install.packages("datasauRus") 43 | ``` 44 | 45 | You can get the latest development version from GitHub, so use 46 | {devtools} to install the package 47 | 48 | ``` r 49 | devtools::install_github("jumpingrivers/datasauRus") 50 | ``` 51 | 52 | ## Usage 53 | 54 | You can use the package to produce Anscombe plots and more. 55 | 56 | ``` r 57 | library("ggplot2") 58 | library("datasauRus") 59 | ggplot(datasaurus_dozen, aes(x = x, y = y, colour = dataset))+ 60 | geom_point() + 61 | theme_void() + 62 | theme(legend.position = "none")+ 63 | facet_wrap(~dataset, ncol = 3) 64 | ``` 65 | 66 | ![](man/figures/datasets-1.png) 67 | 68 | ## Code of Conduct 69 | 70 | Please note that the datasauRus project is released with a [Contributor 71 | Code of 72 | Conduct](https://jumpingrivers.github.io/datasauRus/CODE_OF_CONDUCT.html). 73 | By contributing to this project, you agree to abide by its terms 74 | -------------------------------------------------------------------------------- /man/twelve_from_slant_wide.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/datasaurus-package.R 3 | \docType{data} 4 | \name{twelve_from_slant_wide} 5 | \alias{twelve_from_slant_wide} 6 | \title{Twelve From Slant (wide) data} 7 | \format{ 8 | A data frame with 182 rows and 24 variables: 9 | \itemize{ 10 | \item \strong{bullseye_x}: x-values for the \code{bullseye} dataset 11 | \item \strong{bullseye_y}: y-values for the \code{bullseye} dataset 12 | \item \strong{circle_x}: x-values for the \code{circle} dataset 13 | \item \strong{circle_y}: y-values for the \code{circle} dataset 14 | \item \strong{dots_x}: x-values for the \code{dots} dataset 15 | \item \strong{dots_y}: y-values for the \code{dots} dataset 16 | \item \strong{h_lines_x}: x-values for the \code{h_lines} dataset 17 | \item \strong{h_lines_y}: y-values for the \code{h_lines} dataset 18 | \item \strong{high_lines_x}: x-values for the \code{high_lines} dataset 19 | \item \strong{high_lines_y}: y-values for the \code{high_lines} dataset 20 | \item \strong{slant_x}: x-values for the \code{slant} dataset 21 | \item \strong{slant_y}: y-values for the \code{slant} dataset 22 | \item \strong{slant_down_x}: x-values for the \code{slant_down} dataset 23 | \item \strong{slant_down_y}: y-values for the \code{slant_down} dataset 24 | \item \strong{slant_up_x}: x-values for the \code{slant_up} dataset 25 | \item \strong{slant_up_y}: y-values for the \code{slant_up} dataset 26 | \item \strong{star_x}: x-values for the \code{star} dataset 27 | \item \strong{star_y}: y-values for the \code{star} dataset 28 | \item \strong{v_lines_x}: x-values for the \code{v_lines} dataset 29 | \item \strong{v_lines_y}: y-values for the \code{v_lines} dataset 30 | \item \strong{wide_lines_x}: x-values for the \code{wide_lines} dataset 31 | \item \strong{wide_lines_y}: y-values for the \code{wide_lines} dataset 32 | \item \strong{x_shape_x}: x-values for the \code{x_shape} dataset 33 | \item \strong{x_shape_y}: y-values for the \code{x_shape} dataset 34 | } 35 | } 36 | \usage{ 37 | twelve_from_slant_wide 38 | } 39 | \description{ 40 | A dataset demonstrating the utility of visualization. 41 | These 12 datasets are equal in standard measures: mean, 42 | standard deviation, and Pearson's correlation. 43 | } 44 | \examples{ 45 | #save current settings 46 | state = par("mar", "mfrow") 47 | 48 | # plot 49 | par(mfrow = c(4, 3), mar = c(1, 3, 3, 1)) 50 | 51 | nms = names(twelve_from_slant_wide) 52 | for (i in seq(1, 23, by = 2)) { 53 | nm = substr(nms[i], 1, nchar(nms[i]) - 2) 54 | plot(twelve_from_slant_wide[[nms[i]]], 55 | twelve_from_slant_wide[[nms[i + 1]]], 56 | xlab = "", ylab = "", main = nm) 57 | } 58 | 59 | #reset settings 60 | par(state) 61 | } 62 | \references{ 63 | Matejka, J., & Fitzmaurice, G. (2017). 64 | Same Stats, Different Graphs: Generating Datasets with 65 | Varied Appearance and Identical Statistics through Simulated 66 | Annealing. \emph{CHI 2017 Conference proceedings: ACM SIGCHI 67 | Conference on Human Factors in Computing Systems.} 68 | Retrieved from \url{https://www.research.autodesk.com/publications/same-stats-different-graphs/}. #nolint 69 | } 70 | \keyword{datasets} 71 | -------------------------------------------------------------------------------- /man/twelve_from_slant_alternate_wide.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/datasaurus-package.R 3 | \docType{data} 4 | \name{twelve_from_slant_alternate_wide} 5 | \alias{twelve_from_slant_alternate_wide} 6 | \title{Twelve From Slant Alternate (wide) data} 7 | \format{ 8 | A data frame with 182 rows and 24 variables: 9 | \itemize{ 10 | \item \strong{bullseye_x}: x-values for the \code{bullseye} dataset 11 | \item \strong{bullseye_y}: y-values for the \code{bullseye} dataset 12 | \item \strong{circle_x}: x-values for the \code{circle} dataset 13 | \item \strong{circle_y}: y-values for the \code{circle} dataset 14 | \item \strong{dots_x}: x-values for the \code{dots} dataset 15 | \item \strong{dots_y}: y-values for the \code{dots} dataset 16 | \item \strong{h_lines_x}: x-values for the \code{h_lines} dataset 17 | \item \strong{h_lines_y}: y-values for the \code{h_lines} dataset 18 | \item \strong{high_lines_x}: x-values for the \code{high_lines} dataset 19 | \item \strong{high_lines_y}: y-values for the \code{high_lines} dataset 20 | \item \strong{slant_x}: x-values for the \code{slant} dataset 21 | \item \strong{slant_y}: y-values for the \code{slant} dataset 22 | \item \strong{slant_down_x}: x-values for the \code{slant_down} dataset 23 | \item \strong{slant_down_y}: y-values for the \code{slant_down} dataset 24 | \item \strong{slant_up_x}: x-values for the \code{slant_up} dataset 25 | \item \strong{slant_up_y}: y-values for the \code{slant_up} dataset 26 | \item \strong{star_x}: x-values for the \code{star} dataset 27 | \item \strong{star_y}: y-values for the \code{star} dataset 28 | \item \strong{v_lines_x}: x-values for the \code{v_lines} dataset 29 | \item \strong{v_lines_y}: y-values for the \code{v_lines} dataset 30 | \item \strong{wide_lines_x}: x-values for the \code{wide_lines} dataset 31 | \item \strong{wide_lines_y}: y-values for the \code{wide_lines} dataset 32 | \item \strong{x_shape_x}: x-values for the \code{x_shape} dataset 33 | \item \strong{x_shape_y}: y-values for the \code{x_shape} dataset 34 | } 35 | } 36 | \usage{ 37 | twelve_from_slant_alternate_wide 38 | } 39 | \description{ 40 | A dataset demonstrating the utility of visualization. 41 | These 12 datasets are equal in non-parametric measures: median, 42 | interquartile range, and Spearman's rank correlation. 43 | } 44 | \examples{ 45 | #save current settings 46 | state = par("mar", "mfrow") 47 | 48 | # plot 49 | par(mfrow = c(4, 3), mar = c(1, 3, 3, 1)) 50 | 51 | nms = names(twelve_from_slant_alternate_wide) 52 | for (i in seq(1, 23, by = 2)) { 53 | nm = substr(nms[i], 1, nchar(nms[i]) - 2) 54 | plot(twelve_from_slant_alternate_wide[[nms[i]]], 55 | twelve_from_slant_alternate_wide[[nms[i + 1]]], 56 | xlab = "", ylab = "", main = nm) 57 | } 58 | 59 | #reset settings 60 | par(state) 61 | } 62 | \references{ 63 | Matejka, J., & Fitzmaurice, G. (2017). 64 | Same Stats, Different Graphs: Generating Datasets with 65 | Varied Appearance and Identical Statistics through Simulated 66 | Annealing. \emph{CHI 2017 Conference proceedings: ACM SIGCHI 67 | Conference on Human Factors in Computing Systems.} 68 | Retrieved from \url{https://www.research.autodesk.com/publications/same-stats-different-graphs/}. #nolint 69 | } 70 | \keyword{datasets} 71 | -------------------------------------------------------------------------------- /man/datasaurus_dozen_wide.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/datasaurus-package.R 3 | \docType{data} 4 | \name{datasaurus_dozen_wide} 5 | \alias{datasaurus_dozen_wide} 6 | \title{Datasaurus Dozen (wide) data} 7 | \format{ 8 | A data frame with 142 rows and 26 variables: 9 | \itemize{ 10 | \item \strong{away_x}: x-values for the \code{away} dataset 11 | \item \strong{away_y}: y-values for the \code{away} dataset 12 | \item \strong{bullseye_x}: x-values for the \code{bullseye} dataset 13 | \item \strong{bullseye_y}: y-values for the \code{bullseye} dataset 14 | \item \strong{circle_x}: x-values for the \code{circle} dataset 15 | \item \strong{circle_y}: y-values for the \code{circle} dataset 16 | \item \strong{dino_x}: x-values for \code{dinosaur} dataset! 17 | \item \strong{dino_y}: y-values for \code{dinosaur} dataset! 18 | \item \strong{dots_x}: x-values for the \code{dots} dataset 19 | \item \strong{dots_y}: y-values for the \code{dots} dataset 20 | \item \strong{h_lines_x}: x-values for the \code{h_lines} dataset 21 | \item \strong{h_lines_y}: y-values for the \code{h_lines} dataset 22 | \item \strong{high_lines_x}: x-values for the \code{high_lines} dataset 23 | \item \strong{high_lines_y}: y-values for the \code{high_lines} dataset 24 | \item \strong{slant_down_x}: x-values for the \code{slant_down} dataset 25 | \item \strong{slant_down_y}: y-values for the \code{slant_down} dataset 26 | \item \strong{slant_up_x}: x-values for the \code{slant_up} dataset 27 | \item \strong{slant_up_y}: y-values for the \code{slant_up} dataset 28 | \item \strong{star_x}: x-values for the \code{star} dataset 29 | \item \strong{star_y}: y-values for the \code{star} dataset 30 | \item \strong{v_lines_x}: x-values for the \code{v_lines} dataset 31 | \item \strong{v_lines_y}: y-values for the \code{v_lines} dataset 32 | \item \strong{wide_lines_x}: x-values for the \code{wide_lines} dataset 33 | \item \strong{wide_lines_y}: y-values for the \code{wide_lines} dataset 34 | \item \strong{x_shape_x}: x-values for the \code{x_shape} dataset 35 | \item \strong{x_shape_y}: y-values for the \code{x_shape} dataset 36 | } 37 | } 38 | \usage{ 39 | datasaurus_dozen_wide 40 | } 41 | \description{ 42 | A dataset demonstrating the utility of visualization. 43 | These 12 datasets are equal in standard measures: mean, 44 | standard deviation, and Pearson's correlation. 45 | } 46 | \examples{ 47 | # Save current settings 48 | state = par("mar", "mfrow") 49 | 50 | # Base R Plots 51 | par(mfrow = c(5, 3), mar = c(1, 3, 3, 1)) 52 | 53 | nms = names(datasaurus_dozen_wide) 54 | for (i in seq(1, 25, by = 2)) { 55 | nm = substr(nms[i], 1, nchar(nms[i]) - 2) 56 | plot(datasaurus_dozen_wide[[nms[i]]], 57 | datasaurus_dozen_wide[[nms[i + 1]]], 58 | xlab = "", ylab = "", main = nm) 59 | } 60 | 61 | #reset settings 62 | par(state) 63 | } 64 | \references{ 65 | Matejka, J., & Fitzmaurice, G. (2017). 66 | Same Stats, Different Graphs: Generating Datasets with 67 | Varied Appearance and Identical Statistics through Simulated 68 | Annealing. \emph{CHI 2017 Conference proceedings: ACM SIGCHI 69 | Conference on Human Factors in Computing Systems.} 70 | Retrieved from \url{https://www.research.autodesk.com/publications/same-stats-different-graphs/}. #nolint 71 | } 72 | \keyword{datasets} 73 | -------------------------------------------------------------------------------- /README.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | output: github_document 3 | --- 4 | 5 | 6 | 7 | ```{r, echo = FALSE} 8 | knitr::opts_chunk$set( 9 | collapse = TRUE, 10 | comment = "#>", 11 | fig.path = "man/figures/" 12 | ) 13 | ``` 14 | 15 | # datasauRus 16 | 17 | 18 | [![Lifecycle: stable](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://lifecycle.r-lib.org/articles/stages.html#stable) 19 | [![CRAN status](https://www.r-pkg.org/badges/version/datasauRus)](https://CRAN.R-project.org/package=datasauRus) 20 | [![R-CMD-check](https://github.com/jumpingrivers/datasauRus/workflows/R-CMD-check/badge.svg)](https://github.com/jumpingrivers/datasauRus/actions) 21 | [![R-CMD-check](https://github.com/jumpingrivers/datasauRus/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/jumpingrivers/datasauRus/actions/workflows/R-CMD-check.yaml) 22 | 23 | 24 | This package wraps the awesome Datasaurus Dozen datasets. The Datasaurus Dozen show us why visualisation is important -- summary statistics can be the same but distributions can be very different. In short, this package gives a fun alternative to [Anscombe's Quartet](https://en.wikipedia.org/wiki/Anscombe%27s_quartet), available in R as `anscombe`. 25 | 26 | The original Datasaurus was created by Alberto Cairo. The other Dozen were generated using simulated annealing and the process 27 | is described in the paper "Same Stats, Different Graphs: Generating 28 | Datasets with Varied Appearance and Identical Statistics through 29 | Simulated Annealing" by Justin 30 | Matejka and George Fitzmaurice ([open access materials including manuscript and code](https://www.research.autodesk.com/publications/same-stats-different-graphs/), [official paper](https://doi.org/10.1145/3025453.3025912)). 31 | 32 | In the paper, Justin and George simulate a variety of datasets that the same summary statistics to the Datasaurus but have very different distributions. 33 | 34 | 35 | ```{r, out.width="600px", fig.alt="Sequential dinosaur gif", echo = FALSE} 36 | knitr::include_graphics("https://damassets.autodesk.net/content/dam/autodesk/research/publications-assets/gifs/same-stats-different-graphs/DinoSequentialSmaller.gif") 37 | ``` 38 | 39 | ## Install 40 | The latest stable version is available on CRAN 41 | 42 | ```{r, eval = FALSE} 43 | install.packages("datasauRus") 44 | ``` 45 | 46 | You can get the latest development version from GitHub, so use {devtools} to install the package 47 | 48 | ```{r, eval = FALSE} 49 | devtools::install_github("jumpingrivers/datasauRus") 50 | ``` 51 | 52 | ## Usage 53 | 54 | You can use the package to produce Anscombe plots and more. 55 | 56 | ```{r datasets, fig.height=12, fig.width=9} 57 | library("ggplot2") 58 | library("datasauRus") 59 | ggplot(datasaurus_dozen, aes(x = x, y = y, colour = dataset))+ 60 | geom_point() + 61 | theme_void() + 62 | theme(legend.position = "none")+ 63 | facet_wrap(~dataset, ncol = 3) 64 | ``` 65 | 66 | 67 | ## Code of Conduct 68 | 69 | Please note that the datasauRus project is released with a [Contributor Code of Conduct](https://jumpingrivers.github.io/datasauRus/CODE_OF_CONDUCT.html). By contributing to this project, you agree to abide by its terms 70 | -------------------------------------------------------------------------------- /.github/CODE_OF_CONDUCT.md: -------------------------------------------------------------------------------- 1 | # Contributor Covenant Code of Conduct 2 | 3 | ## Our Pledge 4 | 5 | We as members, contributors, and leaders pledge to make participation in our 6 | community a harassment-free experience for everyone, regardless of age, body 7 | size, visible or invisible disability, ethnicity, sex characteristics, gender 8 | identity and expression, level of experience, education, socio-economic status, 9 | nationality, personal appearance, race, religion, or sexual identity and 10 | orientation. 11 | 12 | We pledge to act and interact in ways that contribute to an open, welcoming, 13 | diverse, inclusive, and healthy community. 14 | 15 | ## Our Standards 16 | 17 | Examples of behavior that contributes to a positive environment for our 18 | community include: 19 | 20 | * Demonstrating empathy and kindness toward other people 21 | * Being respectful of differing opinions, viewpoints, and experiences 22 | * Giving and gracefully accepting constructive feedback 23 | * Accepting responsibility and apologizing to those affected by our mistakes, 24 | and learning from the experience 25 | * Focusing on what is best not just for us as individuals, but for the overall 26 | community 27 | 28 | Examples of unacceptable behavior include: 29 | 30 | * The use of sexualized language or imagery, and sexual attention or 31 | advances of any kind 32 | * Trolling, insulting or derogatory comments, and personal or political attacks 33 | * Public or private harassment 34 | * Publishing others' private information, such as a physical or email 35 | address, without their explicit permission 36 | * Other conduct which could reasonably be considered inappropriate in a 37 | professional setting 38 | 39 | ## Enforcement Responsibilities 40 | 41 | Community leaders are responsible for clarifying and enforcing our standards 42 | of acceptable behavior and will take appropriate and fair corrective action in 43 | response to any behavior that they deem inappropriate, threatening, offensive, 44 | or harmful. 45 | 46 | Community leaders have the right and responsibility to remove, edit, or reject 47 | comments, commits, code, wiki edits, issues, and other contributions that are 48 | not aligned to this Code of Conduct, and will communicate reasons for moderation 49 | decisions when appropriate. 50 | 51 | ## Scope 52 | 53 | This Code of Conduct applies within all community spaces, and also applies 54 | when an individual is officially representing the community in public spaces. 55 | Examples of representing our community include using an official e-mail 56 | address, posting via an official social media account, or acting as an appointed 57 | representative at an online or offline event. 58 | 59 | ## Enforcement 60 | 61 | Instances of abusive, harassing, or otherwise unacceptable behavior may be 62 | reported to the community leaders responsible for enforcement at codeofconduct@rstudio.com. 63 | All complaints will be reviewed and investigated promptly and fairly. 64 | 65 | All community leaders are obligated to respect the privacy and security of the 66 | reporter of any incident. 67 | 68 | ## Enforcement Guidelines 69 | 70 | Community leaders will follow these Community Impact Guidelines in determining 71 | the consequences for any action they deem in violation of this Code of Conduct: 72 | 73 | ### 1. Correction 74 | 75 | **Community Impact**: Use of inappropriate language or other behavior deemed 76 | unprofessional or unwelcome in the community. 77 | 78 | **Consequence**: A private, written warning from community leaders, providing 79 | clarity around the nature of the violation and an explanation of why the 80 | behavior was inappropriate. A public apology may be requested. 81 | 82 | ### 2. Warning 83 | 84 | **Community Impact**: A violation through a single incident or series of 85 | actions. 86 | 87 | **Consequence**: A warning with consequences for continued behavior. No 88 | interaction with the people involved, including unsolicited interaction with 89 | those enforcing the Code of Conduct, for a specified period of time. This 90 | includes avoiding interactions in community spaces as well as external channels 91 | like social media. Violating these terms may lead to a temporary or permanent 92 | ban. 93 | 94 | ### 3. Temporary Ban 95 | 96 | **Community Impact**: A serious violation of community standards, including 97 | sustained inappropriate behavior. 98 | 99 | **Consequence**: A temporary ban from any sort of interaction or public 100 | communication with the community for a specified period of time. No public or 101 | private interaction with the people involved, including unsolicited interaction 102 | with those enforcing the Code of Conduct, is allowed during this period. 103 | Violating these terms may lead to a permanent ban. 104 | 105 | ### 4. Permanent Ban 106 | 107 | **Community Impact**: Demonstrating a pattern of violation of community 108 | standards, including sustained inappropriate behavior, harassment of an 109 | individual, or aggression toward or disparagement of classes of individuals. 110 | 111 | **Consequence**: A permanent ban from any sort of public interaction within the 112 | community. 113 | 114 | ## Attribution 115 | 116 | This Code of Conduct is adapted from the [Contributor Covenant][homepage], 117 | version 2.0, 118 | available at . 119 | 120 | Community Impact Guidelines were inspired by [Mozilla's code of conduct 121 | enforcement ladder](https://github.com/mozilla/diversity). 122 | 123 | [homepage]: https://www.contributor-covenant.org 124 | 125 | For answers to common questions about this code of conduct, see the FAQ at 126 | . Translations are available at . 127 | -------------------------------------------------------------------------------- /inst/extdata/SimpsonsParadox-Wide.tsv: -------------------------------------------------------------------------------- 1 | simpson_1 simpson_1 simpson_2 simpson_2 2 | x y x y 3 | 55.7909718157 69.9365934283 62.2434387845 70.6083992107 4 | 47.2282091405 14.7561070913 52.3349883891 14.705772721 5 | 57.2468738451 51.4617618588 56.3679472613 46.3955379223 6 | 70.8319847999 65.5199077407 66.803945776 66.1748679006 7 | 73.3670855496 91.5959640491 66.5360509146 89.2465806107 8 | 69.9744916206 94.9394275208 62.3812860623 91.4525971096 9 | 40.1049144627 9.58672855908 38.8538312261 6.76356023684 10 | 46.306867762 63.0790021629 39.406283311 63.0623946578 11 | 63.5118905297 91.3188400408 60.8981909102 92.5623698523 12 | 61.3784207464 51.9097664689 56.6229264798 45.7571594797 13 | 24.4368259537 4.28624078996 29.8892499219 6.58005362491 14 | 28.4162104994 5.46796845638 32.0738634721 14.1531733612 15 | 41.6293918156 52.1743334507 41.8251847374 55.4824261842 16 | 40.9624624616 7.50677488138 36.5054087766 10.1583095333 17 | 50.4048080182 40.9862915909 53.1207576225 44.3026622909 18 | 28.064915354 20.6178017531 25.5507775105 20.7388200832 19 | 78.3457430124 91.1442756985 71.8051997136 86.4814775815 20 | 48.576498562 23.373489831 46.241269306 20.5221321867 21 | 68.1415063219 57.0886856283 71.7811928334 61.1889632284 22 | 35.1771191716 21.2889005583 32.3997881671 14.3716866939 23 | 60.277198436 57.723872583 67.2542810388 65.6079688069 24 | 69.0733755677 77.2059289324 73.1173865281 81.1790691518 25 | 54.9438297151 32.941989407 58.5481223323 38.8110033112 26 | 41.8482124647 44.5883095101 44.1040888097 53.1190517597 27 | 29.441699163 5.73279918759 33.2811110226 12.0553165165 28 | 34.3192687687 21.9333645175 32.6388495005 14.5171409112 29 | 54.6812364909 95.4787598334 54.267778726 99.8395673578 30 | 50.830247166 32.553562636 42.2532299599 24.9070402084 31 | 36.4001855327 12.9287725207 34.7631479916 11.7823025614 32 | 40.0448949588 34.973472985 36.7126557379 30.1412965999 33 | 67.6124652252 49.3027674007 74.2780204 59.1457798012 34 | 45.9721509763 23.6637085192 45.8759868214 21.2935901672 35 | 57.8424149318 58.6383610501 65.8825274299 67.4254917282 36 | 51.6168124814 64.531431227 59.4085715643 74.0718570333 37 | 38.3860154463 29.6439075432 36.9961633817 30.0184640143 38 | 82.46724168 95.6593486437 76.360458386 89.4428072999 39 | 22.8861602963 1.0 29.7532455837 3.38317380619 40 | 79.4263516761 99.0 76.9137545243 93.5117462759 41 | 55.807633571 79.0912662501 55.6786939951 77.2130568796 42 | 49.6447592807 31.6975833729 41.7722531422 25.1190637512 43 | 28.7071929512 19.1777034365 27.4284354919 17.3015587512 44 | 45.6904410065 56.6153334155 47.3907588332 55.0957073772 45 | 49.9355452399 38.8000604975 52.744587949 44.724394731 46 | 57.6524875514 67.6529109488 63.369799707 69.8765380012 47 | 61.8960294968 46.3373451262 57.8921037446 43.4549078015 48 | 48.4714332864 44.8087250029 51.4648195016 46.1656865287 49 | 64.8693862824 92.7846809099 60.3103259212 93.7468247846 50 | 48.5282435828 38.7781932107 55.4054060507 46.69509542 51 | 38.9816975616 56.6971386306 37.1541581884 60.0937379901 52 | 22.1525466166 1.87126319037 26.7244644531 4.42829464175 53 | 38.8887808577 32.8430633079 35.6449082971 31.4393232129 54 | 55.0247203448 68.0577628337 57.3638919209 75.9589794967 55 | 62.6428692695 84.1675315407 66.9698602814 86.1463865097 56 | 54.987996807 81.897232613 54.7999369653 78.2343750439 57 | 67.0054190547 93.1570846804 64.7300366558 89.4095394979 58 | 50.3530481629 44.5909244275 56.0674829356 42.1332743252 59 | 46.4315061577 35.5709442115 37.8543879494 29.2860374154 60 | 41.4966700091 33.2331654025 36.6090288878 30.5739213437 61 | 45.571870558 48.93801295 49.943826149 48.5704114132 62 | 59.5518487262 43.0408745162 54.986179506 42.7800379869 63 | 50.3666439178 48.9957895476 49.3299344982 51.1400468911 64 | 59.5083922835 67.2231679587 64.8049315703 68.025548052 65 | 48.7454943377 66.8406774566 58.1189953269 74.7935860661 66 | 50.5354363801 18.1964864096 47.8767910559 18.8551281024 67 | 29.3169060269 6.35268465503 34.4825499887 12.2917200025 68 | 62.8086406129 93.993079221 62.2516201137 91.4087280245 69 | 85.057792881 79.4131993797 81.028199774 72.53857096 70 | 34.2946169945 7.06898620271 38.0835688653 9.04370934353 71 | 33.3845809961 11.7978607101 31.7523760116 14.1949435576 72 | 72.3038815182 94.7885696826 66.6850466715 87.7892763952 73 | 38.9496288838 9.81915009164 35.77453456 10.7387967406 74 | 71.0085263017 81.0677514043 72.4479913643 81.6972689607 75 | 31.2028861704 1.0 35.4114695455 9.66314824078 76 | 44.658260858 19.3585031044 43.2933962176 23.7688888235 77 | 35.6248907864 26.4140627705 37.5257019915 29.6828217583 78 | 59.341379739 63.8125493473 65.2802820152 68.0822569128 79 | 60.2196498836 79.8285218722 69.9248804645 83.2387053847 80 | 58.7258632446 74.3630687968 58.2316075851 74.7684144035 81 | 42.3845648942 55.5576739181 42.4241820105 57.1447689952 82 | 64.9353102974 80.3475669854 66.7097007553 86.2677202689 83 | 49.5564587021 50.6937867532 46.766168213 50.922139871 84 | 72.6187112418 81.4728024583 74.0248646709 79.0417735273 85 | 33.4052196482 22.9983279399 27.2623316124 18.2963375756 86 | 48.6874414786 38.553136122 53.0600562461 44.7161667644 87 | 43.3767323014 49.8525614091 48.006908225 49.5084048396 88 | 58.1384982868 73.7567022368 59.0516319048 74.1156533681 89 | 66.8308084448 73.8965570812 71.9780561091 82.8536317777 90 | 41.836547671 26.0960551519 42.4845265532 24.5057754935 91 | 30.9737261557 16.3020143457 30.3456790514 16.2682633547 92 | 46.1230612646 33.8957370514 41.1668213592 25.729167193 93 | 54.9207537753 55.9941341333 49.9426346305 52.2716948372 94 | 42.4719345739 22.3160191432 44.9644863501 22.1872086659 95 | 31.7340349528 26.19768637 26.373668624 20.6777941288 96 | 59.6602530193 71.1389296118 59.7351278459 73.3419083703 97 | 72.1304361508 92.9543761661 64.9617222145 88.8782400035 98 | 62.3554065495 66.1313980943 64.8210465863 68.0382164226 99 | 29.8253702113 8.27481322951 35.3645105444 10.9472193759 100 | 52.8836434474 62.615927985 60.4077132408 72.4841458308 101 | 32.9291416157 5.29019601905 36.5491391031 8.20967528844 102 | 41.4051061082 33.1126892915 35.6051194399 31.4946993312 103 | 30.2919147933 1.15230198196 35.0363432907 5.03570481684 104 | 37.6451237763 33.9215574513 38.7775178566 28.3606348292 105 | 84.9970089944 97.7031353202 83.0185029285 92.741863979 106 | 56.6641126118 65.8990861195 63.9842995247 68.833868945 107 | 36.8220446576 13.4137085719 34.1981547589 12.275111337 108 | 35.2743113947 12.1660304542 37.4760023503 9.66236878702 109 | 32.2554489511 5.52690352418 36.3494739512 10.7070247734 110 | 36.9797980162 10.192580621 39.2723375859 7.39976821418 111 | 46.2729422339 58.3243666633 46.6969098075 56.0438056164 112 | 26.7819921561 9.57528865767 29.1646187186 16.7840088461 113 | 52.473844867 61.1470483244 47.9127737067 54.5698023947 114 | 50.0637803421 52.3931709543 45.4824544815 53.3366442242 115 | 71.0066775336 90.987444483 66.9676215516 86.2386481302 116 | 46.7681988706 58.0920989194 43.8869704996 57.1986090508 117 | 46.7603725394 52.9512114954 43.1694293102 54.1161277453 118 | 73.050688586 94.0099951388 64.1009321306 88.8947407213 119 | 55.4634109881 71.9748830508 59.6408587008 73.6158709307 120 | 54.8878396148 75.8558634406 53.6970623489 79.3123095571 121 | 46.7175056637 45.3708200727 47.9398878807 50.0060410973 122 | 58.3415929169 87.3669552245 63.8407706096 89.702790829 123 | 30.9406203488 3.68885464012 36.4220612141 7.68834687415 124 | 43.8773877152 16.5994443217 36.6201372163 10.387724563 125 | 41.0650238931 41.8974884321 34.4744559265 32.4207510166 126 | 47.3107900914 55.5072811034 49.0643633082 52.4747542989 127 | 39.9409186214 19.1434218125 34.1992764122 12.5228678511 128 | 41.89231521 23.7448047171 42.1192249761 24.9622382901 129 | 39.445846226 31.9007765222 38.7057897369 28.2735231633 130 | 55.7874243553 63.782837942 58.0006672129 74.8950434406 131 | 32.1116334639 23.9826531817 28.2206940517 18.3509584725 132 | 71.1990034259 97.4032423768 64.4982566115 93.1870322515 133 | 34.1148246116 12.7431090357 34.6273673627 12.1240943805 134 | 60.5866656947 43.6553175251 56.4940129018 44.834828781 135 | 23.7445872074 4.77513142137 33.8762471538 6.63183650855 136 | 44.7348333109 27.9494448323 41.689180565 24.607777977 137 | 47.1306506663 34.9064780376 41.2597662041 25.8856913939 138 | 45.2032821055 79.0635740404 54.3162121966 78.5865437913 139 | 55.2570171341 64.3341018649 58.3661119474 74.5029967652 140 | 45.9529981256 10.2682205525 40.8202472861 6.30691995856 141 | 43.2441938254 24.9094084083 45.1544603893 21.9388078827 142 | 31.2984223513 11.7268672901 33.7796284941 12.9367976004 143 | 59.4640995444 87.6827026135 61.8715099267 91.5789804652 144 | 50.9438026581 41.6379135031 53.1419405799 45.3335233244 145 | 31.7285047479 12.4903829997 30.9396671813 15.2593928553 146 | 32.6256151336 25.1914341709 23.6217983418 22.6232705414 147 | 33.0878368521 23.003823149 26.2020034342 20.8474598713 148 | 49.3641894359 33.0434167997 43.6433755824 23.2971844825 149 | 46.6737581663 48.60672486 49.694458514 48.7661755776 150 | 27.6611771454 9.76520419268 30.9778902072 16.076565687 151 | 71.3981547428 88.8044516258 68.132161143 86.0675247797 152 | 68.5627654306 92.6288756372 68.0408847674 85.3394571936 153 | 64.8599638706 87.8120083903 65.645503436 87.2918504334 154 | 38.213199827 16.1693927462 32.8297271662 12.2826527638 155 | 55.5827357129 54.9490853685 55.0539016141 47.5992683796 156 | 58.4113312297 62.3288413495 64.1976318969 68.5994512813 157 | 61.307161386 94.6561113313 63.114878862 89.9721224683 158 | 71.4795418906 90.7429785871 68.1117371151 85.0547042799 159 | 55.0822017157 44.4810055496 56.6346969092 41.6697734259 160 | 39.8934894552 27.3647048509 38.5517783761 28.5385311781 161 | 26.1719216477 2.59869995461 31.7157984207 5.75024114931 162 | 49.6977718332 58.4988236426 47.1255923939 54.2823884545 163 | 28.6623387823 15.8117338356 29.2961379299 16.995636383 164 | 43.1064912036 13.796717432 40.0916962169 6.62374049947 165 | 61.7024868621 75.1224890664 61.0990882885 71.7290159032 166 | 62.8337035607 69.5484952316 62.8541038017 70.0174747914 167 | 56.3679668225 58.9999823874 66.5038082378 66.6054813172 168 | 58.9971750437 66.3263796115 59.2702645614 71.451220034 169 | 66.5274944321 90.5201714686 66.1612742032 86.8117730528 170 | 29.9904583508 12.7489815693 33.2828248518 12.6955341432 171 | 48.7209699208 53.0936303982 46.1253077991 56.4945993755 172 | 40.7966378241 21.4984485901 42.886621415 24.2543164086 173 | 32.1245373531 9.16881861463 33.9021404949 12.1566055773 174 | 20.8972821989 1.55672804415 24.9826180956 1.42501885326 175 | 41.4522165411 37.0283675664 38.9023932674 28.21067596 176 | 41.8600084666 34.8624241638 34.496824593 32.8011871091 177 | 39.6850464928 15.4768603651 34.6663161324 10.939904154 178 | 38.6001587194 35.7542778685 36.7065432625 29.8584757815 179 | 60.5559326454 63.0295031488 66.6844639145 66.3458510045 180 | 47.5383959004 67.0709325903 39.8472940631 63.0831101669 181 | 55.8142239505 78.5678237321 55.0941567565 77.9901832982 182 | 36.9203337805 18.4859561884 30.4659640738 15.9591937158 183 | 60.1863753971 77.7969936739 56.4089386137 76.5555986682 184 | 30.2404507089 21.5324449299 27.1546213358 19.9899026727 185 | 55.6109417288 54.5998659039 49.5952065091 48.8744017207 186 | 56.2572985091 43.4290597477 55.5458187327 45.5101513894 187 | 20.4729279912 10.3966047821 26.7353376031 18.2035304507 188 | 81.7244622612 78.1146527464 78.6013887465 74.6858848252 189 | 66.4013968042 87.8193348633 66.4838645267 87.7185632764 190 | 30.3877997107 40.1062668898 28.1120710962 38.9813662933 191 | 54.6982538508 61.9810042509 61.7240923513 71.2439148942 192 | 44.9836952823 36.4807812355 41.7645933866 25.3691805647 193 | 44.8570382418 77.053538836 52.9308124522 79.8249061771 194 | 70.2253366178 90.149719683 66.4351189589 87.7209447856 195 | 67.7787268413 82.4259175945 69.0524097506 83.9176191042 196 | 46.2486979561 65.462639425 43.0183805425 59.5931729344 197 | 54.1886032466 76.7160391033 54.2889249188 78.6587620461 198 | 26.0473634346 4.55145937583 27.8589904323 12.0236587905 199 | 34.8166771041 35.6123450364 32.4956587099 34.4073441852 200 | 37.502923267 13.4293539656 37.4710362421 9.50572654509 201 | 36.2540009944 20.0517872923 32.4244054893 14.4566200432 202 | 61.0486173962 63.1521513872 64.7657992136 68.2613206424 203 | 59.4209035713 50.0020296033 55.6674244265 45.9138843376 204 | 28.9489153525 14.5935687693 30.4627297098 16.3162541694 205 | 51.7159471933 67.3200907603 55.1454432469 78.1747011309 206 | 35.1635061627 7.99358479475 35.796089457 10.5820759202 207 | 43.9946570646 41.9812706566 48.2714822998 48.9654753093 208 | 53.4743082986 52.8182627068 52.1431140185 48.7253194827 209 | 49.7734994064 51.020732155 51.5472046729 48.9112066399 210 | 63.0720533077 66.5303891622 62.3932801342 70.5487367136 211 | 52.5010444088 59.536338533 49.3238873867 53.2680744315 212 | 32.763764429 31.4973414962 32.0695524804 35.0866379214 213 | 59.9545110187 73.5826761062 60.5641361712 72.2254343613 214 | 40.9912807052 64.7623760379 41.8056895218 59.3661576595 215 | 44.703946573 18.925907375 42.5648135375 22.8071798115 216 | 41.4328780706 51.0319769334 47.2664897438 50.3662303248 217 | 77.471643704 91.8326445341 74.0728513578 86.0103251526 218 | 66.6007450964 71.9602230601 62.5354642138 70.5124191205 219 | 61.4140789033 75.3714524457 55.7409533387 77.1149926394 220 | 64.2987934542 86.5370841427 65.4965254183 87.4509352511 221 | 37.4395175003 15.7044434331 33.2002589803 13.7139556749 222 | 51.8551019915 63.3036003053 62.8917369438 69.9894072351 223 | 24.9335221421 3.46001244414 29.9903955019 4.13829337337 224 | 54.6821344267 63.9153228262 64.1636791355 68.6116065767 225 | -------------------------------------------------------------------------------- /R/datasaurus-package.R: -------------------------------------------------------------------------------- 1 | #' Box plot data 2 | #' 3 | #' This dataset is the box plot data produced by Matjeka & 4 | #' Fitzmaurice to demonstrate applicability of their process. 5 | #' 6 | #' @name box_plots 7 | #' @format A data frame with 2484 rows and 5 variables: 8 | #'* **left**: data pulled to the left 9 | #'* **lines**: data with arbitrary spikes along a range 10 | #'* **normal**: normally distributed data 11 | #'* **right**: data pulled to the right 12 | #'* **split**: split data 13 | #' @references Matejka, J., & Fitzmaurice, G. (2017). 14 | #' Same Stats, Different Graphs: Generating Datasets with 15 | #' Varied Appearance and Identical Statistics through Simulated 16 | #' Annealing. _CHI 2017 Conference proceedings: ACM SIGCHI 17 | #' Conference on Human Factors in Computing Systems._ 18 | #' Retrieved from [https://www.research.autodesk.com/publications/same-stats-different-graphs/](https://www.research.autodesk.com/publications/same-stats-different-graphs/). #nolint 19 | #' @example inst/examples/box_plots.R 20 | "box_plots" 21 | 22 | #' Box plot data 23 | #' 24 | #' This dataset is the box plot data produced by Matjeka & 25 | #' Fitzmaurice to demonstrate applicability of their process. 26 | #' 27 | #' @name box_plots_long 28 | #' @format A data frame with 12420 rows and 2 variables: 29 | #'* **Plot**: either the left, lines, normal, right or split boxplot 30 | #'* **Values**: the corresponding values from each dataset 31 | #' @references Matejka, J., & Fitzmaurice, G. (2017). 32 | #' Same Stats, Different Graphs: Generating Datasets with 33 | #' Varied Appearance and Identical Statistics through Simulated 34 | #' Annealing. _CHI 2017 Conference proceedings: ACM SIGCHI 35 | #' Conference on Human Factors in Computing Systems._ 36 | #' Retrieved from [https://www.research.autodesk.com/publications/same-stats-different-graphs/](https://www.research.autodesk.com/publications/same-stats-different-graphs/). #nolint 37 | #' @example inst/examples/box_plots.R 38 | "box_plots_long" 39 | 40 | #' Datasaurus Dozen (wide) data 41 | #' 42 | #' A dataset demonstrating the utility of visualization. 43 | #' These 12 datasets are equal in standard measures: mean, 44 | #' standard deviation, and Pearson's correlation. 45 | #' @name datasaurus_dozen_wide 46 | #' @format A data frame with 142 rows and 26 variables: 47 | #'* **away_x**: x-values for the `away` dataset 48 | #'* **away_y**: y-values for the `away` dataset 49 | #'* **bullseye_x**: x-values for the `bullseye` dataset 50 | #'* **bullseye_y**: y-values for the `bullseye` dataset 51 | #'* **circle_x**: x-values for the `circle` dataset 52 | #'* **circle_y**: y-values for the `circle` dataset 53 | #'* **dino_x**: x-values for `dinosaur` dataset! 54 | #'* **dino_y**: y-values for `dinosaur` dataset! 55 | #'* **dots_x**: x-values for the `dots` dataset 56 | #'* **dots_y**: y-values for the `dots` dataset 57 | #'* **h_lines_x**: x-values for the `h_lines` dataset 58 | #'* **h_lines_y**: y-values for the `h_lines` dataset 59 | #'* **high_lines_x**: x-values for the `high_lines` dataset 60 | #'* **high_lines_y**: y-values for the `high_lines` dataset 61 | #'* **slant_down_x**: x-values for the `slant_down` dataset 62 | #'* **slant_down_y**: y-values for the `slant_down` dataset 63 | #'* **slant_up_x**: x-values for the `slant_up` dataset 64 | #'* **slant_up_y**: y-values for the `slant_up` dataset 65 | #'* **star_x**: x-values for the `star` dataset 66 | #'* **star_y**: y-values for the `star` dataset 67 | #'* **v_lines_x**: x-values for the `v_lines` dataset 68 | #'* **v_lines_y**: y-values for the `v_lines` dataset 69 | #'* **wide_lines_x**: x-values for the `wide_lines` dataset 70 | #'* **wide_lines_y**: y-values for the `wide_lines` dataset 71 | #'* **x_shape_x**: x-values for the `x_shape` dataset 72 | #'* **x_shape_y**: y-values for the `x_shape` dataset 73 | #' @references Matejka, J., & Fitzmaurice, G. (2017). 74 | #' Same Stats, Different Graphs: Generating Datasets with 75 | #' Varied Appearance and Identical Statistics through Simulated 76 | #' Annealing. _CHI 2017 Conference proceedings: ACM SIGCHI 77 | #' Conference on Human Factors in Computing Systems._ 78 | #' Retrieved from [https://www.research.autodesk.com/publications/same-stats-different-graphs/](https://www.research.autodesk.com/publications/same-stats-different-graphs/). #nolint 79 | #' @example inst/examples/datasaurus_dozen_wide.R 80 | "datasaurus_dozen_wide" 81 | 82 | #' Datasaurus Dozen data 83 | #' 84 | #' A dataset demonstrating the utility of visualization. 85 | #' These 12 datasets are equal in standard measures: mean, 86 | #' standard deviation, and Pearson's correlation. 87 | #' @name datasaurus_dozen 88 | #' @format A data frame with 1846 rows and 3 variables: 89 | #'* **dataset**: indicates which dataset the data are from 90 | #'* **x**: x-values 91 | #'* **y**: y-values 92 | #' @references Matejka, J., & Fitzmaurice, G. (2017). 93 | #' Same Stats, Different Graphs: Generating Datasets with 94 | #' Varied Appearance and Identical Statistics through Simulated 95 | #' Annealing. _CHI 2017 Conference proceedings: ACM SIGCHI 96 | #' Conference on Human Factors in Computing Systems._ 97 | #' Retrieved from [https://www.research.autodesk.com/publications/same-stats-different-graphs/](https://www.research.autodesk.com/publications/same-stats-different-graphs/). #nolint 98 | #' @example inst/examples/datasaurus_dozen.R 99 | "datasaurus_dozen" 100 | 101 | #' Simpsons Paradox (wide) data 102 | #' 103 | #' A dataset demonstrating Simpson's Paradox with a 104 | #' strongly positively correlated dataset (`simpson_1`) 105 | #' and a dataset with the same positive correlation as `simpson_1`, 106 | #' but where individual groups have a 107 | #' strong negative correlation (`simpson_2`). 108 | #' @name simpsons_paradox_wide 109 | #' @format A data frame with 222 rows and 4 variables: 110 | #'* **simpson_1_x**: x-values from the `simpson_1` dataset 111 | #'* **simpson_1_y**: y-values from the `simpson_1` dataset 112 | #'* **simpson_2_x**: x-values from the `simpson_2` dataset 113 | #'* **simpson_2_y**: y-values from the `simpson_2` dataset 114 | #' @references Matejka, J., & Fitzmaurice, G. (2017). 115 | #' Same Stats, Different Graphs: Generating Datasets with 116 | #' Varied Appearance and Identical Statistics through Simulated 117 | #' Annealing. _CHI 2017 Conference proceedings: ACM SIGCHI 118 | #' Conference on Human Factors in Computing Systems._ 119 | #' Retrieved from [https://www.research.autodesk.com/publications/same-stats-different-graphs/](https://www.research.autodesk.com/publications/same-stats-different-graphs/).#nolint 120 | #' @example inst/examples/simpsons_paradox_wide.R 121 | "simpsons_paradox_wide" 122 | 123 | #' Simpsons Paradox data 124 | #' 125 | #' A dataset demonstrating Simpson's Paradox with a strongly positively 126 | #' correlated dataset (`simpson_1`) 127 | #' and a dataset with the same positive correlation as `simpson_1`, 128 | #' but where individual groups have a 129 | #' strong negative correlation (`simpson_2`). 130 | #' @format A data frame with 444 rows and 3 variables: 131 | #'* **dataset**: indicates which of the two datasets the data are from, `simpson_1` or `simpson_2` 132 | #'* **x**: x-values 133 | #'* **y**: y-values 134 | #' @name simpsons_paradox 135 | #' @references Matejka, J., & Fitzmaurice, G. (2017). 136 | #' Same Stats, Different Graphs: Generating Datasets with 137 | #' Varied Appearance and Identical Statistics through Simulated 138 | #' Annealing. _CHI 2017 Conference proceedings: ACM SIGCHI 139 | #' Conference on Human Factors in Computing Systems._ 140 | #' Retrieved from [https://www.research.autodesk.com/publications/same-stats-different-graphs/](https://www.research.autodesk.com/publications/same-stats-different-graphs/). #nolint 141 | #' @example inst/examples/simpsons_paradox.R 142 | "simpsons_paradox" 143 | 144 | #' Twelve From Slant Alternate (long) data 145 | #' 146 | #' A dataset demonstrating the utility of visualization. 147 | #' These 12 datasets are equal in non-parametric measures: median, 148 | #' interquartile range, and Spearman's rank correlation. 149 | #' @name twelve_from_slant_alternate_long 150 | #' @format A data frame with 2184 rows and 3 variables: 151 | #'* **dataset**: the dataset the data are from 152 | #'* **x**: x-values 153 | #'* **y**: y-values 154 | #' @references Matejka, J., & Fitzmaurice, G. (2017). 155 | #' Same Stats, Different Graphs: Generating Datasets with 156 | #' Varied Appearance and Identical Statistics through Simulated 157 | #' Annealing. _CHI 2017 Conference proceedings: ACM SIGCHI 158 | #' Conference on Human Factors in Computing Systems._ 159 | #' Retrieved from [https://www.research.autodesk.com/publications/same-stats-different-graphs/](https://www.research.autodesk.com/publications/same-stats-different-graphs/). #nolint 160 | #' @example inst/examples/twelve_from_slant_alternate_long.R 161 | "twelve_from_slant_alternate_long" 162 | 163 | #' Twelve From Slant Alternate (wide) data 164 | #' 165 | #' A dataset demonstrating the utility of visualization. 166 | #' These 12 datasets are equal in non-parametric measures: median, 167 | #' interquartile range, and Spearman's rank correlation. 168 | #' @name twelve_from_slant_alternate_wide 169 | #' @format A data frame with 182 rows and 24 variables: 170 | #'* **bullseye_x**: x-values for the `bullseye` dataset 171 | #'* **bullseye_y**: y-values for the `bullseye` dataset 172 | #'* **circle_x**: x-values for the `circle` dataset 173 | #'* **circle_y**: y-values for the `circle` dataset 174 | #'* **dots_x**: x-values for the `dots` dataset 175 | #'* **dots_y**: y-values for the `dots` dataset 176 | #'* **h_lines_x**: x-values for the `h_lines` dataset 177 | #'* **h_lines_y**: y-values for the `h_lines` dataset 178 | #'* **high_lines_x**: x-values for the `high_lines` dataset 179 | #'* **high_lines_y**: y-values for the `high_lines` dataset 180 | #'* **slant_x**: x-values for the `slant` dataset 181 | #'* **slant_y**: y-values for the `slant` dataset 182 | #'* **slant_down_x**: x-values for the `slant_down` dataset 183 | #'* **slant_down_y**: y-values for the `slant_down` dataset 184 | #'* **slant_up_x**: x-values for the `slant_up` dataset 185 | #'* **slant_up_y**: y-values for the `slant_up` dataset 186 | #'* **star_x**: x-values for the `star` dataset 187 | #'* **star_y**: y-values for the `star` dataset 188 | #'* **v_lines_x**: x-values for the `v_lines` dataset 189 | #'* **v_lines_y**: y-values for the `v_lines` dataset 190 | #'* **wide_lines_x**: x-values for the `wide_lines` dataset 191 | #'* **wide_lines_y**: y-values for the `wide_lines` dataset 192 | #'* **x_shape_x**: x-values for the `x_shape` dataset 193 | #'* **x_shape_y**: y-values for the `x_shape` dataset 194 | #' @references Matejka, J., & Fitzmaurice, G. (2017). 195 | #' Same Stats, Different Graphs: Generating Datasets with 196 | #' Varied Appearance and Identical Statistics through Simulated 197 | #' Annealing. _CHI 2017 Conference proceedings: ACM SIGCHI 198 | #' Conference on Human Factors in Computing Systems._ 199 | #' Retrieved from [https://www.research.autodesk.com/publications/same-stats-different-graphs/](https://www.research.autodesk.com/publications/same-stats-different-graphs/). #nolint 200 | #' @example inst/examples/twelve_from_slant_alternate_wide.R 201 | "twelve_from_slant_alternate_wide" 202 | 203 | #' Twelve From Slant (long) data 204 | #' 205 | #' A dataset demonstrating the utility of visualization. 206 | #' These 12 datasets are equal in standard measures: mean, 207 | #' standard deviation, and Pearson's correlation. 208 | #' @name twelve_from_slant_long 209 | #' @format A data frame with 2184 rows and 3 variables: 210 | #'* **dataset**: the dataset the data are from 211 | #'* **x**: x-values 212 | #'* **y**: y-values 213 | #' @references Matejka, J., & Fitzmaurice, G. (2017). 214 | #' Same Stats, Different Graphs: Generating Datasets with 215 | #' Varied Appearance and Identical Statistics through Simulated 216 | #' Annealing. _CHI 2017 Conference proceedings: ACM SIGCHI 217 | #' Conference on Human Factors in Computing Systems._ 218 | #' Retrieved from [https://www.research.autodesk.com/publications/same-stats-different-graphs/](https://www.research.autodesk.com/publications/same-stats-different-graphs/). #nolint 219 | #' @example inst/examples/twelve_from_slant_long.R 220 | "twelve_from_slant_long" 221 | 222 | #' Twelve From Slant (wide) data 223 | #' 224 | #' A dataset demonstrating the utility of visualization. 225 | #' These 12 datasets are equal in standard measures: mean, 226 | #' standard deviation, and Pearson's correlation. 227 | #' @name twelve_from_slant_wide 228 | #' @format A data frame with 182 rows and 24 variables: 229 | #'* **bullseye_x**: x-values for the `bullseye` dataset 230 | #'* **bullseye_y**: y-values for the `bullseye` dataset 231 | #'* **circle_x**: x-values for the `circle` dataset 232 | #'* **circle_y**: y-values for the `circle` dataset 233 | #'* **dots_x**: x-values for the `dots` dataset 234 | #'* **dots_y**: y-values for the `dots` dataset 235 | #'* **h_lines_x**: x-values for the `h_lines` dataset 236 | #'* **h_lines_y**: y-values for the `h_lines` dataset 237 | #'* **high_lines_x**: x-values for the `high_lines` dataset 238 | #'* **high_lines_y**: y-values for the `high_lines` dataset 239 | #'* **slant_x**: x-values for the `slant` dataset 240 | #'* **slant_y**: y-values for the `slant` dataset 241 | #'* **slant_down_x**: x-values for the `slant_down` dataset 242 | #'* **slant_down_y**: y-values for the `slant_down` dataset 243 | #'* **slant_up_x**: x-values for the `slant_up` dataset 244 | #'* **slant_up_y**: y-values for the `slant_up` dataset 245 | #'* **star_x**: x-values for the `star` dataset 246 | #'* **star_y**: y-values for the `star` dataset 247 | #'* **v_lines_x**: x-values for the `v_lines` dataset 248 | #'* **v_lines_y**: y-values for the `v_lines` dataset 249 | #'* **wide_lines_x**: x-values for the `wide_lines` dataset 250 | #'* **wide_lines_y**: y-values for the `wide_lines` dataset 251 | #'* **x_shape_x**: x-values for the `x_shape` dataset 252 | #'* **x_shape_y**: y-values for the `x_shape` dataset 253 | #' @references Matejka, J., & Fitzmaurice, G. (2017). 254 | #' Same Stats, Different Graphs: Generating Datasets with 255 | #' Varied Appearance and Identical Statistics through Simulated 256 | #' Annealing. _CHI 2017 Conference proceedings: ACM SIGCHI 257 | #' Conference on Human Factors in Computing Systems._ 258 | #' Retrieved from [https://www.research.autodesk.com/publications/same-stats-different-graphs/](https://www.research.autodesk.com/publications/same-stats-different-graphs/). #nolint 259 | #' @example inst/examples/twelve_from_slant_wide.R 260 | "twelve_from_slant_wide" 261 | -------------------------------------------------------------------------------- /inst/extdata/SimpsonsParadox-Long.tsv: -------------------------------------------------------------------------------- 1 | dataset x y 2 | simpson_1 55.7909718157 69.9365934283 3 | simpson_1 47.2282091405 14.7561070913 4 | simpson_1 57.2468738451 51.4617618588 5 | simpson_1 70.8319847999 65.5199077407 6 | simpson_1 73.3670855496 91.5959640491 7 | simpson_1 69.9744916206 94.9394275208 8 | simpson_1 40.1049144627 9.58672855908 9 | simpson_1 46.306867762 63.0790021629 10 | simpson_1 63.5118905297 91.3188400408 11 | simpson_1 61.3784207464 51.9097664689 12 | simpson_1 24.4368259537 4.28624078996 13 | simpson_1 28.4162104994 5.46796845638 14 | simpson_1 41.6293918156 52.1743334507 15 | simpson_1 40.9624624616 7.50677488138 16 | simpson_1 50.4048080182 40.9862915909 17 | simpson_1 28.064915354 20.6178017531 18 | simpson_1 78.3457430124 91.1442756985 19 | simpson_1 48.576498562 23.373489831 20 | simpson_1 68.1415063219 57.0886856283 21 | simpson_1 35.1771191716 21.2889005583 22 | simpson_1 60.277198436 57.723872583 23 | simpson_1 69.0733755677 77.2059289324 24 | simpson_1 54.9438297151 32.941989407 25 | simpson_1 41.8482124647 44.5883095101 26 | simpson_1 29.441699163 5.73279918759 27 | simpson_1 34.3192687687 21.9333645175 28 | simpson_1 54.6812364909 95.4787598334 29 | simpson_1 50.830247166 32.553562636 30 | simpson_1 36.4001855327 12.9287725207 31 | simpson_1 40.0448949588 34.973472985 32 | simpson_1 67.6124652252 49.3027674007 33 | simpson_1 45.9721509763 23.6637085192 34 | simpson_1 57.8424149318 58.6383610501 35 | simpson_1 51.6168124814 64.531431227 36 | simpson_1 38.3860154463 29.6439075432 37 | simpson_1 82.46724168 95.6593486437 38 | simpson_1 22.8861602963 1.0 39 | simpson_1 79.4263516761 99.0 40 | simpson_1 55.807633571 79.0912662501 41 | simpson_1 49.6447592807 31.6975833729 42 | simpson_1 28.7071929512 19.1777034365 43 | simpson_1 45.6904410065 56.6153334155 44 | simpson_1 49.9355452399 38.8000604975 45 | simpson_1 57.6524875514 67.6529109488 46 | simpson_1 61.8960294968 46.3373451262 47 | simpson_1 48.4714332864 44.8087250029 48 | simpson_1 64.8693862824 92.7846809099 49 | simpson_1 48.5282435828 38.7781932107 50 | simpson_1 38.9816975616 56.6971386306 51 | simpson_1 22.1525466166 1.87126319037 52 | simpson_1 38.8887808577 32.8430633079 53 | simpson_1 55.0247203448 68.0577628337 54 | simpson_1 62.6428692695 84.1675315407 55 | simpson_1 54.987996807 81.897232613 56 | simpson_1 67.0054190547 93.1570846804 57 | simpson_1 50.3530481629 44.5909244275 58 | simpson_1 46.4315061577 35.5709442115 59 | simpson_1 41.4966700091 33.2331654025 60 | simpson_1 45.571870558 48.93801295 61 | simpson_1 59.5518487262 43.0408745162 62 | simpson_1 50.3666439178 48.9957895476 63 | simpson_1 59.5083922835 67.2231679587 64 | simpson_1 48.7454943377 66.8406774566 65 | simpson_1 50.5354363801 18.1964864096 66 | simpson_1 29.3169060269 6.35268465503 67 | simpson_1 62.8086406129 93.993079221 68 | simpson_1 85.057792881 79.4131993797 69 | simpson_1 34.2946169945 7.06898620271 70 | simpson_1 33.3845809961 11.7978607101 71 | simpson_1 72.3038815182 94.7885696826 72 | simpson_1 38.9496288838 9.81915009164 73 | simpson_1 71.0085263017 81.0677514043 74 | simpson_1 31.2028861704 1.0 75 | simpson_1 44.658260858 19.3585031044 76 | simpson_1 35.6248907864 26.4140627705 77 | simpson_1 59.341379739 63.8125493473 78 | simpson_1 60.2196498836 79.8285218722 79 | simpson_1 58.7258632446 74.3630687968 80 | simpson_1 42.3845648942 55.5576739181 81 | simpson_1 64.9353102974 80.3475669854 82 | simpson_1 49.5564587021 50.6937867532 83 | simpson_1 72.6187112418 81.4728024583 84 | simpson_1 33.4052196482 22.9983279399 85 | simpson_1 48.6874414786 38.553136122 86 | simpson_1 43.3767323014 49.8525614091 87 | simpson_1 58.1384982868 73.7567022368 88 | simpson_1 66.8308084448 73.8965570812 89 | simpson_1 41.836547671 26.0960551519 90 | simpson_1 30.9737261557 16.3020143457 91 | simpson_1 46.1230612646 33.8957370514 92 | simpson_1 54.9207537753 55.9941341333 93 | simpson_1 42.4719345739 22.3160191432 94 | simpson_1 31.7340349528 26.19768637 95 | simpson_1 59.6602530193 71.1389296118 96 | simpson_1 72.1304361508 92.9543761661 97 | simpson_1 62.3554065495 66.1313980943 98 | simpson_1 29.8253702113 8.27481322951 99 | simpson_1 52.8836434474 62.615927985 100 | simpson_1 32.9291416157 5.29019601905 101 | simpson_1 41.4051061082 33.1126892915 102 | simpson_1 30.2919147933 1.15230198196 103 | simpson_1 37.6451237763 33.9215574513 104 | simpson_1 84.9970089944 97.7031353202 105 | simpson_1 56.6641126118 65.8990861195 106 | simpson_1 36.8220446576 13.4137085719 107 | simpson_1 35.2743113947 12.1660304542 108 | simpson_1 32.2554489511 5.52690352418 109 | simpson_1 36.9797980162 10.192580621 110 | simpson_1 46.2729422339 58.3243666633 111 | simpson_1 26.7819921561 9.57528865767 112 | simpson_1 52.473844867 61.1470483244 113 | simpson_1 50.0637803421 52.3931709543 114 | simpson_1 71.0066775336 90.987444483 115 | simpson_1 46.7681988706 58.0920989194 116 | simpson_1 46.7603725394 52.9512114954 117 | simpson_1 73.050688586 94.0099951388 118 | simpson_1 55.4634109881 71.9748830508 119 | simpson_1 54.8878396148 75.8558634406 120 | simpson_1 46.7175056637 45.3708200727 121 | simpson_1 58.3415929169 87.3669552245 122 | simpson_1 30.9406203488 3.68885464012 123 | simpson_1 43.8773877152 16.5994443217 124 | simpson_1 41.0650238931 41.8974884321 125 | simpson_1 47.3107900914 55.5072811034 126 | simpson_1 39.9409186214 19.1434218125 127 | simpson_1 41.89231521 23.7448047171 128 | simpson_1 39.445846226 31.9007765222 129 | simpson_1 55.7874243553 63.782837942 130 | simpson_1 32.1116334639 23.9826531817 131 | simpson_1 71.1990034259 97.4032423768 132 | simpson_1 34.1148246116 12.7431090357 133 | simpson_1 60.5866656947 43.6553175251 134 | simpson_1 23.7445872074 4.77513142137 135 | simpson_1 44.7348333109 27.9494448323 136 | simpson_1 47.1306506663 34.9064780376 137 | simpson_1 45.2032821055 79.0635740404 138 | simpson_1 55.2570171341 64.3341018649 139 | simpson_1 45.9529981256 10.2682205525 140 | simpson_1 43.2441938254 24.9094084083 141 | simpson_1 31.2984223513 11.7268672901 142 | simpson_1 59.4640995444 87.6827026135 143 | simpson_1 50.9438026581 41.6379135031 144 | simpson_1 31.7285047479 12.4903829997 145 | simpson_1 32.6256151336 25.1914341709 146 | simpson_1 33.0878368521 23.003823149 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18.7089683725 35.4695229971 43.1158849517 26.3207111933 62.3962811186 29.4872 51.0256 27.8782642594 50.70890793 27.6340383923 50.5036668574 32.2151786343 65.6009580191 20.9319996761 51.6462354868 28.0896176553 60.512212549 27.186463838 63.5587296478 30.500998079 48.0696996767 32.628463049 54.0637204929 47.552160202 66.4604037217 19 | 40.5701970446 79.3729634752 36.8943276507 47.7065559687 26.1633874648 62.0268148985 32.8205 51.0256 27.3388689181 51.6530467544 32.0371080007 50.5000661229 29.4368412489 67.5545281141 35.4355325251 34.9610381566 31.1357349152 61.4319127116 29.2851466002 63.3836058254 30.4944213623 45.7734755346 32.4386500903 49.9030194916 48.6649246215 64.9610049438 20 | 74.7411813341 21.1016372041 39.0555497751 55.5469737131 25.2137904611 60.2733986018 35.3846 51.4103 27.6784029741 51.1819891691 29.3365106528 50.461893969 40.0500951983 71.8890335708 44.3773828236 57.6847369215 37.700837521 68.4184052804 39.4029453026 51.1508571967 30.4995445502 49.3820997506 35.8382920833 53.3514883396 46.280631958 68.0284231525 21 | 71.7683189223 20.0110618423 46.9570801494 65.2404073935 26.8595295604 63.1418346727 40.2564 51.4103 52.6356576791 51.4185522584 40.816953133 50.4877601957 37.5747950188 69.9887444412 46.313692406 55.6855917703 43.3926884016 43.5975940692 28.8113284396 61.3578540576 50.144392979 47.0142836417 37.1420429198 49.7419879519 50.5632444849 62.6238279659 22 | 76.1669198143 75.9361704048 37.3104527352 45.2587508999 31.7606691716 69.7976553936 44.1026 52.9487 52.0252141055 52.1230110467 42.7390751173 50.483155992 49.3104686263 67.1687603206 46.3860739231 51.0021645381 51.2623974761 49.6665051258 34.3039579069 56.5421259093 49.9128985877 55.5878389121 34.6445467746 54.4441077764 52.6309642318 59.9639357915 23 | 65.6236574431 15.5828033531 40.0096720019 60.9865825077 39.7932905744 75.8079642168 46.6667 54.1026 52.8811647904 50.6215547554 51.8678937691 50.4932732621 47.3480185524 72.5916115408 42.4954400876 56.9036815315 43.2867129203 43.0568603208 29.6027609801 60.157346722 50.3374274859 57.9553142818 35.675404696 49.6256433471 54.6362064446 57.4938424367 24 | 50.8506336394 13.9876016304 48.0143866846 65.7128195934 45.6174391854 78.1199702277 50.0 55.2564 52.9526073133 50.0747390118 43.3710018924 50.5008373234 50.157414434 71.1617258246 44.3005150328 58.1329592781 55.9059162338 54.2526725342 49.1161568603 63.6600006211 50.4189791992 60.5249117346 64.99458547 49.4621728312 51.346027139 61.6005530364 25 | 33.0240700249 24.4678303872 53.7037759268 66.3894820363 53.7714606827 79.3029316435 53.0769 55.641 52.5205524936 51.5024421035 53.0427983006 50.4875038691 58.2100082697 68.0445380719 46.9882833458 55.4206367313 50.198958949 52.2250295948 39.6175458263 62.9251879574 50.2052589714 57.9242171676 66.1291498342 61.6437178853 57.134855354 53.8779658045 26 | 39.7063261674 84.2752871038 63.0674998921 64.0350004581 56.4514336733 79.2471070208 56.6667 56.0256 52.3428220582 51.8619520908 56.6189102967 50.5033740178 56.6089076561 70.4089460888 52.4215231623 47.867005488 55.761587372 54.0305172396 43.23308466 63.1652187223 50.4353151633 56.4393224734 65.790651882 55.9453737171 54.1712415737 58.0598078952 27 | 45.5964849542 9.76334884943 62.0480325076 63.8458632494 66.0937201989 77.0178182606 59.2308 57.9487 51.9275902141 52.2577906065 70.568289819 50.4962361243 52.8338205303 72.5518862725 64.090998189 68.6509850107 58.3231396505 60.0310591646 64.8927879422 65.8141767574 69.5057889042 59.2849248279 67.5017806138 60.8502274659 50.9423823071 62.0973939351 28 | 42.9680469104 17.9454583961 59.8399667124 64.4767644437 56.9258394194 79.2063186226 61.2821 62.1795 52.7137744894 51.1979443188 67.870887292 70.0680261345 61.8185854576 76.1128240228 63.0168717563 68.2252589114 70.171457528 70.2822212385 62.4901493154 74.5842896072 69.5340709833 64.8929773346 67.6295142176 61.4291060701 66.512924456 59.0712818478 29 | 52.4944067819 16.0511142003 55.1609418175 65.2373081694 58.9882538467 78.9172589554 61.5385 66.4103 50.4438027888 82.9418288173 58.8215465891 70.4557817483 61.7276334143 77.0692384058 67.3025779226 64.4134627173 60.600276471 59.7533088802 68.9880844294 63.232147305 69.5101069302 61.3296244282 63.9100640599 62.9723349305 64.3979095023 56.1748754008 30 | 46.0822757831 23.1104578154 61.2797865792 65.766402504 57.9022660533 79.0808678187 61.7949 69.1026 50.2166950326 83.7523429676 60.1869278445 70.4835512458 62.1922520815 76.1568192103 67.2259568904 62.4764105301 63.7602750188 61.1856411696 62.1056186306 75.9908707599 69.5263228876 56.9952791254 66.5493579687 67.7255886599 68.686593294 62.2373439902 31 | 74.2477082092 20.314187812 60.8349175348 64.6037697063 64.8128669568 77.4869389235 57.4359 55.2564 52.1841801076 51.9752506714 59.4355174849 50.4206326073 58.1187658587 73.1189614684 50.309988715 47.2678818553 54.3894952619 53.811901225 32.4618467439 62.8819029189 49.9472399619 48.2295767261 64.0515417112 48.8200939835 65.0277937057 57.0962573525 32 | 64.5682641863 83.6396338956 61.5205906458 64.7918550418 60.3497935166 78.7294458673 54.8718 49.8718 52.7920973459 51.0733956509 49.5858434795 50.4349813825 58.2704123843 35.5968950425 45.4126389249 53.8707976895 55.2412969947 52.5935746267 41.3272006535 49.0702512739 50.0631041291 58.085037556 68.1932895833 46.5435408096 53.3045031712 40.883844549 33 | 74.0216939058 76.1282745076 36.916543863 41.0952474383 48.890555756 16.8065435154 52.5641 46.0256 52.5897198565 51.338090196 41.7797417848 50.4706422808 59.7665303129 32.6212516009 54.0206071815 47.2698972057 49.0701267502 48.8969446811 44.0071499323 46.4496737846 50.489902373 39.2054119682 65.2783891952 44.8203529292 52.9451780881 40.3846256942 34 | 62.3911805626 5.62307076073 38.5021996714 41.5671556174 47.549506348 17.0714045245 48.2051 38.3333 52.0288486671 52.1768375002 46.1276863394 30.4494107792 52.5355347207 27.1741229419 49.8420222483 50.5765522936 46.0181190216 46.2577233718 44.0740606888 34.5532038906 49.6888638379 42.018514414 34.9976613883 28.6329939351 45.225738199 30.5552259226 35 | 74.189036683 68.1335832223 48.6643707344 30.6806668519 30.8894308257 26.3290372348 49.4872 42.1795 52.729246581 51.2017650475 38.0337837705 50.4908995189 50.8925215809 29.1452839382 50.7132995657 48.7722867825 44.7595874356 41.9746066134 44.0013152386 33.9042073544 50.3334811097 43.588671479 33.9237661692 53.5586885227 50.8433046202 37.7446280981 36 | 28.2367819396 56.1395964513 50.2852524042 30.3379221051 28.972835724 29.0504689185 51.0256 44.1026 52.8843132864 50.4414354501 44.5468412844 50.4159133758 49.9675511775 28.4602033343 55.0455549106 45.9862727459 51.6006241332 50.0324668853 45.0063004454 38.2990195507 50.4467514196 57.2305889423 66.0044543688 52.6846691452 49.6592939398 36.273442735 37 | 75.7719387944 69.8292300322 42.276332666 34.5276361236 35.1613607523 22.7868496473 45.3846 36.4103 52.5093008862 51.4162051508 46.2381175975 30.4956129408 51.3478832298 27.3642602259 37.5437012597 32.8840244657 36.4658820182 35.5241876075 44.4438406096 36.0190833012 50.4209091963 43.3479568967 37.0232562464 32.1184816794 47.1427889348 32.0250641734 38 | 75.8552294691 62.5170442862 54.0317756204 29.6723483149 45.8703691685 17.4893933655 42.8205 32.5641 50.8626843321 17.1456310914 49.7397453317 30.4727248868 37.5551162883 24.5376638386 39.7958978066 28.4432191156 33.3947987327 35.0168013145 42.1787133986 26.492119482 50.3740520508 32.9182406167 34.6982700852 36.4753541349 41.2700565259 21.7218171465 39 | 65.9708570175 72.7448559954 37.3293552552 39.6020423074 32.8314206076 24.7975381459 38.7179 31.4103 50.8914922458 17.1413237277 39.1102518936 30.4786951789 39.1995941778 23.6804956588 28.5233910215 39.7966662481 37.5800715749 38.8315722007 44.0445656189 35.6622382764 30.4768299051 39.4246055913 34.8634441715 30.9392745218 47.1301271555 31.3099029222 40 | 21.7780404779 6.61662530728 41.389522551 37.2960562328 39.4855747899 20.0330500489 35.1282 30.2564 25.8551275976 17.0819086886 39.1428377913 30.7928559394 36.3827221955 25.2380052325 31.3996460012 39.2652632229 36.619722059 36.8114141094 41.6404540171 27.0930954213 30.5034743497 38.3485561381 31.0900650789 32.3837547196 40.9159715783 23.5415923929 41 | 67.7597962473 72.4212015285 40.0746666572 34.6236852027 33.4461829314 24.1906434075 32.5641 32.1795 26.0256445518 16.9250135319 34.6254702671 30.4997410297 41.1006943822 26.5526132161 39.2415652162 33.3614166846 36.1634664356 33.819314199 41.9383300069 24.9915229793 30.5048349283 28.7714879939 34.9213061465 35.4184380529 38.6601639737 21.6895496369 42 | 78.6171953363 52.5752573142 35.3496806211 47.1410731332 21.8635812756 46.0525933457 30.0 36.7949 27.8931727187 50.6619634101 34.7542074707 30.4958650002 28.2178118248 28.2802924847 27.7140565712 42.2500516253 33.1677570349 31.2477411672 44.0539275103 33.5563924949 30.4622184647 32.7504720275 30.343361183 32.5245631742 42.6125508262 24.195053774 43 | 68.5077081898 15.4569189652 34.7637004189 47.6247999245 25.1658708449 35.7666139539 33.5897 41.4103 27.6399679354 51.3990974838 36.7537002386 50.5293043035 33.7970720433 28.6997507433 32.9607035213 35.8231112903 42.6449829318 40.1543216386 39.2067193308 51.5337156971 30.491302622 43.5613740753 35.4833288685 44.4916349303 41.2090395008 24.1287091891 44 | 74.8850211598 25.4166063231 37.0266294472 44.4622930512 22.9594789762 51.2877067934 36.6667 45.641 27.892658895 50.7952815163 37.0193512929 50.4926666972 43.9193873055 27.7978445882 35.4047362397 31.9391621952 47.2250252695 44.5756466185 28.7044492315 61.7775254006 30.449653838 44.9951230008 33.2349104271 38.502483721 50.6492211499 37.4837907435 45 | 66.4549036599 19.8366286542 36.4555695327 40.791843032 25.5860552415 60.8465833585 38.2051 49.1026 52.7977329399 50.6860370901 41.4171152753 50.4697734181 44.4164011084 28.9476923345 51.6422264225 50.8383406788 48.3923630032 50.3315285645 31.7086628996 58.837754374 30.4859942157 47.3479464653 35.4399436182 47.7975176939 52.1319019406 39.3646020815 46 | 77.3178020985 48.3983464352 35.5376642131 48.7293868661 26.3143100672 33.2971681038 29.7436 36.0256 27.5806388053 51.5247612607 22.8171074784 30.4360403387 28.0376027706 26.8038243613 34.207620843 33.5938054385 27.2451932001 29.8195074488 42.8117114739 30.0204484197 30.5020767935 36.4412129198 33.8735095227 37.056028453 39.7972712705 16.083549649 47 | 58.9124603193 75.6677562173 20.4089478892 32.2030304205 33.037301251 24.5644624921 29.7436 32.1795 26.4913985349 17.4053909738 34.800701463 30.4979313851 31.6895761296 26.6664002089 36.5234892198 35.6710483891 30.4043143388 29.0795399241 43.300614891 31.5264261979 30.4780588886 28.020580489 28.5641377966 34.6263997171 43.5498814807 24.3072109395 48 | 57.617447817 8.19480060319 23.4957104659 25.3224681542 36.4688894772 21.8761144725 30.0 29.1026 25.9853178159 17.203722132 27.8918897953 30.4849702595 27.2796128523 19.2572809685 28.4192021281 21.3157482481 30.0807992865 27.5880255173 40.3986329069 16.3470083822 30.5011608787 38.3141385582 31.3195486706 31.6639905316 36.7146633371 17.3818098572 49 | 76.0882257967 59.6799300235 29.5575433634 21.3647774591 26.5135531009 32.9187498178 32.0513 26.7949 26.2014192753 17.0938239128 32.8706391587 30.5023417488 31.2218783581 26.4408557812 26.1604969417 23.0422314726 33.9806339675 35.0330581489 40.4356915763 20.2326706762 30.5047730174 26.4166256707 30.796174379 23.5190343602 37.2246101278 17.9800644828 50 | 57.4660505497 1.50441817488 33.0082339121 15.9850714584 36.0270912125 22.1954385395 35.8974 25.2564 25.8575694746 17.113842659 37.7940494081 30.4926383364 33.5458051034 20.0807081014 40.435327397 27.6400711421 34.2506117649 34.4415865654 40.9365466658 16.9130048364 30.509115326 22.8255471196 34.2333554347 22.4959116015 40.5124478537 22.9622145725 51 | 79.4283834934 45.2107942872 53.9803991821 29.3509867092 42.6810404129 18.5425361977 41.0256 25.2564 50.7046843629 17.0237445436 35.6673163042 30.522035054 39.7363306065 23.4948069772 44.544955314 27.3622426221 51.1411835714 20.6183237784 39.6615736653 15.609355577 50.1156321728 16.6940065479 36.6307877065 31.4768312621 40.2220627884 20.6146217044 52 | 76.3565221496 10.4182411281 52.2343086004 29.7116729865 45.0410771273 17.7345177303 44.1026 25.641 50.8119753465 17.1149252621 48.4422995026 30.5017227753 42.8418426525 20.3339004396 40.1237272008 30.0731619402 49.899592066 19.5640835356 40.899259175 20.7985289466 50.4483013392 21.6402564599 34.5486191018 26.1110878654 44.1249612057 23.3662452858 53 | 64.4050752632 78.5841760758 59.5030766063 30.6696739448 47.3524164557 17.1347454264 47.1795 28.718 50.5648455577 17.0777773216 58.6693732631 30.5068378948 47.0937298879 26.9934706312 42.1848064503 28.5736747331 55.5898773424 26.2992451143 41.9686168283 26.4970725985 50.17317743 29.177864393 37.5196356 29.2676058936 42.3462616039 24.5583269437 54 | 40.6350418091 73.3947503698 41.1637810689 34.3157582514 53.8461940322 16.3873180961 49.4872 31.4103 50.9393039127 16.981021883 47.9169554375 30.4867257545 51.4536583539 26.0229417521 40.0026592892 27.4875100027 55.4820563581 22.8274662445 40.3834058238 21.3912255247 49.9227419194 38.4404729124 62.4256973267 34.0442062707 44.1890843708 28.772625473 55 | 43.9498645857 75.9587156671 48.9930401201 32.0303588363 55.1296393835 16.3532468711 51.5385 34.8718 50.4588548392 17.0385789686 51.8941663939 30.494755684 49.3677068167 22.005580703 57.1252310509 42.7390191388 63.5729393821 32.9386176053 56.5381264538 32.4442454697 50.3112993746 26.8604882737 64.320383794 31.7856294846 45.8142453101 31.056979672 56 | 30.9962205791 71.694404938 59.2692803248 29.6407017654 52.1881199617 16.4182174652 53.5897 37.5641 52.9013640729 50.6905627231 51.7490891643 30.4729933399 48.2534809736 29.2708555331 60.375744968 41.3564579961 59.0250770848 28.3220858801 52.9706912828 29.0401966941 50.3994096114 38.9638389234 65.4455325117 43.2734991936 46.6431192691 31.4072190618 57 | 68.2307689907 80.8725016628 45.4691770035 33.8311927323 58.6726630845 16.6633724322 55.1282 40.641 52.6849534438 51.2944692191 58.2465530013 50.4428744949 61.0029597643 30.0153144324 55.1058961252 46.1490510561 62.4237785929 33.4865952868 54.6209525885 30.3445244537 50.3373626448 40.913386144 64.3256315887 41.6159763773 52.1898883047 39.3677964938 58 | 72.0463894612 12.9180067349 62.6912665406 30.2903738312 82.1443116037 33.1952218669 56.6667 42.1795 52.500088943 51.594356165 57.3106968674 50.4825132895 60.8065608049 27.8683134861 61.7372710245 38.5848440052 47.849907703 48.556624645 65.0990443941 27.2415575588 50.2023180965 47.2129480045 65.264338195 35.7175447772 51.1003173587 38.095699984 59 | 46.5927679682 84.9723827774 73.4286708654 48.5778551303 75.6865711383 24.7415103908 59.2308 44.4872 51.8356372562 52.3357655339 67.9680481127 50.4521589097 59.0674270249 26.7882840217 55.7412443246 46.6535769763 52.2685616825 52.8052401961 63.0559909076 29.7090956748 50.2749326091 49.6234993439 66.388704175 38.8447443366 65.4008341477 42.6140320392 60 | 49.2572183396 81.8814032306 70.846426107 52.282253331 85.344964503 52.886419866 62.3077 46.0256 76.9954121013 52.0455286534 65.9493192681 50.494240794 61.4146337175 31.0393750583 58.2964084158 41.5831977346 68.8510215758 39.573434273 70.9601362323 41.2595012867 69.4943803061 52.6124107059 68.1914228192 45.1694864796 64.8520398267 43.1337348404 61 | 42.7817612539 12.9911884302 71.5390198495 45.5218061588 83.8446535127 58.6160602305 64.8718 46.7949 77.310600475 51.7467300374 55.7470928102 50.4789748003 68.9343694692 31.6198930866 57.0425482213 42.418757739 65.6763960251 35.1093154301 69.8958192404 43.4537592655 69.5193397556 47.384533707 68.5406624515 48.4385263389 63.4038060118 44.847493688 62 | 65.475952195 14.2745856444 67.6208658884 38.0065584658 85.66476066 45.5427527692 67.9487 47.9487 77.9260443352 50.3186604237 68.4030100436 50.4986272318 63.0652438592 34.2357465852 76.7756112073 55.2545987009 77.7468851148 47.3718241139 70.5958928563 41.9647438672 69.5291826079 57.8412666804 67.6614312657 51.0685845873 65.3715488709 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69.5510914402 58.4010982686 69.048716493 66.3660457477 72.6546231621 66.8782776113 66 | 31.0052582572 40.682503007 67.7894961571 61.3637015467 57.1446946889 79.1941025922 69.4872 69.4872 75.2280531954 82.9856620201 66.7632573944 70.4738617113 70.8104148392 79.3504618327 64.5963189058 65.952512925 71.5450389858 72.657716331 63.8689598271 70.2137388333 69.5066843151 68.0844667893 67.3082233775 71.5902965807 76.8885781632 72.1654720777 67 | 80.4708943189 49.5021483467 41.6095572691 83.8486865559 46.9624619942 78.6665589963 46.9231 79.8718 50.6583547714 83.1144793358 51.6803004439 89.9970275485 45.8019347617 81.6582677456 46.9095386323 85.6022611586 49.0409566406 77.3625832678 56.5944213157 86.9270062202 50.4441175504 75.0132995059 38.714899408 76.3969778172 43.7446196844 71.2679610751 68 | 71.9641671122 41.8742826668 53.0030253202 84.6747986012 50.5649338128 79.0772985529 48.2051 84.1026 50.2033658114 82.9826568621 53.0965573571 89.540243712 45.835020171 82.7425919919 46.9051031994 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57.997804739 92.1599086113 69.5250693831 93.5980502089 65.3096314498 98.1651568396 76.1056290282 95.3049805754 83 | 55.138737967 30.7257603575 50.4246786898 85.1747475891 56.265834263 79.2529394854 64.8718 97.5641 75.8709636929 88.3864417377 64.7747992444 90.132316736 63.6093798555 83.3600288362 61.5633243835 89.8641388605 65.1507052757 92.5026337368 57.5494740761 90.7489065581 69.5202566002 97.1938760167 65.7140760682 99.2837639519 79.2025626772 92.4072156264 84 | 43.6843934333 32.8230098696 70.6449962629 82.4309187567 57.0758924193 79.1939225382 68.4615 94.1026 75.2443262052 83.9034973788 70.8444476004 89.9242090159 70.7237162322 87.1522075165 61.0865603919 89.2243556475 65.5834276962 93.5170609365 59.5299284566 88.3272741451 69.4989700668 92.1204899292 67.5934531877 90.9519098246 84.8482440854 95.4248045304 85 | 35.9036097344 91.1118630801 63.1490490834 83.9468553485 54.2564023808 79.3138648504 70.7692 91.0256 75.1982952889 83.4623033365 70.6059455116 90.042061963 75.0063926418 82.4258351563 59.2364904011 91.4811005766 69.2889731377 99.5795911254 58.2493910631 92.1296814778 69.5286156637 91.6220882409 66.3880173202 89.2812304744 81.5644753207 83.7929072262 86 | 45.3780188805 29.1692166544 62.8240245172 84.9661859507 62.6856712475 78.1747432719 72.0513 86.4103 75.7010415321 82.9194588559 78.6424883907 89.5512032307 75.0925497814 83.4373907092 66.1540050863 84.1119370252 81.2694405511 80.635944933 58.0245140126 91.6944211685 69.4997669273 84.47968329 67.8061881135 91.6881773501 85.4461864009 83.0782938448 87 | 39.7774828713 43.75581895 70.236869515 82.1711510621 68.0856058616 76.1123175001 73.8462 83.3333 75.4793396586 82.8840513891 70.810692254 89.5926540118 70.3279291834 82.0293546968 67.2313347767 85.055640808 78.5757448361 76.8157030942 58.3821244904 90.5534760692 69.5038013697 81.8154056166 70.9626103129 86.5181213517 80.3124913784 76.5162389518 88 | 38.6644611569 33.3172384774 70.0427352449 80.3850213547 65.1181219632 77.3991884543 75.1282 79.1026 75.1945668739 82.9321157846 66.7699466213 70.4233752108 70.8633308324 79.4081747457 73.7099068702 78.2706108976 74.1867734997 74.3442041467 62.5667590405 77.7439347591 69.4875893963 81.8359875567 71.9936371714 78.8209480695 80.7662322969 79.8960656796 89 | 39.0440366877 84.6760108316 72.5706234458 80.9712184346 68.7456312971 75.7900554548 76.6667 75.2564 74.8202539551 82.9623887856 73.3363681612 70.3169271705 75.7799048195 79.8861994199 76.5804513576 72.8915336827 72.622209414 74.5269656511 72.1758243064 63.1289294176 69.5191157281 71.1159672213 72.4798343312 80.8006830564 82.6701124763 81.711479845 90 | 91.6399614428 79.4066030605 75.1307160373 79.9840931403 63.0902251412 78.041409495 77.6923 71.4103 75.1643404933 83.0349971693 73.1051481627 70.300511881 78.0936590733 77.759558184 78.8765811639 71.5625276804 75.1415195918 73.8196684648 79.4727615693 63.4086861199 69.5043762947 70.4934438205 74.0784292286 72.9515308522 77.1636254828 73.5237189623 91 | 47.4881326771 85.3899333808 83.2939057345 70.7784317876 77.2045893204 69.3121497658 79.7436 66.7949 75.2633555474 82.9452793018 74.6782541432 70.4453141903 76.7457612243 71.9733600919 77.4490021363 72.4473094857 75.168667839 73.9004448762 80.3577008812 63.2954375404 69.5125730109 61.497563947 72.0362880189 69.7160484161 74.0707000309 68.4795642895 92 | 44.5902125769 22.0340116412 79.6642622775 73.9323097157 82.0339084758 62.6818697859 81.7949 60.2564 77.7564189331 51.1517703255 80.240250951 70.3543089613 78.4646034437 69.7493008209 86.8537073476 65.5433240194 85.3242028911 55.3885788252 78.7572361375 53.3326200111 89.4942005622 62.2343269676 72.9981862717 59.6656447092 74.1142308687 71.9263635185 93 | 39.0896145478 70.4661940802 88.4321025254 64.6242470023 81.5779459183 63.8949255005 83.3333 55.2564 77.9544350549 50.4755789715 70.9468490729 50.4727017878 76.7456848486 70.0440572588 79.806531301 52.7231797883 84.090507598 56.301968006 82.5402395934 56.5410522935 89.5001240511 53.2163306165 74.8121312339 57.5898837631 68.942075458 62.635150577 94 | 42.2293783752 19.9140684075 89.1155590082 64.0015066388 85.1801432805 53.7816766607 85.1282 51.4103 77.0833377719 52.1577992662 88.6626638086 50.4553260208 85.161682187 65.5124047088 76.9882734745 54.2027848022 86.2562238205 55.8309303793 86.4358971909 59.7927618087 89.490246254 48.890796551 73.0273050243 46.2741878725 73.2420023309 67.3532711288 95 | 37.0003871448 60.264279248 89.0921976305 57.768193047 84.2868249936 57.2561680896 86.4103 47.5641 76.0635502513 52.1046520613 84.405952832 50.4948658866 91.8083525691 34.9655944844 79.4793575017 49.1410154455 92.5487933153 42.9019079116 79.4886844186 53.6516742641 89.49778562 46.6299066746 77.067111934 51.4869181896 71.8670131631 34.3291575887 96 | 39.0520864793 70.6525028457 91.7260057727 52.6233532573 85.6224867126 45.0241658646 87.9487 46.0256 77.6820163247 51.1656378096 75.9871047068 50.4919669557 91.8874485924 34.7692538555 82.3041595613 46.9909036215 90.3426345089 37.7803861004 81.5304203188 56.0253645659 89.5048511306 48.4234081578 75.0171368727 46.6224426489 71.5691888532 34.699218607 97 | 37.4884147432 60.8144048511 91.7355387598 48.9702108877 85.6044990156 50.7623379055 89.4872 42.5641 76.8780819814 51.8675622958 91.1120854191 50.489389393 92.2484016622 32.3771544295 90.700691247 41.4667030063 85.8191629042 37.5713861819 79.1867885665 53.2347918468 89.5010526463 43.3473436951 76.6653077326 38.440250966 71.4829096934 34.6805169926 98 | 69.3595594592 65.5213545959 91.5078881747 53.3126520851 84.4247858276 38.761410164 93.3333 39.8718 76.9485027211 51.9075165439 98.2881232748 30.6039193719 91.782532741 34.3842946521 95.4434878077 36.1897016148 90.4418181137 40.1331134204 77.8990579454 51.8224583343 89.4888412549 40.6250912209 77.9158742329 45.9268433826 78.1285220512 23.139231835 99 | 43.542775926 62.4603112824 88.239001904 31.4774348786 85.570985078 44.5600964397 95.3846 36.7949 77.8640547124 49.6625455341 95.0652748396 30.5004028652 96.0805193672 28.0536005018 94.9974880471 35.0953011066 92.0175590822 39.9179311324 75.1307142075 23.3724419733 89.5016235276 45.8151791904 73.7420460836 39.1209853231 78.789825254 25.5971933466 100 | 39.8112302539 65.3348328092 88.5305192041 30.4760310098 84.6267242006 39.4870775084 98.2051 33.718 75.7714500936 17.1112512057 95.2492339568 30.4594542144 92.2478988278 28.0707514904 91.517847246 36.8902631263 95.2605278366 46.008830272 76.0580137544 16.3837496882 89.497529662 33.901372671 75.3298157891 32.8303519164 77.7815467962 23.4472144745 101 | 70.0689259404 7.59346560899 55.3651603364 30.4458502777 73.3961549252 22.8162479426 56.6667 40.641 52.3315691301 51.8788603463 49.6561262881 50.4313198677 57.662276619 24.0606079328 62.2926421901 40.0295333141 41.2902998033 42.3115524113 57.6146743876 33.8224476502 50.4390920216 37.670771798 63.4104355141 38.377735614 48.5306293681 34.1524500077 102 | 70.0405435824 77.1438066024 62.5602588694 30.4471356661 73.5074477338 22.8569887644 59.2308 38.3333 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50.5466626677 12.1768930736 37.657209689 17.1758399778 37.3493143351 13.8151142571 10 | 70.8532508347 13.0946015351 64.9488945365 21.3238895667 72.4129293262 19.5285644019 74.2234343161 29.9365075076 72.2693261094 25.4744205578 72.2719941209 26.5963575652 70.867274304 27.9167674891 71.3663081104 23.8012467956 75.6391176725 21.8055710017 68.6520092992 25.427666718 65.7950251817 25.2806547349 75.3003973169 28.0784301604 11 | 42.775107378 11.1760546962 44.0795997559 20.9102874724 26.3684966383 17.7046420134 35.3701853385 9.62922573406 29.5848891035 18.3332343369 33.6668525501 5.39969623814 42.4413044695 5.71657675881 39.8648886776 12.556273676 39.9574737179 18.777009537 31.4227862977 3.23202607679 32.1949000815 5.18898647584 31.9341629094 6.87485022983 12 | 61.4699899287 29.988423363 60.510314401 19.952848952 49.9811722431 17.0482199775 55.8629759671 29.6383941606 59.269111916 28.6591856136 61.5459350672 31.0489656296 57.9837156042 40.63523399 60.0268995612 32.345977651 63.0224149959 29.3862871649 68.5744936232 32.2506267505 64.6539849345 33.6279025288 68.7507446317 38.0882713213 13 | 41.6756860727 33.0975379862 44.3754085193 20.8166816898 50.6092471041 17.3559441314 57.8080741834 29.5975814144 44.2178742721 26.729971038 47.138241688 26.7495939707 42.4441292869 25.7767855445 49.8098767941 22.1274763331 40.7432624786 17.0986938751 50.5083772892 23.7889033344 38.5295464336 18.3049892924 42.1302582214 22.335473782 14 | 48.4734078585 66.280307818 44.3777956556 74.8720494112 51.1971850174 48.1355479751 38.4815139226 69.6831007069 43.160973853 71.9773483833 42.4714502115 63.070319005 37.5973025742 60.2627776835 38.4115278245 68.9242113365 38.6004798453 62.7856808534 50.5229370816 67.1350652103 39.46563803 65.7686990374 43.0864776264 73.4663617619 15 | 75.5217047166 79.61371001 66.5563875378 73.6034590436 74.5008415725 82.893896156 71.3191259472 69.6725096257 71.3331991789 78.0192137094 78.5347248803 76.7284494842 75.0674054737 77.3209861919 78.5835365409 78.4554177933 88.1743708533 62.1805621185 69.3306369961 79.0324023546 68.9131461513 74.838897175 79.4706855234 76.1201351758 16 | 54.8150728506 66.9875581836 62.7165592737 75.25855102 50.2686049601 82.9755929567 64.2824414077 69.6633039536 54.7594508055 75.8827315133 57.1176315681 68.3233350818 64.6912854343 65.5917285576 62.575931879 64.7166914619 56.5018445445 84.8824187987 50.5934644939 78.8908534039 63.4354270118 63.619952281 46.1561969508 68.9012346509 17 | 43.0783672508 63.7773538588 29.7625573504 62.9701989335 52.4636620043 51.540518994 36.4545329489 49.5262071774 34.9761540776 66.790121633 39.3683052928 57.3481448332 46.6594028229 51.3301925817 33.1901729898 60.5536663147 30.2800808492 59.7416430134 31.9883721758 61.6767284602 35.24818549 55.1417538004 46.7210413293 67.3628995927 18 | 88.4392475543 65.4424977511 72.7954595391 69.6893858491 72.4756971383 51.0695364146 81.9057973346 49.6878577262 88.5627991609 68.873075125 87.149805517 57.0971323103 90.9269154848 61.3726489796 81.2744029783 52.9843761153 87.9377177445 62.572162683 82.8700591508 54.7922151216 75.2488934398 59.9057695953 71.1533577125 65.9012713517 19 | 74.397727566 80.6397357201 69.4361659508 71.9610717061 74.3789754816 82.371212652 88.4261334422 89.789680977 87.7165490922 86.0218354733 83.8678042122 90.5051457633 71.5506599788 81.2214458268 92.0842045758 90.7550685039 58.3509360562 91.8460179324 83.7507134192 89.0470302484 76.7345662782 95.7903789033 85.8377465069 92.0370719114 20 | 53.3929167748 9.6909545716 53.3684603793 18.8919363464 51.3000415253 17.4522354447 56.1499215042 9.52473928648 55.2797013138 19.8536545418 55.7663806259 8.6888179471 62.0202954784 5.6230472335 53.4681358972 6.17544437069 39.7317487579 16.6112710374 50.4966347831 7.92656295329 59.9767858564 11.7194820584 42.0339062359 13.1457789906 21 | 50.2401955925 66.7251544267 50.3755771264 76.2934906848 52.3754137041 48.838335582 41.0671483861 69.6930910673 53.7731503629 71.7730894964 47.0176954472 61.2270187283 44.8772391761 52.5962769899 45.4622136315 47.996678992 46.5242239801 62.933618023 50.4963906004 56.2576711816 37.555895378 61.8973206435 46.3105751297 67.8561777133 22 | 73.6385619162 47.2185612169 82.0342884775 54.5837436177 72.3035497832 49.126761868 66.5801582732 49.6370707267 67.3454995769 33.4121315583 72.4861782202 47.9877392942 74.9621411566 53.7993002941 72.9620677907 43.9226795716 74.295552598 47.774868335 69.4038173545 47.6251945241 68.8645555261 47.3789709664 65.4904306312 42.3494103768 23 | 46.9511795593 85.1799716964 53.8497980161 76.5323647896 50.7312343384 82.0871306443 45.1010870075 89.6359196029 43.369247302 84.6288306329 45.2276805752 92.0723693814 38.9867139312 89.0871826174 43.3583952936 91.0406451549 58.8650876604 89.8465124909 50.5034557746 88.5288961133 41.0713041473 85.9241403716 31.4366774132 87.8793889162 24 | 44.2297962772 10.7319127646 51.8141791784 19.2689299129 49.804852969 17.5840330258 41.8682815667 9.5594300983 36.8393401359 14.9461219756 41.7003275847 14.0219598235 41.0420061424 6.54360792948 43.3971641104 15.1802381834 38.6585332859 15.1763718793 50.5686110971 13.5523453485 34.2544218231 14.6810551123 34.3300944594 10.5447873599 25 | 41.8930412673 62.8943615833 46.2804995334 75.1931459273 52.6844263217 48.9072413377 44.9183598062 69.7064370227 47.1138372322 71.7676558262 44.6439155683 60.5889586942 48.9331464872 49.981681159 38.6639008107 67.0536994732 40.1338272099 63.5409502694 50.4879380522 64.7153761482 39.6105052192 57.3602256692 47.6863713156 68.2371127216 26 | 47.2220135867 65.9122377321 42.6362651184 74.2916153375 49.1041292662 47.531099973 49.9836336405 49.5411007292 55.6528341403 68.9030528151 49.7423401581 53.9690541666 47.1898916332 50.7097718384 53.5291683247 55.2703876058 43.3206411345 62.9362870565 50.456119949 56.3360681835 38.7228898708 49.2659407101 55.6381793157 56.2112651171 27 | 62.1175811279 85.4895045139 59.5828623397 75.8456658728 50.7602442676 82.8716477904 63.0489390768 90.1326401867 59.1086758129 79.4376899974 56.1213206005 81.0569766394 55.9645619917 75.3723260298 49.8323030615 77.2310002826 55.4196525403 80.5933345856 50.5083890347 78.1275123883 65.8700682003 84.202529221 42.2845233983 77.7314044329 28 | 59.8396585108 66.1612434992 72.7493582769 69.6206082737 50.3653605902 47.5338901487 65.9779486991 49.5299744131 59.7163496515 69.4189628031 58.1792288609 56.8054794652 51.8286669788 48.2182644184 61.1301260585 59.8749684964 65.2937038511 66.5443256428 50.490038892 56.0767594347 62.5373342909 54.8195971526 51.155171523 62.2064403753 29 | 62.3091833455 85.3291733685 60.1833153682 75.9240227691 73.7750920866 82.6457657722 63.0900986335 69.7035049483 65.840444075 78.0192792185 62.8155813447 75.1705329159 57.6399273025 70.7380676108 70.0137719514 69.522339286 62.2051155567 76.5527714059 68.9444985082 75.5106323999 66.927129087 75.2475142985 72.5222385192 66.7633447673 30 | 53.2287740772 28.5815971459 47.4434001522 20.0627870317 49.2846892359 47.3847330109 53.9622555388 29.519462426 54.1025408987 32.1055794913 55.260420032 37.8733990471 57.4247827197 42.6352343421 60.3679075486 32.8782756226 56.4216512082 31.8443097863 50.4738691224 38.8370545598 62.879973157 35.8581450108 53.2279099318 40.7247610101 31 | 39.3123698294 35.5745324407 29.5942748092 33.3809520603 52.8444810999 49.1194887463 44.940503726 49.5032264782 46.5547352337 33.8539185397 48.439965815 44.0339683769 53.4779577975 45.5475916626 47.1268616619 47.6875298093 44.0088662247 35.411439745 50.5056940821 43.5750715484 39.9182266613 40.6572502747 51.498178531 38.6879013225 32 | 36.8264304161 13.6850287221 38.1699370515 24.3046598874 26.368795162 18.0960306725 24.2894639128 9.71728979838 29.9341093812 23.5274788426 31.9344795339 8.56988494242 30.5380223688 17.0945503435 33.007645402 5.30284972246 39.3307515598 17.3769911959 32.6087239082 7.07069648516 32.7011186751 7.64217796768 33.8961392671 8.38441732947 33 | 64.1384457566 31.4814896094 62.1901510388 20.300733957 49.5300338903 17.0843028053 59.4733114174 29.5077376008 59.2807932878 25.7821658928 61.3926581368 29.6750368361 67.4098964412 34.2200793507 59.4561758107 31.769079898 54.4343351879 29.725772785 68.036676048 30.638833645 63.4160594721 33.5231406736 68.0510226482 39.0577487499 34 | 75.3633450278 80.0259926353 64.3386645541 74.5413235007 73.5423935635 82.4205815323 84.0880764926 89.9619980844 81.5290248181 83.7357858431 81.5840496376 88.7460050809 73.5080662247 79.2099855464 77.0528842503 79.2375160384 58.7351075268 88.6529998634 85.0313543882 85.8641080468 72.8225445818 83.9497070064 84.302149359 88.0648682379 35 | 64.8040597699 10.8218399336 52.3093375143 19.2037540178 73.4063120275 17.7153545231 61.6804508052 9.65851368173 69.1100264472 19.0077912997 63.5596041464 12.7717311268 50.6308491915 18.3257135894 62.3611522818 14.7665176654 65.3879921045 26.9040709129 68.0276293683 14.8727854501 64.7363193945 17.6026116915 72.8582115694 25.5214507511 36 | 50.2019177159 86.0993632098 59.7308086971 76.0016312311 52.1127475692 82.0941145401 56.7552726568 89.6025978471 45.8059147109 77.4742027773 50.1185854501 83.3714927504 46.8599850279 83.2840683372 51.9878222419 79.207669835 59.9407524146 85.6143842598 50.5467109258 82.1971468372 41.2960038226 86.1781579953 36.0775484019 86.2211286622 37 | 57.1119711838 67.0773079536 55.429373533 76.3938783072 50.3713407855 82.9424819102 57.8339971221 69.5858218302 51.4700396011 71.2523547782 52.316461897 65.7505604096 59.8075196591 72.2203686974 56.2822413114 55.3853365562 50.3055424784 63.9892331343 50.5268866997 71.2557475719 64.933899134 68.0456301006 45.6479359878 72.5027798418 38 | 45.0260447473 85.1333069538 52.4193495062 76.8740796661 27.7657958917 80.5567426242 35.2434972375 89.6127083498 33.6594025348 82.672031068 34.5602758643 92.601427282 38.0267843347 93.5433349558 45.4612970623 92.9055474475 59.1411156819 87.7014787302 33.3064896931 94.3075611526 32.6093003269 95.1047101939 31.3065182559 93.3131025046 39 | 40.0964335545 83.563808468 52.1484041739 76.4218306407 51.9126576689 82.0341873086 46.8389615755 89.5180892942 46.7788016941 84.0143214755 42.7616885528 86.9644443227 34.0515928694 95.8769936421 42.6061196342 90.3981594859 55.7182351834 83.2206777679 50.5055053078 83.5120455982 37.5800707249 80.9712016049 40.2970493877 76.6481360928 40 | 60.5351821273 85.7850469004 63.1130359207 75.0709354397 75.0125141025 81.7917967605 62.3815046648 89.5890294553 62.2131288445 84.4577327973 64.1112132083 97.0241743853 58.0845438707 91.3848274192 66.4120940962 96.6543776338 58.9423531711 89.6034571903 69.0855189987 94.3774383809 67.2362999314 93.184535409 76.414249807 95.6766440312 41 | 64.9321423799 84.7895358734 63.4119691462 74.9119601231 73.7725854723 82.5132538505 73.184806549 69.9219475767 66.3765199757 73.3313958529 69.7553397049 70.9579698042 74.3234101869 77.7508915688 70.0017333349 72.7669288275 65.6687699918 65.9287010083 69.0917437115 64.2617434459 65.3776981329 73.8801162531 74.7373562098 69.4249567725 42 | 64.6536486722 64.0812007194 70.9927940636 71.2925232619 74.4419206988 82.745346091 58.2846685874 69.5241836171 62.9025735266 72.6551922501 68.3173818542 67.885687055 67.0082807343 62.6185177403 66.0738583231 67.3646399087 64.6516960305 67.3926609171 68.1487313545 65.2183636428 67.668677901 72.3820224372 73.2934351358 67.8898101605 43 | 55.5456574919 28.4721692578 59.5788056468 19.2788344885 49.3270789652 17.2273174012 64.2254023895 29.6289099903 59.7942974664 24.8479762314 59.2581808443 33.2256040173 55.7778704711 42.1946464603 61.9023395629 34.3438440895 57.1663834117 32.8798756832 50.5105578759 41.1427599607 61.5489507539 37.5261176609 48.1936779788 33.8661680504 44 | 28.2274449828 19.6953257098 40.0230441059 23.0970785225 26.629145689 17.491117236 28.2136857113 29.5775683669 27.5237047427 28.5118165598 30.6880647283 31.8170470289 34.3580123142 35.2143064327 37.2599519755 35.9373567179 39.4095177705 18.1256230468 31.3536314601 30.6024276393 33.681977119 29.101747022 42.8097972569 27.4810168145 45 | 54.4108935793 86.3077970104 54.0570041521 76.406178509 51.8029633136 82.0625979556 54.8573949212 89.7131120445 54.7926544844 79.8805710232 53.9317996538 85.3865445554 66.495604458 84.4295053342 56.7400122715 84.313473813 60.2905339641 84.457096575 50.4941185589 86.6924703161 65.4062845958 88.9258870659 42.13830188 82.8167079439 46 | 46.1348308863 30.1827624166 46.8616887247 20.2533004632 50.1447275709 17.1062750316 48.6989351647 29.5111673702 45.0831728157 19.7173078653 44.8050377252 22.6987372585 43.4920860803 25.7243725667 46.410919498 18.0499970004 39.1055373508 14.8082724705 50.490141218 28.7986373911 38.4385835349 17.4917640949 42.0629913039 24.8016620852 47 | 38.625965654 37.0107920215 31.1277223896 30.8214228923 49.1036802431 48.5683478783 47.2098416215 29.8150505578 41.1568022835 29.9976478802 39.8316284819 39.6213794448 38.5371396574 30.6850100039 38.6483590301 38.9042751137 45.0356964438 45.0907268786 32.4613327269 38.8626120281 36.0157860126 38.13451199 47.4962935844 32.9238593005 48 | 58.5413035968 66.6743353064 58.8530121639 76.1099835024 51.7371859816 82.0756639357 46.735403365 69.7365896346 49.134144764 73.4943732127 54.2261512489 68.3000890559 58.9403201451 70.1319602996 57.6684687588 59.5546612107 53.6934546274 74.4085116269 50.6013476599 67.3603381762 60.527712844 66.1022885037 46.8295222456 67.2781623072 49 | 71.7958766499 39.6744659815 72.4922328097 25.8907129958 72.2766847434 49.2488080492 61.7416127968 49.6225215428 61.6433172409 29.5292601694 64.6525019627 41.1435973982 62.522784643 37.095470271 65.1087691296 37.6211719391 72.7365917102 34.023581537 68.5809513753 43.6493441532 66.7105001249 44.3686471512 65.6465150013 42.1275534705 50 | 36.1473130054 41.951004473 44.7158905405 20.9329043744 51.094177065 51.6140088495 58.1329708262 49.9217877442 43.2025720556 31.6391773287 49.0438595208 42.9555725563 48.234008789 51.0000064766 48.0998132103 49.5326770859 44.0321090117 36.1939205862 50.5194941956 44.3632577577 38.2975021906 48.8519373876 51.9771413805 39.1487390678 51 | 87.9016814604 66.4774729856 73.363846348 69.1487476161 72.6602892164 50.6508690299 70.37822956 69.5940999621 84.0455305148 73.7493737907 80.9207582219 60.1244235979 79.0415416926 70.0991371796 87.3864803538 59.2621670188 86.1375877755 62.9877259799 85.4841524862 64.1555551532 70.1171903288 58.8521665451 68.3698169768 61.29270056 52 | 51.3841924955 28.5967039106 40.4737983708 22.8700246067 51.1998001406 49.9974004388 50.219789802 49.5897995734 53.4178308967 32.3892527357 50.3193301987 42.1121869369 55.4614309273 43.0242708923 54.5485459647 52.7695772322 44.5322697664 35.7025192817 50.4896439847 32.056464478 61.5964022986 43.0585292962 47.3256967414 33.0688551253 53 | 54.7111971589 86.230192552 56.5656125848 76.2574514657 51.6654188478 81.6439096139 57.8998879041 89.5532825826 57.8377717072 77.7148526204 57.6416201395 84.2406303119 60.9016619983 91.6961160127 55.3868209667 83.2565438727 60.8277878585 83.886575509 50.5184960597 86.697015072 61.7078640995 77.1117130532 41.9626428918 78.3734717149 54 | 37.9069153083 37.6365722895 31.83392179 30.06646207 51.7993515269 48.6183469723 38.5860501261 29.5030675716 35.3985584258 23.9646154573 42.1029335967 38.4957429771 41.1841403929 30.5368341683 45.7478831165 48.4135945079 42.7818164601 29.162944837 50.4975288918 40.0069007188 37.3679929625 37.2205713638 49.458739911 34.8811550845 55 | 40.7688248779 61.8796234729 52.0144103886 76.4469864928 51.6249689362 49.9813734701 40.7404061311 49.5019206754 52.9471889594 67.2949854873 47.7436373804 56.5062985216 46.5506349492 51.8106652049 52.7939781307 51.302115573 51.0215608443 65.8751743454 50.5665425103 57.1605458032 40.845738287 54.4915429824 52.9142169306 59.6337711669 56 | 58.9536035376 29.3856565378 58.2475085986 19.4201380692 50.7028126527 17.4363372032 52.746389936 29.5058935981 56.4648123031 25.537074796 61.0346068127 26.0737305291 67.9561385812 33.0535650525 59.6596777126 31.6441783217 58.2216397966 34.2113315098 68.8933277694 33.6424515568 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7.05164861659 76.0006463536 16.0622689786 67.0298582176 2.10035602261 62.081437097 2.01463068947 80.7298314523 3.95398273306 66 | 70.8737945526 37.6876413937 82.8260719709 49.9863983997 72.5811274461 50.5317857244 49.073717039 49.6158124629 60.0152247274 35.6268381885 64.7176835559 44.0360977049 61.2813587997 37.7021111117 65.6493621448 36.8769468905 69.5325497584 43.8870076669 68.6522168371 46.2202044066 66.9299274284 51.9864116672 62.9985025827 45.5135779909 67 | 49.8154355076 66.7225070418 55.7472005158 76.5126513298 50.4126804364 82.7882219604 57.0875607327 69.5203664521 48.193943217 74.9019107475 50.1904861261 69.4430883802 61.9194420233 65.2770159352 43.7228163477 72.0780141689 54.4387487909 78.242323901 50.4805757201 70.6048559087 63.3750746456 73.9273815518 42.7654531552 74.1472134965 68 | 40.3801603334 12.0188300221 41.7981607043 21.9501954483 50.0447088926 17.9758046886 40.4267671705 9.51730277335 35.7997224338 18.8587027396 40.7257338923 15.9110002842 35.4098873355 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60.0343851145 43.5893183244 57.509979246 43.2829846181 65.2813534414 36.5234330414 60.4677962792 31.8088868508 50.658148199 43.1988565784 60.7097725973 44.2267311895 52.5382137225 39.8824614467 78 | 72.4758339815 40.8948338904 68.6314162528 23.0924402141 50.6766258862 48.6273817507 61.3210962782 49.5945353752 52.8951875168 29.6197289118 56.7049700568 43.6290549111 63.5591149446 39.0934410154 48.3494955005 50.3160364868 54.4579248015 29.9867578316 50.5013569779 44.816870933 62.8175533374 45.3758503921 60.5681475759 49.810218933 79 | 35.3274307925 43.4631550148 37.6306619585 24.4001660876 51.6292221751 51.9374584823 49.6868092436 49.7130901444 45.5915342478 32.5486973767 44.9146813375 43.1372264189 48.9286751531 51.7557057474 39.6892504692 41.3496541557 46.3634509876 42.8089857456 50.4906817878 49.3074384861 37.2254197815 45.5997353198 48.0133908665 33.9460164665 80 | 45.1406636424 30.7467347213 41.0341691247 22.5505824362 48.9340430171 17.2546406666 53.7796709791 29.5616422469 49.7076079215 24.3464823347 49.5307512712 23.4993456399 50.847540426 17.1969381729 52.26043971 24.7837687667 38.8740385345 15.7954124534 50.4942366787 19.2711219984 63.2608924115 22.7470751856 43.0570797993 21.4127184527 81 | 71.7269998438 56.1719048971 76.2183487899 66.0465051341 72.7048183097 48.8658737196 78.7614987159 50.0899081218 81.3836844236 68.0720736871 78.3074289111 56.8292862332 71.8639959175 59.4468231137 84.8920715224 56.5335220428 79.8137045722 54.1157521223 68.3603876727 51.9522486263 69.1120171736 62.8861941252 67.5639073047 60.3749042903 82 | 71.9420375549 40.1328860945 80.8608183484 37.2052337767 72.6703443799 51.4857523765 71.7672321408 29.5321491209 76.988194785 28.5120436723 73.7452822404 38.9279294466 65.8433104389 31.4169558237 68.8696997481 41.5070981291 71.9432676716 36.7407440439 68.2385188309 37.2084168067 63.1064337516 36.8552080754 72.202257509 33.7971456887 83 | 40.0433627582 34.6733302469 30.1752929544 32.3800416151 27.4273855075 48.4853290192 29.3643959416 49.5301772936 37.0132393825 30.9360655007 34.0151832414 41.3483492829 32.6804046821 34.5750870474 31.4374312579 33.6571355312 38.2721131809 51.4735796301 31.9110255713 39.2046101432 35.2272488857 43.974984132 50.6795519318 37.4719827309 84 | 42.7366670664 32.1668232021 43.122230703 21.4554138637 50.2461166363 17.1833272229 44.5440520814 29.661281654 40.3210665594 24.4342983663 39.1241989616 24.934955541 35.6761674086 31.799490394 27.3463602333 28.3678783492 38.9130363431 15.4899692415 32.7343804069 23.9749957215 37.833254422 23.6781728484 40.9606244462 21.6569603492 85 | 56.2057628571 9.39345120135 51.0380298406 19.3915703859 49.8324453655 17.4498333191 57.2645294143 9.50558241624 58.4254580034 17.9590705009 59.9921705382 0.583460384601 66.6793778281 2.94106141365 52.9276153444 5.15584397925 74.7555977829 17.9382967887 50.5239527421 7.97872729724 63.5823808853 5.70169896574 43.5337195806 3.27355835764 86 | 46.2854794306 30.2376889411 55.950321447 18.9460970781 50.5693993018 17.2028314914 56.5311192119 29.5982231809 53.8911908152 26.4395912387 56.0541713663 31.7703885683 59.1372990577 42.2222803307 64.557637458 36.1598105314 53.7057060861 29.7249675938 50.5151421492 39.5386252529 64.1797073098 29.6466497216 46.7758685665 31.1362962125 87 | 52.2166367096 67.207749661 48.8283712641 76.3431708548 51.9170401673 47.9770130556 63.51868644 49.6615569244 55.8329769313 66.2961652929 51.2370263755 59.285146518 45.9920892325 53.1092862986 48.8610333618 51.2656992919 50.2563299873 63.4916713626 50.6339962709 63.1908435017 62.1979020881 60.6819112761 51.5075468453 61.4235935846 88 | 72.8698871943 51.776565196 73.9630430123 68.6172486645 52.3946043959 49.3323324542 56.6024044464 49.589807291 55.5524844723 31.9088407577 60.4527852565 48.6849870551 59.7918608638 38.3855916183 56.1217291015 54.154279224 71.9288792205 45.7575680331 68.3684713816 47.4836242917 64.5039020108 56.2467897772 57.8074318855 46.6079171955 89 | 52.4727151026 67.0138017653 54.2591055947 76.6236988377 51.9674908971 81.875813428 48.6608760838 69.5392107996 44.4539255526 77.1335395595 46.7752951104 72.0445407841 52.8593083072 79.6025541225 50.4283006063 78.025897621 54.4064015917 78.0934181879 50.4965578443 63.9311058069 38.0595232563 64.5990042613 47.1695965154 70.793677218 90 | 47.9319147503 29.3950988075 42.2019664814 21.9358697032 49.6437961951 17.3247637188 42.3389038783 29.7191381742 45.2657661955 22.4945773463 43.128286031 27.5122473508 40.366690226 27.0217818895 52.9760042418 25.0949550163 40.4439638644 22.2012509584 50.5098482375 21.8912400238 38.3329292918 32.3103131432 42.5103129015 23.023802132 91 | 73.1626620032 52.4365897832 79.1306168813 62.924256297 72.4292584597 50.6514402104 70.0708400547 49.5371765808 71.1655507094 32.2075380536 69.6362220853 47.6185225257 83.6372211351 47.4767900379 65.1441360455 37.9037180082 78.5568343838 52.7439663471 68.7712333947 51.4083817668 66.4048064406 47.3911763534 59.1222594853 48.7996576855 92 | 46.0011784811 10.2284744715 52.1418226114 19.3755951938 49.2072751445 17.4694179681 50.353965879 9.61659899697 50.3879897882 17.2181498321 46.4709841814 5.21476901715 46.860590485 0.469738228914 48.4170101987 1.0201268829 39.6159864562 17.4813610987 50.5095188094 2.4284637327 36.6873907231 5.26670388266 38.1455324189 5.28846285593 93 | 47.3689354735 65.9902466232 57.5960521126 76.2539795528 50.8356054294 48.7040458369 49.7254488467 49.7691758937 50.5406472777 67.4660279954 52.3864641734 55.0080239919 48.012694723 52.5650998228 51.8445109289 52.9232657814 50.6290476401 63.3157954023 50.8830340978 58.9073829515 64.5883180837 63.2717526521 52.6698653514 59.9672629106 94 | 55.3982974381 9.52915012801 50.532790628 19.3645449911 72.1572879084 19.3611791821 49.6713724887 9.51141090975 65.7175523439 16.6407013983 64.2629374002 8.42485404019 60.9023968743 7.02586295276 57.4847183454 9.8789723702 77.3602148423 15.0333730943 68.4718943272 13.6605643632 65.6492565614 9.21440036191 79.4929759708 12.9013385267 95 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15.6554996225 50.5528084414 12.5285522342 36.363582259 12.8762067776 37.7217019004 18.0122624378 107 | 72.1961753288 55.2401640604 74.262754322 68.2089784417 72.1003255187 50.524411015 64.238099481 49.667282978 70.6063408095 64.3718611942 67.6237266365 55.3555230436 75.7054910962 55.9478419239 63.6295545625 65.3682808958 77.5048078526 63.1701682654 68.1130628556 53.1719870384 66.8616892768 58.3579212055 57.1513160296 54.3043122325 108 | 69.4953801996 83.0388941089 61.9248701522 75.4632512068 73.1152239018 82.2659346111 75.4658231729 89.5701173547 78.7622164499 81.0767016507 78.6944284536 91.0519005025 69.1495584844 83.1773088386 82.8407172021 85.3475346488 60.3042585363 86.1020652574 68.9009665872 91.7659880623 70.4947256575 91.6361883376 89.1638748696 89.3897063991 109 | 44.8756477219 64.5354470563 49.2059494321 75.890997526 51.5168360115 47.8728833231 43.5355511551 69.8022628259 41.4856505182 69.4638623276 44.1914003424 60.3477148741 40.9908223727 58.5953671711 37.8052421178 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49.5655904728 37.1619145725 34.9270199582 36.2615304473 47.1868575228 30.2959037105 40.3533451474 42.4703885061 42.8972842245 42.7892463641 47.3809757547 31.4075931099 39.5576209008 35.5037350539 44.4356192297 54.1787181963 41.9252481403 125 | 35.1883283318 46.0186231248 33.7188307397 28.0245711227 52.4097627886 49.6908809656 52.3794998192 49.6427569923 43.6260889369 31.348637041 45.89536139 43.0091090793 44.6467916493 52.9167718811 41.3303559044 40.5645382132 45.7990320984 41.6203580309 50.5106479209 35.7609310166 40.2407553506 41.2188034396 52.2224415414 39.4857300768 126 | 33.5564810906 15.4379215402 43.7686656094 21.2399260471 27.1641774014 18.0357549152 31.9792879527 29.5098860158 38.4578606572 25.1462223682 36.7428693581 21.318797784 33.0955730397 15.250014781 41.5782930254 13.1583503102 39.2313325864 14.6819535256 31.7447176563 15.1553267336 35.5076996558 20.441871323 39.5894713256 23.1674666016 127 | 41.8493028594 33.1107809186 32.0462240563 29.6006700423 51.8972499409 48.8535411055 41.7323964659 49.9885480173 39.8025029961 31.6694161868 44.2730279654 43.0976170646 48.6558094126 50.9624523811 47.9968834075 47.173794809 44.8572997366 37.5527243552 50.5283792653 45.2925329434 35.7116966795 41.7368369355 51.1445896969 38.1685585049 128 | 91.4645553108 54.276119954 79.9803917596 60.1859548741 72.2591570777 50.9603155156 83.5505483996 49.7433465894 87.6953524862 64.1675056408 90.8189821192 52.9117912654 93.8766866378 58.1551407173 81.0418345985 53.6322952814 87.238260447 63.3603449303 84.7381057579 57.57152424 75.9194605547 55.9642503397 73.954314656 69.025919772 129 | 50.7513289755 28.8254277825 44.1631854006 20.9378734957 51.167521452 17.420245605 51.0466439119 29.6348173571 55.0313695649 24.2329568985 50.2495137191 22.5105551702 47.6916189992 19.8530868781 55.2999913723 27.6725405069 39.7591387019 16.0581069663 50.4960462619 28.775752956 62.5959861342 13.865480591 46.6030787786 29.3203577253 130 | 37.1382313902 56.2347460305 28.1469187485 59.4425893647 27.6203663353 48.5659590648 35.2208281028 49.8246276902 35.7875693564 70.4508935185 34.5302742892 55.1525286173 34.7439317636 63.239139011 24.4391357692 53.256009259 34.6595066063 55.7964072648 30.9024665339 51.3979227214 36.3592061996 49.6894703669 47.0588296198 67.9140671318 131 | 51.3245955776 29.3694279221 48.4596386093 19.8258283407 49.9307207142 47.4673197077 56.1078154776 29.5897014114 45.7153978533 29.5048728853 52.2933015779 39.0275321146 55.7384109333 44.006451194 48.8651686953 47.4953310233 45.0795042397 39.2460626831 50.4990447704 45.219311508 60.9533240866 35.2614209174 51.2996556522 38.151390624 132 | 50.0617177646 66.8388631061 62.6400655563 75.1059514675 52.4681488416 48.4153538361 52.3050356595 49.586029536 66.8294472512 70.9572107699 60.3723009041 59.4480651933 61.6329921268 66.2007109572 59.3629255376 58.1221303308 71.4986084304 63.2664972311 67.4836234184 58.7608751022 61.9844532942 51.5996098175 51.6747930102 61.3069905569 133 | 71.1286744081 82.4103384088 71.3551809876 70.7365209804 74.8559822601 82.9154318552 65.1829663096 69.5356497432 72.7684415802 72.2759424228 70.9826609713 73.9097293958 73.876044464 78.6499451195 73.8686918583 74.365377497 64.6284484268 68.0176220577 67.7912232301 76.6345549657 69.0248544179 74.3658126733 74.4877699677 70.2840828022 134 | 62.7621580839 65.3342922465 62.2211004565 75.2918400224 73.236980457 82.042128935 71.7499169467 69.5816407003 69.2157687261 70.6739445208 69.3511769343 69.6848425533 64.0650107416 67.5431990412 71.0084559182 72.5991473224 62.6643017162 77.3047015503 69.478095457 71.6466840898 69.3283603587 68.2658788994 70.9151728548 64.8743593268 135 | 68.2631690948 83.3928038006 60.8082489671 75.5677831969 73.4620941227 82.5367663286 73.3533905524 89.7280834699 65.2920773809 81.8698809666 64.7606448508 87.5143671854 68.6186574849 83.1450787214 65.8885219735 93.5413221267 60.8881776628 81.8282798624 68.798837139 88.5662367162 64.9587274236 86.788072428 76.9253677933 85.8275947589 136 | 47.7143304937 66.1693254562 56.6133335208 76.2824639356 27.3620534602 81.8157904304 39.0257199089 69.7925041639 34.1693997898 74.6333930333 37.5739383201 68.2599582353 33.7420210016 68.1761618675 39.3239149275 68.2074735382 27.5777447689 62.5184468587 31.2194812334 60.7497323694 35.9029270448 69.9326613341 39.2799713038 79.5306293291 137 | 53.3909749147 9.47659774941 47.828630597 20.0159817648 51.1207066875 17.3947014893 50.9518060784 9.56757750393 52.2972508715 18.4544641946 50.3719234268 18.8511111632 46.0737878855 22.7021766114 47.0362598198 19.4567595279 40.1629157442 18.0084443179 50.5124479961 19.7877612592 62.2287012911 18.4316526088 44.7641917885 25.4829270365 138 | 60.0788225062 29.3294901661 55.0609964096 18.7760367713 50.5367036851 17.3415187427 56.1850795225 29.7272917464 62.8920160731 23.9212796723 58.6103385138 25.3264185715 45.7820627638 22.5254831843 55.2644171521 27.979646521 51.0438829983 27.5320193458 50.5021389989 29.6734435663 65.7610427799 25.4249979812 45.6894919283 30.4982485081 139 | 67.0688514179 62.2757080212 72.0776113868 70.7350247034 52.4107069522 51.5750330675 55.7047279218 49.6997257124 60.2851241478 69.5922617796 60.974068899 52.1737161474 55.300323754 44.8267044955 54.4325284089 55.0420672447 65.344542801 67.0862321349 69.2956313871 53.3111234754 63.717134406 57.9297172295 56.5656104828 55.0790049267 140 | 48.4806233045 9.78936730909 40.5466818982 22.750153119 50.4937192377 17.1776021089 50.6789672694 9.68187618873 55.5667252079 18.7720780041 51.2716198477 17.7989003539 50.9386403876 17.7051520618 50.8620164739 22.2804402981 41.4759633693 16.9687852269 50.9568786264 20.8191365864 60.6414986925 22.3562714183 45.3086600865 26.4068602066 141 | 38.3303831911 36.6401749551 36.2186392875 25.5092494312 27.8390086322 49.7707408011 33.4031658184 29.5662653577 33.6002874631 31.9702490493 32.5306118335 35.1442358024 33.1426658555 37.2820445887 33.6118755686 34.4414392522 41.8602703588 24.871703109 31.179108228 37.2644589281 36.1703232934 38.3905019554 41.6464988427 25.8096287553 142 | 75.5666109354 79.79350494 65.5602641512 74.4661969558 74.1037194415 82.4942552661 67.482077177 69.6257273029 67.7380919972 77.8043458937 67.0286227706 73.7120880682 73.612485663 76.7997003489 67.8046756773 70.4668148519 64.0629875776 70.3632376694 68.0060993451 75.2331666566 66.2606163715 73.3057355058 70.9373508824 64.7354190204 143 | 65.342854895 63.4890309543 75.6693118937 66.9496990316 72.5798472332 50.5879235624 69.5249496468 49.7418302092 72.2912258294 68.2846141662 71.8231621294 51.801994236 75.9661115945 56.1066045137 77.6848664679 49.6495235051 81.4735845494 55.2475375605 69.1966419802 57.0105100516 65.9949495365 50.496009825 65.1226687541 57.2102883078 144 | 58.156326506 86.0884980538 63.3338555681 75.1674610352 52.3573674622 81.8495188222 61.4465491669 89.9299387054 45.5148736705 83.6231138223 51.7258753296 88.2598907844 43.9680967255 83.6925675684 58.6999356553 86.6760645452 57.9679167635 91.4961842339 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69.5954263098 49.7222837847 72.8586203342 51.7819495499 69.1587743442 60.4652675892 70.8583590378 52.7651460265 80.8138690746 54.9542826669 79.6366538678 50.4868166013 69.0395731799 64.7802449882 75.0116853872 45.7754012678 70.2905446213 163 | 35.0328074392 47.0161994793 25.6309460655 46.3527331305 49.684995873 47.1423974148 37.7292304723 49.5220928555 46.8377217616 30.9104421207 39.7684052539 46.2641493457 48.2802163974 53.8210505451 44.6449811273 42.9867249579 39.5584807511 50.7316883959 50.4767272764 42.1540722918 37.3115354145 40.0945652786 46.5279078819 32.0969781607 164 | 36.5966714028 55.1393412864 32.3387625278 66.1520509957 51.7576790559 48.1544667456 48.0477293132 49.6156425795 56.4486800942 66.246165661 49.9037438242 51.5643036666 45.6426860539 54.019210238 56.3557499462 56.3698395265 38.5408249205 51.6509540839 50.5664383268 55.2166554481 39.1455743003 55.9300911182 50.414496968 63.515688046 165 | 60.1789616396 85.8349003354 58.138806143 76.277404642 74.3722689113 82.841644194 69.5499462754 89.9104611135 67.6408762363 81.0638026744 65.9508065333 81.54311104 68.4337980417 83.9871029806 62.7908403333 90.7745139214 60.8818246549 83.691361165 69.4276602398 80.8817003321 64.7004658027 84.2410697359 75.6558080528 70.6232343768 166 | 43.8392804403 10.7675919087 39.3874876095 23.3314675397 50.3586042758 17.3404105797 55.2946405658 9.65280890816 51.0887333136 17.4207079071 45.7255676885 17.6088516715 48.0787604728 19.8586758849 45.6023560911 18.1880002036 39.0779817408 15.1217331245 50.5164841444 18.7714619401 39.3157321673 17.4249064009 38.9350007631 19.9303438004 167 | 71.0631008112 82.413145609 64.7203758414 74.540808488 74.669864644 82.6451967575 77.9209150204 69.6563169591 70.190150431 79.8160434122 73.2476800243 76.3457223656 71.5324408709 78.1226215562 73.8234267297 72.8556524532 63.4211270407 71.8215741252 67.717859049 77.420423327 66.9610968797 78.9177218706 76.8623729688 74.745466245 168 | 61.0078590279 66.0069152952 75.226897994 67.1448239224 73.1702631156 51.291784998 62.3538609914 49.7241800426 65.3288389858 69.9687837238 64.7406276978 56.8564285768 66.3712570227 65.2591682511 63.0282586945 60.248785245 69.6307974648 63.0262676889 67.3252661847 59.2108714884 63.5634172811 51.577217772 68.3848142834 61.6475417891 169 | 40.3937808449 11.929502425 43.862718245 20.6314888182 50.3430362625 17.3356957351 41.7635727792 29.525716257 36.4135340707 20.7575456385 39.6911524411 21.5543827363 35.6989957633 31.4793558586 39.3325931777 11.5122032727 39.9256760618 20.0072995211 31.2807594765 28.4032209379 37.7277740702 20.8742181893 39.8149308163 22.568888749 170 | 73.233261546 46.2728956309 73.4858887436 26.6605188382 72.2763997441 49.8925756023 67.4640887007 49.5186509596 67.3338017288 32.7200934885 65.1849858944 42.3342953927 61.8813203042 38.6979087885 70.6107189529 43.3463018971 71.3351179851 45.4809640835 68.4440956963 47.0594283761 62.9085658938 42.0666778581 57.2787703942 46.0511276298 171 | 36.7463838589 39.6323328812 36.484234146 25.5511017726 51.9595008711 48.6311129889 52.571282651 49.5993534846 50.4660915116 30.3719906993 46.4543030372 42.1013187786 54.7638851939 43.3409145317 42.9876176802 42.5016350341 44.9003990351 38.6621087066 50.4998642204 35.7727775003 37.9688599138 35.9647173411 50.3199373427 36.9141354617 172 | 92.3164841827 53.6036606907 80.2408247052 59.9096419587 72.5113639515 49.5169965661 80.8146550388 49.6273622127 85.0253168668 69.8491345567 85.1014120196 49.879922604 81.0773713532 48.9912003221 83.8500085708 54.4582911121 81.0522739765 55.3514887285 83.2417661385 49.2468151243 71.7804033983 49.0292504888 63.6232793923 44.7199882091 173 | 56.5957268212 66.4861619186 57.3262668062 76.2420475436 49.3188735515 47.7677390556 55.0000382918 69.569366071 57.8179140686 72.4076932358 56.8076429645 61.3912422242 66.2887849338 65.5160158412 58.0981014539 60.8104526507 48.6837128248 63.0244180536 50.4992572732 61.3509393048 62.7178154915 64.8027380739 51.1519525045 61.8363641008 174 | 71.130198532 38.2845665483 77.8174982252 31.6230935286 51.2024349254 47.8424314409 64.7173880599 49.5918779658 60.0625439375 28.814777535 62.089831145 43.2953528917 60.6877687332 37.2357773929 65.9010356456 38.1969153868 70.0033169529 43.4749524852 68.2450551484 41.6621136629 64.7637676696 46.1743035443 64.780951339 43.5362519863 175 | 36.7297971525 56.1719024563 47.5321974167 76.1283827915 51.2493548382 48.8517496193 49.2430004869 49.5134390301 50.4927427013 65.8748574667 50.3739521609 52.7076059349 51.886224266 46.6827069143 50.2942389307 50.4738498423 43.3916127767 46.6762787816 50.5059850839 56.7111827006 64.8919512882 51.951138948 54.702571767 57.4194305761 176 | 69.4168237297 83.1690819018 58.5457781315 76.0633760797 73.2234625736 82.3312387934 56.4831269583 69.5710248572 70.091329673 77.740494552 65.9897949653 78.1267826946 72.0124711531 80.4303870915 67.1584637658 69.8248190808 62.5354760586 75.1119905477 68.9797275966 85.5207962242 63.2603814591 77.3890622663 75.9669447908 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98.1834095833 36.8585151844 99.6860139919 180 | 60.7555046571 66.1070156579 67.3169677374 73.4031840093 73.3294708766 82.3957569897 63.7985862345 69.5619308052 72.3540983643 73.2809688616 71.1123423883 68.8231486421 81.4210661089 71.1417287966 61.1208859477 62.5828571338 78.3696651505 62.940285322 69.0808816745 70.9612413759 66.3985808063 68.4773515844 69.6473297403 63.5093554295 181 | 52.7183843236 28.6738559507 58.2079290267 19.5358274706 49.8036552414 17.3541049007 55.7157783767 29.5022035434 56.3442763738 23.6860012577 52.313818423 27.8787342702 47.199484367 20.5057622347 62.4764683037 35.1340037568 50.6066453722 26.2695348794 50.5031351875 27.890942171 61.6650987853 25.074641231 48.1174941328 33.730109089 182 | 61.5176188718 29.7485037895 65.751489026 21.3497943962 72.2623300967 51.0379274616 63.8475160562 29.5347722075 61.0239552705 33.3590429327 64.0379035674 35.5295377024 65.8682366975 36.7517539486 67.0776479466 39.7066183708 47.9457694142 23.7863278211 68.7125615627 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