{"Package":"scales","Title":"Scale Functions for Visualization","Version":"1.2.1","Authors@R":"c(\nperson(\"Hadley\", \"Wickham\", , \"hadley@rstudio.com\", role = c(\"aut\", \"cre\")),\nperson(\"Dana\", \"Seidel\", role = \"aut\"),\nperson(\"RStudio\", role = c(\"cph\", \"fnd\"))\n)","Description":"Graphical scales map data to aesthetics, and provide methods\nfor automatically determining breaks and labels for axes and legends.","License":"MIT + file LICENSE","URL":"https://scales.r-lib.org, https://github.com/r-lib/scales","BugReports":"https://github.com/r-lib/scales/issues","Depends":"R (>= 3.2)","Imports":"farver (>= 2.0.3), labeling, lifecycle, munsell (>= 0.5), R6,\nRColorBrewer, rlang (>= 1.0.0), viridisLite","Suggests":"bit64, covr, dichromat, ggplot2, hms (>= 0.5.0), stringi,\ntestthat (>= 3.0.0), waldo (>= 0.4.0)","Config/Needs/website":"tidyverse/tidytemplate","Encoding":"UTF-8","LazyLoad":"yes","RoxygenNote":"7.2.1","Config/testthat/edition":"3","NeedsCompilation":"no","Packaged":"2022-08-19 15:35:49 UTC; hadleywickham","Author":"Hadley Wickham [aut, cre],\nDana Seidel [aut],\nRStudio [cph, fnd]","Maintainer":"Hadley Wickham ","Repository":"CRAN","Date/Publication":"2022-08-20 00:10:11 UTC","repoType":"cran","tarballUrl":"ftp://cran.r-project.org/pub/R/src/contrib/scales_1.2.1.tar.gz","jsonAuthors":[{"name":"Hadley Wickham","email":"hadley@rstudio.com","maintainer":true},{"name":"Dana Seidel","email":null,"maintainer":false},{"name":"RStudio ","email":null,"maintainer":false}],"readme":"\n\n\n# scales

\n\n\n\n[![CRAN\nstatus](https://www.r-pkg.org/badges/version/scales)](https://CRAN.R-project.org/package=scales)\n[![R build\nstatus](https://github.com/r-lib/scales/workflows/R-CMD-check/badge.svg)](https://github.com/r-lib/scales/actions)\n[![Codecov test\ncoverage](https://codecov.io/gh/r-lib/scales/branch/main/graph/badge.svg)](https://app.codecov.io/gh/r-lib/scales?branch=main)\n\n\nOne of the most difficult parts of any graphics package is scaling,\nconverting from data values to perceptual properties. The inverse of\nscaling, making guides (legends and axes) that can be used to read the\ngraph, is often even harder! The scales packages provides the internal\nscaling infrastructure used by\n[ggplot2](https://ggplot2.tidyverse.org/), and gives you tools to\noverride the default breaks, labels, transformations and palettes.\n\n## Installation\n\n``` r\n# Scales is installed when you install ggplot2 or the tidyverse.\n# But you can install just scales from CRAN:\ninstall.packages(\"scales\")\n\n# Or the development version from Github:\n# install.packages(\"devtools\")\ndevtools::install_github(\"r-lib/scales\")\n```\n\n## Usage\n\n### Breaks and labels\n\nThe most common use of the scales package is to customise to control the\nappearance of axis and legend labels. Use a `break_` function to control\nhow breaks are generated from the limits, and a `label_` function to\ncontrol how breaks are turned in to labels.\n\n``` r\nlibrary(ggplot2)\nlibrary(dplyr, warn.conflicts = FALSE)\nlibrary(lubridate, warn.conflicts = FALSE)\n\ntxhousing %>% \n mutate(date = make_date(year, month, 1)) %>% \n group_by(city) %>% \n filter(min(sales) > 5e2) %>% \n ggplot(aes(date, sales, group = city)) + \n geom_line(na.rm = TRUE) + \n scale_x_date(\n NULL,\n breaks = scales::breaks_width(\"2 years\"), \n labels = scales::label_date(\"'%y\")\n ) + \n scale_y_log10(\n \"Total sales\",\n labels = scales::label_number(scale_cut = scales::cut_short_scale())\n )\n```\n\n![](man/figures/README-labels-1.png)\n\n``` r\neconomics %>% \n filter(date < ymd(\"1970-01-01\")) %>% \n ggplot(aes(date, pce)) + \n geom_line() + \n scale_x_date(NULL,\n breaks = scales::breaks_width(\"3 months\"), \n labels = scales::label_date_short()\n ) + \n scale_y_continuous(\"Personal consumption expenditures\",\n breaks = scales::breaks_extended(8),\n labels = scales::label_dollar() \n )\n```\n\n![](man/figures/README-labels-2.png)\n\nGenerally, I don’t recommend running `library(scales)` because when you\ntype (e.g.) `scales::label_` autocomplete will provide you with a list\nof labelling functions to job your memory.\n\n### Advanced features\n\nScales colour palettes are used to power the scales in ggplot2, but you\ncan use them in any plotting system. The following example shows how you\nmight apply them to a base plot.\n\n``` r\nlibrary(scales)\n# pull a list of colours from any palette\nviridis_pal()(4)\n#> [1] \"#440154FF\" \"#31688EFF\" \"#35B779FF\" \"#FDE725FF\"\n\n# use in combination with baseR `palette()` to set new defaults\npalette(brewer_pal(palette = \"Set2\")(4))\npar(mar = c(5, 5, 1, 1))\nplot(Sepal.Length ~ Sepal.Width, data = iris, col = Species, pch = 20)\n```\n\n![](man/figures/README-palettes-1.png)\n\nscales also gives users the ability to define and apply their own custom\ntransformation functions for repeated use.\n\n``` r\n# use trans_new to build a new transformation\nlogp3_trans <- trans_new(\n name = \"logp\",\n transform = function(x) log(x + 3),\n inverse = function(x) exp(x) - 3,\n breaks = log_breaks()\n)\n\ndsamp <- sample_n(diamonds, 100)\nggplot(dsamp, aes(carat, price, colour = color)) +\n geom_point() + \n scale_y_continuous(trans = logp3_trans)\n```\n\n![](man/figures/README-transforms-1.png)","jobInfo":{"package":"scales","version":"1.2.1","parsingStatus":"success","parserVersion":2,"parsedAt":"2023-11-28T10:30:28+0000"}}

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