major cleanup

.Rbuildignore

@@ -4,8 +4,11 @@ visual_test
 ^\.Rproj\.user$
 ^\.travis\.yml$
 ^\.codecov\.yml$
+^_pkgdown\.yml$
 ^ISSUE_TEMPLATE\.md$
 ^README\.Rmd$
 ^cran-comments.md$
 ^TODO\.Rmd$
+^docs$
 ^revdep$
+^appveyor\.yml$

.travis.yml

@@ -1,5 +1,10 @@
 language: r
 cache: packages
 
+matrix:
+  include:
+  - r: devel
+  - r: release
+
 after_success:
   - Rscript -e 'covr::codecov()'

DESCRIPTION

@@ -1,16 +1,22 @@
 Package: ggridges
 Type: Package
 Title: Ridgeline Plots in 'ggplot2'
-Version: 0.5.1.9000
-Authors@R: c( 
-    person("Claus O.", "Wilke", , "[email protected]", c("cre", "aut")),
-    person("RStudio", role = c("cph"), comment = "Copyright for ggplot2 code copied to ggridges"))
+Version: 0.5.2.9000
+Authors@R: 
+    person(
+      given = "Claus O.",
+      family = "Wilke",
+      role = c("aut", "cre"),
+      email = "[email protected]",
+      comment = c(ORCID = "0000-0002-7470-9261")
+    )
 Description: Ridgeline plots provide a convenient way of visualizing changes in distributions over time or space. This package enables the creation of such plots in 'ggplot2'.
-URL: https://github.com/clauswilke/ggridges
+URL: https://wilkelab.org/ggridges
+BugReports: https://github.com/wilkelab/ggridges/issues
 Depends:
     R (>= 3.2)
 Imports:
-    ggplot2 (>= 2.2.0),
+    ggplot2 (>= 3.0.0),
     grid (>= 3.0.0),
     plyr (>= 1.8.0),
     scales (>= 0.4.1),
@@ -20,15 +26,13 @@ LazyData: true
 Suggests:
     covr,
     dplyr,
-    gridExtra,
+    patchwork,
     ggplot2movies,
-    DAAG,
     forcats,
     knitr,
     rmarkdown,
     testthat,
-    vdiffr,
-    viridis
+    vdiffr
 VignetteBuilder: knitr
 Collate:
     'data.R'
@@ -38,13 +42,12 @@ Collate:
     'geoms-gradient.R'
     'geom-density-line.R'
     'position.R'
-    'scale-.R'
     'scale-cyclical.R'
     'scale-point.R'
     'scale-vline.R'
     'stats.R'
     'theme.R'
     'utils_ggplot2.R'
     'utils.R'
-RoxygenNote: 6.1.0
+RoxygenNote: 7.0.2
 Roxygen: list(markdown = TRUE)

NAMESPACE

@@ -27,7 +27,6 @@ export(position_raincloud)
 export(scale_alpha_cyclical)
 export(scale_color_cyclical)
 export(scale_colour_cyclical)
-export(scale_discrete_manual)
 export(scale_fill_cyclical)
 export(scale_linetype_cyclical)
 export(scale_point_color_continuous)

NEWS → NEWS.md

@@ -1,5 +1,8 @@
 ggridges 0.5.1.9000
 ----------------------------------------------------------------
+- There is now a project website at https://wilkelab.org/ggridges
+- `scale_discrete_manual()` has been removed, as it has been available in
+  ggplot2 since version 3.0.0.
 - `stat_density_ridges()` now has a parameter `n` that determines at how many
   points along the x axis the density is estimated.
 

R/data.R

@@ -46,3 +46,15 @@
 #'     \item{`Percent`}{The percentage of the voters choosing the given option}
 #' }
 "Catalan_elections"
+
+#' Australian athletes
+#'
+#' This dataset is equivalent to `ais` from the `DAAG` package.
+#'
+#' @references
+#' Telford, R.D. and Cunningham, R.B. 1991. Sex, sport and body-size dependency of hematology in
+#' highly trained athletes. Medicine and Science in Sports and Exercise 23: 788-794.
+#'
+#' @examples
+#' # none yet
+"Aus_athletes"

R/geoms-gradient.R

@@ -43,13 +43,14 @@
 #' library(ggplot2)
 #'
 #' # Example for `geom_ridgeline_gradient()`
-#' library(viridis)
-#' d <- data.frame(x = rep(1:5, 3) + c(rep(0, 5), rep(0.3, 5), rep(0.6, 5)),
-#'                 y = c(rep(0, 5), rep(1, 5), rep(3, 5)),
-#'                 height = c(0, 1, 3, 4, 0, 1, 2, 3, 5, 4, 0, 5, 4, 4, 1))
+#' d <- data.frame(
+#'   x = rep(1:5, 3) + c(rep(0, 5), rep(0.3, 5), rep(0.6, 5)),
+#'   y = c(rep(0, 5), rep(1, 5), rep(3, 5)),
+#'   height = c(0, 1, 3, 4, 0, 1, 2, 3, 5, 4, 0, 5, 4, 4, 1)
+#' )
 #' ggplot(d, aes(x, y, height = height, group = y, fill = factor(x+y))) +
 #'   geom_ridgeline_gradient() +
-#'   scale_fill_viridis(discrete = TRUE, direction = -1) +
+#'   scale_fill_viridis_d(direction = -1) +
 #'   theme(legend.position = 'none')
 #' @importFrom ggplot2 layer
 #' @export
@@ -410,13 +411,15 @@ geom_density_ridges_gradient <- function(mapping = NULL, data = NULL, stat = "de
 #' @examples
 #'
 #' # Example for `geom_density_ridges_gradient()`
-#' ggplot(lincoln_weather, aes(x = `Mean Temperature [F]`, y = `Month`, fill = ..x..)) +
+#' ggplot(lincoln_weather, aes(x = `Mean Temperature [F]`, y = `Month`, fill = stat(x))) +
 #'   geom_density_ridges_gradient(scale = 3, rel_min_height = 0.01) +
-#'   scale_x_continuous(expand = c(0.01, 0)) +
-#'   scale_y_discrete(expand = c(0.01, 0)) +
-#'   scale_fill_viridis(name = "Temp. [F]", option = "C") +
+#'   scale_x_continuous(expand = c(0, 0)) +
+#'   scale_y_discrete(expand = c(0, 0)) +
+#'   scale_fill_viridis_c(name = "Temp. [F]", option = "C") +
+#'   coord_cartesian(clip = "off") +
 #'   labs(title = 'Temperatures in Lincoln NE in 2016') +
-#'   theme_ridges(font_size = 13, grid = TRUE) + theme(axis.title.y = element_blank())
+#'   theme_ridges(font_size = 13, grid = TRUE) +
+#'   theme(axis.title.y = element_blank())
 #' @export
 GeomDensityRidgesGradient <- ggproto("GeomDensityRidgesGradient", GeomRidgelineGradient,
   default_aes = aes(

R/stats.R

@@ -17,7 +17,7 @@
 #'   and sets it to `TRUE`.
 #' @param calc_ecdf If `TRUE`, `stat_density_ridges` calculates an empirical cumulative distribution function (ecdf)
 #'   and returns a variable `ecdf` and a variable `quantile`. Both can be mapped onto aesthetics via
-#'   `..ecdf..` and `..quantile..`, respectively.
+#'   `stat(ecdf)` and `stat(quantile)`, respectively.
 #' @param quantiles Sets the number of quantiles the data should be broken into. Used if either `calc_ecdf = TRUE`
 #'   or `quantile_lines = TRUE`. If `quantiles` is an integer then the data will be cut into that many equal quantiles.
 #'   If it is a vector of probabilities then the data will cut by them.
@@ -32,25 +32,37 @@
 #' library(ggplot2)
 #'
 #' # Examples of coloring by ecdf or quantiles
-#' library(viridis)
-#' ggplot(iris, aes(x=Sepal.Length, y=Species, fill=factor(..quantile..))) +
-#'   stat_density_ridges(geom = "density_ridges_gradient", calc_ecdf = TRUE,
-#'                       quantiles = 5) +
-#'   scale_fill_viridis(discrete = TRUE, name = "Quintiles") + theme_ridges() +
-#'   scale_y_discrete(expand = c(0.01, 0))
+#' ggplot(iris, aes(x = Sepal.Length, y = Species, fill = factor(stat(quantile)))) +
+#'   stat_density_ridges(
+#'     geom = "density_ridges_gradient",
+#'     calc_ecdf = TRUE,
+#'     quantiles = 5
+#'   ) +
+#'   scale_fill_viridis_d(name = "Quintiles") +
+#'   theme_ridges()
 #'
-#' ggplot(iris, aes(x=Sepal.Length, y=Species, fill=0.5 - abs(0.5-..ecdf..))) +
+#' ggplot(iris,
+#'   aes(
+#'     x = Sepal.Length, y = Species, fill = 0.5 - abs(0.5-stat(ecdf))
+#'   )) +
 #'   stat_density_ridges(geom = "density_ridges_gradient", calc_ecdf = TRUE) +
-#'   scale_fill_viridis(name = "Tail probability", direction = -1) + theme_ridges() +
-#'   scale_y_discrete(expand = c(0.01, 0))
+#'   scale_fill_viridis_c(name = "Tail probability", direction = -1) +
+#'   theme_ridges()
 #'
-#' ggplot(iris, aes(x=Sepal.Length, y=Species, fill=factor(..quantile..))) +
-#'   stat_density_ridges(geom = "density_ridges_gradient",
-#'                       calc_ecdf = TRUE, quantiles = c(0.025, 0.975)) +
-#'   scale_fill_manual(name = "Probability",
-#'                     values = c("#FF0000A0", "#A0A0A0A0", "#0000FFA0"),
-#'                     labels = c("(0, 0.025]", "(0.025, 0.975]", "(0.975, 1]")) +
-#'   theme_ridges() + scale_y_discrete(expand = c(0.01, 0))
+#' ggplot(iris,
+#'   aes(
+#'     x = Sepal.Length, y = Species, fill = factor(stat(quantile))
+#'   )) +
+#'   stat_density_ridges(
+#'     geom = "density_ridges_gradient",
+#'     calc_ecdf = TRUE, quantiles = c(0.025, 0.975)
+#'   ) +
+#'   scale_fill_manual(
+#'     name = "Probability",
+#'     values = c("#FF0000A0", "#A0A0A0A0", "#0000FFA0"),
+#'     labels = c("(0, 0.025]", "(0.025, 0.975]", "(0.975, 1]")
+#'   ) +
+#'   theme_ridges()
 #' @export
 stat_density_ridges <- function(mapping = NULL, data = NULL, geom = "density_ridges",
                      position = "identity", na.rm = FALSE, show.legend = NA,
@@ -297,42 +309,57 @@ StatDensityRidges <- ggproto("StatDensityRidges", Stat,
 #'
 #' ggplot(iris, aes(x = Sepal.Length, y = Species, group = Species, fill = Species)) +
 #'   geom_density_ridges(stat = "binline", bins = 20, scale = 2.2) +
-#'   scale_y_discrete(expand = c(0.01, 0)) +
-#'   scale_x_continuous(expand = c(0.01, 0)) +
+#'   scale_y_discrete(expand = c(0, 0)) +
+#'   scale_x_continuous(expand = c(0, 0)) +
+#'   coord_cartesian(clip = "off") +
 #'   theme_ridges()
 #'
 #' ggplot(iris, aes(x = Sepal.Length, y = Species, group = Species, fill = Species)) +
 #'   stat_binline(bins = 20, scale = 2.2, draw_baseline = FALSE) +
-#'   scale_y_discrete(expand = c(0.01, 0)) +
-#'   scale_x_continuous(expand = c(0.01, 0)) +
+#'   scale_y_discrete(expand = c(0, 0)) +
+#'   scale_x_continuous(expand = c(0, 0)) +
 #'   scale_fill_grey() +
-#'   theme_ridges() + theme(legend.position = 'none')
+#'   coord_cartesian(clip = "off") +
+#'   theme_ridges() +
+#'   theme(legend.position = 'none')
 #'
-#' require(ggplot2movies)
-#' require(viridis)
+#' library(ggplot2movies)
 #' ggplot(movies[movies$year>1989,], aes(x = length, y = year, fill = factor(year))) +
 #'    stat_binline(scale = 1.9, bins = 40) +
-#'    theme_ridges() + theme(legend.position = "none") +
-#'    scale_x_continuous(limits = c(1, 180), expand = c(0.01, 0)) +
-#'    scale_y_reverse(expand = c(0.01, 0)) +
-#'    scale_fill_viridis(begin = 0.3, discrete = TRUE, option = "B") +
+#'    scale_x_continuous(limits = c(1, 180), expand = c(0, 0)) +
+#'    scale_y_reverse(expand = c(0, 0)) +
+#'    scale_fill_viridis_d(begin = 0.3, option = "B") +
+#'    coord_cartesian(clip = "off") +
 #'    labs(title = "Movie lengths 1990 - 2005")
+#'    theme_ridges() +
+#'    theme(legend.position = "none")
 #'
-#' count_data <- data.frame(group = rep(letters[1:5], each = 10),
-#'                          mean = rep(1:5, each = 10))
+#' count_data <- data.frame(
+#'   group = rep(letters[1:5], each = 10),
+#'   mean = rep(1:5, each = 10)
+#' )
 #' count_data$group <- factor(count_data$group, levels = letters[5:1])
 #' count_data$count <- rpois(nrow(count_data), count_data$mean)
+#'
 #' ggplot(count_data, aes(x = count, y = group, group = group)) +
-#'   geom_density_ridges2(stat = "binline", aes(fill = group), binwidth = 1, scale = 0.95) +
-#'   geom_text(stat = "bin",
-#'           aes(y = group+0.9*..count../max(..count..),
-#'           label = ifelse(..count..>0, ..count.., "")),
-#'           vjust = 1.2, size = 3, color = "white", binwidth = 1) +
-#'   theme_ridges(grid = FALSE) +
+#'   geom_density_ridges2(
+#'     stat = "binline",
+#'     aes(fill = group),
+#'     binwidth = 1,
+#'     scale = 0.95
+#'   ) +
+#'   geom_text(
+#'     stat = "bin",
+#'     aes(y = group + 0.9*stat(count/max(count)),
+#'     label = ifelse(stat(count) > 0, stat(count), "")),
+#'     vjust = 1.2, size = 3, color = "white", binwidth = 1
+#'   ) +
 #'   scale_x_continuous(breaks = c(0:12), limits = c(-.5, 13), expand = c(0, 0)) +
-#'   scale_y_discrete(expand = c(0.01, 0)) +
+#'   scale_y_discrete(expand = c(0, 0)) +
 #'   scale_fill_cyclical(values = c("#0000B0", "#7070D0")) +
-#'   guides(y = "none")
+#'   guides(y = "none") +
+#'   coord_cartesian(clip = "off") +
+#'   theme_ridges(grid = FALSE)
 #' @importFrom stats quantile
 #' @export
 stat_binline <- function(mapping = NULL, data = NULL,

README.Rmd

@@ -14,34 +14,31 @@ knitr::opts_chunk$set(
 
 # ggridges
 
-[![Build Status](https://travis-ci.org/clauswilke/ggridges.svg?branch=master)](https://travis-ci.org/clauswilke/ggridges)
+<!-- badges: start -->
+[![Build Status](https://travis-ci.org/wilkelab/ggridges.svg?branch=master)](https://travis-ci.org/wilkelab/ggridges)
+[![AppVeyor build status](https://ci.appveyor.com/api/projects/status/github/wilkelab/ggridges?branch=master&svg=true)](https://ci.appveyor.com/project/clauswilke/ggridges)
 [![Coverage Status](https://img.shields.io/codecov/c/github/clauswilke/ggridges/master.svg)](https://codecov.io/github/clauswilke/ggridges?branch=master)
 [![CRAN_Status_Badge](http://www.r-pkg.org/badges/version/ggridges)](https://CRAN.R-project.org/package=ggridges)
 [![CRAN_Downloads_Badge](http://cranlogs.r-pkg.org/badges/ggridges)](http://cranlogs.r-pkg.org/downloads/total/last-month/ggridges)
+<!-- badges: end -->
 
-
-Geoms to make ridgeline plots using ggplot2, written by Claus O. Wilke
-
-This package has now been officially relased on CRAN. Most things should work as expected, and the API should now be relatively stable. For feedback and feature requests, please open issues on github.
-
-## About ridgeline plots
+Draw ridgeline plots using ggplot2.
 
 Ridgeline plots are partially overlapping line plots that create the impression of a mountain range. They can be quite useful for visualizing changes in distributions over time or space. These types of plots have also been called ["joyplots"](
 https://twitter.com/JennyBryan/status/856674638981550080), in reference to the [iconic cover art](https://blogs.scientificamerican.com/sa-visual/pop-culture-pulsar-origin-story-of-joy-division-s-unknown-pleasures-album-cover-video/) for Joy Division's album _Unknown Pleasures_. However, given the [unfortunate origin](https://en.wikipedia.org/wiki/House_of_Dolls) of the name Joy Division, the term "joyplot" is now discouraged.
 
 ## Installation
 
-Stable release:
+Please install the stable release from CRAN:
 
 ```{r eval = FALSE}
 install.packages("ggridges")
 ```
 
-Latest development version:
+Alternatively, you can install the latest development version from github:
 
 ```{r eval = FALSE}
-library(devtools)
-install_github("clauswilke/ggridges")
+remotes::install_github("wilkelab/ggridges")
 ```
 
 ## Usage
@@ -51,9 +48,11 @@ library(ggplot2)
 library(ggridges)
     
 ggplot(diamonds, aes(x = price, y = cut)) +
-  geom_density_ridges(scale = 4) + theme_ridges() +
-  scale_y_discrete(expand = c(0.01, 0)) +   # will generally have to set the `expand` option
-  scale_x_continuous(expand = c(0, 0))      # for both axes to remove unneeded padding
+  geom_density_ridges(scale = 4) + 
+  scale_y_discrete(expand = c(0, 0)) +     # will generally have to set the `expand` option
+  scale_x_continuous(expand = c(0, 0)) +   # for both axes to remove unneeded padding
+  coord_cartesian(clip = "off") + # to avoid clipping of the very top of the top ridgeline
+  theme_ridges()
 ```
 
 ## Documentation and Examples