├── .gitignore ├── DESCRIPTION ├── LICENSE ├── NAMESPACE ├── NEWS.md ├── R ├── aaa.R ├── data.R ├── geom_posterior.R ├── ggdistribute-package.R ├── gghelpers.R ├── helpers.R ├── position_spread.R ├── stat_posterior_density.R ├── stats.R ├── theme.R └── zzz.R ├── README.Rmd ├── README.md ├── cran-comments.md ├── data └── sre.rda ├── ggdistribute.Rproj ├── inst └── CITATION ├── man ├── GeomPosterior.Rd ├── annotate_corner.Rd ├── cmode.Rd ├── data_normal_sample.Rd ├── dmode.Rd ├── example_plot.Rd ├── figures │ ├── README-candy_wrappers-1.png │ ├── README-rainbow_hills-1.png │ ├── README-space_ships-1.png │ └── README-turtle_snails-1.png ├── ggdistribute-package.Rd ├── hdi.Rd ├── label_plot.Rd ├── mejr_geom_defaults.Rd ├── mejr_palette.Rd ├── position_spread.Rd ├── post_int.Rd ├── posterior_plot.Rd ├── scale_add.Rd ├── show_colors.Rd ├── sre.Rd ├── test_mejr_theme.Rd ├── theme_mejr.Rd └── trim_ends.Rd ├── tests ├── testthat.R └── testthat │ ├── test_geom_posterior.R │ ├── test_position_spread.R │ └── test_stat_density.R └── vignettes └── geom_posterior.Rmd /.gitignore: -------------------------------------------------------------------------------- 1 | Meta 2 | doc 3 | .html 4 | .httr-oauth 5 | .junk 6 | .Rapp.history 7 | .Rbuildignore 8 | .RData 9 | .Rhistory 10 | .vscode 11 | *-Ex.R 12 | *.dll 13 | *.DS_Store 14 | *.knit.md 15 | *.o 16 | *.so 17 | *.utf8.md 18 | /*_cache/ 19 | /*.Rcheck/ 20 | /*.tar.gz 21 | /cache/ 22 | src-i386 23 | src-x64 24 | vignettes/*.html 25 | vignettes/*.pdf 26 | 27 | ggdistribute.code-workspace 28 | .Rproj.user 29 | CRAN-RELEASE 30 | -------------------------------------------------------------------------------- /DESCRIPTION: -------------------------------------------------------------------------------- 1 | Package: ggdistribute 2 | Title: A 'ggplot2' Extension for Plotting Unimodal Distributions 3 | Version: 1.0.3 4 | Date: 2018-11-15 5 | Authors@R: c( 6 | person("Joseph M.", "Burling", 7 | email = "josephburling@gmail.com", 8 | role = c("aut", "cre"))) 9 | Description: 10 | The 'ggdistribute' package is an extension for plotting posterior or other 11 | types of unimodal distributions that require overlaying information about a 12 | distribution's intervals. It makes use of the 'ggproto' system to extend 13 | 'ggplot2', providing additional "geoms", "stats", and "positions." 14 | The extensions integrate with existing 'ggplot2' layer elements. 15 | URL: https://github.com/iamamutt/ggdistribute 16 | License: GPL-3 17 | Encoding: UTF-8 18 | LazyData: true 19 | Depends: 20 | R (>= 3.5.0) 21 | Imports: 22 | data.table, 23 | ggplot2 (>= 3.0.0), 24 | tibble, 25 | magrittr, 26 | grDevices, 27 | dplyr 28 | Suggests: 29 | knitr, 30 | testthat, 31 | rmarkdown, 32 | viridisLite, 33 | extrafont 34 | VignetteBuilder: knitr 35 | RoxygenNote: 6.1.1 36 | Roxygen: list(markdown=TRUE) 37 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /NAMESPACE: -------------------------------------------------------------------------------- 1 | # Generated by roxygen2: do not edit by hand 2 | 3 | export(annotate_corner) 4 | export(cmode) 5 | export(data_normal_sample) 6 | export(dmode) 7 | export(example_plot) 8 | export(geom_posterior) 9 | export(hdi) 10 | export(label_plot) 11 | export(mejr_geom_defaults) 12 | export(mejr_palette) 13 | export(position_spread) 14 | export(post_int) 15 | export(posterior_plot) 16 | export(show_colors) 17 | export(sre_data) 18 | export(stat_density_ci) 19 | export(theme_mejr) 20 | export(trim_ends) 21 | import(data.table) 22 | import(ggplot2) 23 | importFrom(grDevices,col2rgb) 24 | importFrom(grDevices,gray) 25 | importFrom(grDevices,rainbow) 26 | importFrom(grDevices,rgb) 27 | importFrom(magrittr,"%>%") 28 | importFrom(stats,density) 29 | importFrom(stats,ecdf) 30 | importFrom(stats,mad) 31 | importFrom(stats,median) 32 | importFrom(stats,na.omit) 33 | importFrom(stats,quantile) 34 | importFrom(stats,rgamma) 35 | importFrom(stats,rnbinom) 36 | importFrom(stats,rnorm) 37 | importFrom(stats,runif) 38 | importFrom(stats,sd) 39 | importFrom(utils,capture.output) 40 | importFrom(utils,modifyList) 41 | -------------------------------------------------------------------------------- /NEWS.md: -------------------------------------------------------------------------------- 1 | # ggdistribute 1.0.3 2 | 3 | * Change all occurences of `midline_color` to `midline` to avoid warnings for `_colour` parameters 4 | 5 | # ggdistribute 1.0.2 6 | 7 | * `show_colors()` theme update for ggplot2 2.3.0 8 | 9 | # ggdistribute 1.0.1 10 | 11 | * Fix R CMD Check notes 12 | * Align geoms to y axis 13 | * Extend README 14 | * Fix reverse in position_spread 15 | 16 | # ggdistribute 1.0.0 17 | 18 | * Upload initial version of package to GitHub 19 | 20 | # ggdistribute 0.0.0.9001 21 | 22 | * Added a `NEWS.md` file to track changes to the package. 23 | -------------------------------------------------------------------------------- /R/aaa.R: -------------------------------------------------------------------------------- 1 | # "fix" R CMD check notes 2 | if (getRversion() >= "2.15.1") { 3 | utils::globalVariables( 4 | c( 5 | ".", ".__adj", ".__by_ht", ".__grp", ".__ht", ".__len", ".__max", ".__mid", 6 | ".__min", ".__n", ".__tmp", ".__y", "PANEL", "group", "grp_max", "grp_min", "x", 7 | "y", "ylower", "ymax", "ymin", "yupper" 8 | ) 9 | ) 10 | } 11 | 12 | if (getRversion() >= "3.1.0") { 13 | utils::suppressForeignCheck( 14 | c( 15 | ".", ".__adj", ".__by_ht", ".__grp", ".__ht", ".__len", ".__max", ".__mid", 16 | ".__min", ".__n", ".__tmp", ".__y", "PANEL", "group", "grp_max", "grp_min", "x", 17 | "y", "ylower", "ymax", "ymin", "yupper" 18 | ) 19 | ) 20 | } 21 | -------------------------------------------------------------------------------- /R/data.R: -------------------------------------------------------------------------------- 1 | #' Raw SRE dataset 2 | #' 3 | #' @format Dataset is an object of class [tibble::tibble]. 4 | #' @seealso [sre_data()] 5 | #' @return A dataset of [tibble::tibble]. 6 | #' @examples 7 | #' sre 8 | #' 9 | #' sre_data() 10 | "sre" 11 | 12 | 13 | #' @describeIn sre create samples from sre data 14 | #' @param n number of samplers per effect and contrast 15 | #' @param seed set.seed number 16 | #' @export 17 | sre_data <- function(n=1000, seed=19850519) { 18 | set.seed(seed) 19 | 20 | # R CMD check 21 | effect <- contrast <- value <- NULL 22 | 23 | as_dtbl(get("sre")) %>% 24 | .[ 25 | , .(value=sample(value, n, replace=TRUE)), 26 | .(effect, contrast) 27 | ] %>% 28 | tibble::as.tibble() 29 | } 30 | 31 | 32 | #' Testing dataset of grouped Normal distributions 33 | #' 34 | #' @param mu Means for each group. A [numeric] vector with the length 35 | #' corresponding to the number of groups. 36 | #' @param n Number of observations for each group. Length 1 [integer]. 37 | #' @param sd_range The min and max to use for standard deviations. Length 2 38 | #' @param seed A seed value to generate the same sample. [numeric] vector. 39 | #' @return A [data.frame] with the following variables: `Group`, `Condition`, 40 | #' `value`. 41 | #' @export 42 | #' @examples 43 | #' data_normal_sample(0, 100) 44 | data_normal_sample <- function(mu=c(-0.5, 4), n=500L, sd_range=c(0.6, 1.4), 45 | seed=19850519) { 46 | set.seed(seed) 47 | 48 | k <- clip_range(length(mu), max=13) 49 | N <- n * k 50 | 51 | if (k == 1) { 52 | adj <- 0 53 | } else { 54 | adj <- median(diff(sort(unique(c(0, mu))))) / max(1, (k - 1)) 55 | } 56 | 57 | adj <- c(-adj, adj) 58 | adj_len <- rep(adj, each=ceiling(n / 2))[1:n] 59 | 60 | value <- unlist(lapply(mu, function(x) { 61 | rnorm(n, mean=x, sd=runif(1L, 62 | min=sd_range[1], 63 | max=sd_range[2])) + adj_len 64 | })) 65 | 66 | scores <- unlist(lapply(mu, function(x) { 67 | sort(runif(2L, x + adj[1], x + adj[2])) 68 | })) 69 | 70 | Condition <- rep(LETTERS[seq_len(k)], each=n) 71 | Group <- sort(rep_len(letters[seq_len(k * 2)], N)) 72 | GroupScore <- rep(scores, each=ceiling(N / k / 2))[1:N] 73 | 74 | tibble::tibble(Condition, Group, GroupScore, value) 75 | } 76 | 77 | ggdist_data <- function(n=500, j=3, k=2, na.rm=TRUE, seed=20130110) { 78 | set.seed(seed) 79 | j <- max(min(j, 26), 1) 80 | k <- max(min(k, 26), 1) 81 | n_jk <- j * k 82 | n_i <- round(max(1, n / n_jk)) 83 | 84 | # R CMD check 85 | jk_i <- qmin <- qmax <- value <- variable <- NULL 86 | 87 | dt <- expand.grid(j=seq_len(j), k=seq_len(k)) %>% 88 | as.data.table() %>% 89 | rep(n_i) %>% 90 | .[, `:=`(j_discrete=LETTERS[j], k_discrete=letters[k])] %>% 91 | .[order(j, k)] %>% 92 | .[, value := rnorm(.N, 2 * j, k^.618), .(j, k)] %>% 93 | cbind(.[, probit_tbl(value, n_jk)]) %>% 94 | .[, variable := runif(.N, qmin, qmax), .(qmin, qmax)] %>% 95 | .[, `:=`(`jk_i`=.GRP), .(j, k)] %>% 96 | .[`jk_i` == max(`jk_i`), value := NA] %>% 97 | .[, .__tmp := min(variable, na.rm=TRUE)] %>% 98 | .[j == min(2, max(j)), variable := { 99 | variable[which.min(variable)] <- .__tmp[1] 100 | variable 101 | }] %>% 102 | .[order(j, k, variable)] %>% 103 | .[1, variable := min(.__tmp, na.rm=TRUE)] %>% 104 | .[, .__tmp := NULL] %>% 105 | .[, I := 1:.N] 106 | 107 | 108 | if (!na.rm) { 109 | return(tibble::as.tibble(dt)) 110 | } 111 | 112 | dt[!is.na(value), ] %>% 113 | tibble::as.tibble() 114 | } 115 | -------------------------------------------------------------------------------- /R/geom_posterior.R: -------------------------------------------------------------------------------- 1 | # Posterior layer wrapper ------------------------------------------------- 2 | 3 | #' @describeIn GeomPosterior geom_posterior Posterior Geom 4 | #' @export 5 | geom_posterior <- function(mapping=NULL, data=NULL, stat="DensityCI", position="spread", 6 | ..., draw_ci=TRUE, draw_sd=TRUE, midline="#767698", 7 | brighten=TRUE, mirror=FALSE, interp_thresh=NULL, na.rm=FALSE, 8 | show.legend=NA, inherit.aes=TRUE) { 9 | layer( 10 | geom=GeomPosterior, stat=stat, data=data, mapping=mapping, position=position, 11 | show.legend=show.legend, inherit.aes=inherit.aes, 12 | params=list( 13 | draw_ci=draw_ci, draw_sd=draw_sd, midline=midline, 14 | mirror=mirror, brighten=brighten, interp_thresh=interp_thresh, 15 | na.rm=na.rm, ... 16 | ) 17 | ) 18 | } 19 | 20 | # ggproto object ---------------------------------------------------------- 21 | 22 | GeomPosterior <- ggproto( 23 | "GeomPosterior", Geom, 24 | default_aes=aes( 25 | weight=1, color="#585872", fill="#8989B2", size=0.5, 26 | alpha=NA, linetype="solid", vjust=1 27 | ), 28 | required_aes=c("x", "y"), non_missing_aes=character(), 29 | optional_aes=c("xmin", "cil", "sdl", "mid", "sdu", "ciu", "xmax"), 30 | draw_key=draw_key_polygon, 31 | 32 | 33 | setup_data=function(self, data, params) { 34 | if (!("y" %Names?% data) && "count" %Names?% data) { 35 | warning("Missing `y` in aesthetics. Defaulting to `count`", call.=FALSE) 36 | data$y <- data$count 37 | } 38 | 39 | mirror <- params$mirror %NA% FALSE 40 | 41 | if (is.null(params$vjust) && mirror) { 42 | params$vjust <- 0.5 43 | } 44 | 45 | compute_vjust(data, "y", params$vjust) 46 | }, 47 | 48 | draw_group=function(self, data, ..., # panel_scales, coord, 49 | draw_ci=TRUE, draw_sd=TRUE, midline="#767698", 50 | brighten=TRUE, mirror=FALSE, interp_thresh=NULL) { 51 | if (nrow(data) == 1) { 52 | return(zeroGrob()) 53 | } 54 | 55 | # handle NA options 56 | draw_ci <- draw_ci %NA% FALSE 57 | draw_sd <- draw_sd %NA% FALSE 58 | 59 | if (is.null(midline)) { 60 | midline <- first_non_na(data$colour) 61 | } 62 | 63 | midline <- midline %NA% NA 64 | brighten <- brighten %NA% FALSE 65 | mirror <- mirror %NA% FALSE 66 | 67 | warn <- is.finite(interp_thresh %:% Inf) %?% FALSE %:% TRUE 68 | interp_thresh <- interp_thresh %:% 0.01 69 | 70 | if (draw_ci) { 71 | assert_names( 72 | c("cil", "ciu"), data, "No confidence intervals found.", 73 | " Set `draw_ci=FALSE` to remove this segment or use ", 74 | "`stat_density_ci` to compute this variable automatically." 75 | ) 76 | } 77 | 78 | if (draw_sd) { 79 | assert_names( 80 | c("sdl", "sdu"), data, "No SD intervals found.", 81 | " Set `draw_sd=FALSE` to remove this segment or use ", 82 | "`stat_density_ci` to compute this variable automatically." 83 | ) 84 | } 85 | 86 | dt <- as_dtbl(data, copy=TRUE) %>% 87 | set_range_data("x", names=c("xmin", "xmax"), force_cols=FALSE, copy=FALSE) %>% 88 | set_range_data("y", names=c("ymin", "ymax"), force_cols=FALSE, copy=FALSE) %>% 89 | set_range_data( 90 | "y", 91 | names=c("grp_min", "grp_max"), force_cols=TRUE, copy=FALSE 92 | ) 93 | 94 | params <- setup_posterior_params( 95 | dt, 96 | draw_ci=draw_ci, draw_sd=draw_sd, midline=midline, 97 | mirror=mirror, brighten=brighten, interp_thresh=interp_thresh, warn=warn 98 | ) 99 | 100 | ggname( 101 | "geom_posterior", 102 | grid::gTree(children=do.call(grid::gList, c( 103 | get_posterior_segment_grobs( 104 | dt, params, ... 105 | ), 106 | get_posterior_line_grobs( 107 | dt, params, ... 108 | ) 109 | ))) 110 | ) 111 | } 112 | ) 113 | 114 | # subfunctions ------------------------------------------------------------ 115 | 116 | setup_posterior_params <- function(data, ...) { 117 | c(..., setup_post_seg_params(data, ...), setup_post_line_params(data, ...)) 118 | } 119 | 120 | setup_post_seg_params <- function(data, brighten, draw_ci, draw_sd, ...) { 121 | fill <- first_non_na(data$fill) %NA% NA 122 | 123 | brighten <- if (is.logical(brighten)) { 124 | if (brighten) { 125 | c(4, 0, 1.333, 0, 4) 126 | } else { 127 | rep(0, 5L) 128 | } 129 | } else { 130 | rep_len(c(brighten, rev(brighten)[-1]), 5L) 131 | } 132 | 133 | segments <- c("xmin", "cil", "sdl", "sdu", "ciu", "xmax") 134 | 135 | if (!draw_ci) { 136 | segments[c(2, 5)] <- NA 137 | brighten[c(2, 4, 5)] <- NA 138 | } 139 | 140 | if (!draw_sd) { 141 | segments[c(3, 4)] <- NA 142 | brighten[c(3, 4)] <- NA 143 | } 144 | 145 | brighten <- na.omit(brighten) 146 | segments <- na.omit(segments) 147 | n <- length(brighten) 148 | segment_list <- structure(vector("list", n), .Names=segments[1:n]) 149 | 150 | for (j in seq_len(n)) { 151 | cols <- segments[c(j, j + 1)] 152 | segment_list[[j]] <- list( 153 | lower_cut=first_non_na(data[[cols[1]]]) %:% NA_real_, 154 | upper_cut=first_non_na(data[[cols[2]]]) %:% NA_real_, 155 | fill=change_brightness(fill, brighten[j]) 156 | ) 157 | } 158 | 159 | c(list(segments=segment_list), fill=fill) 160 | } 161 | 162 | setup_post_line_params <- function(data, midline, draw_ci, draw_sd, ...) { 163 | colour <- first_non_na(data$colour) %NA% NA 164 | 165 | N <- 5 166 | line_pos <- c("cil", "sdl", "mid", "sdu", "ciu") 167 | line_widths <- c(1, 1.45, 2, 1.45, 1) 168 | line_colors <- rep(colour, N) 169 | line_colors[3] <- midline 170 | 171 | omit <- integer() 172 | 173 | if (!draw_ci) { 174 | omit <- append(omit, c(1, 5)) 175 | } 176 | 177 | if (!draw_sd) { 178 | omit <- append(omit, c(2, 4)) 179 | } 180 | 181 | keep <- which(!(seq_len(N) %in% omit)) 182 | n <- length(keep) 183 | line_list <- structure(vector("list", n), .Names=line_pos[keep]) 184 | 185 | for (j in seq_len(n)) { 186 | line_list[[j]] <- list( 187 | ci_column=line_pos[keep[j]], ci_color=line_colors[keep[j]], 188 | ci_lwd_adj=line_widths[keep[j]] 189 | ) 190 | } 191 | 192 | c(list(lines=line_list), colour=colour) 193 | } 194 | 195 | get_posterior_data <- function(data, lower_cut=NULL, upper_cut=NULL, interp_thresh=NULL, 196 | mirror=FALSE, colour="#000000", fill=NA, warn=TRUE) { 197 | dt <- compute_post_seg_data(data, lower_cut, upper_cut, interp_thresh, warn) 198 | 199 | # find the bottom and top parts of the distribution to make a complete line 200 | assert_names(c("ymin", "ymax", "grp_max", "grp_min"), data) 201 | 202 | if (mirror) { 203 | dt[, `:=`(ylower=grp_max / 2 + grp_min - y / 2, yupper=grp_max / 2 + y / 2)] 204 | 205 | adj <- 0 206 | adj <- dt[ 207 | , .(adj=mean(c(ymax, ymin)) - mean(c(grp_min, grp_max))), 208 | .(grp_min, grp_max, ymin, ymax) 209 | ] %>% 210 | .[, adj] 211 | 212 | dt[, `:=`(ylower=ylower + adj, yupper=yupper + adj)] 213 | } else { 214 | dt[, `:=`(ylower=grp_min, yupper=y)] 215 | } 216 | 217 | rbind( 218 | copy(dt) %>% 219 | .[, y := ylower] %>% 220 | .[order(x)], 221 | copy(dt) %>% 222 | .[, y := yupper] %>% 223 | .[order(-x)] 224 | ) %>% 225 | rm_temp_cols(c("colour", "fill")) %>% 226 | .[, `:=`(colour=colour, fill=fill)] %>% 227 | rbind(.[1, ]) 228 | } 229 | 230 | compute_post_seg_data <- function(data, lower_cut=NULL, upper_cut=NULL, 231 | interp_thresh=NULL, warn=TRUE) { 232 | assert_names(c("xmin", "xmax", "x", "y"), data) 233 | 234 | interp_thresh <- interp_thresh %NA% Inf 235 | 236 | if (is.finite(interp_thresh) && interp_thresh <= 0) { 237 | stop("`interp_thresh` must be greater than 0.") 238 | } 239 | 240 | dt <- as_dtbl(data, copy=TRUE) %>% 241 | .[order(x, y), .(x, y)] 242 | 243 | static <- get_static_data(data, dt) 244 | 245 | # use xmin, xmax if not specifying horiz. cutoff points 246 | if (all_missing(lower_cut)) { 247 | lower_cut <- first_non_na(static$xmin) 248 | } 249 | 250 | if (all_missing(upper_cut)) { 251 | upper_cut <- first_non_na(static$xmax) 252 | } 253 | 254 | # check that density data is within cut thresholds 255 | within_range <- dt[, x >= lower_cut & x <= upper_cut] 256 | if (!any(within_range)) { 257 | warning(sprintf( 258 | "No data found between the cutoff points: (%.2f, %.2f)", lower_cut, 259 | upper_cut 260 | ), call.=FALSE) 261 | return(cbind(dt[1, ], static)) 262 | } 263 | 264 | # do further interpolation if cut points are too far away from x density data 265 | within_range <- which(within_range) 266 | 267 | if (!c("cil", "ciu") %Names?% static) { 268 | ci_width <- static$xmax - static$xmin 269 | } else { 270 | ci_width <- static$ciu - static$cil 271 | } 272 | 273 | lower_gap <- (dt[within_range, min(x)] - lower_cut) / ci_width 274 | upper_gap <- (upper_cut - dt[within_range, max(x)]) / ci_width 275 | 276 | if (lower_gap + upper_gap > interp_thresh * 2) { 277 | if (warn) { 278 | gap_info <- sprintf( 279 | paste0( 280 | "The left side at %.2f is missing %.2f%%,", 281 | " and the right side at %.2f is missing %.2f%%", 282 | " of the CI width." 283 | ), lower_cut, lower_gap * 100, 284 | upper_cut, upper_gap * 100 285 | ) 286 | 287 | warning( 288 | "Interpolating gaps. ", gap_info, " Try setting `n` to a higher value,", 289 | " setting `interp_thresh` to a smaller value, ", 290 | "or do `interp_thresh=NA` to turn off interpolation completely.", 291 | call.=FALSE 292 | ) 293 | } 294 | dxy <- interp_low_res( 295 | dt$x, dt$y, lower_cut, upper_cut, max(1 / interp_thresh, 1024) 296 | ) 297 | cbind(dxy, static) 298 | } else { 299 | cbind(dt[within_range, ], static) 300 | } 301 | } 302 | 303 | compute_post_line_data <- function(data, ci_column, ci_color="#000000", ci_lwd_adj=1) { 304 | line_column <- data[[ci_column]] 305 | 306 | if (all_missing(line_column)) { 307 | return(as_dtbl(data[1, ])) 308 | } 309 | 310 | assert_names(c("ylower", "yupper", "x"), data) 311 | 312 | dt <- as_dtbl(data) 313 | vline <- first_non_na(line_column) 314 | 315 | grob_data <- data.table( 316 | x=c(vline, vline), y=interp_vert_line( 317 | dt$x, dt$ylower, dt$yupper, vline 318 | ), 319 | group=c(vline, vline) 320 | ) 321 | 322 | other_data <- get_static_data(dt, grob_data) 323 | grob_data <- cbind(rep(as_dtbl(other_data), nrow(grob_data)), grob_data) 324 | grob_data$colour <- ci_color 325 | grob_data$size <- grob_data$size * ci_lwd_adj 326 | grob_data$fill <- NA 327 | grob_data 328 | } 329 | 330 | 331 | get_posterior_line_grobs <- function(data, params, ...) { 332 | outline_pars <- params[c("interp_thresh", "mirror", "colour")] 333 | outline_pars$data <- data 334 | outline_pars$fill <- NA 335 | lines_data <- do.call(get_posterior_data, outline_pars) 336 | 337 | # path grabs of vertical lines 338 | ci <- lapply(params$lines, function(j) { 339 | j$data <- lines_data 340 | grob_data <- do.call(compute_post_line_data, j) 341 | if (nrow(grob_data) < 2) { 342 | return(zeroGrob()) 343 | } 344 | 345 | 346 | return(GeomPath$draw_panel(grob_data, ...)) 347 | }) 348 | 349 | # path grob of density outline 350 | if (nrow(lines_data) < 2) { 351 | dens <- zeroGrob() 352 | } else { 353 | dens <- grob_posterior(lines_data, ..., use_fill=FALSE, use_color=TRUE) 354 | } 355 | 356 | c(ci, list(dens)) 357 | } 358 | 359 | # polygon grobs of distribution segments 360 | get_posterior_segment_grobs <- function(data, params, ...) { 361 | shared_pars <- params[c("interp_thresh", "warn", "mirror")] 362 | shared_pars$colour <- NA 363 | shared_pars$data <- data 364 | 365 | lapply(params$segments, function(j) { 366 | segment <- do.call(get_posterior_data, c(shared_pars, j)) 367 | 368 | grob_posterior(segment, ..., use_fill=TRUE, use_color=FALSE) 369 | }) 370 | } 371 | 372 | grob_posterior <- function(data, ..., use_fill=FALSE, use_color=TRUE) { 373 | if (nrow(data) == 1) { 374 | return(zeroGrob()) 375 | } 376 | 377 | munch_args <- structure(list(..., data), .Names=c("range", "coord", "data")) 378 | coords <- do.call(coord_munch, munch_args) 379 | 380 | # Sort by group to make sure that colors, fill, etc. come in same order 381 | coords <- coords[order(coords$group), ] 382 | first_rows <- coords[!duplicated(coords$group), ] 383 | 384 | ggname( 385 | "posterior_segment", 386 | grid::polygonGrob( 387 | coords$x, coords$y, 388 | default.units="native", id=coords$group, 389 | gp=grid::gpar( 390 | col=use_color %?% (first_rows$colour %:% NA) %:% NA, 391 | fill=use_fill %?% (first_rows$fill %:% NA) %:% NA, 392 | alpha=all_missing(first_rows$alpha) %?% 393 | 1 %:% ((first_rows$alpha) %:% 1), lwd=first_rows$size, 394 | lex=.pt, lty=first_rows$linetype 395 | ) 396 | ) 397 | ) 398 | } 399 | 400 | compute_vjust <- function(data, axis, vjust=NULL) { 401 | if (is.null(vjust)) { 402 | return(data) 403 | } 404 | 405 | dt <- set_range_data(data, axis, force_cols=TRUE, copy=FALSE) 406 | 407 | if (length(unique(dt$group)) == 1) { 408 | spacing <- dt[, median(.__len, na.rm=TRUE)] 409 | } else { 410 | spacing <- dt[] %>% 411 | .[, .__grp := .GRP, .__min] %>% 412 | .[, .__len := unique_apply(.__min, .__grp, append_diff, NA)] %>% 413 | .[, median(.__len, na.rm=TRUE)] 414 | } 415 | 416 | if (!is.na(spacing)) { 417 | dt[, y := y - (spacing * (1 - vjust))] 418 | } 419 | rm_temp_cols(dt) 420 | } 421 | 422 | # misc -------------------------------------------------------------------- 423 | 424 | interp_low_res <- function(x, y, from, to, n=128) { 425 | x_interp <- seq(from, to, length.out=n) 426 | interp_fun <- stats::approxfun(x, y) 427 | 428 | data.table(x=x_interp, y=interp_fun(x_interp)) 429 | } 430 | 431 | interp_vert_line <- function(x, from, to, v) { 432 | c(stats::approxfun(x, from)(v), stats::approxfun(x, to)(v)) 433 | } 434 | -------------------------------------------------------------------------------- /R/ggdistribute-package.R: -------------------------------------------------------------------------------- 1 | # Package info ------------------------------------------------------------ 2 | 3 | #' A 'ggplot2' Extension for Plotting Unimodal Distributions 4 | #' 5 | #' The `ggdistribute` package is an extension for plotting posterior or other 6 | #' types of unimodal distributions that require overlaying information about a 7 | #' distribution's intervals. It makes use of the `ggproto` system to extend 8 | #' `ggplot2`, providing additional "geoms", "stats", and "positions." The 9 | #' extensions integrate with existing `ggplot2` layer elements. 10 | #' 11 | #' Displaying the distributions relies heavily on stacking distributions using 12 | #' position_spread, which may not align with other geoms. 13 | #' 14 | #' @seealso [ggplot2::geom_density], [ggplot2::position_dodge] 15 | #' 16 | #' See `help(package = "ggdistribute")` for a list of functions. 17 | #' 18 | #' View vignettes with `browseVignettes(package ="ggdistribute")`. 19 | #' 20 | #' @import ggplot2 21 | #' @import data.table 22 | #' @importFrom magrittr "%>%" 23 | #' @importFrom grDevices col2rgb gray rainbow rgb 24 | #' @importFrom stats density ecdf mad median na.omit quantile rgamma rnbinom 25 | #' rnorm runif sd 26 | #' @importFrom utils capture.output modifyList 27 | #' @docType package 28 | #' @name ggdistribute-package 29 | NULL 30 | 31 | 32 | # GeomPosterior / StatDensityCI help ------------------------------------- 33 | 34 | 35 | #' Geom for plotting posterior distributions 36 | #' 37 | #' This geom may be used to plot the density of any type of numeric variable but 38 | #' the displayed intervals may not be informative if the distribution deviates 39 | #' too much from a unimodal, symmetric distribution. 40 | #' 41 | #' @name GeomPosterior 42 | #' 43 | #' @inheritParams ggplot2::geom_violin 44 | #' @param geom Use to override the default connection between `geom_posterior` 45 | #' and `stat_density_ci` 46 | #' @param draw_ci *geom*. Toggles drawing of the confidence interval lines and 47 | #' segments. 48 | #' @param draw_sd *geom*. Toggles drawing of the standard deviation interval 49 | #' lines and segments. 50 | #' @param midline *geom*. Color of the vertical, center line. Set to `NA` 51 | #' to omit the line. 52 | #' @param brighten *geom*. Numeric adjustments to the fill color. A value above 53 | #' 1 increases brightness, below decreases. Should be of length 1 or 5, 54 | #' otherwise values are recycled 55 | #' @param interp_thresh *geom*. If the number of samples used to estimate the 56 | #' density is low, this will result in gaps between segments. This argument 57 | #' decides to interpolate points based on gap proportion for a segment. 58 | #' @param mirror *geom*. Show standard densities (`mirror=FALSE`) or horizontal 59 | #' violin densities (`mirror=TRUE`). 60 | #' @param stat Used to override the default connection between `geom_posterior` 61 | #' and `stat_density_ci`. 62 | #' 63 | #' @param center_stat *stat*. character string of method to compute the 64 | #' distribution's central tendency, such as `"median"`, `"mean"`, or `"mode"`. 65 | #' @param interval_type *stat*. method of computing the interval, either `"hdi"` 66 | #' or `"ci"` 67 | #' @param ci_width *stat*. Width of the distribution's confidence/highest 68 | #' density interval, e.g., 0.95 69 | #' @param trim If a value between 0 and 1 is given, trim the tails of `x` by 70 | #' some proportion according to `trim`. If `NULL` or `NA`, don't trim the 71 | #' tails. See [trim_ends()]. 72 | #' @param cut The values to use for the start and end of the density estimation 73 | #' are `cut` bandwidths (e.g., `0.5*bw`) *beyond* the extremes of the data. 74 | #' This allows the estimated density to drop to approximately zero at the 75 | #' extremes. 76 | #' @param bw The smoothing bandwidth to be used. If numeric, the standard 77 | #' deviation of the smoothing kernel. If character, a rule to choose the 78 | #' bandwidth, as listed in [stats::bandwidth]. If the bandwidth character 79 | #' starts wit a `"."` (e.g., `".nrd0"`), then the average bandwidth will be 80 | #' calculated among all groups in a panel and used for each density estimate. 81 | #' @inheritParams ggplot2::stat_density 82 | #' @section Aesthetics: 83 | #' 84 | #' `geom_posterior` understands the following aesthetics (required aesthetics 85 | #' are in bold): 86 | #' 87 | #' - **x** 88 | #' 89 | #' - **y** 90 | #' 91 | #' - xmin 92 | #' 93 | #' - xmax 94 | #' 95 | #' - alpha - colour 96 | #' 97 | #' - fill 98 | #' 99 | #' - group 100 | #' 101 | #' - linetype 102 | #' 103 | #' - size 104 | #' 105 | #' - weight 106 | #' 107 | #' @section Computed Variables: 108 | #' 109 | #' *stat_density_ci*: 110 | #' 111 | #' - density: density estimate from [stats::density] 112 | #' 113 | #' - scaled: Normalized density values: `density / max(density)` 114 | #' 115 | #' - count: Number of samples at density level: `(density / sum(density)) * n` 116 | #' 117 | #' - xmin: minimum value of `x` from the data 118 | #' 119 | #' - cil: cil cutoff value based on `ci_width` 120 | #' 121 | #' - sdl: central value minus 1 sd of `x` 122 | #' 123 | #' - mid: value of central tendency 124 | #' 125 | #' - sdu: central value plus 1 sd of `x` 126 | #' 127 | #' - ciu: ciu cutoff value based on `ci_width` 128 | #' 129 | #' - xmax: maximum value of `x` from the data 130 | #' 131 | #' *position_spread* 132 | #' 133 | #' - ymin: minimum value of `y` for each group in a panel. 134 | #' 135 | #' - ymax: maximum value of `y` for each group in a panel. 136 | #' 137 | #' @examples 138 | #' \donttest{ 139 | #' library(ggplot2) 140 | #' 141 | #' x <- data_normal_sample(mu = c(-1, 0, 1), n = 500) 142 | #' 143 | #' p <- ggplot(x, aes(x = value)) 144 | #' 145 | #' p + geom_posterior() 146 | #' 147 | #' p + geom_posterior(aes(y = Condition)) 148 | #' 149 | #' p + geom_posterior(aes(y = GroupScore, fill = Condition)) 150 | #' 151 | #' p + geom_posterior(aes(y = GroupScore, fill = Group), 152 | #' brighten = c(1.3, 0, -1.3), 153 | #' position = position_spread( 154 | #' height=0.5, 155 | #' padding = 0)) 156 | #' 157 | #' } 158 | #' @md 159 | NULL 160 | 161 | #' Print an example of the package functions 162 | #' 163 | #' @return NULL 164 | #' @export 165 | example_plot <- function() { 166 | # color palette 167 | colors <- mejr_palette() 168 | 169 | ggplot(sre_data(5000), aes_string(y="effect")) + 170 | 171 | # ggdistribute specific elements ------------------------------------------- 172 | geom_posterior( 173 | # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 174 | # geom_posterior() aesthetics mappings 175 | # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 176 | aes_string(x="value", fill="contrast"), 177 | # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 178 | # options passed to stat_density_ci() for estimating intervals 179 | # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 180 | interp_thresh=.001, # threshold for interpolating segment gaps 181 | center_stat="median", # measure of central tendency 182 | ci_width=0.90, # width corresponding to CI segments 183 | interval_type="ci", # quantile intervals not highest density interval 184 | # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 185 | # options passed to stat_density_ci() for estimating density 186 | # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 187 | bw=".nrd0", # bandwidth estimator type 188 | adjust=1.5, # adjustment to bandwidth 189 | n=1024, # number of samples in final density 190 | trim=.005, # trim `x` this proportion before estimating density 191 | cut=1.5, # tail extension for zero density estimation 192 | # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 193 | # geom_posterior() options 194 | # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 195 | draw_ci=TRUE, # toggle showing confidence interval parts 196 | draw_sd=TRUE, # toggle showing standard deviation parts 197 | mirror=FALSE, # toggle horizontal violin distributions 198 | midline=NULL, # line displaying center of dist. (NULL=aes color) 199 | brighten=c(3, 0, 1.333), # additive adjustment of segment fill colors 200 | # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 201 | # position_spread() options 202 | # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 203 | position=position_spread( 204 | reverse=TRUE, # order of spreaded groups within panels 205 | padding=0.3, # shrink heights of distributions 206 | height="panel" # scale by heights within panels 207 | ), # 208 | # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 209 | # standard ggplot layer options 210 | # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 211 | size=0.15, color=colors$gray, vjust=0.7, show.legend=FALSE 212 | ) + 213 | 214 | # standard ggplot2 elements ------------------------------------------------ 215 | geom_vline(alpha=0.5, color=colors$gray, size=0.333, linetype=1, xintercept=0) + 216 | scale_x_continuous(breaks=seq(-1, 1, .05)) + 217 | facet_grid("contrast ~ .", scales="free_y", space="free_y") + 218 | scale_fill_manual(values=c(colors$yellow, colors$magenta, colors$cyan)) + 219 | labs(x="Difference in accuracy (posterior predictions)") + 220 | theme( 221 | legend.position="none", strip.text.y=element_text(angle=0, hjust=0.5), 222 | panel.border=element_rect(fill=NA, color=colors$lightgray, size=0.67), 223 | panel.grid=element_blank(), panel.ontop=FALSE, axis.title.y=element_blank(), 224 | plot.margin=margin(t=2, r=4, b=2, l=2, unit="pt") 225 | ) 226 | } 227 | -------------------------------------------------------------------------------- /R/gghelpers.R: -------------------------------------------------------------------------------- 1 | #' Print a small example plot with geom_posterior 2 | #' 3 | #' @param data A dataset to use, called at the top layer within `ggplot`. 4 | #' @param x A character string of the `x` axis variable name (e.g., values 5 | #' making up the distribution). 6 | #' @param y A grouping variable for generating groups of distributions. Defaults 7 | #' to `..count..` for no groups and displays the density as counts of the 8 | #' number of samples for the value of `x`. 9 | #' @param ... Additional arguments passed to [geom_posterior()]. 10 | #' 11 | #' @return Object of class `gg`, `ggplot`. 12 | #' @export 13 | #' @examples 14 | #' # Generate a basic example plot if no data is specified. 15 | #' posterior_plot() 16 | posterior_plot <- function(data, x, y="..count..", ...) { 17 | if (missing(data)) { 18 | data <- data_normal_sample(0) 19 | x <- "value" 20 | y <- "..count.." 21 | } 22 | 23 | ggplot(data, aes_string(x=x, y=y)) + geom_posterior(...) 24 | } 25 | 26 | ggname <- function(prefix, grob) { 27 | grob$name <- grid::grobName(grob, prefix) 28 | grob 29 | } 30 | 31 | res_adjust <- function(x, scale_res=1, zero=FALSE) { 32 | x + (resolution(x, zero) * scale_res) 33 | } 34 | 35 | #' scale and add 36 | #' 37 | #' @param base_size start value 38 | #' @param amount multiple by 39 | #' @param adj add after 40 | #' @return numeric 41 | scale_add <- function(base_size, amount=1, adj=0) { 42 | (base_size * amount) + adj 43 | } 44 | 45 | set_range_data <- function(data, axis=c("x", "y"), by="group", force_cols=FALSE, 46 | names=NULL, copy=TRUE) { 47 | axis <- match.arg(axis) 48 | do_ops <- structure(list(min, max, function(x) { 49 | diff(range(x)) 50 | }), .Names=c(".__min", ".__max", ".__len")) 51 | 52 | if (!is.null(names)) { 53 | do_ops <- do_ops[seq_along(names)] 54 | names(do_ops) <- names 55 | } 56 | 57 | dt <- as_dtbl(data, copy=copy) 58 | 59 | Map(function(n, f) { 60 | if (force_cols || all_missing(data[[n]])) { 61 | dt[, eval(n) := f(get(axis)), by=eval(by)] 62 | } 63 | }, names(do_ops), do_ops) 64 | 65 | if (copy) { 66 | as.data.frame(dt) 67 | } else { 68 | dt 69 | } 70 | } 71 | 72 | has_multiple_discrete <- function(data, scales, axis=c("x", "y")) { 73 | axis <- match.arg(axis) 74 | if (is.null(scales[[axis]]) || !scales[[axis]]$is_discrete()) { 75 | return(FALSE) 76 | } 77 | 78 | if (is.null(data[[axis]])) { 79 | return(FALSE) 80 | } 81 | 82 | length(unique(data[[axis]])) > 1 83 | } 84 | 85 | split_discrete_by_group <- function(data, scales, axis=c("x", "y")) { 86 | axis <- match.arg(axis) 87 | 88 | if (!("y" %Names?% data)) { 89 | return(data) 90 | } 91 | 92 | assert_names("group", data) 93 | 94 | dt <- as_dtbl(data) 95 | 96 | if (is.null(dt[[axis]])) { 97 | dt <- dt[order(group)] 98 | } else { 99 | dt <- dt[order(get(axis), group)] 100 | } 101 | 102 | dt[, .__grp := { 103 | if (has_multiple_discrete(.SD, scales, axis)) { 104 | paste0(group, .SD[[axis]]) 105 | } else { 106 | paste0(group, 1) 107 | } 108 | }, group] %>% 109 | .[, group := .GRP, .__grp] %>% 110 | rm_temp_cols() 111 | } 112 | 113 | axis_is_within <- function(min1, max1, min2, max2) { 114 | m <- matrix(c(min1, min2, max1, max2, max1 - min1, max2 - min2), nrow=2) 115 | m <- m[order(-m[, 3]), ] 116 | m[1, 1] <= m[2, 1] & m[1, 2] >= m[2, 2] 117 | } 118 | 119 | # Check for overlap 120 | get_overlaps <- function(data, axis=c("x", "y"), tol=-1e-04) { 121 | axis <- match.arg(axis) 122 | assert_names(c(axis, "group"), data) 123 | dt <- as_dtbl(data) 124 | 125 | if (!("PANEL" %Names?% data)) { 126 | dt$PANEL <- 1L 127 | } 128 | 129 | minmax <- set_range_data(dt, axis, force_cols=TRUE, copy=FALSE) %>% 130 | .[!is.na(.__min) & !is.na(.__max), .(.__grp=.GRP), .(.__min, .__max, group)] 131 | 132 | rm_temp_cols(dt) 133 | 134 | n_grps <- length(unique(minmax$group)) 135 | 136 | if (n_grps == 1) { 137 | pairs <- matrix(c(1, 1), ncol=2) 138 | } else { 139 | pairs <- t(utils::combn(n_grps, 2)) 140 | } 141 | 142 | calc_vals <- list( 143 | pair_height=NA_real_, sep_height=NA_real_, space=NA_real_, 144 | space_ratio=NA_real_, scaled_space=NA_real_, overlap=NA, within=NA 145 | ) 146 | 147 | # R CMD check 148 | h1 <- h2 <- i1 <- i2 <- max1 <- max2 <- min1 <- min2 <- NULL 149 | 150 | overlap_data <- as_dtbl(pairs) %>% 151 | setnames(c("V1", "V2"), c("i1", "i2")) %>% 152 | .[, I := .I] %>% 153 | .[, `:=`( 154 | min1=minmax$.__min[match(i1, minmax$.__grp)], 155 | max1=minmax$.__max[match(i1, minmax$.__grp)], 156 | min2=minmax$.__min[match(i2, minmax$.__grp)], 157 | max2=minmax$.__max[match(i2, minmax$.__grp)], 158 | group1=minmax$group[match(i1, minmax$.__grp)], 159 | group2=minmax$group[match(i2, minmax$.__grp)] 160 | )] %>% 161 | .[, `:=`("h1"=max1 - min1, "h2"=max2 - min2)] %>% 162 | .[, `:=`(i1=NULL, i2=NULL)] %>% 163 | .[, eval(names(calc_vals)) := lapply(calc_vals, identity)] 164 | 165 | if (n_grps == 1) { 166 | return(as.data.frame(overlap_data)) 167 | } 168 | 169 | # R CMD check 170 | overlap <- pair_height <- scaled_space <- sep_height <- space <- space_ratio <- NULL 171 | 172 | overlap_data[] %>% 173 | .[, pair_height := max(max1, max2) - min(min1, min2), I] %>% 174 | .[, sep_height := h1 + h2] %>% 175 | .[, space := pair_height - sep_height] %>% 176 | .[, space_ratio := pair_height / sep_height] %>% 177 | .[space < 0 | space_ratio < 1, scaled_space := scale(c(0, space))[-1]] %>% 178 | .[is.na(scaled_space), scaled_space := 0] %>% 179 | .[, overlap := scaled_space < tol] %>% 180 | .[, within := axis_is_within(min1, max1, min2, max2), I] %>% 181 | as.data.frame() 182 | } 183 | 184 | rescale_groups <- function(data, axis=c("x", "y"), min_col=".__min", ht_col=".__len", 185 | scaler=0, by=NULL) { 186 | axis <- match.arg(axis) 187 | assert_names(unique(c("group", min_col, ht_col, axis)), data) 188 | 189 | dt <- as_dtbl(data) 190 | dt[, .__by_ht := get(ht_col)] 191 | by_grp <- by 192 | 193 | if (is.numeric(by)) { 194 | dt[, .__by_ht := by] 195 | by_grp <- NULL 196 | } 197 | 198 | dt[] %>% 199 | .[, .__adj := scaler * max(.__by_ht), by=eval(by_grp)] %>% 200 | .[, eval(axis) := rescale_as_other( 201 | get(axis), c(get(min_col), get(ht_col) - .__adj + get(min_col)) 202 | ), 203 | by=c("group", min_col, ht_col) 204 | ] %>% 205 | .[] 206 | } 207 | 208 | check_padding <- function(x, size, padding, mult=1.5) { 209 | res <- resolution(x, FALSE) * mult 210 | max_ht <- max(size, na.rm=TRUE) 211 | shrunken_size <- max_ht - (padding * max_ht) 212 | 213 | if (shrunken_size > -res & shrunken_size < res) { 214 | warning("Paddding too large. Data compressed smaller than resolution.", call.=FALSE) 215 | return(0) 216 | } 217 | padding 218 | } 219 | 220 | rm_temp_cols <- function(data, temp_names=NULL) { 221 | if (is.null(temp_names)) { 222 | temp_names <- c( 223 | ".__adj", ".__by_ht", ".__grp", ".__ht", ".__len", ".__max_x", ".__max_y", 224 | ".__max", ".__mid", ".__min_x", ".__min_y", ".__min", ".__n", ".__tmp", ".__y" 225 | ) 226 | } 227 | 228 | temp_names <- temp_names %Names% data 229 | 230 | if (is_none(temp_names)) { 231 | return(data) 232 | } 233 | 234 | as_dtbl(data) %>% 235 | .[, eval(temp_names) := NULL] %>% 236 | .[] 237 | } 238 | 239 | 240 | # colorspace -------------------------------------------------------------- 241 | 242 | 243 | #' plot and show hex values of colors 244 | #' 245 | #' @param colors character vector of hex value colors 246 | #' @param show.legend show the legend with hex values (logical) 247 | #' @param ncols number of columns in the plot 248 | #' @param alpha set alpha level for all colors 249 | #' 250 | #' @return A plot with the index of the color in the tile 251 | #' @export 252 | #' 253 | #' @examples 254 | #' show_colors(mejr_palette()) 255 | #' show_colors(topo.colors(25)) 256 | #' show_colors(cm.colors(64), FALSE) 257 | #' show_colors(viridisLite::viridis(15), alpha = .8) 258 | show_colors <- function(colors, show.legend=TRUE, ncols=NULL, alpha=NA) { 259 | if (missing(colors)) { 260 | colors <- rainbow(10) 261 | } 262 | 263 | n <- length(colors) 264 | 265 | if (n <= 0) { 266 | warning("no colors to plot") 267 | } 268 | 269 | if (!is.null(ncols)) { 270 | ncols <- max(c(1L, ncols)) 271 | } else { 272 | ncols <- sqrt(n) 273 | if (ncols != as.integer(ncols)) { 274 | ncols <- 1L 275 | } 276 | } 277 | 278 | # R CMD check 279 | i <- x <- y <- z <- l <- NULL 280 | 281 | rows <- ceiling(n / ncols) 282 | data <- expand.grid(x=seq_len(ncols), y=seq_len(rows)) 283 | data <- data[with(data, order(y, x)), ] 284 | data$z <- factor(rep_len(colors, nrow(data)), levels=colors, labels=colors) 285 | data$i <- rep_len(1:n, nrow(data)) 286 | data$l <- paste(data$i, data$z, sep=". ") 287 | 288 | ggplot(data, aes(x, y, fill=z)) + geom_raster(aes(fill=z), alpha=alpha) + 289 | scale_fill_manual(values=as.character(colors), breaks=data$z, labels=data$l) + 290 | geom_label(fill="white", hjust=0, nudge_x=-.45, colour=gray(0.5), aes(label=i)) + 291 | scale_y_reverse() + 292 | theme_void() + guides(fill=guide_legend(override.aes=list(alpha=1))) + 293 | theme( 294 | legend.position=ifelse(show.legend, "right", "none"), legend.box="vertical", 295 | legend.box.background=element_blank(), legend.text.align=0, 296 | legend.direction="vertical", legend.title=element_blank() 297 | ) 298 | } 299 | 300 | gray2 <- function(black=127) { 301 | if (is.character(black)) { 302 | return(black) 303 | } 304 | 305 | if (black < 0 | black > 255) { 306 | warning(simpleWarning("black out of range [0, 255]")) 307 | } 308 | 309 | gray(clip_range(black, 0, 255) / 255) 310 | } 311 | 312 | 313 | change_brightness <- function(hex_color, adjust) { 314 | if (adjust == 0 || all_missing(hex_color)) { 315 | return(hex_color) 316 | } 317 | 318 | rgb_mat <- clip_range(col2rgb(hex_color), 1, 254) 319 | rgb_adj <- sigmoid(logit(rgb_mat / 255) + adjust) 320 | 321 | apply(rgb_adj, 2, function(i) { 322 | do.call(rgb, as.list(i)) 323 | }) 324 | } 325 | 326 | 327 | # unexported ggplot2 functions -------------------------------------------- 328 | 329 | 330 | find_subclass <- function(super, class, env) { 331 | name <- paste0(super, camelize(class, first=TRUE)) 332 | obj <- find_global(name, env=env) 333 | if (is.null(name)) { 334 | stop("No ", tolower(super), " called ", name, ".", call.=FALSE) 335 | } else { 336 | if (!inherits(obj, super)) { 337 | stop("Found object is not a ", tolower(super), ".", call.=FALSE) 338 | } 339 | } 340 | obj 341 | } 342 | 343 | find_global <- function(name, env, mode="any") { 344 | if (exists(name, envir=env, mode=mode)) { 345 | return(get(name, envir=env, mode=mode)) 346 | } 347 | nsenv <- asNamespace("ggplot2") 348 | if (exists(name, envir=nsenv, mode=mode)) { 349 | return(get(name, envir=nsenv, mode=mode)) 350 | } 351 | NULL 352 | } 353 | 354 | firstUpper <- function(s) { 355 | paste(toupper(substring(s, 1, 1)), substring(s, 2), sep="") 356 | } 357 | 358 | 359 | camelize <- function(x, first=FALSE) { 360 | x <- gsub("_(.)", "\\U\\1", x, perl=TRUE) 361 | if (first) { 362 | x <- firstUpper(x) 363 | } 364 | x 365 | } 366 | -------------------------------------------------------------------------------- /R/helpers.R: -------------------------------------------------------------------------------- 1 | # missing/null values ----------------------------------------------------- 2 | 3 | # check if x is NULL or zero length. 4 | # if allow_na=TRUE then also accepts all NA values as none. 5 | is_none <- function(x) { 6 | is.null(x) || length(x) < 1L 7 | } 8 | 9 | # check if all values are length zero or NA 10 | all_missing <- function(x) { 11 | is_none(x) || all(is.na(x)) 12 | } 13 | 14 | # try to grab first non NA value in x, if none, return NULL 15 | first_non_na <- function(x) { 16 | val <- na.omit(x)[1] %:% NA 17 | if (is.na(val)) { 18 | return(NULL) 19 | } 20 | val 21 | } 22 | 23 | # return names of object that have non-zero chars 24 | non_empty_names <- function(x, na.rm=TRUE) { 25 | obj_names <- names(x) 26 | obj_names <- obj_names[nzchar(obj_names)] 27 | if (!na.rm) { 28 | return(obj_names) 29 | } 30 | Filter(Negate(is.na), obj_names) 31 | } 32 | 33 | assert_names <- function(required, from_obj, ...) { 34 | err <- list(...) 35 | 36 | if (is_none(err)) { 37 | fr <- sys.call(-1) 38 | fr <- (is.null(fr)) %?% ".GlobalEnv" %:% deparse(fr[1]) 39 | err <- paste0(fr, ".") 40 | } else { 41 | err <- do.call(paste0, err) 42 | } 43 | 44 | if (!(required %Names?% from_obj)) { 45 | stop("names missing: ", paste0(required, collapse=", "), "\n", err, call.=FALSE) 46 | } 47 | } 48 | 49 | assert <- function(..., err=NULL, envir=parent.frame(), print) { 50 | exprs <- eval(substitute(alist(...))) 51 | 52 | force(envir) 53 | 54 | if (missing(print)) { 55 | if (is.null(err)) { 56 | print <- TRUE 57 | } else { 58 | print <- FALSE 59 | } 60 | } 61 | 62 | err <- paste0(err %:% "\b") 63 | 64 | lapply(exprs, function(e) { 65 | result <- eval(e, envir=envir) 66 | expr_str <- dQuote(deparse(e)) 67 | 68 | if (print) { 69 | msg <- paste(err, "expression", expr_str, "failed") 70 | } else { 71 | msg <- err 72 | } 73 | 74 | if (!(length(result) == 1 && is.logical(result))) { 75 | stop("expression ", expr_str, " not logical", call.=FALSE) 76 | } 77 | 78 | if (!result) { 79 | stop(msg, call.=FALSE) 80 | } 81 | 82 | NULL 83 | }) 84 | 85 | invisible() 86 | } 87 | 88 | char_or_names <- function(x) { 89 | if (!is.character(x)) { 90 | x <- non_empty_names(x) 91 | if (is_none(x)) { 92 | stop("Could not determine names from object", call.=FALSE) 93 | } 94 | } 95 | x 96 | } 97 | 98 | # special operators ------------------------------------------------------- 99 | 100 | # If true then object else null 101 | # 102 | # @details This is primarily used in conjuction with the `%:%` operator. See 103 | # examples. 104 | # @param lhs logical scalar. 105 | # @param rhs object to return if `lhs` is `TRUE`. 106 | # @return object on `rhs` or `NULL` 107 | # @examples 108 | # \donttest{ 109 | # `%:%` <- ggdistribute:::`%:%` 110 | # `%?%` <- ggdistribute:::`%?%` 111 | # 112 | # TRUE %?% 1 %:% 0 #> 1 113 | # FALSE %?% 1 %:% 0 #> 0 114 | # 115 | # x <- 0 116 | # 117 | # # expression returns whatever objects are wrapped in between %:% 118 | # (x == 0) %?% "y" %:% "n" 119 | # (x == 1) %?% c("y", "yes") %:% c("n", "No", "false") 120 | # 121 | # # ERROR: The %?% operator captured only 0, which is not logical. 122 | # # x == 0 %?% "y" %:% "n" 123 | # 124 | # # ERROR: The TRUE slot cannot return NULL because NULL is used to 125 | # # decide what to return in %:% 126 | # # `TRUE %?% NULL` and `FALSE %?% obj` would both return NULL. 127 | # # (x == 0) %?% NULL %:% "n" 128 | # } 129 | `%?%` <- function(lhs, rhs) { 130 | if (!is.logical(lhs) || length(lhs) > 1 || anyNA(lhs)) { 131 | stop("Value left of %?% must be logical, of length 1, and not NA.") 132 | } 133 | 134 | if (lhs) { 135 | if (is_none(rhs)) { 136 | stop( 137 | "Values right of %?% cannot return NULL or length zero.", 138 | " Try changing LHS to !LHS and reordering expresions after %?%." 139 | ) 140 | } 141 | rhs 142 | } else { 143 | NULL 144 | } 145 | } 146 | 147 | # if LHS is NULL return RHS 148 | `%:%` <- function(lhs, rhs) { 149 | if (is.null(lhs)) { 150 | rhs 151 | } else { 152 | lhs 153 | } 154 | } 155 | 156 | # if LHS is NULL or length zero return RHS 157 | `%||%` <- function(lhs, rhs) { 158 | if (is_none(lhs)) { 159 | rhs 160 | } else { 161 | lhs 162 | } 163 | } 164 | 165 | # if LHS is NULL or length zero or all NA return RHS 166 | `%NA%` <- function(lhs, rhs) { 167 | if (all_missing(lhs)) { 168 | rhs 169 | } else { 170 | lhs 171 | } 172 | } 173 | 174 | `%Names%` <- function(lhs, rhs) { 175 | lhs <- char_or_names(lhs) 176 | lhs[lhs %chin% non_empty_names(rhs)] 177 | } 178 | 179 | `%!Names%` <- function(lhs, rhs) { 180 | lhs <- char_or_names(lhs) 181 | lhs[!(lhs %chin% non_empty_names(rhs))] 182 | } 183 | 184 | `%Names?%` <- function(lhs, rhs) { 185 | lhs <- char_or_names(lhs) 186 | all(lhs %chin% non_empty_names(rhs)) 187 | } 188 | 189 | # transformations --------------------------------------------------------- 190 | 191 | range_sequence <- function(x, n) { 192 | limits <- range_no_inf(x) 193 | if (all_missing(limits)) { 194 | return(rep(NA, n)) 195 | } 196 | seq(limits[1], limits[2], length.out=n) 197 | } 198 | 199 | # rescale x to the scale of y 200 | rescale_as_other <- function(x, y, scalar_adj=1) { 201 | limits <- range_no_inf(y) 202 | if (all_missing(limits)) { 203 | warning("x left as is. No range detected from y.") 204 | return(x) 205 | } 206 | 207 | center <- mean(limits) 208 | scale <- diff(limits) 209 | 210 | # if the min/max of y is the same then x will start at y and end at y+1 211 | if (scale <= 0) { 212 | scale <- (scalar_adj == 0) %?% 1 %:% scalar_adj 213 | center <- center + scalar_adj * 0.5 214 | } 215 | 216 | center + (norm_vec(x) - 0.5) * scale 217 | } 218 | 219 | # data objects ------------------------------------------------------------ 220 | 221 | # data.frame/table is empty 222 | empty <- function(x) { 223 | is.null(x) || nrow(x) == 0 || ncol(x) == 0 224 | } 225 | 226 | as_dtbl <- function(x, copy=FALSE) { 227 | if (data.table::is.data.table(x)) { 228 | if (copy) { 229 | return(data.table::copy(x)) 230 | } 231 | x 232 | } else { 233 | data.table::as.data.table(x) 234 | } 235 | } 236 | 237 | set_col_order <- function(x, first_names) { 238 | cols <- names(x) 239 | not_first <- cols[!cols %chin% first_names] 240 | data.table::setcolorder(x, c(first_names, not_first)) 241 | invisible(x) 242 | } 243 | 244 | rep.data.table <- function(x, ...) { 245 | args <- list(...) 246 | if (length(args) < 1) { 247 | stop("must provide a `times, lenght.out, or each` argument") 248 | } 249 | n <- c(args[[1]], args$times, args$length.out, args$each)[1] 250 | if (!is.numeric(n)) { 251 | stop("second input argument must be numeric") 252 | } 253 | rbindlist(lapply(seq_len(n), function(...) { 254 | x 255 | })) 256 | } 257 | 258 | get_static_data <- function(from, ref=NULL) { 259 | from <- as_dtbl(from) 260 | 261 | if (is.null(ref)) { 262 | use_names <- non_empty_names(from) 263 | } else { 264 | use_names <- from %!Names% ref 265 | } 266 | 267 | if (length(use_names) < 1) { 268 | return(NULL) 269 | } 270 | 271 | is_static_df <- from[, lapply(.SD, function(i) { 272 | uniqueN(i) == 1 273 | }), .SDcols=use_names] 274 | static_cols <- names(Filter(isTRUE, is_static_df)) 275 | 276 | if (is_none(static_cols)) { 277 | return(NULL) 278 | } 279 | 280 | as.data.frame(from[1, static_cols, with=FALSE]) 281 | } 282 | 283 | # misc -------------------------------------------------------------------- 284 | 285 | append_diff <- function(x, val=NA) { 286 | c(diff(x), val) 287 | } 288 | 289 | 290 | unique_apply <- function(x, i, FUN, ...) { 291 | if (is.null(i)) { 292 | i <- integer(length(x)) + 1L 293 | } 294 | 295 | if (!is.numeric(i)) { 296 | stop("i must be an integer index") 297 | } 298 | 299 | if (length(x) != length(i)) { 300 | stop("i must be the same size as x") 301 | } 302 | 303 | FUN <- match.fun(FUN) 304 | unq_i <- !duplicated(i) 305 | unq_v <- FUN(x[unq_i], ...) 306 | unq_v[i] 307 | } 308 | 309 | unique_simplex <- function(size_vec, grp_idx=NULL, offset_vec=0) { 310 | size <- size_vec - offset_vec 311 | unique_apply(size, grp_idx, function(x) { 312 | x / sum(x) 313 | }) 314 | } 315 | 316 | function2chunk <- function(...) { 317 | functions <- as.character(match.call())[-1L] 318 | function_txt <- capture.output(dump(functions, "", control="all")) 319 | cat("\n\n```r\n") 320 | cat(function_txt, sep="\n") 321 | cat("\n```\n\n") 322 | } 323 | -------------------------------------------------------------------------------- /R/position_spread.R: -------------------------------------------------------------------------------- 1 | # PositionSpread wrapper -------------------------------------------------- 2 | 3 | #' Spread Overlapping Grobs Spread overlapping groups by shrinking them to fit 4 | #' within the data's `y` range. 5 | #' 6 | #' @param height One of `total` (default), `panel`, `single`, a numeric scalar 7 | #' value to give all geoms equal space, or a numeric vector the length of 8 | #' `panels*groups` for manually specifying the height of each group. 9 | #' @param reverse Reverse the order of segments within overlapping `y` ranges. 10 | #' @param padding Multiple of height. Will shrink/enlarge groups to fit within a 11 | #' region. 12 | #' @return NULL 13 | #' @export 14 | #' @examples 15 | #' library(ggplot2) 16 | #' 17 | #' x <- data.frame(y = rnorm(1000), x="", myGroup=sample(1:3, 1000, TRUE)) 18 | #' 19 | #' ggplot(x, aes(x, y))+ 20 | #' geom_point(aes(group=myGroup), position=position_spread(height = 0.5)) 21 | position_spread <- function(height=NULL, reverse=FALSE, padding=0.2) { 22 | ggproto(NULL, PositionSpread, height=height, reverse=reverse, padding=padding) 23 | } 24 | 25 | # ggproto object ---------------------------------------------------------- 26 | 27 | PositionSpread <- ggproto( 28 | "PositionSpread", Position, 29 | required_aes="y", height=NULL, reverse=FALSE, 30 | padding=0.2, 31 | setup_params=function(self, data) { 32 | height_pars <- standardize_height_param(data, self$height %NA% 33 | "single") 34 | 35 | list( 36 | height=height_pars$heights, custom=height_pars$custom, 37 | padding=self$padding %NA% 0, reverse=self$reverse %NA% FALSE 38 | ) 39 | }, 40 | 41 | compute_panel=function(data, params, scales) { 42 | as_dtbl(data) %>% 43 | transform_heights(heights=params$height, padding=params$padding) %>% 44 | spread_by_ymin( 45 | spreader=shrink_and_stack, reverse=params$reverse, 46 | padding=params$padding 47 | ) %>% 48 | rescale_overlapping(scales=scales, skip=params$custom) %>% 49 | set_col_order(c("PANEL", "group", "x", "y") %Names% data) %>% 50 | as.data.frame() 51 | } 52 | ) 53 | 54 | # subfunctions ------------------------------------------------------------ 55 | standardize_height_param <- function(data, height) { 56 | height_type <- c("num", "char")[c(is.numeric(height), is.character(height))] 57 | 58 | if (is_none(height_type)) { 59 | stop("Invalid height argument specified for position_spread: ", height, call.=FALSE) 60 | } 61 | 62 | assert_names(c("PANEL", "group", "y"), data) 63 | 64 | # R CMD check 65 | space <- uspace <- NULL 66 | 67 | height_type <- height_type[1] 68 | n_heights <- nrow(unique(data[, c("PANEL", "group")])) 69 | dt <- as_dtbl(data, copy=TRUE) 70 | 71 | heights <- switch( 72 | height_type, 73 | char={ 74 | grouping <- height_char_groups(height[1]) 75 | 76 | # check if only one group per panel 77 | lonely_groups <- dt[] %>% 78 | set_range_data("y", by=c("group", "PANEL"), force_cols=TRUE, copy=FALSE) %>% 79 | .[, .(.__n=length(unique(.__min))), PANEL] %>% 80 | .[, all(.__n == 1)] 81 | 82 | ht <- if (lonely_groups) { 83 | height <- dt[, .(.__len=max(.__max - (y - .__min))), PANEL] %>% 84 | .[, max(.__len, na.rm=TRUE)] 85 | 86 | rep_len(height, n_heights) 87 | } else { 88 | # get all individual heights ("single") 89 | height_data <- set_range_data(dt, "y", force_cols=TRUE, copy=FALSE) %>% 90 | # order by min and use only these variables 91 | .[order(.__min), .(PANEL, group, .__min, .__max, .__len)] %>% 92 | # unique data 93 | unique() %>% # group by common y min values 94 | .[, .__grp := .GRP, .(.__min)] %>% 95 | # space in between y groups (from min y to next groups min y) 96 | .[, space := unique_apply(.__min, .__grp, append_diff)] %>% 97 | # get last space for the top of the panel 98 | .[ 99 | is.na(space), 100 | space := max(c(.[, max(space %NA% .__len, na.rm=TRUE)], .__len)) 101 | ] %>% 102 | # pick the smallest space according to user group 103 | .[, uspace := min(space), by=grouping] %>% 104 | # height is the max of user heights or y group space 105 | .[, ht := max(c(.__len, uspace)), by=c(grouping, "uspace")] 106 | 107 | height_data$ht 108 | } 109 | 110 | ht 111 | }, num={ 112 | n_user_heights <- length(height) 113 | if (n_user_heights != 1 && n_user_heights != n_heights) { 114 | warning("Height length was size ", n_user_heights, " but ", n_heights, 115 | " heights are required. ", "Recycling heights if any are missing.", 116 | call.=FALSE) 117 | } 118 | 119 | rep_len(height, n_heights) 120 | }, NULL 121 | ) 122 | 123 | heights <- dt[, .(`.__ht`=heights[.GRP]), .(PANEL, group)] 124 | rm_temp_cols(data) 125 | list(heights=heights, type=height_type, custom=height_type == "num") 126 | } 127 | 128 | height_char_groups <- function(height_str=c("total", "panel", "single")) { 129 | switch(tolower(match.arg(height_str)), total=NULL, panel="PANEL", 130 | single=c("PANEL", "group"), NULL) 131 | } 132 | 133 | transform_heights <- function(data, heights, padding) { 134 | assert_names(c("group", "y"), data) 135 | 136 | as_dtbl(data) %>% 137 | merge(heights, by=c("PANEL", "group"), all.y=FALSE) %>% 138 | set_range_data("y", names=".__min", copy=FALSE) %>% 139 | rescale_groups("y", ht_col=".__ht") %>% 140 | rm_temp_cols() 141 | } 142 | 143 | spread_by_ymin <- function(data, spreader, ...) { 144 | assert_names(c("PANEL", "group", "y"), data) 145 | 146 | # NOTE: also creates the variables ymin, ymax in data 147 | as_dtbl(data) %>% 148 | set_range_data("y", 149 | by=c("PANEL", "group"), force_cols=TRUE, 150 | names=c("ymin", "ymax"), copy=FALSE) %>% 151 | .[, .__min := ymin] %>% 152 | .[, spreader(.SD, ...), .(.__min, PANEL)] %>% 153 | rm_temp_cols() 154 | } 155 | 156 | # shrink each group to fit within y range and stack on top of each other. 157 | # groups should be overlapping 158 | shrink_and_stack <- function(SD, reverse=FALSE, padding=0) { 159 | if (all(SD$group < 0)) { 160 | return(SD) 161 | } 162 | 163 | assert_names(c("ymin", "ymax"), SD) 164 | 165 | # must copy if as j within data.table 166 | dt <- as_dtbl(SD, copy=TRUE) 167 | dt[, .__len := ymax - ymin] 168 | 169 | n_groups <- length(unique(dt$group)) 170 | padding <- check_padding(dt$y, dt$.__len, padding) 171 | 172 | if (n_groups <= 1) { 173 | return(rm_temp_cols(rescale_groups(dt, "y", min_col="ymin", scaler=padding))) 174 | } 175 | 176 | # stack order 177 | if ("x" %Names?% dt) { 178 | dt[, .__mid := -x] 179 | } else { 180 | dt[, .__mid := group] 181 | } 182 | 183 | dt <- dt[] %>% 184 | .[, .__mid := mean(range_no_inf(.__mid)), group] %>% 185 | .[order(-.__mid, -group, -y), ] %>% 186 | .[, `:=`(.__grp=.GRP), .(group)] 187 | 188 | if (reverse) { 189 | dt <- dt[order(-.__grp), .__grp := .GRP, .__grp] 190 | } 191 | 192 | pad_adj <- 1 - (padding / max(1, n_groups - 2)) 193 | 194 | dt[] %>% # total height of data 195 | .[, .__len := diff(range_no_inf(y))] %>% # each overlapping groups height 196 | .[, .__ht := ymax - ymin] %>% 197 | # group heights sum to total height 198 | .[, .__len := .__len * unique_simplex(.__ht, .__grp)] %>% 199 | # shrink y by group height segments 200 | .[, .__y := rescale_as_other(y, c(0, .__len * pad_adj)), .(.__grp, .__len)] %>% 201 | # reposition y by stacking based on group height values 202 | .[, .__tmp := unique_apply(.__y * (1 / pad_adj), .__grp, csum_left)] %>% 203 | .[, y := .__y + .__tmp + ymin] %>% 204 | .[, `:=`(ymin=min(y), ymax=min(y) + .__len), .(.__grp)] %>% # junk 205 | rm_temp_cols() 206 | } 207 | 208 | rescale_overlapping <- function(data, scales, skip=FALSE) { 209 | if (skip || is.null(scales$y) || !scales$y$is_discrete()) { 210 | # don't change size of continuous scales or height specified manually 211 | return(data) 212 | } 213 | 214 | o_laps <- get_overlaps(data, "y") 215 | 216 | complete_overlap <- o_laps$within 217 | 218 | if (all_missing(complete_overlap)) { 219 | return(data) 220 | } 221 | 222 | which_overlaps <- o_laps$overlap[!complete_overlap] 223 | 224 | if (any(complete_overlap)) { 225 | warning("Sizes completely overlap with given height.") 226 | } 227 | 228 | if (!any(which_overlaps)) { 229 | return(data) 230 | } 231 | 232 | dt <- set_range_data(data, "y", force_cols=TRUE, copy=FALSE) 233 | 234 | o_laps <- o_laps[which_overlaps, ] 235 | o_laps <- o_laps[order(o_laps$sep_height), ] 236 | o_laps <- o_laps[which.min(o_laps$space), ] 237 | 238 | min_y <- c(o_laps$min1, o_laps$min2) 239 | y_ord <- order(min_y) 240 | min_y <- min_y[y_ord] 241 | max_y <- c(o_laps$max1, o_laps$max2)[y_ord] 242 | h_y <- c(o_laps$h1, o_laps$h2)[y_ord][1] 243 | shrink <- (max_y[1] - min_y[2]) / h_y 244 | h_adj <- 1.2 * h_y * dt[ 245 | group %in% c(o_laps$group1, o_laps$group2), 246 | 1 - (resolution(y, FALSE) / diff(range_no_inf(y))) 247 | ] 248 | 249 | rm_temp_cols(rescale_groups(dt, "y", scaler=shrink, by=h_adj)) 250 | } 251 | -------------------------------------------------------------------------------- /R/stat_posterior_density.R: -------------------------------------------------------------------------------- 1 | # StatDensityCI wrapper --------------------------------------------------- 2 | 3 | #' @describeIn GeomPosterior stat_density_ci Computes a distribution density and 4 | #' confidence intervals for each group 5 | #' @export 6 | stat_density_ci <- function(mapping=NULL, data=NULL, geom="Posterior", 7 | position="spread", ..., center_stat="median", ci_width=0.9, 8 | interval_type="ci", bw="nrd0", adjust=1, kernel="gaussian", 9 | cut=1, n=1024, trim=0.01, na.rm=FALSE, show.legend=NA, 10 | inherit.aes=TRUE) { 11 | layer( 12 | data=data, mapping=mapping, stat=StatDensityCI, geom=geom, position=position, 13 | show.legend=show.legend, inherit.aes=inherit.aes, 14 | params=list(..., 15 | center_stat=center_stat, ci_width=ci_width, 16 | interval_type=interval_type, bw=bw, adjust=adjust, kernel=kernel, 17 | cut=cut, n=n, trim=trim, na.rm=na.rm) 18 | ) 19 | } 20 | 21 | 22 | # ggproto object ---------------------------------------------------------- 23 | 24 | StatDensityCI <- ggproto( 25 | "StatDensityCI", Stat, 26 | required_aes="x", default_aes=aes(y=..count..), 27 | 28 | setup_params=function(data, params) { 29 | params$bw <- calc_avg_bw(data, params$bw) 30 | params 31 | }, 32 | compute_panel=function(self, data, scales, ...) { 33 | if (scales$x$is_discrete()) { 34 | stop("in stat_density_ci, `x` cannot be discrete.") 35 | } 36 | 37 | as_dtbl(data) %>% 38 | split_discrete_by_group(scales, "y") %>% 39 | .[, self$compute_group(.SD, ...), .(PANEL, group)] %>% 40 | as.data.frame() 41 | }, 42 | compute_group=function(data, center_stat="median", ci_width=0.9, 43 | interval_type="ci", adjust=1, kernel="gaussian", cut=1, 44 | n=1024, bw="nrd0", trim=0.01, na.rm=FALSE, ...) { 45 | if (length(unique(data$x)) < 2) { 46 | warning("x contains a single unique value. Dropping group.") 47 | return(NULL) 48 | } 49 | 50 | # density coords 51 | dens <- compute_density( 52 | x=data$x, y=data$y, w=NULL, cut=cut, bw=calc_bw( 53 | data$x, bw 54 | ), 55 | adjust=adjust, kernel=kernel, n=n, trim=trim 56 | ) 57 | 58 | if (all_missing(dens$x)) { 59 | return(NULL) 60 | } 61 | 62 | # confidence interval coords 63 | cint <- compute_conf_ints(data$x, center_stat, ci_width, interval_type) 64 | 65 | # combine with other data not used 66 | dist_data <- cbind(dens, cint) 67 | to_append <- get_static_data(data, dist_data) 68 | 69 | if (is.null(to_append)) { 70 | return(dist_data) 71 | } 72 | 73 | cbind(dist_data, to_append) 74 | } 75 | ) 76 | 77 | # subfunctions ------------------------------------------------------------ 78 | 79 | compute_density <- function(x, y=NULL, w=NULL, cut=1, bw="nrd0", adjust=1, 80 | kernel="gaussian", n=512, trim=NULL) { 81 | x <- is.null(trim) %?% x %:% trim_ends(x, trim, na.rm=TRUE) 82 | n_x <- length(x) 83 | 84 | missing_y <- all_missing(y) 85 | calc_options <- list(density=NA_real_, scaled=NA_real_, count=NA_real_) 86 | calc_list <- missing_y %?% calc_options %:% list(y=NA_real_) 87 | 88 | df <- c(x=NA_real_, calc_list, n=n_x) 89 | 90 | if (n_x < 2) { 91 | warning("Returning NA while trying to compute density. ", 92 | "`x` must be at least length 2, ", "or set `na.rm=TRUE`.", 93 | call.=FALSE) 94 | return(as.data.frame(df)) 95 | } 96 | 97 | dcoord <- stats::density( 98 | x, 99 | weights=w, bw=bw, adjust=adjust, kernel=kernel, n=n, cut=cut 100 | ) 101 | 102 | # actual number of samples 103 | n <- length(dcoord$y) 104 | 105 | # make sure zero density 106 | min_y <- which.min(dcoord$y) 107 | 108 | if (dcoord$y[min_y] > 0) { 109 | left <- min_y / (n / 2) < 1 110 | nearest_min_i <- seq(max(1, min_y - 1), min(n, min_y + 1)) 111 | x_adj <- mean(diff(dcoord$x[nearest_min_i])) 112 | x_adj <- left %?% (-x_adj) %:% x_adj 113 | after <- left %?% (min_y - 1) %:% min_y 114 | n <- n + 1 115 | 116 | dcoord$x <- append(dcoord$x, dcoord$x[min_y] + x_adj, after) 117 | dcoord$y <- append(dcoord$y, 0, after) 118 | } 119 | 120 | # y will be taken from one of these calc cols if not in aes 121 | df$density <- dcoord$y 122 | df$scaled <- dcoord$y / max(dcoord$y) 123 | df$count <- norm_vec_sum(dcoord$y) * df$n 124 | 125 | # y is specified, position density at start of y 126 | if (!missing_y) { 127 | df$y <- min(y, na.rm=TRUE) + norm_vec_sum(dcoord$y) 128 | } 129 | 130 | df$x <- dcoord$x 131 | 132 | as.data.frame(df) 133 | } 134 | 135 | # x coords for cil ci, sd cil, mid, sd ciu, ciu ci 136 | compute_conf_ints <- function(x, center_stat=NULL, ci_width=NULL, interval_type=NULL) { 137 | interval_frame <- data.frame( 138 | mid=NA_real_, sdl=NA_real_, sdu=NA_real_, cil=NA_real_, ciu=NA_real_ 139 | ) 140 | 141 | # setup defaults from missing 142 | ci_width <- max(ci_width %NA% 0.9) 143 | center_stat <- center_stat %NA% "median" 144 | interval_type <- interval_type %NA% "ci" 145 | 146 | # compute interval line positions regardless of being drawn 147 | markers <- post_int(x, mid=center_stat, int=interval_type, widths=ci_width) 148 | 149 | # assign to data which are to be drawn 150 | interval_frame$mid <- markers$c 151 | interval_frame$cil <- markers$l.wide 152 | interval_frame$ciu <- markers$r.wide 153 | interval_frame$sdl <- markers$l.sd 154 | interval_frame$sdu <- markers$r.sd 155 | 156 | interval_frame 157 | } 158 | 159 | # bandwidth calculator for density 160 | calc_bw <- function(x, bw) { 161 | if (is.character(bw)) { 162 | if (length(x) < 2) { 163 | stop("need at least 2 points to select a bandwidth automatically", call.=FALSE) 164 | } 165 | bw <- switch( 166 | tolower(bw), nrd0=stats::bw.nrd0(x), nrd=stats::bw.nrd(x), ucv=stats::bw.ucv(x), 167 | bcv=stats::bw.bcv(x), sj= , `sj-ste`=stats::bw.SJ(x, method="ste"), 168 | `sj-dpi`=stats::bw.SJ(x, method="dpi"), stop("unknown bandwidth rule") 169 | ) 170 | } 171 | bw 172 | } 173 | 174 | # average bandwidth across groups 175 | calc_avg_bw <- function(data, bw, grps="group") { 176 | # if NULL or NA then use individual bandwidth 177 | bw <- bw %NA% "nrd0" 178 | 179 | if (!grepl("^\\.", bw)) { 180 | return(bw) 181 | } 182 | 183 | bw_str <- sub(".", "", bw) 184 | 185 | as_dtbl(data) %>% 186 | .[!is.na(x), .(bw=calc_bw(x, bw_str)), by=grps] %>% 187 | .[, mean(bw, na.rm=TRUE)] 188 | } 189 | -------------------------------------------------------------------------------- /R/stats.R: -------------------------------------------------------------------------------- 1 | # distribution functions -------------------------------------------------- 2 | 3 | 4 | #' Posterior intervals 5 | #' 6 | #' Returns cutoff points from a posterior distribution 7 | #' @return data.table 8 | #' 9 | #' @param x Vector of numeric values. Typically a posterior sample. 10 | #' @param mid Central tendency estimator. Defaults to `"median"`. Other options 11 | #' include `"mean"` and `"mode"`. 12 | #' @param widths interval widths 13 | #' @param adj Bandwidth adjustment used only with the `"mode"` estimator. See 14 | #' [dmode]. 15 | #' @param rope Region of practical equivalence. Check how much of the 16 | #' distribution is within rope value. 17 | #' @param warn Turn off warning for flat intervals found (multiple possible 18 | #' values) 19 | #' @param int interval type, either "hdi" or "ci" 20 | #' @export 21 | #' @examples 22 | #' x <- rpois(5000, 15) 23 | #' ints <- post_int(x, warn = FALSE) 24 | #' hist(x, br=50) 25 | #' abline(v=ints$c, col="cyan") 26 | #' abline(v=ints[, c("l.wide", "r.wide")], col="magenta") 27 | #' 28 | #' post_int(x, "median", warn = FALSE) 29 | #' post_int(x, "mean", warn = FALSE) 30 | #' post_int(x, "mode", adj=2, rope = c(14, 16), warn = FALSE) 31 | post_int <- function(x, mid=c("median", "mean", "mode"), int=c("hdi", "ci"), 32 | widths=c(.50, .95), adj=1.5, rope=NULL, warn=FALSE) { 33 | if (!is.vector(x)) { 34 | stop("`x` must be a vector.") 35 | } 36 | 37 | mid <- match.arg(mid) 38 | int <- match.arg(int) 39 | 40 | center <- NA_real_ 41 | scale <- NA_real_ 42 | 43 | # measure of central tendency 44 | switch(mid, "mean"={ 45 | center <- mean(x) 46 | scale <- sd(x) 47 | }, "median"={ 48 | center <- median(x) 49 | scale <- mad(x) 50 | }, "mode"={ 51 | center <- dmode(x, adjust=adj) 52 | scale <- mad(x) 53 | }, NULL) 54 | 55 | # interval function as an unevaluated function call 56 | int_fun <- switch( 57 | int, "ci"={ 58 | match.call( 59 | quantile, 60 | call("quantile", 61 | x=quote(x), 62 | probs=quote(c((1 - w) / 2, w + ((1 - w) / 2))), names=FALSE) 63 | ) 64 | }, "hdi"={ 65 | match.call(hdi, call("hdi", x=quote(x), prob=quote(w), warn=warn)) 66 | }, NULL 67 | ) 68 | 69 | # mass within 1 sd range 70 | area_sd <- prop_in_range(x, center - scale, center + scale) 71 | 72 | # width identifiers 73 | widths <- sort(widths) 74 | width_ids <- sprintf("%.0f", widths * 100) 75 | width_ids[which.max(widths)] <- "wide" 76 | 77 | # left/right values for each width 78 | intervals <- data.table::rbindlist( 79 | Map(function(w, i) { 80 | ints <- structure( 81 | Reduce(cbind, eval(int_fun)), 82 | dimnames=list(NULL, c("l", "r")) 83 | ) 84 | data.table::data.table(interval=i, ints) 85 | }, c(widths, area_sd), c(width_ids, "sd")) 86 | ) 87 | 88 | central <- interval <- NULL 89 | 90 | # reshape to multivariate format 91 | intervals <- intervals[order(-interval), ] 92 | intervals <- intervals[, central := mid] 93 | intervals <- data.table::dcast( 94 | intervals, central ~ interval, 95 | value.var=c("l", "r"), sep="." 96 | ) 97 | intervals[, c := center] 98 | set_col_order(intervals, c("central", "c", "l.sd", "l.wide", "r.sd", "r.wide")) 99 | 100 | # get rope 101 | if (!is.null(rope)) { 102 | if (is.character(rope) && rope == "max") { 103 | rope <- c(intervals$l.wide, intervals$r.wide) 104 | } 105 | if (length(rope) != 2) { 106 | stop("ROPE must be a minimum and maximum value") 107 | } 108 | rope <- sort(rope) 109 | rope_mass <- prop_in_range(x, rope[1], rope[2]) 110 | intervals[, `:=`(l.rope=rope[1], r.rope=rope[2], rope=rope_mass)] 111 | } 112 | 113 | as.data.frame(intervals) 114 | } 115 | 116 | #' Highest density interval 117 | #' 118 | #' This is a function that will calculate the highest density interval from a 119 | #' posterior sample. 120 | #' 121 | #' The default is to calcualte the highest 95 percent interval. It can be used 122 | #' with any numeric vector instead of having to use one of the specific MCMC 123 | #' classes. This function has been adapted from John K. Kruschke (2011). Doing 124 | #' Bayesian Data Analaysis: A Tutorial with R and BUGS. 125 | #' 126 | #' @param x Numeric vector of a distribution of data, typically a posterior 127 | #' sample 128 | #' @param prob Width of the interval from some distribution. Defaults to `0.95`. 129 | #' @param warn Option to turn off multiple sample warning message Must be in the 130 | #' range of `[0,1]`. 131 | #' @return Numeric range 132 | #' @export 133 | #' @examples 134 | #' x <- qnorm(seq(1e-04, .9999, length.out=1001)) 135 | #' hdi_95 <- hdi(x, .95) 136 | #' hdi_50 <- hdi(x, .50) 137 | #' 138 | #' hist(x, br=50) 139 | #' abline(v=hdi_95, col="red") 140 | #' abline(v=hdi_50, col="green") 141 | #' 142 | #' x <- exp(seq(pi * (1 - (1/16)), pi, len = 1000)) 143 | #' x <- c(x, rev(x)[-1]) 144 | #' x <- c(-x, x) 145 | #' plot(sort(x), type="l") 146 | #' plot(density(x, adjust=0.25)) 147 | #' abline(v=hdi(x, p=.49), col=2) 148 | #' abline(v=hdi(x, p=.50), col=3) 149 | hdi <- function(x, prob=0.95, warn=TRUE) { 150 | if (anyNA(x)) { 151 | stop("HDI: ", "x must not contain any NA values.", call.=FALSE) 152 | } 153 | 154 | N <- length(x) 155 | 156 | if (N < 3) { 157 | if (warn) { 158 | warning("HDI: ", "length of `x` < 3.", " Returning NAs", call.=FALSE) 159 | } 160 | return(c(NA_integer_, NA_integer_)) 161 | } 162 | 163 | x_sort <- sort(x) 164 | window_size <- as.integer(floor(prob * length(x_sort))) 165 | 166 | if (window_size < 2) { 167 | if (warn) { 168 | warning("HDI: ", "window_size < 2.", " `prob` is too small or x does not ", 169 | "contain enough data points.", " Returning NAs.", 170 | call.=FALSE) 171 | } 172 | return(c(NA_integer_, NA_integer_)) 173 | } 174 | 175 | lower <- seq_len(N - window_size) 176 | upper <- window_size + lower 177 | 178 | # vectorized difference between edges of cumulative distribution based on 179 | # scan_length. Values are arranged from left to right scanning. 180 | window_width_diff <- x_sort[upper] - x_sort[lower] 181 | 182 | # find minimum of width differences, check for multiple minima 183 | min_i <- which(window_width_diff == min(window_width_diff)) 184 | n_candies <- length(min_i) 185 | 186 | if (n_candies > 1) { 187 | if (any(diff(sort(min_i)) != 1)) { 188 | if (warn) { 189 | warning("HDI: ", "Identical densities found along ", 190 | "different segments of the distribution.", " Choosing rightmost.", 191 | call.=FALSE) 192 | } 193 | min_i <- max(min_i) 194 | } else { 195 | min_i <- floor(mean(min_i)) 196 | } 197 | } 198 | 199 | # get values based on minimum 200 | c(x_sort[min_i], x_sort[upper[min_i]]) 201 | } 202 | 203 | #' Mode from density estimation 204 | #' 205 | #' Finds the mode using the [density] function and then obtains the maximum value. 206 | #' 207 | #' @param x Value vector. Numeric or integers. 208 | #' @param adjust Bandwidth adjustment. See [density]. 209 | #' 210 | #' @examples 211 | #' x <- rchisq(1000, 3) 212 | #' hist(x, br=50) 213 | #' abline(v = dmode(x), col = "red") 214 | #' abline(v = median(x), col = "green") 215 | #' abline(v = mean(x), col = "blue") 216 | #' @export 217 | dmode <- function(x, adjust=1.5) { 218 | x <- trim_ends(x, 0.05, na.rm=TRUE) 219 | d <- density(x, n=1000, bw="SJ", adjust=adjust) 220 | d$x[which.max(d$y)] 221 | } 222 | 223 | #' Mode from counting frequency 224 | #' 225 | #' Finds the most frequent value from a vector of discrete values 226 | #' 227 | #' @param x an integer vector 228 | #' 229 | #' @return scalar integer value 230 | #' @export 231 | #' 232 | #' @examples 233 | #' cmode(rpois(1000, 20)) 234 | cmode <- function(x) { 235 | i <- as.integer(x) 236 | if (any(x - i != 0)) { 237 | warning("`x` not discrete. Converting vector with `as.integer`") 238 | x <- i 239 | } 240 | x <- sort(x) 241 | y <- rle(x) 242 | y$values[which.max(y$lengths)] 243 | } 244 | 245 | #' Trim extreme values at each end of a vector. 246 | #' 247 | #' @param x A [numeric] vector 248 | #' @param trim Proportion of vector length to trim. Must be between 0 and 1. 249 | #' E.g., a value 0.05 (default) trims 2.5\% off each end of a sorted vector. 250 | #' @param na.rm omit `NA` values. May result in different size vector. 251 | #' 252 | #' @return A [numeric] vector in the original order of `x`, but with trimmed 253 | #' values as `NA` if `na.rm=TRUE` or with these values removed if `FALSE` 254 | #' (which will result in a different sized vector from the input). 255 | #' @export 256 | #' @examples 257 | #' x <- rgamma(10000, 1, 1) 258 | #' range(x) 259 | #' length(x) # <- 10000 260 | #' sum(is.na(x)) # <- 0 261 | #' 262 | #' t <- trim_ends(x, trim = 0.1) 263 | #' range(t) 264 | #' length(t) # <- 9000 265 | #' sum(is.na(t)) # <- 0 266 | #' 267 | #' t <- trim_ends(x, 0.1, na.rm = FALSE) 268 | #' range(t, na.rm = TRUE) 269 | #' length(t) # <- 10000 270 | #' sum(is.na(t)) # <- 1000 271 | trim_ends <- function(x, trim=0.05, na.rm=TRUE) { 272 | if (!is.numeric(trim)) { 273 | stop("`trim` must be a numeric value.") 274 | } 275 | 276 | if (trim < 0 || trim > 1) { 277 | stop("trim amount must be between 0 and 1") 278 | } 279 | 280 | is_na <- is.na(x) 281 | len_finite <- sum(!is_na) 282 | len_trim <- as.integer(floor(len_finite * (trim / 2))) 283 | 284 | # attributes 285 | which_na <- which(is_na) 286 | which_trimmed <- integer() 287 | trim_size <- len_trim * 2L 288 | 289 | if (len_trim > 0L) { 290 | if (len_finite - trim_size < 1) { 291 | warning("trim amount results in zero length vector or all NAs") 292 | x[] <- NA 293 | } else { 294 | x_i_sort <- order(x, na.last=TRUE) 295 | trim_index <- seq_len(len_trim) 296 | trim_index <- unique(c(trim_index, len_finite - trim_index + 1)) 297 | which_trimmed <- sort(x_i_sort[trim_index]) 298 | x[which_trimmed] <- NA 299 | } 300 | } 301 | 302 | if (na.rm) { 303 | x <- x[!is.na(x)] 304 | } 305 | 306 | attr(x, "na.action") <- which_na 307 | attr(x, "trim.action") <- which_trimmed 308 | attr(x, "trim.size") <- trim_size 309 | x 310 | } 311 | 312 | 313 | # misc -------------------------------------------------------------------- 314 | 315 | 316 | prop_in_range <- function(x, lower, upper) { 317 | sum(x > lower & x < upper) / length(x) 318 | } 319 | 320 | probit_tbl <- function(x, k) { 321 | pct <- ecdf(x)(x) 322 | brks <- range_sequence(c(0, 1), k + 1) 323 | idx <- cut(pct, breaks=brks, include.lowest=TRUE, right=FALSE, labels=FALSE) 324 | data.table(qmin=brks[idx], qmax=brks[idx + 1], ecdf=pct) 325 | } 326 | 327 | rgamma2 <- function(n, mean=1, sd=1) { 328 | rgamma(n, (mean / sd)^2, mean / sd^2) 329 | } 330 | 331 | rnbinom2 <- function(n, mean=1, sd=1, min_size=1) { 332 | size <- max(min_size, mean^2 / (sd^2 - mean)) 333 | rnbinom(n, size=size, mu=mean) 334 | } 335 | 336 | csum_left <- function(x, init=0) { 337 | cs <- cumsum(c(init, x)) 338 | na.omit(shift(cs, 1, NA, "lag")) 339 | } 340 | 341 | norm_vec <- function(x, na.rm=TRUE) { 342 | limits <- range_no_inf(x, na.rm=na.rm) 343 | (x - limits[1]) / diff(limits) 344 | } 345 | 346 | norm_vec_sum <- function(x, na.rm=TRUE) { 347 | x / sum(x, na.rm=na.rm) 348 | } 349 | 350 | range_no_inf <- function(x, na.rm=TRUE) { 351 | if (all_missing(x)) { 352 | return(c(NA, NA)) 353 | } 354 | 355 | range(x, na.rm=na.rm) 356 | } 357 | 358 | logit <- function(p) { 359 | p[p < 0 | p > 1] <- NA 360 | log(p / (1 - p)) 361 | } 362 | 363 | sigmoid <- function(x) { 364 | # more computationally stable form of: 1 / (1 + exp(-x)) 365 | # also: logistic(x, l=1, k=1) 366 | exp(-log(1 + exp(-x))) 367 | } 368 | 369 | col_adj <- function(c, a) { 370 | v <- log(exp(c + 1) * sqrt(a)) - 1 371 | pmax(pmin(v, 1), 0) 372 | } 373 | 374 | # Snap a value to either the min or max if outside some range 375 | clip_range <- function(x, min=NULL, max=NULL) { 376 | if (!is.null(max)) { 377 | x <- pmin(x, max) 378 | } 379 | if (!is.null(min)) { 380 | x <- pmax(x, min) 381 | } 382 | return(x) 383 | } 384 | -------------------------------------------------------------------------------- /R/theme.R: -------------------------------------------------------------------------------- 1 | .pt <- 72.27 / 25.4 2 | .stroke <- 96 / 25.4 3 | 4 | 5 | #' theme_mejr color mejr_palette 6 | #' 7 | #' @return list 8 | #' @export 9 | #' 10 | #' @examples 11 | #' mejr_palette() 12 | mejr_palette <- function() { 13 | list( 14 | black="#141214", gray="#939393", lightgray="#E8E8E8", yellow="#E0CF7C", 15 | magenta="#E069C6", cyan="#60ADAB", midline="#767698", fill="#8989B2", 16 | colour="#585872" 17 | ) 18 | } 19 | 20 | #' Custom ggplot2 theme 21 | #' 22 | #' A complete, minimal theme to be used with the `ggplot2` package 23 | #' 24 | #' You can use `theme_update` to change some aspect of this theme after 25 | #' using `theme_set`. 26 | #' 27 | #' @inheritParams ggplot2::theme_gray 28 | #' @param black Values from 0 to 255, indicating the darkest line and 29 | #' text colors (255). 30 | #' @param margin_add additive adjustment of margin spacing and tick length (in "pt" 31 | #' units). May be positive or negative. 32 | #' @param debug Add debug info to text. 33 | #' @param FUN Call a function before returning the theme elements. 34 | #' @param ... Arguments passed to `FUN` 35 | #' @seealso [mejr_geom_defaults], [ggplot2::theme_update], [ggplot2::theme_set] 36 | #' @export 37 | #' @examples 38 | #' \donttest{ 39 | #' library(ggplot2) 40 | #' 41 | #' theme_set(theme_mejr(debug = TRUE)) 42 | #' example_plot() 43 | #' 44 | #' theme_set(theme_mejr()) 45 | #' theme_update(axis.text = element_blank()) # any updates can go here 46 | #' example_plot() 47 | #' } 48 | theme_mejr <- function(base_size=11, base_family=getOption("ggdistribute.font"), 49 | black=67, margin_add=2, debug=FALSE, FUN=NULL, ...) { 50 | black <- gray2(black) 51 | 52 | if (is.null(margin_add) | scale_add(base_size, 1 / 7, margin_add) < 0) { 53 | margin_add <- 0 54 | } 55 | 56 | mejr_geom_defaults(base_size, black) 57 | 58 | if (!is.null(FUN)) { 59 | do.call(match.fun(FUN), list(...)) 60 | } 61 | 62 | # make theme elements -------------------------------------------------------- 63 | 64 | light_gray <- rgb(0, 0, 0, .05) 65 | 66 | theme( 67 | line=element_line( 68 | colour=black, size=scale_add(base_size, 1 / 36), linetype=1, lineend="butt" 69 | ), 70 | rect=element_rect( 71 | fill="transparent", colour=black, 72 | size=scale_add(base_size, 1 / 18), linetype=1 73 | ), 74 | text=element_text( 75 | family=base_family, face="plain", colour=black, size=base_size, 76 | hjust=0.5, vjust=0.5, angle=0, lineheight=1, 77 | margin=margin( 78 | t=scale_add(base_size, 0.45), r=scale_add(base_size, 0.45), 79 | b=scale_add(base_size, 0.45), l=scale_add(base_size, 0.45), 80 | unit="pt" 81 | ), debug=debug 82 | ), 83 | title=element_text( 84 | family=base_family, face="plain", colour=black, 85 | size=scale_add(base_size, 1.1), hjust=0, vjust=0.5, angle=0, lineheight=0.9, 86 | margin=margin( 87 | t=0, r=0, b=scale_add(base_size, 0.25, margin_add), l=0, unit="pt" 88 | ), 89 | debug=debug 90 | ), 91 | axis.line=element_line(), 92 | axis.line.x=NULL, 93 | axis.line.y=NULL, 94 | axis.ticks=element_line(), 95 | axis.ticks.x=element_line(), 96 | axis.ticks.y=element_line(), 97 | axis.ticks.length=grid::unit(clip_range(margin_add / 1.25, 1), "pt"), 98 | axis.text=element_text(size=rel(.75)), 99 | axis.text.x=element_text( 100 | hjust=0.5, vjust=1, 101 | margin=margin( 102 | t=scale_add(base_size, 0.25, margin_add), 103 | r=scale_add(base_size, 0.25, margin_add), b=scale_add(base_size, 0.125), 104 | l=scale_add(base_size, 0.25, margin_add), unit="pt" 105 | ) 106 | ), 107 | axis.text.x.top=element_text( 108 | vjust=0, margin=margin( 109 | t=scale_add(base_size, 0.125), 110 | r=scale_add(base_size, 0.25, margin_add), 111 | b=scale_add(base_size, 0.25, margin_add), 112 | l=scale_add(base_size, 0.25, margin_add), unit="pt" 113 | ) 114 | ), 115 | axis.text.y=element_text(hjust=1, margin=margin( 116 | t=scale_add(base_size, 0.25, margin_add), 117 | r=scale_add(base_size, 0.25, margin_add), 118 | b=scale_add(base_size, 0.25, margin_add), 119 | l=scale_add(base_size, 0.125), unit="pt" 120 | )), 121 | axis.text.y.right=element_text( 122 | hjust=0, margin=margin( 123 | t=scale_add(base_size, 0.25, margin_add), 124 | r=scale_add(base_size, 0.125), 125 | b=scale_add(base_size, 0.25, margin_add), 126 | l=scale_add(base_size, 0.25, margin_add), unit="pt" 127 | ) 128 | ), 129 | axis.title=element_text(face="bold", size=rel(0.875)), 130 | axis.title.x=element_text( 131 | vjust=0.5, hjust=0.5, 132 | margin=margin( 133 | t=scale_add(base_size, 0.4, margin_add / 2), r=scale_add(base_size, 0), 134 | b=scale_add(base_size, 0), l=scale_add(base_size, 0), unit="pt" 135 | ) 136 | ), 137 | axis.title.x.top=element_text( 138 | margin=margin( 139 | t=scale_add(base_size, 0), r=scale_add(base_size, 0), 140 | b=scale_add(base_size, 0.4, margin_add / 2), 141 | l=scale_add(base_size, 0), unit="pt" 142 | ) 143 | ), 144 | axis.title.y=element_text( 145 | angle=90, vjust=0.5, hjust=0, 146 | margin=margin( 147 | t=scale_add(base_size, 0), r=scale_add(base_size, 0.4, margin_add / 2), 148 | b=scale_add(base_size, 0), l=scale_add(base_size, 0), unit="pt" 149 | ) 150 | ), 151 | axis.title.y.right=element_text( 152 | hjust=1, vjust=0.5, angle=270, 153 | margin=margin( 154 | t=scale_add(base_size, 0), r=scale_add(base_size, 0), 155 | b=scale_add(base_size, 0), 156 | l=scale_add(base_size, 0.4, margin_add / 2), unit="pt" 157 | ) 158 | ), 159 | legend.background=element_blank(), 160 | legend.key=element_blank(), 161 | legend.key.size=grid::unit(scale_add(base_size, 1.25), "pt"), 162 | legend.key.height=NULL, 163 | legend.key.width=NULL, 164 | legend.text=element_text(size=rel(0.5)), 165 | legend.text.align=0.5, 166 | legend.title=element_text(face="plain", size=rel(0.7)), 167 | legend.title.align=0.5, 168 | legend.position="bottom", 169 | legend.direction="horizontal", 170 | legend.justification="center", 171 | legend.box="horizontal", 172 | legend.box.just="center", 173 | legend.box.background=element_rect( 174 | colour=black, size=rel(0.25), fill="transparent" 175 | ), 176 | legend.box.margin=margin( 177 | t=scale_add(base_size, 0.125), r=scale_add(base_size, 0.125), 178 | b=scale_add(base_size, 0.125), l=scale_add(base_size, 0.125), unit="pt" 179 | ), 180 | legend.box.spacing=grid::unit(scale_add(base_size, 1 / 3, margin_add / 2), "pt"), 181 | legend.margin=margin( 182 | t=scale_add(base_size, 0.125), r=scale_add(base_size, 1 / 3), 183 | b=scale_add(base_size, 0.125), l=scale_add(base_size, 1 / 3), unit="pt" 184 | ), 185 | legend.spacing=grid::unit(scale_add(base_size, 0.125, margin_add), "pt"), 186 | legend.spacing.x=NULL, 187 | legend.spacing.y=NULL, 188 | panel.background=element_blank(), 189 | panel.border=element_rect(size=rel(0.9), colour=light_gray), 190 | panel.grid=element_blank(), 191 | panel.grid.major=element_blank(), 192 | panel.grid.minor=element_blank(), 193 | panel.grid.major.y=NULL, 194 | panel.grid.minor.y=NULL, 195 | panel.grid.major.x=NULL, 196 | panel.grid.minor.x=NULL, 197 | panel.ontop=FALSE, 198 | panel.spacing=grid::unit(scale_add(base_size, 0.25, margin_add), "pt"), 199 | panel.spacing.x=NULL, 200 | panel.spacing.y=NULL, 201 | strip.background=element_rect(colour=gray(.94), size=rel(0.5), fill=light_gray), 202 | strip.text=element_text(size=rel(0.7), face="bold"), 203 | strip.text.x=element_text( 204 | hjust=0.5, vjust=0.5, 205 | margin=margin( 206 | t=scale_add(base_size, 0.25), r=scale_add(base_size, 0.125), 207 | b=scale_add(base_size, 0.25), l=scale_add(base_size, 0.125), unit="pt" 208 | ) 209 | ), 210 | strip.text.y=element_text( 211 | vjust=0.5, hjust=0.5, angle=270, 212 | margin=margin( 213 | t=scale_add(base_size, 0.125), r=scale_add(base_size, 0.25), 214 | b=scale_add(base_size, 0.125), l=scale_add(base_size, 0.25), unit="pt" 215 | ) 216 | ), 217 | strip.placement="outside", 218 | strip.placement.x=NULL, 219 | strip.placement.y=NULL, 220 | strip.switch.pad.grid=grid::unit(scale_add(base_size, 0.25, margin_add), "pt"), 221 | strip.switch.pad.wrap=grid::unit(scale_add(base_size, 0.25, margin_add), "pt"), 222 | plot.background=element_blank(), 223 | plot.title=element_text( 224 | face="bold", hjust=0, 225 | margin=margin( 226 | t=scale_add(base_size, 0), r=scale_add(base_size, 0), 227 | b=scale_add(base_size, 1 / 3, margin_add / 1.5), 228 | l=scale_add(base_size, 0), unit="pt" 229 | ) 230 | ), 231 | plot.tag=element_text(face="bold"), 232 | plot.tag.position="topleft", 233 | plot.subtitle=element_text( 234 | hjust=0, face="plain", size=rel(0.9), 235 | margin=margin( 236 | t=scale_add(base_size, 0), r=scale_add(base_size, 0), 237 | b=scale_add(base_size, 1 / 3, margin_add), 238 | l=scale_add(base_size, 0), unit="pt" 239 | ) 240 | ), 241 | plot.caption=element_text( 242 | hjust=0.5, size=rel(0.75), face="italic", 243 | margin=margin( 244 | t=scale_add(base_size, 0.5, margin_add), r=scale_add(base_size, 0.75), 245 | b=scale_add(base_size, 0.125), l=scale_add(base_size, 0.75), unit="pt" 246 | ) 247 | ), 248 | plot.margin=margin( 249 | t=scale_add(base_size, 1 / 7), r=scale_add(base_size, 1 / 7), 250 | b=scale_add(base_size, 1 / 7), l=scale_add(base_size, 1 / 7), unit="pt" 251 | ), 252 | complete=TRUE 253 | ) 254 | } 255 | 256 | #' Setup defaults for specific geoms 257 | #' 258 | #' @inheritParams theme_mejr 259 | #' @param gray gray color value (0-255) 260 | #' @param lty linetype 261 | #' @param lwd linewidth 262 | #' @param cex point size 263 | #' @param stroke stroke width 264 | #' @param alpha alpha 265 | #' @param pch point shape 266 | #' @param txt text size 267 | #' @param reset reset all back to default 268 | #' 269 | #' @return NULL 270 | #' @export 271 | #' 272 | #' @examples 273 | #' # This will change the point size and shape for 274 | #' # all geoms in which GeomPoint inherits from. 275 | #' mejr_geom_defaults(cex = 1.1, pch = 19) 276 | #' 277 | #' # Reset defaults back to their original state. 278 | #' mejr_geom_defaults(reset=TRUE) 279 | mejr_geom_defaults <- function(base_size=11, black=51, gray=214, lty=3, lwd=base_size / 280 | 20, 281 | cex=base_size / 9, stroke=base_size * .05, alpha=0.5, 282 | pch=21, txt=base_size / 4, reset=FALSE) { 283 | gray <- gray2(gray) 284 | black <- gray2(black) 285 | 286 | updates <- list( 287 | text=list(geoms="text", opts=list(size=txt, colour=black)), 288 | lines=list(geoms=c( 289 | "line", "hline", "vline", "linerange", "errorbar", 290 | "errorbarh" 291 | ), opts=list(size=lwd, colour=black)), 292 | ablines=list( 293 | geoms=c("hline", "vline", "abline"), 294 | opts=list(size=lwd, colour=gray, linetype=lty) 295 | ), 296 | bar=list(geoms="bar", opts=list(size=lwd, colour=NA)), 297 | smooth=list(geoms="smooth", opts=list( 298 | size=lwd, colour=black, fill=gray 299 | )), 300 | point=list(geoms="point", opts=list( 301 | size=cex, colour=black, shape=pch, 302 | stroke=stroke, fill=NA, alpha=alpha 303 | )) 304 | ) 305 | 306 | gg_defaults <- getOption("ggdistribute.geom.defaults") 307 | gg_edited <- getOption("ggdistribute.geom.edits") 308 | 309 | on.exit({ 310 | options(ggdistribute.geom.defaults=gg_defaults) 311 | options(ggdistribute.geom.edits=gg_edited) 312 | }) 313 | 314 | for (u in seq_along(updates)) { 315 | geoms <- updates[[u]]$geoms 316 | changes <- updates[[u]]$opts 317 | 318 | for (g in geoms) { 319 | if (length(gg_defaults[[g]]) < 1) { 320 | gg_defaults[[g]] <- geom_defaults(g) 321 | } 322 | if (reset) { 323 | update_geom_defaults(g, gg_defaults[[g]]) 324 | } else { 325 | gg_defaults$edited <- TRUE 326 | gg_edited[[g]] <- changes 327 | update_geom_defaults(g, changes) 328 | } 329 | } 330 | } 331 | 332 | invisible() 333 | } 334 | 335 | 336 | geom_defaults <- function(geom) { 337 | if (is.character(geom)) { 338 | g <- find_subclass("Geom", geom, parent.frame()) 339 | } else { 340 | if (inherits(geom, "Geom")) { 341 | g <- geom 342 | } else { 343 | stop("`geom` must be a string (like \"point\")", 344 | " or a Geom object (like GeomPoint).", 345 | call.=FALSE) 346 | } 347 | } 348 | unclass(g$default_aes) 349 | } 350 | 351 | 352 | #' Test theme by printing plots to pdf and viewport 353 | #' 354 | #' @param w pdf width (inches) 355 | #' @param h pdf height (inches) 356 | #' @param eplot list of options for `example_plot` 357 | #' @param mejr list of options for `theme_mejr(...)` 358 | #' @param gg further theme customization with ggplot::theme(...) 359 | #' @param print show the `eplot` plot 360 | #' @param with_test_theme also print the same plot using `ggplot2::theme_test` 361 | #' @param dir directory where to save temp pdf files, such as tempdir() 362 | #' @return NULL 363 | test_mejr_theme <- function(w=6.875, h=4.5, eplot=list(), mejr=list(), gg=theme(), 364 | print=TRUE, with_test_theme=FALSE, dir) { 365 | if (length(eplot) < 1) { 366 | eplot <- list() 367 | } 368 | 369 | if (length(mejr) < 1) { 370 | mejr <- list() 371 | } 372 | 373 | cat(dir) 374 | 375 | if (with_test_theme) { 376 | test_theme_args <- list(base_size=11, debug=FALSE) 377 | test_theme_args <- modifyList( 378 | test_theme_args, Filter(Negate(is.null), mejr[c("base_size", "debug")]), 379 | keep.null=TRUE 380 | ) 381 | 382 | mejr_geom_defaults(reset=TRUE) 383 | theme_set(do.call("theme_test", list())) 384 | 385 | ggsave( 386 | filename=normalizePath(file.path(dir, "theme_test.pdf"), mustWork=F), 387 | example_plot() + do.call("theme_test", test_theme_args) + gg, width=w, 388 | height=h, device="pdf" 389 | ) 390 | } 391 | 392 | theme_set(theme_mejr()) 393 | 394 | p_mejr1 <- example_plot() + do.call(theme_mejr, mejr) + gg 395 | p_mejr2 <- do.call(example_plot, eplot) + do.call(theme_mejr, mejr) + gg 396 | 397 | ggsave( 398 | filename=normalizePath(file.path(dir, "theme_mejr1.pdf"), mustWork=FALSE), 399 | p_mejr1, width=w, height=h, device="pdf" 400 | ) 401 | 402 | ggsave( 403 | filename=normalizePath(file.path(dir, "theme_mejr2.pdf"), mustWork=FALSE), 404 | p_mejr2, width=w, height=h, device="pdf" 405 | ) 406 | 407 | if (print) { 408 | grid::grid.draw(p_mejr2) 409 | } 410 | 411 | invisible() 412 | } 413 | 414 | 415 | #' Write text to one of four corners of a plot 416 | #' 417 | #' @details If multiple panels, will write to all panels. 418 | #' 419 | #' @param text character string 420 | #' @param pos character of `"tl"`, `"tr"`, `"bl"`, `"br"` to 421 | #' indicate position (you may also use the full names, `"bottomleft"`, etc...) 422 | #' @param ... additional options passed to [ggplot2::geom_label] 423 | #' @param geom "text" or "label" geoms 424 | #' 425 | #' @return ggplot layer 426 | #' @export 427 | #' 428 | #' @examples 429 | #' posterior_plot() + annotate_corner("Hi.", "bottomright") 430 | annotate_corner <- function(text, pos="tl", geom=c("text", "label"), ...) { 431 | x <- -Inf 432 | y <- Inf 433 | h <- 0 434 | v <- 1 435 | 436 | switch(tolower(pos), topleft= , tl={ 437 | x <- -Inf 438 | y <- Inf 439 | h <- 0 440 | v <- 1 441 | }, topright= , tr={ 442 | x <- Inf 443 | y <- Inf 444 | h <- 1 445 | v <- 1 446 | }, bottomleft= , bl={ 447 | x <- -Inf 448 | y <- -Inf 449 | h <- 0 450 | v <- 0 451 | }, bottomright= , 452 | br={ 453 | x <- Inf 454 | y <- -Inf 455 | h <- 1 456 | v <- 0 457 | }, stop("incorrect position")) 458 | 459 | annotate(match.arg(geom), x, y, label=text, hjust=h, vjust=v, ...) 460 | } 461 | 462 | 463 | #' Add labels to existing plot 464 | #' 465 | #' Uses a normalized coordinate system to add text anywhere on the current plot. 466 | #' 467 | #' @param labels character vector of labels to use 468 | #' @param x horz positions of items in `labels` 469 | #' @param y vert positions of items in `labels` 470 | #' @param g list of options passed to [grid::gpar] 471 | #' @param ... optional args passed to [grid::grid.text] 472 | #' 473 | #' @return NULL. prints to current graphics device. 474 | #' @export 475 | #' @seealso [grid::grid.text], [grid::gpar] 476 | #' @examples 477 | #' example_plot() 478 | #' label_plot(c('a label', 'another one'), c(.1, .9), c(.95, .1)) 479 | #' 480 | #' # use extra options from grid::grid.text 481 | #' label_plot('last one', 0.5, 0.5, just='center') 482 | label_plot <- function(labels, x, y, g=list(fontsize=14, fontface="bold"), ...) { 483 | l <- length(labels) 484 | if (!all(unlist(lapply(list(labels, x, y), length)) == l)) { 485 | stop("make sure length of labels, x, y are equal") 486 | } 487 | for (i in seq_len(l)) { 488 | grid::grid.text( 489 | label=labels[i], x=unit(x[i], "npc"), y=unit(y[i], "npc"), 490 | gp=do.call(grid::gpar, g), ... 491 | ) 492 | } 493 | } 494 | -------------------------------------------------------------------------------- /R/zzz.R: -------------------------------------------------------------------------------- 1 | set_pkg_opts <- function(..., opt_list=NULL) { 2 | pkg_opts <- c(pairlist(...), as.pairlist(opt_list)) 3 | 4 | if (is.null(pkg_opts)) { 5 | return(invisible()) 6 | } 7 | 8 | opts <- names(options()) 9 | pkg_opt_names <- names(pkg_opts) 10 | new_opts <- nzchar(pkg_opt_names) & !pkg_opt_names %in% opts 11 | 12 | if (any(new_opts)) { 13 | options(pkg_opts[pkg_opt_names[new_opts]]) 14 | } else { 15 | invisible() 16 | } 17 | } 18 | 19 | .onLoad <- function(libname, pkgname) { 20 | set_pkg_opts( 21 | ggdistribute.font="sans", ggdistribute.geom.defaults=list(edited=FALSE), 22 | ggdistribute.geom.edits=list() 23 | ) 24 | invisible() 25 | } 26 | 27 | .onAttach <- function(libname, pkgname) { 28 | packageStartupMessage("ggdistribute loaded") 29 | } 30 | -------------------------------------------------------------------------------- /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 | strip.white=FALSE, 12 | tidy=FALSE, 13 | dpi=300, 14 | fig.align="center", 15 | fig.path="man/figures/README-", 16 | fig.width=5.25, 17 | fig.height=3.8, 18 | out.width="700px" 19 | ) 20 | ``` 21 | 22 | 23 | [![CRAN_Status_Badge](http://www.r-pkg.org/badges/version/ggdistribute)](https://cran.r-project.org/package=ggdistribute) 24 | 25 | # ggdistribute 26 | 27 | A `ggplot2` Extension for Plotting Unimodal Distributions 28 | 29 | The `ggdistribute` package is an extension for plotting posterior or other types of unimodal distributions that require overlaying information about a distribution's intervals. It makes use of the `ggproto` system to extend `ggplot2`, providing additional "geoms", "stats", and "positions." The extensions integrate with existing `ggplot2` layer elements. 30 | 31 | ## Example 32 | 33 | ```{r, echo=FALSE, message=FALSE, results='hide', warning=FALSE} 34 | library(ggplot2) 35 | devtools::load_all(".", export_all = FALSE) 36 | theme_set(theme_mejr()) 37 | ``` 38 | 39 | The package function `example_plot()` is an overview of combining `ggdistribute` with other `ggplot2` elements. The contents of this function are printed below and gives details about the extended parts to `ggplot2`. 40 | 41 | ```{r, eval=FALSE} 42 | library(ggplot2) 43 | library(ggdistribute) 44 | ``` 45 | 46 | ```{r turtle_snails, echo = FALSE, fig.width=5, fig.height=3, out.width="100%"} 47 | plot(example_plot()) 48 | ``` 49 | 50 | ```{r prin_ex_plot, echo=FALSE, results='asis'} 51 | ggdistribute:::function2chunk("example_plot") 52 | ``` 53 | 54 | ## Additional examples from an arbritrary dataset 55 | 56 | ```{r} 57 | # total number of samples in the dataset 58 | N <- 2500 59 | means <- c(-1, 2, 3, 5) 60 | ``` 61 | 62 | The `data` object below is a randomly generated dataset of `r length(means)` different normal distributions. Two factors, `Condition` and `Group`, are assigned to subsets of the generated values. `r N` samples are generated for each value of `mu` for a total of $ `r N*length(means)` $ rows. 63 | 64 | ```{r norm_data} 65 | data <- data_normal_sample(mu = means, n = N) 66 | ``` 67 | 68 | Create a new grouping variable called `Level` based on the column `Group`. 69 | 70 | ```{r norm_data_grps} 71 | # number of levels to make 72 | num_levels <- 8L 73 | 74 | # R version >= 3.5 now let's you assign factors this way. 75 | data$Level <- with(data, factor( 76 | Group, 77 | levels = letters[seq_len(num_levels)], 78 | labels = c(rep("Low", 3), rep("Mid", 2), rep("High", 3)), 79 | ordered = TRUE 80 | )) 81 | ``` 82 | 83 | Show unique groups per `Group`, `Condition`, and `Level` to help understand the data factors. 84 | 85 | ```{r} 86 | unique(data[, c("Group", "Condition", "Level")]) 87 | ``` 88 | 89 | ### Facetting and spreading groups 90 | 91 | ```{r space_ships, fig.width=5, fig.height=4} 92 | ggplot(data) + 93 | aes(x=value, y=Condition, group=Group) + 94 | geom_posterior( 95 | aes(fill=Level), 96 | mirror=TRUE, 97 | show.legend=FALSE, 98 | adjust=1.5, 99 | brighten=c(6, 0, 2.5), 100 | position=position_spread(reverse=TRUE) 101 | ) + 102 | geom_point( 103 | aes(color=Level, shape=Condition), 104 | alpha=.08, 105 | fill=NA, 106 | show.legend=FALSE, 107 | position=position_jitter(0, .45) 108 | ) + 109 | coord_cartesian(ylim=c(0.5, 2.5), expand=FALSE) + 110 | facet_wrap(~ Level, scales="free") + 111 | labs(title="Space Invaders", y="Condition", x="Parameter estimate") 112 | ``` 113 | 114 | ### Changing the appearance of `geom_posterior` 115 | 116 | ```{r candy_wrappers, fig.width=5, fig.height=4} 117 | ggplot(data) + 118 | aes(x=value, y=Group) + 119 | geom_vline( 120 | xintercept=0, size=.6 121 | ) + 122 | geom_posterior( 123 | aes(color=Condition), 124 | midline=NULL, 125 | mirror=TRUE, 126 | fill="#FFFFFF", 127 | draw_sd=FALSE, 128 | interval_type="hdi", 129 | vjust=0, 130 | position=position_spread(height=2) 131 | ) + 132 | labs( 133 | title="Candy Wrappers", 134 | x="Parameter estimate", 135 | y="Sample location" 136 | ) + 137 | scale_x_continuous(breaks=seq(-10, 10, 1)) + 138 | theme( 139 | legend.position=c(.025, .9), 140 | legend.justification=c(0, 0), 141 | panel.grid.major.y=element_line(color=gray(.92)) 142 | ) 143 | ``` 144 | 145 | ### The *y* axis is a repeated, continuous grouping variable 146 | 147 | The variable `GroupScore` is a continuous variable assigned to each `Group`. The distributions will be positioned at the start of the y value for each group, and resized to not overlap with the next group. Resizing can be overriden by specifying `height` in `position_spread`. 148 | 149 | ```{r y_cont} 150 | unique(data[, c("Group", "GroupScore")]) 151 | ``` 152 | 153 | ```{r rainbow_hills, fig.width=5, fig.height=6} 154 | ggplot(data) + 155 | aes(x=value, y=GroupScore) + 156 | geom_vline( 157 | xintercept=0, size=.6 158 | ) + 159 | geom_posterior( 160 | aes(fill=Group), 161 | midline="#FFFFFF", 162 | colour="#FFFFFF", 163 | alpha=0.7, 164 | brighten=c(1.3, 0, -1.3), 165 | interval_type="hdi", 166 | position=position_spread(height=0.5, padding=0) 167 | ) + 168 | labs( 169 | title="Rainbow Hills", 170 | x="Parameter estimate", 171 | y="Group's score" 172 | ) + 173 | scale_x_continuous(breaks=seq(-10, 10, 1)) + 174 | scale_y_continuous(breaks=seq(-10, 10, .5)) 175 | ``` 176 | 177 | ## How to install 178 | 179 | ### Dependencies 180 | 181 | - R: 182 | 183 | A current R installation. 184 | 185 | #### Dependencies for installing the development version of this package 186 | 187 | - `devtools` package: 188 | 189 | The `devtools` package is an R package that makes it easier to install local or remote content as an R package that can be used like any other standard R package. You can install `devtools` by opening up RStudio or an R terminal and running 190 | 191 | ```{r devtools-inst, eval=FALSE} 192 | install.packages("devtools") 193 | ``` 194 | 195 | For Windows users, you *may* be required to install Rtools first before you can use the `devtools` package, if there is any code that needs to be compiled. These are a set of build tools customized for building R packages (see the `devtools` link above for more details). 196 | 197 | - Build tools: 198 | 199 | ### Installing from CRAN 200 | 201 | 202 | If you want to use the last version that was uploaded to the CRAN repository, do the following: 203 | 204 | ```{r cran-inst, eval=FALSE} 205 | install.packages("ggdistribute") 206 | ``` 207 | 208 | ### Installing from the downloaded package content folder 209 | 210 | If you have all of the `ggdistribute` package contents (e.g., an unzipped folder containing `DESCRIPTION`, `NAMESPACE`, `R/`, etc...), you can open up the `ggdistribute.Rproj` file in RStudio and use both `devtools` and RStudio to load or install package. 211 | 212 | The first step is to make sure you have all the package dependencies (other packages that this pacakge relies on) to be able to load or install the `ggdistribute` package materials. You can run the line below to install dependencies first. 213 | 214 | ```{r local-inst-dep, eval=FALSE} 215 | devtools::install_dev_deps() 216 | ``` 217 | 218 | After the dependencies are installed, you can now build and install `ggdistribute` from the current working directory. 219 | Assuming the `ggdistribute` project is loaded in RStudio, you can leave out the first argument. 220 | 221 | ```{r local-inst, eval=FALSE} 222 | devtools::install() 223 | ``` 224 | 225 | If installing from a different working directory, enter the path of the package contents to manually specify what to install. 226 | 227 | ```{r local-inst-alt, eval=FALSE} 228 | devtools::install_dev_deps("/Path/to/the/folder/ggdistribute") 229 | devtools::install("/Path/to/the/folder/ggdistribute") 230 | ``` 231 | 232 | 233 | ### Installing from GitHub 234 | 235 | If `devtools` are installed, you may use the `install_github()` function to download and install the development version of the package from this GitHub repository instead of the one hosted on CRAN. Run the code below to download and install the development version: 236 | 237 | ```{r gh-installation, eval = FALSE} 238 | devtools::install_github("iamamutt/ggdistribute") 239 | ``` 240 | 241 | or to install all suggested packages as well... 242 | 243 | ```{r gh-installation-dep, eval = FALSE} 244 | devtools::install_github("iamamutt/ggdistribute", dependencies=TRUE) 245 | ``` 246 | 247 | ### Loading the package 248 | 249 | If successful, the package should now be installed and can be loaded as any other package. Repeat the last intall step if there are updates to the package, or complete all steps to install on another machine. You should now be able to use the package materials and should see it in your packages tab if using RStudio. It should be loaded like any other package. 250 | 251 | ```{r load-pkg, eval=FALSE} 252 | library(ggdistribute) 253 | ``` 254 | 255 | 256 | ## Getting help 257 | 258 | ### Browsing the vignettes 259 | 260 | Vignettes can be viewed in several different ways. 261 | 262 | - pre-built and saved in the `inst\doc` folder on GitHub. 263 | - calling `vignette("geom_posterior", "ggdistribute")` from within R after the package is installed. 264 | - navigating to packages tab > ggdistribute > User guides, package vignettes... in RStudio. 265 | 266 | ### Viewing the help documentation 267 | 268 | View the package welcome page to navigate to different types of help documents 269 | 270 | ```{r show-welcome, eval=FALSE} 271 | package?ggdistribute 272 | ``` 273 | 274 | Viewing package information and a list of exported objects: 275 | 276 | ```{r show-help, eval=FALSE} 277 | help(package = "ggdistribute") 278 | 279 | # or 280 | library(help="ggdistribute") 281 | ``` 282 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | [![CRAN\_Status\_Badge](http://www.r-pkg.org/badges/version/ggdistribute)](https://cran.r-project.org/package=ggdistribute) 5 | 6 | # ggdistribute 7 | 8 | A `ggplot2` Extension for Plotting Unimodal Distributions 9 | 10 | The `ggdistribute` package is an extension for plotting posterior or 11 | other types of unimodal distributions that require overlaying 12 | information about a distribution’s intervals. It makes use of the 13 | `ggproto` system to extend `ggplot2`, providing additional “geoms”, 14 | “stats”, and “positions.” The extensions integrate with existing 15 | `ggplot2` layer elements. 16 | 17 | ## Example 18 | 19 | The package function `example_plot()` is an overview of combining 20 | `ggdistribute` with other `ggplot2` elements. The contents of this 21 | function are printed below and gives details about the extended parts to 22 | `ggplot2`. 23 | 24 | ``` r 25 | library(ggplot2) 26 | library(ggdistribute) 27 | ``` 28 | 29 | 30 | 31 | ``` r 32 | example_plot <- 33 | function() { 34 | # color palette 35 | colors <- mejr_palette() 36 | 37 | ggplot(sre_data(5000), aes_string(y="effect")) + 38 | 39 | # ggdistribute specific elements ------------------------------------------- 40 | geom_posterior( 41 | # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 42 | # geom_posterior() aesthetics mappings 43 | # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 44 | aes_string(x="value", fill="contrast"), 45 | # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 46 | # options passed to stat_density_ci() for estimating intervals 47 | # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 48 | interp_thresh=.001, # threshold for interpolating segment gaps 49 | center_stat="median", # measure of central tendency 50 | ci_width=0.90, # width corresponding to CI segments 51 | interval_type="ci", # quantile intervals not highest density interval 52 | # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 53 | # options passed to stat_density_ci() for estimating density 54 | # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 55 | bw=".nrd0", # bandwidth estimator type 56 | adjust=1.5, # adjustment to bandwidth 57 | n=1024, # number of samples in final density 58 | trim=.005, # trim `x` this proportion before estimating density 59 | cut=1.5, # tail extension for zero density estimation 60 | # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 61 | # geom_posterior() options 62 | # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 63 | draw_ci=TRUE, # toggle showing confidence interval parts 64 | draw_sd=TRUE, # toggle showing standard deviation parts 65 | mirror=FALSE, # toggle horizontal violin distributions 66 | midline=NULL, # line displaying center of dist. (NULL=aes color) 67 | brighten=c(3, 0, 1.333), # additive adjustment of segment fill colors 68 | # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 69 | # position_spread() options 70 | # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 71 | position=position_spread( 72 | reverse=TRUE, # order of spreaded groups within panels 73 | padding=0.3, # shrink heights of distributions 74 | height="panel" # scale by heights within panels 75 | ), # 76 | # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 77 | # standard ggplot layer options 78 | # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 79 | size=0.15, color=colors$gray, vjust=0.7, show.legend=FALSE 80 | ) + 81 | 82 | # standard ggplot2 elements ------------------------------------------------ 83 | geom_vline(alpha=0.5, color=colors$gray, size=0.333, linetype=1, xintercept=0) + 84 | scale_x_continuous(breaks=seq(-1, 1, .05)) + 85 | facet_grid("contrast ~ .", scales="free_y", space="free_y") + 86 | scale_fill_manual(values=c(colors$yellow, colors$magenta, colors$cyan)) + 87 | labs(x="Difference in accuracy (posterior predictions)") + 88 | theme( 89 | legend.position="none", strip.text.y=element_text(angle=0, hjust=0.5), 90 | panel.border=element_rect(fill=NA, color=colors$lightgray, size=0.67), 91 | panel.grid=element_blank(), panel.ontop=FALSE, axis.title.y=element_blank(), 92 | plot.margin=margin(t=2, r=4, b=2, l=2, unit="pt") 93 | ) 94 | } 95 | ``` 96 | 97 | ## Additional examples from an arbritrary dataset 98 | 99 | ``` r 100 | # total number of samples in the dataset 101 | N <- 2500 102 | means <- c(-1, 2, 3, 5) 103 | ``` 104 | 105 | The `data` object below is a randomly generated dataset of 4 different 106 | normal distributions. Two factors, `Condition` and `Group`, are assigned 107 | to subsets of the generated values. 2500 samples are generated for each 108 | value of `mu` for a total of $ 10^{4} $ rows. 109 | 110 | ``` r 111 | data <- data_normal_sample(mu = means, n = N) 112 | ``` 113 | 114 | Create a new grouping variable called `Level` based on the column 115 | `Group`. 116 | 117 | ``` r 118 | # number of levels to make 119 | num_levels <- 8L 120 | 121 | # R version >= 3.5 now let's you assign factors this way. 122 | data$Level <- with(data, factor( 123 | Group, 124 | levels = letters[seq_len(num_levels)], 125 | labels = c(rep("Low", 3), rep("Mid", 2), rep("High", 3)), 126 | ordered = TRUE 127 | )) 128 | ``` 129 | 130 | Show unique groups per `Group`, `Condition`, and `Level` to help 131 | understand the data factors. 132 | 133 | ``` r 134 | unique(data[, c("Group", "Condition", "Level")]) 135 | #> # A tibble: 8 x 3 136 | #> Group Condition Level 137 | #> 138 | #> 1 a A Low 139 | #> 2 b A Low 140 | #> 3 c B Low 141 | #> 4 d B Mid 142 | #> 5 e C Mid 143 | #> 6 f C High 144 | #> 7 g D High 145 | #> 8 h D High 146 | ``` 147 | 148 | ### Facetting and spreading groups 149 | 150 | ``` r 151 | ggplot(data) + 152 | aes(x=value, y=Condition, group=Group) + 153 | geom_posterior( 154 | aes(fill=Level), 155 | mirror=TRUE, 156 | show.legend=FALSE, 157 | adjust=1.5, 158 | brighten=c(6, 0, 2.5), 159 | position=position_spread(reverse=TRUE) 160 | ) + 161 | geom_point( 162 | aes(color=Level, shape=Condition), 163 | alpha=.08, 164 | fill=NA, 165 | show.legend=FALSE, 166 | position=position_jitter(0, .45) 167 | ) + 168 | coord_cartesian(ylim=c(0.5, 2.5), expand=FALSE) + 169 | facet_wrap(~ Level, scales="free") + 170 | labs(title="Space Invaders", y="Condition", x="Parameter estimate") 171 | ``` 172 | 173 | 174 | 175 | ### Changing the appearance of `geom_posterior` 176 | 177 | ``` r 178 | ggplot(data) + 179 | aes(x=value, y=Group) + 180 | geom_vline( 181 | xintercept=0, size=.6 182 | ) + 183 | geom_posterior( 184 | aes(color=Condition), 185 | midline=NULL, 186 | mirror=TRUE, 187 | fill="#FFFFFF", 188 | draw_sd=FALSE, 189 | interval_type="hdi", 190 | vjust=0, 191 | position=position_spread(height=2) 192 | ) + 193 | labs( 194 | title="Candy Wrappers", 195 | x="Parameter estimate", 196 | y="Sample location" 197 | ) + 198 | scale_x_continuous(breaks=seq(-10, 10, 1)) + 199 | theme( 200 | legend.position=c(.025, .9), 201 | legend.justification=c(0, 0), 202 | panel.grid.major.y=element_line(color=gray(.92)) 203 | ) 204 | ``` 205 | 206 | 207 | 208 | ### The *y* axis is a repeated, continuous grouping variable 209 | 210 | The variable `GroupScore` is a continuous variable assigned to each 211 | `Group`. The distributions will be positioned at the start of the y 212 | value for each group, and resized to not overlap with the next group. 213 | Resizing can be overriden by specifying `height` in `position_spread`. 214 | 215 | ``` r 216 | unique(data[, c("Group", "GroupScore")]) 217 | #> # A tibble: 8 x 2 218 | #> Group GroupScore 219 | #> 220 | #> 1 a -0.885 221 | #> 2 b -0.839 222 | #> 3 c 2.18 223 | #> 4 d 2.33 224 | #> 5 e 2.81 225 | #> 6 f 3.07 226 | #> 7 g 4.90 227 | #> 8 h 4.93 228 | ``` 229 | 230 | ``` r 231 | ggplot(data) + 232 | aes(x=value, y=GroupScore) + 233 | geom_vline( 234 | xintercept=0, size=.6 235 | ) + 236 | geom_posterior( 237 | aes(fill=Group), 238 | midline="#FFFFFF", 239 | colour="#FFFFFF", 240 | alpha=0.7, 241 | brighten=c(1.3, 0, -1.3), 242 | interval_type="hdi", 243 | position=position_spread(height=0.5, padding=0) 244 | ) + 245 | labs( 246 | title="Rainbow Hills", 247 | x="Parameter estimate", 248 | y="Group's score" 249 | ) + 250 | scale_x_continuous(breaks=seq(-10, 10, 1)) + 251 | scale_y_continuous(breaks=seq(-10, 10, .5)) 252 | ``` 253 | 254 | 255 | 256 | ## How to install 257 | 258 | ### Dependencies 259 | 260 | - R: 261 | 262 | A current R installation. 263 | 264 | #### Dependencies for installing the development version of this package 265 | 266 | - `devtools` package: 267 | 268 | 269 | The `devtools` package is an R package that makes it easier to install 270 | local or remote content as an R package that can be used like any other 271 | standard R package. You can install `devtools` by opening up RStudio or 272 | an R terminal and running 273 | 274 | ``` r 275 | install.packages("devtools") 276 | ``` 277 | 278 | For Windows users, you *may* be required to install Rtools first before 279 | you can use the `devtools` package, if there is any code that needs to 280 | be compiled. These are a set of build tools customized for building R 281 | packages (see the `devtools` link above for more details). 282 | 283 | - Build tools: 284 | 285 | ### Installing from CRAN 286 | 287 | If you want to use the last version that was uploaded to the CRAN 288 | repository, do the following: 289 | 290 | ``` r 291 | install.packages("ggdistribute") 292 | ``` 293 | 294 | ### Installing from the downloaded package content folder 295 | 296 | If you have all of the `ggdistribute` package contents (e.g., an 297 | unzipped folder containing `DESCRIPTION`, `NAMESPACE`, `R/`, etc…), you 298 | can open up the `ggdistribute.Rproj` file in RStudio and use both 299 | `devtools` and RStudio to load or install package. 300 | 301 | The first step is to make sure you have all the package dependencies 302 | (other packages that this pacakge relies on) to be able to load or 303 | install the `ggdistribute` package materials. You can run the line below 304 | to install dependencies first. 305 | 306 | ``` r 307 | devtools::install_dev_deps() 308 | ``` 309 | 310 | After the dependencies are installed, you can now build and install 311 | `ggdistribute` from the current working directory. Assuming the 312 | `ggdistribute` project is loaded in RStudio, you can leave out the first 313 | argument. 314 | 315 | ``` r 316 | devtools::install() 317 | ``` 318 | 319 | If installing from a different working directory, enter the path of the 320 | package contents to manually specify what to install. 321 | 322 | ``` r 323 | devtools::install_dev_deps("/Path/to/the/folder/ggdistribute") 324 | devtools::install("/Path/to/the/folder/ggdistribute") 325 | ``` 326 | 327 | ### Installing from GitHub 328 | 329 | If `devtools` are installed, you may use the `install_github()` function 330 | to download and install the development version of the package from this 331 | GitHub repository instead of the one hosted on CRAN. Run the code below 332 | to download and install the development version: 333 | 334 | ``` r 335 | devtools::install_github("iamamutt/ggdistribute") 336 | ``` 337 | 338 | or to install all suggested packages as well… 339 | 340 | ``` r 341 | devtools::install_github("iamamutt/ggdistribute", dependencies=TRUE) 342 | ``` 343 | 344 | ### Loading the package 345 | 346 | If successful, the package should now be installed and can be loaded as 347 | any other package. Repeat the last intall step if there are updates to 348 | the package, or complete all steps to install on another machine. You 349 | should now be able to use the package materials and should see it in 350 | your packages tab if using RStudio. It should be loaded like any other 351 | package. 352 | 353 | ``` r 354 | library(ggdistribute) 355 | ``` 356 | 357 | ## Getting help 358 | 359 | ### Browsing the vignettes 360 | 361 | Vignettes can be viewed in several different ways. 362 | 363 | - pre-built and saved in the `inst\doc` folder on GitHub. 364 | - calling `vignette("geom_posterior", "ggdistribute")` from within R 365 | after the package is installed. 366 | - navigating to packages tab \> ggdistribute \> User guides, package 367 | vignettes… in RStudio. 368 | 369 | ### Viewing the help documentation 370 | 371 | View the package welcome page to navigate to different types of help 372 | documents 373 | 374 | ``` r 375 | package?ggdistribute 376 | ``` 377 | 378 | Viewing package information and a list of exported objects: 379 | 380 | ``` r 381 | help(package = "ggdistribute") 382 | 383 | # or 384 | library(help="ggdistribute") 385 | ``` 386 | -------------------------------------------------------------------------------- /cran-comments.md: -------------------------------------------------------------------------------- 1 | ## Test environments 2 | 3 | * local OS X install, R 3.5.1 4 | * ubuntu 12.04 (on travis-ci), R 3.5.1 5 | * win-builder (devel and release) 6 | 7 | ## R CMD check results 8 | 9 | 0 errors | 0 warnings | 0 note 10 | 11 | ## Reverse dependencies 12 | 13 | There are no reverse dependencies. 14 | 15 | --- 16 | -------------------------------------------------------------------------------- /data/sre.rda: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/iamamutt/ggdistribute/3efffa1ebfe20b6d7adf675412a7833b9feb8c04/data/sre.rda -------------------------------------------------------------------------------- /ggdistribute.Rproj: -------------------------------------------------------------------------------- 1 | Version: 1.0 2 | 3 | RestoreWorkspace: No 4 | SaveWorkspace: No 5 | AlwaysSaveHistory: No 6 | 7 | EnableCodeIndexing: Yes 8 | UseSpacesForTab: Yes 9 | NumSpacesForTab: 2 10 | Encoding: UTF-8 11 | 12 | RnwWeave: knitr 13 | LaTeX: pdfLaTeX 14 | 15 | AutoAppendNewline: Yes 16 | StripTrailingWhitespace: Yes 17 | LineEndingConversion: Posix 18 | 19 | BuildType: Package 20 | PackageUseDevtools: Yes 21 | PackageInstallArgs: --no-multiarch --with-keep.source 22 | PackageCheckArgs: --run-donttest 23 | PackageRoxygenize: rd,collate,namespace 24 | 25 | QuitChildProcessesOnExit: Yes 26 | -------------------------------------------------------------------------------- /inst/CITATION: -------------------------------------------------------------------------------- 1 | citHeader("To cite ggdistribute in publications use:") 2 | 3 | citEntry(entry = "Misc", 4 | title = "{ggdistribute}: {A ggplot2} Extension for Plotting Unimodal Distributions", 5 | author = person("Joseph M.", "Burling"), 6 | note = "R package version 1.0.1", 7 | year = "2018", 8 | url = "https://github.com/iamamutt/ggdistribute", 9 | 10 | textVersion = 11 | paste("Burling, Joseph M. (2018).", 12 | title = "ggdistribute: A ggplot2 Extension for Plotting Unimodal Distributions. R package version 1.0.1.", 13 | "https://github.com/iamamutt/ggdistribute/.") 14 | ) 15 | -------------------------------------------------------------------------------- /man/GeomPosterior.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/geom_posterior.R, R/ggdistribute-package.R, 3 | % R/stat_posterior_density.R 4 | \name{geom_posterior} 5 | \alias{geom_posterior} 6 | \alias{GeomPosterior} 7 | \alias{stat_density_ci} 8 | \title{Geom for plotting posterior distributions} 9 | \usage{ 10 | geom_posterior(mapping = NULL, data = NULL, stat = "DensityCI", 11 | position = "spread", ..., draw_ci = TRUE, draw_sd = TRUE, 12 | midline = "#767698", brighten = TRUE, mirror = FALSE, 13 | interp_thresh = NULL, na.rm = FALSE, show.legend = NA, 14 | inherit.aes = TRUE) 15 | 16 | stat_density_ci(mapping = NULL, data = NULL, geom = "Posterior", 17 | position = "spread", ..., center_stat = "median", ci_width = 0.9, 18 | interval_type = "ci", bw = "nrd0", adjust = 1, 19 | kernel = "gaussian", cut = 1, n = 1024, trim = 0.01, 20 | na.rm = FALSE, show.legend = NA, inherit.aes = TRUE) 21 | } 22 | \arguments{ 23 | \item{mapping}{Set of aesthetic mappings created by \code{\link[=aes]{aes()}} or 24 | \code{\link[=aes_]{aes_()}}. If specified and \code{inherit.aes = TRUE} (the 25 | default), it is combined with the default mapping at the top level of the 26 | plot. You must supply \code{mapping} if there is no plot mapping.} 27 | 28 | \item{data}{The data to be displayed in this layer. There are three 29 | options: 30 | 31 | If \code{NULL}, the default, the data is inherited from the plot 32 | data as specified in the call to \code{\link[=ggplot]{ggplot()}}. 33 | 34 | A \code{data.frame}, or other object, will override the plot 35 | data. All objects will be fortified to produce a data frame. See 36 | \code{\link[=fortify]{fortify()}} for which variables will be created. 37 | 38 | A \code{function} will be called with a single argument, 39 | the plot data. The return value must be a \code{data.frame}, and 40 | will be used as the layer data.} 41 | 42 | \item{stat}{Used to override the default connection between \code{geom_posterior} 43 | and \code{stat_density_ci}.} 44 | 45 | \item{position}{Position adjustment, either as a string, or the result of 46 | a call to a position adjustment function.} 47 | 48 | \item{...}{Other arguments passed on to \code{\link[=layer]{layer()}}. These are 49 | often aesthetics, used to set an aesthetic to a fixed value, like 50 | \code{colour = "red"} or \code{size = 3}. They may also be parameters 51 | to the paired geom/stat.} 52 | 53 | \item{draw_ci}{\emph{geom}. Toggles drawing of the confidence interval lines and 54 | segments.} 55 | 56 | \item{draw_sd}{\emph{geom}. Toggles drawing of the standard deviation interval 57 | lines and segments.} 58 | 59 | \item{midline}{\emph{geom}. Color of the vertical, center line. Set to \code{NA} 60 | to omit the line.} 61 | 62 | \item{brighten}{\emph{geom}. Numeric adjustments to the fill color. A value above 63 | 1 increases brightness, below decreases. Should be of length 1 or 5, 64 | otherwise values are recycled} 65 | 66 | \item{mirror}{\emph{geom}. Show standard densities (\code{mirror=FALSE}) or horizontal 67 | violin densities (\code{mirror=TRUE}).} 68 | 69 | \item{interp_thresh}{\emph{geom}. If the number of samples used to estimate the 70 | density is low, this will result in gaps between segments. This argument 71 | decides to interpolate points based on gap proportion for a segment.} 72 | 73 | \item{na.rm}{If \code{FALSE}, the default, missing values are removed with 74 | a warning. If \code{TRUE}, missing values are silently removed.} 75 | 76 | \item{show.legend}{logical. Should this layer be included in the legends? 77 | \code{NA}, the default, includes if any aesthetics are mapped. 78 | \code{FALSE} never includes, and \code{TRUE} always includes. 79 | It can also be a named logical vector to finely select the aesthetics to 80 | display.} 81 | 82 | \item{inherit.aes}{If \code{FALSE}, overrides the default aesthetics, 83 | rather than combining with them. This is most useful for helper functions 84 | that define both data and aesthetics and shouldn't inherit behaviour from 85 | the default plot specification, e.g. \code{\link[=borders]{borders()}}.} 86 | 87 | \item{geom}{Use to override the default connection between \code{geom_posterior} 88 | and \code{stat_density_ci}} 89 | 90 | \item{center_stat}{\emph{stat}. character string of method to compute the 91 | distribution's central tendency, such as \code{"median"}, \code{"mean"}, or \code{"mode"}.} 92 | 93 | \item{ci_width}{\emph{stat}. Width of the distribution's confidence/highest 94 | density interval, e.g., 0.95} 95 | 96 | \item{interval_type}{\emph{stat}. method of computing the interval, either \code{"hdi"} 97 | or \code{"ci"}} 98 | 99 | \item{bw}{The smoothing bandwidth to be used. If numeric, the standard 100 | deviation of the smoothing kernel. If character, a rule to choose the 101 | bandwidth, as listed in \link[stats:bandwidth]{stats::bandwidth}. If the bandwidth character 102 | starts wit a \code{"."} (e.g., \code{".nrd0"}), then the average bandwidth will be 103 | calculated among all groups in a panel and used for each density estimate.} 104 | 105 | \item{adjust}{A multiplicate bandwidth adjustment. This makes it possible 106 | to adjust the bandwidth while still using the a bandwidth estimator. 107 | For example, \code{adjust = 1/2} means use half of the default bandwidth.} 108 | 109 | \item{kernel}{Kernel. See list of available kernels in \code{\link[=density]{density()}}.} 110 | 111 | \item{cut}{The values to use for the start and end of the density estimation 112 | are \code{cut} bandwidths (e.g., \code{0.5*bw}) \emph{beyond} the extremes of the data. 113 | This allows the estimated density to drop to approximately zero at the 114 | extremes.} 115 | 116 | \item{n}{number of equally spaced points at which the density is to be 117 | estimated, should be a power of two, see \code{\link[=density]{density()}} for 118 | details} 119 | 120 | \item{trim}{If a value between 0 and 1 is given, trim the tails of \code{x} by 121 | some proportion according to \code{trim}. If \code{NULL} or \code{NA}, don't trim the 122 | tails. See \code{\link[=trim_ends]{trim_ends()}}.} 123 | } 124 | \description{ 125 | This geom may be used to plot the density of any type of numeric variable but 126 | the displayed intervals may not be informative if the distribution deviates 127 | too much from a unimodal, symmetric distribution. 128 | } 129 | \section{Functions}{ 130 | \itemize{ 131 | \item \code{geom_posterior}: geom_posterior Posterior Geom 132 | 133 | \item \code{stat_density_ci}: stat_density_ci Computes a distribution density and 134 | confidence intervals for each group 135 | }} 136 | 137 | \section{Aesthetics}{ 138 | 139 | 140 | \code{geom_posterior} understands the following aesthetics (required aesthetics 141 | are in bold): 142 | \itemize{ 143 | \item \strong{x} 144 | \item \strong{y} 145 | \item xmin 146 | \item xmax 147 | \item alpha - colour 148 | \item fill 149 | \item group 150 | \item linetype 151 | \item size 152 | \item weight 153 | } 154 | } 155 | 156 | \section{Computed Variables}{ 157 | 158 | 159 | \emph{stat_density_ci}: 160 | \itemize{ 161 | \item density: density estimate from \link[stats:density]{stats::density} 162 | \item scaled: Normalized density values: \code{density / max(density)} 163 | \item count: Number of samples at density level: \code{(density / sum(density)) * n} 164 | \item xmin: minimum value of \code{x} from the data 165 | \item cil: cil cutoff value based on \code{ci_width} 166 | \item sdl: central value minus 1 sd of \code{x} 167 | \item mid: value of central tendency 168 | \item sdu: central value plus 1 sd of \code{x} 169 | \item ciu: ciu cutoff value based on \code{ci_width} 170 | \item xmax: maximum value of \code{x} from the data 171 | } 172 | 173 | \emph{position_spread} 174 | \itemize{ 175 | \item ymin: minimum value of \code{y} for each group in a panel. 176 | \item ymax: maximum value of \code{y} for each group in a panel. 177 | } 178 | } 179 | 180 | \examples{ 181 | \donttest{ 182 | library(ggplot2) 183 | 184 | x <- data_normal_sample(mu = c(-1, 0, 1), n = 500) 185 | 186 | p <- ggplot(x, aes(x = value)) 187 | 188 | p + geom_posterior() 189 | 190 | p + geom_posterior(aes(y = Condition)) 191 | 192 | p + geom_posterior(aes(y = GroupScore, fill = Condition)) 193 | 194 | p + geom_posterior(aes(y = GroupScore, fill = Group), 195 | brighten = c(1.3, 0, -1.3), 196 | position = position_spread( 197 | height=0.5, 198 | padding = 0)) 199 | 200 | } 201 | } 202 | -------------------------------------------------------------------------------- /man/annotate_corner.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/theme.R 3 | \name{annotate_corner} 4 | \alias{annotate_corner} 5 | \title{Write text to one of four corners of a plot} 6 | \usage{ 7 | annotate_corner(text, pos = "tl", geom = c("text", "label"), ...) 8 | } 9 | \arguments{ 10 | \item{text}{character string} 11 | 12 | \item{pos}{character of \code{"tl"}, \code{"tr"}, \code{"bl"}, \code{"br"} to 13 | indicate position (you may also use the full names, \code{"bottomleft"}, etc...)} 14 | 15 | \item{geom}{"text" or "label" geoms} 16 | 17 | \item{...}{additional options passed to \link[ggplot2:geom_label]{ggplot2::geom_label}} 18 | } 19 | \value{ 20 | ggplot layer 21 | } 22 | \description{ 23 | Write text to one of four corners of a plot 24 | } 25 | \details{ 26 | If multiple panels, will write to all panels. 27 | } 28 | \examples{ 29 | posterior_plot() + annotate_corner("Hi.", "bottomright") 30 | } 31 | -------------------------------------------------------------------------------- /man/cmode.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/stats.R 3 | \name{cmode} 4 | \alias{cmode} 5 | \title{Mode from counting frequency} 6 | \usage{ 7 | cmode(x) 8 | } 9 | \arguments{ 10 | \item{x}{an integer vector} 11 | } 12 | \value{ 13 | scalar integer value 14 | } 15 | \description{ 16 | Finds the most frequent value from a vector of discrete values 17 | } 18 | \examples{ 19 | cmode(rpois(1000, 20)) 20 | } 21 | -------------------------------------------------------------------------------- /man/data_normal_sample.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/data.R 3 | \name{data_normal_sample} 4 | \alias{data_normal_sample} 5 | \title{Testing dataset of grouped Normal distributions} 6 | \usage{ 7 | data_normal_sample(mu = c(-0.5, 4), n = 500L, sd_range = c(0.6, 1.4), 8 | seed = 19850519) 9 | } 10 | \arguments{ 11 | \item{mu}{Means for each group. A \link{numeric} vector with the length 12 | corresponding to the number of groups.} 13 | 14 | \item{n}{Number of observations for each group. Length 1 \link{integer}.} 15 | 16 | \item{sd_range}{The min and max to use for standard deviations. Length 2} 17 | 18 | \item{seed}{A seed value to generate the same sample. \link{numeric} vector.} 19 | } 20 | \value{ 21 | A \link{data.frame} with the following variables: \code{Group}, \code{Condition}, 22 | \code{value}. 23 | } 24 | \description{ 25 | Testing dataset of grouped Normal distributions 26 | } 27 | \examples{ 28 | data_normal_sample(0, 100) 29 | } 30 | -------------------------------------------------------------------------------- /man/dmode.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/stats.R 3 | \name{dmode} 4 | \alias{dmode} 5 | \title{Mode from density estimation} 6 | \usage{ 7 | dmode(x, adjust = 1.5) 8 | } 9 | \arguments{ 10 | \item{x}{Value vector. Numeric or integers.} 11 | 12 | \item{adjust}{Bandwidth adjustment. See \link{density}.} 13 | } 14 | \description{ 15 | Finds the mode using the \link{density} function and then obtains the maximum value. 16 | } 17 | \examples{ 18 | x <- rchisq(1000, 3) 19 | hist(x, br=50) 20 | abline(v = dmode(x), col = "red") 21 | abline(v = median(x), col = "green") 22 | abline(v = mean(x), col = "blue") 23 | } 24 | -------------------------------------------------------------------------------- /man/example_plot.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/ggdistribute-package.R 3 | \name{example_plot} 4 | \alias{example_plot} 5 | \title{Print an example of the package functions} 6 | \usage{ 7 | example_plot() 8 | } 9 | \description{ 10 | Print an example of the package functions 11 | } 12 | -------------------------------------------------------------------------------- /man/figures/README-candy_wrappers-1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/iamamutt/ggdistribute/3efffa1ebfe20b6d7adf675412a7833b9feb8c04/man/figures/README-candy_wrappers-1.png -------------------------------------------------------------------------------- /man/figures/README-rainbow_hills-1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/iamamutt/ggdistribute/3efffa1ebfe20b6d7adf675412a7833b9feb8c04/man/figures/README-rainbow_hills-1.png -------------------------------------------------------------------------------- /man/figures/README-space_ships-1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/iamamutt/ggdistribute/3efffa1ebfe20b6d7adf675412a7833b9feb8c04/man/figures/README-space_ships-1.png -------------------------------------------------------------------------------- /man/figures/README-turtle_snails-1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/iamamutt/ggdistribute/3efffa1ebfe20b6d7adf675412a7833b9feb8c04/man/figures/README-turtle_snails-1.png -------------------------------------------------------------------------------- /man/ggdistribute-package.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/ggdistribute-package.R 3 | \docType{package} 4 | \name{ggdistribute-package} 5 | \alias{ggdistribute-package} 6 | \title{A 'ggplot2' Extension for Plotting Unimodal Distributions} 7 | \description{ 8 | The \code{ggdistribute} package is an extension for plotting posterior or other 9 | types of unimodal distributions that require overlaying information about a 10 | distribution's intervals. It makes use of the \code{ggproto} system to extend 11 | \code{ggplot2}, providing additional "geoms", "stats", and "positions." The 12 | extensions integrate with existing \code{ggplot2} layer elements. 13 | } 14 | \details{ 15 | Displaying the distributions relies heavily on stacking distributions using 16 | position_spread, which may not align with other geoms. 17 | } 18 | \seealso{ 19 | \link[ggplot2:geom_density]{ggplot2::geom_density}, \link[ggplot2:position_dodge]{ggplot2::position_dodge} 20 | 21 | See \code{help(package = "ggdistribute")} for a list of functions. 22 | 23 | View vignettes with \code{browseVignettes(package ="ggdistribute")}. 24 | } 25 | -------------------------------------------------------------------------------- /man/hdi.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/stats.R 3 | \name{hdi} 4 | \alias{hdi} 5 | \title{Highest density interval} 6 | \usage{ 7 | hdi(x, prob = 0.95, warn = TRUE) 8 | } 9 | \arguments{ 10 | \item{x}{Numeric vector of a distribution of data, typically a posterior 11 | sample} 12 | 13 | \item{prob}{Width of the interval from some distribution. Defaults to \code{0.95}.} 14 | 15 | \item{warn}{Option to turn off multiple sample warning message Must be in the 16 | range of \code{[0,1]}.} 17 | } 18 | \value{ 19 | Numeric range 20 | } 21 | \description{ 22 | This is a function that will calculate the highest density interval from a 23 | posterior sample. 24 | } 25 | \details{ 26 | The default is to calcualte the highest 95 percent interval. It can be used 27 | with any numeric vector instead of having to use one of the specific MCMC 28 | classes. This function has been adapted from John K. Kruschke (2011). Doing 29 | Bayesian Data Analaysis: A Tutorial with R and BUGS. 30 | } 31 | \examples{ 32 | x <- qnorm(seq(1e-04, .9999, length.out=1001)) 33 | hdi_95 <- hdi(x, .95) 34 | hdi_50 <- hdi(x, .50) 35 | 36 | hist(x, br=50) 37 | abline(v=hdi_95, col="red") 38 | abline(v=hdi_50, col="green") 39 | 40 | x <- exp(seq(pi * (1 - (1/16)), pi, len = 1000)) 41 | x <- c(x, rev(x)[-1]) 42 | x <- c(-x, x) 43 | plot(sort(x), type="l") 44 | plot(density(x, adjust=0.25)) 45 | abline(v=hdi(x, p=.49), col=2) 46 | abline(v=hdi(x, p=.50), col=3) 47 | } 48 | -------------------------------------------------------------------------------- /man/label_plot.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/theme.R 3 | \name{label_plot} 4 | \alias{label_plot} 5 | \title{Add labels to existing plot} 6 | \usage{ 7 | label_plot(labels, x, y, g = list(fontsize = 14, fontface = "bold"), ...) 8 | } 9 | \arguments{ 10 | \item{labels}{character vector of labels to use} 11 | 12 | \item{x}{horz positions of items in \code{labels}} 13 | 14 | \item{y}{vert positions of items in \code{labels}} 15 | 16 | \item{g}{list of options passed to \link[grid:gpar]{grid::gpar}} 17 | 18 | \item{...}{optional args passed to \link[grid:grid.text]{grid::grid.text}} 19 | } 20 | \value{ 21 | NULL. prints to current graphics device. 22 | } 23 | \description{ 24 | Uses a normalized coordinate system to add text anywhere on the current plot. 25 | } 26 | \examples{ 27 | example_plot() 28 | label_plot(c('a label', 'another one'), c(.1, .9), c(.95, .1)) 29 | 30 | # use extra options from grid::grid.text 31 | label_plot('last one', 0.5, 0.5, just='center') 32 | } 33 | \seealso{ 34 | \link[grid:grid.text]{grid::grid.text}, \link[grid:gpar]{grid::gpar} 35 | } 36 | -------------------------------------------------------------------------------- /man/mejr_geom_defaults.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/theme.R 3 | \name{mejr_geom_defaults} 4 | \alias{mejr_geom_defaults} 5 | \title{Setup defaults for specific geoms} 6 | \usage{ 7 | mejr_geom_defaults(base_size = 11, black = 51, gray = 214, lty = 3, 8 | lwd = base_size/20, cex = base_size/9, stroke = base_size * 0.05, 9 | alpha = 0.5, pch = 21, txt = base_size/4, reset = FALSE) 10 | } 11 | \arguments{ 12 | \item{base_size}{base font size} 13 | 14 | \item{black}{Values from 0 to 255, indicating the darkest line and 15 | text colors (255).} 16 | 17 | \item{gray}{gray color value (0-255)} 18 | 19 | \item{lty}{linetype} 20 | 21 | \item{lwd}{linewidth} 22 | 23 | \item{cex}{point size} 24 | 25 | \item{stroke}{stroke width} 26 | 27 | \item{alpha}{alpha} 28 | 29 | \item{pch}{point shape} 30 | 31 | \item{txt}{text size} 32 | 33 | \item{reset}{reset all back to default} 34 | } 35 | \description{ 36 | Setup defaults for specific geoms 37 | } 38 | \examples{ 39 | # This will change the point size and shape for 40 | # all geoms in which GeomPoint inherits from. 41 | mejr_geom_defaults(cex = 1.1, pch = 19) 42 | 43 | # Reset defaults back to their original state. 44 | mejr_geom_defaults(reset=TRUE) 45 | } 46 | -------------------------------------------------------------------------------- /man/mejr_palette.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/theme.R 3 | \name{mejr_palette} 4 | \alias{mejr_palette} 5 | \title{theme_mejr color mejr_palette} 6 | \usage{ 7 | mejr_palette() 8 | } 9 | \value{ 10 | list 11 | } 12 | \description{ 13 | theme_mejr color mejr_palette 14 | } 15 | \examples{ 16 | mejr_palette() 17 | } 18 | -------------------------------------------------------------------------------- /man/position_spread.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/position_spread.R 3 | \name{position_spread} 4 | \alias{position_spread} 5 | \title{Spread Overlapping Grobs Spread overlapping groups by shrinking them to fit 6 | within the data's \code{y} range.} 7 | \usage{ 8 | position_spread(height = NULL, reverse = FALSE, padding = 0.2) 9 | } 10 | \arguments{ 11 | \item{height}{One of \code{total} (default), \code{panel}, \code{single}, a numeric scalar 12 | value to give all geoms equal space, or a numeric vector the length of 13 | \code{panels*groups} for manually specifying the height of each group.} 14 | 15 | \item{reverse}{Reverse the order of segments within overlapping \code{y} ranges.} 16 | 17 | \item{padding}{Multiple of height. Will shrink/enlarge groups to fit within a 18 | region.} 19 | } 20 | \description{ 21 | Spread Overlapping Grobs Spread overlapping groups by shrinking them to fit 22 | within the data's \code{y} range. 23 | } 24 | \examples{ 25 | library(ggplot2) 26 | 27 | x <- data.frame(y = rnorm(1000), x="", myGroup=sample(1:3, 1000, TRUE)) 28 | 29 | ggplot(x, aes(x, y))+ 30 | geom_point(aes(group=myGroup), position=position_spread(height = 0.5)) 31 | } 32 | -------------------------------------------------------------------------------- /man/post_int.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/stats.R 3 | \name{post_int} 4 | \alias{post_int} 5 | \title{Posterior intervals} 6 | \usage{ 7 | post_int(x, mid = c("median", "mean", "mode"), int = c("hdi", "ci"), 8 | widths = c(0.5, 0.95), adj = 1.5, rope = NULL, warn = FALSE) 9 | } 10 | \arguments{ 11 | \item{x}{Vector of numeric values. Typically a posterior sample.} 12 | 13 | \item{mid}{Central tendency estimator. Defaults to \code{"median"}. Other options 14 | include \code{"mean"} and \code{"mode"}.} 15 | 16 | \item{int}{interval type, either "hdi" or "ci"} 17 | 18 | \item{widths}{interval widths} 19 | 20 | \item{adj}{Bandwidth adjustment used only with the \code{"mode"} estimator. See 21 | \link{dmode}.} 22 | 23 | \item{rope}{Region of practical equivalence. Check how much of the 24 | distribution is within rope value.} 25 | 26 | \item{warn}{Turn off warning for flat intervals found (multiple possible 27 | values)} 28 | } 29 | \value{ 30 | data.table 31 | } 32 | \description{ 33 | Returns cutoff points from a posterior distribution 34 | } 35 | \examples{ 36 | x <- rpois(5000, 15) 37 | ints <- post_int(x, warn = FALSE) 38 | hist(x, br=50) 39 | abline(v=ints$c, col="cyan") 40 | abline(v=ints[, c("l.wide", "r.wide")], col="magenta") 41 | 42 | post_int(x, "median", warn = FALSE) 43 | post_int(x, "mean", warn = FALSE) 44 | post_int(x, "mode", adj=2, rope = c(14, 16), warn = FALSE) 45 | } 46 | -------------------------------------------------------------------------------- /man/posterior_plot.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/gghelpers.R 3 | \name{posterior_plot} 4 | \alias{posterior_plot} 5 | \title{Print a small example plot with geom_posterior} 6 | \usage{ 7 | posterior_plot(data, x, y = "..count..", ...) 8 | } 9 | \arguments{ 10 | \item{data}{A dataset to use, called at the top layer within \code{ggplot}.} 11 | 12 | \item{x}{A character string of the \code{x} axis variable name (e.g., values 13 | making up the distribution).} 14 | 15 | \item{y}{A grouping variable for generating groups of distributions. Defaults 16 | to \code{..count..} for no groups and displays the density as counts of the 17 | number of samples for the value of \code{x}.} 18 | 19 | \item{...}{Additional arguments passed to \code{\link[=geom_posterior]{geom_posterior()}}.} 20 | } 21 | \value{ 22 | Object of class \code{gg}, \code{ggplot}. 23 | } 24 | \description{ 25 | Print a small example plot with geom_posterior 26 | } 27 | \examples{ 28 | # Generate a basic example plot if no data is specified. 29 | posterior_plot() 30 | } 31 | -------------------------------------------------------------------------------- /man/scale_add.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/gghelpers.R 3 | \name{scale_add} 4 | \alias{scale_add} 5 | \title{scale and add} 6 | \usage{ 7 | scale_add(base_size, amount = 1, adj = 0) 8 | } 9 | \arguments{ 10 | \item{base_size}{start value} 11 | 12 | \item{amount}{multiple by} 13 | 14 | \item{adj}{add after} 15 | } 16 | \value{ 17 | numeric 18 | } 19 | \description{ 20 | scale and add 21 | } 22 | -------------------------------------------------------------------------------- /man/show_colors.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/gghelpers.R 3 | \name{show_colors} 4 | \alias{show_colors} 5 | \title{plot and show hex values of colors} 6 | \usage{ 7 | show_colors(colors, show.legend = TRUE, ncols = NULL, alpha = NA) 8 | } 9 | \arguments{ 10 | \item{colors}{character vector of hex value colors} 11 | 12 | \item{show.legend}{show the legend with hex values (logical)} 13 | 14 | \item{ncols}{number of columns in the plot} 15 | 16 | \item{alpha}{set alpha level for all colors} 17 | } 18 | \value{ 19 | A plot with the index of the color in the tile 20 | } 21 | \description{ 22 | plot and show hex values of colors 23 | } 24 | \examples{ 25 | show_colors(mejr_palette()) 26 | show_colors(topo.colors(25)) 27 | show_colors(cm.colors(64), FALSE) 28 | show_colors(viridisLite::viridis(15), alpha = .8) 29 | } 30 | -------------------------------------------------------------------------------- /man/sre.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/data.R 3 | \docType{data} 4 | \name{sre} 5 | \alias{sre} 6 | \alias{sre_data} 7 | \title{Raw SRE dataset} 8 | \format{Dataset is an object of class \link[tibble:tibble]{tibble::tibble}.} 9 | \usage{ 10 | sre 11 | 12 | sre_data(n = 1000, seed = 19850519) 13 | } 14 | \arguments{ 15 | \item{n}{number of samplers per effect and contrast} 16 | 17 | \item{seed}{set.seed number} 18 | } 19 | \value{ 20 | A dataset of \link[tibble:tibble]{tibble::tibble}. 21 | } 22 | \description{ 23 | Raw SRE dataset 24 | } 25 | \section{Functions}{ 26 | \itemize{ 27 | \item \code{sre_data}: create samples from sre data 28 | }} 29 | 30 | \examples{ 31 | sre 32 | 33 | sre_data() 34 | } 35 | \seealso{ 36 | \code{\link[=sre_data]{sre_data()}} 37 | } 38 | \keyword{datasets} 39 | -------------------------------------------------------------------------------- /man/test_mejr_theme.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/theme.R 3 | \name{test_mejr_theme} 4 | \alias{test_mejr_theme} 5 | \title{Test theme by printing plots to pdf and viewport} 6 | \usage{ 7 | test_mejr_theme(w = 6.875, h = 4.5, eplot = list(), mejr = list(), 8 | gg = theme(), print = TRUE, with_test_theme = FALSE, dir) 9 | } 10 | \arguments{ 11 | \item{w}{pdf width (inches)} 12 | 13 | \item{h}{pdf height (inches)} 14 | 15 | \item{eplot}{list of options for \code{example_plot}} 16 | 17 | \item{mejr}{list of options for \code{theme_mejr(...)}} 18 | 19 | \item{gg}{further theme customization with ggplot::theme(...)} 20 | 21 | \item{print}{show the \code{eplot} plot} 22 | 23 | \item{with_test_theme}{also print the same plot using \code{ggplot2::theme_test}} 24 | 25 | \item{dir}{directory where to save temp pdf files, such as tempdir()} 26 | } 27 | \description{ 28 | Test theme by printing plots to pdf and viewport 29 | } 30 | -------------------------------------------------------------------------------- /man/theme_mejr.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/theme.R 3 | \name{theme_mejr} 4 | \alias{theme_mejr} 5 | \title{Custom ggplot2 theme} 6 | \usage{ 7 | theme_mejr(base_size = 11, 8 | base_family = getOption("ggdistribute.font"), black = 67, 9 | margin_add = 2, debug = FALSE, FUN = NULL, ...) 10 | } 11 | \arguments{ 12 | \item{base_size}{base font size} 13 | 14 | \item{base_family}{base font family} 15 | 16 | \item{black}{Values from 0 to 255, indicating the darkest line and 17 | text colors (255).} 18 | 19 | \item{margin_add}{additive adjustment of margin spacing and tick length (in "pt" 20 | units). May be positive or negative.} 21 | 22 | \item{debug}{Add debug info to text.} 23 | 24 | \item{FUN}{Call a function before returning the theme elements.} 25 | 26 | \item{...}{Arguments passed to \code{FUN}} 27 | } 28 | \description{ 29 | A complete, minimal theme to be used with the \code{ggplot2} package 30 | } 31 | \details{ 32 | You can use \code{theme_update} to change some aspect of this theme after 33 | using \code{theme_set}. 34 | } 35 | \examples{ 36 | \donttest{ 37 | library(ggplot2) 38 | 39 | theme_set(theme_mejr(debug = TRUE)) 40 | example_plot() 41 | 42 | theme_set(theme_mejr()) 43 | theme_update(axis.text = element_blank()) # any updates can go here 44 | example_plot() 45 | } 46 | } 47 | \seealso{ 48 | \link{mejr_geom_defaults}, \link[ggplot2:theme_update]{ggplot2::theme_update}, \link[ggplot2:theme_set]{ggplot2::theme_set} 49 | } 50 | -------------------------------------------------------------------------------- /man/trim_ends.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/stats.R 3 | \name{trim_ends} 4 | \alias{trim_ends} 5 | \title{Trim extreme values at each end of a vector.} 6 | \usage{ 7 | trim_ends(x, trim = 0.05, na.rm = TRUE) 8 | } 9 | \arguments{ 10 | \item{x}{A \link{numeric} vector} 11 | 12 | \item{trim}{Proportion of vector length to trim. Must be between 0 and 1. 13 | E.g., a value 0.05 (default) trims 2.5\% off each end of a sorted vector.} 14 | 15 | \item{na.rm}{omit \code{NA} values. May result in different size vector.} 16 | } 17 | \value{ 18 | A \link{numeric} vector in the original order of \code{x}, but with trimmed 19 | values as \code{NA} if \code{na.rm=TRUE} or with these values removed if \code{FALSE} 20 | (which will result in a different sized vector from the input). 21 | } 22 | \description{ 23 | Trim extreme values at each end of a vector. 24 | } 25 | \examples{ 26 | x <- rgamma(10000, 1, 1) 27 | range(x) 28 | length(x) # <- 10000 29 | sum(is.na(x)) # <- 0 30 | 31 | t <- trim_ends(x, trim = 0.1) 32 | range(t) 33 | length(t) # <- 9000 34 | sum(is.na(t)) # <- 0 35 | 36 | t <- trim_ends(x, 0.1, na.rm = FALSE) 37 | range(t, na.rm = TRUE) 38 | length(t) # <- 10000 39 | sum(is.na(t)) # <- 1000 40 | } 41 | -------------------------------------------------------------------------------- /tests/testthat.R: -------------------------------------------------------------------------------- 1 | library(testthat) 2 | library(ggdistribute) 3 | 4 | test_check("ggdistribute") 5 | -------------------------------------------------------------------------------- /tests/testthat/test_geom_posterior.R: -------------------------------------------------------------------------------- 1 | library(ggdistribute) 2 | library(ggplot2) 3 | context("Testing geom posterior") 4 | 5 | test_that("Identity stat", { 6 | dt <- ggdistribute:::ggdist_data() 7 | d <- stats::density(dt$value, n=nrow(dt)) 8 | dt$dx <- d$x 9 | dt$dy <- d$y 10 | dt$mid <- 4 11 | 12 | expect_s3_class( 13 | ggplot(dt, aes(x=dx, y=dy)) + 14 | geom_posterior( 15 | stat="identity", position="identity", 16 | draw_ci=FALSE, draw_sd=FALSE, aes(mid=mid) 17 | ) # 18 | , c("gg", "ggplot") 19 | ) 20 | }) 21 | -------------------------------------------------------------------------------- /tests/testthat/test_position_spread.R: -------------------------------------------------------------------------------- 1 | library(ggdistribute) 2 | library(data.table) 3 | context("Testing position spread") 4 | 5 | test_that("Overlaps", { 6 | y_vals <- matrix(c( 7 | 0, 1, 1, 2.5, 1.1, 2.5, 3, 4.9, 8 | 4, 5, 4.7, 5.5, 5.49999, 5.5 9 | ), nrow = 2) 10 | 11 | data <- data.table( 12 | group = as.integer(seq_len(ncol(y_vals))), 13 | PANEL = rep(1L, ncol(y_vals)) 14 | ) 15 | data <- data[ 16 | , .(y = seq(y_vals[1L, group], 17 | y_vals[2L, group], 18 | length.out = 3)), 19 | .(PANEL, group) 20 | ] 21 | data[group == max(group), group := group + 2L] 22 | 23 | o <- ggdistribute:::get_overlaps(data, "y", tol = -1e-3) 24 | expect_identical(which(o$overlap), c(7L, 16L)) 25 | expect_identical(which(o$within), c(7L, 21L)) 26 | 27 | o <- ggdistribute:::get_overlaps(data[group == 9], "y", tol = -1e-3) 28 | expect_true(is.na(o$space)) 29 | }) 30 | -------------------------------------------------------------------------------- /tests/testthat/test_stat_density.R: -------------------------------------------------------------------------------- 1 | library(ggdistribute) 2 | context("Testing stat density computations") 3 | 4 | set.seed(20130110) 5 | x <- rnorm(1000, 100, 15) 6 | 7 | test_that("List modification", { 8 | expect_named(compute_density(x), c( 9 | "x", "density", "scaled", 10 | "count", "n" 11 | )) 12 | 13 | defaults <- compute_conf_ints(x) 14 | 15 | expect_named(defaults, c("mid", "sdl", "sdu", "cil", "ciu")) 16 | expect_identical(defaults, compute_conf_ints(x, NA, NA, NA)) 17 | }) 18 | -------------------------------------------------------------------------------- /vignettes/geom_posterior.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "Plotting Posterior Distributions with `ggdistribute`" 3 | author: "Joseph M. Burling" 4 | date: "`r Sys.Date()`" 5 | output: rmarkdown::html_vignette 6 | vignette: > 7 | %\VignetteIndexEntry{GeomPosterior} 8 | %\VignetteEngine{knitr::rmarkdown} 9 | %\VignetteEncoding{UTF-8} 10 | --- 11 | 12 | ```{r setup, include = FALSE, warning=FALSE, message=FALSE} 13 | knitr::opts_chunk$set( 14 | collapse = TRUE, 15 | comment = "#>", 16 | strip.white = FALSE, 17 | tidy = FALSE, 18 | fig.align = "center", 19 | dpi = 300, 20 | fig.width = 5.25, 21 | fig.height = 3.8, 22 | out.width = "90%" 23 | ) 24 | ``` 25 | 26 | ```{r load-and-opts, echo=FALSE, message=FALSE} 27 | library(ggplot2) 28 | theme_set(theme_gray(10)) 29 | ``` 30 | 31 | # Introduction 32 | 33 | The `ggdistribute` package is an extension for plotting posterior or other types of unimodal distributions that require overlaying information about a distribution's intervals. It makes use of the `ggproto` system to extend `ggplot2`, providing additional "geoms", "stats", and "positions." The extensions integrate with existing `ggplot2` layer elements. 34 | 35 | To load the package to access the exported `ggplot2` extensions, do the following, 36 | 37 | ```{r load-ggdistribute} 38 | library(ggdistribute) 39 | ``` 40 | 41 | The `data` object below is a randomly generated dataset of 5 different normal distributions. Two factors `Condition` and `Group` are assigned according to the generated values. 1000 samples are generated for each value of `mu`. 42 | 43 | ```{r make-data} 44 | data <- data_normal_sample(mu = c(-1, 0, 2, 5, 10), n = 1000) 45 | ``` 46 | 47 | # Extensions passed to `ggplot2::layer` 48 | 49 | ## `geom_posterior(...)`, `GeomPosterior` 50 | 51 | This is the main function wrapper to generate the posterior distribution grobs. See `help("geom_posterior")` for a list of options passed to the ggproto object `GeomPosterior`. The `geom_posterior()` function with no arguments and no `y` aesthetic defaults to estimating the normalized density (integerates to 1) times the number of data points. This is the same as `aes(y = ..count..)` with `geom_posterior`. 52 | 53 | ```{r ex1} 54 | b_cond <- data[data$Condition == "B", ] 55 | 56 | ggplot(b_cond, aes(x = value))+ 57 | geom_posterior() 58 | ``` 59 | 60 | If `y` is discrete, the densities are justified at the bottom of the `y` value. 61 | 62 | ```{r ex2} 63 | ggplot(data, aes(x = value, y = Condition))+ 64 | geom_posterior() 65 | ``` 66 | 67 | Mirroring can be turned on to generate densities similar to `geom_violin`, and `geom_posterior` can be used like other `geoms` in `ggplot2`, like specifying facetting. 68 | 69 | ```{r ex3, fig.height=6} 70 | ggplot(data, aes(x = value, y = Condition))+ 71 | geom_posterior(mirror = TRUE)+ 72 | facet_grid(Group~., scales = "free_y") 73 | ``` 74 | 75 | ## `stat_density_ci(...)`, `StatDensityCI` 76 | 77 | The `StatDensityCI` class is the default stat for the `geom_posterior` wrapper. It computes densities for each group and finds the confidence intervals. See `help("stat_density_ci")` for additional options and computed variables. 78 | 79 | ```{r ex4} 80 | ggplot(data, aes(x = value, y = Condition)) + 81 | stat_density_ci(n = 1024, interp_thresh = .001) 82 | ``` 83 | 84 | The `stat_density_ci` wrapper can also be used with other geoms that make use of `density`, `count`, and `scaled` variables. 85 | 86 | ```{r ex5} 87 | ggplot(data, aes(x = value, y = ..density.., fill = Condition)) + 88 | stat_density_ci( 89 | alpha = 0.5, 90 | n = 1024, 91 | geom = "density", 92 | position = "identity" 93 | ) 94 | ``` 95 | 96 | ## `position_spread(...)`, `PositionSpread` 97 | 98 | The `PositionSpread` class spreads out overlapping densities within the range of their y axis value. For instance, there are two different groups for `Condition` A-D, but only one group in E. Distributions within each `Condition` are resized and spread over the group's y interval. Padding is turned off below to see where distributions begin and end. 99 | 100 | ```{r ex6} 101 | data$Group[data$Condition == "E"] <- "z" 102 | 103 | ggplot(data, aes(x = value, y = Condition, group = Group)) + 104 | geom_posterior(position = position_spread(padding = 0)) + 105 | theme(panel.grid.major.y = element_line(color = gray(.8))) 106 | ``` 107 | 108 | 109 | # Extended example 110 | 111 | The package function `example_plot()` is an overview of combining `ggdistribute` with other `ggplot2` elements. The contents of this function are printed below and gives details about the extended parts to `ggplot2`. 112 | 113 | ```{r ex-plot-print, fig.width=5, fig.height=3, out.width="100%"} 114 | # color palette 115 | colors <- mejr_palette() 116 | 117 | plt <- 118 | ggplot(sre_data(5000), 119 | aes(y=effect)) + 120 | # ggdistribute specific elements ------------------------------------------- 121 | geom_posterior( 122 | # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 123 | # geom_posterior() aesthetics mappings 124 | # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 125 | aes(x=value, fill=contrast), 126 | # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 127 | # options passed to stat_density_ci() for estimating intervals 128 | # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 129 | interp_thresh=.001, # threshold for interpolating segment gaps 130 | center_stat="median", # measure of central tendency 131 | ci_width=0.90, # width corresponding to CI segments 132 | interval_type="ci", # quantile intervals not highest density interval 133 | # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 134 | # options passed to stat_density_ci() for estimating density 135 | # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 136 | bw=".nrd0", # bandwidth estimator type 137 | adjust=1.5, # adjustment to bandwidth 138 | n=1024, # number of samples in final density 139 | trim=.005, # trim `x` this proportion before estimating density 140 | cut=1.5, # tail extension for zero density estimation 141 | # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 142 | # geom_posterior() options 143 | # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 144 | draw_ci=TRUE, # toggle showing confidence interval parts 145 | draw_sd=TRUE, # toggle showing standard deviation parts 146 | mirror=FALSE, # toggle horizontal violin distributions 147 | midline=NULL, # line displaying center of dist. (NULL=aes color) 148 | brighten=c(3, 0, 1.333), # additive adjustment of segment fill colors 149 | # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 150 | # position_spread() options 151 | # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 152 | position=position_spread( 153 | reverse=TRUE, # order of spreaded groups within panels 154 | padding=0.3, # shrink heights of distributions 155 | height="panel" # scale by heights within panels 156 | ), # 157 | # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 158 | # standard ggplot layer options 159 | # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 160 | size=0.15, color=colors$gray, vjust=0.7, show.legend=FALSE 161 | ) + 162 | # standard ggplot2 elements ------------------------------------------------ 163 | geom_vline(alpha=0.5, color=colors$gray, size=0.333, linetype=1, xintercept=0) + 164 | scale_x_continuous(breaks=seq(-1, 1, .05)) + 165 | facet_grid("contrast ~ .", scales="free_y", space="free_y") + 166 | scale_fill_manual(values=c(colors$yellow, colors$magenta, colors$cyan)) + 167 | labs(x="Difference in accuracy (posterior predictions)") + 168 | theme( 169 | legend.position="none", strip.text.y=element_text(angle=0, hjust=0.5), 170 | panel.border=element_rect(fill=NA, color=colors$lightgray, size=0.67), 171 | panel.grid=element_blank(), panel.ontop=FALSE, axis.title.y=element_blank(), 172 | plot.margin=margin(t=2, r=4, b=2, l=2, unit="pt") 173 | ) 174 | 175 | plot(plt) 176 | ``` 177 | 178 | ```{r other-tests, eval=FALSE, echo=FALSE} 179 | library(ggplot2) 180 | 181 | # other visual inspections 182 | dt <- ggdistribute:::ggdist_data(1000, j = 5) 183 | 184 | ggplot(dt) + aes(x = value) + 185 | geom_posterior(n = 512, interp_thresh = .001) + labs(title = "no y") 186 | 187 | ggplot(dt) + aes(x = value, y = 10) + 188 | geom_posterior(n = 512, interp_thresh = .001) + labs(title = "scalar y") 189 | 190 | ggplot(dt) + aes(x = value, y = j_discrete) + 191 | geom_posterior(n = 512, interp_thresh = .001) + labs(title = "char y") 192 | 193 | ggplot(dt) + aes(x = value, y = j_discrete, group = k_discrete) + 194 | geom_posterior(n = 512, interp_thresh = .001) + 195 | labs(title = "char y, w/ group") 196 | 197 | ggplot(dt) + aes(x = value, y = k_discrete) + 198 | geom_posterior(n = 512, interp_thresh = .001, aes(group = j_discrete)) + 199 | labs(title = "char y, w/ group switched") 200 | 201 | ggplot(dt) + aes(x = value) + 202 | geom_posterior(n = 512, interp_thresh = .001, aes(group = j_discrete)) + 203 | labs(title = "no y, w/ group") 204 | 205 | ggplot(dt) + aes(x = value, y = j_discrete) + 206 | geom_posterior(n = 512, interp_thresh = .001, aes(group = j_discrete)) + 207 | labs(title = "char y, w/ same y group") 208 | 209 | ggplot(dt) + aes(x = value, y = j_discrete) + 210 | geom_posterior(n = 512, interp_thresh = .001, aes(fill = k_discrete)) + 211 | labs(title = "char y, w/ diff group") 212 | 213 | ggplot(dt) + aes(x = value, y = I) + 214 | geom_posterior(n = 512, interp_thresh = .001, aes(group = j_discrete)) + 215 | labs(title = "integer y, w/ group") 216 | 217 | ggplot(dt) + aes(x = value, y = j_discrete) + 218 | geom_posterior( 219 | n = 512, interp_thresh = .001, 220 | position = position_spread(height = 20)) + 221 | labs(title = "manual height, w/ group") 222 | 223 | ggplot(dt) + aes(x = value, y = variable) + 224 | geom_posterior( 225 | n = 512, interp_thresh = .001, 226 | mirror = TRUE, aes(group = j_discrete)) + 227 | labs(title = "cont. y, w/ group, mirrored") 228 | 229 | ggplot(dt) + aes(x = value, y = variable * 10) + 230 | geom_posterior( 231 | n = 512, interp_thresh = .001, mirror = TRUE, 232 | aes(group = j_discrete)) + facet_wrap(~k_discrete) + 233 | labs(title = "cont. y, w/ group, wrap, mirrored") 234 | 235 | dt$k[dt$j == 1 & dt$k == 2] <- NA 236 | 237 | ggplot(dt) + aes(x = value, y = j_discrete, group = k_discrete) + 238 | geom_posterior(n = 512, interp_thresh = .001) + 239 | facet_grid(j_discrete ~ ., scales = "free_y") + 240 | labs(title = "char y, w/ group, grid, missing group") 241 | 242 | ggplot(dt) + aes(x = value, group = k_discrete) + 243 | geom_posterior(n = 512, interp_thresh = .001) + 244 | facet_grid(j_discrete ~ ., scales = "free_y") + 245 | labs(title = "no y, w/ group, grid, missing group") 246 | ``` 247 | --------------------------------------------------------------------------------