prep for release

* fix r cmd check issues from #282
* fix ggplot test issues (closes #289)
* add @TeemuSailynoja as contributor

DESCRIPTION

@@ -1,15 +1,16 @@
 Package: bayesplot
 Type: Package
 Title: Plotting for Bayesian Models
-Version: 1.9.0.9000
-Date: 2022-03-09
+Version: 1.10.0
+Date: 2022-11-10
 Authors@R: c(person("Jonah", "Gabry", role = c("aut", "cre"), email = "[email protected]"),
              person("Tristan", "Mahr", role = "aut"),
              person("Paul-Christian", "Bürkner", role = "ctb"),
              person("Martin", "Modrák", role = "ctb"),
              person("Malcolm", "Barrett", role = "ctb"),
              person("Frank", "Weber", role = "ctb"),
              person("Eduardo", "Coronado Sroka", role = "ctb"),
+             person("Teemu", "Sailynoja", role = "ctb"),
              person("Aki", "Vehtari", role = "ctb"))
 Maintainer: Jonah Gabry <[email protected]>
 Description: Plotting functions for posterior analysis, MCMC diagnostics,
@@ -25,7 +26,7 @@ URL: https://mc-stan.org/bayesplot/
 BugReports: https://github.com/stan-dev/bayesplot/issues/
 SystemRequirements: pandoc (>= 1.12.3), pandoc-citeproc
 Depends:
-    R (>= 3.1.0)
+    R (>= 3.5.0)
 Imports:
     dplyr (>= 0.8.0),
     ggplot2 (>= 3.0.0),

NEWS.md

@@ -1,8 +1,14 @@
 <!-- See http://style.tidyverse.org/news.html for advice on writing news -->
 
-# bayesplot 1.9.0.9000
-
-Items for next release go here
+# bayesplot 1.10.0
+
+* New function `mcmc_rank_ecdf()` for rank ecdf plots with confidence bands for
+assessing if two or more chains sample the same distribution (#282,
+@TeemuSailynoja)
+* New functions `ppc_pit_ecdf()`, `ppc_pit_ecdf_grouped()`, PIT ecdf plots with
+confidence bands to assess if `y` and `yrep` contain samples from the same
+distribution. (#282, @TeemuSailynoja)
+* Fix failing tests due to latest ggplot2 release (#289)
 
 # bayesplot 1.9.0
 

R/bayesplot-colors.R

@@ -221,7 +221,7 @@ plot_scheme <- function(scheme = NULL) {
     value = rep(1, length(x))
   )
 
-  ggplot(color_data, aes_(x = ~ name, y = ~ value, fill = ~ group)) +
+  ggplot(color_data, aes(x = .data$name, y = .data$value, fill = .data$group)) +
     geom_bar(
       width = .5,
       stat = "identity",

R/helpers-gg.R

@@ -100,7 +100,7 @@ set_hist_aes <- function(freq = TRUE, ...) {
   if (freq) {
     aes_(x = ~ value, ...)
   } else {
-    aes_(x = ~ value, y = ~ stat(density), ...)
+    aes_(x = ~ value, y = ~ after_stat(density), ...)
   }
 }
 

R/helpers-ppc.R

@@ -448,21 +448,19 @@ interpolate_gamma <- function(N, K, prob, L) {
 #' @noRd
 get_interpolation_values <- function(N, K, L, prob) {
   for (dim in c("L", "prob")) {
-    if (all(get(dim) != bayesplot:::gamma_adj[, dim])) {
+    if (all(get(dim) != gamma_adj[, dim])) {
       stop(paste(
         "No precomputed values to interpolate from for '", dim, "' = ",
         get(dim),
         ".\n",
         "Values of '", dim, "' available for interpolation: ",
-        paste(unique(bayesplot:::gamma_adj[, dim]), collapse = ", "),
+        paste(unique(gamma_adj[, dim]), collapse = ", "),
         ".",
         sep = ""
       ))
     }
   }
-  vals <- bayesplot:::gamma_adj[
-    bayesplot:::gamma_adj$L == L & bayesplot:::gamma_adj$prob == prob,
-  ]
+  vals <- gamma_adj[gamma_adj$L == L & gamma_adj$prob == prob, ]
   if (N > max(vals$N)) {
     stop(paste(
       "No precomputed values to interpolate from for sample length of ",

R/mcmc-diagnostics-nuts.R

@@ -154,7 +154,7 @@ mcmc_nuts_acceptance <-
                            Mean = mean(.data$Value),
                            Median = median(.data$Value))
 
-    hists <- ggplot(data, aes_(x = ~ Value, y = ~ ..density..)) +
+    hists <- ggplot(data, aes_(x = ~ Value, y = ~ after_stat(density))) +
       geom_histogram(
         fill = get_color("l"),
         color = get_color("lh"),
@@ -354,7 +354,7 @@ mcmc_nuts_treedepth <- function(x, lp, chain = NULL, ...) {
   treedepth <- dplyr::filter(x, .data$Parameter == "treedepth__")
   accept_stat <- dplyr::filter(x, .data$Parameter == "accept_stat__")
 
-  hist_td <- ggplot(treedepth, aes_(x = ~ Value, y = ~ ..density..)) +
+  hist_td <- ggplot(treedepth, aes_(x = ~ Value, y = ~ after_stat(density))) +
     geom_histogram(
       fill = get_color("l"),
       color = get_color("lh"),
@@ -450,7 +450,7 @@ mcmc_nuts_energy <-
     clrs <- set_names(get_color(c("lh", "mh")), c("E_fill", "Ediff_fill"))
     aes_labs <- c(expression(pi[E]), expression(pi[paste(Delta, E)]))
 
-    graph <- ggplot(data, aes_(y = ~ ..density..)) +
+    graph <- ggplot(data, aes_(y = ~ after_stat(density))) +
       geom_histogram(
         aes_(
           x = ~ Ediff_centered,

R/mcmc-traces.R

@@ -504,7 +504,7 @@ mcmc_rank_ecdf <-
     x = x
   )
   data <- data %>%
-    group_by(parameter, chain) %>%
+    group_by(.data$parameter, .data$chain) %>%
     dplyr::group_map(~ data.frame(
       parameter = .y[1],
       chain = .y[2],

R/ppc-distributions.R

@@ -70,12 +70,12 @@
 #' \donttest{
 #' # ppc_ecdf_overlay with continuous data (set discrete=TRUE if discrete data)
 #' ppc_ecdf_overlay(y, yrep[sample(nrow(yrep), 25), ])
-#' }
+#'
 #' # ECDF and ECDF difference plot of the PIT values of ´y´ compared to ´yrep
 #' # with 99% simultaneous confidence bands.
 #' ppc_pit_ecdf(y, yrep, prob = 0.99, plot_diff = FALSE, interpolate_adj = FALSE)
 #' ppc_pit_ecdf(y, yrep, prob = 0.99, interpolate_adj = FALSE)
-#'
+#' }
 #'
 #' # for ppc_hist,dens,freqpoly,boxplot definitely use a subset yrep rows so
 #' # only a few (instead of nrow(yrep)) histograms are plotted
@@ -104,9 +104,11 @@
 #'
 #' ppc_ecdf_overlay_grouped(y, yrep[1:25, ], group = group)
 #'
+#' \donttest{
 #' # ECDF difference plots of the PIT values by group
 #' # with 99% simultaneous confidence bands.
 #' ppc_pit_ecdf_grouped(y, yrep, group=group, prob=0.99, interpolate_adj=FALSE)
+#' }
 #'
 #' # don't need to only use small number of rows for ppc_violin_grouped
 #' # (as it pools yrep draws within groups)
@@ -596,7 +598,7 @@ ppc_pit_ecdf <- function(y,
 
   if (is.null(pit)) {
     pit <- ppc_data(y, yrep) %>%
-      group_by(y_id) %>%
+      group_by(.data$y_id) %>%
       dplyr::group_map(~ mean(.x$value[.x$is_y] >= .x$value[!.x$is_y])) %>%
       unlist()
     if (is.null(K)) {
@@ -661,7 +663,7 @@ ppc_pit_ecdf_grouped <-
 
     if (is.null(pit)) {
       pit <- ppc_data(y, yrep, group) %>%
-        group_by(y_id) %>%
+        group_by(.data$y_id) %>%
         dplyr::group_map(~ mean(.x$value[.x$is_y] >= .x$value[!.x$is_y])) %>%
         unlist()
       if (is.null(K)) {

tests/testthat/test-bayesplot_grid.R

@@ -30,7 +30,8 @@ test_that("bayesplot_grid throws correct errors", {
 test_that("bayesplot_grid works", {
   expect_message(
     a <- bayesplot_grid(p1, p2, xlim = c(-200, 200), ylim = c(0, 200)),
-    "Adding another scale for 'y'"
+    "is already present",
+    fixed = TRUE
   )
   expect_silent(
     b <- bayesplot_grid(plots = list(p1, p2),

tests/testthat/test-convenience-functions.R

@@ -5,41 +5,38 @@ context("Convenience functions (for ggplot objects)")
 
 # abline_01, vline_ and hline_ ------------------------------------------
 test_that("abline_01 returns the correct object", {
-  expect_equal(
-    abline_01(color = "green", linetype = 2),
-    geom_abline(intercept = 0, slope = 1, color = "green", linetype = 2, na.rm = TRUE),
-    check.environment = FALSE
-  )
+  a <- abline_01(color = "green", linetype = 2)
+  b <- geom_abline(intercept = 0, slope = 1, color = "green", linetype = 2, na.rm = TRUE)
+  a$constructor <- b$constructor <- NULL
+  expect_equal(a, b, check.environment = FALSE)
 })
 test_that("vline_* and hline_* return correct objects", {
-  expect_equal(
-    vline_0(color = "red"),
-    geom_vline(xintercept = 0, color = "red", na.rm = TRUE),
-    check.environment = FALSE
-  )
-  expect_equal(
-    hline_0(size = 2, linetype = 3),
-    geom_hline(yintercept = 0, size = 2, linetype = 3, na.rm = TRUE),
-    check.environment = FALSE
-  )
-  expect_equal(
-    vline_at(c(3,4), na.rm = FALSE),
-    geom_vline(xintercept = c(3,4)),
-    check.environment = FALSE
-  )
-  expect_equal(
-    hline_at(c(3,4), na.rm = FALSE),
-    geom_hline(yintercept = c(3,4)),
-    check.environment = FALSE
-  )
+  a <- vline_0(color = "red")
+  b <- geom_vline(xintercept = 0, color = "red", na.rm = TRUE)
+  a$constructor <- b$constructor <- NULL
+  expect_equal(a, b, check.environment = FALSE)
+
+  a <- hline_0(linewidth = 2, linetype = 3)
+  b <- geom_hline(yintercept = 0, linewidth = 2, linetype = 3, na.rm = TRUE)
+  a$constructor <- b$constructor <- NULL
+  expect_equal(a, b, check.environment = FALSE)
+
+  a <- vline_at(c(3,4), na.rm = FALSE)
+  b <- geom_vline(xintercept = c(3,4))
+  a$constructor <- b$constructor <- NULL
+  expect_equal(a, b, check.environment = FALSE)
+
+  a <- hline_at(c(3,4), na.rm = FALSE)
+  b <- geom_hline(yintercept = c(3,4))
+  a$constructor <- b$constructor <- NULL
+  expect_equal(a, b, check.environment = FALSE)
 })
 test_that("vline_at with 'fun' works", {
   x <- example_mcmc_draws(chains = 1)
-  expect_equal(
-    vline_at(x, colMeans),
-    geom_vline(xintercept = colMeans(x), na.rm = TRUE),
-    check.environment = FALSE
-  )
+  a <- vline_at(x, colMeans)
+  b <- geom_vline(xintercept = colMeans(x), na.rm = TRUE)
+  a$constructor <- b$constructor <- NULL
+  expect_equal(a, b, check.environment = FALSE)
 })
 test_that("calc_v (internal function) works", {
   a <- 1:4
@@ -70,8 +67,8 @@ test_that("lbub works", {
 # plot and facet backgrounds ----------------------------------------------
 test_that("grid_lines returns correct theme object", {
   thm <- theme_default() + grid_lines(size = 1.5, color = "purple")
-  expect_equal(thm$panel.grid.major, element_line(size = 1.5, color = "purple"))
-  expect_equal(thm$panel.grid.minor, element_line(size = 0.75, color = "purple"))
+  expect_equal(thm$panel.grid.major, element_line(linewidth = 1.5, color = "purple"))
+  expect_equal(thm$panel.grid.minor, element_line(linewidth = 0.75, color = "purple"))
 })
 test_that("panel_bg returns correct theme object", {
   bg1 <- panel_bg()
@@ -182,9 +179,8 @@ test_that("yaxis_ticks returns correct theme object", {
 # overlay functions -------------------------------------------------------
 test_that("overlay_function returns the correct object", {
   expect_error(overlay_function(), 'argument "fun" is missing')
-  expect_equal(
-    overlay_function(fun = "dnorm"),
-    stat_function(fun = "dnorm", inherit.aes = FALSE),
-    check.environment = FALSE
-  )
+  a <- overlay_function(fun = "dnorm")
+  b <- stat_function(fun = "dnorm", inherit.aes = FALSE)
+  a$constructor <- b$constructor <- NULL
+  expect_equal(a, b, check.environment = FALSE)
 })

tests/testthat/test-extractors.R

@@ -121,8 +121,7 @@ test_that("cmdstanr methods work", {
 
   fit <- cmdstanr::cmdstanr_example("logistic", iter_sampling = 500, chains = 2)
   np <- nuts_params(fit)
-  np_names <- paste0(c("treedepth", "divergent", "accept_stat", "stepsize",
-                       "n_leapfrog", "energy"), "__")
+  np_names <- paste0(c("treedepth", "divergent", "energy", "accept_stat", "stepsize", "n_leapfrog"), "__")
   expect_identical(levels(np$Parameter), np_names)
   expect_equal(range(np$Iteration), c(1, 500))
   expect_equal(range(np$Chain), c(1, 2))