Merge branch 'master' into ppc-rootogram-grouped

Author: jgabry

.github/workflows/test-coverage.yaml

@@ -18,7 +18,7 @@ jobs:
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
 
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
 
       - uses: r-lib/actions/setup-r@v2
         with:

DESCRIPTION

@@ -26,7 +26,7 @@ Description: Plotting functions for posterior analysis, MCMC diagnostics,
 License: GPL (>= 3)
 URL: https://mc-stan.org/bayesplot/
 BugReports: https://github.com/stan-dev/bayesplot/issues/
-SystemRequirements: pandoc (>= 1.12.3), pandoc-citeproc
+SystemRequirements: pandoc (>= 1.12.3)
 Depends:
     R (>= 4.1.0)
 Imports:

NEWS.md

@@ -1,5 +1,7 @@
 # bayesplot (development version)
 
+* Use `rlang::warn()` and `rlang::inform()` for selected PPC user messages instead of base `warning()` and `message()`.
+* Standardize input validation errors in `ppc_km_overlay()` and interpolation helpers to use `rlang::abort()` for consistent error handling.
 * Fix assignment-in-call bug in `mcmc_rank_ecdf()` (#).
 * Replaced deprecated `dplyr` and `tidyselect` functions (`top_n`, `one_of`, `group_indices`) with their modern equivalents to ensure future compatibility. (#431)
 * Documentation added for all exported `*_data()` functions (#209)
@@ -8,6 +10,9 @@
 * Fixed test in `test-ppc-distributions.R` that incorrectly used `ppc_dens()` instead of `ppd_dens()` when testing PPD functions
 * New functions `mcmc_dots` and `mcmc_dots_by_chain` for dot plots of MCMC draws by @behramulukir (#402)
 * Default to `quantiles=100` for all dot plots by @behramulukir (#402)
+* Use `"neff_ratio"` consistently in diagnostic color scale helpers to avoid relying on partial matching of `"neff"`.
+* Replace `expand = c(mult, add)` with `ggplot2::expansion()` helper in scale functions for consistency with ggplot2 >= 3.3.0 style.
+* Replace uses of `geom_bar(stat = "identity")` with the more idiomatic ggplot2 form `geom_col()` 
 
 # bayesplot 1.15.0
 

R/bayesplot-colors.R

@@ -189,7 +189,7 @@ color_scheme_view <- function(scheme = NULL) {
 
 #' @export
 print.bayesplot_scheme <- function(x, ...) {
-  tab <- data.frame(unlist(x, use.names = FALSE), stringsAsFactors = FALSE)
+  tab <- data.frame(unlist(x, use.names = FALSE))
   colnames(tab) <- attr(x, "scheme_name") %||% "hex_color"
   print(tab, ...)
 }
@@ -222,9 +222,8 @@ plot_scheme <- function(scheme = NULL) {
   )
 
   ggplot(color_data, aes(x = .data$name, y = .data$value, fill = .data$group)) +
-    geom_bar(
+    geom_col(
       width = .5,
-      stat = "identity",
       color = "white",
       linewidth = 0.1
     ) +

R/helpers-gg.R

@@ -37,10 +37,10 @@ modify_aes <- function(mapping, ...) {
 facet_wrap_parsed <- function(...) {
   facet_wrap(..., labeller = label_parsed)
 }
-dont_expand_y_axis <- function(expand = c(0,0)) {
+dont_expand_y_axis <- function(expand = expansion(0, 0)) {
   scale_y_continuous(expand = expand)
 }
-dont_expand_x_axis <- function(expand = c(0,0)) {
+dont_expand_x_axis <- function(expand = expansion(0, 0)) {
   scale_x_continuous(expand = expand)
 }
 dont_expand_axes <- function() {

R/helpers-ppc.R

@@ -456,7 +456,7 @@ interpolate_gamma <- function(N, K, prob, L) {
 get_interpolation_values <- function(N, K, L, prob) {
   for (dim in c("L", "prob")) {
     if (all(get(dim) != .gamma_adj[, dim])) {
-      stop(paste(
+      abort(paste(
         "No precomputed values to interpolate from for '", dim, "' = ",
         get(dim),
         ".\n",
@@ -469,7 +469,7 @@ get_interpolation_values <- function(N, K, L, prob) {
   }
   vals <- .gamma_adj[.gamma_adj$L == L & .gamma_adj$prob == prob, ]
   if (N > max(vals$N)) {
-    stop(paste(
+    abort(paste(
       "No precomputed values to interpolate from for sample length of ",
       N,
       ".\n",
@@ -480,7 +480,7 @@ get_interpolation_values <- function(N, K, L, prob) {
     ))
   }
   if (N < min(vals$N)) {
-    stop(paste(
+    abort(paste(
       "No precomputed values to interpolate from for sample length of ",
       N,
       ".\n",
@@ -491,7 +491,7 @@ get_interpolation_values <- function(N, K, L, prob) {
     ))
   }
   if (K > max(vals[vals$N <= N, ]$K)) {
-    stop(paste(
+    abort(paste(
       "No precomputed values available for interpolation for 'K' = ",
       K,
       ".\n",
@@ -502,7 +502,7 @@ get_interpolation_values <- function(N, K, L, prob) {
     ))
   }
   if (K < min(vals[vals$N <= N, ]$K)) {
-    stop(paste(
+    abort(paste(
       "No precomputed values available for interpolation for 'K' = ",
       K,
       ".\n",

R/mcmc-diagnostics-nuts.R

@@ -183,7 +183,7 @@ mcmc_nuts_acceptance <-
         )
     }
     hists <- hists +
-      dont_expand_y_axis(c(0.005, 0)) +
+      dont_expand_y_axis(expansion(mult = 0.005, add = 0)) +
       facet_wrap(vars(.data$Parameter), scales = "free") +
       yaxis_text(FALSE) +
       yaxis_title(FALSE) +
@@ -483,8 +483,8 @@ mcmc_nuts_energy <-
       ) +
       scale_fill_manual("", values = fills, labels = aes_labs) +
       scale_color_manual("", values = clrs, labels = aes_labs) +
-      dont_expand_y_axis(c(0.005, 0)) +
-      scale_x_continuous(expand = c(0.2, 0)) +
+      dont_expand_y_axis(expansion(mult = 0.005, add = 0)) +
+      scale_x_continuous(expand = expansion(mult = 0.2, add = 0)) +
       labs(y = NULL, x = expression(E - bar(E))) +
       bayesplot_theme_get() +
       space_legend_keys()  +

R/mcmc-diagnostics.R

@@ -170,8 +170,8 @@ mcmc_rhat <- function(rhat, ..., size = NULL) {
     labs(y = NULL, x = expression(hat(R))) +
     scale_fill_diagnostic("rhat") +
     scale_color_diagnostic("rhat") +
-    scale_x_continuous(breaks = brks, expand = c(0, .01)) +
-    scale_y_discrete(expand = c(.025,0)) +
+    scale_x_continuous(breaks = brks, expand = expansion(mult = 0, add = 0.01)) +
+    scale_y_discrete(expand = expansion(mult = 0.025, add = 0)) +
     yaxis_title(FALSE) +
     yaxis_text(FALSE) +
     yaxis_ticks(FALSE)
@@ -199,7 +199,7 @@ mcmc_rhat_hist <- function(rhat, ..., binwidth = NULL, bins = NULL, breaks = NUL
     scale_color_diagnostic("rhat") +
     scale_fill_diagnostic("rhat") +
     labs(x = expression(hat(R)), y = NULL) +
-    dont_expand_y_axis(c(0.005, 0)) +
+    dont_expand_y_axis(expansion(mult = 0.005, add = 0)) +
     bayesplot_theme_get() +
     yaxis_title(FALSE) +
     yaxis_text(FALSE) +
@@ -255,14 +255,14 @@ mcmc_neff <- function(ratio, ..., size = NULL) {
       linetype = 2,
       linewidth = 0.25) +
     labs(y = NULL, x = expression(N[eff]/N)) +
-    scale_fill_diagnostic("neff") +
-    scale_color_diagnostic("neff") +
+    scale_fill_diagnostic("neff_ratio") +
+    scale_color_diagnostic("neff_ratio") +
     scale_x_continuous(
       breaks = breaks,
       # as.character truncates trailing zeroes, while ggplot default does not
       labels = as.character(breaks),
       limits = c(0, max(1, max_ratio) + 0.05),
-      expand = c(0, 0)) +
+      expand = expansion(0, 0)) +
     bayesplot_theme_get() +
     yaxis_text(FALSE) +
     yaxis_title(FALSE) +
@@ -287,10 +287,10 @@ mcmc_neff_hist <- function(ratio, ..., binwidth = NULL, bins = NULL, breaks = NU
       binwidth = binwidth,
       bins = bins,
       breaks = breaks) +
-    scale_color_diagnostic("neff") +
-    scale_fill_diagnostic("neff") +
+    scale_color_diagnostic("neff_ratio") +
+    scale_fill_diagnostic("neff_ratio") +
     labs(x = expression(N[eff]/N), y = NULL) +
-    dont_expand_y_axis(c(0.005, 0)) +
+    dont_expand_y_axis(expansion(mult = 0.005, add = 0)) +
     yaxis_title(FALSE) +
     yaxis_text(FALSE) +
     yaxis_ticks(FALSE) +
@@ -424,12 +424,12 @@ diagnostic_points <- function(size = NULL) {
 
 # Functions wrapping around scale_color_manual() and scale_fill_manual(), used to
 # color the intervals by rhat value
-scale_color_diagnostic <- function(diagnostic = c("rhat", "neff")) {
+scale_color_diagnostic <- function(diagnostic = c("rhat", "neff_ratio")) {
   d <- match.arg(diagnostic)
   diagnostic_color_scale(d, aesthetic = "color")
 }
 
-scale_fill_diagnostic <- function(diagnostic = c("rhat", "neff")) {
+scale_fill_diagnostic <- function(diagnostic = c("rhat", "neff_ratio")) {
   d <- match.arg(diagnostic)
   diagnostic_color_scale(d, aesthetic = "fill")
 }
@@ -541,9 +541,7 @@ drop_NAs_and_warn <- function(x) {
       bayesplot_theme_get()
     if (style == "bar") {
       graph <- graph +
-        geom_bar(
-          position = "identity",
-          stat = "identity",
+        geom_col(
           linewidth = 0.2,
           fill = get_color("l"),
           color = get_color("lh"),
@@ -574,7 +572,7 @@ drop_NAs_and_warn <- function(x) {
       scale_x_continuous(
         limits = c(-0.5, lags + 0.5),
         breaks = function(x) as.integer(pretty(x, n = 3)),
-        expand = c(0, 0)
+        expand = expansion(0, 0)
       ) +
       labs(x = "Lag", y = "Autocorrelation") +
       force_axes_in_facets()

R/mcmc-distributions.R

@@ -332,7 +332,7 @@ mcmc_dens_chains <- function(
     labs(color = "Chain") +
     scale_y_discrete(
       limits = unique(rev(data$parameter)),
-      expand = c(0.05, .6)
+      expand = expansion(mult = 0.05, add = 0.6)
     ) +
     scale_color +
     bayesplot_theme_get() +
@@ -523,7 +523,7 @@ mcmc_dots_by_chain <- function(
   }
 
   graph +
-    dont_expand_y_axis(c(0.005, 0)) +
+    dont_expand_y_axis(expansion(mult = 0.005, add = 0)) +
     bayesplot_theme_get() +
     yaxis_text(FALSE) +
     yaxis_title(FALSE) +
@@ -642,7 +642,7 @@ mcmc_dots_by_chain <- function(
   }
 
   graph +
-    dont_expand_y_axis(c(0.005, 0)) +
+    dont_expand_y_axis(expansion(mult = 0.005, add = 0)) +
     bayesplot_theme_get() +
     yaxis_text(FALSE) +
     yaxis_ticks(FALSE) +
@@ -703,7 +703,7 @@ mcmc_dots_by_chain <- function(
   }
 
   graph +
-    dont_expand_y_axis(c(0.005, 0)) +
+    dont_expand_y_axis(expansion(mult = 0.005, add = 0)) +
     bayesplot_theme_get() +
     yaxis_text(FALSE) +
     yaxis_title(FALSE) +

R/mcmc-parcoord.R

@@ -132,8 +132,8 @@ mcmc_parcoord <-
 
 
     divg <- sym("Divergent")
-    draws <- dplyr::filter(data, UQ(divg) == 0)
-    div_draws <- dplyr::filter(data, UQ(divg) == 1)
+    draws <- dplyr::filter(data, !!divg == 0)
+    div_draws <- dplyr::filter(data, !!divg == 1)
     has_divs <- isTRUE(nrow(div_draws) > 0)
 
     graph <- ggplot(draws, aes(
@@ -159,7 +159,7 @@ mcmc_parcoord <-
     }
 
     graph +
-      scale_x_discrete(expand = c(0,0), labels = levels(draws$Parameter)) +
+      scale_x_discrete(expand = expansion(0, 0), labels = levels(draws$Parameter)) +
       expand_limits(x = nlevels(draws$Parameter) + 0.25) +
       labs(x = NULL, y = NULL)
   }
@@ -191,7 +191,7 @@ mcmc_parcoord_data <-
       # 'Parameter' and 'Value' so need to be a little careful)
       divs <- np %>%
         validate_nuts_data_frame() %>%
-        dplyr::filter(UQ(param) == "divergent__") %>%
+        dplyr::filter(!!param == "divergent__") %>%
         select(- !!param) %>%
         rename("Divergent" = !!value)