Update github ci and deps

.github/workflows/R-CMD-check.yaml

@@ -20,31 +20,32 @@ jobs:
       matrix:
         config:
 # running Windows and Mac with --no-examples to avoid spurious Stan crashes in examples
-          - {os: windows-latest, r: 'release', suffix: 'quick', not_cran: 'false', check_options: '"--no-build-vignettes", "--no-examples"'}
-          - {os: macOS-latest, r: 'release', suffix: 'quick', not_cran: 'false', check_options: '"--no-build-vignettes", "--no-examples"'}
-          - {os: ubuntu-latest, r: 'devel', suffix: '', not_cran: 'true', check_options: 'NULL', rspm: "https://packagemanager.rstudio.com/cran/__linux__/bionic/latest"}
+          - {os: windows-latest, r: 'release', suffix: 'quick', not_cran: 'false', check_options: '"--no-build-vignettes", "--ignore-vignettes", "--no-examples"'}
+          - {os: macOS-latest, r: 'release', suffix: 'quick', not_cran: 'false', check_options: '"--no-build-vignettes", "--ignore-vignettes", "--no-examples"'}
+          - {os: ubuntu-latest, r: 'devel', suffix: '', not_cran: 'true', check_options: 'NULL', rspm: "https://packagemanager.rstudio.com/cran/__linux__/bionic/latest", http-user-agent: 'release'}
           - {os: ubuntu-latest, r: 'release', suffix: '', not_cran: 'true', check_options: 'NULL', rspm: "https://packagemanager.rstudio.com/cran/__linux__/bionic/latest"}
-          - {os: ubuntu-latest, r: 'release', suffix: 'quick', not_cran: 'false', check_options: '"--no-build-vignettes"', rspm: "https://packagemanager.rstudio.com/cran/__linux__/bionic/latest"}
-          - {os: ubuntu-latest, r: 'oldrel', suffix: 'quick', not_cran: 'false', check_options: '"--no-build-vignettes"', rspm: "https://packagemanager.rstudio.com/cran/__linux__/bionic/latest"}
-          - {os: ubuntu-latest, r: '4.1', suffix: 'quick', not_cran: 'false', check_options: '"--no-build-vignettes"', rspm: "https://packagemanager.rstudio.com/cran/__linux__/bionic/latest"}
-          - {os: ubuntu-latest, r: '4.0', suffix: 'quick', not_cran: 'false', check_options: '"--no-build-vignettes"', rspm: "https://packagemanager.rstudio.com/cran/__linux__/bionic/latest"}
-          - {os: ubuntu-latest, r: '3.6', suffix: 'quick', not_cran: 'false', check_options: '"--no-build-vignettes"', rspm: "https://packagemanager.rstudio.com/cran/__linux__/bionic/latest"}
+          - {os: ubuntu-latest, r: 'release', suffix: 'quick', not_cran: 'false', check_options: '"--no-build-vignettes", "--ignore-vignettes"', rspm: "https://packagemanager.rstudio.com/cran/__linux__/bionic/latest"}
+          - {os: ubuntu-latest, r: 'oldrel', suffix: 'quick', not_cran: 'false', check_options: '"--no-build-vignettes", "--ignore-vignettes"', rspm: "https://packagemanager.rstudio.com/cran/__linux__/bionic/latest"}
+          - {os: ubuntu-latest, r: '4.1', suffix: 'quick', not_cran: 'false', check_options: '"--no-build-vignettes", "--ignore-vignettes"', rspm: "https://packagemanager.rstudio.com/cran/__linux__/bionic/latest"}
+          - {os: ubuntu-latest, r: '4.0', suffix: 'quick', not_cran: 'false', check_options: '"--no-build-vignettes", "--ignore-vignettes"', rspm: "https://packagemanager.rstudio.com/cran/__linux__/bionic/latest"}
+          - {os: ubuntu-latest, r: '3.6', suffix: 'quick', not_cran: 'false', check_options: '"--no-build-vignettes", "--ignore-vignettes"', rspm: "https://packagemanager.rstudio.com/cran/__linux__/bionic/latest"}
 
     env:
       R_REMOTES_NO_ERRORS_FROM_WARNINGS: true
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
       _R_CHECK_FORCE_SUGGESTS_: false
 
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v4
+
+      - uses: r-lib/actions/setup-pandoc@v2
 
       - uses: r-lib/actions/setup-r@v2
         with:
           r-version: ${{ matrix.config.r }}
+          http-user-agent: ${{ matrix.config.http-user-agent }}
           use-public-rspm: true
 
-      - uses: r-lib/actions/setup-pandoc@v2
-
       - name: Query dependencies
         run: |
           install.packages('remotes')
@@ -64,13 +65,9 @@ jobs:
         env:
           RHUB_PLATFORM: linux-x86_64-ubuntu-gcc
         run: |
-          Rscript -e "remotes::install_github('r-hub/sysreqs')"
-          sysreqs=$(Rscript -e "cat(sysreqs::sysreq_commands('DESCRIPTION'))")
+          Rscript -e "install.packages('pak')"
+          sysreqs=$(Rscript -e "cat(tryCatch(pak::pkg_sysreqs('.', dependencies = TRUE), error = function(e) pak::pkg_sysreqs('.'))[['install_scripts']])")
           sudo -s eval "$sysreqs"
-          sudo apt-get install jags cargo libudunits2-dev libgdal-dev
-# cargo: for gifski
-# libudunits2-dev   for units (transformr)
-# libgdal-dev       for sf (transformr)
 
       - name: Install dependencies
         run: |

DESCRIPTION

@@ -45,7 +45,6 @@ Suggests:
     rstanarm (>= 2.19.2),
     emmeans,
     broom (>= 0.4.3),
-    dotwhisker,
     MCMCglmm,
     bayesplot,
     modelr,
@@ -59,6 +58,7 @@ Suggests:
     gganimate,
     gifski,
     png,
+    ragg,
     pkgdown,
     distributional,
     transformr

README.Rmd

@@ -371,14 +371,6 @@ bind_rows(linear_results, bayes_results) %>%
 
 Shrinkage towards the overall mean is visible in the Bayesian results.
 
-Compatibility with `broom::tidy` also gives compatibility with `dotwhisker::dwplot`:
-
-```{r dwplot, warning = FALSE}
-bind_rows(linear_results, bayes_results) %>%
-  rename(term = condition) %>%
-  dotwhisker::dwplot() 
-```
-
 ### Posterior prediction and complex custom plots
 
 The tidy data format returned by `spread_draws` also facilitates additional computation on variables followed by the construction of more complex custom plots. For example, we can generate posterior predictions easily, and use the `.width` argument (passed internally to `median_qi`) to generate any number of intervals from the posterior predictions, then plot them alongside point summaries and the data:
@@ -502,7 +494,7 @@ p = mtcars %>%
   transition_states(.draw, 0, 1) +
   shadow_mark(past = TRUE, future = TRUE, alpha = 1/20, color = "gray50")
 
-animate(p, nframes = ndraws, fps = 2.5, width = 672, height = 480, res = 100, dev = "png", type = "cairo")
+animate(p, nframes = ndraws, fps = 2.5, width = 672, height = 480, units = "px", res = 100, dev = "ragg_png")
 ```
 
 See `vignette("tidybayes")` for a variety of additional examples and more explanation of how it works.

README.md

@@ -318,21 +318,21 @@ m %>%
     ## # Groups:   condition [1]
     ##    condition condition_mean .chain .iteration .draw response_sd
     ##    <fct>              <dbl>  <int>      <int> <int>       <dbl>
-    ##  1 A                 0.234       1          1     1       0.640
-    ##  2 A                 0.156       1          2     2       0.455
-    ##  3 A                 0.0598      1          3     3       0.713
-    ##  4 A                 0.426       1          4     4       0.681
-    ##  5 A                 0.0164      1          5     5       0.626
-    ##  6 A                 0.131       1          6     6       0.501
-    ##  7 A                 0.397       1          7     7       0.537
-    ##  8 A                 0.325       1          8     8       0.543
-    ##  9 A                 0.363       1          9     9       0.504
-    ## 10 A                 0.152       1         10    10       0.620
-    ## 11 A                 0.263       1         11    11       0.473
-    ## 12 A                 0.328       1         12    12       0.463
-    ## 13 A                -0.114       1         13    13       0.705
-    ## 14 A                 0.486       1         14    14       0.564
-    ## 15 A                 0.0881      1         15    15       0.492
+    ##  1 A                0.00544      1          1     1       0.576
+    ##  2 A               -0.0836       1          2     2       0.576
+    ##  3 A                0.0324       1          3     3       0.551
+    ##  4 A                0.113        1          4     4       0.576
+    ##  5 A                0.157        1          5     5       0.583
+    ##  6 A                0.218        1          6     6       0.621
+    ##  7 A                0.276        1          7     7       0.641
+    ##  8 A                0.0130       1          8     8       0.637
+    ##  9 A                0.152        1          9     9       0.609
+    ## 10 A                0.192        1         10    10       0.521
+    ## 11 A                0.154        1         11    11       0.558
+    ## 12 A                0.298        1         12    12       0.552
+    ## 13 A                0.349        1         13    13       0.531
+    ## 14 A                0.471        1         14    14       0.566
+    ## 15 A                0.313        1         15    15       0.568
 
 The condition numbers are automatically turned back into text (“A”, “B”,
 “C”, …) and split into their own column. A long-format data frame is
@@ -428,11 +428,11 @@ m %>%
     ## # A tibble: 5 × 7
     ##   condition condition_mean .lower .upper .width .point .interval
     ##   <fct>              <dbl>  <dbl>  <dbl>  <dbl> <chr>  <chr>    
-    ## 1 A                  0.195 -0.141  0.540   0.95 median qi       
-    ## 2 B                  0.999  0.647  1.34    0.95 median qi       
+    ## 1 A                  0.199 -0.142  0.549   0.95 median qi       
+    ## 2 B                  1.01   0.651  1.34    0.95 median qi       
     ## 3 C                  1.84   1.48   2.19    0.95 median qi       
-    ## 4 D                  1.02   0.663  1.35    0.95 median qi       
-    ## 5 E                 -0.886 -1.23  -0.528   0.95 median qi
+    ## 4 D                  1.02   0.681  1.37    0.95 median qi       
+    ## 5 E                 -0.890 -1.23  -0.529   0.95 median qi
 
 ### Comparison to other models via compatibility with `broom`
 
@@ -481,11 +481,11 @@ bayes_results
     ## # A tibble: 5 × 8
     ##   condition estimate conf.low conf.high .width .point .interval model
     ##   <fct>        <dbl>    <dbl>     <dbl>  <dbl> <chr>  <chr>     <chr>
-    ## 1 A            0.195   -0.141     0.540   0.95 median qi        Bayes
-    ## 2 B            0.999    0.647     1.34    0.95 median qi        Bayes
+    ## 1 A            0.199   -0.142     0.549   0.95 median qi        Bayes
+    ## 2 B            1.01     0.651     1.34    0.95 median qi        Bayes
     ## 3 C            1.84     1.48      2.19    0.95 median qi        Bayes
-    ## 4 D            1.02     0.663     1.35    0.95 median qi        Bayes
-    ## 5 E           -0.886   -1.23     -0.528   0.95 median qi        Bayes
+    ## 4 D            1.02     0.681     1.37    0.95 median qi        Bayes
+    ## 5 E           -0.890   -1.23     -0.529   0.95 median qi        Bayes
 
 This makes it easy to bind the two results together and plot them:
 
@@ -499,17 +499,6 @@ bind_rows(linear_results, bayes_results) %>%
 
 Shrinkage towards the overall mean is visible in the Bayesian results.
 
-Compatibility with `broom::tidy` also gives compatibility with
-`dotwhisker::dwplot`:
-
-``` r
-bind_rows(linear_results, bayes_results) %>%
-  rename(term = condition) %>%
-  dotwhisker::dwplot() 
-```
-
-![](man/figures/README/dwplot-1.png)<!-- -->
-
 ### Posterior prediction and complex custom plots
 
 The tidy data format returned by `spread_draws` also facilitates
@@ -667,7 +656,7 @@ p = mtcars %>%
   transition_states(.draw, 0, 1) +
   shadow_mark(past = TRUE, future = TRUE, alpha = 1/20, color = "gray50")
 
-animate(p, nframes = ndraws, fps = 2.5, width = 672, height = 480, res = 100, dev = "png", type = "cairo")
+animate(p, nframes = ndraws, fps = 2.5, width = 672, height = 480, units = "px", res = 100, dev = "ragg_png")
 ```
 
 ![](man/figures/README/hops-1.gif)<!-- -->
@@ -689,6 +678,6 @@ have encountered, but I would love to make it cover more!
 
 ## Citing `tidybayes`
 
-Matthew Kay (2023). *tidybayes: Tidy Data and Geoms for Bayesian
-Models*. R package version 3.0.4, <https://mjskay.github.io/tidybayes/>.
+Matthew Kay (2024). *tidybayes: Tidy Data and Geoms for Bayesian
+Models*. R package version 3.0.6, <https://mjskay.github.io/tidybayes/>.
 DOI: [10.5281/zenodo.1308151](https://doi.org/10.5281/zenodo.1308151).

tests/testthat/test.tidy_draws.R

@@ -140,6 +140,7 @@ test_that("tidy_draws works with rjags", {
 })
 
 test_that("tidy_draws works with jagsUI", {
+  skip_if_not_installed("rjags")
   skip_if_not_installed("jagsUI")
 
   # this test model is kind of dumb because jagsUI doesn't seem to allow you to not input data

vignettes/tidy-brms.Rmd

@@ -547,7 +547,7 @@ p = mtcars %>%
   transition_states(.draw, 0, 1) +
   shadow_mark(future = TRUE, color = "gray50", alpha = 1/20)
 
-animate(p, nframes = ndraws, fps = 2.5, width = 432, height = 288, res = 96, dev = "png", type = "cairo")
+animate(p, nframes = ndraws, fps = 2.5, width = 432, height = 288, units = "px", res = 96, dev = "ragg_png")
 ```
 
 ```{r echo=FALSE, results='asis'}
@@ -757,7 +757,7 @@ p = mtcars_clean %>%
   scale_fill_brewer(palette = "Dark2") +
   transition_manual(.draw)
 
-animate(p, nframes = ndraws, fps = 2.5, width = 576, height = 192, res = 96, dev = "png", type = "cairo")
+animate(p, nframes = ndraws, fps = 2.5, width = 576, height = 192, units = "px", res = 96, dev = "ragg_png")
 ```
 
 ```{r echo=FALSE, results='asis'}

vignettes/tidybayes-residuals.Rmd

@@ -381,7 +381,7 @@ p = cens_df %>%
   geom_abline() +
   transition_manual(residual_draw)
 
-animate(p, nframes = k, width = 384, height = 384, res = 96, dev = "png", type = "cairo")
+animate(p, nframes = k, width = 384, height = 384, units = "px", res = 96, dev = "ragg_png")
 ```
 
 ```{r echo=FALSE, results='asis'}
@@ -710,7 +710,7 @@ p = bin_df %>%
   geom_abline() +
   transition_manual(.residual_draw)
 
-animate(p, nframes = k, width = 384, height = 384, res = 96, dev = "png", type = "cairo")
+animate(p, nframes = k, width = 384, height = 384, units = "px", res = 96, dev = "ragg_png")
 ```
 
 ```{r echo=FALSE, results='asis'}

vignettes/tidybayes.Rmd

@@ -38,10 +38,11 @@ if (requireNamespace("pkgdown", quietly = TRUE) && pkgdown::in_pkgdown()) {
   large_height = 5.25
 }
 
+eval_chunks = if (isTRUE(exists("params"))) params$EVAL else FALSE
 knitr::opts_chunk$set(
   fig.width = small_width,
   fig.height = small_height,
-  eval = if (isTRUE(exists("params"))) params$EVAL else FALSE
+  eval = eval_chunks
 )
 if (capabilities("cairo") && Sys.info()[['sysname']] != "Darwin") {
   knitr::opts_chunk$set(
@@ -104,7 +105,6 @@ library(ggdist)
 library(tidybayes)
 library(ggplot2)
 library(cowplot)
-library(emmeans)
 library(broom)
 library(rstan)
 library(rstanarm)
@@ -643,9 +643,9 @@ m_linear = lm(response ~ condition, data = ABC)
 
 Combining `emmeans::emmeans` with `broom::tidy`, we can generate tidy-format summaries of conditional means from the above model:
 
-```{r}
+```{r, eval = eval_chunks && requireNamespace("emmeans", quietly = TRUE)}
 linear_results = m_linear %>% 
-  emmeans(~ condition) %>% 
+  emmeans::emmeans(~ condition) %>%
   tidy(conf.int = TRUE) %>%
   mutate(model = "OLS")
 
@@ -733,19 +733,19 @@ m_rst = stan_glm(response ~ condition, data = ABC)
 
 We can use `emmeans::emmeans()` to get conditional means with uncertainty:
 
-```{r}
+```{r, eval = eval_chunks && requireNamespace("emmeans", quietly = TRUE)}
 m_rst %>%
-  emmeans( ~ condition) %>%
+  emmeans::emmeans( ~ condition) %>%
   gather_emmeans_draws() %>%
   median_qi()
 ```
 
 Or `emmeans::emmeans()` with `emmeans::contrast()` to do all pairwise comparisons:
 
-```{r}
+```{r, eval = eval_chunks && requireNamespace("emmeans", quietly = TRUE)}
 m_rst %>%
-  emmeans( ~ condition) %>%
-  contrast(method = "pairwise") %>%
+  emmeans::emmeans( ~ condition) %>%
+  emmeans::contrast(method = "pairwise") %>%
   gather_emmeans_draws() %>%
   median_qi()
 ```
@@ -754,20 +754,20 @@ See the documentation for `emmeans::pairwise.emmc()` for a list of the numerous
 
 As before, we can plot the results instead of using a table:
 
-```{r fig.width = tiny_width, fig.height = tiny_height}
+```{r fig.width = tiny_width, fig.height = tiny_height, eval = eval_chunks && requireNamespace("emmeans", quietly = TRUE)}
 m_rst %>%
-  emmeans( ~ condition) %>%
-  contrast(method = "pairwise") %>%
+  emmeans::emmeans( ~ condition) %>%
+  emmeans::contrast(method = "pairwise") %>%
   gather_emmeans_draws() %>%
   ggplot(aes(x = .value, y = contrast)) +
   stat_halfeye()
 ```
 
 `gather_emmeans_draws()` also supports `emm_list` objects, which contain estimates from different reference grids (see `emmeans::ref_grid()` for more information on reference grids). An additional column with the default name of `.grid` is added to indicate the reference grid for each row in the output:
 
-```{r}
+```{r, eval = eval_chunks && requireNamespace("emmeans", quietly = TRUE)}
 m_rst %>%
-  emmeans(pairwise ~ condition) %>%
+  emmeans::emmeans(pairwise ~ condition) %>%
   gather_emmeans_draws() %>%
   median_qi()
 ```
@@ -789,8 +789,8 @@ Now we can use `emmeans()` and `gather_emmeans_draws()` exactly as we did with `
 
 ```r
 m_glmm %>%
-  emmeans( ~ condition, data = ABC) %>%
-  contrast(method = "pairwise") %>%
+  emmeans::emmeans( ~ condition, data = ABC) %>%
+  emmeans::contrast(method = "pairwise") %>%
   gather_emmeans_draws() %>%
   ggplot(aes(x = .value, y = contrast)) +
   stat_halfeye()