Replaced gridextra with patchwork. Relegated ggplot2 from Depends to …

…Import.

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: ale
 Title: Interpretable Machine Learning and Statistical Inference with Accumulated Local Effects (ALE)
-Version: 0.2.0.20240207
+Version: 0.2.0.20240208
 Authors@R: c(
     person("Chitu", "Okoli", , "[email protected]", role = c("aut", "cre"),
            comment = c(ORCID = "0000-0001-5574-7572")),
@@ -14,9 +14,9 @@ Roxygen: list(markdown = TRUE)
 RoxygenNote: 7.3.1
 Suggests: 
     ALEPlot,
-    gridExtra,
     knitr,
     mgcv,
+    patchwork,
     readr,
     rmarkdown,
     testthat (>= 3.0.0)
@@ -28,6 +28,7 @@ Imports:
     ellipsis,
     furrr,
     future,
+	  ggplot2,
     glue,
     grDevices,
     insight,
@@ -42,8 +43,7 @@ Imports:
     univariateML,
     yaImpute
 Depends: 
-    R (>= 3.5.0),
-	  ggplot2
+    R (>= 3.5.0)
 Remotes:
     tidyverse/ggplot2#5592
 URL: https://github.com/tripartio/ale, https://tripartio.github.io/ale/

R/ale_core.R

@@ -273,10 +273,9 @@
 #'       arguments to understand how these are determined.
 #'
 #' @examples
-# Sample 1000 rows from the diamonds dataset (for a simple example)
-#' diamonds
+# Sample 1000 rows from the ggplot2::diamonds dataset (for a simple example)
 #' set.seed(0)
-#' diamonds_sample <- diamonds[sample(nrow(diamonds), 1000), ]
+#' diamonds_sample <- ggplot2::diamonds[sample(nrow(ggplot2::diamonds), 1000), ]
 #'
 #' # Split the dataset into training and test sets
 #' # https://stackoverflow.com/a/54892459/2449926
@@ -309,7 +308,8 @@
 #' )
 #'
 #' # Plot the ALE data
-#' gridExtra::grid.arrange(grobs = ale_gam_diamonds$plots, ncol = 2)
+#' ale_gam_diamonds$plots |>
+#'   patchwork::wrap_plots()
 #'
 #' # Bootstrapped ALE
 #' # This can be slow, since bootstrapping runs the algorithm boot_it times
@@ -322,7 +322,8 @@
 #' )
 #'
 #' # Bootstrapped ALEs print with confidence intervals
-#' gridExtra::grid.arrange(grobs = ale_gam_diamonds_boot$plots, ncol = 2)
+#' ale_gam_diamonds_boot$plots |>
+#'   patchwork::wrap_plots()
 #'
 #'
 #' # If the predict function you want is non-standard, you may define a
@@ -339,7 +340,8 @@
 #' )
 #'
 #' # Plot the ALE data
-#' gridExtra::grid.arrange(grobs = ale_gam_diamonds_custom$plots, ncol = 2)
+#' ale_gam_diamonds_custom$plots |>
+#'   patchwork::wrap_plots()
 #'
 #' }
 #'
@@ -449,10 +451,9 @@ ale <- function (
 #'
 #' @examples
 #'
-# Sample 1000 rows from the diamonds dataset (for a simple example)
-#' diamonds
+# Sample 1000 rows from the ggplot2::diamonds dataset (for a simple example)
 #' set.seed(0)
-#' diamonds_sample <- diamonds[sample(nrow(diamonds), 1000), ]
+#' diamonds_sample <- ggplot2::diamonds[sample(nrow(ggplot2::diamonds), 1000), ]
 #'
 #' # Split the dataset into training and test sets
 #' # https://stackoverflow.com/a/54892459/2449926
@@ -484,8 +485,11 @@ ale <- function (
 #'
 #' # Print interaction plots
 #' ale_ixn_gam_diamonds$plots |>
-#'   purrr::walk(\(.x1) {  # extract list of x1 ALE outputs
-#'     gridExtra::grid.arrange(grobs = .x1, ncol = 2)  # plot all x1 plots
+#'   # extract list of x1 ALE outputs
+#'   purrr::walk(\(.x1) {
+#'     # plot all x2 plots in each .x1 element
+#'     patchwork::wrap_plots(.x1) |>
+#'       print()
 #'   })
 #' }
 #'

R/model_bootstrap.R

@@ -150,7 +150,8 @@
 #' mb_gam$model_coefs
 #'
 #' # Plot ALE
-#' gridExtra::grid.arrange(grobs = mb_gam$ale$plots, ncol = 2)
+#' mb_gam$ale$plots |>
+#'   patchwork::wrap_plots()
 #' }
 #'
 #'

R/stats.R

@@ -121,9 +121,9 @@
 #'
 #' @examples
 #' \donttest{
-#' # Sample 1000 rows from the diamonds dataset (for a simple example)diamonds
+#' # Sample 1000 rows from the ggplot2::diamonds dataset (for a simple example)
 #' set.seed(0)
-#' diamonds_sample <- diamonds[sample(nrow(diamonds), 1000), ]
+#' diamonds_sample <- ggplot2::diamonds[sample(nrow(ggplot2::diamonds), 1000), ]
 #'
 #' # Split the dataset into training and test sets
 #' # https://stackoverflow.com/a/54892459/2449926
@@ -168,7 +168,8 @@
 #' )
 #'
 #' # Plot the ALE data. The horizontal bands in the plots use the p-values.
-#' gridExtra::grid.arrange(grobs = ale_gam_diamonds$plots, ncol = 2)
+#' ale_gam_diamonds$plots |>
+#'   patchwork::wrap_plots()
 #'
 #' }
 #'

README.Rmd

@@ -74,10 +74,9 @@ Here is a simple example that demonstrates the usage of the model. First, we tra
 ```{r gam-and-ale, fig.width=7, fig.height=11}
 library(ale)
 
-# Sample 1000 rows from the diamonds dataset (for a simple example).
-# diamonds is included with ggplot2, which is imported by the ale package.
+# Sample 1000 rows from the ggplot2::diamonds dataset (for a simple example).
 set.seed(0)
-diamonds_sample <- diamonds[sample(nrow(diamonds), 1000), ]
+diamonds_sample <- ggplot2::diamonds[sample(nrow(ggplot2::diamonds), 1000), ]
 
 # Split the dataset into training and test sets
 # https://stackoverflow.com/a/54892459/2449926
@@ -97,17 +96,19 @@ gam_diamonds <- mgcv::gam(
   data = diamonds_train
 )
 
-# Create ALE data and plot it
+# Create ALE data
 ale_gam_diamonds <- ale(
   diamonds_test, gam_diamonds,
   model_packages = 'mgcv'  # required for parallel processing
 )
 
-gridExtra::grid.arrange(grobs = ale_gam_diamonds$plots, ncol = 2)
+# Plot the ALE data
+ale_gam_diamonds$plots |> 
+  patchwork::wrap_plots(ncol = 2)
 ```
 
 ## Getting help
 
-If you find a bug, please report it on [GitHub](https://github.com/tripartio/ale/issues). If you have a question about how to use the package, you can post it on [Stack Overflow with the “ale” tag](https://stackoverflow.com/questions/tagged/ale). I will follow that tag, so I will try my best to respond quickly. However, be sure to always include a minimal reproducible example for your usage requests. If you cannot include your own dataset in the question, then use one of the built-in datasets to frame your help request: `var_cars`, `census`, or `diamonds`. 
+If you find a bug, please report it on [GitHub](https://github.com/tripartio/ale/issues). If you have a question about how to use the package, you can post it on [Stack Overflow with the “ale” tag](https://stackoverflow.com/questions/tagged/ale). I will follow that tag, so I will try my best to respond quickly. However, be sure to always include a minimal reproducible example for your usage requests. If you cannot include your own dataset in the question, then use one of the built-in datasets to frame your help request: `var_cars` or `census`. You may also use `ggplot2::diamonds` for a larger sample. 
 
 

README.md

@@ -107,12 +107,10 @@ the `ggplot` plot objects.
 
 ``` r
 library(ale)
-#> Loading required package: ggplot2
 
-# Sample 1000 rows from the diamonds dataset (for a simple example).
-# diamonds is included with ggplot2, which is imported by the ale package.
+# Sample 1000 rows from the ggplot2::diamonds dataset (for a simple example).
 set.seed(0)
-diamonds_sample <- diamonds[sample(nrow(diamonds), 1000), ]
+diamonds_sample <- ggplot2::diamonds[sample(nrow(ggplot2::diamonds), 1000), ]
 
 # Split the dataset into training and test sets
 # https://stackoverflow.com/a/54892459/2449926
@@ -132,13 +130,15 @@ gam_diamonds <- mgcv::gam(
   data = diamonds_train
 )
 
-# Create ALE data and plot it
+# Create ALE data
 ale_gam_diamonds <- ale(
   diamonds_test, gam_diamonds,
   model_packages = 'mgcv'  # required for parallel processing
 )
 
-gridExtra::grid.arrange(grobs = ale_gam_diamonds$plots, ncol = 2)
+# Plot the ALE data
+ale_gam_diamonds$plots |> 
+  patchwork::wrap_plots(ncol = 2)
 ```
 
 <img src="man/figures/README-gam-and-ale-1.png" width="100%" />
@@ -153,5 +153,5 @@ tag](https://stackoverflow.com/questions/tagged/ale). I will follow that
 tag, so I will try my best to respond quickly. However, be sure to
 always include a minimal reproducible example for your usage requests.
 If you cannot include your own dataset in the question, then use one of
-the built-in datasets to frame your help request: `var_cars`, `census`,
-or `diamonds`.
+the built-in datasets to frame your help request: `var_cars` or
+`census`. You may also use `ggplot2::diamonds` for a larger sample.

vignettes/ale-intro.Rmd

@@ -32,7 +32,7 @@ library(dplyr)
 
 ## diamonds dataset
 
-For this introduction, we use the `diamonds` dataset, built-in with the `ggplot2` graphics system. We cleaned the original version by [removing duplicates](https://lorentzen.ch/index.php/2021/04/16/a-curious-fact-on-the-diamonds-dataset/ "errors in the diamonds dataset") and invalid entries where the length (x), width (y), or depth (z) is 0.
+For this introduction, we use the `diamonds` dataset, included with the `{ggplot2}` graphics system. We cleaned the original version by [removing duplicates](https://lorentzen.ch/index.php/2021/04/16/a-curious-fact-on-the-diamonds-dataset/ "errors in the diamonds dataset") and invalid entries where the length (x), width (y), or depth (z) is 0.
 
 ```{r diamonds_print}
 # Clean up some invalid entries
@@ -130,7 +130,6 @@ The `ale()` function returns a list with various elements. The two main ones are
 ale_gam_diamonds <- ale(
   diamonds_test, gam_diamonds,
   model_packages = 'mgcv',  # required for parallel processing
-  silent = TRUE,  # progress bars disabled for the vignette
   parallel = 2  # CRAN limit (delete this line on your own computer)
 )
 ```
@@ -144,11 +143,12 @@ To access the plot for a specific variable, we can call it by its variable name
 ale_gam_diamonds$plots$carat
 ```
 
-To iterate the list and plot all the ALE plots, we provide here some demonstration code using the `gridExtra` package for arranging multiple plots in a common plot grid using `gridExtra::grid.arrange`. We need to pass the list of plots to the `grobs` argument and we can specify that we want two plots per row with the `ncol` argument.
+To iterate the list and plot all the ALE plots, we provide here some demonstration code using the `patchwork` package for arranging multiple plots in a common plot grid using `patchwork::wrap_plots()`. We need to pass the list of plots to the `grobs` argument and we can specify that we want two plots per row with the `ncol` argument.
 
 ```{r print ale_simple, fig.width=7, fig.height=11}
 # Print all plots
-gridExtra::grid.arrange(grobs = ale_gam_diamonds$plots, ncol = 2)
+ale_gam_diamonds$plots |> 
+  patchwork::wrap_plots(ncol = 2)
 ```
 
 ## Bootstrapped ALE
@@ -173,12 +173,12 @@ ale_gam_diamonds_boot <- ale(
   # Normally boot_it should be set to 100, but just 10 here for a faster demonstration
   boot_it = 10,
   model_packages = 'mgcv',  # required for parallel processing
-  silent = TRUE,  # progress bars disabled for the vignette
   parallel = 2  # CRAN limit (delete this line on your own computer)
 )
 
 # Bootstrapping produces confidence intervals
-gridExtra::grid.arrange(grobs = ale_gam_diamonds_boot$plots, ncol = 2)
+ale_gam_diamonds_boot$plots |> 
+  patchwork::wrap_plots(ncol = 2)
 ```
 
 In this case, the bootstrapped results are mostly similar to single (non-bootstrapped) ALE result. In principle, we should always bootstrap the results and trust only in bootstrapped results. The most unusual result is that values of `x_length` (the length of the diamond) from 6.2 mm or so and higher are associated with lower diamond prices. When we compare this with the `y_width` value (width of the diamond), we suspect that when both the length and width (that is, the size) of a diamond become increasingly large, the price increases so much more rapidly with the width than with the length that the width has an inordinately high effect that is tempered by a decreased effect of the length at those high values. This would be worth further exploration for real analysis, but here we are just introducing the key features of the package.
@@ -192,25 +192,28 @@ Another advantage of ALE is that it provides data for two-way interactions betwe
 ale_ixn_gam_diamonds <- ale_ixn(
   diamonds_test, gam_diamonds,
   model_packages = 'mgcv',  # required for parallel processing
-  silent = TRUE,  # progress bars disabled for the vignette
   parallel = 2  # CRAN limit (delete this line on your own computer)
 )
 
 ```
 
 Like the `ale()` function, the `ale_ixn()` returns a list with one element per input x1 variable, as well as a `.common_data` element with details about the outcome (y) variable. However, in this case, each variable's element consists of a list of all the x2 variables for which the x1 interaction is calculated. Each x2 element then has two elements: the ALE data for that variable and a `ggplot` plot object that plots that ALE data. In the interaction plots, the x1 variable is always shown on the x axis and the x2 variable on the y axis.
 
-Again, we provide here some demonstration code to plot all the ALE plots. It is a little more complex this time because of the two levels of interacting variables in the output data, so we use the `purrr` package to iterate the list structure. `purrr::walk` takes a list as its first argument and then we specify an anonymous function for what we want to do with each element of the list. We specify the anonymous function as `\(.x1) {...}` where `.x1` in our case represents each individual element of `ale_ixn_gam_diamonds$plots` in turn, that is, a sublist of plots with which the x1 variable interacts. We print the plots of all the x1 interactions as a combined grid of plots with `gridExtra::grid.arrange`, as before.
+Again, we provide here some demonstration code to plot all the ALE plots. It is a little more complex this time because of the two levels of interacting variables in the output data, so we use the `purrr` package to iterate the list structure. `purrr::walk()` takes a list as its first argument and then we specify an anonymous function for what we want to do with each element of the list. We specify the anonymous function as `\(.x1) {...}` where `.x1` in our case represents each individual element of `ale_ixn_gam_diamonds$plots` in turn, that is, a sublist of plots with which the x1 variable interacts. We print the plots of all the x1 interactions as a combined grid of plots with `patchwork::wrap_plots()`, as before.
 
 ```{r print all ale_ixn, fig.width=7, fig.height=7}
 # Print all interaction plots
 ale_ixn_gam_diamonds$plots |>
-  purrr::walk(\(.x1) {  # extract list of x1 ALE outputs
-    gridExtra::grid.arrange(grobs = .x1, ncol = 2)  # plot each x1 plot
+  # extract list of x1 ALE outputs
+  purrr::walk(\(.x1) {
+    # plot all x2 plots in each .x1 element
+    patchwork::wrap_plots(.x1, ncol = 2) |>
+      print()
   })
+
 ```
 
-Because we are printing all plots together with the same `gridExtra::grid.arrange` statement, some of them might appear vertically distorted because each plot is forced to be of the same height. For more fine-tuned presentation, we would need to refer to a specific plot. For example, we can print the interaction plot between carat and depth by referring to it thus: `ale_ixn_gam_diamonds$plots$carat$depth`.
+Because we are printing all plots together with the same `patchwork::wrap_plots()` statement, some of them might appear vertically distorted because each plot is forced to be of the same height. For more fine-tuned presentation, we would need to refer to a specific plot. For example, we can print the interaction plot between carat and depth by referring to it thus: `ale_ixn_gam_diamonds$plots$carat$depth`.
 
 ```{r print specific ixn, fig.width=5, fig.height=3}
 ale_ixn_gam_diamonds$plots$carat$depth