reduce size of the tarball; resubmit

CRAN-RELEASE

@@ -1,2 +1,2 @@
 This package was submitted to CRAN on 2021-04-12.
-Once it is accepted, delete this file and tag the release (commit 312f97bc).
+Once it is accepted, delete this file and tag the release (commit 76c332ca).

README.Rmd

@@ -18,7 +18,7 @@ options(
 
 knitr::opts_chunk$set(
   collapse = TRUE,
-  dpi = 300,
+  dpi = 150, # change to 300 once on CRAN
   warning = FALSE,
   message = FALSE,
   out.width = "100%",
@@ -232,43 +232,17 @@ ggbetweenstats(
 `r emo::ji("check")` Bayesian estimation <br>
 
 A number of other arguments can be specified to make this plot even more
-informative or change some of the default options. 
+informative or change some of the default options. Additionally, there is also a
+`grouped_` variant of this function that makes it easy to repeat the same
+operation across a **single** grouping variable:
 
-```{r ggbetweenstats2}
-# for reproducibility
-set.seed(123)
-library(ggplot2)
-
-# plot
-ggbetweenstats(
-  data = ToothGrowth,
-  x = supp,
-  y = len,
-  type = "r", # robust statistics
-  k = 3, # number of decimal places for statistical results
-  xlab = "Supplement type", # label for the x-axis variable
-  ylab = "Tooth length", # label for the y-axis variable
-  title = "The Effect of Vitamin C on Tooth Growth", # title text for the plot
-  ggtheme = ggthemes::theme_fivethirtyeight(), # choosing a different theme
-  ggstatsplot.layer = FALSE, # turn off `ggstatsplot` theme layer
-  package = "wesanderson", # package from which color palette is to be taken
-  palette = "Darjeeling1" # choosing a different color palette
-)
-```
-
-Additionally, there is also a `grouped_` variant of this function that makes it
-easy to repeat the same operation across a **single** grouping variable:
-
-```{r ggbetweenstats3, fig.height = 14, fig.width = 12}
+```{r ggbetweenstats2, fig.height = 8, fig.width = 12}
 # for reproducibility
 set.seed(123)
 
 # plot
 grouped_ggbetweenstats(
-  data = dplyr::filter(
-    .data = movies_long,
-    genre %in% c("Action", "Action Comedy", "Action Drama", "Comedy")
-  ),
+  data = dplyr::filter(movies_long, genre %in% c("Action", "Comedy")),
   x = mpaa,
   y = length,
   grouping.var = genre, # grouping variable
@@ -282,7 +256,7 @@ grouped_ggbetweenstats(
   caption = substitute(paste(italic("Source"), ": IMDb (Internet Movie Database)")),
   palette = "default_jama",
   package = "ggsci",
-  plotgrid.args = list(nrow = 2),
+  plotgrid.args = list(nrow = 1),
   annotation.args = list(title = "Differences in movie length by mpaa ratings for different genres")
 )
 ```
@@ -503,16 +477,13 @@ gghistostats(
 There is also a `grouped_` variant of this function that makes it
 easy to repeat the same operation across a **single** grouping variable:
 
-```{r gghistostats2, fig.height = 10, fig.width = 10}
+```{r gghistostats2, fig.height = 6, fig.width = 10}
 # for reproducibility
 set.seed(123)
 
 # plot
 grouped_gghistostats(
-  data = dplyr::filter(
-    .data = movies_long,
-    genre %in% c("Action", "Action Comedy", "Action Drama", "Comedy")
-  ),
+  data = dplyr::filter(movies_long, genre %in% c("Action", "Comedy")),
   x = budget,
   test.value = 50,
   type = "nonparametric",
@@ -523,7 +494,7 @@ grouped_gghistostats(
   ggtheme = ggthemes::theme_tufte(),
   # modify the defaults from `ggstatsplot` for each plot
   ggplot.component = ggplot2::labs(caption = "Source: IMDB.com"),
-  plotgrid.args = list(nrow = 2),
+  plotgrid.args = list(nrow = 1),
   annotation.args = list(title = "Movies budgets for different genres")
 )
 ```
@@ -601,7 +572,7 @@ As with the rest of the functions in this package, there is also a `grouped_`
 variant of this function to facilitate looping the same operation for all levels
 of a single grouping variable.
 
-```{r ggdotplotstats2, fig.height = 8, fig.width = 12}
+```{r ggdotplotstats2, fig.height = 6, fig.width = 12}
 # for reproducibility
 set.seed(123)
 
@@ -660,13 +631,13 @@ The available marginal distributions are-
 
 Number of other arguments can be specified to modify this basic plot-
 
-```{r ggscatterstats2, fig.width=8}
+```{r ggscatterstats2, fig.width=10}
 # for reproducibility
 set.seed(123)
 
 # plot
 ggscatterstats(
-  data = dplyr::filter(.data = movies_long, genre == "Action"),
+  data = dplyr::filter(movies_long, genre == "Action"),
   x = budget,
   y = rating,
   type = "robust", # type of test that needs to be run
@@ -691,16 +662,13 @@ note that, as opposed to the other functions, this function does not return a
 using `ggplot.component` argument (available for all functions, but especially
 useful here):
 
-```{r ggscatterstats3, fig.height = 12, fig.width = 14}
+```{r ggscatterstats3, fig.height = 6, fig.width = 14}
 # for reproducibility
 set.seed(123)
 
 # plot
 grouped_ggscatterstats(
-  data = dplyr::filter(
-    .data = movies_long,
-    genre %in% c("Action", "Action Comedy", "Action Drama", "Comedy")
-  ),
+  data = dplyr::filter(movies_long, genre %in% c("Action", "Comedy")),
   x = rating,
   y = length,
   grouping.var = genre, # grouping variable
@@ -711,8 +679,8 @@ grouped_ggscatterstats(
   ggplot.component = list(
     ggplot2::scale_x_continuous(breaks = seq(2, 9, 1), limits = (c(2, 9)))
   ),
-  plotgrid.args = list(nrow = 2),
-  annotation.args = list(title = "Relationship between movie length by IMDB ratings for different genres")
+  plotgrid.args = list(nrow = 1),
+  annotation.args = list(title = "Relationship between movie length and IMDB ratings")
 )
 ```
 
@@ -765,16 +733,13 @@ minimum, median, and maximum number of pairs used for correlation tests.
 There is also a `grouped_` variant of this function that makes it
 easy to repeat the same operation across a **single** grouping variable:
 
-```{r ggcorrmat2, fig.height = 10, fig.width = 10}
+```{r ggcorrmat2, fig.height = 6, fig.width = 10}
 # for reproducibility
 set.seed(123)
 
 # plot
 grouped_ggcorrmat(
-  data = dplyr::filter(
-    .data = movies_long,
-    genre %in% c("Action", "Action Comedy", "Action Drama", "Comedy")
-  ),
+  data = dplyr::filter(movies_long, genre %in% c("Action", "Comedy")),
   type = "robust", # correlation method
   colors = c("#cbac43", "white", "#550000"),
   grouping.var = genre, # grouping variable
@@ -844,6 +809,8 @@ ggpiestats(
   data = mtcars,
   x = am,
   y = cyl,
+  package = "wesanderson",
+  palette = "Royal1",
   title = "Dataset: Motor Trend Car Road Tests", # title for the plot
   legend.title = "Transmission", # title for the legend
   caption = substitute(paste(italic("Source"), ": 1974 Motor Trend US magazine"))
@@ -859,55 +826,23 @@ ggpiestats(
 `r emo::ji("check")` Bayesian hypothesis-testing <br>
 `r emo::ji("check")` Bayesian estimation <br>
 
-In case of repeated measures designs, setting `paired = TRUE` will produce
-results from McNemar's chi-squared test-
-
-```{r ggpiestats2, fig.height=4}
-# for reproducibility
-set.seed(123)
-
-# data
-df_paired <-
-  data.frame(
-    "before" = c("Approve", "Approve", "Disapprove", "Disapprove"),
-    "after" = c("Approve", "Disapprove", "Approve", "Disapprove"),
-    counts = c(794, 150, 86, 570),
-    check.names = FALSE
-  )
-
-# plot
-ggpiestats(
-  data = df_paired,
-  x = before,
-  y = after,
-  counts = counts,
-  title = "Survey results before and after the intervention",
-  label = "both",
-  paired = TRUE, # within-subjects design
-  package = "wesanderson",
-  palette = "Royal1"
-)
-```
-
-Additionally, there is also a `grouped_` variant of this function that makes it
+There is also a `grouped_` variant of this function that makes it
 easy to repeat the same operation across a **single** grouping variable.
 Following example is a case where the theoretical question is about proportions
 for different levels of a single nominal variable:
 
-```{r ggpiestats3, fig.height = 10, fig.width = 10}
+```{r ggpiestats2, fig.height = 6, fig.width = 10}
 # for reproducibility
 set.seed(123)
 
 # plot
 grouped_ggpiestats(
-  data = movies_long,
-  x = genre,
-  grouping.var = mpaa, # grouping variable
+  data = mtcars,
+  x = cyl,
+  grouping.var = am, # grouping variable
   label.repel = TRUE, # repel labels (helpful for overlapping labels)
   package = "ggsci", # package from which color palette is to be taken
-  palette = "default_ucscgb", # choosing a different color palette
-  annotation.args = list(title = "Composition of MPAA ratings for different genres"),
-  plotgrid.args = list(nrow = 2)
+  palette = "default_ucscgb" # choosing a different color palette
 )
 ```
 
@@ -972,33 +907,20 @@ ggbarstats(
 
 And, needless to say, there is also a `grouped_` variant of this function-
 
-```{r ggbarstats2, fig.height = 12, fig.width = 10}
+```{r ggbarstats2, fig.height = 6, fig.width = 12}
 # setup
 set.seed(123)
 
-# smaller dataset
-df <-
-  dplyr::filter(
-    .data = forcats::gss_cat,
-    race %in% c("Black", "White"),
-    relig %in% c("Protestant", "Catholic", "None"),
-    !partyid %in% c("No answer", "Don't know", "Other party")
-  )
-
 # plot
 grouped_ggbarstats(
-  data = df,
-  x = relig,
-  y = partyid,
-  grouping.var = race,
-  label = "both",
-  xlab = "Party affiliation",
+  data = mtcars,
+  x = am,
+  y = cyl,
+  grouping.var = vs,
   package = "wesanderson",
   palette = "Darjeeling2",
   ggtheme = ggthemes::theme_tufte(base_size = 12),
-  ggstatsplot.layer = FALSE,
-  annotation.args = list(title = "Race, religion, and political affiliation"),
-  plotgrid.args = list(nrow = 2)
+  ggstatsplot.layer = FALSE
 )
 ```
 
@@ -1241,8 +1163,7 @@ the following packages that manage different aspects of statistical analyses:
 ## `statsExpressions`
 
 The `statsExpressions` package forms the statistical backend that processes data
-and creates expressions containing results from statistical tests and are by
-default displayed in as plot **subtitle** and **caption**.
+and creates expressions containing results from statistical tests.
 
 For more exhaustive documentation for this package, see:
 <https://indrajeetpatil.github.io/statsExpressions/>
@@ -1258,8 +1179,8 @@ For more exhaustive documentation for this package, see:
 
 ## `ipmisc`
 
-The `ipmisc` package contains some of the data wrangling/cleaning functions and
-a few other miscellaneous functions.
+The `ipmisc` package contains the data wrangling/cleaning functions and a few
+other miscellaneous functions.
 
 For more exhaustive documentation for this package, see:
 <https://indrajeetpatil.github.io/ipmisc/>
@@ -1282,7 +1203,7 @@ package.
 
 The hexsticker was generously designed by Sarah Otterstetter (Max Planck
 Institute for Human Development, Berlin). This package has also benefited from
-the larger `rstats` community on Twitter and StackOverflow.
+the larger `rstats` community on Twitter and `StackOverflow`.
 
 Thanks are also due to my postdoc advisers (Mina Cikara and Fiery Cushman at
 Harvard University; Iyad Rahwan at Max Planck Institute for Human Development)