Suggested improvement to Histograms (grouped and non grouped)

R/combine_plots.R

@@ -1,4 +1,3 @@
-#'
 #' @title Combining multiple plots using `cowplot::plot_grid()` with a
 #'   combination of title, caption, and annotation label
 #' @name combine_plots
@@ -65,10 +64,10 @@
 #' \url{https://cran.r-project.org/package=ggstatsplot/vignettes/combine_plots.html}
 #'
 #' @examples
-#' 
+#'
 #' # loading the necessary libraries
 #' library(ggplot2)
-#' 
+#'
 #' # preparing the first plot
 #' p1 <-
 #'   ggplot2::ggplot(
@@ -77,7 +76,7 @@
 #'   ) +
 #'   geom_point() +
 #'   labs(title = "setosa")
-#' 
+#'
 #' # preparing the second plot
 #' p2 <-
 #'   ggplot2::ggplot(
@@ -86,7 +85,7 @@
 #'   ) +
 #'   geom_point() +
 #'   labs(title = "versicolor")
-#' 
+#'
 #' # combining the plot with a title and a caption
 #' combine_plots(
 #'   p1,

R/data.R

@@ -1,4 +1,3 @@
-#'
 #' @title Movie information and user ratings from IMDB.com (wide format).
 #' @name movies_wide
 #' @details Modified dataset from `ggplot2movies` package.
@@ -34,8 +33,6 @@
 #' head(movies_wide)
 "movies_wide"
 
-
-#'
 #' @title Movie information and user ratings from IMDB.com (long format).
 #' @name movies_long
 #' @details Modified dataset from `ggplot2movies` package.
@@ -94,8 +91,6 @@
 #' head(Titanic_full)
 "Titanic_full"
 
-
-#'
 #' @title Moral judgments about third-party moral behavior.
 #' @name intent_morality
 #' @details This dataset contains data from a recent study about how people

R/ggcoefstats.R

@@ -1,4 +1,3 @@
-#'
 #' @title Model coefficients for fitted models with the model summary as a
 #'   caption.
 #' @name ggcoefstats
@@ -126,7 +125,7 @@
 #' \url{https://cran.r-project.org/package=ggstatsplot/vignettes/ggcoefstats.html}
 #'
 #' @examples
-#' 
+#'
 #' set.seed(123)
 #' ggcoefstats(x = lm(formula = mpg ~ cyl * am, data = mtcars))
 #' @export

R/ggcorrmat.R

@@ -1,4 +1,3 @@
-#'
 #' @title Visualization of a correlalogram (or correlation matrix)
 #' @name ggcorrmat
 #' @aliases ggcorrmat
@@ -125,7 +124,7 @@
 #' \url{https://cran.r-project.org/package=ggstatsplot/vignettes/ggcorrmat.html}
 #'
 #' @examples
-#' 
+#'
 #' # to get the correlalogram
 #' # note that the function will run even if the vector with variable names is
 #' # not of same length as the number of variables
@@ -134,14 +133,14 @@
 #'   cor.vars = sleep_total:bodywt,
 #'   cor.vars.names = c("total sleep", "REM sleep")
 #' )
-#' 
+#'
 #' # to get the correlation matrix
 #' ggstatsplot::ggcorrmat(
 #'   data = ggplot2::msleep,
 #'   cor.vars = sleep_total:bodywt,
 #'   output = "r"
 #' )
-#' 
+#'
 #' # setting output = "p-values" (or "p") will return the p-value matrix
 #' ggstatsplot::ggcorrmat(
 #'   data = ggplot2::msleep,
@@ -150,15 +149,15 @@
 #'   p.adjust.method = "fdr",
 #'   output = "p"
 #' )
-#' 
+#'
 #' # setting output = "ci" will return the confidence intervals for unique
 #' # correlation pairs
 #' ggstatsplot::ggcorrmat(
 #'   data = ggplot2::msleep,
 #'   cor.vars = sleep_total:bodywt,
 #'   output = "ci"
 #' )
-#' 
+#'
 #' # modifying few elements of the correlation matrix by changing function defaults
 #' ggstatsplot::ggcorrmat(
 #'   data = datasets::iris,

R/gghistostats.R

@@ -1,4 +1,3 @@
-#'
 #' @title Histogram for distribution of a numeric variable
 #' @name gghistostats
 #' @aliases gghistostats
@@ -190,6 +189,12 @@ gghistostats <-
     data %<>%
       tibble::as_data_frame(x = .)
 
+    # Adding some binwidth sanity checking
+        if (is.null(binwidth)) {
+          binwidth <- (max(data$x) - min(data$x))/sqrt(length(data$x))
+        }
+
+
     # ========================================== stats ==================================================================
 
     if (isTRUE(results.subtitle)) {

R/ggpiestats.R

@@ -1,4 +1,3 @@
-#'
 #' @title Pie charts with statistical tests
 #' @name ggpiestats
 #' @aliases ggpiestats

R/ggscatterstats.R

@@ -1,4 +1,3 @@
-#'
 #' @title Scatterplot with marginal distributions
 #' @name ggscatterstats
 #' @aliases ggscatterstats

R/grouped_ggbetweenstats.R

@@ -1,4 +1,3 @@
-#'
 #' @title Violin plots for group or condition comparisons in between-subjects
 #'   designs repeated across all levels of a grouping variable.
 #' @name grouped_ggbetweenstats

R/grouped_ggcorrmat.R

@@ -1,4 +1,3 @@
-#'
 #' @title Visualization of a correlalogram (or correlation matrix) for all
 #'   levels of a grouping variable
 #' @name grouped_ggcorrmat
@@ -38,7 +37,7 @@
 #'
 #'
 #' @examples
-#' 
+#'
 #' # for getting plot
 #' ggstatsplot::grouped_ggcorrmat(
 #'   data = ggplot2::msleep,
@@ -49,15 +48,15 @@
 #'   nrow = 2,
 #'   ncol = 2
 #' )
-#' 
+#'
 #' # for getting correlations
 #' ggstatsplot::grouped_ggcorrmat(
 #'   data = ggplot2::msleep,
 #'   grouping.var = vore,
 #'   cor.vars = sleep_total:bodywt,
 #'   output = "correlations"
 #' )
-#' 
+#'
 #' # for getting confidence intervals
 #' # if robust correlation is selected, confidence intervals will not be
 #' # available

R/grouped_gghistostats.R

@@ -1,4 +1,3 @@
-#'
 #' @title Grouped histograms for distribution of a numeric variable
 #' @name grouped_gghistostats
 #' @aliases grouped_gghistostats
@@ -88,7 +87,6 @@ grouped_gghistostats <- function(data,
                                  messages = TRUE,
                                  ...) {
   # ================== preparing dataframe ==================
-
   # getting the dataframe ready
   df <- dplyr::select(
     .data = data,
@@ -100,6 +98,24 @@ grouped_gghistostats <- function(data,
       title.text = !!rlang::enquo(grouping.var)
     )
 
+  binmax <- dplyr::select(
+    .data = data,
+    !!rlang::enquo(x)
+  ) %>% max
+
+  binmin <- dplyr::select(
+    .data = data,
+    !!rlang::enquo(x)
+  ) %>% min
+
+  bincount <- as.integer(data %>% dplyr::count())
+
+  # Adding some binwidth sanity checking
+  if (is.null(binwidth)) {
+    binwidth <- (binmax - binmin)/sqrt(bincount)
+  }
+
+
   # creating a nested dataframe
   df %<>%
     dplyr::mutate_if(

R/grouped_ggscatterstats.R

@@ -1,4 +1,3 @@
-#'
 #' @title Scatterplot with marginal distributions for all levels of a grouping
 #'   variable
 #' @name grouped_ggscatterstats
@@ -36,10 +35,10 @@
 #' @inherit ggscatterstats return details
 #'
 #' @examples
-#' 
+#'
 #' # to ensure reproducibility
 #' set.seed(123)
-#' 
+#'
 #' # basic function call
 #' ggstatsplot::grouped_ggscatterstats(
 #'   data = dplyr::filter(
@@ -53,7 +52,7 @@
 #'   formula = y ~ x + I(x^3),
 #'   grouping.var = genre
 #' )
-#' 
+#'
 #' # using labeling
 #' ggstatsplot::grouped_ggscatterstats(
 #'   data = dplyr::filter(ggplot2::mpg, cyl != 5),
@@ -66,7 +65,7 @@
 #'   label.expression = hwy > 25 & displ > 2.5,
 #'   messages = FALSE
 #' )
-#' 
+#'
 #' # labeling without expression
 #' ggstatsplot::grouped_ggscatterstats(
 #'   data = dplyr::filter(

R/helpers_effsize_ci.R

@@ -1,4 +1,3 @@
-#'
 #' @title A heteroscedastic one-way ANOVA for trimmed means with confidence
 #'   interval for effect size.
 #' @name t1way_ci
@@ -469,7 +468,6 @@ chisq_v_ci <- function(data,
 }
 
 
-#'
 #' @title Robust correlation coefficient and its confidence interval
 #' @name robcor_ci
 #' @description Custom function to get confidence intervals for percentage bend
@@ -620,7 +618,6 @@ robcor_ci <- function(data,
   return(results_df)
 }
 
-#'
 #' @title Confidence intervals for partial eta-squared and omega-squared for
 #'   linear models.
 #' @name lm_effsize_ci

R/theme_ggstatsplot.R

@@ -1,4 +1,3 @@
-#'
 #' @title Default theme used in all `ggstatsplot` package plots
 #' @name theme_ggstatsplot
 #' @author Indrajeet Patil
@@ -68,7 +67,6 @@ theme_ggstatsplot <- function(ggtheme = ggplot2::theme_bw(), ggstatsplot.layer =
   }
 }
 
-#'
 #' @title Default theme used in all `ggstatsplot` package plots
 #' @name theme_mprl
 #' @author Indrajeet Patil