Introduction

The stat_frequency function in the ggplot2 package allows users to create informative and visually appealing plots of categorical data. In this article, we’ll explore how to use the stat_frequency function with ggplot2 to add labels to error bars in a plot. The example will demonstrate how to count occurrences of each X/color group in the data.

Background

In the provided Stack Overflow question, there is an issue when adding labels to error bars. To resolve this problem, we’ll look into ways of calculating counts and creating new data frames to display these values.

Calculating Counts with Base table

Firstly, let’s discuss how one can use base R’s table function to get the count of each unique value in a column or color group.

myCounts <- table(data[[var]], data[[color]])

However, this approach may not be the best choice as it assumes there is only one unique value within a given X/color grouping. For multiple unique values, we should use apply to get all of the unique values.

Standard Evaluation with Tidyverse

One possible solution when dealing with multiple color groups and many variables can be achieved by utilizing the tidyverse and its tidyverse package for standard evaluation.

getPlotList <- function(param.list, data=db, y, color){
  param.list %>% sapply(function(var){

    # Groupby data to create new data frame with counts of each unique value.
    ggplot(data=data, aes_string(x=var, y=y, color=color))+
      stat_summary(fun.data = mean_cl_normal, position = position_dodge(0.3), na.rm = TRUE) +
      stat_summary(fun.y = mean, geom = "point", position = position_dodge(0.3), na.rm = TRUE) +
      ylim(0, NA) +
      # Create new data frame to display counts of each unique value.
      geom_text(aes_string(x = var, y = "y", label = "label", color = NA)
                , data %>%
                  count(!!as.name(var), !!as.name(color)) %>%
                  group_by(!!as.name(var)) %>%
                  summarise(
                    label = paste("n =", paste(unique(n), collapse = ";"))
                  ) %>%
                  mutate(y = 0.5)
                , show.legend = FALSE)

  }, simplify = FALSE, USE.NAMES = TRUE)
}

In the example above, count(!!as.name(var), !!as.name(color)) calculates the counts of each unique value within a given X/color grouping and groups by variable to create new data frame for plotting.

Conclusion

This solution provides a flexible way to count occurrences of each X/color group in your data. It also shows how you can use tidyverse functions to standardize the code, which can improve readability.

Last modified on 2024-11-24