Creating Percent Stacked Shapes with ggplot: A Deep Dive

Introduction

In recent years, the popularity of data visualization tools like ggplot2 has grown significantly. One of the key features that make ggplot2 stand out is its ability to create complex and informative plots with ease. In this article, we’ll explore one such feature – creating percent stacked shapes using ggplot2’s geom_rect() layer.

Problem Statement

Many users have asked if it’s possible to create a percent stacked plot instead of a traditional bar chart. The answer lies in the use of custom shapes and clever manipulation of data. In this article, we’ll show you how to achieve this using ggplot2.

Background

The geom_rect() layer is used to create rectangular shapes on a plot. By default, it’s used for creating plots with continuous x-values, but we can also use it to create custom shapes. The magic happens when we combine this layer with the position="fill" argument and stat="identity".

Solution

To achieve percent stacked shapes, we need to manipulate our data using the dplyr package. We’ll group by the shape’s coordinates (x1, y1, x2, y2) and the separation variable (t). This will allow us to calculate the percentage of each shape for a given row.

library(ggplot2)
library(dplyr)

df %>%
  group_by(x1, y1, x2, y2, t) %>%
  count() %>%
  ungroup() %>%
  group_by(x1, y1) %>%
  mutate(
    perc = n / sum(n),
    percLabel = paste(100 * perc, "%", sep = ""),
    transf = abs(x2 - x1) * perc,
    newx1 = ifelse(t == "a", x1, x2 - transf),
    newx2 = ifelse(t == "a", x1 + transf, x2)
  )

In the code above, we’re using group_by() to group our data by the shape’s coordinates and separation variable. We then use count() to calculate the number of occurrences for each group.

Next, we ungroup() to combine all groups back into one dataframe. Finally, we use mutate() to create new columns that will be used in our ggplot2 plot. These columns include the percentage (perc), percentage label (percLabel), transformation value (transf), and new x-coordinates (newx1, newx2).

Plotting with ggplot2

Now that we have our transformed data, it’s time to create the plot! We’ll use ggplot() to create a new plot and add our custom shapes.

ggplot(df, aes(xmin = newx1, xmax = newx2, ymin = y1, ymax = y2, fill = t)) +
  geom_rect(col = "black") +
  geom_text(aes(x = newx1 + ((newx2 - newx1) / 2), 
                y = y1 + ((y2 - y1) / 2), 
                label = percLabel)) +
  labs(x = "X", y = "Y")

In the code above, we’re using ggplot() to create a new plot. We add our custom shapes with geom_rect() and labels with geom_text(). Finally, we add labels to our x and y axes.

Conclusion

Creating percent stacked shapes with ggplot2 is easier than you think! By manipulating your data using the dplyr package and cleverly combining it with ggplot2’s features, you can create complex and informative plots. Remember to experiment with different arguments and options to get the desired result for your own projects.

Additional Tips

• When working with percent stacked shapes, make sure to use position="fill" in your geom_rect() layer to ensure that each shape is correctly positioned.
• Experiment with different shapes and colors to create visually appealing plots.

Last modified on 2023-10-09