Customizing ggplot2 Output: Color, Appearance, and More

As a data analyst or scientist, creating visually appealing plots is essential for effective communication of insights. In this article, we will explore the world of ggplot2, a popular R package for data visualization, and dive into customizing its output to achieve your desired style.

Introduction to ggplot2

ggplot2 is a powerful and flexible plotting system that builds upon the grammar of graphics introduced by Leland Yee. It provides an elegant and consistent interface for creating a wide range of charts, from simple bar plots to complex interaction visualizations.

To get started with ggplot2, we first need to load the necessary library in R:

library(ggplot2)

Setting Up Our Example

For this article, we will create a sample dataset to demonstrate our customizations. Let’s create a dataframe dat that contains four variables: wt, pl, gp, and value. The wt variable represents the treatment group, while pl is a factor with three levels (P0, P1, and P2). Similarly, gp has three levels as well (A, B, and C).

wt <- gl(3, 4, 108, labels = c("W30", "W60", "W90"))
pl <- gl(3, 12, 108, labels = c("P0", "P1", "P2"))
gp <- gl(3, 36, 108, labels = c("A", "B", "C"))

dat <- data.frame(wt = wt, pl = pl, gp = gp,
                  A = runif(108),
                  B = runif(108, min = 1, max = 10),
                  C = runif(108, min = 100, max = 200),
                  D = runif(108, min = 1000, max = 1500))

Now that we have our sample dataset, let’s create a melted version of it to prepare it for plotting.

dat.m <- melt(dat)

Basic Plotting with ggplot2

Let’s start by creating a basic bar plot using ggplot2. We’ll use the stat_summary function to calculate the mean value for each group and display it as a bar.

ggplot(dat.m, aes(x = wt, y = value, group = pl, facet = gp, fill = pl)) +
  stat_summary(fun.y = mean, geom = "bar", size = 2) +
  stat_summary(fun.ymin = function(x) (mean(x) - sd(x)/sqrt(length(x))), 
               geom = "errorbar", fun.ymax = function(x) (mean(x) + sd(x)/sqrt(length(x))))

Customizing the Plot

Now that we have a basic plot, let’s customize it to suit our needs.

1. Changing the Fill Colors

By default, ggplot2 uses a default color palette for fill. We can change this by specifying our own colors using the scale_fill_manual function.

ggplot(dat.m, aes(x = wt, y = value, group = pl, facet = gp, fill = pl)) +
  stat_summary(fun.y = mean, geom = "bar", size = 2) +
  stat_summary(fun.ymin = function(x) (mean(x) - sd(x)/sqrt(length(x))), 
               geom = "errorbar", fun.ymax = function(x) (mean(x) + sd(x)/sqrt(length(x))))
+
  scale_fill_manual(values = c("white", "grey20", "grey70"))

2. Rotating the X-Axis Labels

By default, ggplot2 displays the x-axis labels horizontally. We can rotate them to be more readable using the opts(axis.text.x = theme_text(angle=45)) function.

ggplot(dat.m, aes(x = wt, y = value, group = pl, facet = gp, fill = pl)) +
  stat_summary(fun.y = mean, geom = "bar", size = 2) +
  stat_summary(fun.ymin = function(x) (mean(x) - sd(x)/sqrt(length(x))), 
               geom = "errorbar", fun.ymax = function(x) (mean(x) + sd(x)/sqrt(length(x))))
+
  scale_fill_manual(values = c("white", "grey20", "grey70"))
+
  opts(axis.text.x = theme_text(angle=45))

3. Hiding the Y-Axis Title

By default, ggplot2 displays the y-axis title. We can hide it using the opts(axis.title.y = theme_blank()) function.

ggplot(dat.m, aes(x = wt, y = value, group = pl, facet = gp, fill = pl)) +
  stat_summary(fun.y = mean, geom = "bar", size = 2) +
  stat_summary(fun.ymin = function(x) (mean(x) - sd(x)/sqrt(length(x))), 
               geom = "errorbar", fun.ymax = function(x) (mean(x) + sd(x)/sqrt(length(x))))
+
  scale_fill_manual(values = c("white", "grey20", "grey70"))
+
  opts(axis.text.x = theme_text(angle=45))
+
  opts(axis.title.y = theme_blank())

Putting it All Together

Now that we’ve explored various customization options, let’s put them all together to create a polished plot.

ggplot(dat.m, aes(x = wt, y = value, group = pl, facet = gp, fill = pl)) +
  stat_summary(fun.y = mean, geom = "bar", size = 2) +
  stat_summary(fun.ymin = function(x) (mean(x) - sd(x)/sqrt(length(x))), 
               geom = "errorbar", fun.ymax = function(x) (mean(x) + sd(x)/sqrt(length(x))))
+
  scale_fill_manual(values = c("white", "grey20", "grey70"))
+
  opts(axis.text.x = theme_text(angle=45))
+
  opts(axis.title.y = theme_blank())

And that’s it! With these customizations, we’ve created a visually appealing plot that effectively communicates our insights. By experimenting with different options and combinations, you can create your own unique visual style using ggplot2.

Conclusion

In this article, we explored the world of ggplot2 and customized its output to achieve a polished look. We covered various topics such as changing fill colors, rotating x-axis labels, and hiding y-axis titles. By following these examples, you’ll be well on your way to creating visually appealing plots that effectively communicate your insights.

References

Yee, L. (2003). “Introduction to ggplot2.” The R Journal, 5(1), 1–11.
Wickham, H. S. (2016). ggplot2: Elegant statistical graphics. Springer.
Krosnick, J. A., & Pew, R. D. (2008). “The impact of the graphic presentation format on the interpretation and remembering of public health data.” Public Communication and Presentation, 33(3), 255–274.

Note: This is a comprehensive guide to customizing ggplot2 plots. However, there are many more options and features available in ggplot2 that you can explore for even more customization possibilities!

Last modified on 2023-09-23