Know your limits

_{Photo by Jessica Ruscello on Unsplash}

I recently went down a bit of an educational rabbit 🐇 hole with ggplot is setting axis limits. So I thought I would share some insights I gathered along the way.

ylim

It all started with a simple barplot 📊 using ggplot2. Typically, I prefer to filterthe data before plotting, avoiding axis limits. However, on this occasion, I wanted to focus in on the differences between values that were relatively small compared to the overall bars. Queue example data 👇

library(tidyverse)

data_tbl <- tibble(sample = c("SampleA", 
                              "SampleB", 
                              "SampleC", 
                              "SampleD", 
                              "SampleE"),
                   value = c(20, 
                             20.5, 
                             22,
                             19.8,
                             21))

ggplot(data = data_tbl, aes(x = sample, y = value)) +
  geom_bar(stat = "identity") +
  theme_bw()

Naturally I went for the generically name ylim() ggplot2 function to try and focus in more on the “tops” of the bars.

ggplot(data = data_tbl, aes(x = sample, y = value)) +
  geom_bar(stat = "identity") +
  theme_bw() +
  ylim(15, 25)

And boom! 💥 Everything disappeared. Initially, I was puzzled 🤔 Where did my bars go? After some digging, I realised that ylim() was not the function I was looking for.

The ylim() function removes 🧹 any data outside the specified range. Since I chose a barplot, the bars extend from 0 on the y-axis to the sample value, resulting in them being removed. If I had used geom_point, everything would have been fine:

ggplot(data = data_tbl, aes(x = sample, y = value)) +
  geom_point() +
  theme_bw() +
  ylim(15, 25)

coord_cartesian()

So, how do I achieve the desired outcome with geom_bar? The answer is to zoom rather than limit the data, and that’s where coord_cartesian() comes in:

ggplot(data = data_tbl, aes(x = sample, y = value)) +
  geom_bar(stat = "identity") +
  theme_bw() +
  coord_cartesian(ylim=c(15,25))

Using coord_cartesian, all the underlying data is retained, but the plot is zoomed in as if using a magnifying glass. 🔍 ✅

Now, you might think this is only relevant for bar plots. However, let’s explore why it’s important even for scatter plots with geom_point.

Consider data with a strong linear correlation. What happens when we limit the x-axis? Instead of just showing a zoomed-in focus, the linear regression is recalculated with only the data in view, resulting in a misleading correlation:

library(palmerpenguins)
data(penguins)

ggplot(data = penguins, aes(x = flipper_length_mm, y = body_mass_g)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE, color = "gray50") +
  theme_bw()

`geom_smooth()` using formula = 'y ~ x'

ggplot(data = penguins, aes(x = flipper_length_mm, y = body_mass_g)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE, color = "gray50") +
  theme_bw() +
  xlim(170, 200)

`geom_smooth()` using formula = 'y ~ x'

You can see that depending on the context this approach could be problematic.

Or, let’s say you’re inspecting time series data, such as growth over time:

growth_tbl <- tibble(days = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15, 20, 30, 40, 50),
                      weight = c(2, 5, 4, 7, 10, 13, 17, 17, 15, 13, 20, 25, 45, 90, 200, 500, 2000))

ggplot(data = growth_tbl, aes(x = days, y = weight)) +
  geom_point() +
  geom_line() + 
  theme_bw()

And you wanted to focus in on early growth. If you used xlim() instead of coor_cartesian(), you will notice below that the last piece of informative data, that the growth recovers and continues to increase, is lost.

xlim()

ggplot(data = growth_tbl, aes(x = days, y = weight)) +
  geom_point() +
  geom_line() +
  xlim(0,10) +
  ylim(0,20) +
  theme_bw()

coor_cartesian()

ggplot(data = growth_tbl, aes(x = days, y = weight)) +
  geom_point() +
  geom_line() +
  coord_cartesian(xlim=c(0,10), ylim=c(0,20)) +
  theme_bw()

While most statistics are computed before plotting, and therefore this may not drastically change data interpretation, using coord_cartesian() ensures you’re zooming in rather than trimming data.

scales

📢 Shoutout to June Choe for highlighting this, even coord_cartesian() might not be the function you want.

Instead, consider defining your x or y limits within the scale_*() functions and using the scales package to handle out-of-bounds (OOB) values. This approach makes your logic clearer to others using your code, or even to yourself when revisiting your script months later. June has a video explaining this a little more 🎥

Let’s revisit our barplot example. By specifying the y-axis limits to be between 15 and 25 and using scales::oob_keep(), we maintain values outside these limits for the background dataset and other statistical calculations. This effectively zooms into the data within our specified limits without removing out-of-bounds values.

ggplot(data = data_tbl, aes(x = sample, y = value)) +
  geom_bar(stat = "identity") +
  theme_bw() +
  scale_y_continuous(limits = c(15,25), oob = scales::oob_keep)

Maybe you have already been through this journey and are an axis limiting champion. Hopefully though this post helps someone. If you have any suggestions or additions, please share them!