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How to Solve R Error: continuous value supplied to discrete scale

by | Programming, R, Tips

This error occurs if you use a numeric variable for the fill, color, and shape aesthetics in ggplot when it expects a factor variable. You can solve this error by using a factor class variable as the grouping variable. You can convert numeric values to factor using the factor() function.

This tutorial will go through the error in detail and how to solve it with code examples.


Example

Consider a dataset consisting of 500 random values sampled from the normal distribution. Each of the samples falls into one of five catogories.

set.seed(0)
df <- data.frame(x = rnorm(500), category= 1:5)
head(df)

Let’s run the code to see the data frame:

   x category
1  1.2629543        1
2 -0.3262334        2
3  1.3297993        3
4  1.2724293        4
5  0.4146414        5
6 -1.5399500        1

Next, we will attempt to create five box plots to show the distribution of the values within the five data groups.

library("ggplot2")

ggplot(df, aes(x, group = category, fill = category)) +    
  geom_boxplot() + 
  scale_fill_manual(values = c("magenta", "steelblue",
                               "blue", "purple",
                               "yellow"))

Let’s run the code to see what happens:

Error: Continuous value supplied to discrete scale

The error occurs because the fill argument in the aes() function call expects a factor variable not numeric. We can verify that category is numeric using is.numeric():

is.numeric(df$category)
[1] TRUE

Solution: Use factor() To convert numerical values to factor

We can solve this error by converting the category variable to a factor using the built-in factor function. Let’s look at the revised code:

library("ggplot2")

ggplot(df, aes(x, group = category, fill = factor(category))) +    
  geom_boxplot() + 
  scale_fill_manual(values = c("magenta", "steelblue",
                               "blue", "purple",
                               "yellow"))

Let’s run the code to get the result:

Five category box-plot
Five category box-plot

We successfully plotted the five box plots.

Example #2: Default data set example mtcars and ggplot2

Let’s look at an example of plotting three variables from the mtcars dataset. We will attempt to plot miles-per-gallon (mpg) against weight (wt) with the number of cylinders (cyl) as the colour and shape of the points. We will also plot lines of best fit for the three cyl categories (4, 6, 8).

ggplot(mtcars, aes(x=wt, y=mpg, color=cyl, shape=cyl)) +
  geom_point() + 
  geom_smooth(method=lm, se=FALSE, fullrange=TRUE)+
  scale_shape_manual(values=c(3, 16, 17))+ 
  scale_color_manual(values=c('#999999','#E69F00', '#56B4E9'))+
  theme(legend.position="top")

Let’s run the code to see what happens:

Error: Continuous value supplied to discrete scale

The error occurs because the color and shape arguments in the aes() function call need to be factors not numeric.

Solution

We can solve this error by converting cyl from numeric to factor using the factor() function. Let’s look at the revised code:

ggplot(mtcars, aes(x=wt, y=mpg, color=factor(cyl), shape=factor(cyl))) +
  geom_point() + 
  geom_smooth(method=lm, se=FALSE, fullrange=TRUE)+
  scale_shape_manual(values=c(3, 16, 17))+ 
  scale_color_manual(values=c('#999999','#E69F00', '#56B4E9'))+
  theme(legend.position="top")

Let’s run the code to get the result:

Mtcars plot mpg vs wt vs cyl line of best fit
Mtcars plot mpg vs wt vs cyl

We successfully created the plot.

Summary

Congratulations on reading to the end of this tutorial!

For further reading on R related errors, go to the articles: 

Go to the online courses page on R to learn more about coding in R for data science and machine learning.

Have fun and happy researching!

Research Scientist at Moogsoft | + posts

Suf is a research scientist at Moogsoft, specializing in Natural Language Processing and Complex Networks. Previously he was a Postdoctoral Research Fellow in Data Science working on adaptations of cutting-edge physics analysis techniques to data-intensive problems in industry. In another life, he was an experimental particle physicist working on the ATLAS Experiment of the Large Hadron Collider. His passion is to share his experience as an academic moving into industry while continuing to pursue research. Find out more about the creator of the Research Scientist Pod here and sign up to the mailing list here!