When working with ggplot2
in R, you may encounter the error ‘ggplot2 doesn’t know how to deal with data of class character’. This error typically occurs when trying to pass character data to a function that expects a different data type, such as numeric or factor. In this post, we will go through the causes of the error and how to solve it with easy-to-follow steps.
Cause of the Error
In R, data comes in different types or ‘classes’ like numeric, factor, and character. The data visualisation ggplot2
package, expects certain classes for various types of plots. When you try to map a character variable (such as a string) to an axis, ggplot2
doesn’t know how to interpret it as it expects numerical or categorical (factor) data for plotting.
The error often appears when:
- You map a character variable to an aesthetic (such as
x
,y
,color
, orsize
). - You’re trying to use a function or plot type that requires numeric or factor data but receive character data instead.
Example: Reproducing the Error with color Aesthetic
Let’s look at a simple example to reproduce this error. Suppose you have a data frame with a character column. If you try to map the category column to the colour aesthetic in a scatter plot you may throw the error.
# Create a data frame with numeric and character columns df1 <- data.frame( category = c("Group1", "Group2", "Group3", "Group4"), x = c(5, 10, 15, 20), y = c(3, 6, 9, 12) ) # Attempt to map the 'category' character column to the color aesthetic ggplot(df1, aes(x = x, y = y, color = category)) + geom_point()
You may not get the error in the most recent of ggplot2
, but in old versions, this would result in Error: ggplot2 doesn't know how to deal with data of class character
.
Solution: Convert Character Data to Factor
To resolve this issue, convert the category column to a factor:
# Convert the 'category' column to a factor df1$category <- as.factor(df1$category) # Create the plot again ggplot(df1, aes(x = x, y = y, color = category)) + geom_point()
Now that category has been converted to a factor, ggplot2
can properly map colours based on the categorical variable.
Example 2: Reproducing the Error with geom_line()
Let’s look at a simple example that will produce this error when trying to create a line plot.
# Create a data frame with character and numeric data df <- data.frame( category = c("A", "B", "C", "D"), value = c(10, 15, 20, 25) ) # Attempt to create a line plot (which expects a numeric x-axis) ggplot(df, aes(x = category, y = value)) + geom_line()
When you run this code, you will encounter the error:
Error: `geom_line()` requires the following missing aesthetics: x
And the warning:
`geom_line()`: Each group consists of only one observation. ℹ Do you need to adjust the group aesthetic?
This error occurs because geom_line()
expects both x
and y
to be numeric. However, category
is of class character, and since a line plot requires continuous numeric data for the x
aesthetic, it doesn’t know how to handle the character data.
Solution: Convert Character Data to Factor
To fix this, convert the category column from character to factor:
# Convert the 'category' column to a factor df$category <- as.factor(df$category) # Create the plot again ggplot(df, aes(x = category, y = value)) + geom_line()
Handling the “Each group consists of only one observation” Warning
After converting category to a factor, you might still encounter the following warning:
`geom_line()`: Each group consists of only one observation. ℹ Do you need to adjust the group aesthetic?
This happens because geom_line()
is trying to connect points for each factor level individually, but since there’s only one observation per factor, it can’t draw a line.
To fix this issue, you need to explicitly define a group aesthetic. In this case, you can group all the observations by adding group = 1
to the aesthetics, like this:
# Convert the 'category' column to a factor df$category <- as.factor(df$category) # Create the plot again and add group = 1 ggplot(df, aes(x = category, y = value, group = 1)) + geom_line()
Why This Works:
- The
group = 1
aesthetic tellsggplot2
to treat all points as part of the same group, allowinggeom_line()
to draw a single line connecting all the points.
This adjustment removes the warning and ensures the line plot works as expected.
General Fix: Check Data Types Before Plotting
Before you start plotting, it’s good practice to check the class of your data to ensure you’re using the appropriate types for your plot.
# Check the class of the columns in your data str(df2)
This will help you identify if any columns are being treated as characters when they should be factors or numerics.
Common Use Cases
- Bar Plots with Character Data: When creating bar plots, it’s fine to use characters as categorical variables, but they should be converted to factors.
- Scatter or Line Plots: These types of plots require numeric values for the x-axis and y-axis. If you have a character class in your data, convert it to numeric if applicable, or change the plot type if the data represents categories.
Final Thoughts
The “ggplot2 doesn’t know how to deal with data of class character” error occurs when ggplot2
is given character data for a plot type that expects numeric or factor data. By converting character data to factors or using an appropriate plot type for categorical data, you can easily resolve this error and produce the desired visualization.
Next time you encounter this error, check your data types and choose the correct plot type for your data. This quick adjustment will allow you to avoid the error and plot effectively with ggplot2
.
For further reading on R-related errors, go to the articles:
- How to Solve R Error as.Date.numeric(x) : ‘origin’ must be supplied
- How to Solve R Error: Incorrect number of dimensions
- How to Solve R Error: ggplot2 doesn’t know how to deal with data of class matrix
- How to Solve R Error: StatBin requires a continuous x variable: the x variable is discrete. Perhaps you want stat=”count”?
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!
Suf is a senior advisor in data science with deep expertise in Natural Language Processing, Complex Networks, and Anomaly Detection. Formerly a postdoctoral research fellow, he applied advanced physics techniques to tackle real-world, data-heavy industry challenges. Before that, he was a particle physicist at the ATLAS Experiment of the Large Hadron Collider. Now, he’s focused on bringing more fun and curiosity to the world of science and research online.