Solving the R error: ggplot2 doesn’t know how to deal with data of class list

A ggplot2 doesn’t know how to deal with data of class list error is often people’s first introduction to the ggplot2 visualization library. This is in part because of how simple R typically makes most operations related to data science. You can usually move different data types into different libraries without needing to change their formatting around too much. But ggplot2 does have some strict requirements for some of its input data. And fixing this error typically just requires editing the R code to take those requirements into account.

Understanding How ggplot2 Manipulates Information

The core of the error typically comes down to the fact that the base R datatypes often have a lot in common with each other. The various types are often so similar to each other that it can be easy to confuse them. And this is the most common reason for the class list error when using the ggplot2 package. You might have the right metrics, but within the wrong type of container.

But to understand why it’s also important to understand some of ggplot2’s functionality. Ggplot2 is a complex package with a wide variety of advanced functionality. But the heart of the package comes from how it can visually represent different informational points. For example, you could provide a collection of different data points which every variable representing a single measurement. The R package can then be used to visually plot out your results. And each metric can be fit into the larger context of a variety of data analysis subjects.

Likewise, it’s easy to change the output to fit your particular needs. You can create a scatter point graph from each geom point or create a bar chart or any other form of data visualization that fits your needs. You can even take the result of a scatter plot or other presentation and modify the organization into the most appealing form. All with little to no extra effort. You can create some amazing results with a single function call. Or even by simply changing the arguments within an already present function. For example, using added color or geom smooth to automatically add a trend line over your ggplot2 plot. In a similar way, it’s easy to make a presentation more readable with axis labels or reading data from a categorical variable. You can even embed it within R markdown documents to create dynamic reports.

Automation and Formatting of Metrics

Ggplot2’s level of automation is impressive, but it does have a few caveats. And this ties into the most common source of the error. Automatically manipulating data typically requires said information to be properly formatted in a way the system can understand. And in the case of ggplot2, that means a data frame.

The “class list” error stems from the use of a list container instead of a data set. The two containers are extremely similar in many respects. In terms of this specific error, the larger issue is a list’s extra flexibility. For example, elements in a list don’t have to have equal lengths or exist as vectors. Generally, the more predictable a structure’s contents are the easier it is for automated processes to work with it. But the reason for it aside, there’s just a simple fact that a ggplot2 function typically expects a data frame rather than a list. Similar errors often occur when other factors interfere with predictability. For example, providing information to the library out of proper order could create a class uneval error.

How To Fix the Error

The most self-apparent way to fix the error is to simply modify your list and turn it into a data frame. However, as previously noted there are a lot of similarities between the two containers. In fact, there are enough similarities that you can easily convert the lists causing the initial error into data frames by leveraging R’s built-in functionality.


ourList <- list(a = 1:10, b = 11:20)
ourDataFrame <-
ggplot(ourDataFrame, aes(x=a, y=b)) + geom_point()

This example begins by importing ggplot2 before moving on to create a list of values ranging from 1 to 20 in two separate categories. Next, we take that ourList and convert it into ourDataFrame. The new frame is then sent to ggplot2. The aes and geom_point functions are used to create a styled scatter point graph of the items in ourDataFrame. Which, of course, originated from the ourList list.

Of course, the exact procedure used will vary depending on where your list originates. You might even want to create a separate function to automatically handle any list conversions based on common elements found within the material you’re working with. But the previous example outlines the overall principle needed to get around the class error with ggplot2. It simply comes down to ensuring you’re passing information in the correct format.

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