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How to Solve R Error in data.frame undefined columns selected

by | Programming, R, Tips

If you try to subset a data frame without using a comma, you will raise the error: undefined columns selected. The syntax for subsetting a data frame is:

dataframe[rows_to_subset, columns_to_subset]

To solve this error, you need to use a comma after the rows you want to subset, even if you want rows from all columns. For example,

data[data$col1>5, col1] ,

selects rows in column 1 with values greater than 5.

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

Example: Error in data.frame undefined columns selected

Let’s look at an example with a data frame with two variables.

dat <- data.frame(x = c(0, 1, 2, 3, 4, 5),
y = c(11, 2, 5, 7, 9, 3))

  x  y
1 0 11
2 1  2
3 2  5
4 3  7
5 4  9
6 5  3

Let’s try to select the rows in column y that are greater than 5:

Error in `[.data.frame`(dat, dat$y > 5) : undefined columns selected

R raises the error because we did not use a comma after the row subset expression to inform R which columns we want to select.

Solution: Use a comma for the row and column expressions

We need to add a comma after the row subset expression to solve this error. Let’s look at the revised code:

dat[dat$y>5, "y"]

Note that we have to put the column name in quotes. Let’s run the code to see the result:

[1] 11  7  9

If we want to return values from all columns, we can leave the space after the comma blank.

dat[dat$y>5, ]
  x  y
1 0 11
4 3  7
5 4  9

If we know the total number of columns, we can use an equivalent command dat[dat$y>5, 1:2]. Let’s look at the revised code:

dat[dat$y>5, 1:2]
  x  y
1 0 11
4 3  7
5 4  9

We successfully retrieved the rows where at least one of the values is greater than five.


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!