This error occurs when you try to pass a 1-dimensional vector to the
colSums function, which expects a 2-dimensional input. If we want to subset a data frame column, we can use the drop argument to preserve the data frame object. For example,
df <- data.frame(x1 = rnorm(10), x2 = rnorm(10), x3 = rnorm(10)) colSums(df[,1, drop=FALSE])
This tutorial will go through the error and how to solve it with code examples.
Let’s look at an example to reproduce the error:
# Create a data frame df <- data.frame(x = sample.int(100, 10), y = sample.int(50, 10), z = sample.int(200,10)) df
x y z 1 56 39 24 2 17 32 166 3 26 15 87 4 12 35 189 5 23 3 156 6 59 33 144 7 33 17 170 8 44 47 157 9 67 6 126 10 64 49 75
Next, we will attempt to sum up the values in the first column using the
first_col_sum <- colSums(df[, 1])
Let’s run the code to see what happens:
Error in colSums(df[, 1]) : 'x' must be an array of at least two dimensions
The error occurs because
df[, 1] is a 1-dimensional vector:
 56 17 26 12 23 59 33 44 67 64
However, the colSums function expects an array of two or more dimensions.
We can solve the error by subsetting the column with the
drop argument set to
FALSE. By specifying
FALSE we ensure that R does not convert the column to a vector object.
Let’s look at the revised code:
# Extract column from data frame col <- df[, 1, drop = FALSE] # Check object is 2-dimensional dim(col) # Check object is a data frame is.data.frame(col)
 10 1  TRUE
Let’s use the
colSums function to get the sum of the column values:
# Attempt to get the sum of the column first_col_sum <- colSums(col) first_col_sum
Congratulations on reading to the end of this tutorial!
For further reading on R-related errors, go to the articles:
- How to Solve R Error: $ operator is invalid for atomic vectors
- How to Solve R Warning: longer object length is not a multiple of shorter object length
- How to Solve R Error: Subscript out of bounds
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 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!