# How to Solve R Error in colSums – ‘x’ must be an array of at least two dimensions

by | Programming, R, Tips

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.

## Example

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 `colSums` function:

`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:

`df[,1]`
`  56 17 26 12 23 59 33 44 67 64`

However, the colSums function expects an array of two or more dimensions.

### Solution

We can solve the error by subsetting the column with the `drop` argument set to `FALSE`. By specifying `drop` as `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```
`401`

## 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!

##### Suf
Research Scientist at | + 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!