A dataframe in R is a data structure used to store data in a tabular form – in rows and columns. In this tutorial, we will look at how to get the number of columns in an R dataframe with the help of some examples.

## How do I count the columns in a dataframe in R?

You can use the built-in `ncol()`

function to count the number of columns in a dataframe in R. Pass the dataframe as an argument.

The following is the syntax –

# number of columns in dataframe ncol(dataframe)

It returns the number of columns in the dataframe.

## Examples

Let’s now look at some examples of using the above syntax to count columns in a dataset in R.

### Example 1 – Count columns in an R dataframe

First, let’s create a dataframe that we will be operating on.

# create a dataframe employees_df = data.frame( "Name"= c("Jim", "Dwight", "Angela", "Tobi", "Kevin"), "Age"= c(26, 28, 29, 32, 30), "Department"= c("Sales", "Sales", "Accounting", "HR", "Accounting"), "Salary" = c(80000, 81000, 72000, 65000, 72000) ) # display the dataframe print(employees_df)

Output:

Name Age Department Salary 1 Jim 26 Sales 80000 2 Dwight 28 Sales 81000 3 Angela 29 Accounting 72000 4 Tobi 32 HR 65000 5 Kevin 30 Accounting 72000

We now have a dataframe containing information about some employees working in an office. The dataframe has columns “Name”, “Age”, “Department”, and “Salary”.

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We can see that the above dataframe has four columns. Let’s now try to get this column count programmatically in R.

To get the column count of the above dataframe, we pass the dataframe as an argument to the `ncol()`

function.

# number of columns in employees_df print(ncol(employees_df))

Output:

[1] 4

We get the number of columns in the above dataframe as 4.

### Example 2 – Number of columns in an R dataframe with `NA`

values

What happens if the dataframe you want to count the columns for has `NA`

values?

Let’s find out.

First, we will create a dataframe with some `NA`

values.

# create a dataframe employees_df = data.frame( "Name"= c("Jim", "Dwight", "Angela", "Tobi", "Kevin"), "Age"= c(26, 28, NA, 32, 30), "Department"= c("Sales", "Sales", "Accounting", "HR", "Accounting"), "Salary" = c(80000, 81000, 72000, NA, 72000) ) # display the dataframe print(employees_df)

Output:

Name Age Department Salary 1 Jim 26 Sales 80000 2 Dwight 28 Sales 81000 3 Angela NA Accounting 72000 4 Tobi 32 HR NA 5 Kevin 30 Accounting 72000

Here, we created a similar dataframe from the above example by replacing some values with `NA`

. You can see that columns “Age” and “Salary” have one `NA`

value each.

Let’s now apply the `ncol()`

function to this dataframe.

# number of columns in employees_df print(ncol(employees_df))

Output:

[1] 4

We get the number of columns in the above dataframe as 4. Same as the result we got when the dataframe didn’t have any `NA`

values. We can say that the `ncol()`

function counts the columns in the dataframe irrespective of the `NA`

values present.

You might also be interested in –

- How to Create a DataFrame in R?
- How to Add a Row to a Dataframe in R?
- R – Get Vector of Dataframe Column Names

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