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 rows in an R dataframe with the help of some examples.

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

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

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

The following is the syntax –

# number of rows in dataframe nrow(dataframe)

It returns the number of rows in the dataframe (including the rows with `NA`

values).

## Examples

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

### Example 1 – Count rows 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”.

We can see that the above dataframe has five rows. Let’s now try to get this row count programmatically in R.

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

function.

# number of rows in employees_df print(nrow(employees_df))

Output:

[1] 5

We get the number of rows in the above dataframe as 5.

### Example 2 – Count rows in an R dataframe with `NA`

values

What happens if the dataframe you want to count the rows 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 rows 3 and 4 have one `NA`

value each.

Let’s now apply the `nrow()`

function to this dataframe.

# number of rows in employees_df print(nrow(employees_df))

Output:

[1] 5

We get the number of rows in the above dataframe as 5. Same as the result we got when the dataframe didn’t have any `NA`

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

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

values present.

If you do not want to count rows with **any** `NA`

values, use the `na.omit()`

function before applying the `nrow()`

function.

# count rows without any NA values print(nrow(na.omit(employees_df)))

Output:

[1] 3

Now we get the row count as 3. This is the number of rows that do not have any missing values in the above dataframe.

## Summary – Number of Rows in R dataframe

In this tutorial, we looked at how to count the number of rows in a dataframe in R. Here’s a short summary of the steps mentioned in this tutorial –

- Use the
`nrow()`

function to get the number of rows of a dataframe in R. It counts the rows (including the ones with`NA`

values). - To omit the rows with any missing values, apply the
`na.omit()`

function before applying the`nrow()`

function. This will give the number of rows without any missing values in the dataframe.

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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