You can use the following methods in dplyr to filter the rows of a data frame in R based on a factor variable:

**Method 1: Filter Based on Factor Labels**

library(dplyr) #filter rows where team column is equal to factor label 'A' or 'C' df %>% filter(team %in% c('A', 'C'))

**Method 2: Filter Based on Factor Levels**

library(dplyr) #filter rows where factor level of team column is greater than 2 df %>% filter(as.integer(team)>2)

The following examples shows how to use each method in practice with the following data frame in R that contains information about various basketball players:

#create data frame df <- data.frame(team=as.factor(c('A', 'A', 'A', 'B', 'B', 'C', 'C', 'D')), points=c(12, 34, 20, 25, 22, 28, 34, 19)) #view data frame df team points 1 A 12 2 A 34 3 A 20 4 B 25 5 B 22 6 C 28 7 C 34 8 D 19

**Example 1: Filter Based on Factor Labels**

We can use the following syntax to filter the data frame to only contain rows where the factor labels of the **team** column are equal to A or C:

library(dplyr) #filter rows where team column is equal to factor label 'A' or 'C' df %>% filter(team %in% c('A', 'C')) team points 1 A 12 2 A 34 3 A 20 4 C 28 5 C 34

Notice that the resulting data frame only contains rows where the value in the **team** column is equal to either A or C.

**Example 2: Filter Based on Factor Levels**

We can use the following syntax to filter the data frame to only contain rows where the factor levels of the **team** column are greater than 2:

library(dplyr) #filter rows where factor level of team column is greater than 2 df %>% filter(as.integer(team)>2) team points 1 C 28 2 C 34 3 D 19

In this particular example, the **as.integer** function converts the factor labels of the **team** column to integers.

For example:

- Factor level ‘A’ becomes
**1**. - Factor level ‘B’ becomes
**2**. - Factor level ‘C’ becomes
**3**. - Factor level ‘D’ becomes
**4**.

Thus, when we filter for rows where the factor level is greater than 2, only the rows with a value of C or D in the **team** column are kept.

**Additional Resources**

The following tutorials explain how to perform other common functions in dplyr:

How to Remove Rows Using dplyr

How to Select Columns by Index Using dplyr

How to Filter Rows that Contain a Certain String Using dplyr