Table of Contents
dplyr is a popular R package that can be used to manipulate data frames. One of its main functions is to filter data based on a factor, which is a categorical variable with a limited number of levels. This can be done by using the filter() function in dplyr, which allows users to specify the factor variable and the desired levels to be included in the filtered data set. This makes it easy to subset and analyze specific groups within a data frame based on their factor values.
You can use the following methods in 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.
Additional Resources
The following tutorials explain how to perform other common functions in dplyr: