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Dplyr is a popular R package used for data manipulation and analysis. One of its key features is the ability to filter data based on different criteria. When dealing with categorical data, such as factors, the filter function in dplyr can be used to subset the data based on specific factor levels. This allows for easy and efficient data exploration and analysis, as only the desired factor levels will be included in the resulting dataset. By using the filter function in dplyr, researchers and analysts can effectively filter their data based on factors, enabling them to focus on specific subsets of their data for further analysis.
dplyr: Filter Based on Factor
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: