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The “not in” filter in dplyr is a useful tool for excluding specific values from a dataset. This filter allows users to specify a list of values that they do not want to include in their data, effectively creating a subset of the original dataset. This feature is particularly helpful when working with large datasets, as it allows for quick and efficient removal of unwanted values. To use the “not in” filter, users simply need to specify the values they want to exclude in the filter function, providing a convenient and intuitive way to clean and manipulate data.
dplyr: Use a “not in” Filter
You can use the following basic syntax in to filter for rows in a data frame that are not in a list of values:
df %>% filter(!col_name %in% c('value1', 'value2', 'value3', ...))
The following examples show how to use this syntax in practice.
Example 1: Filter for Rows that Do Not Contain Value in One Column
Suppose we have the following data frame in R:
#create data frame df <- data.frame(team=c('A', 'A', 'B', 'B', 'C', 'C', 'D', 'D'), position=c('G', 'G', 'F', 'G', 'F', 'C', 'C', 'C'), points=c(12, 14, 19, 24, 36, 41, 18, 29)) #view data frame df team position points 1 A G 12 2 A G 14 3 B F 19 4 B G 24 5 C F 36 6 C C 41 7 D C 18 8 D C 29
The following syntax shows how to filter for rows where the team name is not equal to ‘A’ or ‘B’:
#filter for rows where team name is not 'A' or 'B'
df %>% filter(!team %in% c('A', 'B'))
team position points
1 C F 36
2 C C 41
3 D C 18
4 D C 29Example 2: Filter for Rows that Do Not Contain Value in Multiple Columns
Suppose we have the following data frame in R:
#create data frame df <- data.frame(team=c('A', 'A', 'B', 'B', 'C', 'C', 'D', 'D'), position=c('G', 'G', 'F', 'G', 'F', 'C', 'C', 'C'), points=c(12, 14, 19, 24, 36, 41, 18, 29)) #view data frame df team position points 1 A G 12 2 A G 14 3 B F 19 4 B G 24 5 C F 36 6 C C 41 7 D C 18 8 D C 29
The following syntax shows how to filter for rows where the team name is not equal to ‘A’ and where the position is not equal to ‘C’:
#filter for rows where team name is not 'A' and position is not 'C'
df %>% filter(!team %in% c('A') & !position %in% c('C'))
team position points
1 B F 19
2 B G 24
3 C F 36The following tutorials explain how to perform other common functions in dplyr:
Cite this article
stats writer (2024). How can I use the “not in” filter in dplyr to exclude certain values from my data?. PSYCHOLOGICAL SCALES. Retrieved from https://scales.arabpsychology.com/stats/how-can-i-use-the-not-in-filter-in-dplyr-to-exclude-certain-values-from-my-data/
stats writer. "How can I use the “not in” filter in dplyr to exclude certain values from my data?." PSYCHOLOGICAL SCALES, 6 May. 2024, https://scales.arabpsychology.com/stats/how-can-i-use-the-not-in-filter-in-dplyr-to-exclude-certain-values-from-my-data/.
stats writer. "How can I use the “not in” filter in dplyr to exclude certain values from my data?." PSYCHOLOGICAL SCALES, 2024. https://scales.arabpsychology.com/stats/how-can-i-use-the-not-in-filter-in-dplyr-to-exclude-certain-values-from-my-data/.
stats writer (2024) 'How can I use the “not in” filter in dplyr to exclude certain values from my data?', PSYCHOLOGICAL SCALES. Available at: https://scales.arabpsychology.com/stats/how-can-i-use-the-not-in-filter-in-dplyr-to-exclude-certain-values-from-my-data/.
[1] stats writer, "How can I use the “not in” filter in dplyr to exclude certain values from my data?," PSYCHOLOGICAL SCALES, vol. X, no. Y, ص Z-Z, May, 2024.
stats writer. How can I use the “not in” filter in dplyr to exclude certain values from my data?. PSYCHOLOGICAL SCALES. 2024;vol(issue):pages.