How to Use “not in” operator in Filter

The post How to Use “not in” operator in Filter appeared first on Data Science Tutorials

How to Use “not in” operator in Filter, To filter for rows in a data frame that is not in a list of values, use the following basic syntax in dplyr.

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df %>%
  filter(!col_name %in% c('value1', 'value2', 'value3', ...))

The examples below demonstrate how to utilize this syntax in practice.

Example 1: Rows that do not have a value in one column are filtered out.

Let’s say we have the following R data frame.

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Let’s create a data frame

df <- data.frame(team=c('P1', 'P2', 'P3', 'P4', 'P5', 'P6', 'P7', 'P8'),
                 points=c(110, 120, 80, 16, 105, 185, 112, 112),
                 assists=c(133, 128, 131, 139, 134,55,66,135),
                 rebounds=c(18, 18, 14, 13, 12, 15, 17, 12))

Now we can view the data frame

df
    team points assists rebounds
1   P1    110     133       18
2   P2    120     128       18
3   P3     80     131       14
4   P4     16     139       13
5   P5    105     134       12
6   P6    185      55       15
7   P7    112      66       17
8   P8    112     135       12

The following syntax demonstrates how to search for rows where the team name is not ‘P1’ or ‘P2’.

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Find rows where the team name isn’t ‘P1’ or ‘P2’.

df %>%
  filter(!team %in% c('P1', 'P2'))
   team points assists rebounds
1   P3     80     131       14
2   P4     16     139       13
3   P5    105     134       12
4   P6    185      55       15
5   P7    112      66       17
6   P8    112     135       12

Example 2: Filter for rows that don’t have a value in more than one column

The following syntax demonstrates how to filter for rows with a team name that does not equal ‘P1’ and a position that does not equal ‘P3’.

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filter for rows with a team name other than ‘P1’ and a position other than ‘P3’.

df <- data.frame(team=c('P1', 'P2', 'P3', 'P4', 'P5', 'P6', 'P7', 'P8'),
                 points=c('A', 'A', 'B', 'B', 'C', 'C', 'C', 'D'),
                 assists=c(133, 128, 131, 139, 134,55,66,135),
                 rebounds=c(18, 18, 14, 13, 12, 15, 17, 12))
df
   team points assists rebounds
1   P1      A     133       18
2   P2      A     128       18
3   P3      B     131       14
4   P4      B     139       13
5   P5      C     134       12
6   P6      C      55       15
7   P7      C      66       17
8   P8      D     135       12
df %>%
  filter(!team %in% c('P1') & !points %in% c('D'))
   team points assists rebounds
1   P2      A     128       18
2   P3      B     131       14
3   P4      B     139       13
4   P5      C     134       12
5   P6      C      55       15
6   P7      C      66       17

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