Remove Rows from the data frame in R

Remove Rows from the data frame in R, To remove rows from a data frame in R using dplyr, use the following basic syntax.

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1. Remove any rows containing NA’s.

df %>%  na.omit()

2. Remove any rows in which there are no NAs in a given column.

df %>%  filter(!is.na(column_name))

3. Get rid of duplicates

df %>%  distinct()

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4. Remove rows based on their index position.

df %>%  filter(!row_number() %in% c(1, 2, 4))

5. Based on the condition, remove rows.

df %>%  filter(column1=='A' | column2 > 8)

With the given data frame, the following examples explain how to apply each of these approaches in practice.

library(dplyr)

Now we can create a data frame.

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df <- data.frame(player = c('P1', 'P2', 'P3', 'P4', 'P5', 'P6', 'P7'),
points = c(122, 144, 154, 155, 120, 218, 229),
assists = c(43, 55, 77, 18, 114, NA,29))

Let’s view the data frame

df

df <- data.frame(player = c('P1', 'P2', 'P3', 'P4', 'P5', 'P6', 'P7'),
points = c(122, 144, 154, 155, 120, 218, 229),
assists = c(43, 55, 77, 18, 114, NA,29))

Approach 1: Remove Any Row with NA’s

The following code explains how to eliminate any rows from the data frame that have NA values.

delete any row that has the letter NA in it.

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df %>%  na.omit()

  player points assists
1     P1    122      43
2     P2    144      55
3     P3    154      77
4     P4    155      18
5     P5    120     114
7     P7    229      29

Approach 2: Delete any rows that contain NAs in specific columns.

The following code demonstrates how to delete any row in a column containing NA values.

delete any rows in the ‘points’ column that have a NA.

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df %>%   filter(!is.na(assists))

   player points assists
1     P1    122      43
2     P2    144      55
3     P3    154      77
4     P4    155      18
5     P5    120     114
6     P7    229      29

Approach 3: Rows that are duplicated should be removed.

The code below demonstrates how to eliminate duplicate rows.

duplicate rows should be removed

df %>%  distinct()

   player points assists
1     P1    122      43
2     P2    144      55
3     P3    154      77
4     P4    155      18
5     P5    120     114
6     P6    218      NA
7     P7    229      29

Approach 4: Rows are removed based on their index position.

The code below demonstrates how to eliminate rows based on their index position.

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Rows 1, 2, and 4 should be removed.

df %>%  filter(!row_number() %in% c(1, 2, 4))

  player points assists
1     P3    154      77
2     P5    120     114
3     P6    218      NA
4     P7    229      29

Approach 5: Rows are removed based on their condition.

The code below demonstrates how to eliminate rows based on certain criteria.

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only keep rows when the team letter is ‘A’ or the number of points is more than eight.

df %>%  filter(player=='P1' | assists >100)

   player points assists
1     P1    122      43
2     P5    120     114