You can use the following methods from the dplyr package to remove rows with NA values:
Method 1: Remove Rows with NA Values in Any Column
library(dplyr) #remove rows with NA value in any column df %>% na.omit()
Method 2: Remove Rows with NA Values in Certain Columns
library(dplyr) #remove rows with NA value in 'col1' or 'col2' df %>% filter_at(vars(col1, col2), all_vars(!is.na(.)))
Method 3: Remove Rows with NA Values in One Specific Column
library(dplyr) #remove rows with NA value in 'col1' df %>% filter(!is.na(col1))
The following examples show how to use these methods in practice with the following data frame:
#create data frame with some missing values
df <- data.frame(team=c('A', 'A', 'B', 'B', 'C'),
points=c(99, 90, 86, 88, NA),
assists=c(33, NA, 31, 39, 34),
rebounds=c(NA, 28, 24, 24, 28))
#view data frame
df
team points assists rebounds
1 A 99 33 NA
2 A 90 NA 28
3 B 86 31 24
4 B 88 39 24
5 C NA 34 28
Method 1: Remove Rows with NA Values in Any Column
The following code shows how to remove rows with NA values in any column of the data frame:
library(dplyr) #remove rows with NA value in any column df %>% na.omit() team points assists rebounds 3 B 86 31 24 4 B 88 39 24
The only two rows that are left are the ones without any NA values in any column.
Method 2: Remove Rows with NA Values in Certain Columns
The following code shows how to remove rows with NA values in any column of the data frame:
library(dplyr) #remove rows with NA value in 'points' or 'assists' columns df %>% filter_at(vars(points, assists), all_vars(!is.na(.))) team points assists rebounds 1 A 99 33 NA 2 B 86 31 24 3 B 88 39 24
The only rows left are the ones without any NA values in the ‘points’ or ‘assists’ columns.
Method 3: Remove Rows with NA Values in One Specific Column
The following code shows how to remove rows with NA values in one specific column of the data frame:
library(dplyr) #remove rows with NA value in 'points' column df %>% filter(!is.na(points)) team points assists rebounds 1 A 99 33 NA 2 A 90 NA 28 3 B 86 31 24 4 B 88 39 24
The only rows left are the ones without any NA values in the ‘points’ column.
Additional Resources
The following tutorials explain how to perform other common operations using dplyr:
dplyr: How to Filter Rows that Contain Certain String
dplyr: How to Replace NA Values with Zero
dplyr: How to Use a “not in” Filter
