How to Remove Rows with NA in R

How do you remove rows with NA values (missing values) from an R DataFrame (data.frame)? NA stands for Not Available and it is not a number that is considered a missing value. So our task is to remove the rows that contain either some or all NA values. In this article, we’ll cover how to remove rows that contain any NA values, as well as those that contain all NA values.

If a row contains some NA values, the following methods are used to drop these rows however, you can also replace NA with 0 or replace NA with an empty string.

1. na.omit()
2. complete.cases()
3. rowSums()
4. drop_na()

If a row contains all NA values, these two methods are used to remove them.

1. rowSums() with ncol
2. filter() with rowSums()

1. Quick Examples of Removing Rows with NA Values

Following are quick examples of how to remove/delete rows with NA on R DataFrame (data.frame).

# Below are the quick examples

# Example 1: Remove rows with NA's using na.omit()
df <- na.omit(df)

# Example 2: Remove rows with NA's using complete.cases
df <- df[complete.cases(df), ] 

# Example 3: Remove rows with NA's using rowSums()
df <- df[rowSums(is.na(df)) == 0, ] 

# Example 4: Import the tidyr package                 
library("tidyr")

# Remove rows with NA's using drop_na()
df <- df %>% drop_na()

# Example 5: Remove rows that contains all NA's
df <- df[rowSums(is.na(df)) != ncol(df), ]

# Example 6: Load the dplyr package                      
library("dplyr") 

# Remove rows that contains all NA's
df <- filter(df, rowSums(is.na(df)) != ncol(df))

Let’s create a data frame with 5 rows and 3 columns such that one row contains all NA and some rows contain at least one NA.

# Create dataframe with 5 rows and 3 columns
df=data.frame(id=c(2,1,3,4,NA),
       name=c('sravan',NA,'chrisa','shivgami',NA),
       gender=c(NA,'m',NA,'f',NA))

# Display dataframe
print(df)

Yields below output.

2. Remove Rows with NA From the R Dataframe

By using na.omit(), complete.cases(), rowSums(), and drop_na() methods you can remove rows that contain NA ( missing values) from the R data frame. Let’s see an example for each of these methods.

2.1. Remove Rows with NA using na.omit()

The na.omit() function is used to remove any rows with NA values from a data frame and returns the modified data frame.

Syntax of na.omit():

# Syntax of na.omit()
na.omit(df)

Where df is the input data frame

Example:

In this example, we will apply na.omit() to the given data frame and drop the rows that contain some NA values.

# Remove rows with NA's using na.omit()
print(na.omit(df))

Yields below output.

Notice that the above resultant data frame has no rows with NA values.

2.2. Remove Rows with NA using complete.cases()

The complete.cases() function removes rows that contain some NA values and returns the modified data frame having no NA values.

Syntax of complete.cases() function

# Syntax of complete.cases()
df[complete.cases(df), ] 

Example:

In this example, you can apply this function to a given data frame, it will remove the rows which contain some NA. Let’s pass the data frame into this function to remove the rows having at least one NA value.

# Remove rows with NA's using complete.cases
print(df[complete.cases(df), ] )

Output:

# Output
  id     name gender
4  4 shivgami      f

We can see that the above row has no NA values.

2.3. Remove rows with NA using rowSums()

In this example, you can use the rowSums() function to filter out rows without any NA values. rowSums(is.na(df)) == 0 this syntax calculates the sum of NA values for each row in the dataframe. (is.na(df) creates a logical matrix of the same dimensions as df. TRUE for every NA value and FALSE otherwise), and then checks if the sum is equal to 0. Using this condition you can remove the rows having NA values.

Syntax of rowSums() function:

# Syntax of rowSums() function 
df[rowSums(is.na(df)) == 0, ] 

Example:

In this example, we will apply rowSums() to the data frame and remove the rows having some NA. df[rowSums(is.na(df)) == 0, ], this syntax subsets the dataframe, keeping only the rows where the condition is TRUE. In other words, it selects rows that have no NA values in any of their columns.

# Remove rows with NA's using rowSums()
print(df[rowSums(is.na(df)) == 0, ]  )

Output:

# Output
  id     name gender
4  4 shivgami      f

2.4. Remove rows with NA using drop_na()

drop_na() function will drop the rows that contain at least one NA value. It is available in tidyr package. tidyr is a third-party library hence, to use the tidyr library, first, you need to install it by using install.packages(‘tidyr’). Once installation is completed, load the tidyr library to use this dro_na() method. To load a library in R language you can use library("tidyr").

Syntax:

# Syntax
df %>% drop_na()

where df is the input data frame and %>% loads the method to the data frame.

Example:

In this example, we will apply drop_na() to remove rows with some NA. Let’s apply this method and get the rows without having the NA values.

#import the tidyr package                 
library("tidyr")

# Remove rows with NA's using drop_na()
print(df %>% drop_na())

Output:

# Output
  id     name gender
4  4 shivgami      f

3. Remove Rows Containing all NA Values in the R Dataframe

So far, we have seen how to remove rows that have NA on any columns. In this section, we will remove the rows with NA on all columns in an R data frame (data.frame).

3.1. Remove Rows with All NA using rowSums() with ncol

Here, we are comparing rowSums() count with ncol() count, if they are not equal, we can say that the row doesn’t contain all NA values. Hence the row that contains all NA will not be selected. When the counts are equal then the row is considered with all NA values and the row is considered to be removed from the R dataframe.

Syntax of rowSums():

# Syntax
df[rowSums(is.na(df)) != ncol(df), ]

Example:

In this example, we will apply rowSums() and ncol() methods to remove rows with all NA.

#Remove rows that contains all NA's
print(df[rowSums(is.na(df)) != ncol(df), ])

Output:

# Output
   id     name gender
1  2   sravan   <NA>
2  1     <NA>      m
3  3   chrisa   <NA>
4  4 shivgami      f

We can see that the id with the value 5 row is deleted since it contains all NA values.

3.2. Delete Rows with NA using filter() with rowSums()

It is similar to the above method, we are comparing rowSums() count with ncol() count, if they are not equal, we can say that the row doesn’t contain all NA values. Hence the row that contains all NA will not be selected. To do this, we have to use the filter() method.

Syntax:

# Syntax
filter(df, rowSums(is.na(df)) != ncol(df))

Example:

In this example, we will apply filter() with rowSums() to remove rows with all NA.

#Load the dplyr package                      
library("dplyr") 

#Remove rows that contains all NA's
print(filter(df, rowSums(is.na(df)) != ncol(df)))

Output:

# Output
  id     name gender
1  2   sravan   <NA>
2  1     <NA>      m
3  3   chrisa   <NA>
4  4 shivgami      f

We can see that the id-5 row is deleted since it contains all NA.

4. Conclusion

From this article, we have seen how to remove the rows that contain NA values from the R dataframe. If you want to remove the rows that contain all NA values, you can use the combination of rowSums(), ncol(), and filter() from the dplyr package. If you want to delete the rows that have some NA values, you can use rowSums(), drop_na() from tidyr package, na.omit() and complete.cases()

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References

1. Missing values in R
2. rowSums()