Count non zero values in each column of R dataframe

Count the number of NA values in a DataFrame column in R

Last Updated : 26 Mar, 2021

A null value in R is specified using either NaN or NA. In this article, we will see how can we count these values in a column of a dataframe.

Approach

Create dataframe
Pass the column to be checked to is.na() function

Syntax: is.na(column)

Parameter:

column: column to be searched for na values

Returns:

A vector with boolean values, TRUE for NA otherwise FALSE

From the vector add the values which are TRUE
Display this number
- Here, 0 means no NA value

Given below are few examples

Example 1:

R

df<-data.frame(x = c(1,2,NA), y = rep(NA, 3)) 
print("dataframe is ") 
print(df) 
  
print("vector is") 
vec = is.na(df[,1]) 
print(vec) 
  
count = sum(vec) 
  
print("count of NA in first column is" ) 
print(count)

Output:

Example 2:

R

df<-data.frame(x = c("kapil","rahul",NA,NA), y = c(1,2,NA,3)) 
print("dataframe is ") 
print(df) 
  
print("vector is") 
vec = is.na(df[,1]) 
print(vec) 
  
count = sum(vec) 
  
print("count of NA in first column is" ) 
print(count)

Output:

Count non zero values in each column of R dataframe

K

kapilm180265ca

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