The is.na() function in R is used to check for missing values (NA) in the data frame, dataset, or vector.
This function is extremely helpful in data cleaning and preparation, as it helps identify and handle missing values in a dataset.
Syntax
is.na(df)
Parameters
df: It is a data frame or set to be tested.
Return value
It returns a boolean or logical value: TRUE if it finds the NA value and FALSE if it does not.
Example 1: Using is.na() function with DataFrame
If you have a data frame and you are not sure how many NA values are there in the data frame, you can use the is.na() function and pass the data frame will return a data frame where NA values are replaced by TRUE and, in another case, FALSE.
df <- data.frame(
col1 = c(1, NA, 3),
col2 = c(NA, 5, NA),
col3 = c(7, NA, 9)
)
is.na(df)
Output
Example 2: Working with Vector
From the visual representation, you can see that we created a vector with two NA values and use is.na() function that will return TRUE for NA values and FALSE otherwise.
vec <- c(11, 21, 19, NA, 46, NA)
is.na(vec)
Output
[1] FALSE FALSE FALSE TRUE FALSE TRUE
Example 3: Working with any() function
The any() function returns whether any values are NA in the input object.
data <- c(11, 21, 19, NA, 46, NA)
any(is.na(data))
Output
[1] TRUE
In this example, any() function returns TRUE because the vector data contains at least one NA value. If it does not have a single NA value, then it returns FALSE.
data <- c(11, 21, 19, 46, 18)
any(is.na(data))
Output
[1] FALSE
Example 4: Counting NA values in a data frame
When you are doing exploratory data analysis, finding and removing NA values is the most important part and these functions will help you find it.
If you want to count total NA values in a data frame, use the combination of is.na() and sum() functions.
Let’s take an example data frame df and count the NA values.
df <- data.frame(
col1 = c(1, NA, 3),
col2 = c(NA, 5, NA),
col3 = c(7, NA, 9)
)
num_na_df <- sum(is.na(df))
num_na_df
Output
[1] 4
Example 5: Counting NA values in a vector
You can count the number of NA values in a vector using the combination of sum() and is.na() functions.
vec <- c(11, 21, 19, NA, 46, NA)
sum(is.na(vec))
Output
[1] 2
To deal with NA values, you might use functions like na.omit() to remove rows with NA or functions like replace(), mean(), median(), etc., to impute missing values.
Krunal Lathiya is a seasoned Computer Science expert with over eight years in the tech industry. He boasts deep knowledge in Data Science and Machine Learning. Versed in Python, JavaScript, PHP, R, and Golang. Skilled in frameworks like Angular and React and platforms such as Node.js. His expertise spans both front-end and back-end development. His proficiency in the Python language stands as a testament to his versatility and commitment to the craft.