How to Find and Count Missing Values in R DataFrame

In R programming, missing values are commonly represented as NA. To identify and handle these values effectively, we can use the is.na() function, which checks whether a data point is missing.

Syntax

which(is.na(data))

sum(is.na(data))

Parameters:

• is.na(data): Identifies missing values and returns TRUE for each NA.
• which(is.na(data)): Returns the index positions of missing values.
• sum(is.na(data)): Calculates the total number of missing values.

Find and count the Missing values From the entire Data Frame

We create a data frame named stats and use which(is.na()) to get the positions of missing values and sum(is.na()) to get the total number.

• data.frame: creates tabular data from vectors.
• is.na: checks whether a value is missing (NA).
• which: returns the positions of TRUE values in a logical vector.
• sum: counts TRUE values by summing them (as TRUE = 1, FALSE = 0).

stats <- data.frame(player = c('A', 'B', 'C', 'D'),
                    runs = c(100, 200, 408, NA),
                    wickets = c(17, 20, NA, 5))

print("Position of missing values ")
which(is.na(stats))

print("Count of total missing values  ")
sum(is.na(stats))

Output:

Count the number of Missing Values with summary

We use summary() to get statistical details of each column, including the number of missing values.

• summary: gives descriptive statistics and NA counts per column.

stats <- data.frame(player=c('A', 'B', 'C', 'D'),
                    runs=c(NA, 200, 408, NA),
                    wickets=c(17, 20, NA, 8))


summary(stats)

Output:

Count the number of Missing Values with colSums

We use colSums() with is.na() to count NA values in each column.

• colSums: computes the sum of each column, here to count NAs.
• is.na: checks for missing values.

stats <- data.frame(player=c('A', 'B', 'C', 'D'),
                    runs=c(NA, 200, 408, NA),
                    wickets=c(17, 20, NA, 8))

colSums(is.na(stats))

Output:

Find and count the Missing values in one column of a Data Frame

We check the missing values in specific columns using dataframe$column.

• $ operator: accesses a specific column from a data frame.

stats <- data.frame(player = c('A', 'B', 'C', 'D'),
                    runs = c(NA, 200, 408, NA),
                    wickets = c(17, 20, NA, 8))

print("Location of missing values in runs column")
which(is.na(stats$runs))

print("Count of missing values in wickets column")
sum(is.na(stats$wickets))

Output:

Find and count missing values in all columns in Data Frame

We use sapply() to apply functions column-wise and identify NA positions and counts.

• sapply: applies a function to each column and returns a list or vector.
• function(x): defines an anonymous function to check NAs per column.
• which: returns positions of missing values.
• sum: counts total missing values in each column.

stats <- data.frame(player = c('A', 'B', 'C', 'D'),
                    runs = c(100, 200, 408, NA),
                    wickets = c(17, 20, NA, 5))

print("Position of missing values by column wise")
sapply(stats, function(x) which(is.na(x)))

print("Count of missing values by column wise")
sapply(stats, function(x) sum(is.na(x)))

Output:

The output shows the position and count of missing values in each column. The runs column has a missing value at position 4 and wickets has one at position 3, while player has no missing values. This helps quickly locate and quantify missing data column-wise.