# Count Non-NA Values in R (2 Examples)

In this article youâ€™ll learn how to get the number of non-NA values in R programming.

The tutorial will contain this information:

Letâ€™s get started.

## Example 1: Count Non-NA Values in Vector Object

In Example 1, Iâ€™ll demonstrate how to find the number of non-missing values in a vector object.

For this example, we first have to create an exemplifying vector:

```vec <- c(1, NA, 2, NA, NA, 1, 2, 1, NA)            # Create example vector
vec                                                # Print example vector
# [1]  1 NA  2 NA NA  1  2  1 NA```

Next, we can apply the sum and is.na functions to this vector to get the number of non-NA values:

```sum(!is.na(vec))                                   # Get number of non-NA values
# [1] 5```

As you can see based on the previous output of the RStudio console, our vector object contains five non-NA values.

## Example 2: Count Non-NA Values in Columns & Rows of Data Frame

In this example, Iâ€™ll explain how to return the number of non-NA values in a data frame.

Letâ€™s create some example data:

```data <- data.frame(x1 = c(5, NA, 7, NA, NA, 3),    # Create example data frame
x2 = c("a", "b", NA, NA, "a", "c"))
data                                               # Print example data frame```

By executing the previous code, we have managed to construct Table 1, i.e. a data frame containing six rows and two columns. Both of the columns contain some NA values.

If we want to count the non-NA values in each column of this data frame, we can apply the colSums and is.na functions as shown below:

```colSums(!is.na(data))                              # Get number of non-NA values in columns
# x1 x2
#  3  4```

The column x1 contains three non-NA values, and the column x2 contains four non-NA values.

It is also possible to count the non-NA values by rows. For this, we have to exchange the colSums function by the rowSums function:

```rowSums(!is.na(data))                              # Get number of non-NA values in rows
# [1] 2 1 1 0 1 2```

The previously shown vector shows the number of non-NA values in each of the six rows in our data frame.

## Video & Further Resources

Do you need more info on the topics of this article? Then I recommend having a look at the following video on my YouTube channel. In the video, I explain the contents of this article in the R programming language:

In addition, you might want to read some of the related articles on my homepage.

In this article you have learned how to count the number of non-NA values in the R programming language. If you have any additional questions, kindly let me know in the comments section.

Subscribe to the Statistics Globe Newsletter