# Report Missing Values in Data Frame in R (2 Examples)

In this R tutorial you’ll learn how to illustrate missing data in a data table in an elegant way.

Here’s the step-by-step process!

## Creating Example Data

First, we need to construct some data that we can use in the following examples:

```set.seed(873264) # Create example data data <- round(data.frame(x1 = rnorm(100), x2 = runif(100), x3 = rpois(100, 1)), 2) data\$x1[rbinom(100, 1, 0.2) == 1] <- NA data\$x2[rbinom(100, 1, 0.4) == 1] <- NA data\$x3[rbinom(100, 1, 0.6) == 1] <- NA head(data) # First rows of example data # x1 x2 x3 # 1 -0.35 NA NA # 2 NA 0.98 NA # 3 1.69 0.87 2 # 4 -0.99 0.00 NA # 5 NA NA NA # 6 NA 0.03 NA```

The previous output of the RStudio console shows the structure of our exemplifying data: It’s a data frame containing three numeric columns. Each of the columns has a non-neglectable amount of NA values.

## Example 1: Count Missing Values in Columns

When inspecting the missing data structure of a data frame, the first step should always be to count the missing values in each variable. This Example therefore illustrates how to get the number of NAs in each column. For this task, we can use the colSums and the is.na functions as shown below:

```colSums(is.na(data)) # Count missing values by column # x1 x2 x3 # 20 44 58```

The previous output of the RStudio console shows that our example data contains 20 missing values in the variable x1, 44 missing values in the variable x2, and 58 missing values in the variable x3.

Those total numbers are hard to interpret without taking the size of our data table into account. The following R code therefore computes the percentages of missing values by column:

```colSums(is.na(data)) / nrow(data) # Percentage of missing values by column # x1 x2 x3 # 0.20 0.44 0.58```

x1 has 20% missings, x2 has 44% missings, and x3 has 58% missings. This result would definitely be alarming in practice!

## Example 2: Visualize Missing Values Using VIM Package

It is also important to inspect the missing data structure. Hence, this Example explains how to show the structure of missing values in a graphic using the VIM add-on package. If we want to use the functions of the VIM package, we first have to install and load VIM:

```install.packages("VIM") # Install VIM package library("VIM") # Load VIM```

Now, we can use the aggr() function of the VIM package to create an aggregation plot of our missing data:

`aggr(data) # Create aggregation plot` Figure 1 shows how the aggregation plot of our data looks like. Based on the plot you can see the amount of missing values in each column and you can see how often multiple variables are missing simultaneously.

## Video & Further Resources

Do you need more info on the content of this page? Then you might want to watch the following video of my YouTube channel. In the video, I’m explaining the topics of this tutorial.

Furthermore, you could have a look at the related tutorials of my website. Note that this page showed only a small part of the possible analysis methods for missing values. Make sure to analyze your missing data as good as possible and treat the missing values properly via imputation methods or other missing data approaches.

To summarize: In this R tutorial you learned how to visualize and count missing values. If you have further questions, please let me know in the comments section below.

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