R Tutorial : How do we visualize missing values?

Want to learn more? Take the full course at https://learn.datacamp.com/courses/dealing-with-missing-data-in-r at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- We now know what missing values are, how they work, how to count and summarise them - now let's look at some of the built-in visualizations that come with naniar. Data summaries are very useful, but sometimes an idea or a thought can be quickly captured with a visualization. naniar provides a friendly family of missing data visualization functions, each presenting different visualizations missingness summaries. In fact, each of these visualizations is a nice compact shorthand for the data summaries. While you could create similar and more complex visualizations using the summary information from the previous lesson, this can be repetitive. The visualizations in naniar reduce repetition and increase iteration, so you can operate closer to the speed of thought. In this lesson, we cover how to get a bird's eye view of the data, how to look at missings in the variables and cases, and how to generate visualizations for missing spans and across groups in the data. When you first get a dataset, it can be difficult to get a visceral sense of where the missings are. To get an overview of the amount of missingness, use the vis_miss function from the visdat package. vis_miss produces a "heatmap" of the missingness - like as if the plot corresponded to the dataset as a giant spreadsheet, with values colored black for missing, and grey for present. vis_miss also provides missingness summary statistics, showing the overall percentage of missingness in the legend, and the amount of missings in each variable. These can be turned off in its options, described in the helpfile. vis_miss also allows for clustering of the missing data by setting cluster = TRUE: this orders the rows by missingness to identify common co-occurrences. To quickly show the miss