Steven Sanderson counts what doesn’t exist:
Welcome back, R enthusiasts! Today, we’re going to explore a fundamental task in data analysis: counting the number of missing (NA) values in each column of a dataset. This might seem straightforward, but there are different ways to achieve this using different packages and methods in R.
Let’s dive right in and compare how to accomplish this task using base R, dplyr, and data.table. Each method has its own strengths and can cater to different preferences and data handling scenarios.
Read on for 3 1/2 separate methods.