R Tutorial : Using mappers to clean up your data

Want to learn more? Take the full course at https://learn.datacamp.com/courses/intermediate-functional-programming-with-purrr at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Now that we have seen the basics of mappers let's use them to clean your data! First, let's start with set_names() a function from purrr that, as its name suggests, sets the names of a list. For example, here, the visits2016 vector is not named, so we'll add the month abbreviation. We're doing this because it's easier to work on a named list. For example, if you want to filter elements out of the list, you'll know which elements you have extracted, as they are named. Note that the second element of set_names() must have the same length as the first element. As you can see, we can use set_names() inside a map() function, by building a mapper that will be applied to each element of the .x argument. What we are doing here is taking every element of the all_visits vector and applying set_names() to each of these elements, with the month abbreviation as a parameter. keep() is a convenient function that helps to make a selection inside a list, by only keeping the elements that satisfy a condition. This condition can be a standard test like is.numeric(), or a mapper, as you can see on this slide. For example, if we need to know which months had more than 30,000 visits during the year 2016, we can use keep() to extract the months that satisfy the condition described in the mapper. discard() is the counterpart of keep(): it removes all the elements that satisfy a condition. As you can see, while on the last slide I have kept the elements that showed a sum above 30000, I'm removing them when using discard(). Use keep() and discard() along with map() to clean your list. Here, for example, we want to keep only the factor columns of each data.frame. The first line of code creates a list that contains the first six row of the