R Tutorial: Count With Your Data

Want to learn more? Take the full course at https://learn.datacamp.com/courses/working-with-data-in-the-tidyverse 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 gotten to know our data better, it's time to do some careful counting. While glimpse and skim give nice overviews, counting values within variables will help you to better understand the underlying structure of your data and can help guide you toward asking good questions about your data. We'll work with the "bakers" data again, but this time we'll use the full data for all bakers who have appeared on "The Great British Bake Off". Here is a glimpse of bakers. We have 95 observations, or bakers, and 10 variables. Let's start with a simple question: how many series of the TV show do we have data for? More specifically, how many distinct series are there? For that, we can use the distinct function from the dplyr package. And we see that there are 8! So we have 8 series, and we know that each row is a baker. How can we count the number of distinct bakers per series? We'll use a new dplyr verb: count. The argument to count is a variable or variables to group by. Count adds a new column named "n" to store the counts. Since each row is a baker, we know that most series feature a dozen bakers. Series 1 had only 10, and series 4 had a baker's dozen or 13. Using count gives you the same output as using group underscore by followed by a summarize to count the rows by group. Count is a clearer and faster way We can also count by more than one variable. Here we count by two variables: aired underscore us and series. We can see that series 4, 5, 6, and 7 have aired in the US. The n column is still the number of bakers. Count helpfully does an extra ungroup step for you. Let's say that after counting, we wanted to add a mutate to make a new column with the proportion of bakers in each series for the whole show. If we use count