R Tutorial: Counts vs. proportions

Want to learn more? Take the full course at https://learn.datacamp.com/courses/exploratory-data-analysis at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- You may have noticed in the last exercises that sometimes raw counts of cases can be useful, but often it's the proportions that are more interesting. We can do our best to compute these proportions in our head or we could do it explicitly. Let's return to our table of counts of cases by identity and alignment. If we wanted to instead get a sense of the proportion of all cases that fell into each category, we can take the original table of counts, saved as tab underscore cnt, and provide it as input to the prop dot table function. We see here that the single largest category are characters that are bad and secret at about 29% of characters. Also note that because these are all proportions out of the whole dataset, the sum of all of these proportions is 1. If we're curious about systematic associations between variables, we should look to conditional proportions. An example of a conditional proportion is the proportion of public identity characters that are good. To build a table of these conditional proportions, add a 1 as the second argument, specifying that you'd like to condition on the rows. We see here that around 57% of all secret characters are bad. Because we're conditioning on identity, it's every row that now sums to one. To condition on the columns instead, change that argument to 2. Now it's the columns that sum to one and we learn, for example, that the proportion of bad characters that are secret is around 63%. As the number of cells in these tables gets large, it becomes much easier to make sense of your data using graphics. The bar chart is still a good choice, but we're going to need to add some options. Here is the code for the bar chart based on counts. We want to condition on whatever is on the x axis and stretch those bars to