R Tutorial: Baseline Conversion Rates

Want to learn more? Take the full course at https://learn.datacamp.com/courses/ab-testing-in-r at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- In the previous lesson, we learned some of the principles of A/B testing and took a look at our preliminary dataset. Let's spend some more time looking at our pre-experiment, or baseline values for an experiment. Before starting any A/B testing experiment you'll want to know your baseline value, or the current value before any experimental changes happen. Why is this? Well, let's come back to our hypothesis. We said we expect a different photo to result in more conversions, but what does "more" really mean in this context? Is it compared to the conversion rate in the last year? today? next week? or what about relative to when the experiment is actually run? If you're not planning to run your experiment for a couple of months there could be other factors that change your conversion rates between now and when the experiment is run. To have a clearly defined hypothesis and experiment you need to know what your baseline for comparison is, otherwise you can't really know if your experiment had an effect or not. For our experiment, to start, we'll compute the current, pre-experiment conversion rate over all of the time. As mentioned earlier, for most of our analyses we'll use a suite of packages referred to as the tidyverse. Here, these packages will help us manipulate and plot our baseline data. We'll also read in our click_data just as we did in the previous exercises. From here we can find the mean of our clicked_adopt_today column to see what percentage of the time people clicked, also known as our conversion rate. We can do that with the dplyr function summarize, using the pipe to connect our data to the function. We then use the mean() function to compute the conversion rate (averaging the 1s and 0s in the clicked_adopt_today column). If we look at t