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Let's use lada's approach of transforming variables on our Facebook data set. Most of our variables, such as friend count, likes, comments, wall posts and others are variables that I would call engagement variables, and they all have very long tails. Some users have 10 times, or even 100 ,the median value. Another way to say this is that some people have an order of magnitudes, more likes, clicks, or comments, than any other users. In statistics, we say that the data is over dispersed. Often it helps to transform these values so we can see standard deviations, or orders of magnitudes, so we are in effect, shortening the tail. Here was our histogram of friend count from before, and notice, we still have that long tail. We can transform this variable by taking the log, either using the natural log, log base 2, or log base 10. We could use other functions, such as the square root, and doing so helps us to see patterns more clearly, without being distracted by the tails. A lot of common statistical techniques, like linear regression, are based on the assumption that variables have normal distributions. So by taking the log of this variable, we can transform our data to turn it into maybe a normal distribution or something that more closely resembles a normal distribution, if we'd be using linear regression or some other modelling technique. Now, I know we're not doing modelling here, but let's just see what it looks like to transform the variable. First, I'm going to just do this in the summary command. So, here's our regular summary of friend count. Looks like the median friend count is 82, and the mean is 196. I can take the log base 10 of this friend count and get a different table. Now this seems a little bit unusual since I have negative infinity for the minimum and negative infinity for the mean. Hm. So what must be going on? Well, some of our users have a friend count of zero. So, when we take the log of base 10 of zero, that would be undefined. For those familiar with Calculus, the limit would be negative infinity, which is why that appears here. To avoid this, we're going to add one to friend count, so that way we don't get an undefined answer, or negative infinity. There, that looks much better. And, just to show you another function, let's also use the square root on friend count. This would be another type of transformation. For me, log base 10 is an easier transformation to wrap my head around, since I'm just comparing friend counts on orders of magnitude of 10. Basically a tenfold scale like the pH scale. Now that you've seen transformations within summaries, let's see if you can apply a similar transformation to the histogram. Check out the instructor notes to learn how to use scales and how to create multiple graphs on one page. Once you've read through those links, and you think you're ready, try this next programming exercise. In it, you're going to create three different histograms. The first one will be our original friend count histogram, and then the second one will have the friend count transformed using log 10. And then the last histogram will have the friend count transformed using square root. Now, I haven't given you all the information to do this exercise, but I want you to be resourceful. Part of being a good programmer is being able to read documentation and know how to find your answers to the questions you may have. Now, we've already done the legwork for you by providing the resource. So, let's see if you can figure it out.