Excel is great for spreadsheet use. R is great for data analysis, reporting, data visualization and modeling. I can remember taking accounting in college, when I was not aware of Excel (it was a long time ago). We used sheets of paper that we had to adjust manually (erasers were invaluable). Some of the figures were adjusted so many times, that the paper would wear through.
Correlation Between Data Attributes
Let’s say that you want to know if the number of your visitors are related to how much you spend on media. The number of visitors and media spend are both data attributes. If we want to understand the relationship between these two data attributes (beyond just eyeballing them), we have to understand if they covary. That means that when one variable moves from its mean, we would expect that a related variables would change in a similar way. This is the correlation between data attributes.
In this post I will walk through what covariance is, how it is calculated and use R to test for it. This is a very valuable technique to determine relationships between variables and it is not difficult to apply.
Favorite Google Analytics Features
I thought about what features I liked most about Google Analytics, what Google Analytics features make digital analysis and optimization easier and more insightful. Below are some of my favorite Google Analytics features.