So what is dplyr and why should you use it? There are three popular toolkits for data manipulation in R. The dplyr cheat sheet is also extremely helpful. A good resource for learning more is R for Data Science. This lesson will only scratch the surface of dplyr, although we'll also pick up a few more dplyr tricks in later lessons as well. We'll make heavy use of dplyr and other Tidyverse packages throughout these lessons. Today we'll focus on basic data manipulation and summary statistics using the dplyr package, part of the Tidyverse family of R packages. When you read a newspaper columnist arguing that \(X\) is to blame for the massive change in \(Y\), how often do you take a look at the data to see if \(Y\) is really changing at all? Or if it's actually changing in the opposite direction? These are easy wins and we should make the most of them, particularly given that many common misconceptions are predictable. Simple descriptive statistics can be extremely powerful. This lesson and the next one will introduce you to some powerful tools for exploratory data analysis, using the gapminder dataset as an example. 9.6.5 How does %>% compare to + in ggplot2?.9.6.4 All About that Base: R's "Native" Pipe.9.6 Put that in your pipe and smoke it!.9.5 Pivoting: From Wider to Longer and Back Again.9.4.3 across() as an alternative to rowwise().9.3 Column-wise Operations with across().7.5.2 Conditional Distributions of Bivariate Normal.7.5.1 Affine Transformations of a Multivariate Normal.7.4.3 What's the Square Root of a Matrix?.
4.4 Heteroskedasticity-Robust Standard Errors and Tests.3.9.6 Adding Interactions With :, *, and ^.3.9.5 Transforming Outcomes and Predictors.2.4 Faceting - Plots for Multiple Subsets.1.10 Change an existing variable or create a new one with mutate.
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