When someone asks me what’s the main advantage of R over python I almost always answer: “It’s dplyr! The way you can handle data.frames is a dream.”

Pythonista: “But pandas has also Data.Frames. They are built to resemble their counterparts in R.”

Me: “That’s true. But they manage the functionality of plain R. Actually R has made several steps ahead with dplyr.”

But dplyr is built mainly for interactive data exploration. So it’s very easy to select, mutate, group and summarize your data.frame (or tibble). The reason is non-standard evaluation (NSE) (See more in Hadley Wickham’s book Advanced R. NSE occures when you use a column-name without any quoting.

But when it’s up to programming it get’s a little more complicated.

So let’s look at an example:

As I mentioned two and a half year ago it’s a little tricky to use otf-fonts in ggplot-graphics in LaTeX-documents.

When you are use XeLaTeX instead of pdfladtex you have to use another option:

Dplyr 0.7.0 has been published. One of the greatest improvements is the enhancement for standard evaluation.

Let’s look at an example. Let’s say I want to apply a function to a column of a data.frame. But the name of the data.frame can change from call to call. So the actual column is the value of a string.

Earlier to dplyr 0.7.0 I used the following methods using lazyeval:

One of the most annoying features of ggplot2 was the missing x-axis scale on facet-plots with some shorter columns.

To illustrate the problem see the following (pre-2.2-version) example:

I recently used the function spread with a factor. Spread is defined in Hadley Wickham‘s package tidyR. It’s used to convert a data.frame from long format to wide format.

Here’s an example showing my problem: