Abdul Majed Raja shows how to use the cowplot library in R to merge together independent plots into a single image:

The way it works in cowplot is that, we have assign our individual ggplot-plots as an R object (which is by default of type ggplot). These objects are finally used by cowplot to produce a unified single plot.

In the below code, We will build three different histograms using the R’s in-built dataset iris and then assign one by one to an R object. Finally, we will use cowplot function plot_grid() to combine the two plots of our interest.

The only thing that disappointed me with cowplot is that its name has nothing to do with cattle.

Max Gordon has some updates to the htmlTable package:

Even more common than grouping columns is probably grouping data by rows. The htmlTable allows you to do this by rgroup and tspanner. The most common approach is by using rgroupas the first row-grouping element but with larger tables you frequently want to separate concepts into separate sections. Here’s a more complex example. This has previously been a little cumbersome to to counting the rows of each tspanner but now you’re able to (1) leave out the last row, (2) specify the number of rgroups instead of the number of rows. The latter is convenient as the n.tspanner must align with the underlying rgroup. 

I haven’t used this package before, but it does look interesting. H/T R-bloggers

Thomas Lin Pedersen announces that gganimate is now available on CRAN:

While this commit was done in the autumn 2017, nothing further happened until I decided to make gganimate the center of my useR 2018 keynote, at which point I was forced (by myself) to have some sort of package ready by the summer of 2018.
A fair amount of users have shown displeasure in the breaking changes this history has resulted in. Many blog posts have already been written focusing on the old API, as well as code on numerous computers that will no longer work. I understand this frustration, of course, but both me and David agreed that doing it this way was for the best in the end. I’m positive that the new API has already greatly exceeded the mind-share of the old API and given a year the old API will be all but a distant memory…

Read on for information on these breaking changes, and how the changes will make life easier in the long run. And stay for the fireworks. H/T R-Bloggers

Sebastian Sauer shows us a few techniques for visualizing multivariate data, using ggplot2 in some cases and plotly in others:

Plotting univariate (sampled) normal data
Well, that’s obvious.
d %>% ggplot(aes(x = X1)) + geom_density()

It gets much less obvious from there.  It was also interesting learning about ggplotly, a function which translates ggplot2 visuals to plotly visuals.

Laura Ellis has an updated ggmap tutorial:

For those of you who have been following along with issue #51 in the ggmap repo, you’ll notice that there have been a number of changes in the Google Maps Static API service. Unfortunately these have caused some breakage in previous ggmap functionality.
If you used this package prior to July 2018, you may were likely able to do so without signing up for the Google Static Map API service yourself. As indicated on the the ggmap github repo – “Google has recently changed its API requirements, and ggmap users are now required to provide an API key and enable billing.  The billing enablement especially is a bit of a downer, but you can use the free tier without incurring charges. Also, the service being exposed through an easy to use r package that extends ggplot2 is pretty great so I’ll allow it.

This recent API change hurts.  But click through for the tutorial, which doesn’t hurt.

Sebastian Sauer shows how to display multiple ggplot2 diagrams together using facets as well as a combination of the nest() and map() functions:

One simple way is to plot several facets according to the grouping variable:

d %>% ggplot() + aes(x = hp, y = mpg) + geom_point() + facet_wrap(~ cyl)

Faceting is great, but it’s good to know the other technique as well.