Category: Visualization

The folks at Jumping Rivers conclude a series:

As with many aspects of programming, when you are working by yourself you can be (slightly) more lax with styles and set-up. However, as you start working in a team, different styles can quickly become a hindrance and lead to errors.

Using {knitr} is no different. When you work on documents with different team members, it’s helpful to have a consistent set of settings. If the default for eval changes, this can easily waste time as you try to track down an error. At Jumping Rivers, we use {knitr} a lot. From our training courses, to providing feedback to clients, to constructing monthly reports on clients infrastructure. The great thing about {knitr} is it’s really easy to customise. The bad thing is that without some care, it’s really easy for every member of the team to have different default options. This proliferation of different default options, means that when we pick up someone else document, mistakes may creep in.

Read on for different options they use to keep things consistent.

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The folks at Jumping Rivers continue a series on knitr and rmarkdown:

In this third post, we’ll look at including eternal images, such as figures and logos in HTML documents. This is relevant for all R markdown files, including fancy things like {bookdown}, {distill} and {pkgdown}. The main difference with the images discussed in this post, is that the image isn’t generated by R. Instead, we’re thinking of something like a photograph. When including an image in your web-page, the two key points are

– What size is your image?
– What’s the size of your HTML/CSS container on your web-page?

Read the whole thing.

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Sebastian Sauer gives us a quick solution to plotting one graph per variable:

Say we have a data frame where we would like to plot each numeric variables’s distribution.

There are a number of good solutions outthere such as this one, or here, or here.

When I read this, my first thought was along the lines of, “Why not use facets or something like cowplot?” But then it clicked that this is per-variable plotting, whereas faceting requires you choose a variable and see the plots based on that variable’s distinct values..

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The Jumping Rivers team shares some insight on image creation:

In this series of posts we’ll consider the (simple?) task of generating and including figures for the web using R & {knitr}. Originally this was going to be a single post, but as the length increase, we’ve decided to separate it into a separate articles. The four posts we intend to cover are

– setting the image size (this post)
– selecting the image type, PNG vs JPEG vs SVG
– including non-generated files in a document
– setting global {knitr} options.

Read on for the first post in the series.

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Kenneth Tay takes a look at a sunflower plot:

A sunflower plot is a type of scatterplot which tries to reduce overplotting. When there are multiple points that have the same (x, y) values, sunflower plots plot just one point there, but has little edges (or “petals”) coming out from the point to indicate how many points are really there.

My first thought on it is that it’s too busy and doesn’t do its job of portraying a mass of data points very well. When you have just a few observations, then yeah, it’s not too bad. But once you have any reasonable amount of density on the plot, it’s better to use jitter and transparency (as Kenneth points out). H/T R-bloggers

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