Category: Visualization

Laura Ellis shows us how to create formatted tables using the formattable package in R:

We are going to narrow down the data set to focus on 4 key health metrics. Specifically the prevalence of obesity, tobacco use, cardiovascular disease and obesity. We are then going to select only the indicator name and yearly KPI value columns. Finally we are going to make extra columns to display the 2011 to 2016 yearly average and the 2011 to 2016 metric improvements.

Tables are an area of data visualization that we tend to forget at our own peril.

Jason C. Fisher walks us through a color scheme generator based on Paul Tol’s research;

Choosing colors for a graphic is a bit like taking a trip down the rabbit hole, that is, it can take much longer than expected and be both fun and frustrating at the same time. Striking a balance between colors that look good to you and your audience is important. Keep in mind that color blindness affects many individuals throughout the world and it is incumbent on you to choose a color scheme that works in color-blind vision. Luckily there are a number of excellent R packages that address this very issue, such as the colorspace,RColorBrewer, and viridis packages. And because this is R, where diversity is king, why not offer one more function for creating color blind friendly palettes.

Let me introduce the GetTolColors function in the R-package inlmisc. This function generates a vector of colors from qualitative, diverging, and sequential color schemes by Paul Tol (2018). The original inspiration for developing this function came from Peter Carl’s blog post describing color schemes from an older issue of Paul Tol’s Technical Note (issue 2.2, released Dec. 2012). And the qualitative color schemes described in his blog post found their way into the ptol_pal function in the R-package ggthemes. My intent with this document is to exhibit the latest Tol color schemes (issue 3.0, released May 2018) and show that they are not only visually pleasing but also well thought out.

Read on for step-by-step instructions and to see some of the palettes.  The package authors have taken care in color design, so check it out.

Simon Jackson shows how you can use the secondary axis to label line endings in ggplot2:

Now we can use scale_y_*, with the argument sec.axis to create a second axis on the right, with numbers to be displayed at breaks, defined by our vector of line ends:

ggplot(d, aes(age, circumference, color = Tree)) +
      geom_line() +
      scale_y_continuous(sec.axis = sec_axis(~ ., breaks = d_ends))

This is good.  I’d really prefer to show the labels instead of the value; that way it’d be possible to eliminate the legend altogether.  H/T R-Bloggers.

The folks at Jumping Rivers show how to use gganimate to, in this case, track goal differences for Premier League teams:

Now we can see not only when Arsenal picked up points, but when they dropped points as well. For example, on the 27th of August, they got beat by 4 goals as their goal difference shifted from 0 to -4.

We’re not done there! For the gif, we want to be able to display the current status of the team on each day i.e. Champions League (4th or above), Europa League (5th – 7th), Top Half (8th – 10th), Bottom Half (11th – 17th) or Relgations Zone (18th or below). To do this, on each day, we first need to retrieve the order of each team based on their points and goal difference

Click through to see the example.

The folks at Jumping Rivers show an example of creating a nice-looking plot with ggplot2:

The changes we’ve made so far would impossible for any package to do for us – how would the package know the plot title? We can now improve the look and feel of the plot. There are two ways of complementary ways of doing this: scales and themes. The ggplot scales control things like colours and point size. In the latest version of ggplot2, version 3.0.0, the Viridis colour palette was introduced. This palette is particularly useful for creating colour-blind friendly palettes

g + scale_colour_viridis_d() # d for discrete

With a few lines of code, those default graphs can look a lot nicer.