Category: Visualization

Bernando Lares shows off some stuff his lares can do around visualizing time series data:

The overall idea of these functions is to visualize your stocks and portfolio’s performance with a just a few lines of simple code. I’ve created individual functions for each of the calculations and plots, and some other functions that gather all of them into a single list of objects for further use.

On the other hand, the lares package is “my personal library used to automate and speed my everyday work on Analysis and Machine Learning tasks”. I am more than happy to share it with you for your personal use. Feel free to install, use, and comment on any of its code and functionalities and I’ll happy to help you with it. I have previously shared other uses of the library in other posts which might also interest you: Visualizing ML Results (binary), Visualizing ML Results (continuous)and AutoML to understand datasets.

• NOTE 1: The following post was written by a non-economist or professional investor. I am open to your comments and technical corrections if needed. Glad to learn as always!

• NOTE 2: I will be using the less customizable functions in this post so we can focus more on the outputs than in the coding part; but once again, feel free to use the functions and dive into the library to understand or change them!

• NOTE 3: All currency units are USD ($).

It does seem to be easy to use for this scenario.

David Smith looks at an interesting package in R:

Tyler describes the rayshader package in a gorgeous blog post: his goal was to generate 3-D representations of landscape data that “looked like a paper weight”. (Incidentally, you can use this package to produce actual paper weights with 3-D printing.) To this end, he went beyond simply visualizing a 3-D surface in rgl and added a rectangular “base” to the surface as well as shadows cast by the geographic features. He also added support for detecting (or specifying) a water level: useful for representing lakes or oceans (like the map of the Monterey submarine canyon shown below) and for visualizing the effect of changing water levels like this animation of draining Lake Mead.

It looks great.

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.