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Category: Visualization

Including and Resizing External Images in knitr

Published 2021-03-03 by Kevin Feasel

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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Choosing an Image File Type

Published 2021-02-19 by Kevin Feasel

The folks at Jumping Rivers continue a series on image optimization:

As the JPEG compression algorithm significantly reduces file size, JPEG files are ubiquitous across the web. If you take a photo on your camera, it’s almost certainly using a JPEG storage format. Historically the file extension was .jpg as Microsoft Windows only handled three character file extensions (also .htm vs .html). But today both extensions are used (personally I prefer .jpeg, but I’m not very consistent if I’m totally honest).

If you did a little Googling on which file format to use for images, then the answer you would come across is that JPEG’s are the default choice. But remember, figures are different from standard images!

Click through for a review of three viable image formats.

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Adding Data Labels in Excel

Published 2021-02-15 by Kevin Feasel

Elizabeth Ricks shows how to include data labels on a line chart in Excel:

Today’s post is a tactical one for folks creating visuals in Excel: how to embed labels for your data series in your graphs, instead of relying on default Excel legends.

To illustrate, let’s look at an example from storytelling with data: Let’s Practice!. The graph below shows demand and capacity (in project hours) over time.

It is significantly more complicated than you might first think.

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Visualizing SQL Server Graph Tables via TGF

Published 2021-02-12 by Kevin Feasel

Louis Davidson shows how you can visualize data stored in SQL Server graph tables:

Each node object has its own surrogate key values that start at 0, so if you are going to use the code for more than one node at a time, you have to make the surrogate values unique for the TGF file (see the last blog on importing for more details on that). In the code I make a temp table to stage the objects, so if you have > 1 node, the second set of keys need to start off where the previous ones left off. So the code uses an identity column, and joins to that identity column by schema, table, and edgeId, outputting the unique key:

Read on to see how Louis translates the data into the right format for visualization.

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Image Sizing in RMarkdown Documents

Published 2021-02-11 by Kevin Feasel

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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Plotting Multiple Plots in R using map and ggplot

Published 2021-02-11 by Kevin Feasel

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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Sunflower Plots in R

Published 2021-02-05 by Kevin Feasel

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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Scrolling Credits in Video Meetings

Published 2021-02-05 by Kevin Feasel

Rob Farley continues a thread:

The basic concept is that once you have a greenscreen application background in place, you can easily show text or drawings or whatever on the screen, superimposed over your webcam feed. I show how to do this in this post: http://blogs.lobsterpot.com.au/2021/01/30/presentation-trickery-online-glassboard-like-lightboard-but-using-just-free-software/. Scott Hanselman also made a video about it at https://youtu.be/-oaikJCR6ec, and used the idea to make scrolling credits, which is a really neat idea that everyone loves.

Click through to see how it works.

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Histograms versus Bar Charts

Published 2021-02-03 by Kevin Feasel

Alex Velez explains the difference between a histogram and a bar (or column) chart:

Consider the above illustration of two data visualizations. 

A histogram is on the left, and to the right is a bar chart (also known as a bar graph). Histograms and bar charts look almost identical, yet they are dramatically different. Understanding their differences is important, so you know when to use each one and accurately convey—or consume—the insights they contain. 

Let’s take a closer look. 

Click through for that closer look.

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