New Version of ggforce Available

Thomas Lin Pedersen announces a new version of ggforce for R:

If there is one thing of general utility lacking in ggplot2 it is probably the ability to annotate data cleanly. Sure, there’s geom_text()/geom_label()but using them requires a fair bit of fiddling to get the best placement and further, they are mainly relevant for labeling and not longer text. ggrepelhas improved immensely on the fiddling part, but the lack of support for longer text annotation as well as annotating whole areas is still an issue.

In order to at least partly address this, ggforce includes a family of geoms under the geom_mark_*() moniker. They all behaves equivalently except for how they encircle the given area(s). 

There are some really interesting features in the ggforce package, so check them out.

Simplify Visuals: No Unnecessary Lines

Stephanie Evergreen shows how you can improve your visuals by removing most of the lines:

The Lines section of the Data Visualization Checklist helps us enhance reader interpretability by handling a lot of the junk, or what Edward Tufte called the “noise” in the graph. I’m referring to all of the parts of the graph that don’t actually display data or assist reader cognition. Create more readability by deleting unnecessary lines. 

The default chart, on the left, has black gridlines. These stand out quite a bit because of how well black contrasts against the white chart background. But the gridlines shouldn’t be standing out so much because they are not the most important part of the graph 

I like that Stephanie keeps the gridlines. I’ve seen Tufte advocate removing them altogether but there’s a lot of value in keeping them in; just don’t make them the sharpest focus color.

MLB Run Scoring Trends Shiny App

Martin Monkman has an update to the MLB run scoring app in Shiny, just in time for spring training:

2. feather instead of csv
The app relied on some pre-wrangled csv files; these have been replaced by files stored using the .feather format, which makes for a signficant performance improvement.

Martin has made a significant number of changes and it’s cool to see the full list of changes. H/T R-bloggers

Eye-Friendly Palettes

Shannon Holck has shared a Power BI theme using a color-safe and easy to view palette:

Edward Tufte recommended use of soft colors that do not tire the eyes.  I’ve actually never read his books (yet), but a former boss of mine was a devout disciple and produced some beautifully soft color palettes.

Stephen Few, in “Show Me the Numbers,” reiterated Tufte’s color theories and recommended three sets of hues:

Light – for large shapes, e.g. bars
Medium – for small shapes, e.g. points
Dark/Bright – for calling attention to data

Click through for more including where you can get this Power BI theme. I’m not exactly the world’s biggest fan of the default palette so I’ll have to check this one out.

Conditional Formatting in Power BI

Reza Rad shows us a few ways to perform conditional formatting in Power BI:

I have given many presentations and talks about Data Visualization, and still, I am amazed by how many visualizations I see which is not following the basic rules. In this article, I want to focus on table visual. A table is a visual that most of us are using it on many occasions, in fact, many users, like to see the data in table format. However, a table can be visualized in a way that is not readable. In this article, I’m showing you the most common style of a table which many report developers use, and then challenge it with a better style. The mystery is of course in conditional formatting. Like all my other articles, this article is demonstrating this technique in Power BI. If you like to learn more about Power BI, read Power BI book from Rookie to Rock Star.

Some of these formats are better than others, but you do have the power to do quite a bit with it in Power BI.

Displaying Filters In Power BI Tooltips

Marco Russo shows how you can create a filter dump measure to show all active filters as part of your tooltip:

The Tooltips can display a string with multiple lines. This is useful for the DumpFilters measure that creates a new line for every column with a filter. You might wonder why the DumpFilters measure is required considering that Power BI can already display any filters and slicers affecting a visual. The reason is that the DumpFilters measure isolates the filters of a single cell and can show the effects of filters that are not visible in the standard visualization provided by Power BI.

This is interesting reading and a good way of sharing to users how they got to the current view of data.

Improving Plots With ggformula

Sebastian Sauer shows how you can use the ggformula package combined with ggplot2 to enhance your R visuals:

Since some time, there’s a wrapper for ggplot2 available, bundled in the package ggformula. One nice thing is that in that it plays nicely with the popular R package mosaicmosaic provides some useful functions for modeling along with a tamed and consistent syntax. In this post, we will discuss some “ornaments”, that is, some details of beautification of a plot. I confess that every one will deem it central, but in some cases in comes in handy to know how to “refine” a plot using ggformula.

Note that this “refinement” is primarily controlled via the function gf_refine() (most stuff), gf_lab() (for labs), and gf_lims() (for axis limits). Themes can be adjusted using gf_theme().

Click through for several examples.

Extracting Numerical Data Points From Images

Matt Allington visualizes changes in the Gartner magic quadrant for BI tools:

Today Gartner released the 2019 magic quadrant for Business Intelligence.  As expected (by me at least), Microsoft is continuing its trail blazing and now has a clear lead over Tableau in both ability to execute and completeness of vision.  I thought it would be interesting to see a trend over time for the last 5 years, as this is the time period that I have been a professional Power BI Consultant.  I needed some way to extract the numerical data points from the images I had collected.  This article shows you how to do that.  Here is the final output – a scatter chart with a play axis in Power BI of course.

I was just commenting the other day about how somebody should do this and Matt went and did it.

Power BI Violin Plots

Meagan Longoria shows off a violin plot custom visual in Power BI:

violin plot is a nifty chart that shows both distribution and density of data. It’s essentially a box plot with a density plot on each side. Box plots are a common way to show variation in data, but their limitation is that you can’t see frequency of values. In other words, you can see statistics such as min, max, median, mean, or quartiles, but you can’t see the individual values nor how often they occurred.

Read on for a review of the custom visual available for violin plots, including areas where it does well and where it falls short at present.

Generating Plots Like The BBC

David Smith has some notes on bbplot, a ggplot2 extension the BBC uses for its graphics:

If you’re looking a guide to making publication-ready data visualizations in R, check out the BBC Visual and Data Journalism cookbook for R graphics. Announced in a BBC blog post this week, it provides scripts for making line charts, bar charts, and other visualizations like those below used in the BBC’s data journalism. 

I’m still reading through the linked cookbook but it’s a good one.

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