Disentangling Data Science
Tag: dashboard
Rob Gilmore curates a github repo listing resources for working with Shiny, the R web framework and dashboarding tool.
Nan Xiao curates a second repository, listing awesome R packages offer that extensions to Shiny, like extended UI or server components.
They should be your go-to resources when looking for anything Shiny!
My colleague prof. Jack van Wijk pointed me towards these great guidelines by Deloitte on how to design an effective dashboard.
Some of these rules are more generally applicable to data visualization. Yet, the Deloitte 10 commandments form a good checklist when designing a dashboard.
Here’s my interpretation of the 10 rules:
In terms of recycling the good work by others operating in the data visualization field, check out:
I just love how Deloitte uses example visualizations to help convey what makes a good (dashboard) chart:
John Hopkins University’s Center for Systems Science and Engineering maintains this Corona virus dashboard showing the latest statistics and other information regarding the 2019-2020 outbreak in Wuhan.
The dashboard is updated every 15 minutes and demonstrates, among others, the total infected, death toll, recovery rate, and geographical spread.
R markdown creates interactive reports from R code, including interactive reports, documents, dashboards, presentations, and even books. Have a look at this awesome gallery of R markdown examples.
Yan Holtz recently created a neat little overview of handy R Markdown tips and tricks that improve the appearance of output documents. He dubbed this overview Pimp my RMD. Have a look, it’s worth it!
In my second Harry Plotter blog (22-Aug-2017), I wrote:
I would like to demonstrate how regular expressions can be used to retrieve (sub)strings that follow a specific format. We could use regex to examine, for instance, when, and by whom, which magical spells are cast.
Well, Prusinowskik (real name unknown) beat me to it, and how! S/He formed a comprehensive list of all spells found in the Harry Potter saga (see below), and categorized these into “spells“, “charms“, and “curses“, and into “popular“, “dueling” and “unforgivable” purposes. Next, Prusinowskik built an interactive Shiny application with lovely JavaScript graphs (package: rCharts) for us to discover precisely when during the saga which spells are cast (see also below). Moreover, the analysis was repeated for both the books and the movies.
Truly excellent work Prusinowskik! The Shiny app can be found here.
If you have ever programmed in R, you are probably familiar with the Grammar of Graphics due to ggplot2. You can read more about the Grammar of Graphics here, but the general idea behind it is that visualizations can be build up through various layers, each of which have certain characteristics (aesthetics in ggplot2).
ggplot(tips, aes(x = total_bill, y = tip)) +
geom_point(aes(color = sex)) +
geom_smooth(method = 'lm')
This post is not about ggplot2 nor specifically the Grammar of Graphics, but rather a summary of the official release of Vega-Lite 2, a high-level language for rapidly creating interactive visualizations which you might know from the R-package ggvis.
Vega-Lite has four operators to compose charts: layer, facet, concat and repeat. Layer stacks charts on top of each other in an orderly fashion. Facet divides and charts the data into groups. Concat combines multiple charts into dashboard layouts and, finally, repeat concatenate charts. Most importantly is that these operators can be combined! The example below compares weather data in New York and Seattle, layering data for individual years and averages within a repeated template for different measurements.
Vega-Lite 2 is especially useful because of the included interaction options. Programmers can specify how users can interactive select the data in their visualizations (e.g., a point or interval selection), along with possible transformations. With these interactions, users can for instance filter data, highlight points, or pan or zoom a plot. The plot below uses an interval selection, which causes the chart to include an interactive brush (shown in grey). The brush selection parameterizes the red guideline, which visualizes the average value within the selected interval.
However, this is not all! When multiple visualizations are combined in a dashboard, interactive selections can apply to all. Below, you see an interval selection being applied over a set of histograms. As a viewer adjusts the selection, they can immediately see how the other distributions change in response.
According to the developers Vega and Vega-Lite will be included in Jupyter Lab (the next generation of Jupyter Notebooks). Please find more details about Vega-Lite in the documentation or view the InfoVis 2016 research paper on the Vega-Lite language design. Moreover, check out the example gallery or these Vega-Lite applications. The source code you can find on GitHub. For updates, follow the Vega project on Twitter at @vega_vis. For an overview of the features you may watch the OpenVis Conference Video with the developers.
paulvanderlaken.com 