A Quick Look At Data Visualization Tools

Vincent Wong walks us through data visualization tools on the market today:

Highcharts

When we talk about Echarts, we will usually compare it with Highcharts. The relationship between them is a bit like the relationship between WPS and Office.

Highcharts is also a visualization library which you have to pay for it if you are going to use it. It has many advantages, for example, its documents and tutorials, JS scripts, and CSS are very detailed. It saves time and allows you to pay more attention to learning and developing. What’s more, it is very stable.

There are some good tools on this list.

Histomaps

Jennifer Lyons shows us a few places where we can use histomaps to good effect:

Histomaps can be a good option when we are looking to visualize qualitative trends over time. The trick is that you need to be using two mutually exclusive variables. In Spark’s case he used time and power. In the example below, I am using time and staff’s satisfaction with their work environment. Imagine you are collecting open-ended survey data every quarter during your grant term. You code a person’s response into the mutually exclusive category of met, partially met, and not met regarding their expressed satisfaction.

Read the whole thing.

Tips For Creating Population Share Maps

Lisa Charlotte Rost uses election results to give us some tips on building map-based comparisons:

This map shows us that both parties received a higher vote share in the east than in the west. But it also artificially increases the polarisation: If the AfD gets just one more vote than the Linke, the whole district flips from pink to blue. And we would need to create a third category, “tied”, for the nine election districts in which there were exactly as many AfD voters as Linke voters. (The New York Times created that category for their “Extremely Detailed Map of the 2016 Election”.)

There is another option: We could show the percentage point difference between the two shares. To do so, we subtract the AfD votes from the Linke votes. If the result is positive, we show the district in blue. If it’s negative, we show it in pink.

This is a case where there’s not a huge difference between methods, but it can make a big difference in other situations.

Animating With gganimate

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.

Why Graph Text Should Be Horizontal

Stephanie Evergreen explains why you don’t want diagonal or vertical text in your charts:

In languages based on the Latin alphabet, we read horizontally, from left to right. Reading on a diagonal produces cramped necks. Reading vertical text is just not going to happen. So, as much as possible, the text in our graphs should be horizontal.

Let’s walk through a demo. I was trying to combat my sense of hopelessness about the world by exploring the latest dataviz related to the Sustainable Development Goals (don’t we all?). The vast majority of their viz is pretty awesome, especially given how complicated the data can be. I saw this graph about how few developing countries have representation on international development councils.

Watch as Stephanie starts with a column chart with diagonal (and cut-off) text and converts it to a visual which is much easier to read.

Examples Of Charts In Different Languages

David Smith points out a great repository of information on generating different types of charts in different libraries:

The visualization tools include applications like Excel, Power BI and Tableau; languages and libraries including R, Stata, and Python’s matplotlib); and frameworks like D3. The data visualizations range from the standard to the esoteric, and follow the taxonomy of the book Data Visualisation (also by Andy Kirk). The chart categories are color coded by row: categorical (including bar charts, dot plots); hierarchical (donut charts, treemaps); relational (scatterplots, sankey diagrams); temporal (line charts, stream graphs) and spatial (choropleths, cartograms).

Check out the Chartmaker Directory.

Styling In ggplot2

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.

NFL Player Stats In Power BI

Dustin Ryan shares his NFL player stats and analysis Power BI desktop file:

I’ve had a lot of people ask me for this over the past few months and its finally (mostly) ready! There are still a few things I’d like to do with the data models and reports but I wanted to go ahead and get the content shared out since I know many people use this for the Fantasy Football drafts which generally happen during the third week of the NFL preseason.

So here it is. I’ve spent a decent amount of time scraping the data from a few different websites in order to put something together I thought would be useful and fun, so please take a look and enjoy it!

Click through for the file and a YouTube video with more info.

Creating Timelines With dbatools

Marcin Gminski shows how to pull SQL Agent and backup history out of SQL Server and display it as a visual history timeline:

Currently, the output from the following commands is supported:

  • Get-DbaAgentJobHistory
  • Get-DbaBackupHistory

You will run the above commands as you would normally do but pipe the output to ConvertTo-DbaTimeline, the same way as you would with any other ConverTo-* PowerShell function. The output is a string that most of the time you will save as file using the Out-File command in order to open it in a browser.

Then, with the ConvertTo-DbaTimeline cmdlet, you can convert that into an HTML page which looks pretty good.

Making A Readable Presentation Template

Meagan Longoria has some advice for presentation templates:

The title text is 36pt Segoe UI Light, the subheading text is 24pt Segoe UI, and the speaker info text is 14 pt Segoe UI.

Those font sizes alone make it very hard to read from the back of even the smaller rooms at the conference.

In addition to being too small, the gray text for the speaker info doesn’t have enough contrast from the white background. We want to get a contrast ratio of at least 4.5:1 (but 7:1 would be better). The contrast ratio for these colors is 4.0.

While sans serif fonts are generally thought to be easier to read in presentations, it’s better to use fonts with a stroke width that is not too thin – not necessarily wider characters, but thicker lines that make up each letter. So Segoe UI Light would not be my first choice for a title font, but Segoe UI or Segoe UI Bold might be ok.

Also, the red used on the right half of the slide is VERY bright for an element that is purely decorative, to the point that it might be distracting for some people. And the reason we need to squish our title into two lines of too-small text is because that giant red shape takes up half the page. What is more important: a “pretty” red shape to make our slide look snazzy or being able to clearly read the title of the presentation?

There’s a lot along these lines, and it’s great food for thought.  Meagan includes a set of recommendations at the end, so be sure to catch those.

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