Tag: visualization

Online Workshop Tidy Data Science in R, by Jake Thompson

Online Workshop Tidy Data Science in R, by Jake Thompson

Here’s a website hosting for a five-day hands-on workshop based on the book “R for Data Science”.

The workshop was originally offered as part of the Stats Camp: Summer Statistical Institute in Lawrence, KS and hosted by the Center for Research Methods and Data Analysis and the Achievement and Assessment Instituteat the University of Kansas. It is designed for those who want to learn practical applications of R for data analysis.

You can download the Workshop files, but I suggest you do so via the original workshop webpage.

This workshop is designed for those who want to learn how to use R to analyze data. The material is based on Hadley Wickham and Garrett Grolemund’s R for Data Science. We’ll talk about how to conduct a complete data analysis from data import to final reporting in R using a suite of packages known as the tidyverse. The two goals of this workshop are: 1) learn how to use R to answer questions about our data; and 2) write code that is human readable and reproducible. We will also talk about how to share our code and analyses with others.

You should take this workshop if you are new to R, or to the tidyverse, and want to learn how to take advantage of this ecosystem to do data analysis. You’ll get the most from the workshop if you are primarily interested in applying pre-existing R packages and functions to your own data. We will give minimal tutorials on how to write your own functions; however, the main focus will be on using existing tools, rather than building our own.

About this workshop

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3D visual representations of common neural network architectures

3D visual representations of common neural network architectures

Came across this awesome Youtube video that blew my mind. Definitely a handy resource if you want to explain the inner workings of neural networks. Have a look!

Reminded me of my other go-to resource when it comes to explaining neural nets, the playlists by 3Blue1Brown:

I’ll surely add these to the other neural network resources I’ve written about on my blog:

Google Fonts: 915 free font families

Google Fonts: 915 free font families

Looking for a custom typeface to use in your data visualizations? Google Fonts is an awesome databank of nearly a thousands font families you can access, download, and use for free.

If you’re into design, the website includes a blog featuring articles on font design.

Google Fonts among others provided the font for my dissertation cover so I definitely recommend it.

Learn from the Pros: How media companies visualize data

Learn from the Pros: How media companies visualize data

Past months, multiple companies shared their approaches to data visualization and their lessons learned.

Click the companies in the list below to jump to their respective section


Financial Times

The Financial Times (FT) released a searchable database of the many data visualizations they produced over the years. Some lovely examples include:

Graphic showing what May needs to happen to get her deal over the line when MPs vote on Friday
Data visualization belonging to a recent Brexit piece by the FT, viahttps://www.ft.com/graphics
Dutch housing graphic
Searching the FT database for European House Prices via https://www.ft.com/graphics returns this map of the Netherlands.

BBC

The BBC released a free cookbook for data visualization using R programming. Here is the associated Medium post announcing the book.

The BBC data team developed an R package (bbplot) which makes the process of creating publication-ready graphics in their in-house style using R’s ggplot2 library a more reproducible process, as well as making it easier for people new to R to create graphics.

Apart from sharing several best practices related to data visualization, they walk you through the steps and R code to create graphs such as the below:

One of the graphs the BBC cookbook will help you create, via https://bbc.github.io/rcookbook/

Economist

The data team at the Economist also felt a need to share their lessons learned via Medium. They show some of their most misleading, confusing, and failing graphics of the past years, and share the following mistakes and their remedies:

  • Truncating the scale (image #1 below)
  • Forcing a relationship by cherry-picking scales
  • Choosing the wrong visualisation method (image #2 below)
  • Taking the “mind-stretch” a little too far (image #3 below)
  • Confusing use of colour (image #4 below)
  • Including too much detail
  • Lots of data, not enough space

Moreover, they share the data behind these failing and repaired data visualizations:

Via https://medium.economist.com/mistakes-weve-drawn-a-few-8cdd8a42d368
Via https://medium.economist.com/mistakes-weve-drawn-a-few-8cdd8a42d368
Via https://medium.economist.com/mistakes-weve-drawn-a-few-8cdd8a42d368
Via https://medium.economist.com/mistakes-weve-drawn-a-few-8cdd8a42d368

FiveThirtyEight

I could not resist including this (older) overview of the 52 best charts FiveThirtyEight claimed they made.

All 538’s data visualizations are just stunningly beautiful and often very
ingenious, using new chart formats to display complex patterns. Moreover, the range of topics they cover is huge. Anything ranging from their traditional background — politics — to great cover stories on sumo wrestling and pricy wine.

Viahttps://fivethirtyeight.com/features/the-52-best-and-weirdest-charts-we-made-in-2016/
Via https://fivethirtyeight.com/features/the-52-best-and-weirdest-charts-we-made-in-2016/ You should definitely check out the original cover story via https://projects.fivethirtyeight.com/sumo/
Via https://fivethirtyeight.com/features/the-52-best-and-weirdest-charts-we-made-in-2016/

Daily Art by Saskia Freeke

Daily Art by Saskia Freeke

Saskia Freeke (twitter) is a Dutch artist, creative coder, interaction designer, visual designer, and educator working from Amsterdam. She has been creating an awesome digital art piece for every day since January 1st 2015. Her ever-growing collection includes some animated, visual masterpieces.

My personal favorites are Saskia’s moving works, her GIFs:

Saskia uses Processing to create her art. Processing is a Java-based language, also used often by Daniel Shiffmann whom we know from the Coding Train.

18 Pitfalls of Data Visualization

18 Pitfalls of Data Visualization

Maarten Lambrechts is a data journalist I closely follow online, with great delight. Recently, he shared on Twitter his slidedeck on the 18 most common data visualization pitfalls. You will probably already be familiar with most, but some (like #14) were new to me:

  1. Save pies for dessert
  2. Don’t cut bars
  3. Don’t cut time axes
  4. Label directly
  5. Use colors deliberately
  6. Avoid chart junk
  7. Scale circles by area
  8. Avoid double axes
  9. Correlation is no causality
  10. Don’t do 3D
  11. Sort on the data
  12. Tell the story
  13. 1 chart, 1 message
  14. Common scales on small mult’s
  15. #Endrainbow
  16. Normalise data on maps
  17. Sometimes best map is no map
  18. All maps lie

Even though most of these 18 rules below seem quite obvious, even the European Commissions seems to break them every now and then:

Can you spot what’s wrong with this graph?