Seminar Schedule and Reading List

Week 1: Digital Searching tips and tricks

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Week 2: Introduction to databases and analysis
An introduction to database basics including ‘what is a database’, why and how do we sample data, data checking and cleaning, and some basic statistics often used by historians

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Week 3: Intro to R
Included: what is R; the different sections of the R-Studio screen; writing scripts; including metadata in scripts; data types; saving; opening files; storing objects; importing and exporting data; basic data manipulation in R; useful terminology used by R to help with Google searches. Worked through these in a hands on session.

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Week 4: Databases in R

Included: a refresher on importing data; an overview of different ways we could manipulate data - in data tables, using the tidyverse, and in using SQL; using SQL in R to interrogate a small relational database; the difference between NULL and zero; exporting queries; and left joins.

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Weeks 5 and 6: Textual Analysis in R

Included: An introduction to APIs - what are they, building up a GET request in R to call on digital.nz, looping the GET request to collect all of the results, and API etiquette basics; textual analysis in R, creating basic word clouds, and running sentiment analysis across a soldier diary, a settler diary, and the results from our earlier API request.

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Week 7: Websites and Mapping

An introduction to Weebly, but much of it would apply to any website creation platform. Included thinking about the look and feel of the website, copyright issues, how to ‘build’ pages and embed HTML features from external sources, Google Analytics, SEO, and some thoughts on Blogs and writing for the web. 

Began with an overview of types of maps, and more detail on choropleth maps. Moved on to playing with Google Maps and Google Earth Pro, and maps in Google Charts.

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Week 8: Visualisations

A broad overview of visualisations, the good and the bad and why they are used to communicate information. Included playing with some Google Charts coding generally, then all together for a Bar Chart. Very quick overview at the end of the class of such tools as Timeline, StoryMap, Juxtapose, and Genially.
 
Suggested reading:

• Alberto Cairo, The Functional Art: an introduction to information graphics and visualization, New Riders, 2013

• Alberto Cairo, The Truthful Art: data, charts, and maps for communication, New Riders, 2016

• Alberto Cairo, http://www.thefunctionalart.com/

• Nathan Yau, Visualize This: the flowingdata guide to design, visualization, and statistics, Wiley Publishing, Indianapolis, 2011

• Nathan Yau, Data Points: Visualization that means something, John Wiley and Sons Inc, Indianapolis, 2013

• Nathan Yau, https://flowingdata.com/

• Stephen Few, Show me the numbers: designing tables and graphs to enlighten, Analytics Press, Burlingame, 2012

• Stephen Few, http://www.perceptualedge.com/

• Georgia Lupi and Stefanie Posavec, Dear Data, Particular Books, 2016

• Georgia Lupi and Stefanie Posavec, http://www.dear-data.com/

• Nate Silver, https://fivethirtyeight.com/

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Week 9: Network analysis in R

We worked through an online tutorial by Jesse Sadler in which she uses historical data and creates a series of increasingly complex network analysis maps in R. This also introduced students to GitHub and exporting data and code from there.

• Network Analysis with R

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Week 10: R maps and visualisations

Students chose from a selection of online tutorials regarding either mapping or advanced visualisations in R and worked through these across the 2 hour workshop. These online tutorials were chosen primarily on the basis of me being able to see clearly who the author is, being happy that they are someone who knows what they are doing, and on the content being written/aimed at an audience who was, essentially, students who have a bit of R knowledge but are still finding their way.  

Suggested online tutorials included:

Maps

• Geocomputation with R: https://geocompr.robinlovelace.net/index.html 

• Drawing beautiful maps programmatically with R, sf and ggplot2 (Published 25 October 2018, by Mel Moreno and Mathieu Basille)

  • Part 1: Basics: https://r-spatial.org/r/2018/10/25/ggplot2-sf.html  

  • Part 2: Layers: https://r-spatial.org/r/2018/10/25/ggplot2-sf-2.html

  • Part 3: Layouts: https://r-spatial.org/r/2018/10/25/ggplot2-sf-3.html

• Using Spatial Data with R (Last updated 11 February 2019. By Claudia A Engel)

  • https://cengel.github.io/R-spatial/index.html

  • Skip straight to making maps in R here: https://cengel.github.io/R-spatial/mapping.html

• Creating Maps and mapping data with ggplot2: a YouTube R tutorial by Dr Paul Christiansen of the Institute of Public Health at the University of Liverpool.

  • https://www.youtube.com/watch?v=AgWgPSZ7Gp0

  • Files associated with the tutorial are available here: https://drive.google.com/drive/folders/13kUgC2ZbirHosoECX2eVPpOjtpSvKyR9

• Map Making in R: Course notes from ‘Case Studies in Reproducible Research: a spring seminar at UCSC’, 8 July 2017, Eric C. Anderson, Kristen C. Ruegg, Tina Cheng. Note that because this is from 2017 parts of it will be out of date by now.

  • This link will take you to the particular class on map making, but you can view the full syllabus on this same page.https://eriqande.github.io/rep-res-eeb-2017/map-making-in-R.html

Data Visualisations

• R for Data Science. Hadley Wickham and Garreet Grolemund: https://r4ds.had.co.nz/index.html

  • Chapter 3 of this book is the Data Visualisation chapter, but all of the other chapters are useful references for those continuing on with R adventures. https://r4ds.had.co.nz/data-visualisation.html

  • Chapter 28 is Graphics for Communication and takes you through some steps to make your ggplot2 graphs a bit more audience friendly. https://r4ds.had.co.nz/graphics-for-communication.html

 

• R Graphics Cookbook, Winston Chang.​

  • ​“…a practical guide that provides more than 150 recipes to help you generate high-quality graphs quickly, without having to comb through all the details of R’s graphing systems. Each recipe tackles a specific problem with a solution you can apply to your own project, and includes a discussion of how and why the recipe works.”

  • https://r-graphics.org/index.html

 

• Youtube tutorials: Plotting Anything with ggplot2

One of the people who maintains ggplot2 (it is over 10 years old now) is Thomas Lin Pedersen, about whom you can learn more here: https://www.data-imaginist.com/about

He live streamed and has since posted on YouTube two workshops about ggplot2 that you might prefer, if you like to have someone show you through the steps.

• Part 1: https://www.youtube.com/watch?v=h29g21z0a68  (2 ½ hours)

• Part 2: https://www.youtube.com/watch?v=0m4yywqNPVY  (2 hours)

• Resources for these workshops available here: https://github.com/thomasp85/ggplot2_workshop

 

• Cheatsheets

Here are a couple of handy ‘cheatsheets’ about the package – useful wee reminders of some of the features and how to use them:

https://github.com/rstudio/cheatsheets/blob/main/data-visualization-2.1.pdf

 

• ggplot2: Elegant Graphics for Data Analysis

https://ggplot2-book.org/

“While this book gives some details on the basics of ggplot2, its primary focus is explaining the Grammar of Graphics that ggplot2 uses, and describing the full details. It is not a cookbook, and won’t necessarily help you create any specific graphic that you need. But it will help you understand the details of the underlying theory, giving you the power to tailor any plot specifically to your needs.

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All of the above are written by people directly involved in the creation or maintenance of the ggplot2 package, so I thought it probably useful to include at least one outside voice. When making a Gannt chart in ggplot2 earlier this year to show the periods of the provincial councils of NZ for the PoPP Shiny App I found this tutorial and skipped ahead to the section on colour and was able to get quite a lot out of it just dipping my toe into the tutorial like that. So feel free to work all the way through it, or just dip in and out as you see fit. 

• The tutorial (last updated 24 March 2022): https://www.cedricscherer.com/2019/08/05/a-ggplot2-tutorial-for-beautiful-plotting-in-r/

• The author is Cédric Scherer – for more about him, see here: https://www.cedricscherer.com/top/about/

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Week 11: Working on student specific tools for their final assignments
At this stage in the year each student had different needs for their final assignments, so an individual plan was made for each of them for this class. Tools covered included additional direction on network maps; further instruction on Google Charts; a refresher on infographics, Timeline, and Google Maps; some further guidance on R coding for particular issues.

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Week 12: Student presentations