In this course participants will learn to use the R programming language, with a particular focus on using R for handling, visualising, analysing research data, and communicate research outputs. These are important skills for today's scientists (including agriscience), economists (including agricultural economics) and business professionals (including agribusiness). This course will highlight strategies for developing an efficient workflow centred around R and RStudio. After learning the basics, we will focus on using R for exploratory data analysis, the production of more complex research visualisations, statistical modelling, and employing R for research communication. Additionally, we will look into the basics of working with databases in R and managing our research data and output with git.
With a hands-on approach, each participant will be able to import data with R, navigate and manipulate data tables and represent data graphically from very early in the course.
At the end of the course, participants will have reached an advanced knowledge of R and should be equipped to deal with almost all aspects of using R to analyse their research data.
- R (>4.0) and RStudio will need to be installed prior to the course
- A second screen has been recommended by past participants
- Course Book: R for Data Science
- Course Book Solutions: [Unofficial solutions for "R for Data Science"](https://jrnold.github.io/r4ds-exercise-solutions/](https://jrnold.github.io/r4ds-exercise-solutions/)
- Course Code Repository: Github Repository
- Course Chat: See Paper Outline or Email
- Course Zoom Meeting Link: See Paper Outline or Email
- Course Moodle: See Paper Outline or Email
- RMarkdown: RMarkdown Reference Guide
- Databases: Databases using R
- Git: Head First Git excerpts will be provided
- tidymodels: Tidy Modeling with R
- R Shiny: Mastering Shiny
- General: RStudio Cheatsheets
- Visualisations (33%): Assesment tasks will be handed out on 18/03/22. Please submit it by 01/04/22 NZT.
- Basic Data Analysis (33%): Assesment tasks will be handed out on 27/04/22. Please submit it by 11/05/22 NZT.
- Data Retrieval & Modelling (33%): Assesment tasks will be handed out on 08/06/22. Please submit it by 22/06/22 NZT.
Dr. Thomas Koentges is an honorary teaching fellow at Waikato University and the founder of You Say Data, a New Zealand-based digital upskilling and data analysis company. He has lectured in Computer Science, Digital Humanities, and Data Science at the University of Leipzig and currently holds an honorary position as Fellow for Historical Language Processing and Data Analysis at Harvard University's Center for Hellenic Studies. Dr Koentges is also a certified RStudio Education partner.
Chapters covered:
- 1-2 Introduction
- 3 Data Visualisation
Chapters covered:
- 4 Workflow Basics
- 6 Workflow Scripts
- 8 Workflow Projects
- (not in book) Record Keeping with Notebooks and Markdown
Chapters covered:
- 5 Data Transformation
Assesment task 1 "Visualisation" will be handed out
Class 4: Exploratory Data Analysis (Changed to Git and Data Transformation)| Friday 25/03/22, 09:00am - 12:00pm
Chapters covered:
-
(not in book) Working with git (and GitHub) to find, re-use, version control, collaborate on and store code
-
5 Data Transformation
-
Postponed to next week: 7 Exploratory Data Analysis
Chapters covered:
- 5 Data Transformation
- 7 Exploratory Data Analysis
Chapters covered:
- 5 Data Transformation
- 7 Exploratory Data Analysis
Assessment 1 due
Chapters covered:
- 7 Exploratory Data Analysis
- 11 Data import
Chapters covered:
- 11 Data import
- 12 Tidy data
Class 9: Programming Principles | Data Types Focus: Strings, Factors, Dates | Relational Data | 27/04/22, Wednesday 09:00am - 12:00pm
Chapters covered:
- (not in book) Intro Programming
- 20 Vectors
- 10 Tibbles
- 14 Strings
- 15 Factors
- 18 Pipes
Assesment task 2 "Basic Analysis" will be handed out
Chapters covered:
- 13 Relational data
- 16 Dates and times
Chapters covered:
- 16 Dates and times
- 19 Functions
Chapters covered:
- 21 Iteration
- (not in book) Parallelisation
- 23 Models (22--24)
Assessment 2 due
- (not in book) Parallelisation
- 23 Models (22--24)
Chapters covered:
- (not in book) Recap
- (not in book) tidymodels
Chapters covered:
- 28 Graphics for communication
- (not in book) patchwork
Chapters covered:
- 27 R Markdown
- 29 R Markdown formats
- (not in book) Quarto
- 30 R Markdown workflow
- (not in book) Working with git (and GitHub) to find, re-use, version control, collaborate on and store code
- (not in book) Finding data in a data warehouse (e.g. Snowflake), getting it out for analysis, and putting it in if required.
Assesment task 3 "Data Retrieval & Modelling" will be handed out
- (not in book) Building Dashboards with RShiny
- (not in book) Brief introduction to geospatial visualisation (maps) and analysis
Final Assessment 3 due by 22/06/22 NZT