April 6, Course introduction and getting started with R

Before class

• Install R, RStudio and tidyverse. You must do this before the session, as I will not devote lecture time to it! If you are having trouble, attend the office hours at 9am on April 7.

• Required reading: Variable types

• Read through the showcase document. No need to study this in detail yet - the document briefly demonstrates most of the techniques that we'll cover in this class, so you'll know what to look forward to!

Materials

• Lecture: slides, 2020 recording, 2021 recording

• Lab: Using R for calculation

• Exercises: problems, solutions

Extras

• Showcase document: published and RMarkdown source

• Data tables: weather.csv, rides.csv

April 8, R objects, variable types and data tables

Before class

• Suggested reading: Summary statistics

Materials

• Lecture: slides, 2020 recording, 2021 recording

• Lab: Basic R objects

• Exercises: problems, solutions

Extras

• RStudio shortcut reference

• Data table: 2016-presidential-election-county-results.csv

April 13, Data visualisation with ggplot, part 1

Before class

• Required reading: R for Data Science, chapter 3: Data visualisation, 2021 recording

Materials

• Lecture: slides, 2020 recording, 2021 recording

• Lab: Data visualization

• Exercises: problems, solutions

Extras

• ggplot reference sheet

• Code for generating efficiency

• Data tables: worldbank_data_tidy.csv

  • Data sourced directly from world bank website: worldbank_data_raw.csv

  • Script for tidying data table: worldbank_ETL.R

  • Information about the raw data table: worldbank_metadata.csv

April 15, Data visualisation with ggplot, part 2

Note: Extras and "before class" from the previous lecture are also relevant to this one

Materials

• Lecture: slides, 2020 recording, 2021 recording

• Lab: Layering in ggplot

April 20, dplyr: data manipulation and functions

Before class

• Required reading: R for Data Science, chapter 4: Data transformation

Materials

• Lecture: slides, 2020 recording, 2021 recording

• Lab: Data transformation

• Exercises: problems, solutions

Extras

• Data Wrangling cheat sheet (Note: this sheet uses gather and spread, which are out-dated. We'll, talk about this sort of think in Lecture 7)

April 22, Rmarkdown documents, presentations and workflow

Before class

• Instead of slides, this lecture will be conducted using two R Markdown documents, so download them and open them in RStudio before the lecture.

Materials

• Lecture: introductory and advanced (this one needs to be modified before you will be able to compile it - figure out how!) R Markdown documents, and 2020 recording, 2021 recording

• Lab: Reproducible research

• Exercises: problems, solutions

Extras

• Data table: airbnb_nyc_2019.csv

• RMarkdown cheat sheet

• An in-depth guide to R Markdown: https://bookdown.org/yihui/rmarkdown/

April 23, Project proposal due

April 27, Pastes, merges and joins: combining tables and dataset grammar

• Lecture: slides, 2020 recording, 2021 recording

• Lab: Data joining and maps - this lab uses the following datsets:

  • 2016_US_Presdential_Results_for_class.csv

  • county_map_fips.rds

• Exercises: problems, solutions

Extras

• Data table: Drought_data.csv

• Advanced mapping with R: Getting started with ggmap

April 29, Useful tips, packages and FAQs

Materials

• Lecture: slides, 2020 recording, 2021 recording

• Lab: Data parsing and stat graphics - this lab uses the following datset:

  • cricket_data.csv

Extras

• lubridate cheat sheet

May 7, Final project due