Before we Start

  • Use RStudio to write and run R programs.
  • Use install.packages() to install packages (libraries).

Introduction to R

  • Access individual values by location using [].
  • Access arbitrary sets of data using [c(...)].
  • Use logical operations and logical vectors to access subsets of data.

Starting with Data

  • Use read_csv to read tabular data in R.
  • Use factors to represent categorical data in R.

Data Wrangling with dplyr

  • Use the dplyr package to manipulate dataframes.
  • Use select() to choose variables from a dataframe.
  • Use filter() to choose data based on values.
  • Use group_by() and summarize() to work with subsets of data.
  • Use mutate() to create new variables.

Data Wrangling with tidyr

  • Use the tidyr package to change the layout of data frames.
  • Use pivot_wider() to go from long to wide format.
  • Use pivot_longer() to go from wide to long format.

Data Visualisation with ggplot2

  • ggplot2 is a flexible and useful tool for creating plots in R.
  • The data set and coordinate system can be defined using the ggplot function.
  • Additional layers, including geoms, are added using the + operator.
  • Boxplots are useful for visualizing the distribution of a continuous variable.
  • Barplots are useful for visualizing categorical data.
  • Faceting allows you to generate multiple plots based on a categorical variable.

Writing Good Software

  • Keep your project folder structured, organized and tidy.
  • Document what and why, not how.
  • Break programs into short single-purpose functions.
  • Write re-runnable tests.
  • Don’t repeat yourself.
  • Be consistent in naming, indentation, and other aspects of style.

Getting started with R Markdown (optional)

  • R Markdown is a useful language for creating reproducible documents combining text and executable R-code.
  • Specify chunk options to control formatting of the output document