Introduction to the RStudio Server

11am - 12pm

In this section we will start using the RStudio server and become familiar with the interface.

  1. Getting a username and password
  2. Logging on
  3. Exploring the R Studio interface
  4. Packages
  5. Getting help

A brief overview is shown on this link. In the session I will spend some time explaining the interface in more detail with attention to the help available within RStudio.

http://r.bournemouth.ac.uk:82/taster_sessions/Starting_RStudio_Server.html

http://r.bournemouth.ac.uk:82/books/modern_R/_book/getting-to-know-rstudio.html#keyboard-shortcuts

Simple programming in base R

12 - 1pm

In this section I will introduce the basic elements of the R language. If you are already familiar with R you may take a coffee break, but this will be new to those who have never seen R before.

  1. Typing commands in the console
  2. Assigning data to variables
  3. Vectors and data frames
  4. Data types
  5. Writing data to a file

http://r.bournemouth.ac.uk:82/taster_sessions/simple-R.html

http://r.bournemouth.ac.uk:82/books/modern_R/_book/data-types-and-objects.html

Lunch break

Timing slightly flexible, but aim to take to a break from around 1pm to 2pm.

Organising workflows using projects

In this section we will look at how to organise work in R efficiently and neatly using projects and markdown documents. This ensures reproducibility.

2pm - 2:30

  1. Starting a project
  2. Uploading data
  3. Working with markdown documents
  4. Reading in data
  5. Compiling reports

http://r.bournemouth.ac.uk:82/taster_sessions/Starting_Projects.html

Importing data from the internet

This section will begin to demonstrate some more advanced features of R. R can be linked to online data in many ways. This example is a relatively simple one.

2:30 - 3.30 pm

  1. Scraping data from web sites
  2. Cleaning data
  3. Iterating operations using functions

http://r.bournemouth.ac.uk:82/taster_sessions/Loading_from_the_web.html

Introduction to dplyr and the “tidyverse”

Modern data processing in R is based around the “tidyverse”. This is a set of R packages designed to make data procesing in R efficient and human readable. Learning to take full advantage of this takes some time. In this session you will get a quick taste for the tidy way of working with data.

3pm - 4pm

  1. Filtering
  2. Grouping
  3. Summarising
  4. Visualising

http://r.bournemouth.ac.uk:82/taster_sessions/Using_dplyr.html