Introduction to R

This Advanced Quantitative Methods training session is open to all ESRC and non-ESRC funded Social Science PGR students within the seven WRDTP partner institutions. The course is aimed at people who are completely new to R and RStudio and is suitable for students from all of the interdisciplinary Pathways.

This one-day course provides a hands-on introduction to R and R Studio, framed within the context of Data Science. R originated as a language for statistical computing and graphics and is open-source and free to use. RStudio is an R-specific Integrated Development Environment (IDE) that is also open-source and freely available. This provides a convenient environment in which to manage your projects and use R. Although highly popular amongst statisticians, R is also a standard tool for doing Data Science. Using R and pre-existing libraries, it is possible to conduct statistical analysis and modelling and perform many useful data science tasks, such as data transformation, data visualization, machine learning, text mining, etc.

Audience:

The course is aimed at people who are completely new to R and RStudio. The course is suitable for people from any background and no prior knowledge or experience is necessary. However, it does help if you are analytically-minded and interested in processing and analysing data. The course introduces R and RStudio within the context of Data Science.

Learning objectives:

On successful completion of the module, you will be able to:

• Install and navigate R and RStudio for carrying out data-related tasks;
• Install and update R libraries (called packages) that extend the base functionality of R;
• Describe and manipulate various R objects for storing and data (vectors, lists and data frames);
• Use R like a scientific calculator and in more advanced ways;
• Load data into R from different sources (e.g. plain text, HTML, CSV and Excel files)
• Write data from R using different formats (e.g. plain text and CSV);
• Manipulate and transform datasets (e.g. selecting data subsets, joining datasets etc.)
• Explore and summarise data using simple descriptive statistics and data visualization.

Structure of the sessions:

The course consists of 4 sessions on key topics in R and RStudio. For each session there will be a short introduction from the tutor and then you can work through a handout in self-study style. For each session you will be provided with the following:

• A handout to follow in self-study;
• An R script containing all R code for the session (and script with answers to exercises);
• Copies of relevant resources;
• Datasets to use within the session