Advanced Quantitative Methods: Introduction to R

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. R Studio 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.

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.

Workshop leaders

Places are limited to 25 for this workshop

This training is open to all ESRC and non-ESRC funded students within the WRDTP universities. It is of particular interest to students who are using Advanced Quantitative Methods as part of their PhD research.

Students are responsible for arranging travel to and from this AQM training session. The WRDTP cannot reimburse travel costs in this instance.