Advanced Quantitative Methods: 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. 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 visualisation, machine learning, text mining etc.
On successful completion of this training, you will be able to
- Install and navigate R and R Studio 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)
- 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 visualisation
Training/ day structure
The training day consists of 4 sessions on key topics in R and R Studio. For each session there will be a short introduction and then participants will 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
Paul Clough is a Professor of Search and Analytics at the University of Sheffield Information School. Further information regarding his research interests and academic history can be found at his University staff profile.
This training session will be delivered via Blackboard Collaborate.
PLEASE NOTE: Our online training sessions will be recorded and will be available on the VIRE in an edited format for those students who cannot attend. If you wish to join this session but do not wish for your contributions to be included in the edited VIRE resource, please ensure that you select NO when prompted in the online booking form regarding recording.