What is R?
R is an open-source, widely used statistical programming language, that is easy to learn and, thanks to its many extensions, can be used as a general-purpose programming language. R was first released in 1993 and has since risen greatly in popularity, being used in academic institutions as well as companies such as the BBC, NHS, Google, Facebook, Twitter, Microsoft, Wellcome, Sanger Institute, New York Times and Mozilla.
R is simple to use and easy to read, this makes sharing your code easier and enables you to write your code faster. This is taken further by the Tidyverse ecosystem within R, that provides even easier to read code as well as excellent documentation. R is one of the best choices for data science and machine learning due to its wide pool of libraries for statistics, data manipulation and wrangling, data visualisation, and modelling; it is used across many sectors such as finance, healthcare, technology and retail for these purposes. R is the gold standard choice for data visualisation in data science thanks to the ggplot2 library and its many extensions. R also has one of the best open-source Integrated Development Environments (IDE) available in RStudio, not only does it make programming in R easy, it makes it simple to create documents with your code and outputs in various output formats such as HTML, Word, Powerpoint, and PDF.
R vs Python: Which is right for you?
Python is another very popular programme language for data science and both have their pros and cons without there being a clear winner. Python is easier to integrate with other software and is more versatile. Python is the better choice when it comes to web applications, software development, task automation and integration of analysis with web applications and production databases. The R analysis ecosystem is superior and provides a much larger number of libraries specialized in different types of statistical analysis and its ggplot library is still the gold standard when it comes to data visualization.
Programming experience: Python and R are both easy to learn with many online resources available to continue to learn independently. If you already know another object-oriented programming language Python might feel more natural to you.
Your environment: Which programming language do your peers, fellow students or teachers use? What is more common in your field of study? Do future employers or job sectors that you might target after graduation have a preference?
Your goals: Some models and visualizations might be better supported in R than Python. Also consider your long-term career goals. If you aspire to become a software developer, you might prefer Python.
The DSL R workshops have an open format, which are split into two workshop series.
The Fundamentals series takes you from getting started in R, key data types, using logic, and loading datasets; you’ll be doing some basic data cleaning and analysis along the way.
The R Data Wrangling and Visualisation series introduces popular R data science libraries, allowing you to do complex data manipulations, perform summary statistics, and make a wide array of data visualisations.
All workshops will be held in-person and are for two hours per session.
This workshop takes place in LBR.R.08 located on the lower ground floor of the library following the School’s Health and Safety guidelines. Computers are provided.
This workshop is not available online.
All software is provided on lab computers. If you would like to use your own laptop you will need install the following software:
R ( Mac / Windows)
RStudio Desktop (Mac/Windows - you must install R first to use RStudio)
XQuartz (install if using Mac with operating system greater than 10.5)
We have also curated some self-study courses that can be accessed via our Moodle page. If you can't find what you're looking for below, please email firstname.lastname@example.org or attend one of our drop-in sessions for advice.
We also have workshops and self-study courses for Python.
General guidance to using the open sessions:
Book onto an open session for the R series you are working on. This will be either Fundamentals or Data Wrangling and Visualisation. You are advised to start with the R Fundamentals series and with R Fundamentals 1 – Introduction to R unless you already have experience using R
Keep booking onto open sessions to complete whatever R workshop is next in the series for you. You will likely complete one and a half or two workshops per session
Use our workshop flow graphic for guidance.
Here are some example scenarios to help you know where to start and how progression works:
- Scenario 1: You have completed R Fundamentals 1 and 2 -> come to an open Fundamentals session and start with R Fundamentals 3
- Scenario 2: You have completed R Fundamentals 1 through to 6 -> come to an open Fundamentals session and start with R Fundamentals 7, if you finish start with R Data Wrangling 1 (from the Data Wrangling and Visualisation series)
- Scenario 3: You have used R before and feel a bit comfortable but might want a refresher -> come to an open session of either Fundamentals or Data Wrangling and Visualisation and pick a workshop where you think you want to get a refresher. You might need a review of using conditional logic in R then come to the Fundamentals series and R Fundamentals 6. You might want a refresher on joining datasets, then you will want to come to the Data Wrangling and Visualisation series and R Data Wrangling 3.
Click on the link below to check availability and book your place: