Course summary

The “Introduction to R for Health and Social Sciences” is a 2-day open-to-public instructor-led course which is designed to provide you with a streamlined, hands-on training in essential skills in the R programming language for data wrangling, statistical analysis and data visualisations. During this “jargon-free” and focused training course you will learn the most important concepts of R language for data science with specific examples of methods and approaches used in health and social sciences. The course will enable you to begin working with your data, create exploratory data visualisations, compute descriptive statistics about your datasets, and implement a selection of hypothesis testing statistical methods.

The course covers modern approaches in data science using R language and its ecosystem of external libraries including tidyverse family of packages e.g. dplyr, ggplot2, tidyr, readr, tibble and other essential R libraries for data wrangling and statistics.

This course also includes a 1-month follow-up period during which you are encouraged to ask questions directly to the tutor (via email) e.g. to advise you on methods or specific R functions which may be suitable in your analysis.

Who is this course for?

This course is especially recommended to health and social scientists (e.g. post-doc researchers, PhD candidates, Masters students, data analysts and scientists) interested in data science and R language who are looking for a hands-on, structured, focused and tutor-led online training provided by a recognised and experienced organisation.

What is included in the course fee?

At Mind Project, we pride ourselves for delivering great quality, goal-directed, interactive and cost-effective training. Our remote, hybrid, and on-premises tutor-led courses are designed to maximise your opportunity to learn complex topics in shortest possible time while being able to discuss and interact with our tutor and other fellow attendees. Therefore, the registration fee for this course includes a number of benefits that many other training providers don’t offer:

• a remotely delivered live training with our expert tutor over 2 days (from 9:30 until 16:30 on both days) – you will be able to ask questions, interact and discuss the topic with other attendees from the comfort of your home/office,
• you will have access to course online materials for 1 year via our Mind Project Learning Platform e.g. course video recordings, exercises and quizzes, R code scripts used during tutorials and live sessions, example datasets, optional and mandatory reading (e.g. blog articles, academic papers, industry reports), and other external recommended resources e.g. online books,
• one-month post-course follow-up period during which you will be able to ask questions to our tutor related to the material presented during the course and receive our recommendations of R methods/functions suitable for your analysis and data,
• exercises and tasks completed by you during the live sessions will help you in better material retention and will enhance your learning progress,
• small group size allows easy interaction and stimulating environment for successful learning,
• you will receive a certificate of course completion for attending this course.

Programme outline

During this course, you will learn a number of data science approaches for data wrangling, exploratory analysis, visualisations and statistics with the R programming language. The course will be structured in four parts (each part will be followed with a short exercise) and will be run according to the following schedule:

Part 1: First steps with R language and data import/export

• Introduction to R language, RStudio and the ecosystem of packages in R,
• Importing/exporting data,
• A gentle introduction to data structures in R.

Part 2: Data wrangling and describing data with R

• Essential data wrangling operations: e.g. subsetting, filtering, renaming variables, recoding values and creating new data,
• Measures of central tendency, dispersion/variability and other basic descriptive and summary statistics,
• Value counts, cross-tabulations and data aggregations with tidyverse.

Part 3: Data visualisations with R

• Plotting descriptives with ggplot2: examples of different static plots/charts,
• Creating a publication-ready plots – adjusting elements of a plot and its graphical settings,
• Examples of more advanced plots e.g. grouped and aggregated plots, grid layouts and themes with ggplot2 and associated R packages.

Part 4: Inferential statistics and hypothesis testing with R

• Examples of parametric tests of differences and relationships,
• Implementing multiple linear regression with R – understanding regression metrics, model evaluation, comparing regression models,
• Final comments and questions.