Overview

This course aims to familiarise participants with the R interface and the R language in order to enable them to conduct statistical analysis.

Content

This self-paced course is aimed at researchers who want to learn how to use the statistical software R to conduct statistical analysis.

You'll be expected to have a basic understanding of common statistical tests and concepts as these will not be taught during this course.

The topics covered using R will include:

• Introduction to the R platform and interface
• Introduction to basic scripting commands
• Input of data sets
• Summary measures
• Graphical displays
• Statistical tests (e.g. t-test)
• Add-ons and packages in R
• Help in R

Learning outcomes

At the end of the course, you'll be able to conduct a basic statistical analysis in R using your own data or data supplied in the course materials.

You should have reasonable confidence in further independent progress through access to help files.

By the end of the course you should:

• be familiar with the Rstudio interface and menu options.
• have the ability to use R as a basic calculator
• understand the general structure of the language including objects, the use of brackets and functions.
• be able to subset and manipulate the data into a format ready for statistical analysis
• run functions to create basic summary statistics of the data, produce graphs of a publishable standard and perform statistical significance tests.
• have confidence in learning and installing additional packages.

Course structure and teaching

This is a 6 hour online, self-paced course that includes:

• Full electronic notes
• Short lecture videos (recorded outside of the classroom with screen recordings and annotation) that follow closely with the notes
• Interactive quizzes for each chapter
• Revision summaries
• Support will also be available through a forum, where you can ask questions related to the course materials.

Entry requirements

A basic level of statistical literacy is required as a prerequisite. You should have a basic understanding of standard errors, p-values and confidence intervals.

If you've completed the five-day Introduction to Statistics and Research Methods course then you'll have the necessary skills.

Cost and concessions

The standard price is £100.

A 50% discount is available for UCL staff, students, alumni. If you're eligible for a discount, email ich.statscou@ucl.ac.uk before booking to be sent the discount code.

The course is available for free to those associated with the Institute of Child Health or Great Ormond Street Hospital, and SLMS doctoral students. Please also email ich.statscou@ucl.ac.uk to gain a booking code.

Certificates

You can download a certificate of participation once you have completed all the session quizzes.

Find out about other statistics courses

CASC's stats courses are suitable for anyone requiring an understanding of research methodology and statistical analyses. The courses allow non-statisticians to interpret published research and/or undertake their own research studies. Find out more about CASC's full range of statistics courses.

Course team

Dr Dean Langan

Dean works as a lecturer, jointly based within the School of Life and Medical Sciences (SLMS) and the Centre for Applied Statistics Courses (CASC) at University College London (UCL). He is responsible for the development and implementation of online statistical training principally aimed at doctoral students within SLMS. He took up this position in August 2021 having worked for 6 years solely within CASC, delivering face-to-face short courses to a wide range of delegates, including students and staff within UCL Great Ormond Street Institute of Child Health, and other working professionals from a wide range of industries.

Previously, Dean has also worked as a medical statistician at the Clinical Trials Research unit (CTRU, University of Leeds), working on a number of phase II and III clinical trials in a range of disease areas including stroke and myeloma cancer. He undertook a PhD full-time at the University of York from 2012 to 2015, researching statistical methods for meta-analysis. The title of the PhD was 'estimating the heterogeneity variance in a random-effects meta-analysis'.

Course information last modified: 17 Jan 2023, 13:17