ONLINE COURSE – Introduction to statistics using R and Rstudio (IRRS03) This course will be delivered live
17 March 2021 - 18 March 2021£275.00
This course will now be delivered live by video link in light of travel restrictions due to the COVID-19 (Coronavirus) outbreak.
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
TIME ZONE – UK local time (GMT+0) – however all sessions will be recorded and made available allowing attendees from different time zones to follow a day behind with an additional 1/2 days support after the official course finish date (please email firstname.lastname@example.org for full details or to discuss how we can accommodate you).
In this two day course, we provide a comprehensive introduction to R and how it can be used for data science and statistics. We begin by providing a thorough introduction to RStudio, which is the most popular and powerful interfaces for using R. We then introduce all the fundamentals of the R language and R environment: variables and assignment, data structures, operators, functions, scripts, packages, projects, etc. We then provide an introduction to data processing and formatting (aka, data wrangling), an introduction to data visualization, an introduction to RMarkdown, and introduce how to some of the most widely used statistical methods such as linear regression, Anovas, etc. From this course, you will gain a comprehensive introduction to R, which will serve as foundation for progressing further with R to any kind of data analysis, data science, or statistics.
THIS IS ONE COURSE IN OUR R SERIES – LOOK OUT FOR COURSES WITH THE SAME COURSE IMAGE TO FIND MORE IN THIS SERIES
This course is aimed at anyone who is interested in using R for data science or statistics. R is widely used in all areas of academic scientific research, and also widely throughout the public, and private sector.
Venue – Delivered remotely
Time zone – GMT+0
Availability – TBC
Duration – 2 days
Contact hours – Approx. 15 hours
ECT’s – Equal to 1 ECT’s
Language – English
PLEASE READ – CANCELLATION POLICY: Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact email@example.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.
Dr. Mark Andrews
Works at – Senior Lecturer, Psychology Department, Nottingham Trent University, England
Teaches – Introduction to statistics using R and Rstudio; Introduction data visualization using GG plot 2; Introduction data wrangling using R and Rstudio; Introduction to generalised linear models using R and Rstudio; Introduction to mixed models using R an d Rstudio; Introduction to Bayesian data analysis for social and behavioural sciences using R and Stan; Structural Equation Models, Path Analysis, Causal Modelling and Latent Variable Models Using R; Generalised Linear, Nonlinear and General Additive Models; Python for data science, machine learning, and scientific computing
Mark Andrews is a Senior Lecturer in the Psychology Department at Nottingham Trent University in Nottingham, England. Mark is a graduate of the National University of Ireland and obtained an MA and PhD from Cornell University in New York. Mark’s research focuses on developing and testing Bayesian models of human cognition, with particular focus on human language processing and human memory. Mark’s research also focuses on general Bayesian data analysis, particularly as applied to data from the social and behavioural sciences. Since 2015, he and his colleague Professor Thom Baguley have been funded by the UK’s ESRC funding body to provide intensive workshops on Bayesian data analysis for researchers in the social sciences.
This course will be largely practical, hands-on, and workshop based. For each topic, there will first be some lecture style presentation, i.e., using slides or blackboard, to introduce and explain key concepts and theories. Then, we will cover how to perform the various statistical analyses using R. Any code that the instructor produces during these sessions will be uploaded to a publicly available GitHub site after each session. For the breaks between sessions, and between days, optional exercises will be provided. Solutions to these exercises and brief discussions of them will take place after each break.
The course will take place online using Zoom. On each day, the live video broadcasts will occur during UK local time (GMT+0) at:
All sessions will be video recorded and made available to all attendees as soon as possible, hopefully soon after each 2hr session.
If some sessions are not at a convenient time due to different time zones, attendees are encouraged to join as many of the live broadcasts as possible. For example, attendees from North America may be able to join the live sessions from 3pm-5pm and 6pm-8pm, and then catch up with the 12pm-2pm recorded session once it is uploaded. By joining any live sessions that are possible will allow attendees to benefit from asking questions and having discussions, rather than just watching prerecorded sessions.
At the start of the first day, we will ensure that everyone is comfortable with how Zoom works, and we’ll discuss the procedure for asking questions and raising comments.
Although not strictly required, using a large monitor or preferably even a second monitor will make the learning experience better, as you will be able to see my RStudio and your own RStudio simultaneously.
All the sessions will be video recorded, and made available immediately on a private video hosting website. Any materials, such as slides, data sets, etc., will be shared via GitHub.
Assumed quantitative knowledge
We will assume only a minimal amount of familiarity with some general statistical and mathematical concepts. These concepts will arise when we discuss statistics and data analysis. Anyone who has taken any undergraduate (Bachelor’s) level course on (applied) statistics can be assumed to have sufficient familiarity with these concepts.
Assumed computer background
No prior experience with R or any other programming language is required. Of course, any familiarity with any other programming will be helpful, but is not required.
Equipment and software requirements
Attendees of the course will need to use a computer on which RStudio can be installed. This includes Mac, Windows, and Linux, but not tablets or other mobile devices. Instructions on how to install and configure all the required software, which is all free and open source, will be provided before the start of the course. We will also provide time during the workshops to ensure that all software is installed and configured properly.
UNSURE ABOUT SUITABLILITY THEN PLEASE ASK firstname.lastname@example.org
Wednesday 17th – Classes from 12:00 to 20:00
Topic 1: The What and Why of R. We’ll start by briefly explaining what R is, what is used for, and why is has become so popular.
Topic 2: Guided tour of RStudio. RStudio is the most widely used interface to R. We will provide a tour of all its parts and features and how to use it effectively.
Topic 3: First steps in R. Now, we cover all the fundamentals of R and the R environment. These include variables and assignment, data structures such as vectors, data frames, lists, etc, operations on data structures, functions, scripts, installing and loading packages, using RStudio projects, reading in data, etc. This topic will be detailed so that everyone obtains a solid grasp on these fundamentals, which makes all subsequent learning much easier.
Thursday 18th – Classes from 12:00 to 20:00
Topic 4: Introducing wrangling. Data wrangling, which is the art of cleaning and restructuring data is a big topic. Here, we just provide an introduction (subsequent courses in this series will cover wrangling in depth). Here, we will primarily focus on filtering, slicing, selecting, renaming, and mutating data frames.
Topic 5: Data visualization. Data visualization is another big and important topics. Here, we just provide an introduction, specifically an introduction to ggplot (subsequent courses in this serious will cover visualization in depth). We’ll cover scatterplots, boxplots, histograms, and their variants.
Topic 6: RMarkdown. RMarkdown is a powerful tool for creating reproducible research reports, as well as slides, scientific website, posters, etc. In an RMarkdown document, we mix R code and the narrative text of the report, and the outputs of the R code, including figures, are included in the final document.
Topic 7: Introduction to Statistics using R. There are many thousands of statistical methods built into R. Here, we will simply provide an introduction to some of the most widely used methods. In particular, we will cover linear regression, Anova, and some other simple test. The aim of this section is to get a sense of how statistical analysis is done in a R, and how to perform some of the most widely used methods.