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FREE 1 DAY INTRO TO R AND R STUDIO (FIRR01)
26th August 2021 @ 8:00 am - 5:00 pmFree – £20.00
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 email@example.com for full details or to discuss how we can accommodate you).
In this free one 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.
IF YOU ARE IN A POSITION TO CONTRIBUTE TOWARDS YOUR PLACE IT WOULD BE APPRECIATED SO WE CAN USE ANY REVENUE TO FUND OTHER FREE COURSES – THERE IS THE OPTION TO PURCHASE A TICKET FOR £20.00
WE ALSO RECOMMEND ORGANISING A SAMALL GROUP IF YOU HAVE COLLEAGUES WHO ALSO WANT TO ATTEND, THIS WAY WE CAN MAXIMISE HOW MANY PEOPEL CAN ACCESS THE COURSE
THIS IS A FREE INTRODUCTORY COURSE – LOOK OUT FOR COURSES WITH THE SAME COURSE IMAGE TO FIND MORE PAID COURSES 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 – 1 days
Contact hours – Approx. 6 hours
ECT’s – NA
Language – English
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 and R studio; 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 a 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.
The course will take place online using Zoom. 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.
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.
WE RECOMMEND ORGANISING A SMALL GROUP IF YOU HAVE COLLEAGUES WHO ALSO WANT TO ATTEND, THIS WAY WE CAN MAXIMISE HOW MANY PEOPLE CAN ACCESS THE COURSE
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.
WE RECOMMEND ORGANISING A SAMALL GROUP IF YOU HAVE COLLEAGUES WHO ALSO WANT TO ATTEND, THIS WAY WE CAN MAXIMISE HOW MANY PEOPEL CAN ACCESS THE COURSE