ONLINE COURSE – Introduction to statistics using R and Rstudio (IRRS01) This course will be delivered live
4 June 2020 - 5 June 2020£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 – Western European Time – 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 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 module of a five module series – you do not need to attend them all but they are designed to complement each other. Please see the links below
“1” June 4th – 5th Introduction to statistics using R and Rstudio
“2” June 18th – 19th Introduction data visualization using GG plot 2 (R and R studio)
“3” July 9th – 10th Introduction data wrangling using R and Rstudio
“4”July 23rd – 24th Introduction to generalised linear models using R and Rstudio
“5” August 6th – 7th Introduction to mixed models using R an d Rstudio
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 – Western European Time
Availability – 20 places
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 firstname.lastname@example.org. 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 (and accommodation fees if booked through PS statistics) will be credited.
Dr. Mark Andrews
This course will be hands-on and workshop based. Throughout each day, there will be a minimal amount of lecture style presentation, i.e., using slides, introducing and explaining key concepts. However, even in these cases, the topics being covered will include practical worked examples that will work through together.
Teaching will be done online via video link using Zoom. 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 recorded, and made available immediately on a private video hosting website. All materials will be shared via Git, which will allow for instantaneous sharing of code etc.
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.
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Thursday 4th – Classes from 09:30 to 17:30
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.
Friday 5th – Classes from 09:30 to 17:30
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.