Introduction to R for University Administrators (online)

Date:

26/03/2025

Organised by:

Statistical Services Centre Ltd

Presenter:

James Gallagher and Sandro Leidi

Level:

Intermediate (some prior knowledge)

Contact:

James Gallagher
07873873617
jamesgallagher1929@gmail.com

video conference logo

Venue: Online

Description:

Overview of 1-day course
The statistical package R (www.r-project.org) is a freely available and flexible software environment for statistical computing and graphics. R is part of an international collaboration and has become very popular; it is used for data analysis in many application areas including education and is widely used in academia. In particular it has the facilities to perform advanced statistical modelling methods, and is equally well suited to more elementary statistics and graphics.  

The course begins with a general overview of the concepts involved in using R, such as object orientation. It then moves onto importing data from other applications such as Excel, considers common data manipulation techniques and finishes with an introduction to graphics and summary statistics for numerical data.

The course will use the R Gui application on a Windows operating system. Note the software is not based on using point-and-click mode, but a written command syntax.

Cost
£270 (inclusive of 20% VAT) 

Delivery Mode
All training is online and will be delivered live on each day between 10:00 and 16:30 (GMT+1). The delivery platform is Zoom, which may be freely accessed. Questions may be asked using Zoom's chat box. Note we are a team of two presenters, so one of us is always available to provide additional support.  We also use Zoom meetings rather than webinars to encourage further interaction during an online course.​

Who Should Attend?
Administrators in educational establishments working in Policy, Planning and Strategy units; Data and Insight units; Business Intelligence units; those involved in extracting actionable insights from student records and in reporting to policy makers or committees. Anyone in these positions wanting an introduction to R will benefit greatly from this course.
Although the course is aimed at HE professional, this introduction to R is more widely applicable. No previous experience of the R software is required.

How You Will Benefit
You will be able to describe the essential concepts related to using R; use R to import and manipulate data and for basic statistical analysis including graphics. You will also be able to:

  • Install CRAN packages for specific analyses

  • Write a script to (a) efficiently reproduce output and (b) have an auditable record of your analysis

  • Make effective use of the many resources which are freely available to learn about the R software for your particular areas of interest. 

What Do We Cover?

  • Introductory concepts of R and the R environment: windows, objects, data frames, working directory, attributes of an object in particular class, functions, scripts, workspaces and CRAN packages; help system

  • Importing data from other applications: CSV, Excel and Access into a data frame

  • Data manipulation: using R as a calculator, recoding variables, sorting and subsetting; missing values; changing the class of an object; factors and their labelling

  • Exploratory stage: statistical graphics, graphics window and history; summary statistics for numerical data

  • Sources of information about R.

Software
Practical work will be done in R.
Note:

  • For practical work, participants must download and install the R software prior to the start of the course

  • Practical work is based on the Windows operating system.

Cost:

£270

Website and registration:

Register for this course

Region:

International

Keywords:

Data Quality and Data Management , Data Management , Data Editing, Variable recoding, Quantitative Data Handling and Data Analysis, Descriptive Statistics, ICT and Software, Quantitative Software, R, Student analytics, Statistical graphics


Related publications and presentations from our eprints archive:

Data Quality and Data Management
Data Management
Data Editing
Variable recoding
Quantitative Data Handling and Data Analysis
Descriptive Statistics
ICT and Software
Quantitative Software
R

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