From SPSS to R



Organised by:

National Centre for Social Research (an NCRM Centre Partner)


Joe Crowley


Entry (no or almost no prior knowledge)



View in Google Maps  (EC1V 0AX)


35 Northampton Square


This in-person course is an introduction to R for users of the analysis software SPSS. It provides an introduction to the RStudio working environment, fundamentals of coding in R, R data types and structures, and a grounding in tidyverse style coding for standard data management tasks. At the end of the course attendees should be able to work confidently with data within R in preparation for analysis, as well as produce simple descriptives to explore and understand their data.  This course may also be suitable for users of Stata.

Attendees should install R / RStudio on their own device in advance of the session, and verify that they can install packages successfully.  It will not possible to provide technical support for this during the training, as issues can be specific to each user, e.g. their operating system or organisational IT policies.  This device must be brought to the session to be used during the training.  

The course will cover:

  • Understand the RStudio working environment, including as the purpose of the Source, Console, Environment and Files windows, how to refer to objects stored in the Environment with code and the concept of assigning values to objects, and how to access R’s help tab. 
  • How to use functions from packages in R. Packages are collections of R functions – essentially a bundle of tools and resources - that extend the functionality of the primary R software. Whatever challenge you encounter, there is usually a package that can help with it. 
  • How to deal with common issues such as conflicts between packages, and how to find documentation and support for the use of new packages.
  • The different object types (e.g. vectors, data frames and lists) and data types (e.g. numeric, character and logical) which R commonly uses and how to identify and manage them. 
  • Explore the concept of object-orientated programming, how to apply functions in R, core features of applying R functions, and how these interact with different data and object types. 
  • How R handles missing values and how to manage data with missing values.   
  • Fundamentals of good coding practice in R. 
  • An introduction to the R tidyverse: a collection of key R packages designed to make data science tasks easier and more efficient. These packages work together seamlessly to provide a consistent and powerful toolkit for data manipulation, visualization, and analysis.
  • Key tools for data wrangling using tidyverse functions, including importing and exporting data of different types, how to explore and understand your data in R and produce basic descriptive analysis, and then how to edit your data - including recoding, sub-setting, sorting, and aggregation of data. 



The fee per teaching day is £35 per day for students / £75 per day for staff working for academic institutions, Research Councils and other recognised research institutions, registered charity organisations and the public sector / £250 per day for all other participants. In the event of cancellation by the delegate a full refund of the course fee is available up to two weeks prior to the course. NO refunds are available after this date. If it is no longer possible to run a course due to circumstances beyond its control, NCRM reserves the right to cancel the course at its sole discretion at any time prior to the event. In this event every effort will be made to reschedule the course. If this is not possible or the new date is inconvenient a full refund of the course fee will be given. NCRM shall not be liable for any costs, losses or expenses that may be incurred as a result of its cancellation of a course, including but not limited to any travel or accommodation costs. The University of Southampton’s Online Store T&Cs also continue to apply.

Website and registration:


Greater London


Data Management , ICT and Software, Quantitative Software, R, SPSS

Related publications and presentations:

Data Management
ICT and Software
Quantitative Software

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