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Cathie Marsh Institute for Social Research (CMI)

Intermediate R

Date: 6 February 2020 and 22 May 2020
Time: 9.45am–5pm
Instructor: Dr Alexandru Cernat 
Level: Introductory
Fee: £195 (£140 for those from educational, government and charitable institutions). 

CMI offers up to five subsidised places at a reduced rate of £60 per course day to research staff and students within Humanities at The University of Manchester. These places are awarded in order of application. 

Humanities PGR students at The University of Manchester can apply for a methods@manchester bursary to help cover their costs. All applications will be considered on a case-by-case basis and applicants will be required to provide a supporting statement from their supervisor. Applications for bursaries must be submitted at least two weeks in advance of the course date; applications submitted after this time will not be accepted. Retrospective applications cannot be made if courses have already taken place or payment has already been made.

Please click here to make a booking. If you are applying for a subsidised place, select the £60 University of Manchester option on the booking form. For queries about methods@manchester bursaries, contact methods@manchester.ac.uk (please note, you must have a confirmed place on the course before requesting a bursary application form). For any other queries about short courses, please contact cmi-shortcourses@manchester.ac.uk.

Please note: this is not guaranteed and is considered on a case by case basis. Please contact us for more information.

Outline

Cleaning data is one of the most important and time-consuming aspects of being a data analyst and researcher. Most courses typically teach statistical models or basic use of statistical software but few of these teach students how to efficiently clean real-world data.

This course will tackle this important topic. We will do this by introducing the tidyverse package in R. This is a large package that brings together some of the best tools for data cleaning and visualization in R. Inspired by the concept of “tidy data” the package enables users to import, merge, recode, restructure and plot data very efficiently. Half of the course will focus on data cleaning while the other half will focus on data visualization. The course will combine the use of lectures with hands-on practical sessions. In the practical part, we will be using real-world data to get the students used to the typical challenges they are expected to encounter when working with that. This will also help prepare them for working independently on their own data.

Course objectives

  • To understand the concept of tidy data
  • To learn how to efficiently connect multiple commands in R using the pipe operator
  • To learn how to efficiently transform variables and prepare for analysis
  • To learn how to work with factor variables To learn how to visualize data using R

Skills covered

  • Filtering cases and selecting variables
  • Working with factors
  • Transforming variables
  • Merging data
  • Using the pipe operator
  • Visualizing data in R

Prerequisites

Basic knowledge of R and R-Studio

Recommended reading

  • R for Data Science - Garrett Grolemund, Hadley Wickham - https://r4ds.had.co.nz/

About the instructor

  • Dr Alexandru Cernat is a lecturer in Social Statistics at the University of Manchester. Previously he was Research Associate at the National Centre for Research Methods. He has been awarded a PhD in survey methodology from the University of Essex where he has investigated data quality in longitudinal studies. His research interests cover latent variable modelling, measurement error, missing data, survey methodology, methods for longitudinal data collection and analysis.

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