Working with quantitative data entails being proficient with data management and exploration in order to do analysis and get insights. And while there are multiple ways of doing this, R has emerged as one of the most popular tools for these tasks. R is a free and open source software for statistics and data science that is increasingly used with social survey data.
Important: you will need some familiarity with quantitative data, and an understanding of variables, datasets, and re-coding. A basic knowledge of R is also a highly recommended.
In this course you will be introduced to the use of R and will learn how to prepare and visualize data. We will focus on the use of the ‘Tidyverse’ package. Inspired by the concept of “tidy data” this package enables users to import, merge, recode, restructure and visualize data very efficiently.
Half of the course will focus on how to efficiently transform variables and prepare them for analysis while the other half will focus on visualization. The course will combine presentations of the key concepts, hands on practical sessions and discussions of the solutions. In the practical part we will be using real world data to prepare the participants for working with their own data.
By the end of the course participants will:
- Know how to import data
- Understand the concept of “tidy data”
- Know how to clean data effectively
- Know how to explore and visualise data
- importing data
- cleaning data
- visualization of data
Who will benefit?
- People who have experience with quantitative data but want to transition from a different statistical software to R.
- People with basic knowledge of R who want to get experience with working with real world data.
- People who want to learn how to visualize data in R.
To understand the concept of tidy data
- To learn how to efficiently transform variables and prepare them for analysis
- To learn how to work with factor variables
- To learn how to visualize data using R
Alexandru Cernat is a senior lecturer in social statistics at the University of Manchester since 2016. He received a PhD in survey methodology from the University of Essex and was a post-doc at the National Centre for Research Methods.
His expertise covers: latent variable modelling, non-response, new forms of data, longitudinal data design, longitudinal analysis.
He has taught from multiple organizations such as the National Centre for Research Methods, European Survey Research Association, International Program in Data Science and the African Institute of Mathematical science.
You can find more about his research and activities at www.alexcernat.com
This course contributes 6 hours to the MRS CPD programme.