An R Approach to Data Cleaning and Wrangling for Education Research
Abstract
Data wrangling, also known as data cleaning and preprocessing, is a critical step in the data analysis process, particularly in the context of learning analytics. This chapter provides an introduction to data wrangling using R and covers topics such as data importing, cleaning, manipulation, and reshaping with a focus on tidy data. Specifically, readers will learn how to read data from different file formats (e.g. CSV, Excel), how to manipulate data using the dplyr package, and how to reshape data using the tidyr package. Additionally, the chapter covers techniques for combining multiple data sources. By the end of the chapter, readers should have a solid understanding of how to perform data wrangling tasks in R.
Main Authors
Format
Books
Book part
Published
2024
Subjects
Publication in research information system
Publisher
Springer
The permanent address of the publication
https://urn.fi/URN:NBN:fi:jyu-202407045132Use this for linking
Parent publication ISBN
978-3-031-54463-7
Review status
Peer reviewed
DOI
https://doi.org/10.1007/978-3-031-54464-4_4
Language
English
Is part of publication
Learning Analytics Methods and Tutorials : A Practical Guide Using R
Citation
- Kopra, J., Tikka, S., Heinäniemi, M., López-Pernas, S., & Saqr, M. (2024). An R Approach to Data Cleaning and Wrangling for Education Research. In M. Saqr, & S. López-Pernas (Eds.), Learning Analytics Methods and Tutorials : A Practical Guide Using R (pp. 95-119). Springer. https://doi.org/10.1007/978-3-031-54464-4_4
License
Copyright© 2024 The Author(s)