An R Approach to Data Cleaning and Wrangling for Education Research
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| Titel: | An R Approach to Data Cleaning and Wrangling for Education Research |
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| Autoren: | Tikka Santtu, Abdelgalil Mohamed Mohamed Sacr, López Pernas Sonsoles, Kopra Juho Johannes, Heinäniemi Merja Hannele |
| Weitere Verfasser: | Saqr, Mohammed, López-Pernas, Sonsoles |
| Quelle: | Learning Analytics Methods and Tutorials ISBN: 9783031544637 |
| Verlagsinformationen: | Springer Nature Switzerland, 2024. |
| Publikationsjahr: | 2024 |
| Schlagwörter: | R programming, learning analytics, oppiminen, 4. Education, Statistics, ohjelmointi, tidyverse, data wrangling, Tilastotiede, data cleaning, tietojenkäsittely |
| Beschreibung: | 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 package, and how to reshape data using the 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. |
| Publikationsart: | Part of book or chapter of book Article |
| Dateibeschreibung: | application/pdf; fulltext |
| Sprache: | English |
| DOI: | 10.1007/978-3-031-54464-4_4 |
| Zugangs-URL: | http://urn.fi/URN:NBN:fi:jyu-202407045132 |
| Rights: | CC BY |
| Dokumentencode: | edsair.doi.dedup.....d255cdfd8061c41efb48fb565cde59e1 |
| Datenbank: | OpenAIRE |
Nájsť tento článok vo Web of Science | 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 package, and how to reshape data using the 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. |
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| DOI: | 10.1007/978-3-031-54464-4_4 |