Advanced Topics in Data Preparation Using R (MT)

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The data we obtain from survey and experimental platforms (for behavioural science) can be very messy and not ready for analysis. For social science researchers, survey data are the most common type of data to deal with. But typically the data are not obtained in a format that permits statistical analyses without first conducting considerable time re-formatting, re-arranging, manipulating columns and rows, de-bugging, re-coding, and linking datasets. In this module students will be introduced to common techniques and tools for preparing and cleaning data ready for analysis to proceed. The module consists of four lab exercises where students make use of real life, large-scale, datasets to obtain practical experience of generating codes and debugging.

• Postgraduate students and staff
• Further details regarding eligibility criteria are available here

Some experience using R software. If you have not used R before then it is recommended that you first take the ‘Introduction to R’ module.

Number of sessions: 4

Overarching goal: Learn how to prepare messy datasets and clean data ready for analysis using R software

• How to process datasets in batch
• How to match, link and merge datasets (combine columns; combine rows)
• Techniques to deal with unwanted cases, including cases that do not meet the inclusion criteria, duplicates, and outliers, etc.
• How to deal with missing data
• How to recode and mutate variables
• How to revise original data files without messing up the format
• Other topics that you are interested in after we finish the above ones

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