Join Mike Chapple for an in-depth discussion in this video Common data problems, part of Cleaning Bad Data in R.
- [Instructor] Now that you have some exposure…to the basic concepts of tidy data,…let's spend some time talking about the common problems…that appear in untidy datasets.…The five categories are datasets…where the column header contain values instead…of variable names, datasets that store multiple variables…in a single column, datasets that store variables in both…rows and columns, datasets that store different types…of observational units in the same table,…and datasets where a single observational unit…is spread across multiple tables.…
Let's take a look at each one…of these problems in more detail.…First, you know from the concepts of tidy data…that columns should contain variables,…therefore, it makes sense that the column header…would contain a variable name.…One common issue with datasets is…that column headers might contain actual values instead…of variable names.…This has the end result of spreading…what should be a single variable across multiple columns…and makes it very hard to analyze the data.…
Here's an example of a dataset that has this problem.…
Where possible, instructor Mike Chapple shows how to correct the issues using R, but the same principles can be applied to any statistical programing language.
- Missing data
- Duplicate rows and values
- Converting data
- Formatting data
- Working with tidy data
- Tidying data sets
- Dealing with suspicious data
Skill Level Beginner
1. Missing Data
2. Duplicated Data
3. Formatting Data
5. Tidy Data
6. Red Flags
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