Join Mike Chapple for an in-depth discussion in this video Outliers in subgroups, part of Cleaning Bad Data in R.
- [Instructor] In addition to straightforward…outlier detection, you should also examine…your data set for outliers that might…appear in subsets of your data.…This is another case where applying…domain knowledge is quite helpful.…Consider as an example, a data set…containing test scores for students…in an elementary school that were…administered a grade level standardized test.…I've provided the code here to load that data file.…Let's go ahead and load the tidyverse,…set our working directory, and then…read in the tests data set.…
I'm going to start by looking…at some summary statistics.…I see that I have a student identifier…that's an integer value, an age that's a numeric value,…a grade level, and a test score.…And one thing that jumps out at me right away…is that the ages in this data set…range from five to 39.…Now that sounds suspicious for an elementary school.…Let's dig into that variable more…by looking at a box plot.…Now there certainly shouldn't be…students in elementary school that are…in their 20's and 30's, but I see here…
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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