Join Mike Chapple for an in-depth discussion in this video Unit conversions, part of Cleaning Bad Data in R.
- [Instructor] One of the most perplexing issues…in data analysis arises when you encounter…a data set that doesn't explain itself very well.…These issues can often be traced to a lack of metadata…and, in particular, a failure to specify units.…Sometimes we can make inferences about the units involved…simply by taking a look at the data set.…For example, take a look at this variable, weight.…When it stands alone, there's not much…that we can figure out or do with this data.…What is it weighing?…What units of measure are specified here?…Are these pounds, kilograms, ounces?…We really can't tell just from looking at…this single variable.…
But then let's add a little context to the data.…Now we know that this is a table of information about people…and, specifically, teenage boys.…We can apply a little domain knowledge here…and then make the assumption that these measurements…are in pounds.…Other units of measure wouldn't make sense…for a healthy group of young men.…It's not always that easy, however.…Let's take another look at the Mexican weather data set…
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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