Join Mike Chapple for an in-depth discussion in this video Missing values, part of Cleaning Bad Data in R.
- [Instructor] The first type of missing data…that we'll address occurs when we have missing values…from one or more observations in our data set.…For example, imagine that we have a data set…consisting of a few facts about three different people,…Tom, Mary and Bob.…We might do a quick survey of Tom, Mary and Bob…and ask them their height, weight and age…collecting the data set that we have on the screen.…However Tom might refuse to tell us his age…leaving us with a missing value.…
Missing values can occur for many different reasons.…We might have situations like the survey…I just described where someone refuses to provide data,…or we have simply might not have gotten around…to collecting the data yet…or we might have a data element that is not applicable…for a given observation.…For example, if we ask someone who has no children…the age of their oldest child,…that would result in a missing value.…You can probably think of other situations…that would result in missing data.…
Different programming languages handle missing values…
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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