Join Mike Chapple for an in-depth discussion in this video Aggregations and missing values, part of Cleaning Bad Data in R.
- [Narrator] One particular place where you need to…exercise caution is when you use aggregate functions…on data sets that contain missing values.…You'll want to understand what those missing values mean,…and how they impact your analysis.…Aggregate functions summarize data in a data set…to help us better understand the data.…You likely use them all the time.…Common examples of aggregate functions…are calculating the mean or average of a variable,…determining the median value of the variable,…finding the maximum or minimum value…and calculating the sum of all the values from a variable.…
Think for a moment about how missing values…might impact these functions.…For example, consider the National Forest data set…that we looked at in the last video.…Here's the original data set that we loaded.…The sum of these values is 192,272,000 acres…and we have 42 rows of data.…Now how would you calculate the mean of this data set?…Well, you'd probably divide the total by the number of rows…and you'd get an answer of 4,577,905 acres.…
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
What's next?1m 5s
- Mark as unwatched
- Mark all as unwatched
Are you sure you want to mark all the videos in this course as unwatched?
This will not affect your course history, your reports, or your certificates of completion for this course.Cancel
Take notes with your new membership!
Type in the entry box, then click Enter to save your note.
1:30Press on any video thumbnail to jump immediately to the timecode shown.
Notes are saved with you account but can also be exported as plain text, MS Word, PDF, Google Doc, or Evernote.