1. John Spencer
MEASURE Evaluation
University of North Carolina at Chapel Hill
Webinar
August 24, 2017
The life changing magic of
tidying up your data
The art and science of
making data usable

2. Keep only those things
that bring a “spark of
joy”
The life changing
magic of tidying up
Marie Kondo

3. https://www.measureevaluation.org/news/tidy-data-and-how-to-get-it

4. Outline
What is tidy
data?
Why is it
important in
GIS?
What tools
exist to help?

5. Imagefrom:RforDataScience
Grolemund, Wickham
http://r4ds.had.co.nz

6. File format is the specific way information is
encoded for storage in a computer file.
File format vs file structure
Wikipedia

7. File structure how the data is stored in the file.
File format vs file structure

8. You’ve found a great new data repository and
you can’t wait to get data from it and start doing
stuff like this

9. Onceyouget the
data,itwill need
tobecleaned up
beforeusingit.
Messy
Data

10. Making
Messy
Data Tidy
Messy data needs to be tidied up before it
can be used.

11. Tidy
Data
Organized structure for data.
1. Each variable forms a column.
2. Each observation forms a row.
3. Each type of observational unit
forms a table.
Wickham, H. (2014). Tidy data. Journal of Statistical
Software, 59(10).

12. Untidy
Data
1. Column names represent data
values instead of variable names
2. A single column contains data
on multiple variables instead of
a single variable
3. Variables are contained in both
rows and columns instead of
just columns
4. A single table contains more
than one observational unit
5. Data about an observational
unit is spread across multiple
data sets
Wickham, H. (2014). Tidy data. Journal of Statistical
Software, 59(10).

13. “Happy families are all alike;
every unhappy family is
unhappy in its own way.”
–– Leo Tolstoy
“Tidy datasets are all alike, but
every messy dataset is messy
in its own way.”
–– Hadley Wickham

14. Let’s get messy

15. Class
Mammal Number of feet
Horse 4
Dog 4
Cat 4
Reptile
Snake 0
Turtle 4
Bird
Eagle 2
Ostrich 2
Multiple data classes
and species mixed in
the same column
Blank rows
Easy for human to
read, hard for a
computer

16. Animal Number of feet Class
Horse 4 Mammal
Eagle 2 Bird
Turtle 4 Reptile
Dog 4 Mammal
Snake 0 Reptile
Ostrich 2 Bird
Cat 4 Mammal
Tidy data

17. Make it easier for computer
programs to read data

18. Often found on websites or in reports

19. TidyuntidyUnitedNations’migrationdatawithtidyr
KanNashida
https://blog.exploratory.io/tidy-untidy-united-nations-migration-data-with-tidyr-167cbd24c5c2

21. Messy data and GIS

23. GIS wants to see well structured data
Facility ID Name Latitude Longitude Number of
staff
3K4R200 Eastern Health
Clinic
-47.48516 61.69449 13
27LS611 Southern Health
Clinic
-6.05422 19.66357 4
1N291B2 Western Health
Clinic
-48.36875 109.76463 9

24. Applicable beyond GIS data

25. Following basic tidy data protocols will make analysis
with many other software programs easier to do.

26. Hadley Wickham has an R
package, TidyR that can be
very helpful in tidying data.
R
https://cran.r-project.org/web/packages/tidyr/vignettes/tidy-data.html

27. Nicholas Hould has an
overview of tools in Python
programming language
Tidy data in Python.
Python
http://www.jeannicholashould.com/tidy-data-in-python.html

28. Stata provides tools; an
overview of some of them are
available via the Carolina
Population Center Website
Stata
http://www.cpc.unc.edu/research/tools/data_analysis/statatutorial

29. Excel is not necessarily the
best tool to change untidy
data into tidy data, but there
are some things it can do.
Microsoft has a page
describing how to clean data
and offers some plugins that
could be helpful:
Excel
https://goo.gl/WGiUvp

30. A good overview of some
useful Excel functions can be
found here:
Excel
http://myexcelonline.com/blog/top-excel-data-cleansing-techniques/

31. Other Data
Formats
XML
• Extensible Markup Language
• Designed to store and transport data
• Well defined schema
JSON
• JavaScript Object Notation
• Increasingly Common
• GeoJSON variation for geographic data
By definition the data is “tidy”

32. Advice

33. Advice for
data
producers
• Include tidy data download
options
• Think about potential users
of your data and what they
need to use data effectively

34. Advice for
data users
• Look for tools that make the
job easier
• Look for alternative
download sources that
provide the data in tidy
format
• Share tools that you create

35. https://www.measureevaluation.org/news/tidy-data-and-how-to-get-it

36. This presentation was produced with the support of the United States Agency for
International Development (USAID) under the terms of MEASURE Evaluation
cooperative agreement AID-OAA-L-14-00004. MEASURE Evaluation is
implemented by the Carolina Population Center, University of North Carolina at
Chapel Hill in partnership with ICF International; John Snow, Inc.; Management
Sciences for Health; Palladium; and Tulane University. Views expressed are not
necessarily those of USAID or the United States government.
www.measureevaluation.org