1. Fall 2018 Research Workshop Cindy Traub, PhD
R for Mere Mortals
Fall 2018 Research Workshop
Cindy Traub, PhD

2. What is R?
“a language and environment for statistical computing and graphics” [
Open source
Extendable by independently written packages
Addresses computational needs of different disciplines

3. Useful beyond traditional statistics
Accessing and/or “Cleaning” data
Working with tabular data too large for Excel to handle
Retrieving data from websites
Analyzing text
Creating visualizations, databases, maps
…and much more

4. What we will learn about R today
How do you get data in and out of R?
Where does your data live? (Before, during, after your analysis)
How can you “see” your data in R?
How can you get help when you get stuck?

5. Where is R in the research cycle?
Ask a Good Question
Collect/ Obtain Raw Data
Clean and Prep Data
Generate Statistical Results
Adapted in part from:
“An introduction to data cleaning with R”
(de Jonge and van der Loo, 2013)

6. Tiny Example: City/State/Zip
What do you notice?

7. Tiny Example: City/State/Zip
Missouri vs. MO
St. vs. Saint
Right data, wrong spot (CaseNo 5)
Wrong data (zip in CaseNo 3)
Q: How would you find these errors in a “big” data set?

8. Simple tasks R does well:
Read in (or write out) a csv file
Display/count unique values in a column
Select rows matching 1 or more conditions
Aggregate (group together) data by 1 or more features
Plot your data
Split apart or glue together text strings
Reproducibility (aka “show your work”) through scripts or R Markdown
Can handle tabular data too big for Excel to open

9. How does R compare to Excel?
Similarities
Differences
Good with tabular data
Can define new values based on old
Can create charts
Can take actions based on values
Can filter/subset data based on attributes
Many different functions available
Hard to track/describe steps taken in Excel (winner for reproducibility: R)
Entries are static in R, can be dynamic in Excel (winner: Excel)
Capacity to handle large files (winner: R)
“Fancy” formatting/visual display of tabular data (winner: Excel)

10. What does R look like?
Unlike traditional office productivity software, there are many ways to access and interact with the software: -Command line -Point-click graphical interface like R Studio -Learning: many options -Datacamp, TryR -Coursera, swirl

11. What else can I do in R? CRAN Task Views Look for vignettes
Sorted by topic
Look for vignettes
Make a sound
choice of technique

12. R Studio intro Please open R Studio on your machine.
Go to to get files:
Within R Studio, open sampleRcode.R
Tips included in file gettingstaRted.pdf

13. Cleaning your data[frame]
Look at your data.
Quantities as numbers (not text)?
One column  one type of data?
One row  one observation?
Any typos/name standardization?
Any extra characters?
Values make sense in context?
Create/consolidate any columns?
Reshape (wide to tall or opposite)?
Did dates read in correctly?
Useful commands: head(mydata) tail(mydata) str(mydata) summary(mydata) names(mydata) unique(mydata$col1) table(mydata$col1) mydata[row(s), col(s)] mydata$newCol_name<- … plot(mydata$col2) boxplot(mydata$col3) pairs(mydata)

14. Thanks for coming! Slides are available on my R libguide:
Any questions?
Please complete the survey about today's session and sign the attendance sheet. Thanks!
Contact Cindy at

15. Bonus slides follow More common functions Other useful data tools
Data structures in R

16. Common useful functions
Assuming "obj" is name of a data frame, vector, etc. length(obj) str(obj) class(obj) names(obj) c(obj1, obj2, …) cbind(obj1, obj2, …) rbind(obj1, obj2, …) ls() (note: that is lowercase LS) dir()

17. What can you store and where?
Data Types
Storage Objects
Numbers
Integers (1, 2, -15, etc.)
Decimal-valued (3.14, -2.9)
Text
Character strings (“Atticus”, “C:/Users/Labuser/Desktop”)
Boolean
TRUE or FALSE
Constant
Vector
List
Matrix
Data Frame
Data Table
Shapefile

18. Useful tools + technology for data
R (Resources at
Excel (Data Validation, Filters)
Python and Jupyter Notebooks (
OpenRefine (
Gephi for network viz (
Mallet for NLP (
D3 JS (