1. Introduction to R Studio
Basic Features, Rudimentary Data Analysis, & Graphing Options

2. Organization 1. What is R Studio & Why should you use it?
2.Where to get R Studio
3. Layout of R Studio
4. Installing Packages
5. Loading Packages
6. Bringing Data into R Studio/Exporting Data
7. Data Types in R
8. Helpful Resources

3. 1-A: What is R Studio? An alternative to base R Benefits of R Studio
Friendlier user interface
Open source, but paid option as well (not worth it for typical users)
Same install process
Benefits of R Studio
Easier transition from Stata to R
Multiple window views at once – Results, Code, Working Environment, and Graphs

4. 1-B: What is R Studio? Benefits of R in General
Open Source
Means free
Can make a working object out of anything
Lets you save:
Results
Graphs
This is really helpful – can add elements to graphs after you create them!
Data frames
Vectors
Etc…

5. 1-C: What is R Studio? An Important Note
Citing R Studio & Packages
If using R or R Studio for analyses that will be published, should always cite the version of R and the versions of the packages you have used
Results can change across versions, not typical for versions of R, but fairly typical for package versions
Is this a reason not to use R/R Studio?
No, Stata has the same problems but no one ever acknowledges it
See -
Past users of psmatch2 were obtaining systematically biased results (it has been fixed now, to the extent of my knowledge).

6. 1-D: Why Should You Use R Studio?
Versatility
Packages are created and updated frequently
Adapts to newer methods faster than Stata or SPSS
Double-edged blade with this, though
Superior graphing capabilities
Yup, I said it….
Seriously – graphs may be stored and have plots added/changed after they have been made
Great package for excellent plots – ggplot2 (I’ll be demonstrating plots with this later)
Knowledge of Code
Transferrable to other programs (kind of)
E.g. Matlab, SAS, C++, etc…
Have I mentioned that it is free?

7. 2-A: Where to get R or R Studio
Options for using R
Base R package
An alternative – R Studio
I find it to be much more user-friendly
Also, an easier transition from Stata
Look and feel of the program is similar
Differences
As far as I can tell, none for the typical user outside of the different interface
Important note
Must install base R package before R Studio will work

8. 3-A: Layout of R Studio Four Primary Windows Console Source
Equivalent of results window in Stata
Source
Equivalent of Do file editor in Stata
Not open by default
Environment
Closest equivalent is Properties window in Stata
File/Plot/Packages/Etc…Viewer
Similar to Properties window in Stata but has much more functionality

9. 3-B: Layout of R Studio Default Setup

10. 3-C: Layout of R Studio With Source Code Window

11. 4-A: Installing Packages into R Studio
Works similarly to adding packages into Stata (e.g., psmatch2, gllamm, etc…)
Two methods:
Point and Click
Syntax

12. 4-B: Installing Packages into R Studio Point and Click Method
Step 1: Tools  Install Packages
Step 2: Enter package name, check dependencies

13. 4-C: Installing Packages into R Studio Point and Click Method
Step 3: Install package
Step 4: Verify install in Console

14. 4-D: Installing Packages into R Studio Syntax Method
Step 1: Write then run code
Step 2: Verify install in Console

15. 5-A: Loading Packages in R Studio
Unlike Stata, packages other than the base package are not loaded by default
They need to be explicitly loaded by the user
This can be adjusted through Tools  Global Preferences menu
Again, two methods:
Point and click
Syntax

16. 5-B: Loading Packages in R Studio Point and Click Method
Step 1. Packages  Check Box
Step 2: Verify load in Console

17. 5-C: Loading Packages in R Studio Syntax Method
Step 1: Write then run code
Step 2: Verify load in Console

18. 6-A: Bringing Data into R Studio
Native R data forms
.RData or .rda
Can import multiple types of files, just like Stata
Text data (.txt, .TXT, etc…)
Can be imported using the read.table(…) function
Spreadsheet data (.csv, etc…)
Must use sep option (e.g., read.table(“file name here”, sep=“,”) )for comma separated files

19. 7-A: Basic Features of R Studio Data Storage Types
Numeric
Equivalent in Stata is “byte”
Meaningful numbers
Character
Equivalent in Stata is “string”
Contains alpha characters
Logical
True or False
Special
NA (Not available)
Typical missing value indicator
 Can have a variety of classes
Inf and –Inf
Basically, what is returned when the number is too large or small
NaN
Not a number
Null
Distinct from NA
Not recognized in vectors, has no class
Best thought of as “undefined”

20. 7-B: Basic Features of R Studio Types of Data
Vectors
May be numeric, character, or logical
Matrices
Combination of vectors
Must all be the same storage type (e.g., numeric) and length
Arrays
2+ Dimensional versions of matrices
Same restrictions apply
Data Frames
Most important for our purposes
Vectors comprising it may have multiple storage types
Lists
Serve a number of purposes
Can be made up of any combination of data storage types
Factors
May explicitly tell R Studio that a variable is nominal or ordinal (similar to “encode” function in Stata)
Integers are mapped to character values, character value are kept as labels

21. 8-A: Helpful Resources Websites
Coursera – Johns Hopkins Data Science Series
R Website (PDF)
Stack Overflow
Swirl – Interactive Learning in R (I used this)

22. 8-B: Helpful Resources (Cheap!) Books
R Cookbook
OReilly-Cookbooks-Paul- Teetor/dp/ /ref=sr_1_1?ie=UT F8&qid= &sr=8- 1&keywords=r+cookbook
R Graphics Cookbook (uses ggplot2)
Cookbook-Winston- Chang/dp/ /ref=sr_1_1?ie=U TF8&qid= &sr=8- 1&keywords=r+graphics+cookbook