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- Help with R/SAS/SPSS/Stata

Support for locating and working with datasets, statistical information, and geographic data.

**This page is a collection of links for help with using R, SAS, SPSS, and Stata. The guides are very "from-the-ground-up" and cover multiple topics, from the basics of getting data into the program to various common data-management tasks to introductory data analysis.**

**These guides generally focus on using syntax to work with and analyze data in statistical software. While there are learning curves of varying degrees of steepness with each of these applications, a syntax-based approach to working with data is a more robust and reproducible means of doing empirical analysis and is the flip side of proper citation with regard to the coin of transparency in quantitative research. Simply put, syntax is documentation that spells out what you did to process and analyze the data and produce your findings, and access to that syntax thus shows others how you got your results. Some journals such as the American Economic Review and the American Journal of Political Science even require submission of syntax for cleaning and analyzing data as part of their submission policies on data availability.**

**For additional guidance on working with data, see "Stata for Researchers: Project Management" (written for Stata users, but applicable more generally) and the Teaching Integrity in Empirical Research Project's recommended specifications and processes for working with data.**

**"Cookbook for R," by Winston Chang**

**"Getting Started in Data Analysis Using Stata and R," by Oscar Torres-Reyna, Princeton University**

**"Quick-R - 'Accessing the Power of R'"**

**"R for Data Science," by Garrett Grolemund and Hadley Wickham**

**"R - "Quick List of Useful R Packages"**

**"R for SAS and SPSS Users," by Robert A. Muenchen, the University of Tennessee (.pdf)**

**"R for Stata Users," by Matthieu Gomez , Princeton University**

**"Resources to Help You Learn and Use R," from UCLA's Academic Technology Services**

**"Tidyverse - R Packages for Data Science," by Hadley Wickham**

**"Using R to Analyse Key UK Surveys," from the UK Data Service (.pdf)**

**"Documentation for SAS Products and Solutions," from the SAS Institute**

**"Knowledge Base / Samples and SAS Notes," from the SAS Institute**

**"Resources to Help You Learn and Use SAS," from UCLA's Academic Technology Services**

**"SAS Classes and Seminars," from UCLA's Academic Technology Services**

**"Topics in SAS Programming," from UNC's Carolina Population Center**

**"Resources to Help You Learn and Use SPSS," from UCLA's Academic Technology Services**

**"SPSS Classes and Seminars," from UCLA's Academic Technology Services**

**"SPSS Tutorials," from Ruben Geert Van Den Berg in the Netherlands**

**"What Is SPSS 20 For Windows?" from the UK Data Service (.pdf)**

**"A Little Bit of Stata Programming Goes A Long Way," from Christopher Baum at Boston College (.pdf)**

**"Getting Started in Data Analysis Using Stata and R," by Oscar Torres-Reyna, Princeton University**

**"Introduction to Stata," by Germán Rodríguez, Princeton University**

**"Frequently Asked Questions," from the Stata Corporation**

**"Resources for Learning Stata," from the Stata Corporation**

**"Resources to Help you Learn and Use Stata," from UCLA's Academic Technology Services**

**"A SAS User's Guide to Stata," from the UNC Carolina Population Center**

**"Stata Classes and Seminars," from UCLA's Academic Technology Services**

**"Stata Cheat Sheets," from the "Fundamentals of Data Analysis and Visualization" Project**

Robert O'Reilly, Ph.D

404-727-6129 (P)

roreill@emory.edu

Emory Center for Digital Scholarship (ECDS)

Woodruff Library, 3rd Floor

ECDS Hours: Monday-Friday, 9:00AM-5:00PM

We help researchers with locating quantitative data and with cleaning and preparing data for analysis.

Website
roreill@emory.edu

Emory Center for Digital Scholarship (ECDS)

Woodruff Library, 3rd Floor

ECDS Hours: Monday-Friday, 9:00AM-5:00PM

We help researchers with locating quantitative data and with cleaning and preparing data for analysis.