<p>Lecture, four hours; discussion, one hour. Designed for students who never thought they would become programmers. No prior experience with R required. Students learn fundamental data management and data manipulation skills necessary for creating analysis datasets. Students gain fundamental skills of data management, which are important regardless of which programming language is used, and develop a strong foundation in the R programming language. P/NP or letter grading.</p>

Social Science

College of Letters and Science

SOC SC 45 - R for Social Sciences

R for Social Sciences

Investigate data patterns 
– Sort datasets in ways that generate insights about data structure 
– Select specific observations and specific variables in order to identify data  structure and to examine whether variables are created correctly 
– Create summary statistics of particular variables to diagnose errors in data 4. 


Visualize data 
– Create plots using the ggplot2 library 
– Customize plots through color palettes, labels, line shapes, etc. 


Learn guidelines and practical strategies for ensuring data quality when cleaning data  and creating analysis variables 

Manipulate the organizational structure of datasets 
– Summarize and collapse observations by group 
– Reshape and “tidy” untidy data 


Understand fundamental concepts of object-oriented programming – Understand basic object types and how they apply to statistical analysis – Understand object attributes and how they apply to statistical analysis 

Become familiar with Base R approach and Tidyverse approach to data manipulation 3. 


Create variables 
– Create variables that require calculations across columns 
– Create variables that require processing across rows 


Combine multiple datasets 
– Join (merge) datasets 
– Append (stack) datasets 


General Catalog

R for Social Sciences

SOC SC 45

5 units