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Data Services Class Descriptions

Information, materials, and schedules for all currently offered Data Services classes.

R is a programming language for statistical analysis of data. This tutorial will introduce you to the basic elements of R, to working with data sets in R, to visualizing them, and to implementing common statistical procedures.

Software:	Computer workstations with R and RStudio installed are available for in-person tutorials in Bobst 617. For remote tutorials, while some patrons decide to approach tutorials as a demonstration of the software, other patrons approach tutorials with a more “hands-on” approach and wish to interact with the software during the tutorial. If the latter is the case, we recommend referencing our supported software page for additional information on accessing/downloading the software prior to the tutorial.
Duration:	120 min
Room description:	Some tutorials are held remotely and require NYU sign on to access, while others are held in person, without a remote component. Please note the correct modality and location of the tutorial when registering
Prerequisites:	Basic computer literacy, understanding files and folders
Skills Taught / Learning Outcomes:	Interface of R and RStudio Data types and indexing (assignments, objects, vectors, matrices, data frames, lists) R built-in functions and syntax Working directory Packages Importing data from CSV, Excel, .txt, SPSS, Stata files etc... Exporting data as CSV Preparing data for analysis (renaming variables, variable types, missing values) Computing, transforming and recoding variables Subset datasets by row and by columns Descriptive statistics, frequency tables, cross tabulation tables Graphics (boxplot, histogram, scatterplot, partitioning window) Basic analyses (t-test, correlation, ANOVA, regression, chi-square)
Class Materials:	Dataset.csv Syntax.R Introduction to R.Rmd Code_Preview.pdf
Related Classes:	Data Visualization with Tableau Data Cleaning Using OpenRefine Introduction to Research Data Management Introduction to Python
Additional Training Materials:	guides.nyu.edu/r
Feedback:	bit.ly/feedbackds

Upcoming sessions for this tutorial