R is a language and environment for statistical computing and graphics. It is a GNU project that provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, etc.) and graphical techniques, and is highly extensible. While a software like SAS can be more amendable to large datasets, R is versatile, well documented and free to use.
For more information, visit www.r-project.org.
R is a programming language that is especially powerful for data exploration, visualization, and statistical analysis. With hands-on exercises, we will learn how to import and manage datasets, create R objects, install and load R packages, conduct basic statistical analyses, and create common graphical displays. This solid foundation of skills is an excellent starting point for using R for everyday and sophisticated research analyses. Read More.
This introduction to the popular ggplot2 R graphics package will show you how to create a wide variety of graphical displays in R. Topics covered included aesthetic mapping and scales, faceting, and themes. This is an intermediate level workshop appropriate for those already familiar with R. Participants should be familiar with importing and saving data, data types (e.g., numeric, factor, character), and manipulating data.frames in R. Read More.
This hands-on, intermediate R course will demonstrate a variety of statistical procedures using the open-source statistical software program, R. Topics covered include multiple regression, multilevel models, and multiple imputation. We expect that users enrolled in this course are already familiar with the statistical processes that we cover and are interested in learning how to run these procedures in R. Read More.
This hands-on, intermediate course will guide you through a variety of programming functions in the open-source statistical software program, R. It is intended for those already comfortable with using R for data analysis who wish to move on to writing their own functions. To the extent possible this workshop uses real-world examples. Concepts will be introduced as they are needed for a realistic analysis task. In the course of working through a realistic project we will learn about interacting with web services, regular expressions, iteration, functions, control flow and more. Read More.
Statistics courses usually use clean and well-behaved data for examples and homework. This leaves many unprepared for the messiness and chaos of data in the real world. This workshop aims to prepare you for dealing with messy data by walking you through real-life example.
The duration of the workshop is 3 hours. Computers with R installed are available on a first-come, first-served basis. If you will use your own laptop, please install R from https://cran.r-project.org and Rstudio from https://www.rstudio.com/products/rstudio/download/#download in advance.
Notes and materials for this workshop are available at https://dss.iq.harvard.edu/workshop-materials#widget-1.
Audience: Harvard Faculty, Students, and Staff are all welcome.
Pre-Requisites: Introduction to R
Software Version: R version >= 3.5 and RStudio version >= 1.1
Cost: none
Late Drop/Cancel Fee: none
Please email sworthington@iq.harvard.edu with questions.
