Please consult the NIH Library Training Calendar for course offerings. Each topic area divided into two separate classes. The Introduction to R series is divided into three topic areas: (1) Introduction to R, (2) Introduction to Data Wrangling in R, and (3) Data Visualization in R. The NIH Library Data Services team has created an Introduction to R series that is offered monthly. The series is modeled after Data Carpentry, and is designed to teach non-programmers to write modular code and to introduce best practices for using R for data analysis. Learning about R and RStudio NIH Library Introduction to R Classes Learn more about using R to conduct research that can be easily recreated, understood, and verified.
Because it is open source and uses literate programming (combining content and code), R facilitates research reproducibility. R and RStudio are useful for a wide variety of data manipulation, analysis, and visualization tasks. To learn more about RStudio, visit the RStudio homepage. RStudio is an open source user interface for R, and it also provides access to some packages not available in the basic R environment. To learn more about R, visit the R Project for Statistical Computing. For additional information about working with different data formats in R, see R Data Import/Export. XLSX), files from a variety of statistical packages (including SAS, SPSS, and Stata), and more.
#DOWNLOAD R AND RSTUDIO SOFTWARE#
The R environment provides an integrated suite of software facilities for data manipulation, calculation and graphical display. R can import and export data in a variety of formats, both open source and proprietary, including plain text and comma-separated values (CSV), Excel spreadsheets (.XLS and. R is a programming language and environment for statistical computing and graphics.
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