Difference between revisions of "Training:SummerSchool2016:Programme:R"
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| − | The R programming language | + | = Programming with R = |
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| + | The R programming language has become the standard tool for data analysis, statistics, and bioinformatics. It owes much of this popularity due to its free, open-source, and highly extensible nature. There are tens of thousands of R extensions available, and each adds the ability to perform new types of analyses and operations. This tutorial is intended as a one-day introduction to the language. After the workshop, students will be able to write R code using RStudio, analyze and manipulate data in R, create publication-quality graphics, parallelize and performance-optimize their scripts, and run analyses both on their own computer and clusters operated by organizations like CAC, SciNet, and SHARCNET. | ||
'''Instructor:''' Jeff Stafford - Centre for Advanced Computing, Queen's University | '''Instructor:''' Jeff Stafford - Centre for Advanced Computing, Queen's University | ||
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R - [https://www.r-project.org/ https://www.r-project.org/] | R - [https://www.r-project.org/ https://www.r-project.org/] | ||
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RStudio Release Preview - [https://www.rstudio.com/products/rstudio/download/preview/ https://www.rstudio.com/products/rstudio/download/preview/] | RStudio Release Preview - [https://www.rstudio.com/products/rstudio/download/preview/ https://www.rstudio.com/products/rstudio/download/preview/] | ||
Latest revision as of 21:31, 29 June 2016
Programming with R
The R programming language has become the standard tool for data analysis, statistics, and bioinformatics. It owes much of this popularity due to its free, open-source, and highly extensible nature. There are tens of thousands of R extensions available, and each adds the ability to perform new types of analyses and operations. This tutorial is intended as a one-day introduction to the language. After the workshop, students will be able to write R code using RStudio, analyze and manipulate data in R, create publication-quality graphics, parallelize and performance-optimize their scripts, and run analyses both on their own computer and clusters operated by organizations like CAC, SciNet, and SHARCNET.
Instructor: Jeff Stafford - Centre for Advanced Computing, Queen's University
Prerequisites: No programming experience required
Required Software:
R - https://www.r-project.org/
RStudio Release Preview - https://www.rstudio.com/products/rstudio/download/preview/
