R
Free software environment for statistical computing and graphics.
R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, timeseries analysis, classification, clustering, …) and graphical techniques, and is highly extensible. The S language is often the vehicle of choice for research in statistical methodology, and R provides an Open Source route to participation in that activity.
One of R’s strengths is the ease with which welldesigned publicationquality plots can be produced, including mathematical symbols and formulae where needed. Great care has been taken over the defaults for the minor design choices in graphics, but the user retains full control.
R is an integrated suite of software facilities for data manipulation, calculation and graphical display. It includes;
 an effective data handling and storage facility,
 a suite of operators for calculations on arrays, in particular matrices,
 a large, coherent, integrated collection of intermediate tools for data analysis,
 graphical facilities for data analysis and display either onscreen or on hardcopy, and
 a welldeveloped, simple and effective programming language which includes conditionals, loops, userdefined recursive functions and input and output facilities.
Keen to improve your software skills? Prism offer a range of onsite statistical training workshops featuring R, including...
 Statistical Programming  R Starter and R Programming  Courses 1&2
 Statistical Programming  Getting Started with R
 Statistical Programming  R Programming  Course 2
 Statistical Programming  R Improver  Course 3
To download R, please visit www.rproject.org
Related content

R Starter and R Programming  Courses 1&2
This workshop combines the F106  Getting Started with R module on Day 1 and the F107 R Programming module on Day 2. Day 1 and Day 2 may be taken separately.
On Day 1, this workshop will introduce the open source R software for statistical computing and equip you to use R for basic data handling and statistical analysis. In the first part of the workshop, you will learn how to get data into R, how to manipulate that data in R and how to explore it using numerical, tabular and graphical summaries. In the second part, you will discover how to analyse data in R using linear regression and analysis of variance. You will also be equipped with ways to find help on R, so that following the workshop you will be able to build on the fundamentals you have learnt to expand your knowledge of R.
On Day 2, this workshop will increase your understanding of how to program in R to enable you to work more efficiently by writing code for reuse in routine analyses. It will also equip you to run simulations and implement novel or customised methods. This workshop introduces the fundamentals of R programming, looks at ways to improve the efficiency of R code, considers how to debug code and how to make use of multiple cores (on Windows).
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Getting Started with R
This workshop will introduce the open source R software for statistical computing and equip you to use R for basic data handling and statistical analysis. In the first part of the workshop, you will learn how to get data into R, how to manipulate that data in R and how to explore it using numerical, tabular and graphical summaries. In the second part, you will discover how to analyse data in R using linear regression and analysis of variance. You will also be equipped with ways to find help on R, so that following the workshop you will be able to build on the fundamentals you have learnt to expand your knowledge of R.
Read More 
R Programming  Course 2
The base R distribution and the vast number of addon packages provide a wealth of functions for data management and statistical analyses. However understanding how to program in R will enable you to work more efficiently by writing code for reuse in routine analyses. It will also equip you to run simulations and implement novel or customised methods. This workshop introduces the fundamentals of R programming, looks at ways to improve the efficiency of R code, considers how to debug code and how to make use of multiple cores (on Windows).
Read More