R is both programming language for statistical computing and graphics, and a software (ie application) to execute programs written in R. R was developed in mid 90s at the University of Auckland (New Zealand).
Since then R has become one of the dominant software environments for data analysis and is used by a variety of scientific disciplines.
BTW why it is called so strange (R)? Long time ago it was popular to use short names for computer languages (C for example). At AT&T Bell Labs (John Chambers) in mid 70s a language oriented towards statistical computing was developed and called S (from Statistics). R is one letter before S in an alphabet.
Rstudio is an environment through which to use R. In Rstudio one can simultaneously write code, execute code it, manage data, get help, view plots. Rstudio is a commercial product distributed under dual-license system by RStudio, Inc. Key developer of RStudio is Hadley Wickham another brilliant New Zealander (cf Hadley Wickham )
Microsoft invest heavily into R development recently. It bought Revolution Analytics a key developer of R and provider of commercial versions of the system. With MS support the system is expected to gain more popularity (for example by integrating it with popular MS products)
Abandoning the habit of secrecy in favor of process transparency and peer review was the crucial step by which alchemy became chemistry. Eric S. Raymond, E. S. The art of UNIX programming: Addison-Wesley.
Replicability: independent experiment targetting the same question will produce a result consistent with the original study. Reproducibility: ability to repeat the experiment with exactly the same outcome as originally reported [description of method/code/data is needed to do so].
Computational science is facing a credibility crisis: it’s impossible to verify most of the computational results presented at conferences and in papers today. (Donoho D. et al 2009)
Hot topic: google: "reproducible research" = 649000 (January/2021)
Enter data in Excel/OOCalc to clean and/or make explanatory analysis. [Excel handles missing data inconsistently and sometimes incorrectly Many common functions are poor or missing in Excel. Poor quality graphics]
Import data from Spreadsheet into SPSS/SAS/Stata for serious analysis [Using SPSS/SAS/Stata in point-and-click mode for serious statistical analyses.]
Prepare report/paper [Copy/paste output from SPSS/SAS/Stata to Word/OpenOffice, add description. Send to publisher]
Repeat 1–4 [revision/recomputation etc…]
Tedious/time-wasting/costly.
Even small data/method change requires extensive recomputation effort/careful report/paper revision and update.
Error-prone: difficult to record/remember a ‘click history’.
Famous example: Reinhart and Rogoff controversy Countries very high GDP–debt ratio suffer from low growth. However the study suffers serious, but easy identifiable flaws which were discovered when RR published the dataset they used in their analysis (cf Growth_in_a_Time_of_Debt)
Abandon spreadsheets.
Abandon point-and-click mode. Use statistical scripting languages and run program/scripts.
Steeper learning curve.
Perhaps higher costs in short run.
Duplication of effort (or mess if scripts/programs are poorly documented).
Literate programming concept: Code and description in one document. Create software as works of literature, by embedding source code inside descriptive text, rather than the reverse (as in most programming languages), in an order that is convenient for human readers.
A program is like a WEB tangled and weaved (turned into a document), with relations and connections in the program parts. We express a program as a web of ideas. WEB is a combination of – a document formatting language and – a program language.
General idea of Literate statistical programming mimics Knuth’s WEB system.
Statistical computing code is embedded inside descriptive text. Literate statistical program is weaved (turned) into report/paper by executing code and inserting the results obtained. data/method changes.
Reliability: Easier to find/fix bugs. The results produced will not change when recomputed (in theory at least).
Efficiency: Reuse allows to avoid duplication of effort (Payoff in the long run.)
Transparency: increased citation rate, broader impact, improved institutional memory
Institutional memory is a collective set of facts, concepts, experiences and know-how held by a group of people.
Flexibility: When you don’t ‘point-and-click’ you gain many new analytic options.
Many incl. costs and learning curve
Document formatting language: LaTeX (not recommended) or Markdown (or many others, ie. orgmode). LaTeX is a word processor/a document markup language. Markdown: lightweight document markup language based on email text formatting. Easy to write, read and publish as-is.
Program language: R (or Python)
Frontends: RStudio
The basic idea is that instead of manually registering changes one has made to data, documents etc, one can use software to help him manage the whole process. Such software is called Version Control Systems or VCS
VCS not only manages content, registering each modification of it, but control access to the content as well. Thus many individuals can work on common project (compare this to common scenario of mailing spreadsheets to each other–highly inefficient at least)
There are highly reliable and publicly available VCS services and GitHub is the most popular of them.
GitHub is owned by Microsoft (do not use if you boycott MS :-))
I use GitHub as an educational tool: to distribute learning content to my students and to store content they produce for me (ie projects)
The free GitHub account is public. It is OK for me. If it is not OK for you, you can buy a license for commercial account or do not use GitHub.