R Project Challenges: Test Your Skills and Knowledge with Fun and Interactive Exercises
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R was started by professors Ross Ihaka and Robert Gentleman as a programming language to teach introductory statistics at the University of Auckland.[13] The language took heavy inspiration from the S programming language with most S programs able to run unaltered in R[6] as well as from Scheme's lexical scoping allowing for local variables.[1] The name of the language comes from being an S language successor and the shared first letter of the authors, Ross and Robert.[14] Ihaka and Gentleman first shared binaries of R on the data archive StatLib and the s-news mailing list in August 1993.[15] In June 1995, statistician Martin Mächler convinced Ihaka and Gentleman to make R free and open-source under the GNU General Public License.[15][16] Mailing lists for the R project began on 1 April 1997 preceding the release of version 0.50.[17] R officially became a GNU project on 5 December 1997 when version 0.60 released.[18] The first official 1.0 version was released on 29 February 2000.[13]
The R Core Team was formed in 1997 to further develop the language.[23][24] As of January 2022[update], it consists of Chambers, Gentleman, Ihaka, and Mächler, plus statisticians Douglas Bates, Peter Dalgaard, Kurt Hornik, Michael Lawrence, Friedrich Leisch, Uwe Ligges, Thomas Lumley, Sebastian Meyer, Paul Murrell, Martyn Plummer, Brian Ripley, Deepayan Sarkar, Duncan Temple Lang, Luke Tierney, and Simon Urbanek, as well as computer scientist Tomas Kalibera. Stefano Iacus, Guido Masarotto, Heiner Schwarte, Seth Falcon, Martin Morgan, and Duncan Murdoch were members.[15][25] In April 2003,[26] the R Foundation was founded as a non-profit organization to provide further support for the R project.[27]
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Other R package resources include R-Forge,[59][50] a platform for the collaborative development of R packages. The Bioconductor project provides packages for genomic data analysis, including object-oriented data handling and analysis tools for data from Affymetrix, cDNA microarray, and next-generation high-throughput sequencing methods.[60]
The R Journal is an open access, refereed journal of the R project. It features short to medium-length articles on the use and development of R, including packages, programming tips, CRAN news, and foundation news.
R is a popular and widely used software for data science. Like any large open source software project, it takes a lot of work to maintain and develop. Much of the work is done by a small number of volunteers and there is a need to grow the contributor community.
Further sponsors welcome to support participant travel, accommodation and/or social events. Sponsors will be acknowledged on this website, on the R Contributors Twitter and Mastodon accounts, and in reports of the sprint. Please contact r-project-sprint-2023@gaggle.email to discuss!
Every new project likely fills you with enthusiasm and excitement. And it should. You are about to find answers to your research questions, and you hopefully come out more knowledgeable due to it. However, there are likely certain aspects of data analysis that you find less enjoyable. I can think of two:
While we cover data wrangling in great detail in the next Chapter (Chapter 7), I would like to share some insights from my work that helped me stay organised and, consequently, less frustrated. The following applies to small and large research projects, which makes it very convenient no matter the situation and the scale of the project. Of course, feel free to tweak my approach to whatever suits you. However, consistency is king.
All the files that you generate are in the same place. Your data, your coding, your exported plots, your reports, etc., all are in one place together without you having to manage the files manually. This is due to the fact that RStudio sets the root directory to whichever folder your project is saved to.
This section is not directly related to RStudio, R or data analysis in general. Instead, I want to convey to you that a good folder structure can go a long way. It is an excellent habit to start thinking about folder structures before you start working on your project. Placing your files into dedicated folders, rather than keeping them loosely in one container, will speed up your work and save you from the frustration of not finding the files you need. I have a template that I use regularly. You can either create it from scratch in RStudio or open your file browser and create the folders there. RStudio does not mind which way you do it. If you want to spend less time setting this up, you might want to use the function create_project_folder() from the r4np package. It creates all the folders as shown in Figure 6.1.