Introduction: Why Choose R?
As a young geoscientist, you will sooner or later face the difficult question: “Which software do you use for your data processing and analysis?”. Conventionally scientists and geoscientists have been forced to use the software provided by their department or research group [1]. This may be an in-house software package specially developed for the scientific tasks faced on a daily basis, or additionally a complicated and complex proprietary software offering.
Often getting started in these programs can be a virtual nightmare - help files are full of jargon, and often virtually no tutorials exist. Perhaps at best as a 'newbie' you may be provided with some scripts by a supervisor. Regardless starting in an unknown software package is always daunting and can be simply frustrating!!
This online module offers the possibility of using an open source, community developed software that is built by scientists for scientists. R is a fully developed, and tested suite of tools, functions and packages to deal with virtually any scientific task you can expect to face.
Developed in 1993, at the University of Auckland, New Zealand, The R Project For Statistical Computing, or simply R as it is commonly know has grown into one of the most commonly used and popular statistical environments available. The active community of users have themselves contributed over 5000 additional packages to the base installation [2].
Across the diverse fields of mathematics, quantitative finance, biology, astrophysics, and statistics - one can access and exploit specifically designed tools to work with many types of data.
This online course, or condensed manual is meant to serve as a quick 'point of referral' for your own continued exploration of the R environment. It does not aim to overwhelm the new user with technicalities and unnecessary jargon but instead provide the 'nuts and bolts" to navigate R, and complete common tasks.
It is structured to progressively teach basic skills first, and then expand developing the techniques needed to successfully complete more lengthy tasks. Thus it is recommended to read and follow through the examples sequentially, as each ensuing chapter builds on the fundamentals presented in earlier sections.
[1]: For specific tasks such as processing of geophysical data, or in cases where packages were developed by industry (i.e. Oil and Gas) this may be unavoidable. For any other tasks R is well-suited.
[2]: The full range of packages can be found here:
