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We prefer to think of it of an environment within which many classical and modern statistical techniques have been implemented.
Most classical statistics and much of the latest methodology is available for use with R, but users may need to be prepared to do a little work to find it.
There is an important difference in philosophy between S (and hence R) and the other main statistical systems.
In S a statistical analysis is normally done as a series of steps, with intermediate results being stored in objects.
S system, which was developed at Bell Laboratories by John Chambers et al. The term “environment” is intended to characterize it as a fully planned and coherent system, rather than an incremental accretion of very specific and inflexible tools, as is frequently the case with other data analysis software.
It provides a wide variety of statistical and graphical techniques (linear and nonlinear modelling, statistical tests, time series analysis, classification, clustering, ...). R is very much a vehicle for newly developing methods of interactive data analysis.
This manual provides information on data types, programming elements, statistical modelling and graphics. It has developed rapidly, and has been extended by a large collection of , yet many people use R as a statistics system.
See Graphics, which can be read at almost any time and need not wait until all the preceding sections have been digested.
Many users will come to R mainly for its graphical facilities.
We would like to extend warm thanks to Bill Venables (and David Smith) for granting permission to distribute this modified version of the notes in this way, and for being a supporter of R from way back. This should give some familiarity with the style of R sessions and more importantly some instant feedback on what actually happens.