R is easily extensible through functions and extensions, and its community is noted for contributing packages. R and its libraries implement various statistical and graphical techniques, including linear and nonlinear modeling, classical statistical tests, spatial and time-series analysis, classification, clustering, and others. The first official "stable beta" version (v1.0) was released on 29 February 2000.
The Comprehensive R Archive Network (CRAN) was officially announced 23 April 1997 with 3 mirrors and 12 contributed packages. Heiner Schwarte, Guido Masarotto, Stefano Iacus, Seth Falcon, and Duncan Murdoch were members.
As of 2021, it consisted of Gentleman, Ihaka, and Maechler, plus Douglas Bates, John Chambers, Peter Dalgaard, Kurt Hornik, Tomas Kalibera, Michael Lawrence, Friedrich Leisch, Uwe Ligges, Thomas Lumley, Martin Morgan, Paul Murrell, Martyn Plummer, Brian Ripley, Deepayan Sarkar, Duncan Temple Lang, Luke Tierney, and Simon Urbanek. The R Core Team was formed in 1997 to further develop the language.
In 1995, Martin Maechler convinced Ihaka and Gentleman to make R free and open-source software under the GNU General Public License. It was named partly after the first names of the first two R authors and partly as a play on the name of S.
In 1991 Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, embarked on an S implementation, independent of S-PLUS. Many codes written for S-PLUS run unaltered in R. A commercial version of S was offered as S-PLUS starting in 1988. S was created by John Chambers in 1976 while at Bell Labs. R is an implementation of the S programming language combined with lexical scoping semantics.
Multiple third-party graphical user interfaces are available, such as RStudio, an integrated development environment and Jupyter, a notebook interface. Precompiled executables are provided for various operating systems. It is written primarily in C, Fortran, and R itself (partially self-hosting) and is available under the GNU General Public License. The official R software environment is a GNU package. Polls, data mining surveys, and studies of scholarly literature databases show that R is highly popular since August 2021, R ranks 14th in the TIOBE index, a measure of programming language popularity. It is widely used among statisticians and data miners for developing statistical software and data analysis. It is supported by the R Core Team and the R Foundation for Statistical Computing. This way if either var1 or var2 is missing, then no action is taken by the IF command, and newvar remains 0.R is a programming language and free software environment for statistical computing and graphics. If you need newvar to be set equal to 1 when you know var1 = 1 (i.e., var1 not missing) and var2 = 2 (i.e., var2 not missing) and you want newvar set to 0 in all other cases, then the simplest way is to first initialize newvar to 0, and then flip it to 1 only when var = 1 and var2 = 2: There's an easier solution to the original problem.
NOTE: this was meant as an illustration of how a DO IF-ELSE IF structure handles missing values. SPSS then decides "VAR1=1 and VAR2=2: I can't determine." The case is neither true or false, so the case falls out of the DO IF structure without a NEWVAR value being assigned SPSS looks at the first case where VAR1=1 and VAR2 is system-missing. The case then falls out of the DO IF structure and SPSS goes to the next case. SPSS will process the DO IF command for this case and say "VAR1=1: true" and go to the COMPUTE command so NEW_VAR=1. Suppose that for our first case VAR1=1 and VAR2 is system-missing. If either VAR1 or VAR2 is system-missing, then SPSS cannot determine if this selection criteria is either true or false, so SPSS returns a system-missing value for the NEWVAR variable.Įven the order of commands can affect the outcome of the target variable. If it is not the case where VAR1=1 and VAR=2, then SPSS returns a value of "false" and goes to the ELSE command. Why is this happening? SPSS evaluates case criteria as "true" or "false." In the above example, SPSS looks at a case and asks "VAR1=1 and VAR2=2: true or false?" If VAR1=1 and VAR2=2, then this statement is "true" and SPSS performs the subsequent COMPUTE command. SPSS is evaluating the cases as it should.