R (programming language)
R is a programming language and free software environment for statistics.[6][7][8][9][10][11] R is a language built for a specific purpose. It is strictly designed for statistical analysis. The algorithms for many statistical models are devised in R. Precisely R is the language of Statistical Analyzers. It’s an open source and the best suite for the statisticians to develop statistical softwares. Usage in other areasThe R language was originally made for statistics. But today, it is also used in many scientific fields including ecology.[12][13] Development historyA list of changes in R releases is maintained in various "news" files at CRAN (Comprehensive R Archive Network).[14] Some highlights are listed below for several major releases.
CommunitiesR has local communities worldwide for users to share ideas and learn.[21][22] There are a growing number of R events bringing its users together, such as conferences (e.g. useR!, WhyR?, conectaR, SatRdays)[23][24] and other meetups.[25] useR! conferencesThe official annual gathering of R users is called "useR!".[26] The first such event was useR! 2004 in May 2004, Vienna, Austria.[27] After skipping 2005, the useR! conference has been held annually.[28] Subsequent conferences have included:[26]
Future conferences planned are as follows:[26][29]
The R JournalThe R Journal is the open access refereed journal of the R project. It features articles on the use and development of the R language. Basic syntaxThe following examples illustrate the basic syntax of the language and use of the command-line interface. In R, the generally preferred[30] assignment operator is an arrow made from two characters > x <- 1:6 # Create vector.
> y <- x^2 # Create vector by formula.
> print(y) # Print the vector’s contents.
[1] 1 4 9 16 25 36
> mean(y) # Arithmetic mean of vector.
[1] 15.16667
> var(y) # Sample variance of vector.
[1] 178.9667
> model <- lm(y ~ x) # Linear regression model y = A + B * x.
> print(model) # Print the model’s results.
Call:
lm(formula = y ~ x)
Coefficients:
(Intercept) x
-9.333 7.000
> summary(model) # Display an in-depth summary of the model.
Call:
lm(formula = y ~ x)
Residuals:
1 2 3 4 5 6
3.3333 -0.6667 -2.6667 -2.6667 -0.6667 3.3333
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -9.3333 2.8441 -3.282 0.030453 *
x 7.0000 0.7303 9.585 0.000662 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 3.055 on 4 degrees of freedom
Multiple R-squared: 0.9583, Adjusted R-squared: 0.9478
F-statistic: 91.88 on 1 and 4 DF, p-value: 0.000662
> par(mfrow = c(2, 2)) # Create a 2 by 2 layout for figures.
> plot(model) # Output diagnostic plots of the model.
References
Other websites
|
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Index:
pl ar de en es fr it arz nl ja pt ceb sv uk vi war zh ru af ast az bg zh-min-nan bn be ca cs cy da et el eo eu fa gl ko hi hr id he ka la lv lt hu mk ms min no nn ce uz kk ro simple sk sl sr sh fi ta tt th tg azb tr ur zh-yue hy my ace als am an hyw ban bjn map-bms ba be-tarask bcl bpy bar bs br cv nv eml hif fo fy ga gd gu hak ha hsb io ig ilo ia ie os is jv kn ht ku ckb ky mrj lb lij li lmo mai mg ml zh-classical mr xmf mzn cdo mn nap new ne frr oc mhr or as pa pnb ps pms nds crh qu sa sah sco sq scn si sd szl su sw tl shn te bug vec vo wa wuu yi yo diq bat-smg zu lad kbd ang smn ab roa-rup frp arc gn av ay bh bi bo bxr cbk-zam co za dag ary se pdc dv dsb myv ext fur gv gag inh ki glk gan guw xal haw rw kbp pam csb kw km kv koi kg gom ks gcr lo lbe ltg lez nia ln jbo lg mt mi tw mwl mdf mnw nqo fj nah na nds-nl nrm nov om pi pag pap pfl pcd krc kaa ksh rm rue sm sat sc trv stq nso sn cu so srn kab roa-tara tet tpi to chr tum tk tyv udm ug vep fiu-vro vls wo xh zea ty ak bm ch ny ee ff got iu ik kl mad cr pih ami pwn pnt dz rmy rn sg st tn ss ti din chy ts kcg ve
Portal di Ensiklopedia Dunia
