By Nicholas J. Horton, Ken Kleinman

An up to date, All-in-One source for utilizing SAS and R to accomplish widespread Tasks
The first version of this renowned consultant supplied a direction among SAS and R utilizing an easy-to-understand, dictionary-like strategy. holding a similar available structure, SAS and R: information administration, Statistical research, and images, moment version explains the best way to simply practice an analytical job in either SAS and R, with no need to navigate throughout the broad, idiosyncratic, and occasionally unwieldy software program documentation. The ebook covers many universal projects, similar to info administration, descriptive summaries, inferential techniques, regression research, and pix, in addition to extra advanced applications.

New to the second one Edition
This variation now covers RStudio, a robust and easy-to-use interface for R. It features a variety of extra themes, together with utilizing program application interfaces (APIs), having access to facts via database administration platforms, utilizing reproducible research instruments, and statistical research with Markov chain Monte Carlo (MCMC) tools and finite mix versions. it is usually prolonged examples of simulations and lots of new examples.

Enables effortless Mobility among the 2 Systems
Through the vast indexing and cross-referencing, clients can without delay locate and enforce the cloth they wish. SAS clients can lookup initiatives within the SAS index after which locate the linked R code whereas R clients can enjoy the R index in the same demeanour. a number of instance analyses exhibit the code in motion and facilitate additional exploration. The datasets and code can be found for obtain at the book’s site.

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Additional info for SAS and R: Data Management, Statistical Analysis, and Graphics (2nd Edition)

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MERGING, COMBINING, AND SUBSETTING DATASETS ✐ 31 subset(ds, select = -c(x2, x5)) # drop x2 and x5 Note: The first examples create a new dataframe consisting of the variables x1 and xk. An alternative is to specify the variables to be excluded (in this case the second). The last example can be used to drop specified variables. More sophisticated ways of listing the variables to be kept are available. 12). 3). SAS /* sample without replacement */ proc surveyselect data=ds out=outds n=nsamp method=srs; run; /* sample with replacement */ proc surveyselect data=ds out=outds n=nsamp method=urs; run; /* sample with replacement to observed sample size (bootstrap) */ proc surveyselect data=ds out=outds rate=1 method=urs; run; or /* sample without replacement */ data ds2; set ds; order = uniform(0); run; proc sort data=ds2; by order; run; data ds3; set ds2; if _n_ le nsamp; run; Note: The former code shows how proc surveyselect can be used in various ways.

The edit() function can be used to view names and edit values. ); run; Note: Applying any function which operates on a string (or character) variable when given a numeric variable will force it to be treated as a character variable. , v3 = v1||v2) will result in a string. 3 for a discussion of informats, which apply variable types when reading in data. 7). ; if x ne . then newcat = (x ge minval) + (x ge cutpoint1) + ... + (x ge cutpointn); run; Note: Each expression within parentheses is a logical test returning 1 if the expression is true, 0 otherwise.

Because we describe moving from R to SAS, we begin with the R entry. 8 ✐ CHAPTER 1. sas7bdat") Note: The first set of code (obviously) requires a working version of SAS. The second can be used with any SAS formatted data set; it is based on reverse-engineering of the SAS data set format, which SAS has not made public. xml'; /* /* /* /* /* SPSS */ BMDP */ SAS vers. ext' out=ds dbms=excel; run; /* Excel */ ... dbms=access; ... /* Access */ ... dbms=dta; ... /* Stata */ Note: The libname statements above refer to files, rather than directories.

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