03804nam a22004215i 4500001001800000003000900018005001700027007001500044008004100059020001800100020001900118024003500137040000900172082001400181100002800195245007400223264004600297300002100343336002600364337002600390338003900416347002400455490001100479505016200490520228700652650001602939650002902955650001602984650005003000710003403050773002003084776003603104830001103140856010303151942001203254999001703266952009903283978-0-387-74731-6DE-He21320260521091950.0cr nn 008mamaa100301s2008    xxu|    s    |||| 0|eng d  a9780387747316  a997803877473167 a10.1007/978-0-387-74731-62doi  cCICY04a519.52231 aSpector, Phil.eauthor.10aData Manipulation with Rh[recurso electrónico] /cby Phil Spector. 1aNew York, NY :bSpringer New York,c2008.  bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  arecurso en líneabcr2rdacarrier  atext filebPDF2rda1 aUse R!0 aData in R -- Reading and Writing Data -- R and Databases -- Dates -- Factors -- Subscripting -- Character Manipulation -- Data Aggregation -- Reshaping Data.  aSince its inception, R has become one of the preeminent programs for statistical computing and data analysis. The ready availability of the program, along with a wide variety of packages and the supportive R community make R an excellent choice for almost any kind of computing task related to statistics. However, many users, especially those with experience in other languages, do not take advantage of the full power of R. Because of the nature of R, solutions that make sense in other languages may not be very efficient in R. This book presents a wide array of methods applicable for reading data into R, and efficiently manipulating that data. In addition to the built-in functions, a number of readily available packages from CRAN (the Comprehensive R Archive Network) are also covered. All of the methods presented take advantage of the core features of R: vectorization, efficient use of subscripting, and the proper use of the varied functions in R that are provided for common data management tasks. Most experienced R users discover that, especially when working with large data sets, it may be helpful to use other programs, notably databases, in conjunction with R. Accordingly, the use of databases in R is covered in detail, along with methods for extracting data from spreadsheets and datasets created by other programs. Character manipulation, while sometimes overlooked within R, is also covered in detail, allowing problems that are traditionally solved by scripting languages to be carried out entirely within R. For users with experience in other languages, guidelines for the effective use of programming constructs like loops are provided. Since many statistical modeling and graphics functions need their data presented in a data frame, techniques for converting the output of commonly used functions to data frames are provided throughout the book. Using a variety of examples based on data sets included with R, along with easily simulated data sets, the book is recommended to anyone using R who wishes to advance from simple examples to practical real-life data manipulation solutions. Phil Spector is Applications Manager of the Statistical Computing Facility and Adjunct Professor in the Department of Statistics at University of California, Berkeley. 0aSTATISTICS. 0aMATHEMATICAL STATISTICS.14aSTATISTICS.24aSTATISTICS AND COMPUTING/STATISTICS PROGRAMS.2 aSpringerLink (Online service)0 tSpringer eBooks08iPrinted edition:z9780387747309 0aUse R!40uhttp://dx.doi.org/10.1007/978-0-387-74731-6zVer el texto completo en las instalaciones del CICY  2ddccER  c34562d34562  00102ddc40708LEaCICYbCICYcELd2025-07-10l0o519.5r2025-07-10 08:40:21w2025-07-10yER