R：如何以名称获取空白的数据集

本文介绍了R：如何以名称获取空白的数据集的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！ 问题描述 如何在数据集数据集中获取一个带有空格的R数据集，例如'BJsales.lead（BJsales）？How can one get an R dataset with blanks in its name, such as'BJsales.lead (BJsales)' in package "datasets" ?pkg = "datasets"cat( "Summary of all the datasets in package", pkg, "--\n" )d = data( package=pkg ) $results # "Package" "LibPath" "Item" "Title"names = d[ , "Item" ]titles = d[ , "Title" ] # sum( duplicated( names )) ??for( j in 1:len(names) ){ name = names[[j]] cat( name, ":\n" ) data( list=name ) x = get( name ) # <-- Error if blank in name m = paste( dim( as.matrix( x )), collapse=" " ) # grr cat( class(x), m, " freq", frequency(x), "\n" )}# -> Error in get(name) : object 'BJsales.lead (BJsales)' not found确定， code> get 只能查找有效的名称，这是合理的。 但是怎么办 - 如何获取BJsales.lead（BJsales）的数据？OK, get can only lookup valid names, that's reasonable.But what to do -- how can one get the data for 'BJsales.lead (BJsales)' ? R版本3.1.3（2015-03-09） 平台：x86_64-apple-darwin10.8.0（64位） 运行于：OS X 10.8.3（Mountain Lion）R version 3.1.3 (2015-03-09)Platform: x86_64-apple-darwin10.8.0 (64-bit)Running under: OS X 10.8.3 (Mountain Lion)推荐答案其实 get（） 可以查找无效名称：`x y` <- 3;get('x y');## [1] 3这里的问题是 data（）返回的结果列的项目不总是包含数据集的确切名称;在某些情况下，它有一个括号后缀，虽然我不知道为什么。The issue here is that the Item column of the results matrix returned by data() does not always contain the exact name of the data set; in some cases, it has a parenthetical suffix, although I've no idea why.你可以用 gsub（），然后通过 get（）加载。You can strip it off with gsub(), and then loading via get() should work.另外，你不应该需要数据（list = name）调用。Also, you shouldn't need the data(list=name) call.另外，没有 len ）（不幸的是）;我想你的意思是 length（）。Also, there's no len() (unfortunately); I think you mean length().因此：pkg <- 'datasets';cat('Summary of all the datasets in package',pkg,'--\n');d <- data(package=pkg)$results; # 'Package' 'LibPath' 'Item' 'Title'names <- d[,'Item'];titles <- d[,'Title'];for (j in 1:length(names)) { name <- names[j]; cat(name,':\n'); x <- get(gsub('\\s.*','',name)); m <- paste(dim(as.matrix(x)),collapse=' '); cat(class(x),m,' freq',frequency(x),'\n');};## Summary of all the datasets in package datasets --## AirPassengers :## ts 144 1 freq 12## BJsales :## ts 150 1 freq 1## BJsales.lead (BJsales) :## ts 150 1 freq 1## BOD :## data.frame 6 2 freq 1## CO2 :## nfnGroupedData nfGroupedData groupedData data.frame 84 5 freq 1## ChickWeight :## nfnGroupedData nfGroupedData groupedData data.frame 578 4 freq 1## DNase :## nfnGroupedData nfGroupedData groupedData data.frame 176 3 freq 1## EuStockMarkets :## mts ts matrix 1860 4 freq 260## Formaldehyde :## data.frame 6 2 freq 1## HairEyeColor :## table 32 1 freq 1## Harman23.cor :## list 3 1 freq 1## Harman74.cor :## list 3 1 freq 1## Indometh :## nfnGroupedData nfGroupedData groupedData data.frame 66 3 freq 1## InsectSprays :## data.frame 72 2 freq 1## JohnsonJohnson :## ts 84 1 freq 4## LakeHuron :## ts 98 1 freq 1## LifeCycleSavings :## data.frame 50 5 freq 1## Loblolly :## nfnGroupedData nfGroupedData groupedData data.frame 84 3 freq 1## Nile :## ts 100 1 freq 1## Orange :## nfnGroupedData nfGroupedData groupedData data.frame 35 3 freq 1## OrchardSprays :## data.frame 64 4 freq 1## PlantGrowth :## data.frame 30 2 freq 1## Puromycin :## data.frame 23 3 freq 1## Seatbelts :## mts ts 192 8 freq 12## Theoph :## nfnGroupedData nfGroupedData groupedData data.frame 132 5 freq 1## Titanic :## table 32 1 freq 1## ToothGrowth :## data.frame 60 3 freq 1## UCBAdmissions :## table 24 1 freq 1## UKDriverDeaths :## ts 192 1 freq 12## UKgas :## ts 108 1 freq 4## USAccDeaths :## ts 72 1 freq 12## USArrests :## data.frame 50 4 freq 1## USJudgeRatings :## data.frame 43 12 freq 1## USPersonalExpenditure :## matrix 5 5 freq 1## VADeaths :## matrix 5 4 freq 1## WWWusage :## ts 100 1 freq 1## WorldPhones :## matrix 7 7 freq 1## ability.cov :## list 3 1 freq 1## airmiles :## ts 24 1 freq 1## airquality :## data.frame 153 6 freq 1## anscombe :## data.frame 11 8 freq 1## attenu :## data.frame 182 5 freq 1## attitude :## data.frame 30 7 freq 1## austres :## ts 89 1 freq 4## beaver1 (beavers) :## data.frame 114 4 freq 1## beaver2 (beavers) :## data.frame 100 4 freq 1## cars :## data.frame 50 2 freq 1## chickwts :## data.frame 71 2 freq 1## co2 :## ts 468 1 freq 12## crimtab :## table 42 22 freq 1## discoveries :## ts 100 1 freq 1## esoph :## data.frame 88 5 freq 1## euro :## numeric 11 1 freq 1## euro.cross (euro) :## matrix 11 11 freq 1## eurodist :## dist 21 21 freq 1## faithful :## data.frame 272 2 freq 1## fdeaths (UKLungDeaths) :## ts 72 1 freq 12## freeny :## data.frame 39 5 freq 1## freeny.x (freeny) :## matrix 39 4 freq 1## freeny.y (freeny) :## ts 39 1 freq 4## infert :## data.frame 248 8 freq 1## iris :## data.frame 150 5 freq 1## iris3 :## array 600 1 freq 1## islands :## numeric 48 1 freq 1## ldeaths (UKLungDeaths) :## ts 72 1 freq 12## lh :## ts 48 1 freq 1## longley :## data.frame 16 7 freq 1## lynx :## ts 114 1 freq 1## mdeaths (UKLungDeaths) :## ts 72 1 freq 12## morley :## data.frame 100 3 freq 1## mtcars :## data.frame 32 11 freq 1## nhtemp :## ts 60 1 freq 1## nottem :## ts 240 1 freq 12## npk :## data.frame 24 5 freq 1## occupationalStatus :## table 8 8 freq 1## precip :## numeric 70 1 freq 1## presidents :## ts 120 1 freq 4## pressure :## data.frame 19 2 freq 1## quakes :## data.frame 1000 5 freq 1## randu :## data.frame 400 3 freq 1## rivers :## numeric 141 1 freq 1## rock :## data.frame 48 4 freq 1## sleep :## data.frame 20 3 freq 1## stack.loss (stackloss) :## numeric 21 1 freq 1## stack.x (stackloss) :## matrix 21 3 freq 1## stackloss :## data.frame 21 4 freq 1## state.abb (state) :## character 50 1 freq 1## state.area (state) :## numeric 50 1 freq 1## state.center (state) :## list 2 1 freq 1## state.division (state) :## factor 50 1 freq 1## state.name (state) :## character 50 1 freq 1## state.region (state) :## factor 50 1 freq 1## state.x77 (state) :## matrix 50 8 freq 1## sunspot.month :## ts 3177 1 freq 12## sunspot.year :## ts 289 1 freq 1## sunspots :## ts 2820 1 freq 12## swiss :## data.frame 47 6 freq 1## treering :## ts 7980 1 freq 1## trees :## data.frame 31 3 freq 1## uspop :## ts 19 1 freq 0.1## volcano :## matrix 87 61 freq 1## warpbreaks :## data.frame 54 3 freq 1## women :## data.frame 15 2 freq 1 这篇关于R：如何以名称获取空白的数据集的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！