如何找到具有某些特定屬性的數據集？

Question

包datasets和各種軟件包帶有相當數量的有用數據集，但是當您需要它用於您的軟件包示例，用於教學目的或者要求/回答問題時，似乎沒有簡單的方法來找到您的完美數據集在這裏。

說例如我想要一個數據集是一個data.frame，至少有2 character列，並且少於100行。

如何探索每個可用的數據集並查看最多的相關信息以作出我的選擇？

我過去的嘗試很混亂，需要時間，並且碰到一些包含不尋常物體結構的軟件包，如caret。

Answer 1

擴展/修改自己的喜好。

library(data.table) 
dt = as.data.table(data(package = .packages(all.available = TRUE))$results) 
dt = dt[, `:=`(Item = sub(' \\(.*', '', Item), 
       Object = sub('.*\\((.*)\\)', '\\1', Item))] 

dt[, { 
     data(list = Object, package = Package) 
     d = eval(parse(text = Item)) 

     classes = if (sum(class(d) %in% c('data.frame')) > 0) unlist(lapply(d, class)) 
       else NA_integer_ 

     .(class = paste(class(d), collapse = ","), 
     nrow  = if (!is.null(nrow(d))) nrow(d) else NA_integer_, 
     ncol  = if (!is.null(ncol(d))) ncol(d) else NA_integer_, 
     charCols = sum(classes == 'character'), 
     facCols = sum(classes == 'factor')) 
    } 
    , by = .(Package, Item)] 
#  Package   Item            class nrow ncol charCols facCols 
# 1: datasets AirPassengers             ts NA NA  NA  NA 
# 2: datasets  BJsales             ts NA NA  NA  NA 
# 3: datasets BJsales.lead             ts NA NA  NA  NA 
# 4: datasets   BOD           data.frame 6 2  0  0 
# 5: datasets   CO2 nfnGroupedData,nfGroupedData,groupedData,data.frame 84 5  0  3 
# ---                          
#492: survival transplant           data.frame 815 6  0  3 
#493: survival  uspop2            array 101 2  NA  NA 
#494: survival  veteran           data.frame 137 8  0  1 
#495: viridis viridis.map           data.frame 1024 4  1  0 
#496: xtable   tli           data.frame 100 5  0  3 

Answer 2

在包datasets沒有data.frame類的數據集滿足您的條件，更確切地說，如果他們是data.frame類和至多100列，那麼他們都沒有character類的兩個或多個列。我剛剛發現了以下代碼的第一個版本。

library(datasets) 
res <- library(help = "datasets") 

dat <- unlist(lapply(strsplit(res$info[[2]], " "), '[[', 1)) 
dat <- dat[dat != ""] 
df_names <- NULL 
for(i in seq_along(dat)){ 
    d <- tryCatch(get(dat[i]), error = function(e) e) 
    if(inherits(d, "data.frame")){ 
     if(nrow(d) <= 100){ 
      char <- sum(sapply(d, is.character)) 
      fact <- sum(sapply(d, is.factor)) 
      if(char >= 2 || fact >= 2){ 
       print(dat[i]) 
       df_names <- c(df_names, dat[i]) 
      } 
     } 
    } 
} 

df_names 
[1] "CO2"  "esoph"  "npk"  "sleep"  "warpbreaks" 

所以我不得不包含額外的指令來處理類factor的列。默認情況下，使用stringsAsFactors = TRUE創建數據框。如果你能做到這一點，那麼你有它，他們的名字是矢量df_names。爲了讓它們在全球環境中可用，只需要get即可。

Answer 3

我返工@ EDDI的kickass回答這樣：

• 這是一個功能
• 它不雜亂的工作空間（它只是加載data.table）
• 我改名列短，更增添了幾分
• 列表元素的類也被檢查
• 我返回對象和數據集標題
• Annoyin G封裝的警告可以隱藏

你只是在運行這個（前提是你已經安裝data.table）：

ds <- dataset_summary() # around 5 seconds if you have a lot of packages like me 

它的工作原理與使用有問題（見編輯歷史）的caret包

subset(ds,Package == "caret") 
#  Package   Object    Item             Title  class nrow ncol char fact ord num int list df 
# 143 caret  GermanCredit  GermanCredit          German Credit Data data.frame 1000 62 0 1 0 54 7 0 0 
# 144 caret  Sacramento  Sacramento        Sacramento CA Home Prices data.frame 932 9 0 3 0 3 3 0 0 
# 145 caret   tecator   absorp   Fat, Water and Protein Content of Meat Samples  matrix 215 100 NA NA NA NA NA NA NA 
# 146 caret  BloodBrain   bbbDescr        Blood Brain Barrier Data data.frame 208 134 0 0 0 118 16 0 0 
# 147 caret    cars    cars Kelly Blue Book resale data for 2005 model year GM cars data.frame 804 18 0 0 0 1 17 0 0 
# 148 caret    cox2  cox2Class          COX-2 Activity Data  factor NA NA NA NA NA NA NA NA NA 

易子集，並尋找一個特定的數據集的list項目和data.frame列類計數。

subset(ds,class == 'list' & df > 0,select=-c(2,4)) 
#   Package   Item class nrow ncol char fact ord num int list df 
# 225  ecodist  iris.fit list NA NA 0 0 0 1 0 0 1 
# 238 ElemStatLearn orange10.test list NA NA 0 0 0 0 0 0 50 
# 239 ElemStatLearn orange10.train list NA NA 0 0 0 0 0 0 50 
# 240 ElemStatLearn orange4.test list NA NA 0 0 0 0 0 0 50 
# 241 ElemStatLearn orange4.train list NA NA 0 0 0 0 0 0 50 
# 346   lava missingdata list NA NA 0 0 0 0 0 0 4 

工作區是乾淨

ls() 
# [1] "dataset_summary" "ds" 

沒有從data.table加載開。

search() 
# [1] ".GlobalEnv"   "package:data.table" "package:Matrix"  "package:sp"   "package:timeSeries" "package:timeDate" 
# [7] "tools:rstudio"  "package:stats"  "package:graphics" "package:grDevices" "package:utils"  "package:datasets" 
# [13] "package:methods" "Autoloads"   "package:base" 

功能

dataset_summary <- function(silent = TRUE){ 
    if(silent){ 
    w <- options()$warn 
    options(warn = -1) 
    on.exit(options(warn = w)) 
    } 
    ws <- ls(envir=.GlobalEnv) 
    library(data.table) 
    dt = as.data.table(data(package = .packages(all.available = TRUE))$results) 
    dt = dt[, `:=`(Item = sub(' \\(.*', '', Item), 
       Object = sub('.*\\((.*)\\)', '\\1', Item))] 

    df <- as.data.frame(dt[, { 
    data(list = Object, package = Package) 
    d = eval(parse(text = Item)) 

    classes = if (sum(class(d) %in% c('data.frame','list')) > 0) unlist(lapply(d, class)) 
    else NA_integer_ 

    .(class = paste(class(d), collapse = ","), 
     nrow  = if (!is.null(nrow(d))) nrow(d) else NA_integer_, 
     ncol  = if (!is.null(ncol(d))) ncol(d) else NA_integer_, 
     char  = sum(classes == 'character'), 
     fact  = sum(classes == 'factor'), 
     ord  = sum(classes == 'ordered'), 
     num  = sum(classes == 'numeric'), 
     int  = sum(classes == 'integer'), 
     list  = sum(classes == 'list'), 
     df  = sum(classes == 'data.frame')) 
    } 
    , by = .(Package, Item)]) 
    rm(list=setdiff(ls(envir=.GlobalEnv),ws),envir=.GlobalEnv) 
    df 
} 

Answer 4

通過myfun()返回的表可以通過適當的條件下過濾，和數據集的列可通過其在類coulmn給定類進行鑑定。

caret包的問題是它沒有任何數據框或矩陣對象。數據集可以存在於列表對象內的caret中。我不確定，caret包中的一些列表對象包含一系列函數。

此外，如果感興趣，您可以使myfun()函數更具體地返回有關數據幀或矩陣對象的信息。

myfun <- function(package) 
{ 
    t(sapply(ls(paste0('package:', package)), function(x){ 
    y <- eval(parse(text = paste0(package, "::`", x, "`"))) 
    data.frame(data_class = paste0(class(y), collapse = ","), 
       nrow = ifelse(any(class(y) %in% c("data.frame", "matrix")), 
           nrow(y), 
           NA_integer_), 
       ncol = ifelse(any(class(y) %in% c("data.frame", "matrix")), 
           ncol(y), 
           NA_integer_), 
       classes = ifelse(any(class(y) %in% c("data.frame", "matrix")), 
            paste0(unlist(lapply(y, class)), collapse = ","), 
            NA), 
       stringsAsFactors = FALSE) 

    })) 
} 

library(datasets) 
meta_data <- myfun(package = "datasets") 
head(meta_data) 
#    data_class nrow ncol classes               
# ability.cov "list"  NA NA NA                
# airmiles  "ts"   NA NA NA                
# AirPassengers "ts"   NA NA NA                
# airquality "data.frame" 153 6 "integer,integer,numeric,integer,integer,integer"     
# anscombe  "data.frame" 11 8 "numeric,numeric,numeric,numeric,numeric,numeric,numeric,numeric" 
# attenu  "data.frame" 182 5 "numeric,numeric,factor,numeric,numeric" 

meta_data[ "ChickWeight", ] 
# $data_class 
# [1] "nfnGroupedData,nfGroupedData,groupedData,data.frame" 
# 
# $nrow 
# [1] 578 
# 
# $ncol 
# [1] 4 
# 
# $classes 
# [1] "numeric,numeric,ordered,factor,factor" 

library('caret') 
meta_data <- myfun(package = "caret") 
#    data_class nrow ncol classes 
# anovaScores "function" NA NA NA  
# avNNet  "function" NA NA NA  
# bag   "function" NA NA NA  
# bagControl "function" NA NA NA  
# bagEarth  "function" NA NA NA  
# bagEarthStats "function" NA NA NA 

如果加載的包需要應用myfun()功能上的包後卸載，試試這個：

loaded_pkgs <- search() 
library('caret') 
meta_data <- myfun(package = "caret") 
unload_pkgs <- setdiff(search(), loaded_pkgs) 
for(i in unload_pkgs) { 
    detach(pos = which(search() %in% i)) 
}