软件包data.frame和各种软件包附带了大量有用的数据集，但是当您需要用于包装示例，教学目的或者需要时，似乎没有简单的方法可以找到您的完美数据集。在这里询问/回答问题。
比方说，我想要一个character的数据集，至少有2 caret列，并且长度少于100行。
如何浏览每个可用的数据集并查看最多相关信息以供我选择？
我过去的尝试很混乱，需要时间，并且遇到了一些具有{{1}}等不寻常对象结构的软件包。
答案 0 :(得分：3)
我重写了@ eddi的kickass回答：
data.table）您只需运行此功能（如果您已安装data.table）：
ds <- dataset_summary() # around 5 seconds if you have a lot of packages like me
它适用于曾经存在问题的caret包（请参阅编辑历史记录）
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
易于子集化并查找特定数据集，会计算list项和data.frame列的类别。
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
工作区很干净
ls()
# [1] "dataset_summary" "ds"
除了data.table之外没有任何东西加载。
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
}
答案 1 :(得分：2)
根据自己的喜好进行扩展/修改。
library(data.table)
dt = as.data.table(data(package = .packages(all.available = TRUE))$results)
dt = dt[, `:=`(Item = sub(' \\(.*', '', Item),
Object = sub('.*\\((.*)\\)', '\\1', Item))]
dt[, {
data(list = Object, package = Package)
d = eval(parse(text = Item))
classes = if (sum(class(d) %in% c('data.frame')) > 0) unlist(lapply(d, class))
else NA_integer_
.(class = paste(class(d), collapse = ","),
nrow = if (!is.null(nrow(d))) nrow(d) else NA_integer_,
ncol = if (!is.null(ncol(d))) ncol(d) else NA_integer_,
charCols = sum(classes == 'character'),
facCols = sum(classes == 'factor'))
}
, by = .(Package, Item)]
# Package Item class nrow ncol charCols facCols
# 1: datasets AirPassengers ts NA NA NA NA
# 2: datasets BJsales ts NA NA NA NA
# 3: datasets BJsales.lead ts NA NA NA NA
# 4: datasets BOD data.frame 6 2 0 0
# 5: datasets CO2 nfnGroupedData,nfGroupedData,groupedData,data.frame 84 5 0 3
# ---
#492: survival transplant data.frame 815 6 0 3
#493: survival uspop2 array 101 2 NA NA
#494: survival veteran data.frame 137 8 0 1
#495: viridis viridis.map data.frame 1024 4 1 0
#496: xtable tli data.frame 100 5 0 3
答案 2 :(得分：1)
在包datasets中，没有类data.frame的数据集可以满足您的条件，更确切地说，如果它们属于类data.frame并且最多有100列，那么它们都没有两列或更多列character列。我刚刚发现以下代码的第一个版本。
library(datasets)
res <- library(help = "datasets")
dat <- unlist(lapply(strsplit(res$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"
所以我必须包含额外的指令来处理类factor的列。默认情况下，数据框是使用stringsAsFactors = TRUE创建的。如果你可以使用它，那么它们的名字就在向量df_names中。为了使它们在全球环境中可用get您想要的那个。
答案 3 :(得分：1)
myfun()返回的表可以使用适当的条件进行过滤，数据集的列可以通过类coulmn中给出的类来标识。
caret包的问题在于它没有任何数据框或矩阵对象。数据集可以存在于列表对象内的caret中。我不确定，caret包中的一些列表对象包含一系列函数。
此外，如果感兴趣，您可以使myfun()函数更具体，仅用于返回有关数据框或矩阵对象的信息。
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
如果在包上应用myfun()函数后需要卸载已加载的包，请尝试以下操作：
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 ) )
}