datasets 包和各种包都带有大量有用的数据集，但是当您需要它用于包示例、教学目的或询问/在这里回答一个关于 SO 的问题。
例如，我想要一个数据集，它是 data.frame，至少有 2 个 character 列，并且长度小于 100 行。
如何探索每个可用的数据集并查看最多的相关信息来做出选择？
我过去的尝试很混乱，需要时间，并且由于某些具有不寻常对象结构的包（例如 caret）而崩溃。
【问题讨论】：
标签： r
我已经将一个解决方案打包在一个单一功能的 github 包中。
我正在复制底部的整个代码，但最简单的是：
remotes::install_github("moodymudskipper/datasearch")
library(datasearch)
“dplyr”包中的所有数据集
dplyr_all <-
datasearch("dplyr")
View(dplyr_all)
受条件限制的“数据集”包中的数据集
datasets_ncol5 <-
datasearch("datasets", filter = ~is.data.frame(.) && ncol(.) == 5)
View(datasets_ncol5)
所有已安装包的所有数据集，无限制
# might take more or less time, depends what you have installed
all_datasets <- datasearch()
View(all_datasets)
# subsetting the output
my_subset <- subset(
all_datasets,
class1 == "data.frame" &
grepl("treatment", names_collapsed) &
nrow < 100
)
View(my_subset)
datasearch <- function(pkgs = NULL, filter = NULL){
# make function silent
w <- options()$warn
options(warn = -1)
search_ <- search()
file_ <- tempfile()
file_ <- file(file_, "w")
on.exit({
options(warn = w)
to_detach <- setdiff(search(), search_)
for(pkg in to_detach) eval(bquote(detach(.(pkg))))
# note : we still have loaded namespaces, we could unload those that we ddn't
# have in the beginning but i'm worried about surprising effects, I think
# the S3 method tables should be cleaned too, and maybe other things
# note2 : tracing library and require didn't work
})
# convert formula to function
if(inherits(filter, "formula")) {
filter <- as.function(c(alist(.=), filter[[length(filter)]]))
}
## by default fetch all available packages in .libPaths()
if(is.null(pkgs)) pkgs <- .packages(all.available = TRUE)
## fetch all data sets description
df <- as.data.frame(data(package = pkgs, verbose = FALSE)$results)
names(df) <- tolower(names(df))
item <- NULL # for cmd check note
df <- transform(
df,
data_name = sub('.*\\((.*)\\)', '\\1', item),
dataset = sub(' \\(.*', '', item),
libpath = NULL,
item = NULL
)
df <- df[order(df$package, df$data_name),]
pkg_data_names <- aggregate(dataset ~ package + data_name, df, c)
pkg_data_names <- pkg_data_names[order(pkg_data_names$package, pkg_data_names$data_name),]
env <- new.env()
n <- nrow(pkg_data_names)
pb <- progress::progress_bar$new(
format = "[:bar] :percent :pkg",
total = n)
row_dfs <- vector("list", n)
for(i in seq(nrow(pkg_data_names))) {
pkg <- pkg_data_names$package[i]
data_name <- pkg_data_names$data_name[i]
datasets <- pkg_data_names$dataset[[i]]
pb$tick(tokens = list(pkg = format(pkg, width = 12)))
sink(file_, type = "message")
data(list=data_name, package = pkg, envir = env)
row_dfs_i <- lapply(datasets, function(dataset) {
dat <- get(dataset, envir = env)
if(!is.null(filter) && !filter(dat)) return(NULL)
cl <- class(dat)
nms <- names(dat)
nc <- ncol(dat)
if (is.null(nc)) nc <- NA
nr <- nrow(dat)
if (is.null(nr)) nr <- NA
out <- data.frame(
package = pkg,
data_name = data_name,
dataset = dataset,
class = I(list(cl)),
class1 = cl[1],
type = typeof(dat),
names = I(list(nms)),
names_collapsed = paste(nms, collapse = "/"),
nrow = nr,
ncol = nc,
length = length(dat))
if("data.frame" %in% cl) {
classes <- lapply(dat, class)
cl_flat <- unlist(classes)
out <- transform(
out,
classes = I(list(classes)),
types = I(list(vapply(dat, typeof, character(1)))),
logical = sum(cl_flat == 'logical'),
integer = sum(cl_flat == 'integer'),
numeric = sum(cl_flat == 'numeric'),
complex = sum(cl_flat == 'complex'),
character = sum(cl_flat == 'character'),
raw = sum(cl_flat == 'raw'),
list = sum(cl_flat == 'list'),
data.frame = sum(cl_flat == 'data.frame'),
factor = sum(cl_flat == 'factor'),
ordered = sum(cl_flat == 'ordered'),
Date = sum(cl_flat == 'Date'),
POSIXt = sum(cl_flat == 'POSIXt'),
POSIXct = sum(cl_flat == 'POSIXct'),
POSIXlt = sum(cl_flat == 'POSIXlt'))
} else {
out <- transform(
out,
nrow = NA,
ncol = NA,
classes = NA,
types = NA,
logical = NA,
integer = NA,
numeric = NA,
complex = NA,
character = NA,
raw = NA,
list = NA,
data.frame = NA,
factor = NA,
ordered = NA,
Date = NA,
POSIXt = NA,
POSIXct = NA,
POSIXlt = NA)
}
if(is.matrix(dat)) {
out$names <- list(colnames(dat))
out$names_collapsed = paste(out$names, collapse = "/")
}
out
})
row_dfs_i <- do.call(rbind, row_dfs_i)
if(!is.null(row_dfs_i)) row_dfs[[i]] <- row_dfs_i
sink(type = "message")
}
df2 <- do.call(rbind, row_dfs)
df <- merge(df, df2)
df
}
【讨论】：
根据自己的喜好扩展/修改。
library(data.table)
dt = as.data.table(data(package = .packages(all.available = TRUE))$results)
dt = dt[, `:=`(Item = sub(' \\(.*', '', Item),
Object = sub('.*\\((.*)\\)', '\\1', Item))]
dt[, {
data(list = Object, package = Package)
d = eval(parse(text = Item))
classes = if (sum(class(d) %in% c('data.frame')) > 0) unlist(lapply(d, class))
else NA_integer_
.(class = paste(class(d), collapse = ","),
nrow = if (!is.null(nrow(d))) nrow(d) else NA_integer_,
ncol = if (!is.null(ncol(d))) ncol(d) else NA_integer_,
charCols = sum(classes == 'character'),
facCols = sum(classes == 'factor'))
}
, by = .(Package, Item)]
# Package Item class nrow ncol charCols facCols
# 1: datasets AirPassengers ts NA NA NA NA
# 2: datasets BJsales ts NA NA NA NA
# 3: datasets BJsales.lead ts NA NA NA NA
# 4: datasets BOD data.frame 6 2 0 0
# 5: datasets CO2 nfnGroupedData,nfGroupedData,groupedData,data.frame 84 5 0 3
# ---
#492: survival transplant data.frame 815 6 0 3
#493: survival uspop2 array 101 2 NA NA
#494: survival veteran data.frame 137 8 0 1
#495: viridis viridis.map data.frame 1024 4 1 0
#496: xtable tli data.frame 100 5 0 3
【讨论】：
在包datasets 中没有满足您条件的data.frame 类数据集，更准确地说，如果它们属于data.frame 类并且最多有100 列，那么它们都没有两列或更多列character。我刚刚通过以下代码的第一个版本发现了这一点。
library(datasets)
res <- library(help = "datasets")
dat <- unlist(lapply(strsplit(res$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factor 类的列。默认情况下，数据框使用stringsAsFactors = TRUE 创建。如果你能处理这些，你就有了，它们的名字在矢量df_names 中。为了使它们在全球环境中可用，只需 get 您想要的。
【讨论】：
data 函数可以返回数据集，您可以将其限制在某些包中，使用它可能会很有趣。但令我惊讶的是，我们看到的每个涉及日期的示例都是一个人随机浏览 100 个数据集列表的结果，或者像你一样编写自定义函数。
myfun()返回的表可以用适当的条件过滤，数据集的列可以通过类列中给定的类来标识。
caret 包的问题在于它没有任何数据框或矩阵对象。数据集可能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函数列表。
另外，如果有兴趣，您可以使myfun() 函数更具体地返回有关数据帧或矩阵对象的信息。
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
如果加载的包需要在对包应用myfun()函数后卸载，试试这个：
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 ) )
}
【讨论】：
ls('package:...') 的想法，因为它可以访问其他对象，这可以用来做更酷的事情，比如通过正则表达式查找函数或进行更多工作查找例如，按参数 up 函数。但它没有“看到”某些数据集是有问题的，例如来自caret 包的数据集。