包装底座R重塑，便于使用 [英] Wrapping base R reshape for ease-of-use
本文介绍了包装底座R重塑，便于使用的处理方法，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
taRifx包的下一个版本中。然而，在我这样做之前，我想征求改进意见。
这是我的版本，来自@RichieCotton的更新：
# reshapeasy: Version of reshape with way, way better syntax
# Written with the help of the StackOverflow R community
# x is a data.frame to be reshaped
# direction is "wide" or "long"
# vars are the names of the (stubs of) the variables to be reshaped (if omitted, defaults to everything not in id or vary)
# id are the names of the variables that identify unique observations
# vary is the variable that varies. Going to wide this variable will cease to exist. Going to long it will be created.
# omit is a vector of characters which are to be omitted if found at the end of variable names (e.g. price_1 becomes price in long)
# ... are options to be passed to stats::reshape
reshapeasy <- function( data, direction, id=(sapply(data,is.factor) | sapply(data,is.character)), vary=sapply(data,is.numeric), omit=c("_","."), vars=NULL, ... ) {
if(direction=="wide") data <- stats::reshape( data=data, direction=direction, idvar=id, timevar=vary, ... )
if(direction=="long") {
varying <- which(!(colnames(data) %in% id))
data <- stats::reshape( data=data, direction=direction, idvar=id, varying=varying, timevar=vary, ... )
}
colnames(data) <- gsub( paste("[",paste(omit,collapse="",sep=""),"]$",sep=""), "", colnames(data) )
return(data)
}
请注意，您可以从宽到长移动，而无需更改方向以外的选项。对我来说，这是可用性的关键。
如果您与我聊天或通过电子邮件发送您的信息，我很乐意在功能帮助文件中对任何实质性的改进表示感谢。
改进可能体现在以下方面：
- 命名函数及其参数
- 使它更通用(目前它处理一个相当具体的案例，我认为这是迄今为止最常见的，但它还没有用尽stats：：Rehape的功能)
- 代码改进
示例
样本数据
x.wide <- structure(list(surveyNum = 1:6, pio_1 = structure(c(2L, 2L, 1L,
2L, 1L, 1L), .Names = c("1", "2", "3", "4", "5", "6"), .Label = c("1",
"2"), class = "factor"), pio_2 = structure(c(2L, 1L, 2L, 1L,
2L, 2L), .Names = c("1", "2", "3", "4", "5", "6"), .Label = c("1",
"2"), class = "factor"), pio_3 = structure(c(2L, 2L, 1L, 1L,
2L, 1L), .Names = c("1", "2", "3", "4", "5", "6"), .Label = c("1",
"2"), class = "factor"), caremgmt_1 = structure(c(2L, 1L, 1L,
2L, 1L, 2L), .Names = c("1", "2", "3", "4", "5", "6"), .Label = c("1",
"2"), class = "factor"), caremgmt_2 = structure(c(1L, 2L, 2L,
2L, 2L, 1L), .Names = c("1", "2", "3", "4", "5", "6"), .Label = c("1",
"2"), class = "factor"), caremgmt_3 = structure(c(1L, 2L, 1L,
2L, 1L, 1L), .Names = c("1", "2", "3", "4", "5", "6"), .Label = c("1",
"2"), class = "factor"), prev_1 = structure(c(1L, 2L, 2L, 1L,
1L, 2L), .Names = c("1", "2", "3", "4", "5", "6"), .Label = c("1",
"2"), class = "factor"), prev_2 = structure(c(2L, 2L, 1L, 2L,
1L, 1L), .Names = c("1", "2", "3", "4", "5", "6"), .Label = c("1",
"2"), class = "factor"), prev_3 = structure(c(2L, 1L, 2L, 2L,
1L, 1L), .Names = c("1", "2", "3", "4", "5", "6"), .Label = c("1",
"2"), class = "factor"), price_1 = structure(c(2L, 1L, 2L, 5L,
3L, 4L), .Names = c("1", "2", "3", "4", "5", "6"), .Label = c("1",
"2", "3", "4", "5", "6"), class = "factor"), price_2 = structure(c(6L,
5L, 5L, 4L, 4L, 2L), .Names = c("1", "2", "3", "4", "5", "6"), .Label = c("1",
"2", "3", "4", "5", "6"), class = "factor"), price_3 = structure(c(3L,
5L, 2L, 5L, 4L, 5L), .Names = c("1", "2", "3", "4", "5", "6"), .Label = c("1",
"2", "3", "4", "5", "6"), class = "factor")), .Names = c("surveyNum",
"pio_1", "pio_2", "pio_3", "caremgmt_1", "caremgmt_2", "caremgmt_3",
"prev_1", "prev_2", "prev_3", "price_1", "price_2", "price_3"
), idvars = "surveyNum", rdimnames = list(structure(list(surveyNum = 1:24), .Names = "surveyNum", row.names = c("1",
"2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13",
"14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24"
), class = "data.frame"), structure(list(variable = structure(c(1L,
1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L), .Label = c("pio",
"caremgmt", "prev", "price"), class = "factor"), .id = c(1L,
2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L)), .Names = c("variable",
".id"), row.names = c("pio_1", "pio_2", "pio_3", "caremgmt_1",
"caremgmt_2", "caremgmt_3", "prev_1", "prev_2", "prev_3", "price_1",
"price_2", "price_3"), class = "data.frame")), row.names = c(NA,
6L), class = c("cast_df", "data.frame"))
x.long <- structure(list(.id = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), pio = structure(c(2L,
2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L,
1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L,
1L, 2L, 2L, 1L, 2L, 1L, 1L), .Label = c("1", "2"), class = "factor"),
caremgmt = structure(c(2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L,
2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L,
1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L,
1L, 2L, 2L), .Label = c("1", "2"), class = "factor"), prev = structure(c(1L,
2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L,
2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L,
2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L), .Label = c("1",
"2"), class = "factor"), price = structure(c(2L, 1L, 2L,
5L, 3L, 4L, 1L, 5L, 4L, 3L, 1L, 2L, 6L, 6L, 5L, 4L, 6L, 3L,
5L, 6L, 3L, 1L, 2L, 4L, 3L, 5L, 2L, 5L, 4L, 5L, 6L, 6L, 4L,
6L, 4L, 1L, 2L, 3L, 1L, 2L, 2L, 5L, 1L, 6L, 1L, 3L, 4L, 3L,
6L, 5L, 5L, 4L, 4L, 2L, 2L, 2L, 6L, 3L, 1L, 4L, 4L, 5L, 1L,
3L, 6L, 1L, 3L, 5L, 1L, 3L, 6L, 2L), .Label = c("1", "2",
"3", "4", "5", "6"), class = "factor"), surveyNum = c(1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 1L, 2L,
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L,
16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 1L, 2L, 3L,
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L,
17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L)), .Names = c(".id",
"pio", "caremgmt", "prev", "price", "surveyNum"), row.names = c(NA,
-72L), class = "data.frame")
示例
> x.wide
surveyNum pio_1 pio_2 pio_3 caremgmt_1 caremgmt_2 caremgmt_3 prev_1 prev_2 prev_3 price_1 price_2 price_3
1 1 2 2 2 2 1 1 1 2 2 2 6 3
2 2 2 1 2 1 2 2 2 2 1 1 5 5
3 3 1 2 1 1 2 1 2 1 2 2 5 2
4 4 2 1 1 2 2 2 1 2 2 5 4 5
5 5 1 2 2 1 2 1 1 1 1 3 4 4
6 6 1 2 1 2 1 1 2 1 1 4 2 5
> reshapeasy( x.wide, "long", NULL, id="surveyNum", vary="id", sep="_" )
surveyNum id pio caremgmt prev price
1.1 1 1 2 2 1 2
2.1 2 1 2 1 2 1
3.1 3 1 1 1 2 2
4.1 4 1 2 2 1 5
5.1 5 1 1 1 1 3
6.1 6 1 1 2 2 4
1.2 1 2 2 1 2 6
2.2 2 2 1 2 2 5
3.2 3 2 2 2 1 5
4.2 4 2 1 2 2 4
5.2 5 2 2 2 1 4
6.2 6 2 2 1 1 2
1.3 1 3 2 1 2 3
2.3 2 3 2 2 1 5
3.3 3 3 1 1 2 2
4.3 4 3 1 2 2 5
5.3 5 3 2 1 1 4
6.3 6 3 1 1 1 5
> head(x.long)
.id pio caremgmt prev price surveyNum
1 1 2 2 1 2 1
2 1 2 1 2 1 2
3 1 1 1 2 2 3
4 1 2 2 1 5 4
5 1 1 1 1 3 5
6 1 1 2 2 4 6
> head(reshapeasy( x.long, direction="wide", id="surveyNum", vary=".id" ))
surveyNum pio.1 caremgmt.1 prev.1 price.1 pio.3 caremgmt.3 prev.3 price.3 pio.2 caremgmt.2 prev.2 price.2
1 1 2 2 1 2 2 1 2 3 2 1 2 6
2 2 2 1 2 1 2 2 1 5 1 2 2 5
3 3 1 1 2 2 1 1 2 2 2 2 1 5
4 4 2 2 1 5 1 2 2 5 1 2 2 4
5 5 1 1 1 3 2 1 1 4 2 2 1 4
6 6 1 2 2 4 1 1 1 5 2 1 1 2
推荐答案
我还希望看到一个对输出进行排序的选项，因为这是我不喜欢在基本R中进行重塑的原因之一。例如，让我们使用您已经熟悉的Stata Learning Module: Reshaping data wide to long。我看到的例子是"1岁和2岁的儿童身高和体重"的例子。
我通常使用reshape()：
# library(foreign)
kidshtwt = read.dta("http://www.ats.ucla.edu/stat/stata/modules/kidshtwt.dta")
kidshtwt.l = reshape(kidshtwt, direction="long", idvar=1:2,
varying=3:6, sep="", timevar="age")
# The reshaped data is correct, just not in the order I want it
# so I always have to do another step like this
kidshtwt.l = kidshtwt.l[order(kidshtwt.l$famid, kidshtwt.l$birth),]
由于这是一个烦人的步骤，我在重塑数据时总是要经历这一步，所以我认为将其添加到您的函数中会很有用。
我还建议至少提供一个选项，对从long到wide的最终列顺序执行相同的操作。
列排序的示例函数
我不确定将其集成到您的函数中的最佳方式，但我将其组合在一起是为了根据变量名称的基本模式对数据框进行排序。
col.name.sort = function(data, patterns) {
a = names(data)
b = length(patterns)
subs = vector("list", b)
for (i in 1:b) {
subs[[i]] = sort(grep(patterns[i], a, value=T))
}
x = unlist(subs)
data[ , x ]
}
它可以通过以下方式使用。假设我们已经将reshapeasylongtowide示例的输出保存为名为a的数据框，并且我们希望它按以下顺序进行排序：
col.name.sort(a, c("sur", "car", "pre", "pio", "pri"))
这篇关于包装底座R重塑，便于使用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IT屋！
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