我有一个包含大约150万行和5列的数据帧。一个变量（VARIABLE）属于这种类型NATIONALITY_YEAR（例如SPAIN_1998），我想将它分成两列，一列包含国籍，这是在下划线之前的名称的左侧，另一列包含年份，右侧下划线。我尝试使用concat.split，这应该是最简单的方法：
aa <- concat.split(mydata, "VARIABLE", sep = "_", drop = F)
但是运行2小时后它没有产生任何输出。我不确定是否应该让它运行更长时间，或者是否有非耗时的方式来执行此操作。
非常感谢有关此问题的任何帮助！
这是一个可重现的（子集！）样本：
mydata<- structure(list(PROVINCE = c(1L, 4L, 7L, 8L, 11L, 14L, 17L, 20L,
24L, 28L, 30L, 33L, 36L, 41L, 44L, 46L, 48L, 3L, 6L, 8L, 10L,
13L, 15L, 18L, 23L, 26L, 29L, 31L, 35L, 38L, 41L, 46L, 47L, 2L,
4L, 8L, 8L, 11L, 15L, 17L, 21L, 24L, 28L, 30L, 33L, 37L, 41L,
45L, 46L, 49L, 3L, 6L, 8L, 10L, 13L, 15L, 19L, 23L, 27L, 29L,
32L, 36L, 39L, 43L, 46L, 48L, 2L, 5L, 8L, 8L, 12L, 15L, 18L,
21L, 24L, 28L, 30L, 33L, 37L, 41L, 45L, 46L, 50L, 3L, 7L, 8L,
10L, 14L, 16L, 20L, 23L, 27L, 29L, 32L, 36L, 39L, 43L, 46L, 48L,
3L, 6L, 8L, 8L, 12L, 15L, 18L, 21L, 25L, 28L, 31L, 34L, 38L,
41L, 45L, 46L, 50L, 3L, 7L, 8L, 11L, 14L, 17L, 20L, 23L, 27L,
29L, 33L, 36L, 40L, 43L, 46L, 48L, 3L, 6L, 8L, 9L, 12L, 15L,
18L, 22L, 25L, 28L, 31L, 35L, 38L, 41L, 45L, 46L, 50L, 4L, 7L,
8L, 11L, 14L, 17L, 20L, 24L, 28L, 30L, 33L, 36L, 41L, 43L, 46L,
48L, 3L, 6L, 8L, 10L, 13L, 15L, 18L, 22L, 26L, 28L, 31L, 35L,
38L, 41L, 46L, 47L, 1L, 4L, 8L, 8L, 11L, 14L, 17L, 20L, 24L,
28L, 30L, 33L, 36L, 41L, 44L, 46L, 49L, 3L, 6L), AGE5 = structure(c(1L,
5L, 9L, 7L, 6L, 7L, 5L, 8L, 3L, 3L, 3L, 5L, 8L, 2L, 3L, 6L, 9L,
5L, 7L, 4L, 3L, 5L, 8L, 8L, 2L, 8L, 2L, 9L, 7L, 9L, 9L, 2L, 7L,
2L, 9L, 1L, 8L, 8L, 1L, 8L, 1L, 6L, 4L, 6L, 7L, 2L, 3L, 1L, 7L,
5L, 6L, 9L, 5L, 6L, 8L, 9L, 3L, 4L, 3L, 4L, 4L, 1L, 3L, 1L, 2L,
2L, 6L, 6L, 2L, 9L, 2L, 2L, 1L, 5L, 9L, 5L, 8L, 9L, 7L, 4L, 3L,
7L, 2L, 8L, 2L, 6L, 9L, 1L, 5L, 1L, 6L, 6L, 6L, 7L, 3L, 6L, 3L,
3L, 4L, 1L, 1L, 2L, 9L, 6L, 4L, 3L, 8L, 3L, 7L, 1L, 5L, 2L, 6L,
6L, 8L, 5L, 9L, 5L, 6L, 2L, 3L, 1L, 4L, 8L, 9L, 8L, 1L, 5L, 1L,
6L, 4L, 6L, 2L, 3L, 3L, 5L, 9L, 5L, 5L, 4L, 7L, 8L, 4L, 2L, 5L,
7L, 8L, 9L, 8L, 3L, 7L, 7L, 5L, 6L, 3L, 6L, 1L, 2L, 2L, 3L, 7L,
1L, 9L, 5L, 8L, 4L, 5L, 4L, 1L, 3L, 7L, 7L, 9L, 3L, 9L, 7L, 5L,
7L, 8L, 1L, 4L, 4L, 6L, 1L, 8L, 7L, 8L, 6L, 8L, 4L, 3L, 4L, 5L,
9L, 2L, 6L, 6L, 1L, 5L, 7L), .Label = c("10-14", "15-19", "20-24",
"25-29", "30-34", "35-39", "40-44", "45-49", "50-54"), class = "factor"),
ZONA91OK = c(101L, 4079L, 712L, 8205L, 11022L, 14021L, 1714L,
20067L, 2414L, 2810L, 300799L, 3305L, 36026L, 41024L, 4405L,
4607L, 48015L, 308L, 610L, 8121L, 1006L, 1307L, 1511L, 1813L,
2308L, 2605L, 2910L, 310799L, 35026L, 3811L, 411199L, 4601L,
4708L, 202L, 405L, 8015L, 837L, 11033L, 1502L, 1702L, 2112L,
2408L, 28047L, 30015L, 3305L, 3709L, 410199L, 4511L, 1202L,
490699L, 3063L, 610L, 827L, 1006L, 1301L, 15036L, 1901L,
2310L, 2709L, 29025L, 3201L, 36008L, 390899L, 4301L, 46184L,
4805L, 206L, 504L, 817L, 813L, 12135L, 1519L, 1810L, 2104L,
2402L, 28130L, 30030L, 3305L, 3707L, 411399L, 45165L, 46181L,
5008L, 305L, 7026L, 803L, 1006L, 1413L, 16078L, 200999L,
2312L, 2712L, 29069L, 3210L, 3616L, 391199L, 4313L, 46105L,
4805L, 310L, 6153L, 8252L, 8205L, 1205L, 1505L, 1808L, 2110L,
2508L, 2810L, 311399L, 3405L, 3807L, 41024L, 4507L, 46102L,
500599L, 3014L, 706L, 8121L, 11028L, 14042L, 1712L, 20045L,
2314L, 27031L, 29901L, 33024L, 3614L, 400199L, 4307L, 46021L,
4805L, 3066L, 6153L, 8015L, 901L, 12040L, 1522L, 1806L, 2203L,
2508L, 28047L, 311099L, 35004L, 3801L, 410199L, 4515L, 46017L,
501199L, 407L, 7027L, 827L, 1102L, 1404L, 17155L, 200599L,
24089L, 2812L, 30019L, 33024L, 3612L, 41038L, 4301L, 4628L,
4805L, 307L, 6153L, 817L, 1004L, 1309L, 1508L, 1804L, 2206L,
2606L, 28130L, 310799L, 35011L, 38022L, 411399L, 4622L, 4701L,
1036L, 4079L, 807L, 803L, 1108L, 1410L, 1708L, 201399L, 2410L,
28058L, 30043L, 33024L, 3610L, 410399L, 4401L, 4621L, 490499L,
3059L, 6153L), VARIABLE = structure(c(1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 13L, 13L), .Label = c("SPAIN_1998",
"EU15DC_1998", "ROE_1998", "MAGREB_1998", "SSA_1998", "LA_1998",
"ASIA_1998", "ROW_1998", "Total_1998", "SPAIN_1999", "EU15DC_1999",
"ROE_1999", "MAGREB_1999", "SSA_1999", "LA_1999", "ASIA_1999",
"ROW_1999", "Total_1999", "SPAIN_2000", "EU15DC_2000", "ROE_2000",
"MAGREB_2000", "SSA_2000", "LA_2000", "ASIA_2000", "ROW_2000",
"Total_2000", "SPAIN_2001", "EU15DC_2001", "ROE_2001", "MAGREB_2001",
"SSA_2001", "LA_2001", "ASIA_2001", "ROW_2001", "Total_2001",
"SPAIN_2002", "EU15DC_2002", "ROE_2002", "MAGREB_2002", "SSA_2002",
"LA_2002", "ASIA_2002", "ROW_2002", "Total_2002", "SPAIN_2003",
"EU15DC_2003", "ROE_2003", "MAGREB_2003", "SSA_2003", "LA_2003",
"ASIA_2003", "ROW_2003", "Total_2003", "SPAIN_2004", "EU15DC_2004",
"ROE_2004", "MAGREB_2004", "SSA_2004", "LA_2004", "ASIA_2004",
"ROW_2004", "Total_2004", "SPAIN_2005", "EU15DC_2005", "ROE_2005",
"MAGREB_2005", "SSA_2005", "LA_2005", "ASIA_2005", "ROW_2005",
"Total_2005", "SPAIN_2006", "EU15DC_2006", "ROE_2006", "MAGREB_2006",
"SSA_2006", "LA_2006", "ASIA_2006", "ROW_2006", "Total_2006",
"SPAIN_2007", "EU15DC_2007", "ROE_2007", "MAGREB_2007", "SSA_2007",
"LA_2007", "ASIA_2007", "ROW_2007", "Total_2007", "SPAIN_2008",
"EU15DC_2008", "ROE_2008", "MAGREB_2008", "SSA_2008", "LA_2008",
"ASIA_2008", "ROW_2008", "Total_2008", "SPAIN_2009", "EU15DC_2009",
"ROE_2009", "MAGREB_2009", "SSA_2009", "LA_2009", "ASIA_2009",
"ROW_2009", "Total_2009", "SPAIN_2010", "EU15DC_2010", "ROE_2010",
"MAGREB_2010", "SSA_2010", "LA_2010", "ASIA_2010", "ROW_2010",
"Total_2010", "SPAIN_2011", "EU15DC_2011", "ROE_2011", "MAGREB_2011",
"SSA_2011", "LA_2011", "ASIA_2011", "ROW_2011", "Total_2011",
"SPAIN_2012", "EU15DC_2012", "ROE_2012", "MAGREB_2012", "SSA_2012",
"LA_2012", "ASIA_2012", "ROW_2012", "Total_2012", "NOTSPAIN_1998",
"NOTSPAIN_1999", "NOTSPAIN_2000", "NOTSPAIN_2001", "NOTSPAIN_2002",
"NOTSPAIN_2003", "NOTSPAIN_2004", "NOTSPAIN_2005", "NOTSPAIN_2006",
"NOTSPAIN_2007", "NOTSPAIN_2008", "NOTSPAIN_2009", "NOTSPAIN_2010",
"NOTSPAIN_2011", "NOTSPAIN_2012", "AFRICA_1998", "AFRICA_1999",
"AFRICA_2000", "AFRICA_2001", "AFRICA_2002", "AFRICA_2003",
"AFRICA_2004", "AFRICA_2005", "AFRICA_2006", "AFRICA_2007",
"AFRICA_2008", "AFRICA_2009", "AFRICA_2010", "AFRICA_2011",
"AFRICA_2012", "DWC_1998", "DWC_1999", "DWC_2000", "DWC_2001",
"DWC_2002", "DWC_2003", "DWC_2004", "DWC_2005", "DWC_2006",
"DWC_2007", "DWC_2008", "DWC_2009", "DWC_2010", "DWC_2011",
"DWC_2012"), class = "factor"), FREQUENCY = c(614, 1943,
59, 201, 188, 10859, 93,
1494, 60, 1001, 1000, 689, 675, 934, 51,
1240, 165, 13, 0, 14, 2, 2,
2, 0, 3, 0, 40, 1, 18, 41, 1, 0, 3, 0, 0, 0, 1, 0,
0, 0, 0, 0, 7, 1, 0, 0, 0, 0, 0, 0, 0, 0, 80, 0,
0, 0, 4, 0, 0, 15, 0, 0, 1, 1, 3, 4, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 2, 0, 1, 0, 0, 2, 11, 0, 0, 0, 3, 2, 1, 5,
64, 1, 4, 1, 3, 4, 8, 1, 1, 1, 1, 0, 0, 0,
0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 2173, 907, 9059, 839,
4303, 100, 1727, 663, 694, 1210, 623,
1261, 772, 697, 490, 1031, 490, 956, 704,
1293, 1011, 739, 927, 755, 3340, 1190, 1254, 12880, 528,
3244, 277, 892, 837, 1, 2, 10, 1, 1, 2, 2, 0, 0, 1, 8, 3,
12, 0, 2, 1, 0, 4, 0, 0, 0, 0, 0, 0, 1, 12, 0, 7, 0, 0, 0,
0, 0, 5, 2)), .Names = c("PROVINCE", "AGE5", "ZONA91OK",
"VARIABLE", "FREQUENCY"), row.names = c(1L, 501L, 1001L, 1501L,
2001L, 2501L, 3001L, 3501L, 4001L, 4501L, 5001L, 5501L, 6001L,
6501L, 7001L, 7501L, 8001L, 8501L, 9001L, 9501L, 10001L, 10501L,
11001L, 11501L, 12001L, 12501L, 13001L, 13501L, 14001L, 14501L,
15001L, 15501L, 16001L, 16501L, 17001L, 17501L, 18001L, 18501L,
19001L, 19501L, 20001L, 20501L, 21001L, 21501L, 22001L, 22501L,
23001L, 23501L, 24001L, 24501L, 25001L, 25501L, 26001L, 26501L,
27001L, 27501L, 28001L, 28501L, 29001L, 29501L, 30001L, 30501L,
31001L, 31501L, 32001L, 32501L, 33001L, 33501L, 34001L, 34501L,
35001L, 35501L, 36001L, 36501L, 37001L, 37501L, 38001L, 38501L,
39001L, 39501L, 40001L, 40501L, 41001L, 41501L, 42001L, 42501L,
43001L, 43501L, 44001L, 44501L, 45001L, 45501L, 46001L, 46501L,
47001L, 47501L, 48001L, 48501L, 49001L, 49501L, 50001L, 50501L,
51001L, 51501L, 52001L, 52501L, 53001L, 53501L, 54001L, 54501L,
55001L, 55501L, 56001L, 56501L, 57001L, 57501L, 58001L, 58501L,
59001L, 59501L, 60001L, 60501L, 61001L, 61501L, 62001L, 62501L,
63001L, 63501L, 64001L, 64501L, 65001L, 65501L, 66001L, 66501L,
67001L, 67501L, 68001L, 68501L, 69001L, 69501L, 70001L, 70501L,
71001L, 71501L, 72001L, 72501L, 73001L, 73501L, 74001L, 74501L,
75001L, 75501L, 76001L, 76501L, 77001L, 77501L, 78001L, 78501L,
79001L, 79501L, 80001L, 80501L, 81001L, 81501L, 82001L, 82501L,
83001L, 83501L, 84001L, 84501L, 85001L, 85501L, 86001L, 86501L,
87001L, 87501L, 88001L, 88501L, 89001L, 89501L, 90001L, 90501L,
91001L, 91501L, 92001L, 92501L, 93001L, 93501L, 94001L, 94501L,
95001L, 95501L, 96001L, 96501L, 97001L, 97501L, 98001L, 98501L,
99001L, 99501L), class = "data.frame")
答案 0 :(得分：7)
请改为尝试：
library(data.table)
dt = data.table(mydata)
dt[, `:=`(NATIONALITY = sub('(.*)_(.*)', '\\1', VARIABLE),
YEAR = sub('(.*)_(.*)', '\\2', VARIABLE))]
答案 1 :(得分：6)
似乎我需要考虑更新我的concat.split函数！
您尝试使用的函数的版本使用read.table，它确实倾向于使用大型数据集。我使用了read.table，因为它有一个方便的text参数，可以让您在data.frame中指定一列作为输入。使用小型数据集时这非常方便，但显然不适用于较大的数据集：）
据我所知，“data.table”包中的fread没有类似的功能，但由于R倾向于快速写文件，我认为值得尝试一下与我在concat.split中使用fread代替read.table的方法类似。
这是概念：
fread将其重读。fread在某个地方获得text参数？这是一个概念作为一个函数（根据@ eddi在评论中的建议进行了编辑更新）：
csDataTable <- function(dataset, splitcol, sep, drop = FALSE) {
if (is.numeric(splitcol)) splitcol <- names(dataset)[splitcol]
if (!is.data.table(dataset)) dataset <- data.table(dataset)
if (sep == ".") {
dataset[, (splitcol) := gsub(".", "|", get(splitcol), fixed = TRUE)]
sep <- "|"
}
if (!is.character(dataset[[splitcol]])) {
dataset[, (splitcol) := as.character(get(splitcol))]
}
x <- tempfile()
writeLines(dataset[[splitcol]], x)
Split <- fread(x, sep=sep, header = FALSE)
setnames(Split, paste(splitcol, seq_along(Split), sep = "_"))
if (isTRUE(drop)) dataset[, (splitcol) := NULL]
cbind(dataset, Split)
}
这是行动中的功能：
## Expand your sample data to 1.5 million rows to test
out <- mydata[rep(rownames(mydata), 1500000/nrow(mydata)), ]
csDataTable(out, "VARIABLE", "_")
# PROVINCE AGE5 ZONA91OK VARIABLE FREQUENCY VARIABLE_1 VARIABLE_2
# 1: 1 10-14 101 SPAIN_1998 614 SPAIN 1998
# 2: 4 30-34 4079 SPAIN_1998 1943 SPAIN 1998
# 3: 7 50-54 712 SPAIN_1998 59 SPAIN 1998
# 4: 8 40-44 8205 SPAIN_1998 201 SPAIN 1998
# 5: 11 35-39 11022 SPAIN_1998 188 SPAIN 1998
# ---
# 1499996: 44 35-39 4401 ROE_1999 0 ROE 1999
# 1499997: 46 35-39 4621 ROE_1999 0 ROE 1999
# 1499998: 49 10-14 490499 ROE_1999 0 ROE 1999
# 1499999: 3 30-34 3059 MAGREB_1999 5 MAGREB 1999
# 1500000: 6 40-44 6153 MAGREB_1999 2 MAGREB 1999
在这个测试中，至少，解决方案比我预期的要好得多：
subFun <- function() {
dt = data.table(out)
dt[, `:=`(NATIONALITY = sub('(.*)_(.*)', '\\1', VARIABLE),
YEAR = sub('(.*)_(.*)', '\\2', VARIABLE))]
}
freadFun <- function() {
csDataTable(out, "VARIABLE", "_")
}
library(microbenchmark)
microbenchmark(subFun(), freadFun(), times = 20)
# Unit: seconds
# expr min lq median uq max neval
# subFun() 3.814174 4.244820 4.273834 4.345358 4.480520 20
# freadFun() 1.356533 2.064262 2.152159 2.226465 2.300886 20
答案 2 :(得分：3)
以下是拆分因子标签的解决方案
VARIABLE_LEVELS <- cbind("VARIABLE"=levels(mydata$VARIABLE),
as.data.frame(do.call("rbind",
strsplit(levels(mydata$VARIABLE), split="_")))
mydata <- merge(mydata, VARIABLE_LEVELS)
#
# Insted of merege you can use VARIABLE (in mydata) as index
#
mydata <- cbind(mydata, VARIABLE_LEVELS[as.integer(mydata$VARIABLE),c("V1","V2")])