r data.frame創建新變量

Question

我有一個大約150萬行和5列的數據框。一個變量（VARIABLE）屬於這種類型的NATIONALITY_YEAR（例如SPAIN_1998），我想將它分成兩列，一列包含國籍，即下劃線之前名稱的左側，另一列包含年份，右側下劃線。我試着用concat.split應該是最簡單的方法：

aa <- concat.split(mydata, "VARIABLE", sep = "_", drop = F) 

但是運行2小時後它沒有產生任何輸出。我不確定是否應該讓它長時間運行，或者是否有非常耗時的方式來完成此操作。

對這個問題的任何幫助將非常感謝！

這是一個可重複的（子！）樣本：

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,L, 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") 

Answer 1

試試這個：

library(data.table) 
dt = data.table(mydata) 

dt[, `:=`(NATIONALITY = sub('(.*)_(.*)', '\\1', VARIABLE), 
      YEAR  = sub('(.*)_(.*)', '\\2', VARIABLE))] 

Answer 2

好像我需要尋找到更新我的concat.split功能！

您嘗試使用的函數的版本使用read.table，它傾向於與大型數據集糾纏。我曾使用read.table，因爲它有一個方便的text參數，可以讓您指定data.frame中的一列作爲輸入。這在處理small-ish數據集時非常方便，但顯然不適用於較大的數據集:)

據我所知，來自「data.table」包的fread沒有類似的功能，但自R傾向於很快寫入文件，我認爲值得嘗試一下類似的方法，就像我在concat.split中用fread而不是read.table那樣。

這裏的概念：

1. 編寫需要被劃分到新文件中的變量。
2. 使用超快fread讀回。
3. 等待fread獲得text爭論的地方下了線？

下面是一個概念，作爲一個函數（在評論與編輯更新每@ EDDI的建議）：

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) 
} 

這裏的工作中的作用：

## 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 

---

在這個測試，至少，解決方案的票價比我預期的好得多：

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 

Answer 3

下面是一些解決方案與分裂因子標籤

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")])