问题描述
我目前正在使用标识列为“ ZA.Betriebnr。”的数据集。
我想将此10位数的标识号更改为另一个，其唯一ID从1到600（“ AUI.ID”）。为此，我有另一个数据集，每个“ ZA.Betriebnr”都有一个对应的“ AUI”。 “ .ID”（ZA.Betriebsnr。和AUI.Betriebsnummer是等效的。）Feldkalender矩阵具有70000行和IDConverter 600，因此不同的“ ZA.Betriebnr。”可以采用相同的“ AUI.ID”。
我的代码：
Read-in data and loading of libraries for the years 2009 to 2018
d9<-read.csv2("Feldkalender_2009.csv",header=T,stringsAsFactors = F)
d10<-read.csv2("Feldkalender_2010.csv",stringsAsFactors = F)
d11<-read.csv2("Feldkalender_2011.csv",stringsAsFactors = F)
d12<-read.csv2("Feldkalender_2012.csv",stringsAsFactors = F)
d13<-read.csv2("Feldkalender_2013.csv",stringsAsFactors = F)
d14<-read.csv2("Feldkalender_2014.csv",stringsAsFactors = F)
d15<-read.csv2("Feldkalender_2015.csv",stringsAsFactors = F)
d16<-read.csv2("Feldkalender_2016.csv",stringsAsFactors = F)
d17<-read.csv2("Feldkalender_2017.csv",stringsAsFactors = F)
d18<-read.csv2("Feldkalender_2018.csv",stringsAsFactors = F)
ds<-list(d9,d10,d11,d12,d13,d14,d15,d16,d17,d18)
创建2009年至2018年的媒介
years<-c(2009,2010,2011,2012,2013,2014,2015,2016,2017,2018)
使用AUI.Betriebsnr的相应AUI.ID加载文件。
IDConverter<-read.csv2("Zuweisung_BetrNr_AUI_ID.csv",stringsAsFactors = F)
替换ZA.Betriebnsr。标识变量由唯一的AUI.ID标识变量
options("scipen"=100,"digits"=4)
for ( i in 1:length(years)){
for (j in 1:length(ds[[i]]$ZA.Betriebsnr.)){
for(k in 1:length(IDConverter$AUI.Betriebsnummer)){
o3<-IDConverter
o<-ds[[i]]
o$ZA.Betriebsnr.[o$ZA.Betriebsnr.[j] == o3$AUI.Betriebsnummer[k]]<-o3$AUI.ID[k]
}
ds[[i]]$ZA.Betriebsnr.[j]<-o$ZA.Betriebsnr.[j]
}
}
我制作了一个三重循环效果，但结束时间太长。有谁知道如何使其运行更快？
数据：
ds（（1））
[1] 10100001151 10100001248 10100001252 10100001392 10100001445 10100001633
[7] 10100048900 10100091095 10100091571 10200002050 10200002133 10200002162
[13] 10200002248 10200002260 10200002280 10200002299 10200002300 10200002304
[19] 10200002324 10200002328 10200002380 10200002509 10200002517 10200002518
[25] 10200002549 10200002550 10200002553 10200002900 10200024541 10200049529
[31] 10200049709 10200050203 10200050694 10200050716 10200123451 10300003001
[37] 10300003195 10300003333 10300003379 10300003469 10300003546 10300003757
[43] 10300003770 10300043107 10300043152 10300052076 10300053285 10300054306
[49] 10300056544 10300058629 10400004248 10400004709 10400004894 10400004913
[55] 10400004922 10400047954 10500005013 10500005027 10500005032 10500005069
[61] 10500005105 10500005153 10500005160 10500006021 10500006030 10500006047
[67] 10500006084 10500006098 10500007001 10500007027 10500007030 10500073183
[73] 11000010059 11000010067 11000010071 11000010108 11000010142 11000010185
[79] 11000010358 11000010367 11000010405 11000010413 11000010507 11000010537
[85] 11000010542 11000010679 11000010694 11000010704 11000010721 11000010768
[91] 11000010779 11000010789 11100011215 11100011293 11100011346 11100011354
[97] 11100011356 11100011364 11100011517 11100011592 11100011643 11200012286
[103] 11200012567 11200012701 11200012738 11200012784 11200012793 11200012868
IDConverter：
structure(list(AUI.ID = 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,25L,26L,27L,28L,29L,30L,31L,32L,33L,34L,35L,37L,39L,40L,41L,42L,43L,45L,46L,47L,48L,49L,50L,51L,52L,53L,54L,55L,56L,57L,58L,59L,60L,61L,62L,63L,64L,65L,66L,67L,68L,69L,70L,71L,72L,73L,74L,75L,76L,77L,78L,79L,80L,81L,82L,83L,84L,85L,86L,87L,88L,89L,90L,91L,92L,93L,94L,95L,96L,97L,98L,99L,100L,101L,102L,103L,104L,105L,106L,107L,108L,110L,111L,112L,113L,114L,115L,116L,117L,118L,119L,120L,121L,122L,123L,124L,125L,126L,127L,128L,129L,130L,133L,134L,135L,136L,137L,138L,139L,140L,141L,142L,143L,144L,145L,146L,147L,148L,149L,150L,151L,153L,154L,155L,156L,157L,158L,159L,160L,161L,162L,163L,164L,165L,166L,167L,168L,169L,170L,171L,172L,173L,174L,175L,176L,177L,178L,179L,180L,181L,182L,184L,185L,186L,187L,188L,189L,190L,191L,192L,193L,195L,196L,197L,198L,199L,200L,201L,202L,203L,204L,205L,206L,207L,208L,209L,210L,213L,214L,215L,216L,217L,218L,219L,220L,221L,222L,223L,224L,226L,227L,228L,230L,231L,232L,233L,234L,235L,236L,237L,238L,240L,241L,242L,243L,244L,245L,246L,247L,248L,249L,251L,252L,253L,254L,255L,256L,257L,258L,259L,260L,261L,262L,264L,265L,266L,267L,268L,269L,270L,271L,272L,273L,276L,277L,278L,279L,280L,281L,282L,283L,284L,285L,286L,287L,288L,289L,290L,291L,292L,293L,294L,295L,296L,297L,298L,299L,300L,301L,302L,303L,304L,305L,306L,307L,308L,309L,310L,311L,312L,313L,314L,315L,316L,317L,318L,319L,320L,321L,322L,323L,324L,325L,326L,327L,328L,329L,330L,331L,332L,333L,334L,335L,336L,337L,338L,339L,340L,341L,342L,343L,344L,345L,346L,347L,348L,349L,350L,351L,352L,353L,354L,355L,356L,357L,358L,359L,360L,361L,362L,363L,364L,365L,366L,367L,368L,369L,370L,371L,372L,373L,374L,375L,376L,377L,378L,379L,381L,384L,385L,386L,387L,388L,389L,392L,393L,394L,395L,396L,397L,402L,403L,406L,407L,409L,410L,411L,412L,413L,414L,415L,416L,417L,418L,420L,421L,422L,423L,425L,426L,428L,429L,430L,433L,434L,435L,436L,437L,438L,439L,440L,441L,442L,443L,444L,445L,446L,447L,448L,449L,450L,451L,452L,453L,454L,455L,456L,457L,458L,459L,460L,461L,463L,464L,465L,466L,467L,468L,469L,470L,471L,472L,473L,474L,475L,476L,477L,478L,479L,480L,481L,482L,483L,484L,485L,486L,487L,488L,489L,490L,491L,492L,493L,494L,495L,496L,497L,498L,499L,500L,501L,502L,503L,504L,505L,506L,507L,508L,509L,510L,511L,512L,513L,514L,515L,516L,517L,518L,519L,520L,521L,522L,523L,524L,525L,526L,527L,528L,529L,530L,531L,532L,533L,534L,535L,536L,537L,538L,539L,540L,541L,542L,543L,544L,545L,546L,547L,548L,549L,550L),AUI.Betriebsnummer = c(10100001151,10100001248,10100001252,10100001392,10100001445,50023000140,10100001595,50023000141,10100001633,10100048900,10100091095,10100091571,10200002050,10200002133,10200052063,10200002162,10200002248,10200002560,10200002260,10200002280,10200002299,10200002300,10200002304,10200002312,10200002324,10200002583,10200002328,10200023281,10200002380,10200024541,10200002454,10200002461,10200123451,10200002490,10200002491,10200002509,10200002517,10200002518,10200002541,10200002545,10200002549,10200002550,10200002553,10200002900,10200092900,10200049529,10200049709,10200050203,10200050694,10200050716,10300003001,10300003195,10300003333,10300003379,10300003469,10300003532,10300067247,10300003546,10300003757,10300003770,10300043107,10300043152,10300052076,10300053285,10300054306,10300056436,10300056544,10300058629,10400004248,10400004709,10400004894,10400004913,10400004922,12100054023,10400004940,10400047954,10500005013,10500005027,10500005032,10500005069,10500005105,10500005106,10500005153,10500005160,10500006021,40600003014,10500006030,10500006047,10500006084,10500006098,10500007001,10500007027,10500007030,10500073183,11000010059,11000010067,11000010071,11000010107,11000010108,11000010796,11000010142,11000010185,11000010358,11000010367,11000010405,11000010413,11000010507,11000010537,11000010542,11000010679,11000010694,11000010704,11000010721,11000010732,11000010746,11000010768,11000010779,11000010789,11000010792,11100011215,11100011293,11100011346,11100011354,11100011356,11100011364,11100011421,11100011517,11100011592,11100011643,11100050411,11100061583,11200012286,11200012567,11200012701,40500012701,11200012738,11200012784,11200012793,11200012868,40500012868,11200012878,11200042615,11300013064,11300001473,11300013142,11300013391,11300004307,11300013153,11300001142,11300013160,11300001355,11300013173,11300001252,11300013175,11300001692,11300013207,11300001524,11300013253,11300001691,11300013275,11300003062,11300013297,11300001527,11300013316,11300001038,11300013321,11300002180,11300013334,11300013344,11300002012,11300013358,11300001424,11300013359,11300113368,11300013368,11300013373,11300013419,11300002032,11500015038,40500015038,11500015053,11600015053,11200015053,11500015070,40500015070,11500015086,40500015086,11500015161,11600012207,11600015183,40500015183,11600016019,11600016369,40500016369,11600016479,40500016479,11600016501,11600016505,40500016505,11600016511,11600046326,11600046631,11700017220,11700017231,11700017234,12100012140,10500006130,40600003031,12100020125,12100020129,33800020129,12100020234,12100202371,12100020237,12100020244,40600003045,12100020257,10500006119,12100020285,12100020302,12100020309,12100203641,12100020364,12100076053,12100020423,12100044092,10500006134,40600003004,12100056058,12100065060,12100066090,12100068039,10500006121,40600003022,12100068051,12100068082,12100021029,12100068186,12100069117,12100073027,12100076004,12100077055,12100078018,10500006116,40600002026,12100078031,12100078036,12100079017,12100082016,12100083070,12100086025,12100086206,12100087022,10500006122,12100087028,33800087028,33800040081,12100087039,12100087054,12100090030,11000010857,12100091003,12100095028,20101001048,20101002536,20101011068,20101522505,20101018528,20101018556,20101021506,20101023523,20101024001,20101025504,20101025510,20101045022,20101025511,20101039043,20101026536,20101030020,20101036505,20101040508,20101044050,20101045012,20101045514,20101047508,20101049005,20101049523,20101056043,20101057501,20101082014,20101089004,20101089046,20101089056,20101089521,20101104520,20101105017,20101105026,20101105502,20101112004,20101122031,20101112523,20101123002,20101125018,20101501505,20101510507,20111000129,20111003006,20111012905,20300003110,20300003139,20300003928,20300003196,20300003203,20300003277,20300003309,20300003334,20300003750,50023000133,20300003850,20300003930,20400014491,20400014572,20400014691,20414603014,20414611012,20414605011,20414606022,20414607001,20414615005,20414607009,20414610012,20414609013,20414611011,20414681008,20414689013,20414693224,20414693950,20414699906,20500005113,20500005407,20600006010,20600006688,20600006694,20600006093,20700007184,20700007423,20700007561,20700007592,20700007630,20600006342,20700007739,20700007925,22400024056,20400024056,22400024386,22424615008,22400024808,22400024813,20400024813,22400024819,22400024873,22400024892,22400024895,22424605004,20424605004,22424605007,22424606007,22424607001,22424614003,22424607006,22424607009,22424613017,22424607010,22424683003,20424683003,20424690006,22424690006,22424690008,22424691007,20424691007,22424693915,22424693916,22500025602,22500025922,22600026611,33800010595,33800038215,33800038245,33800038293,33800038297,33800038650,33800038710,40400038710,33800038839,33800038841,33800038845,33800038850,33800038869,33800038879,33800039342,33800039455,40400039455,33800039484,33800039697,33800038967,33800039741,33800039814,33800042204,34000040001,34000010001,34000040007,34000040014,34200040014,34000040029,34000040036,34000010036,34000040064,34000010064,34000011077,34000010071,34000040071,34000040142,34000010142,34000040148,34000040236,34000010236,34000010276,34000040276,34000040330,34000040349,34200040349,34000010349,34000040350,34000040745,34000010745,34000040750,34000010750,34200042003,34200042192,34000020533,34200042237,34200042282,34200020282,34200042308,34200042683,34200020683,34200042706,34200020706,34200042744,40100001069,40100001129,40700001129,40100001198,40200002033,40200033333,40200064112,40200014010,40200064073,40300050012,40300050040,40300050050,40300050060,40300050130,40300050280,40300050300,40300050340,40300050450,40300050465,40300059999,20101105510,11100011432,12100020374,22424610001,40200014020,40200014041,40200014060,40200014143,40200015003,10200049005,10300003015,10300043087,10300055348,10300056171,10300058510,11100021278,11100051278,11300013392,11300004444,20400014298,20414609026,34000040174,34200040174,34000010174,34000040188,40100001161,40739621104,40200064231,40300050640,10200002336,10200002487,10200002601,10200002493,10200002505,10200002579,10200049289,10200050612,10400050736,11600012905,40500012905,12100079050,12100103904,20101006001,20600006785,20600006806,22424613003,34200042689,34200030689,40200014119,40200044205,40300060005,20484610012,10200002347,10200002494,10200002584,10200051553,10300233561,10400004860,10500005080,11200001330,40500001330,11600016543,40500016543,12100020408,12100021025,12100047309,20101015520,20101032522,20101089525,20101104527,20101122023,20600006798,20600006808,34200042248,34200020248,40100001076,40100001077,40300050730,10400050794,20101035542,20101045535,20111018901,40300050740,34200020737,10300061548,10300062011,10400047179,10400050776,10500000001,11100070131,12100046094,10400050787,20101014026,20101021530,20101043527,20424608001,34200030559,34200030675,40300050467,50023000101,50023000102,50023000104,50023000105,50023000107,50023000108,50023000109,50023000111,50023000112,50023000113,50023000114,50023000115,50023000117,50023000119,50023000120,50023000121,50023000123,50023000124,50023000125,50023000126,50023000127,50023000129,40200064160,10300023157,10400047256,10400004658,40600002024,40600002090,40200044179,10400050790,11100051027,10300054270,40200064215,20111019049,50023000122,50023000134,50023000137,50023000138,50023000139,20101002552,11000090575,12100013085,12100013088,12100021051,12100248133,20101107522,20101112529,34000010768,34200030877,40200044188,40200064025,40400039414,50023000136,50023000142,50023000143,50023000147,50023000153,50023000155,50023000158,50023000165,50023000166,50023000167,50023000170,20600005170,11300004623,50023000181,50023000150,50023000202)),class = "data.frame",row.names = c(NA,-636L))
解决方法
首先，为了优化数据加载，可以执行一个循环，为每个文件构建路径（因为它们遵循清晰的模式），然后将文件直接放入列表中，而不是分配变量然后列出它们： / p>
if (resultCode == Activity.RESULT_OK) {
// your toast here
}
else{
// the Activity result is null,and so your 'if' statement is not working
}
通过此循环，您已经有10年的时间了，因此您无需创建向量并测量其长度。但是，如果您想这样做，则更简单的方法是进行ds = list()
for(i in 1:10){
ds[[i]] = read.csv2(paste("Feldkalender_",2008+i,".csv",sep=""),header=T,stringsAsFactors = F)}
。
对于您的三重循环，您需要向我们提供一些数据以供我们运行代码。复制并粘贴years = 2009:20018（列表中的第一项）和dput(ds[[1]])的输出。
假设您的IDConverter看起来像这样
> IDConverter
AUI.Betriebsnummer AUI.ID
1 a lovely
2 b flap
3 c drum
4 d cars
5 e flight
6 f pretend
7 g fuel
8 h self
9 i line
10 j letter
然后，您可以将其转换为以AUI.Betriebsnummer作为名称的命名向量。
> IDConverter <- with(IDConverter,setNames(AUI.ID,AUI.Betriebsnummer))
> IDConverter
a b c d e f g h i j
"lovely" "flap" "drum" "cars" "flight" "pretend" "fuel" "self" "line" "letter"
此转换后，您可以通过例如
直接将一组项目映射到另一个项目unname(IDConverter[c("a","b","f")])
给你
> unname(IDConverter[c("a","f")])
[1] "lovely" "flap" "pretend"
将此方法与其他一些优化措施一起使用，您的代码将变为
years < -c(2009,2010,2011,2012,2013,2014,2015,2016,2017,2018)
IDConverter <- read.csv2("Zuweisung_BetrNr_AUI_ID.csv",header = T,stringsAsFactors = F)
IDConverter <- with(IDConverter,AUI.Betriebsnummer))
dfs <- lapply(years,function(y) {
df <- read.csv2(paste0("Feldkalender_",y,".csv"),stringsAsFactors = F)
within(df,ZA.Betriebsnr. <- unname(IDConverter[ZA.Betriebsnr.]))
})
应该比以前的效率高得多。
更新
您得到NA是因为ZA.Betriebsnr.包含数字，而不是字符。您只需要将其转换为字符向量即可。做这样的事情
dfs <- lapply(years,ZA.Betriebsnr. <- unname(IDConverter[as.character(ZA.Betriebsnr.)]))
})