我有的数据：
1、23年的贸易数据，分别有23个csv文件
2、根据commodity.code这一变量，将商品分为了36种类型
想要达成的目的:
1、根据商品的36种类型，将不同类型的商品筛选出来，并添加一个名为icsnb的新变量，变量的值依次为1-36
2、对每年的贸易数据都这样处理
目前写的代码:
前期准备：

setwd("D:/data")
list.files()
library(xlsx)
a<-read.xlsx("匹配结果最终版 - 副本.xlsx" ,1,encoding = "UTF-8",stringsAsFactors = FALSE)[,5]
a2<-strsplit(a,split=",")
a2[23]
#> [[1]]
#>   [1] "20110"  "20120"  "20130"  "20210"  "20220"  "20230"  "20311" 
#>   [8] "20312"  "20319"  "20321"  "20322"  "20329"  "20410"  "20421" 
#>  [15] "20422"  "20423"  "20430"  "20441"  "20442"  "20443"  "20450" 
#>  [22] "20500"  "20610"  "20621"  "20622"  "20629"  "20630"  "20641" 
#>  [29] "20649"  "20680"  "20690"  "20710"  "20721"  "20722"  "20723" 
#>  [36] "20731"  "20739"  "20741"  "20742"  "20743"  "20750"  "20810" 
#>  [43] "20820"  "20890"  "21011"  "21012"  "21019"  "21020"  "21090" 
#>  [50] "160100" "160210" "160220" "160231" "160239" "160241" "160242"
#>  [57] "160249" "160250" "160290" "160300" "40110"  "40120"  "40130" 
#>  [64] "40210"  "40221"  "40229"  "40291"  "40299"  "40310"  "40390" 
#>  [71] "40410"  "40490"  "40500"  "40610"  "40620"  "40630"  "40640" 
#>  [78] "40690"  "40700"  "40811"  "40819"  "40891"  "40899"  "100110"
#>  [85] "100190" "100200" "100300" "100400" "100510" "100590" "100610"
#>  [92] "100620" "100630" "100640" "100700" "100810" "100820" "100830"
#>  [99] "100890" "110100" "110210" "110220" "110230" "110290" "110311"
#> [106] "110312" "110313" "110314" "110319" "110321" "110329" "110411"
#> [113] "110412" "110419" "110421" "110422" "110423" "110429" "110430"
#> [120] "110710" "110720" "190110" "190120" "190190" "190211" "190219"
#> [127] "190240" "190410" "190510" "190520" "190530" "190540" "190590"
#> [134] "70110"  "70190"  "70200"  "70310"  "70320"  "70390"  "70410" 
#> [141] "70420"  "70490"  "70511"  "70519"  "70521"  "70529"  "70610" 
#> [148] "70690"  "70700"  "70810"  "70820"  "70890"  "70910"  "70920" 
#> [155] "70930"  "70940"  "70951"  "70952"  "70960"  "70970"  "70990" 
#> [162] "71010"  "71021"  "71022"  "71029"  "71030"  "71080"  "71090" 
#> [169] "71110"  "71120"  "71130"  "71140"  "71190"  "71210"  "71220" 
#> [176] "71230"  "71290"  "71310"  "71320"  "71331"  "71332"  "71333" 
#> [183] "71339"  "71340"  "71350"  "71390"  "71410"  "71420"  "71490" 
#> [190] "80110"  "80120"  "80130"  "80211"  "80212"  "80221"  "80222" 
#> [197] "80231"  "80232"  "80240"  "80250"  "80290"  "80300"  "80410" 
#> [204] "80420"  "80430"  "80440"  "80450"  "80510"  "80520"  "80530" 
#> [211] "80540"  "80590"  "80610"  "80620"  "80710"  "80720"  "80810" 
#> [218] "80820"  "80910"  "80920"  "80930"  "80940"  "81010"  "81020" 
#> [225] "81030"  "81040"  "81090"  "81110"  "81120"  "81190"  "81210" 
#> [232] "81220"  "81290"  "81310"  "81320"  "81330"  "81340"  "81350" 
#> [239] "81400"  "110510" "110520" "110610" "110620" "110630" "121010"
#> [246] "121020" "121210" "121230" "121291" "121292" "121299" "190300"
#> [253] "200110" "200120" "200190" "200210" "200290" "200310" "200320"
#> [260] "200410" "200490" "200520" "200530" "200540" "200551" "200559"
#> [267] "200560" "200570" "200590" "200600" "200710" "200791" "200799"
#> [274] "200811" "200819" "200820" "200830" "200840" "200850" "200860"
#> [281] "200870" "200880" "200892" "200899" "200911" "200919" "200920"
#> [288] "200930" "200940" "200950" "200960" "200970" "200980" "200990"
#> [295] "40900"  "170111" "170112" "170191" "170199" "170210" "170220"
#> [302] "170230" "170240" "170260" "170290" "170310" "170390" "170410"
#> [309] "170490" "90111"  "90112"  "90121"  "90122"  "90130"  "90140" 
#> [316] "90210"  "90220"  "90230"  "90240"  "90300"  "90411"  "90412" 
#> [323] "90420"  "90500"  "90610"  "90620"  "90700"  "90810"  "90820" 
#> [330] "90830"  "90910"  "90920"  "90930"  "90940"  "90950"  "91010" 
#> [337] "91020"  "91030"  "91040"  "91050"  "91091"  "91099"  "180100"
#> [344] "180310" "180320" "180400" "180500" "180610" "180620" "180631"
#> [351] "180632" "180690" "210110" "210130" "41000"  "71040"  "150100"
#> [358] "151710" "151790" "190220" "190230" "190490" "200510" "200580"
#> [365] "200891" "210120" "210210" "210220" "210230" "210310" "210320"
#> [372] "210330" "210390" "210410" "210420" "210500" "210610" "210690"
#> [379] "220900" "220110" "220190" "220210" "220290" "220300" "220410"
#> [386] "220421" "220429" "220430" "220510" "220590" "220600" "220810"
#> [393] "220820" "220830" "220840" "220850" "220890" "120100" "120210"
#> [400] "120220" "120300" "120400" "120500" "120600" "120710" "120720"
#> [407] "120730" "120740" "120750" "120760" "120791" "120792" "120799"
#> [414] "120810" "120890" "20900"  "150200" "150300" "150410" "150420"
#> [421] "150430" "150510" "150590" "150600" "150710" "150790" "150810"
#> [428] "150890" "150910" "150990" "151000" "151110" "151190" "151211"
#> [435] "151219" "151221" "151229" "151311" "151319" "151321" "151329"
#> [442] "151410" "151490" "151511" "151519" "151521" "151529" "151530"
#> [449] "151540" "151550" "151560" "151590" "151610" "151620" "151800"
#> [456] "151911" "151912" "151913" "151919" "151920" "152110" "152190"
#> [463] "152200" "110811" "110812" "110813" "110814" "110819" "110820"
#> [470] "110900" "843780" "843790" "843810" "843820" "843830" "843840"
#> [477] "843850" "843860" "843880" "843890" "847920"
#给每行数据命名
ICS1<-unlist(a2[1])
ICS2<-unlist(a2[2])
ICS3<-unlist(a2[3])
ICS4<-unlist(a2[4])
ICS5<-unlist(a2[5])
ICS6<-unlist(a2[6])
ICS7<-unlist(a2[7])
ICS8<-unlist(a2[8])
ICS9<-unlist(a2[9])
ICS10<-unlist(a2[10])
ICS11<-unlist(a2[11])
ICS12<-unlist(a2[12])
ICS13<-unlist(a2[13])
ICS14<-unlist(a2[14])
ICS15<-unlist(a2[15])
ICS16<-unlist(a2[16])
ICS17<-unlist(a2[17])
ICS18<-unlist(a2[18])
ICS19<-unlist(a2[19])
ICS20<-unlist(a2[20])
ICS21<-unlist(a2[21])
ICS22<-unlist(a2[22])
ICS23<-unlist(a2[23])
ICS24<-unlist(a2[24])
ICS25<-unlist(a2[25])
ICS26<-unlist(a2[26])
ICS27<-unlist(a2[27])
ICS28<-unlist(a2[28])
ICS29<-unlist(a2[29])
ICS30<-unlist(a2[30])
ICS31<-unlist(a2[31])
ICS32<-unlist(a2[32])
ICS33<-unlist(a2[33])
ICS34<-unlist(a2[34])
ICS35<-unlist(a2[35])
ICS36<-unlist(a2[36])
ICS<-list(ICS1,ICS2,ICS3,ICS4,ICS5,ICS6,ICS7,ICS8,ICS9,ICS10,ICS11,ICS12,ICS13,ICS14,ICS15,ICS16,ICS17,ICS18,ICS19,ICS20,ICS21,ICS22,ICS23,ICS24,ICS25,ICS26,ICS27,ICS28,ICS29,ICS30,ICS31,ICS32,ICS33,ICS34,ICS35,ICS36)

<sup>Created on 2019-05-13 by the reprex package (v0.2.1)</sup>

第一种:

ICS<list(ICS1,ICS2,ICS3,ICS4,ICS5,ICS6,ICS7,ICS8,ICS9,ICS10,ICS11,ICS12,ICS13,ICS14,ICS15,ICS16,ICS17,ICS18,ICS19,ICS20,ICS21,ICS22,ICS23,ICS24,ICS25,ICS26,ICS27,ICS28,ICS29,ICS30,ICS31,ICS32,ICS33,ICS34,ICS35,ICS36)
icsn<-c(1:36)
setwd("E:/论文数据/双边贸易数据/美国贸易数据")
file.names<-list.files()
data <- lapply(file.names, function(x){
  read.csv(x, header=T)}) 
ICSM=NULL
result=NULL
for (i in 1:length(data)) {
  for (j in 1:length(ICS)) {
    result=list(ICSM<-rbind(ICSM,(mutate(filter(as.data.frame(data[[i]]),Commodity.Code%in%ICS[[j]]),icsnb=icsn[j]))))
  }
}

第二种：

result=NULL
for (i in 1:length(data)) {
  for (j in 1:length(ICS)) {
    result=list(result,sapply(as.data.frame(data[i]),function(x){
      mutate(filter(x,Commodity.Code%in%ICS[[j]]),icsnb=icsn[j]))})
  }
}

但是两个代码都没有算出结果，第一种代码我觉得是for循环的结果赋值错了，第二种不知道哪里出错了。