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findFragment<-function(Dataset){
df1 <<- data.frame(Col9=character(),aid=character(),month=as.Date(character()),year=as.Date(character()),Outcome=character(),ser_no=character(),Similar=character(),stringsAsFactors=FALSE)
rowind<<-0
start.time <- Sys.time()
apply(Dataset,1,function(slic){
rowind<<-rowind+1
fragment<-subset(Dataset, subset = ser_no %in% slic[1] &
Outcome %in% slic[2] &
year %in% slic[3] &
month %in% slic[4] &
code %in% slic[5] &
name %in% slic[6] &
!(aid %in% slic[7]) &
((as.numeric(Percentage)<=(as.numeric(slic[8])+0.01) &
as.numeric(Percentage)>=as.numeric(slic[8])-0.01)
)
)
#Refiltering results
#If result includes more than 3 rows then refilter back on these rows and include only those rows that have percentage+-0.0001
if(nrow(fragment)>3){
fragment<<-subset(fragment, subset = ((as.numeric(Percentage)<=(as.numeric(slic[8])+0.0001) &
as.numeric(Percentage)>=as.numeric(slic[8])-0.0001)
))
}
#Writing data is extremely slow in below way(takes 30+ minutes).
#fragmentize$Similiar[rowind]<<-paste(as.character(unlist(fragment[7])),collapse=",")
#Writing data this way takes total execution time to 9 minutes
# df1<<-rbind(df1,data.frame(Col9=slic[9],
# aid=slic[7],
# ser_no=slic[1],
# Outcome=slic[2],
# month=slic[4],
# year=slic[3],
# Similar=paste(as.character(unlist(fragment[7])),collapse=",")),make.row.names = FALSE)
})
# df1<<-merge(x = Dataset, y = df1, by = c("Col9","aid","ser_no","Outcome","month","year"), all = TRUE)
cat("Completed in",Sys.time()-start.time)
}
fragmentize$Similiar<-0
findFragment(fragmentize)
fragmentize<-data.frame(ser_no=rep("A1",35243),Outcome=rep("A2",35243),year=rep("A3",35243),month=rep("A4",35243),code=rep("A5",35243),name=rep("A6",35243),aid=rep(letters[1:4],35243),Percentage=rep(1,35243),col9=rep("A9",35243),col10=rep("A10",35243),col11=rep("A11",35243),col12=rep("A12",35243),col13=rep("A13",35243),col4=rep("A14",35243),col15=rep("A15",35243),col16=rep("A16",35243),col7=rep("A17",35243),col8=rep("A18",35243),col19=rep("A19",35243),col20=rep("A20",35243),col21=rep("A21",35243),col22=rep("A22",35243),col23=rep("A23",35243),col24=rep("A24",35243),col25=rep("A25",35243),col26=rep("A26",35243),col27=rep("A27",35243),col28=rep("A28",35243),col29=rep("A29",35243))
fragmentize<-data.frame(col9=rep("A9",35243),col10=rep("A10",35243),col11=rep("A11",35243),col12=rep("A12",35243),col13=rep("A13",35243),col4=rep("A14",35243),col15=rep("A15",35243),col16=rep("A16",35243),col7=rep("A17",35243),col8=rep("A18",35243),col19=rep("A19",35243),col20=rep("A20",35243),col21=rep("A21",35243),col22=rep("A22",35243),col23=rep("A23",35243),col24=rep("A24",35243),col25=rep("A25",35243),col26=rep("A26",35243),col27=rep("A27",35243),col28=rep("A28",35243),col29=rep("A29",35243))
library(random)
ser_noVal<-rep(1:831)
OutcomeVal<-c("Aggressive","Balanced","Positive","Negative","Neutral","Conservative")
yearVal<-c(2013:2017)
monthVal<-c(1:12)
codeVal <- c("A", "B", "C")
nameVal<-randomStrings(n=33, len=2, digits=FALSE,loweralpha=TRUE, unique=TRUE, check=TRUE)
aidVal<-randomStrings(n=222, len=4, digits=TRUE,loweralpha=TRUE, unique=TRUE, check=TRUE)
percentVal<-c(1:1561)
fragmentize$ser_no[sample(1:nrow(fragmentize), nrow(fragmentize), FALSE)] <- rep(ser_noVal, c(6,70,4,83,1,92,1,1,6,16,8,3,376,63,735,23,28,3,24,1,84,13,119,7,5,4,1,29,1,27,7,3,9,7,4,11,7,14,2,1,1,16,5,150,31,10,1,1049,2,47,36,2,41,37,6,81,55,6,11,22,3,10,30,4,8,4,175,9,6,1,1,83,20,1,34,38,1,3,41,6,19,1,13,65,42,115,53,18,19,36,5,16,20,38,1,36,1,1,1,4,7,5,19,7,8,39,113,4,1,21,21,2,12,7,6,11,33,19,1,1,53,2,195,79,1,1,2,2,3,1,7,3,11,5,2,1,16,2,14,2,2,15,4,54,4,3,2,40,49,2,1,3,22,9,25,5,42,8,5,6,8,8,3,179,2,4,16,131,113,20,1,13,27,57,52,34,7,4,1,3,22,21,577,16,28,31,82,1,1,74,26,25,1,23,1,29,116,33,1,3,9,8,11,12,1,2,3,11,1,1,13,3,22,13,1,15,2,4,20,1,2,7,2,2,18,147,8,2,50,5,25,2,12,1,98,6,6,37,55,20,9,6,3,8,4,2,2,9,2,32,6,183,10,141,755,34,1,13,3,1,83,1,10,1,566,27,1,38,1,45,7,44,43,11,18,259,36,64,6,19,31,33,355,70,14,26,41,619,139,1,2,45,76,2,49,5,19,51,30,16,32,12,10,1,4,2,80,25,45,84,50,346,125,60,61,321,6,14,17,13,37,7,4,61,79,207,68,111,49,75,425,92,50,329,4,22,2,7,88,1265,3,22,41,10,29,1,37,3,1,13,20,35,10,33,26,5,1,1,1,1,1,2,3,6,14,2,4,2,20,921,132,9,8,114,438,57,37,10,1778,21,10,44,1,4,3,10,48,1,100,123,6,15,234,3,15,3,14,13,46,39,2,72,3,97,97,10,13,2,38,3,4,17,49,143,5,76,61,11,17,16,40,1,1,1,1,1,9,6,1,2,20,28,30,4,30,14,9,80,1,32,7,20,4,26,2,66,4,2,1,2,12,2,8,2,12,56,9,1023,33,19,1,3,46,1,6,88,40,84,85,35,28,314,3,7,61,79,34,55,2,23,1,10,1,2,77,6,70,40,1,4,93,1,48,3,5,17,2,8,1,2,1,7,27,13,23,4,4,4,7,1,2,1,1,2,18,13,44,32,1,2,2,8,103,1,6,366,4,4,5,2,6,15,6,30,10,1,3,1,2,4,20,8,1,86,3,3,3,2,4,76,3,436,4,1,10,28,17,39,1,1,896,21,12,24,1,177,29,8,3,36,14,2,6,9,1,17,5,2,113,48,2,8,15,155,34,465,23,1,222,1,22,14,23,4,11,3,18,12,17,2,5,3,7,4,2,1,1,1,2,2,9,185,22,11,1,1,14,3,3,2,11,2,4,2,1,4,17,4,213,7,62,1,210,126,38,1,391,2,6,67,44,21,19,16,98,14,4,1,1,2,197,8,31,1,48,1,10,9,36,24,54,65,1,5,5,12,224,13,41,28,7,339,50,5,9,2,3,3,1,1,1,2,7,1,35,11,25,1,2,12,23,4,14,6,2,3,20,36,7,2,6,10,22,1,2,6,2,18,14,15,10,24,11,3,78,2,1,10,236,293,25,43,5,14,4,32,29,4,1,6,6,9,1,202,173,1,12,1,18,1,55,56,3,9,4,3,12,4,2,32,3,22,7,45,15,4,5,4,3,2,1,7,7,12,4,1,2,8,166,1,10,9,15,1,1,11,8,26,67,1,288,39,3,31,4,25,6,7,4,22,5,3,1,71,19,3,5,19,4,27,21,4,22,5,1,52,1,7,70,27,277,1,4,1,80,1,141,10,4,6,3,11,5,6,15,1,1,1,6,1,2))
fragmentize$Outcome[sample(1:nrow(fragmentize), nrow(fragmentize), FALSE)] <- rep(OutcomeVal, c(21775,3034,126,10,10277,21))
fragmentize$year[sample(1:nrow(fragmentize), nrow(fragmentize), FALSE)] <- rep(yearVal, c(11,2709,8476,11308,12739))
fragmentize$month[sample(1:nrow(fragmentize), nrow(fragmentize), FALSE)] <- rep(monthVal, c(2536, 2535, 2780, 2616, 2902, 3190, 3274, 3553, 3623, 3515, 2339, 2380))
fragmentize$code[sample(1:nrow(fragmentize), nrow(fragmentize), FALSE)] <- rep(codeVal, c(7610,24718,2915))
fragmentize$name[sample(1:nrow(fragmentize), nrow(fragmentize), FALSE)] <- rep(nameVal, c(218, 917, 1736, 555, 42, 76, 79, 267, 1988, 116, 194, 161, 12, 353, 261, 285, 382, 6050, 2053, 45, 1, 276, 4598, 7543, 337, 14, 1, 591, 1020, 657, 139, 3995, 281))
fragmentize$aid[sample(1:nrow(fragmentize), nrow(fragmentize), FALSE)] <- rep(aidVal, c(310, 82, 26, 6, 493, 175, 31, 4, 19, 160, 263, 248, 68, 20, 666, 303, 6, 125, 190, 8, 108, 93, 206, 11, 278, 2, 273, 3, 3, 4, 285, 1, 555, 44, 93, 21, 94, 5309, 46, 25, 7, 249, 67, 20, 3, 15, 15, 16, 5, 12, 5, 17, 67, 44, 332, 57, 358, 25, 204, 8, 612, 108, 47, 273, 16, 20, 516, 16, 344, 33, 153, 4, 43, 73, 14, 37, 88, 7, 26, 23, 116, 33, 28, 66, 24, 21, 18, 32, 96, 6, 16, 3, 176, 121, 109, 177, 8, 30, 156, 117, 24, 90, 199, 236, 24, 25, 34, 20, 50, 14, 19, 30, 8, 20, 3, 10, 55, 24, 26, 17, 17, 29, 147, 148, 6, 2031, 65, 1135, 632, 91, 544, 1073, 11, 617, 15, 18, 2, 226, 182, 89, 513, 23, 149, 6, 398, 148, 13, 129, 323, 26, 4, 4, 155, 63, 32, 64, 23, 2, 120, 1, 2, 1, 10, 25, 120, 993, 5, 335, 40, 539, 413, 116, 78, 15, 38, 2, 15, 34, 271, 3, 604, 375, 52, 47, 459, 457, 177, 28, 293, 49, 266, 96, 1836, 18, 127, 18, 246, 5, 8, 4, 11, 102, 24, 21, 63, 57, 25, 22, 2, 1, 1, 51, 74, 56, 154, 97, 21, 31, 4, 3, 1, 11))
fragmentize$Percentage[sample(1:nrow(fragmentize), nrow(fragmentize), FALSE)] <- rep(percentVal, c(116,84,64,108,25,36,104,6,17,21,129,70,32,34,18,234,37,14,102,4,5,24,57,19,130,7,22,81,123,9,1,6,4,7,103,22,30,2,17,18,44,176,3,12,71,7,20,52,11,10,7,81,7,6,5,3,45,15,9,116,10,78,5,39,36,7,34,7,44,5,14,58,7,23,386,13,46,1,79,12,18,4,15,6,1009,6,47,55,36,18,15,2,1,2,297,39,6,18,50,33,18,37,632,5,26,28,31,187,15,26,9,1,38,27,9,25,2,4,486,49,11,104,130,6,3,9,6,3,16,5,9,392,96,9,4,7,1,39,35,8,3,12,14,94,309,59,3,15,1,18,85,277,13,6,3,4,68,204,2,7,59,5,19,189,1,440,2,44,109,151,2,45,6,3,131,18,23,17,334,1,103,27,18,2,27,2,75,98,7,19,2,72,1,10,82,17,256,20,17,1,92,2,1,13,71,3,21,13,86,1,16,1,83,103,226,1,26,20,1,63,1,7,9,10,51,2,155,70,11,4,10,2,49,152,9,2,42,9,21,53,33,11,1,101,8,49,1,3,1,2,4,141,9,17,163,44,7,36,121,17,32,6,4,2,26,9,4,72,1,22,70,3,1,4,1,74,24,41,39,30,8,1,27,4,30,1,73,4,21,10,9,8,117,9,65,3,6,24,14,2,4,89,6,2,20,49,40,266,3,4,1,23,1,28,14,17,22,28,20,1,6,58,25,10,4,6,37,168,11,8,3,58,4,99,62,20,22,15,2,20,1,32,3,3,9,4,19,1,7,33,1,18,4,1,13,13,11,38,27,1,20,176,18,10,1,1,15,20,3,21,13,4,49,6,10,22,2,1,12,10,78,7,5,4,13,7,22,5,8,10,72,2,17,1,9,1,13,14,129,21,2,12,1,13,51,12,138,3,3,9,9,6,17,11,13,4,1,6,15,11,1,102,15,2,1,4,5,48,7,12,4,2,2,6,20,9,2,28,25,1,1,12,16,30,12,10,4,3,2,88,13,1,216,13,9,1,3,11,12,9,7,1,1,48,6,2,16,2,1,4,2,12,11,16,11,1,7,67,3,2,1,6,323,23,1,25,5,2,5,57,10,50,5,97,4,4,19,5,2,1,5,5,4,7,4,2,6,4,1,1,2,1,5,2,13,13,1,7,1,6,3,43,3,1,47,8,5,1,179,97,5,10,40,1,5,4,3,11,1,4,2,8,1,1,3,7,5,1,54,1,7,2,3,43,1,1,3,3,1,191,27,1,3,1,19,51,3,3,3,33,4,41,2,15,2,2,6,114,1,1,1,2,2,13,1,1,3,1,1,3,3,1,1,107,2,7,2,10,2,1,1,30,1,42,1,1,67,1,1,11,3,48,32,1,4,2,58,1,1,149,2,17,1,40,97,1,2,6,1,20,1,1,28,127,30,1,1,14,13,5,84,5,2,4,1,86,4,13,15,18,18,11,8,3,1,12,49,92,5,3,2,100,12,81,1,6,64,7,15,6,20,13,82,46,19,26,7,67,2,6,9,1,29,3,1,10,2,64,5,18,107,203,9,2,2,101,52,2,6,1,70,7,10,86,1,1,1,7,1,15,1,1,51,4,44,5,15,2,50,79,27,12,64,1,17,32,54,44,114,1,34,4,12,96,56,1,31,5,1,3,37,4,130,5,4,3,1,26,2,20,41,9,1,37,4,18,1,2,157,30,5,5,27,6,30,1,20,42,1,51,34,7,12,16,1,57,1,1,40,29,1,37,36,32,2,14,43,3,4,10,2,2,17,36,27,10,1,53,101,111,1,10,56,2,1,43,152,8,103,2,29,2,44,2,18,44,87,49,5,43,16,13,1,53,26,30,6,17,7,2,24,36,4,41,2,17,1,24,1,7,5,8,14,1,38,45,14,38,22,10,2,11,8,13,3,28,1,19,1,18,14,15,2,26,2,15,27,1,3,22,28,49,10,2,1,20,22,77,1,2,4,122,1,1,44,1,14,15,1,70,4,4,25,54,10,34,13,17,2,2,23,30,13,1,2,10,15,1,14,30,23,1,1,21,15,12,1,13,2,1,6,26,3,12,1,62,10,15,3,21,34,14,3,10,2,35,18,6,1,90,44,1,1,172,3,7,79,13,37,2,13,23,8,2,10,59,1,12,1,107,6,11,9,25,4,1,2,26,14,18,8,322,1,19,1,6,162,1,9,19,2,9,5,3,12,50,4,16,9,5,34,14,10,2,1,46,40,15,3,13,55,20,93,8,1,2,25,8,7,58,14,17,3,1,7,2,5,3,4,1,131,3,1,2,7,18,45,6,3,12,8,11,18,5,13,7,35,7,1,8,4,5,15,49,6,1,1,80,11,2,5,1,5,19,20,179,22,1,10,1,9,48,111,3,21,1,3,17,20,2,2,2,7,3,6,1,6,8,12,5,5,1,129,1,1,2,10,6,8,16,8,2,2,9,20,1,74,5,42,5,1,1,1,2,14,4,12,9,47,12,38,1,1,3,8,34,1,5,1,4,4,21,2,1,1,14,95,23,14,2,1,90,7,7,32,8,6,1,2,19,12,1,2,7,30,4,1,10,1,2,7,1,7,3,16,1,9,4,3,5,1,76,3,17,8,1,6,70,3,1,11,3,7,27,1,2,40,2,3,7,6,8,3,1,49,14,56,1,17,2,5,5,70,5,13,3,10,2,3,10,1,2,4,5,94,1,3,2,1,5,2,6,4,4,5,6,12,1,16,68,1,4,11,4,4,1,9,1,6,3,9,5,4,50,3,1,12,4,1,5,2,24,35,2,3,2,60,1,3,2,6,3,2,2,9,7,1,11,12,5,4,3,56,7,1,5,1,1,4,1,18,5,1,1,9,159,1,11,2,8,2,3,1,1,9,3,7,2,68,2,5,43,2,4,38,1,5,2,26,1,4,2,1,5,10,1,4,2,1,8,2,6,86,2,2,1,10,3,1,4,10,3,35,17,3,4,14,1,1,17,4,6,39,3,13,50,6,3,3,38,4,1,3,2,26,1,5,28,2,5,1,1,21,1,13,6,2,4,6,13,3,5,9,3,2,1,32,1,8,20,2,2,8,2,2,30,1,9,2,4,4,4,1,13,1,45,2,5,3,1,1,23,12,1,2,1,1,1,26,1,14,1,1,6,1,10,1,10,7,2,2,1,1,1,4,11,4,2,2,1,3,2,19,8,5,4,3,1,1,52,4,1,1,2,3,4,3,1,23,23,2,2,2,1,1,9,6,2,26,1,1,2,2,1,1,1,1,10,4,7,27,4,2,1,1,24,3,3,2,1,3,5,2,4,14,1,1,4,3,2,1,18,1,1,2,4,2,1,5,2,1,5,1,4,1,1,5,1,5,1,1,3,2,1,5,1,3,1,1,1,3,3,2,1,5,1,4,5,4,3,2,1,1,1,4,6,2,1,1,1,9,1,2,1,3,1,1,1,5,5,8,1,1,1,2,6,2,2,4,1,3,2,2,1,9,1,2,4,1,3,25))
rm(ser_noVal,OutcomeVal,yearVal,monthVal,codeVal,nameVal,aidVal,percentVal)
最佳答案
据我了解这个问题，OP希望在他的生产数据集中找到类似的记录，这些记录在 ser_no 中具有相同的值。 , Outcome , year , month , code , 和 name和 Percentage 中的值大致相等(在给定的公差内)。 OP 已请求附加 aid除 aid 之外的任何匹配行的值实际行的值。
可能的方法是使用 data.table 的非等效自联接:
library(data.table)
eps <- 0.01
system.time(
setDT(fragmentize, key = c("ser_no", "Outcome", "year", "month", "code", "name", "aid"))[
, Percentage := as.numeric(Percentage)][
, similar := fragmentize[
.(ser_no = ser_no, Outcome = Outcome, year = year, month = month,
code = code, name = name, aid = aid,
lb = Percentage * (1 - eps), ub = Percentage * (1 + eps)),
on = .(ser_no, Outcome, year, month, code, name,
Percentage >= lb, Percentage <= ub),
by = .EACHI, toString(setdiff(unique(x.aid), i.aid))][, V1]]
)
User System Elapsed
0.61 0.00 0.64
fragmentize获得了一个额外的专栏
similar :
str(fragmentize)
Classes ‘data.table’ and 'data.frame': 35243 obs. of 30 variables:
$ col9 : Factor w/ 1 level "A9": 1 1 1 1 1 1 1 1 1 1 ...
$ col10 : Factor w/ 1 level "A10": 1 1 1 1 1 1 1 1 1 1 ...
$ col11 : Factor w/ 1 level "A11": 1 1 1 1 1 1 1 1 1 1 ...
$ col12 : Factor w/ 1 level "A12": 1 1 1 1 1 1 1 1 1 1 ...
$ col13 : Factor w/ 1 level "A13": 1 1 1 1 1 1 1 1 1 1 ...
$ col4 : Factor w/ 1 level "A14": 1 1 1 1 1 1 1 1 1 1 ...
$ col15 : Factor w/ 1 level "A15": 1 1 1 1 1 1 1 1 1 1 ...
$ col16 : Factor w/ 1 level "A16": 1 1 1 1 1 1 1 1 1 1 ...
$ col7 : Factor w/ 1 level "A17": 1 1 1 1 1 1 1 1 1 1 ...
$ col8 : Factor w/ 1 level "A18": 1 1 1 1 1 1 1 1 1 1 ...
$ col19 : Factor w/ 1 level "A19": 1 1 1 1 1 1 1 1 1 1 ...
$ col20 : Factor w/ 1 level "A20": 1 1 1 1 1 1 1 1 1 1 ...
$ col21 : Factor w/ 1 level "A21": 1 1 1 1 1 1 1 1 1 1 ...
$ col22 : Factor w/ 1 level "A22": 1 1 1 1 1 1 1 1 1 1 ...
$ col23 : Factor w/ 1 level "A23": 1 1 1 1 1 1 1 1 1 1 ...
$ col24 : Factor w/ 1 level "A24": 1 1 1 1 1 1 1 1 1 1 ...
$ col25 : Factor w/ 1 level "A25": 1 1 1 1 1 1 1 1 1 1 ...
$ col26 : Factor w/ 1 level "A26": 1 1 1 1 1 1 1 1 1 1 ...
$ col27 : Factor w/ 1 level "A27": 1 1 1 1 1 1 1 1 1 1 ...
$ col28 : Factor w/ 1 level "A28": 1 1 1 1 1 1 1 1 1 1 ...
$ col29 : Factor w/ 1 level "A29": 1 1 1 1 1 1 1 1 1 1 ...
$ ser_no : int 1 1 1 1 1 1 2 2 2 2 ...
$ Outcome : chr "Aggressive" "Aggressive" "Aggressive" "Aggressive" ...
$ year : int 2015 2015 2016 2017 2015 2016 2014 2014 2015 2015 ...
$ month : int 11 11 5 5 2 10 5 10 2 5 ...
$ code : chr "A" "B" "B" "B" ...
$ name : chr "wt" "Ds" "UF" "Of" ...
$ aid : chr "UuaR" "uwIL" "9WAx" "h5eH" ...
$ Percentage: num 255 1295 168 549 85 ...
$ similar : chr "" "" "" "" ...
- attr(*, ".internal.selfref")=<externalptr>
- attr(*, "sorted")= chr "ser_no" "Outcome" "year" "month" ...
similar绝大多数行是空的，我们只显示非空行和相关列。设置键已经排序
fragmentize这使得验证结果更容易:
fragmentize[similar != "", .(ser_no, Outcome, year, month, code, name, aid,
Percentage, similar)]
ser_no Outcome year month code name aid Percentage similar
1: 13 Aggressive 2016 3 B gZ 21So 525 59PL
2: 13 Aggressive 2016 3 B gZ 59PL 529 21So
3: 15 Aggressive 2017 1 B nt C2i4 1311 uwIL
4: 15 Aggressive 2017 1 B nt uwIL 1323 C2i4
5: 15 Aggressive 2017 6 B Wj hMo4 308 mrDx
6: 15 Aggressive 2017 6 B Wj mrDx 308 hMo4
7: 48 Aggressive 2016 11 B gZ 4LVK 1216 FtSG
8: 48 Aggressive 2016 11 B gZ FtSG 1205 4LVK
9: 48 Aggressive 2017 5 B nt 59PL 85 f1Fh
10: 48 Aggressive 2017 5 B nt f1Fh 85 59PL
11: 48 Aggressive 2017 7 B Wj lVpw 1021 mz3h
12: 48 Aggressive 2017 7 B Wj mz3h 1021 lVpw
13: 252 Aggressive 2016 6 B gZ bkk6 75 spPd
14: 252 Aggressive 2016 6 B gZ spPd 75 bkk6
15: 255 Aggressive 2015 9 B Wj 59PL 29 dceG
16: 255 Aggressive 2015 9 B Wj dceG 29 59PL
17: 265 Aggressive 2017 9 B FB FodL 756 twvT
18: 265 Aggressive 2017 9 B FB twvT 759 FodL
19: 276 Aggressive 2016 11 A gZ 59PL 949 M6sO
20: 276 Aggressive 2016 11 A gZ M6sO 944 59PL
21: 288 Aggressive 2017 6 B gZ 21So 878 Y9gk
22: 288 Aggressive 2017 6 B gZ Y9gk 882 21So
23: 340 Aggressive 2015 7 B nt FtSG 763 kBpV
24: 340 Aggressive 2015 7 B nt kBpV 767 FtSG
25: 340 Aggressive 2016 4 B Ds 21So 731 bkk6
26: 340 Aggressive 2016 4 B Ds bkk6 727 21So
27: 340 Aggressive 2017 10 B nt B4fM 673 M6sO
28: 340 Aggressive 2017 10 B nt M6sO 678 B4fM
29: 340 Neutral 2017 8 A Oa 59PL 872 Vyl1
30: 340 Neutral 2017 8 A Oa Vyl1 872 59PL
31: 340 Neutral 2017 9 B FB 59PL 723 75iU
32: 340 Neutral 2017 9 B FB 75iU 723 59PL
33: 370 Aggressive 2015 6 A gZ 3Xre 132 DWZh
34: 370 Aggressive 2015 6 A gZ DWZh 132 3Xre
35: 370 Aggressive 2016 5 B gZ 1reu 1162 jSL1
36: 370 Aggressive 2016 5 B gZ jSL1 1158 1reu
37: 370 Aggressive 2017 3 B Wj 21So 872 spPd
38: 370 Aggressive 2017 3 B Wj spPd 867 21So
39: 370 Aggressive 2017 4 B FB 0Xza 1547 NXGE
40: 370 Aggressive 2017 4 B FB NXGE 1535 0Xza
41: 379 Aggressive 2015 2 B FB mJAy 133 zQZw
42: 379 Aggressive 2015 2 B FB zQZw 133 mJAy
43: 379 Aggressive 2015 7 B gZ FtSG 201 spPd
44: 379 Aggressive 2015 7 B gZ spPd 201 FtSG
45: 379 Aggressive 2016 8 B Wj 75iU 95 HzTb
46: 379 Aggressive 2016 8 B Wj HzTb 95 75iU
47: 379 Aggressive 2016 9 B gZ F9c3 244 LpB1
48: 379 Aggressive 2016 9 B gZ LpB1 246 F9c3
49: 379 Aggressive 2016 12 B nt 4DGD 507 zYVN
50: 379 Aggressive 2016 12 B nt zYVN 504 4DGD
51: 379 Aggressive 2017 1 B Wj LpB1 85 gzvo
52: 379 Aggressive 2017 1 B Wj gzvo 85 LpB1
53: 379 Aggressive 2017 9 B FB Xo8U 60 hSJN
54: 379 Aggressive 2017 9 B FB hSJN 60 Xo8U
55: 379 Aggressive 2017 9 B Wj 75iU 12 Puss
56: 379 Aggressive 2017 9 B Wj Puss 12 75iU
57: 379 Aggressive 2017 11 B Wj 1reu 817 N7dg, SCPN
58: 379 Aggressive 2017 11 B Wj N7dg 809 SCPN, 1reu
59: 379 Aggressive 2017 11 B Wj SCPN 809 N7dg, 1reu
60: 379 Aggressive 2017 12 B gZ B4fM 17 hMo4
61: 379 Aggressive 2017 12 B gZ hMo4 17 B4fM
62: 379 Neutral 2016 9 B Wj L58K 103 hMo4
63: 379 Neutral 2016 9 B Wj hMo4 103 L58K
64: 379 Neutral 2017 6 B gZ 21So 1016 I46B
65: 379 Neutral 2017 6 B gZ I46B 1012 21So
66: 379 Neutral 2017 9 B Wj 21So 1244 LpB1
67: 379 Neutral 2017 9 B Wj LpB1 1240 21So
68: 379 Neutral 2017 11 B gZ 3Vpo 483 spPd
69: 379 Neutral 2017 11 B gZ spPd 483 3Vpo
70: 393 Aggressive 2015 2 B FB 8SzN 323 cKuN
71: 393 Aggressive 2015 2 B FB cKuN 322 8SzN
72: 458 Aggressive 2015 1 B FB 75iU 972 GWLn
73: 458 Aggressive 2015 1 B FB GWLn 977 75iU
74: 458 Neutral 2017 1 B Wj 21So 483 59PL
75: 458 Neutral 2017 1 B Wj 59PL 483 21So
76: 458 Neutral 2017 6 B iN hMo4 802 spPd
77: 458 Neutral 2017 6 B iN spPd 807 hMo4
78: 526 Aggressive 2017 3 B Wj 4DGD 992 59PL
79: 526 Aggressive 2017 3 B Wj 59PL 991 4DGD
80: 552 Aggressive 2015 7 B Wj 9oyt 95 OWxi
81: 552 Aggressive 2015 7 B Wj OWxi 95 9oyt
82: 552 Aggressive 2017 10 B Ds 59PL 890 9WAx
83: 552 Aggressive 2017 10 B Ds 9WAx 894 59PL
84: 561 Aggressive 2015 1 B gZ f1Fh 949 spPd
85: 561 Aggressive 2015 1 B gZ spPd 952 f1Fh
86: 561 Aggressive 2016 4 B Wj I46B 776 hpRD
87: 561 Aggressive 2016 4 B Wj hpRD 771 I46B
88: 561 Aggressive 2016 8 B gZ eKpA 809 rp75
89: 561 Aggressive 2016 8 B gZ rp75 807 eKpA
90: 561 Aggressive 2016 9 B Wj 4LVK 882 CF4V, M6sO
91: 561 Aggressive 2016 9 B Wj CF4V 878 4LVK, M6sO
92: 561 Aggressive 2016 9 B Wj M6sO 882 CF4V, 4LVK
93: 651 Aggressive 2017 2 B Ds 59PL 179 SCPN
94: 651 Aggressive 2017 2 B Ds SCPN 179 59PL
95: 735 Aggressive 2017 8 B iN M6sO 760 tNgx
96: 735 Aggressive 2017 8 B iN tNgx 758 M6sO
97: 817 Neutral 2016 6 B gZ I46B 197 SCPN
98: 817 Neutral 2016 6 B gZ SCPN 198 I46B
ser_no Outcome year month code name aid Percentage similar
59PL类似，而第 2 行指向
21So .也有两种情况，其中已识别出 3 个相似的行。
setDT()强制fragmentize到 data.table对象，从而在某些列上设置键。这不是连接所必需的，而是排序 fragmentize这有助于验证结果的正确性。此外，它可能会加快加入速度。 Percentage被强制输入 double以防止连接期间的类型转换。在测试数据框 II 中，OP 创建了 Percentage如 integer type 而用于范围连接的下限和上限的类型为 double .请注意 Percentage通过引用或就地更新，即不复制整个数据对象以节省时间和内存。 similar是使用聚合非等自连接的结果创建的。 fragmentize与自身的选定列正确连接。这些被指定为 list使用缩写 .() .另外，lb和 ub被创建为与 Percentage 近似匹配的下限和上限使用相对公差 eps . on子句指定在连接中应该完全匹配的列以及非对等连接条件。 AFAIK，不可能在单个列上指定反联接。因此，条件aid != aid必须以另一种方式对待。 by = .EACHI参数请求为符合连接条件的每组行同时连接和聚合。这避免了创建可能包含所有多个匹配项的大型中间表。 toString(setdiff(unique(x.aid), i.aid)) 给出.在多个匹配的情况下，每个 aid值应该只出现一次。然后，setdiff()删除 aid实现 OP 要求的结果中实际行的值 aid != aid .最后，结果被折叠为单个字符串。 [, V1]表达式仅提取具有聚合值的列，该列最终成为新列 similar . 关于r - 在数据框中查找子集并写入结果，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/47776525/
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