我想在 R 中优化以下代码。这个循环需要很长时间才能运行。 我想知道是否有人可以帮助我优化这段代码，因为它需要很长时间才能运行？ 谢谢大家!
SIN_FM5:是一个大约有 300.000 行和 7 列的数据库。
# Make Combination
SIN_FM5$Combination=(SIN_FM5$SINISTRE)
Count.Comb=data.frame(table(SIN_FM5$Combination))
# Calculate number of combinations
Total.Comb=nrow(Count.Comb)
# Loop through all combinations and calculate statistics
Statistics=array(0,dim=c(Total.Comb,5))
for (i in 1:Total.Comb) {
Subset=subset(SIN_FM5, SIN_FM5$Combination==Count.Comb[i,1])
Statistics[(i),]=c(Count.Comb[i,1],mean(Subset$MONTANT_PAIEMENT),
median(Subset$MONTANT_PAIEMENT),min(Subset$MONTANT_PAIEMENT),
max(Subset$MONTANT_PAIEMENT))
}
resultatN=cbind(Count.Comb,Statistics)
dput(head(SIN_FM5))
控制台~/
"TRSP-5194", "TRSP-5197", "TRSP-5201", "TRSP-5202", "TRSP-5204",
"TRSP-5205", "TRSP-5207", "TRSP-5212", "TRSP-5214", "TRSP-5215",
"TRSP-5218", "TRSP-5222", "TRSP-5230", "TRSP-5238", "TRSP-5243",
"TRSP-5247", "TRSP-5248", "TRSP-5253", "TRSP-5254", "TRSP-5255",
"TRSP-5257", "TRSP-5259", "TRSP-5262", "TRSP-5263", "TRSP-5266",
"TRSP-5267", "TRSP-5268", "TRSP-5270", "TRSP-5271", "TRSP-5274",
"TRSP-5277", "TRSP-5279", "TRSP-5281", "TRSP-5283", "TRSP-5288",
"TRSP-5289", "TRSP-5293", "TRSP-5296", "TRSP-5299", "TRSP-5301",
"TRSP-5303", "TRSP-5304", "TRSP-5306", "TRSP-5308", "TRSP-5310",
"TRSP-5311", "TRSP-5312", "TRSP-5313", "TRSP-5335", "TRSP-5343",
"TRSP-5348", "TRSP-5352", "TRSP-5357", "TRSP-5363", "TRSP-5366",
"TRSP-5372", "TRSP-5373", "TRSP-5384", "TRSP-5386", "TRSP-5388",
"TRSP-5391", "TRSP-5392", "TRSP-5428", "TRSP-5436", "VANBILSENYolanda",
"VanLierop", "VirgaJesseZiekenhuis", "WanetGeorges", "WANETThierry",
"WILLEMSMichel", "WUESTENBERGHSAlain", "X01", "X01CR", "X02CR",
"X03CR", "X04CR", "X05CR", "X06CR", "X07CR", "Y01", "Y01CR",
"Y02", "ZOPO-5344"), class = "factor"), Combination = c(73010009L,
73010009L, 73010014L, 73010014L, 73010014L, 73010014L)), .Names = c("SINISTRE",
"victimeid", "Nature.Injury", "LocationL", "DurationITT", "Code.Nace2008",
"POLICE", "TYPE_DE_PAIEMENT", "MODE_DE_PAIEMENT", "CODE_NATURE_DE_PAIEMENT",
"MONTANT_PAIEMENT", "BENEFICIARE", "Combination"), row.names = c(NA,
6L), class = "data.frame")
最佳答案
SIN_FM5 <- data.frame(Combination = sample(1:10, 100, repl=TRUE), MONTANT_PAIEMENT=rnorm(100))
bySIN <- by(SIN_FM5, list(SIN_FM5[['Combination']]), FUN= function(subd) {
data.frame(counts = nrow(subd),
meanMont = mean(subd$MONTANT_PAIEMENT),
medMont = median(subd$MONTANT_PAIEMENT),
minMont = min(subd$MONTANT_PAIEMENT),
maxMont = max(subd$MONTANT_PAIEMENT) ) } )
> sapply(bySIN, as.vector)
1 2 3 4 5 6 7 8
counts 11 7 14 9 11 16 10 8
meanMont 0.3499753 -0.188964 0.1740817 -0.1505312 -0.6335896 -0.1434513 -0.2148642 -0.2978299
medMont 0.4381513 -0.06965143 0.05762425 -0.2247187 -0.7682626 -0.2288606 -0.1467318 -0.3315809
minMont -1.122418 -0.9638749 -1.634259 -1.336908 -2.068224 -1.974108 -2.15415 -1.295045
maxMont 1.50662 0.1653189 1.215114 1.243138 0.4643551 1.29805 1.154282 0.7097163
9 10
counts 4 10
meanMont 0.2575141 0.146613
medMont 0.07692888 0.1047567
minMont -0.534418 -1.006938
maxMont 1.410617 1.4973
这是一个 data.table 解决方案。可能会快得多:
require(data.table)
dtb <- data.table(SIN_FM5)
setkey(dtb, "Combination")
dtb[ , list(counts=length(MONTANT_PAIEMENT),
meanMont = mean(MONTANT_PAIEMENT),
medMont = median(MONTANT_PAIEMENT),
minMont = min(MONTANT_PAIEMENT),
maxMont = max(MONTANT_PAIEMENT) ), by="Combination"]
#-----------------------------------------------
Combination counts meanMont medMont minMont maxMont
[1,] 1 11 0.3499753 0.43815133 -1.1224179 1.5066198
[2,] 2 7 -0.1889640 -0.06965143 -0.9638749 0.1653189
[3,] 3 14 0.1740817 0.05762425 -1.6342586 1.2151136
[4,] 4 9 -0.1505312 -0.22471868 -1.3369085 1.2431378
[5,] 5 11 -0.6335896 -0.76826264 -2.0682244 0.4643551
[6,] 6 16 -0.1434513 -0.22886060 -1.9741083 1.2980502
[7,] 7 10 -0.2148642 -0.14673175 -2.1541500 1.1542819
[8,] 8 8 -0.2978299 -0.33158086 -1.2950452 0.7097163
[9,] 9 4 0.2575141 0.07692888 -0.5344180 1.4106168
[10,] 10 10 0.1466130 0.10475674 -1.0069382 1.4972998
关于r - 优化 R 中的循环，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/11286316/