根据data.frame（大型数据集）中的条件循环进行累积添加

时间：2017-03-06 16:00:40

标签： r for-loop

我在优化循环时遇到问题，根据data.frame中的条件累积添加数字。下面是输入data.frame，其中包含接近一百万行的较大数据集中的几行：

inputData <- structure(list(SNP_pos = structure(c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 1L, 2L), .Label = c("SNP_1", "SNP_2", "SNP_3", "SNP_4", "SNP_5", "SNP_6", "SNP_7", "SNP_8", "SNP_9", "SNP_10", "SNP_11", "SNP_12", "SNP_13", "SNP_14"), class = "factor"), sample_id = c(8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L), allele1 = structure(c(2L, 2L, 2L, 1L, 2L, 2L, 4L, 4L, 2L, 3L, 3L, 1L, 4L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 4L, 4L, 2L, 3L, 3L, 1L, 4L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 4L, 4L, 2L, 3L, 3L, 1L, 4L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 4L, 4L, 2L, 3L, 3L, 1L, 4L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 4L, 4L, 2L, 3L, 3L, 1L, 4L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 4L, 4L, 2L, 3L, 3L, 1L, 4L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 4L, 4L, 2L, 3L, 3L, 1L, 4L, 1L, 2L, 2L), .Label = c("A", "G", "T", "C"), class = "factor"), sample_id_x = c(8739L, 8739L, 8739L, 8739L, 8739L, 8739L, 8739L, 8739L, 8739L, 8739L, 8739L, 8739L, 8739L, 8739L, 8832L, 8832L, 8832L, 8832L, 8832L, 8832L, 8832L, 8832L, 8832L, 8832L, 8832L, 8832L, 8832L, 8832L, 8888L, 8888L, 8888L, 8888L, 8888L, 8888L, 8888L, 8888L, 8888L, 8888L, 8888L, 8888L, 8888L, 8888L, 9056L, 9056L, 9056L, 9056L, 9056L, 9056L, 9056L, 9056L, 9056L, 9056L, 9056L, 9056L, 9056L, 9056L, 9058L, 9058L, 9058L, 9058L, 9058L, 9058L, 9058L, 9058L, 9058L, 9058L, 9058L, 9058L, 9058L, 9058L, 9062L, 9062L, 9062L, 9062L, 9062L, 9062L, 9062L, 9062L, 9062L, 9062L, 9062L, 9062L, 9062L, 9062L, 9072L, 9072L, 9072L, 9072L, 9072L, 9072L, 9072L, 9072L, 9072L, 9072L, 9072L, 9072L, 9072L, 9072L, 9152L, 9152L), allele2 = structure(c(2L, 2L, 1L, 1L, 2L, 2L, 1L, 4L, 2L, 3L, 3L, 2L, 3L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 4L, 4L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 4L, 4L, 2L, 3L, 3L, 2L, 3L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 4L, 4L, 2L, 3L, 3L, 2L, 4L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 4L, 4L, 2L, 3L, 3L, 2L, 3L, 1L, 1L, 2L, 2L, 1L, 3L, 2L, 4L, 4L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 4L, 4L, 2L, 3L, 3L, 2L, 3L, 1L, 1L, 2L), .Label = c("A", "G", "T", "C"), class = "factor"), snp_diff = c(0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0), IBS = c(1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1), IBD = c(1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1)), .Names = c("SNP_pos", "sample_id", "allele1", "sample_id_x", "allele2", "snp_diff", "IBS", "IBD"), row.names = c(NA, 100L), class = "data.frame")

以下是预期的输出数据。框架：

outputData <- structure(list(SNP_pos = structure(c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 1L, 2L), .Label = c("SNP_1", "SNP_2", "SNP_3", "SNP_4", "SNP_5", "SNP_6", "SNP_7", "SNP_8", "SNP_9", "SNP_10", "SNP_11", "SNP_12", "SNP_13", "SNP_14"), class = "factor"), sample_id = c(8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L, 8685L), allele1 = structure(c(2L, 2L, 2L, 1L, 2L, 2L, 4L, 4L, 2L, 3L, 3L, 1L, 4L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 4L, 4L, 2L, 3L, 3L, 1L, 4L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 4L, 4L, 2L, 3L, 3L, 1L, 4L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 4L, 4L, 2L, 3L, 3L, 1L, 4L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 4L, 4L, 2L, 3L, 3L, 1L, 4L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 4L, 4L, 2L, 3L, 3L, 1L, 4L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 4L, 4L, 2L, 3L, 3L, 1L, 4L, 1L, 2L, 2L), .Label = c("A", "G", "T", "C"), class = "factor"), sample_id_x = c(8739L, 8739L, 8739L, 8739L, 8739L, 8739L, 8739L, 8739L, 8739L, 8739L, 8739L, 8739L, 8739L, 8739L, 8832L, 8832L, 8832L, 8832L, 8832L, 8832L, 8832L, 8832L, 8832L, 8832L, 8832L, 8832L, 8832L, 8832L, 8888L, 8888L, 8888L, 8888L, 8888L, 8888L, 8888L, 8888L, 8888L, 8888L, 8888L, 8888L, 8888L, 8888L, 9056L, 9056L, 9056L, 9056L, 9056L, 9056L, 9056L, 9056L, 9056L, 9056L, 9056L, 9056L, 9056L, 9056L, 9058L, 9058L, 9058L, 9058L, 9058L, 9058L, 9058L, 9058L, 9058L, 9058L, 9058L, 9058L, 9058L, 9058L, 9062L, 9062L, 9062L, 9062L, 9062L, 9062L, 9062L, 9062L, 9062L, 9062L, 9062L, 9062L, 9062L, 9062L, 9072L, 9072L, 9072L, 9072L, 9072L, 9072L, 9072L, 9072L, 9072L, 9072L, 9072L, 9072L, 9072L, 9072L, 9152L, 9152L), allele2 = structure(c(2L, 2L, 1L, 1L, 2L, 2L, 1L, 4L, 2L, 3L, 3L, 2L, 3L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 4L, 4L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 4L, 4L, 2L, 3L, 3L, 2L, 3L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 4L, 4L, 2L, 3L, 3L, 2L, 4L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 4L, 4L, 2L, 3L, 3L, 2L, 3L, 1L, 1L, 2L, 2L, 1L, 3L, 2L, 4L, 4L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 4L, 4L, 2L, 3L, 3L, 2L, 3L, 1L, 1L, 2L), .Label = c("A", "G", "T", "C"), class = "factor"), snp_diff = c(0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0), IBS = c(1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1), IBD = c(NA, NA, 0, 1, 2, 3, 0, 1, 2, 3, 4, 0, 0, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 0, 0, 0, 1, 1, 2, 0, 1, 2, 3, 4, 5, 6, 7, 8, 0, 0, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 0, 1, 2, 1, 2, 0, 1, 2, 3, 4, 5, 6, 7, 8, 0, 0, 1, 0, 1, 2, 3, 0, 1, 2, 3, 4, 5, 0, 0, 0, 1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 0, 0, 1, 0, 1)), .Names = c("SNP_pos", "sample_id", "allele1", "sample_id_x", "allele2", "snp_diff", "IBS", "IBD"), row.names = c(NA, 100L), class = "data.frame")

以下是我用来生成输出文件的代码：

for (i in 1:nrow(inputData)) { inputData$IBD<-ifelse(inputData$IBD==0,inputData$IBD<-inputData$IBD,ifelse (inputData$allele1==inputData$allele2&inputData$sample_id_x!=shift(inputData$sample_id_x),inputData$IBD<-inputData$IBD,ifelse (inputData$allele1==inputData$allele2&inputData$sample_id_x==shift(inputData$sample_id_x),inputData$IBD<-shift(inputData$IBD)+1,inputData$IBD<-inputData$IBD))) }

  1. 第一个条件比较列IBD == 0，如果是，则将IBD保留为0。
  2. 第二个条件然后检查列allele1 = = allele2以及sample_id_x是否不等于先前的sample_id_x（它上面的那个）。如果满足此条件，则IBD应保持不变。
  3. 最后，如果列allele1 == allele2和sample_id_x ==前一个sample_id_x（它上面的那个），则将IBD添加到之前的IBD（上面的那个），否则保持原样。 上面的代码可以工作，但运行了很长时间，我需要一些更有资源的东西，我的for循环。

    4. 请帮助优化代码或提出更好的代码......

1 个答案:

答案 0 :(得分：0)

#First, create a vector with boolean where sub-conditions of the third condition are met    
temp = as.numeric(c(FALSE, sapply(2:nrow(inputData), function(i)
    inputData$sample_id_x[i] == inputData$sample_id_x[i-1])) & #1st sub-condition
        (inputData$allele1 == inputData$allele2) & #2nd sub-condition
        inputData$IBD != 0) #3rd sub-condition

#If the value in 'IBD' is zero, then temp2 = 0, otherwise 1
temp2 = as.numeric(temp + inputData$IBD != 0)

ave(temp2, 
#Everytime 'temp' is zero, it starts a new group
cumsum(sapply(1:length(temp), function(x) ifelse(temp[x]==0, 1, 0) )),
FUN = cumsum)
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