Question

以下代码生成所需的输出。然而，缺乏矢量化意味着它运行得非常慢。我怎样才能加快速度？

我将dput结果放在部分指示性数据中。

输入dput s

StandRef输入

structure(list(id = structure(c(43L, 50L, 17L, 45L, 9L, 5L, 49L, 
33L, 48L, 39L, 71L, 64L, 44L, 47L, 58L, 24L, 15L, 37L, 14L, 11L, 
26L, 57L, 4L, 30L, 72L, 21L, 23L, 60L, 38L, 59L, 29L, 19L, 6L, 
46L, 36L, 3L, 63L, 55L, 51L, 35L, 10L, 7L, 16L, 73L, 42L, 52L, 
41L, 27L, 25L, 61L, 20L, 70L, 53L, 18L, 31L, 22L, 1L, 8L, 2L, 
40L, 65L, 67L, 28L, 56L, 13L, 32L, 54L, 66L, 68L, 34L, 12L, 69L, 
62L), .Label = c("ID 1009445", "ID 120763", "ID 133883", "ID 136398", 
"ID 171850", "ID 192595", "ID 197597", "ID 216406", "ID 21888", 
"ID 230940", "ID 23777", "ID 282791", "ID 306348", "ID 309745", 
"ID 326928", "ID 344897", "ID 34974", "ID 350157", "ID 391831", 
"ID 402479", "ID 43010", "ID 484078", "ID 484697", "ID 537134", 
"ID 562259", "ID 562455", "ID 567042", "ID 572866", "ID 578945", 
"ID 595683", "ID 59759", "ID 598460", "ID 603611", "ID 603757", 
"ID 607991", "ID 60976", "ID 622720", "ID 646989", "ID 656144", 
"ID 668807", "ID 669435", "ID 720522", "ID 740555", "ID 745499", 
"ID 746001", "ID 783969", "ID 78979", "ID 792426", "ID 793541", 
"ID 797860", "ID 806559", "ID 810517", "ID 826054", "ID 837609", 
"ID 839287", "ID 867918", "ID 869788", "ID 875380", "ID 876870", 
"ID 882220", "ID 893116", "ID 895909", "ID 899050", "ID 900143", 
"ID 908100", "ID 912185", "ID 916371", "ID 916620", "ID 957879", 
"ID 966195", "ID 993247", "ID 998911", "ID 999610"), class = "factor"), 
    region = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L), location = c(259090L, 559306L, 2227063L, 2369217L, 4026978L, 
    4211264L, 4679449L, 5105226L, 5106345L, 5344670L, 5473601L, 
    5476528L, 5871970L, 6461228L, 6700029L, 6708265L, 7639959L, 
    9297695L, 10254788L, 10328812L, 11102816L, 11568295L, 11720437L, 
    12843457L, 14012506L, 14156669L, 14632300L, 14641938L, 15298211L, 
    15468425L, 15534406L, 16279682L, 16699353L, 17226952L, 17320785L, 
    269017L, 453097L, 828833L, 954610L, 954842L, 1066378L, 1217332L,  
    1253530L, 1277716L, 1292857L, 1337952L, 1439657L, 1452989L, 
    1712345L, 1758035L, 2601630L, 2640359L, 2778095L, 3151129L, 
    3369931L, 3399080L, 3529525L, 3810217L, 3821120L, 3841588L, 
    3901557L, 4111633L, 4220440L, 4528632L, 4665450L, 5099307L, 
    5260242L, 5958770L, 5966356L, 6137405L, 6246065L, 6297231L, 
    6807949L)), .Names = c("id", "region", "location"), class = "data.frame", row.names = c(NA, 
-73L))

两个样本输入

样本1

        structure(list(region = c(1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L), 
        begin = c(0L, 2259252L, 5092077L, 9158205L, 0L, 135094L, 
        941813L, 5901391L, 6061324L), finish = c(2259252L, 5092077L, 
        9158205L, 20463033L, 135094L, 941813L, 5901391L, 6061324L, 
        7092402L), sed = c(3.98106154985726, 7.51649828394875, 5.15440228627995, 
        2.67456624889746, 7.54309412557632, 4.17413910385221, 7.47043058509007, 
        6.13362524658442, 1.00084994221106)), .Names = c("region", 
        "begin", "finish", "sed"), class = "data.frame", row.names = c(NA, 
        -9L))

样本2

        structure(list(region = c(1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L), 
        begin = c(0L, 2253252L, 7091077L, 9120205L, 0L, 135094L, 
        941813L, 5901391L, 6061324L), finish = c(2253252L, 7091077L, 
        9120205L, 17463033L, 135094L, 941813L, 5901391L, 6061324L, 
        7092402L), sed = c(3.31830840984048, 1.38014704208403, 6.13049140975458, 
        2.10349875097134, 0.48170587509345, 0.13058713509175, 9.13509713513509, 
        6.13047153058701, 3.81734081501503)), .Names = c("region", 
        "begin", "finish", "sed"), class = "data.frame", row.names = c(NA, 
        -9L))

未实施代码

matchLocationsToRegions <- function(path) {     
# get list of data files (around 500 of these; only dput of 2 given: sample262519 and sample252519)
setwd(path,sep="",collapse=NULL)
data_files <- list.files()

# read in template file with complete regional boundaries
standRef <- read.table(paste(path, "StandRef.txt",sep="",collapse=NULL), header=TRUE, sep="\t")

# pre-allocate a df with row dimensions of standRef and num of columns according to num of data files
sediment.df <- as.data.frame(matrix(NA,nrow=nrow(standRef),ncol=length(data_files)))
colnames(sediment.df) <- data_files
rownames(sediment.df) <- standRef[,1]

# create a counter for columns filled
col_counter <- 1    

for (file in data_files) {
    # read in current, processed data
    sample <- read.table(file, header=TRUE, sep="\t")          

    # pre-allocate vectors for sedimentation data vector
    sed <- rep(NA, nrow(standRef))

    # create a variable to track end boundary for a particular sample_ID
    end_tracker <- 1

    index <- unlist(lapply (unique(standRef$region), function(reg) {
             reg.filter <- which(standRef$region == reg)
             samp.filter <- which(sample$region == reg)
             samp.filter[cut(standRef$location[reg.filter],c(0L,sample$finish[samp.filter]),labels=F)]
        }))
    sed <- sample$sed[index]

    # fill in next, unfilled column of relevant df with data from relevant vector
    sediment.df[col_counter] <- sed   

    # update column counter variable 
    col_counter <- col_counter + 1
}       

# save df as a table
write.table(sediment.df,file="samples_sed.txt", row.names=TRUE, sep="\t") 
}

正在运行Rprof表明了这一点 "scan" "read.table" "matchLocationsToRegions"和 "type.convert" "read.table" "matchLocationsToRegions" 主导运行时。据推测，在这条线上循环是一个瓶颈：

sample <- read.table(file, header=TRUE, sep="\t")

更新：区域上的for循环已被更快执行的代码替换（h / t Simon Urbanek）。但是，其余的都很慢。

Answer 1

您可以轻松删除循环：

sediment.df <- as.data.frame(lapply(data_files, function(file) {
   sample <- read.table(file, header=TRUE, sep="\t")          
   index <- unlist(lapply (unique(standRef$region), function(reg) {
         reg.filter <- which(standRef$region == reg)
         samp.filter <- which(sample$region == reg)
          samp.filter[cut(standRef$location[reg.filter],c(0L,sample$finish[samp.filter]),labels=F)]
    }))
    sample$sed[index]
}))
colnames(sediment.df) <- data_files
rownames(sediment.df) <- standRef[,1]

但是，在read.table中花费大量时间并不是不太可能，因此您可以考虑a）使用scan，b）仅使用所有样本创建一个文件（例如，使用额外的列定义样本）所以你不需要加载很多文件。

使用嵌套的循环来矢量化/加速代码

1 个答案: