矢量化/加速嵌套循环代码

Question

下面的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缓慢。我如何加快速度？

我已将dput结果从部分指示性数据中提取出来。

输入dput小号

1. 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)) 
   

2. 两个样本输入

样品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)) 

样品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)) 

Unvectorized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") 
} 

运行Rprof表明 "scan" "read.table" "matchLocationsToRegions"和 "type.convert" "read.table" "matchLocationsToRegions" 为主运行。据推测，有一个瓶颈，通过套住超过此线：

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

更新：for环比地区已被替换，执行快很多（H/T西蒙Urbanek）。但其余的很慢。

Answer 1

您可以删除容易循环：

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] 

然而，这不是不可能，很多的时间在read.table花费，所以你可以使用scan，考虑A）B）只创建一个文件与所有样本（例如使用额外的列来定义样本），因此您不需要加载许多文件。