在R中重新组织唯一（纽约MTA旋转栅门）数据集

我有一个独特的数据集（纽约MTA旋转栅门数据），我需要以某种方式重新组织以执行一些分析。我已经写了一些代码，但是效率不高，因为它是一个非常大的数据集。我希望有人能提出更好的办法

该数据集有43列。第1-3列为唯一标识符（即特定车站的旋转栅门）。然后第4-8列标识计量时间、计量类型、条目，然后标识退出。9-13，然后其余的列（最多43列）遵循相同的模式。数据集很难看，所以我不想在这里发布，但你可以在下面的链接中找到它。您必须查看2014年10月18日之前的数据

这是可行的，但我知道有一种更有效的方法。任何帮助都将不胜感激

编辑：

我应该补充一点，因为我遗漏了一些可能改变这一方法的关键部分。在我用read.csv读入数据后，我只使用两个仪表（第2列）将数据子集。因为我喜欢这个建议，所以我将子集数据转换为一个字符串，如下所示。这实际上是相当体面的表现，但任何进一步的建议将不胜感激

out1 <- function() {
  data <- read.csv(name, header=FALSE)

##Isolate data for stations included in network area
  station <- subset(data, V2%in% station_names)
  data <- apply(station, 1, paste, collapse=",")
  starts <- seq(from=4, to=43, by=5)
  new_data <- rbindlist(lapply(strsplit(data, ","), function(x) {
  rbindlist(lapply(starts, function(y) {
  as.list(x[c(1:3, y:(y+4))])
  }))
})) 
setnames(new_data, colnames(new_data), c("C.A", "UNIT", "SCP", "DATE",  "TIME","DESC", "ENTRIES", "EXIT"))
new_data <- as.data.frame(new_data)
}

---

out1如果您不介意在数据加载时进行处理：

# data via http://web.mta.info/developers/resources/nyct/turnstile/ts_Field_Description_pre-10-18-2014.txt

data <- readLines(textConnection("A002,R051,02-00-00,03-21-10,00:00:00,REGULAR,002670738,000917107,03-21-10,04:00:00,REGULAR,002670738,000917107,03-21-10,08:00:00,REGULAR,002670746,000917117,03-21-10,12:00:00,REGULAR,002670790,000917166,03-21-10,16:00:00,REGULAR,002670932,000917204,03-21-10,20:00:00,REGULAR,002671164,000917230,03-22-10,00:00:00,REGULAR,002671181,000917231,03-22-10,04:00:00,REGULAR,002671181,000917231
A002,R051,02-00-00,03-22-10,08:00:00,REGULAR,002671220,000917324,03-22-10,12:00:00,REGULAR,002671364,000917640,03-22-10,16:00:00,REGULAR,002671651,000917719,03-22-10,20:00:00,REGULAR,002672430,000917789,03-23-10,00:00:00,REGULAR,002672473,000917795,03-23-10,04:00:00,REGULAR,002672474,000917795,03-23-10,08:00:00,REGULAR,002672516,000917876,03-23-10,12:00:00,REGULAR,002672652,000917934
A002,R051,02-00-00,03-23-10,16:00:00,REGULAR,002672879,000917996,03-23-10,20:00:00,REGULAR,002673636,000918073,03-24-10,00:00:00,REGULAR,002673683,000918079,03-24-10,04:00:00,REGULAR,002673683,000918079,03-24-10,08:00:00,REGULAR,002673722,000918171,03-24-10,12:00:00,REGULAR,002673876,000918514,03-24-10,16:00:00,REGULAR,002674221,000918594,03-24-10,20:00:00,REGULAR,002675082,000918671
A002,R051,02-00-00,03-25-10,00:00:00,REGULAR,002675153,000918675,03-25-10,04:00:00,REGULAR,002675153,000918675,03-25-10,08:00:00,REGULAR,002675190,000918752,03-25-10,12:00:00,REGULAR,002675345,000919053,03-25-10,16:00:00,REGULAR,002675676,000919118,03-25-10,20:00:00,REGULAR,002676557,000919179,03-26-10,00:00:00,REGULAR,002676688,000919207,03-26-10,04:00:00,REGULAR,002676694,000919208
A002,R051,02-00-00,03-26-10,08:00:00,REGULAR,002676735,000919287,03-26-10,12:00:00,REGULAR,002676887,000919607,03-26-10,16:00:00,REGULAR,002677213,000919680,03-26-10,20:00:00,REGULAR,002678039,000919743,03-27-10,00:00:00,REGULAR,002678144,000919756,03-27-10,04:00:00,REGULAR,002678145,000919756,03-27-10,08:00:00,REGULAR,002678155,000919777,03-27-10,12:00:00,REGULAR,002678247,000919859
A002,R051,02-00-00,03-27-10,16:00:00,REGULAR,002678531,000919908,03-27-10,20:00:00,REGULAR,002678892,000919964,03-28-10,00:00:00,REGULAR,002678929,000919966,03-28-10,04:00:00,REGULAR,002678929,000919966,03-28-10,08:00:00,REGULAR,002678935,000919982,03-28-10,12:00:00,REGULAR,002679003,000920006,03-28-10,16:00:00,REGULAR,002679231,000920059,03-28-10,20:00:00,REGULAR,002679475,000920098"))


library(data.table)

starts <- seq(from=4, to=43, by=5)

new_data <- rbindlist(lapply(strsplit(data, ","), function(x) {

  rbindlist(lapply(starts, function(y) {
    as.list(x[c(1:3, y:(y+4))])
  }))

}))

setnames(new_data, colnames(new_data), c("control_area", "unit", "scp", "date", "time", "description", "entries", "exits"))

dplyr::glimpse(new_data)

## Observations: 48
## Variables:
## $ control_area (fctr) A002, A002, A002, A002, A002, A002, A002, A002, A002, A002, A002, A002, A002, A0...
## $ unit         (fctr) R051, R051, R051, R051, R051, R051, R051, R051, R051, R051, R051, R051, R051, R0...
## $ scp          (fctr) 02-00-00, 02-00-00, 02-00-00, 02-00-00, 02-00-00, 02-00-00, 02-00-00, 02-00-00, ...
## $ date         (fctr) 03-21-10, 03-21-10, 03-21-10, 03-21-10, 03-21-10, 03-21-10, 03-22-10, 03-22-10, ...
## $ time         (fctr) 00:00:00, 04:00:00, 08:00:00, 12:00:00, 16:00:00, 20:00:00, 00:00:00, 04:00:00, ...
## $ description  (fctr) REGULAR, REGULAR, REGULAR, REGULAR, REGULAR, REGULAR, REGULAR, REGULAR, REGULAR,...
## $ entries      (fctr) 002670738, 002670738, 002670746, 002670790, 002670932, 002671164, 002671181, 002...
## $ exits        (fctr) 000917107, 000917107, 000917117, 000917166, 000917204, 000917230, 000917231, 000...

#数据通过http://web.mta.info/developers/resources/nyct/turnstile/ts_Field_Description_pre-10-18-2014.txt
数据< P>这是一种可供选择的方法。它使用“stringi”包和我的“splitstackshape”包

我们将使用中确定的字段描述中的名称

让我们试一下：

## Try a dataset where we know there are unbalanced numbers of observations...
data <- readLines("http://web.mta.info/developers/data/nyct/turnstile/turnstile_130615.txt")


与@hrbmstr的方法相比，时间安排如下：

funHRB <- function() {
  starts <- seq(from=4, to=43, by=5)
  new_data <- rbindlist(lapply(strsplit(data, ","), function(x) {
    rbindlist(lapply(starts, function(y) {
      as.list(x[c(1:3, y:(y+4))])
    }))
  }))
  setnames(new_data, colnames(new_data), 
           c("control_area", "unit", "scp", "date",
             "time", "description", "entries", "exits"))
  new_data
}
system.time(out2 <- funHRB())
#    user  system elapsed 
#   23.59    0.03   23.77 

这太棒了。谢谢你的帮助！
library(splitstackshape)
library(stringi)

Names <- scan(what = "character", sep = ",", 
              text = paste0(
                "C/A,UNIT,SCP,DATE1,TIME1,DESC1,ENTRIES1,EXITS1,",
                "DATE2,TIME2,DESC2,ENTRIES2,EXITS2,DATE3,TIME3,DESC3,",
                "ENTRIES3,EXITS3,DATE4,TIME4,DESC4,ENTRIES4,EXITS4,",
                "DATE5,TIME5,DESC5,ENTRIES5,EXITS5,DATE6,TIME6,DESC6,",
                "ENTRIES6,EXITS6,DATE7,TIME7,DESC7,ENTRIES7,EXITS7,",
                "DATE8,TIME8,DESC8,ENTRIES8,EXITS8"))

## What are the unique variable "stubs"?
isRepeated <- unique(gsub("\\d", "", Names[4:length(Names)]))

funAM <- function(invec) {
  temp <- stri_split_fixed(invec, ",", simplify = TRUE)
  temp <- `dim<-`(stri_trim_both(temp), dim(temp))
  DT <- setnames(as.data.table(temp), Names)
  merged.stack(getanID(DT, 1:3), var.stubs = isRepeated,
               sep = "var.stubs")
}

## Try a dataset where we know there are unbalanced numbers of observations...
data <- readLines("http://web.mta.info/developers/data/nyct/turnstile/turnstile_130615.txt")

system.time(out <- funAM(data)) ## Reasonably fast
#    user  system elapsed 
#    1.25    0.02    1.29 
out
#      C/A UNIT      SCP .id .time_1     DATE     TIME    DESC   ENTRIES     EXITS
# 1:  A002 R051 02-00-00   1       1 06-08-13 00:00:00 REGULAR 004153504 001427135
# 2:  A002 R051 02-00-00   1       2 06-08-13 04:00:00 REGULAR 004153535 001427138
# 3:  A002 R051 02-00-00   1       3 06-08-13 08:00:00 REGULAR 004153559 001427177
# 4:  A002 R051 02-00-00   1       4 06-08-13 12:00:00 REGULAR 004153683 001427255
# 5:  A002 R051 02-00-00   1       5 06-08-13 16:00:00 REGULAR 004153959 001427320
# ---                                                                              
# 241492: TRAM2 R469 00-05-01   6       4                                              
# 241493: TRAM2 R469 00-05-01   6       5                                              
# 241494: TRAM2 R469 00-05-01   6       6                                              
# 241495: TRAM2 R469 00-05-01   6       7                                              
# 241496: TRAM2 R469 00-05-01   6       8 

funHRB <- function() {
  starts <- seq(from=4, to=43, by=5)
  new_data <- rbindlist(lapply(strsplit(data, ","), function(x) {
    rbindlist(lapply(starts, function(y) {
      as.list(x[c(1:3, y:(y+4))])
    }))
  }))
  setnames(new_data, colnames(new_data), 
           c("control_area", "unit", "scp", "date",
             "time", "description", "entries", "exits"))
  new_data
}
system.time(out2 <- funHRB())
#    user  system elapsed 
#   23.59    0.03   23.77 

system.time(DF <- read.csv(
  header = FALSE, col.names = Names, 
  strip.white = TRUE, 
  colClasses = rep("character", length(Names)), 
  text = data))
#    user  system elapsed 
#   66.01    0.07   66.91