直接从 R 读取 .dat 和 .dct

Question

我需要使用 .dct 文件读取 .dat 文件。有没有人用R做过？

格式为:

dictionary {
  # how many lines per record
  _lines(1)
  # start defining the first line
  _line(1)

  # starting column / storage type / variable name / read format / variable label
  _column(1)    str8    aid    %8s    "respondent identifier"
  ...
}

“读取格式”类似于:

%2f        2 column integer variable
%12s      12 column string variable
%8.2f      8 column number with 2 implied decimal places. 

此处描述了存储类型: http://www.stata.com/help.cgi?datatypes

其他用于提供信息的网站:

http://library.columbia.edu/indiv/dssc/technology/stata_write.html

http://www.stata.com/support/faqs/data-management/reading-fixed-format-data/

.dat 文件是一串数字，对应于 .dct 文件中指定的变量。 (大概这是固定宽度列中的数据)。

这是一个真实的例子:

.dtc 文件
http://goo.gl/qHZOk

数据
http://goo.gl/FRGRF

来自 stata 站点的一个具体示例是:
.dat文件(在本例中为“test.raw”)

C1245A101George Costanza
B1223B011Cosmo Kramer

.dct文件

dictionary using test2.raw {
 _column(1)     str5     code   %5s
 _column(2)     int      call   %4f
 _column(6)     str1     city   %1s
 _column(7)     int      neigh  %3f
 _column(10)    str16    name   %16s
}

结果数据文件:

      +-----------------------------------------------+
      |  code   call   city   neigh              name |
      |-----------------------------------------------|
   1. | C1245   1245      A     101   George Costanza |
   2. | B1223   1223      B      11      Cosmo Kramer |
      +-----------------------------------------------+

Answer 1

@thelatemail 是关于如何进行的现场。这是我拼凑起来的一个小函数，让您开始使用更强大的解决方案:

read.dat.dct <- function(dat, dct) {
    temp <- readLines(dct)
    pattern <- "_column\\(([0-9]+)\\)\\s+([a-z0-9]+)\\s+([a-z0-9_]+)\\s+%([0-9]+).*"
    classes <- c("numeric", "character", "character", "numeric")
    metadata <- setNames(lapply(1:4, function(x) {
        out <- gsub(pattern, paste("\\", x, sep = ""), temp)
        out <- gsub("^\\s+|\\s+$|.*\\{|\\}", "", out)
        out <- out[out != ""]
        class(out) <- classes[x] ; out }), 
                         c("StartPos", "Str", "ColName", "ColWidth"))
    read.fwf(dat, widths = metadata[["ColWidth"]], 
             col.names = metadata[["ColName"]])
}

在错误检查、泛化函数等方面，您还有很多工作要做。例如，此函数不适用于重叠列，如@thelatemail 添加到您的问题的示例中所示。 “StartPos[n] + ColWidth[n]”应等于“StartPos[n+1]”形式的一些错误检查可用于停止读取文件，如果错误消息不正确。此外，结果数据的类也可以从函数生成的“元数据”列表中提取，并在 read.fwf 中分配。使用 colClasses争论。

这是一个 dat 文件和一个 dct 文件来演示:

将以下两行复制并粘贴到文本编辑器中，并将其作为“test.dat”保存在您的工作目录中。

C1245A101George Costanza
B1223B011Cosmo Kramer

将以下行复制并粘贴到文本编辑器中，并将其作为“test.dct”保存在您的工作目录中

dictionary using test.dat {
    _column(1)     str1     code   %1s
    _column(2)     int      call   %4f
    _column(6)     str1     city   %1s
    _column(7)     int      neigh  %3f
    _column(10)    str16    name   %16s
}

现在，运行函数:

read.dat.dct(dat = "test.dat", dct = "test.dct")
#   code call city neigh            name
# 1    C 1245    A   101 George Costanza
# 2    B 1223    B    11    Cosmo Kramer

更新:改进的功能(还有很大的改进空间)

read.dat.dct <- function(dat, dct, labels.included = "no") {
    temp <- readLines(dct)
    temp <- temp[grepl("_column", temp)]
    switch(labels.included,
           yes = {
               pattern <- "_column\\(([0-9]+)\\)\\s+([a-z0-9]+)\\s+(.*)\\s+%([0-9]+)[a-z]\\s+(.*)"
               classes <- c("numeric", "character", "character", "numeric", "character")
               N <- 5
               NAMES <- c("StartPos", "Str", "ColName", "ColWidth", "ColLabel")
           },
           no = {
               pattern <- "_column\\(([0-9]+)\\)\\s+([a-z0-9]+)\\s+(.*)\\s+%([0-9]+).*"
               classes <- c("numeric", "character", "character", "numeric")
               N <- 4
               NAMES <- c("StartPos", "Str", "ColName", "ColWidth")
           })
    metadata <- setNames(lapply(1:N, function(x) {
        out <- gsub(pattern, paste("\\", x, sep = ""), temp)
        out <- gsub("^\\s+|\\s+$", "", out)
        out <- gsub('\"', "", out, fixed = TRUE)
        class(out) <- classes[x] ; out }), NAMES)

    metadata[["ColName"]] <- make.names(gsub("\\s", "", metadata[["ColName"]]))

    myDF <- read.fwf(dat, widths = metadata[["ColWidth"]], 
             col.names = metadata[["ColName"]])
    if (labels.included == "yes") {
        attr(myDF, "col.label") <- metadata[["ColLabel"]]
    }
    myDF
}

它如何处理您的数据？

temp <- read.dat.dct(dat = "http://dl.getdropbox.com/u/18116710/21600-0009-Data.txt", 
                     dct = "http://dl.getdropbox.com/u/18116710/21600-0009-Setup.dct",
                     labels.included = "yes")
dim(temp)                     # How big is the dataset?
# [1] 180  40
head(temp[, 1:6])             # What do the first few columns & rows look like?
#   CASEID      AID RRELNO RPREGNO H3PC1.H3PC1 H3PC2.H3PC2
# 1      1 57118381      5       1           1           1
# 2      2 57134970      1       2           1           1
# 3      3 57135078      1       1           1           1
# 4      4 57135078      5       1           1           1
# 5      5 57164981      1       1           7           3
# 6      6 57191909      1       3           1           1
head(attr(temp, "col.label")) # What are the variable labels?
# [1] "CASE IDENTIFICATION NUMBER"             "RESPONDENT IDENTIFIER"                 
# [3] "ROMANTIC RELATIONSHIP NUMBER"           "RELATIONSHIP PREGNANCY NUMBER"         
# [5] "S23Q1 1 TOLD PARTNER PREGNANT-W3"       "S23Q2 MONTHS PREG WHEN TOLD PARTNER-W3"

原始示例怎么样？

read.dat.dct("test.dat", "test.dct", labels.included = "no")
#   code call city neigh            name
# 1    C 1245    A   101 George Costanza
# 2    B 1223    B    11    Cosmo Kramer