直接从R读取.dat和.dct

Question

我需要使用.dct文件读取.dat文件.有人用R做过吗？

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"
  ...
}

'阅读格式'如下:

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

存储类型如下所述:http://www.stata.com/help.cgi？datatypes

用于信息的其他网站:

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

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

.dat文件是一组与.dct文件中指定的变量对应的数字.(据推测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

stata站点的一个具体示例是:

该.dat文件(本例中为"test.raw")

C1245A101George Costanza
B1223B011Cosmo Kramer

该.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
}

生成的数据文件:

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

Answer 1

@thelatemail是关于如何继续进行的.这是一个小功能,我把它放在一起,让你开始一个更强大的解决方案:

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,概括功能等方面还有很多工作要做.例如,此函数不适用于重叠列,如@thelatemail添加到您的问题中的示例中所示.如果出现错误消息,则"StartPos [n] + ColWidth [n]"形式的某些错误检查应该等于"StartPos [n + 1]"可以用来停止读取文件.另外,结果数据的类也可以从函数生成的"元数据"列表中提取,并read.fwf使用colClasses参数进行分配.

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,运行该功能:

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(仍有很大的改进空间)

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

Answer 2

您可以dat使用读取文件,?read.fwf因为.dat数据本质上只是一个固定宽度的数据文件.

请参阅此处 - 组织Messy Notepad数据 - 使用字典文件中的column(X)值.dct作为宽度.

可以使用readLines提取信息来抓取字典文件,然后可以将其传递给调用中的参数read.fwf.

例如:'变量名'与col.names=参数对齐,'存储类型'与colClasses=参数对齐.

尽管如此,还是会有一些人工处理.