@thelatemail is spot-on about how to proceed. Here's a small function I threw together to get you started on a more robust solution:
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"]])
}
There is still a lot you would have to do with respect to error checking, generalizing the function, and so on. For example, this function does not work with overlapping columns, as are present in the example that @thelatemail added to your question. Some error checking in the form of "StartPos[n] + ColWidth[n]" should equal "StartPos[n+1]" could be used to stop reading the file if this is not true with an error message. Additionally, the classes of the resulting data can also be extracted from the "metadata" list generated by the function and assigned in read.fwf using the colClasses argument.
Here is a dat file and a dct file to demonstrate:
Copy and paste the following two lines into a text editor and save it in your working directory as "test.dat".
C1245A101George Costanza
B1223B011Cosmo Kramer
Copy and paste the following lines into a text editor and save it in your working directory as "test.dct"
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
}
Now, run the function:
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
Update: An improved function (with still a lot of room for improvement)
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
}
How does it work with your data?
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"
What about with the original example?
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
.dctfile? What specific.datfiletype are you talking about? We are going to need more detailed information to answer you. – Nicolellamemiscpackage will be useful, as suggested in the help forread.dta, which you would have navigated towards after reading the wonderful Data Import / Export Manual – Timbal