How to create, structure, maintain and update data codebooks in R?

A more advanced version would be to use S4 classes. For example, in bioconductor the ExpressionSet is used to store microarray data with its associated experimental meta data.

The MIAME object described in Section 4.4, looks very similar to what you are after:

experimentData <- new("MIAME", name = "Pierre Fermat",
          lab = "Francis Galton Lab", contact = "[email protected]",
          title = "Smoking-Cancer Experiment", abstract = "An example ExpressionSet",
          url = "www.lab.not.exist", other = list(notes = "Created from text files"))

You could add any special attribute to any R object with the attr function. E.g.:

x <- cars
attr(x,"source") <- "Ezekiel, M. (1930) _Methods of Correlation Analysis_.  Wiley."

And see the given attribute in the structure of the object:

> str(x)
'data.frame':   50 obs. of  2 variables:
 $ speed: num  4 4 7 7 8 9 10 10 10 11 ...
 $ dist : num  2 10 4 22 16 10 18 26 34 17 ...
 - attr(*, "source")= chr "Ezekiel, M. (1930) _Methods of Correlation Analysis_.  Wiley."

And could also load the specified attribute with the same attr function:

> attr(x, "source")
[1] "Ezekiel, M. (1930) _Methods of Correlation Analysis_.  Wiley."

If you only add new cases to your data frame, the given attribute will not be affected (see: str(rbind(x,x)) while altering the structure will erease the given attributes (see: str(cbind(x,x))).

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UPDATE: based on comments

If you want to list all non-standard attributes, check the following:

setdiff(names(attributes(x)),c("names","row.names","class"))

This will list all non-standard attributes (standard are: names, row.names, class in data frames).

Based on that, you could write a short function to list all non-standard attributes and also the values. The following does work, though not in a neat way... You could improve it and make up a function :)

First, define the uniqe (=non standard) attributes:

uniqueattrs <- setdiff(names(attributes(x)),c("names","row.names","class"))

And make a matrix which will hold the names and values:

attribs <- matrix(0,0,2)

Loop through the non-standard attributes and save in the matrix the names and values:

for (i in 1:length(uniqueattrs)) {
    attribs <- rbind(attribs, c(uniqueattrs[i], attr(x,uniqueattrs[i])))
}

Convert the matrix to a data frame and name the columns:

attribs <- as.data.frame(attribs)
names(attribs) <- c('name', 'value')

And save in any format, eg.:

write.csv(attribs, 'foo.csv')

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To your question about the variable labels, check the read.spss function from package foreign, as it does exactly what you need: saves the value labels in the attrs section. The main idea is that an attr could be a data frame or other object, so you do not need to make a unique "attr" for every variable, but make only one (e.g. named to "varable labels") and save all information there. You could call like: attr(x, "variable.labels")['foo'] where 'foo' stands for the required variable name. But check the function cited above and also the imported data frames' attributes for more details.

I hope these could help you to write the required functions in a lot neater way than I tried above! :)