问题
Recently I encountered the following problem in my R code. In a function, accepting a data frame as an argument, I needed to add (or replace, if it exists) a column with data calculated based on values of the data frame's original column. I wrote the code, but the testing revealed that data frame extract/replace operations, which I've used, resulted in a loss of the object's special (user-defined) attributes.
After realizing that and confirming that behavior by reading R documentation (http://stat.ethz.ch/R-manual/R-patched/library/base/html/Extract.html), I decided to solve the problem very simply - by saving the attributes before the extract/replace operations and restoring them thereafter:
myTransformationFunction <- function (data) {
# save object's attributes
attrs <- attributes(data)
<data frame transformations; involves extract/replace operations on `data`>
# restore the attributes
attributes(data) <- attrs
return (data)
}
This approach worked. However, accidentally, I ran across another piece of R documentation (http://stat.ethz.ch/R-manual/R-patched/library/base/html/Extract.data.frame.html), which offers IMHO an interesting (and, potentially, a more generic?) alternative approach to solving the same problem:
## keeping special attributes: use a class with a
## "as.data.frame" and "[" method:
as.data.frame.avector <- as.data.frame.vector
`[.avector` <- function(x,i,...) {
r <- NextMethod("[")
mostattributes(r) <- attributes(x)
r
}
d <- data.frame(i = 0:7, f = gl(2,4),
u = structure(11:18, unit = "kg", class = "avector"))
str(d[2:4, -1]) # 'u' keeps its "unit"
I would really appreciate if people here could help by:
Comparing the two above-mentioned approaches, if they are comparable (I realize that the second approach as defined is for data frames, but I suspect it can be generalized to any object);
Explaining the syntax and meaning in the function definition in the second approach, especially
as.data.frame.avector
, as well as what is the purpose of the lineas.data.frame.avector <- as.data.frame.vector
.
回答1:
I'm answering my own question, since I have just found an SO question (How to delete a row from a data.frame without losing the attributes), answers to which cover most of my questions posed above. However, additional explanations (for R beginners) for the second approach would still be appreciated.
UPDATE:
Another solution to this problem has been proposed in an answer to the following SO question: indexing operation removes attributes. Personally, however, I better like the approach, based on creating a new class, as it's IMHO semantically cleaner.
来源:https://stackoverflow.com/questions/23841387/approaches-to-preserving-objects-attributes-during-extract-replace-operations