On 16/01/2015 3:02 PM, Paul Johnson wrote:> Dear R friends
>
> I wanted a function to make a simple percent table that would be easy for
> students to use. The goal originally was to have a simple thing people
> would call like this
>
> pctable(rowvar, colvar, data)
>
> and the things "rowvar" and "colvar" might be names of
variables in data. I
> wanted to avoid the usage of "with" (as we now see in the table
help).
>
> Then some people wanted more features, and I agreed with the suggestion to
> create a formula interface that people can call like so:
>
> pctable(rowvar ~ colvar, data)
>
> I end up with a generic function pctable and methods pctable.default,
> pctable.formula, pctable.character.
>
> I got that working, mostly I understand what's going on.
>
> Except the following, which, actually, is a good lesson to me about
> promises and method dispatch in R. An S3 generic will not send a call with
> a promise in the first argument to pctable.default (as I had mistakenly
> hoped). I'll paste in all the code below, but I think you will know the
> answer even without running it.
>
> pctable is a generic function. In workspace, I have no objects x and y,
> but there are variables inside data.frame dat named x and y. Since y is
> not an object, the method dispatch fails thus:
>
>> pctable(y, x, dat)
> Error in pctable(y, x, dat) (from #3) : object 'y' not found
>
> This direct call on pctable.default works (recall y and x are promises):
I think you are using the word "promise" differently than the standard
R
usage. In a sense, all arguments are promises, but you seem to mean
something more. I think you mean that you want nonstandard evaluation
for x and y.
>
>> pctable.default(y, x, dat)
> Count (column %)
> x
> y 1 2 3 4 Sum
> A 5(20%) 3(12%) 5(20%) 6(24%) 19
> B 9(36%) 5(20%) 4(16%) 6(24%) 24
> C 1(4%) 6(24%) 3(12%) 2(8%) 12
> D 4(16%) 4(16%) 6(24%) 5(20%) 19
> E 6(24%) 7(28%) 7(28%) 6(24%) 26
> Sum 25 25 25 25 100
>
> All the methods work fine when the first argument is a language object.
>
> This works (dispatches to pctable.formula)
>
>> pctable(y ~ x, dat)
> Count (column %)
> x
> y 1 2 3 4 Sum
> A 5(20%) 3(12%) 5(20%) 6(24%) 19
> B 9(36%) 5(20%) 4(16%) 6(24%) 24
> C 1(4%) 6(24%) 3(12%) 2(8%) 12
> D 4(16%) 4(16%) 6(24%) 5(20%) 19
> E 6(24%) 7(28%) 7(28%) 6(24%) 26
> Sum 25 25 25 25 100
>
>
> This works (dispatches to pctable.default)
>> pctable(dat$y, dat$x)
> Count (column %)
> dat$x
> dat$y 1 2 3 4 Sum
> A 5(20%) 3(12%) 5(20%) 6(24%) 19
> B 9(36%) 5(20%) 4(16%) 6(24%) 24
> C 1(4%) 6(24%) 3(12%) 2(8%) 12
> D 4(16%) 4(16%) 6(24%) 5(20%) 19
> E 6(24%) 7(28%) 7(28%) 6(24%) 26
> Sum 25 25 25 25 100
>
> However, this fails because y is not an object with a type
I would say it fails because object y does not exist in the evaluation
frame of the function, where you are implicitly evaluating it.
>
>> pctable(y, x, dat)
> Error in pctable(y, x, dat) (from #3) : object 'y' not found
>
> Can R be tricked to send that call to pctable.default, where it does work?
Yes, but it is probably a bad idea. The idea of S3 dispatch is that it
depends on the type of the argument used for dispatching, by default the
first argument. You don't have a variable named y, so you can't do
that.
There are a few ways to do the tricking. You could add a data parameter
to the generic function, and construct a new environment from it before
you evaluate the first argument. Then y would be found in the data
parameter, and dispatch could work.
You could use exists() to find if the first argument exists, and make an
explicit call to pctable.default if it doesn't. But this will fail if
you have a global variable named y, because the test will find that one
and use it for dispatch, rather than using dat$y.
So I would conclude: don't do that. If you want to use a data
argument, use the formula method. That's what other functions do, and
so that's what your users would expect. If you don't use a formula,
then the variables should all use standard evaluation, i.e. they should
exist in the frame where you are calling pctable.
One more comment inline below.
>
> Here's the code, I'm working on documentation, will put in package
> rockchalk eventually, but hate to leave this problem until I fully
> understand it.
>
>
> pctable <- function(rv, ...)
> {
> UseMethod("pctable")
> }
>
> ## rv: row variable, quoted or not
> ## cv: column variable, quoted or not
> pctable.default <- function(rv, cv, data = parent.frame(),
> rvlab = NULL, cvlab = NULL,
> colpct = TRUE, rowpct = FALSE,
> exclude = c(NA, NaN), rounded = FALSE)
> {
> rvlabel <- if (!missing(rv)) deparse(substitute(rv))
> cvlabel <- if (!missing(cv)) deparse(substitute(cv))
> rvlab <- if (is.null(rvlab)) rvlabel else rvlab
> cvlab <- if (is.null(cvlab)) cvlabel else cvlab
>
> rvin <- eval(substitute(rv), envir = data, enclos = parent.frame())
> cvin <- eval(substitute(cv), envir = data, enclos = parent.frame())
>
> t1 <- table(rvin, cvin, dnn = c(rvlab, cvlab), exclude = exclude)
> rownames(t1)[is.na(rownames(t1))] <- "NA" ## symbol to
letters
> colnames(t1)[is.na(colnames(t1))] <- "NA"
> if (rounded) t1 <- round(t1, -1)
> t2 <- addmargins(t1, c(1,2))
> t1colpct <- round(100*prop.table(t1, 2), 1)
> t1rowpct <- round(100*prop.table(t1, 1), 1)
> t1colpct <- apply(t1colpct, c(1,2), function(x)
gsub("NaN", "", x))
> t1rowpct <- apply(t1rowpct, c(1,2), function(x)
gsub("NaN", "", x))
> res <- list("count" = t2, "colpct" = t1colpct,
"rowpct" = t1rowpct,
> call = match.call())
> class(res) <- "pctable"
> print(res, colpct = colpct, rowpct = rowpct)
> invisible(res)
> }
>
>
> pctable.formula <- function(formula, data = NULL, rvlab = NULL,
> cvlab = NULL, colpct = TRUE, rowpct = FALSE,
> exclude = c(NA, NaN), rounded = FALSE,
> ..., subset = NULL)
>
> {
> if (missing(data) || !is.data.frame(data)) stop("pctable requires
a
> data frame")
This test seems too strong. If x and y had been global variables of the
right shape in your example, then pctable(y ~ x) should work. I would
let model.frame (which you call down below) establish the rules for what
is allowed.
Duncan Murdoch
> if (missing(formula) || (length(formula) != 3L))
> stop("pctable requires a two sided formula")
> mt <- terms(formula, data = data)
> if (attr(mt, "response") == 0L) stop("response variable
is required")
> mf <- match.call(expand.dots = FALSE)
> keepers <- match(c("formula", "data",
"subset", "na.action"),
> names(mf), 0L)
> mf <- mf[c(1L, keepers)]
> mf$drop.unused.levels <- FALSE
> mf[[1L]] <- quote(stats::model.frame)
> mf <- eval(mf, parent.frame())
> ## response is column 1
> rvlab <- if (missing(rvlab)) colnames(mf)[1] else rvlab
> cvlab <- if (missing(cvlab)) colnames(mf)[2] else cvlab
>
> res <- pctable.default(mf[[1L]], mf[[2L]], data = mf,
> rvlab = rvlab, cvlab = cvlab,
> colpct = colpct, rowpct = rowpct,
> exclude = exclude, rounded = rounded)
> invisible(res)
> }
>
> pctable.character <- function(rowvar, colvar, data = NULL, rvlab = NULL,
> cvlab = NULL, colpct = TRUE,
> rowpct = FALSE, exclude = c(NA, NaN), rounded
> FALSE,
> ..., subset = NULL)
>
> {
> if (missing(data) || !is.data.frame(data)) stop("pctable requires
a
> data frame")
> ## colvar <- if (!is.character(colvar)) deparse(substitute(colvar))
> else colvar
> colvar <- as.character(substitute(colvar))[1L]
>
> rvlab <- if (missing(rvlab)) rowvar else rvlab
> cvlab <- if (missing(cvlab)) colvar else cvlab
>
> t1 <- with(data, table(data[[rowvar]], data[[colvar]], dnn =
c(rvlab,
> cvlab), exclude = exclude))
> rownames(t1)[is.na(rownames(t1))] <- "NA" ## symbol to
letters
> colnames(t1)[is.na(colnames(t1))] <- "NA"
> if (rounded) t1 <- round(t1, -1)
> t2 <- addmargins(t1, c(1,2))
> t1colpct <- round(100*prop.table(t1, 2), 1)
> t1rowpct <- round(100*prop.table(t1, 1), 1)
> t1colpct <- apply(t1colpct, c(1,2), function(x)
gsub("NaN", "", x))
> t1rowpct <- apply(t1rowpct, c(1,2), function(x)
gsub("NaN", "", x))
>
> res <- list("count" = t2, "colpct" = t1colpct,
"rowpct" = t1rowpct,
> call = match.call())
> class(res) <- "pctable"
> print(res, colpct = colpct, rowpct = rowpct)
> invisible(res)
> }
>
>
> ## OK, I see now I'm doing the same work over and over, will extract
> ## a middle chunk out of each of those methods. And finally my cool print
> method.
>
> print.pctable <- function(tab, colpct = TRUE, rowpct = FALSE){
> count <- tab[["count"]]
>
> t3 <- count
> if (colpct && !rowpct) {
> cpct <- tab[["colpct"]]
> for(j in rownames(cpct)){
> for(k in colnames(cpct)){
> t3[j, k] <- paste0(count[j, k], "(", cpct[j,
k], "%)")
> }
> }
> cat("Count (column %)\n")
> print(t3)
> return(invisible(t3))
> }
>
> ## rowpct == TRUE< else would have returned
> rpct <- tab[["rowpct"]]
> for(j in rownames(rpct)){
> for(k in colnames(rpct)){
> t3[j, k] <- paste0(count[j, k], "(", rpct[j, k],
"%)")
> }
> }
>
> if (!colpct) {
> cat("Count (row %)\n")
> print(t3)
> return(invisible(t3))
> } else {
> cpct <- tab[["colpct"]]
> t4 <- array("", dim = c(1, 1) +
c(2,1)*dim(tab$colpct))
> t4[seq(1, NROW(t4), 2), ] <- t3
> rownames(t4)[seq(1, NROW(t4), 2)] <- rownames(t3)
> rownames(t4)[is.na(rownames(t4))] <- ""
> colnames(t4) <- colnames(t3)
> for(j in rownames(tab[["colpct"]])) {
> for(k in colnames(tab[["colpct"]])){
> t4[1 + which(rownames(t4) == j) ,k] <-
> paste0(tab[["colpct"]][j, k], "%")
> }
>
> }
>
> names(dimnames(t4)) <- names(dimnames(count))
>
> cat("Count (row %)\n")
> cat("column %\n")
> print(t4, quote = FALSE)
> return(invisible(t4))
> }
> }
>
>
> And usage examples
>
>
>
> dat <- data.frame(x = gl(4, 25),
> y = sample(c("A", "B", "C",
"D", "E"), 100, replace> TRUE))
>
>
> ## Here's what I was aiming for, in the beginning
> pctable(y ~ x, dat)
> pctable(y ~ x, dat, exclude = NULL)
> pctable(y ~ x, dat, rvlab = "My Outcome Var", cvlab = "My
Columns")
> ## People who like row percents asked for this
> pctable(y ~ x, dat, rowpct = TRUE, colpct = FALSE)
> ## Some people want both. Tiresome.
> pctable(y ~ x, dat, rowpct = TRUE, colpct = TRUE)
> pctable(y ~ x, dat, rowpct = TRUE, colpct = TRUE, exclude = NULL)
> tab <- pctable(y ~ x, dat, rvlab = "My Outcome Var", cvlab =
"My Columns")
> print(tab, rowpct = TRUE, colpct = FALSE)
> print.pctable(tab, rowpct = TRUE, colpct = TRUE)
>
>
>
>
> ## I also wanted an interface that would allow calls like
> ## pctable(y, x, dat)
> ## which I was able to do, but not when pctable is a method.
> ## As long as one writes in an existing variable, this dispatches
> ## pctable.default and result is OK
> pctable(dat$y, dat$x)
> pctable(dat$y, dat$x, rowpct = TRUE, colpct = FALSE)
> pctable(dat$y, dat$x, rowpct = TRUE, colpct = TRUE)
> pctable(dat$y, dat$x, rowpct = TRUE, colpct = TRUE, exclude = NULL)
>
> tab <- pctable(dat$y, dat$x)
> print(tab, rowpct = TRUE, colpct = FALSE)
> print(tab, rowpct = TRUE, colpct = TRUE)
>
> pctable(dat$y, dat$x, rowpct = TRUE, colpct = TRUE, exclude = c(NA,
"E"))
> pctable(dat$y, dat$x, rowpct = TRUE, colpct = TRUE, exclude =
c("E"))
> ## Why do NA's get excluded
> pctable(dat$y, dat$x, rowpct = TRUE, colpct = TRUE, exclude =
c("B", "2"))
>
> ## This succeeds
> pctable.default(y, x, dat)
> ## Next causes error
> pctable(y, x, dat)
>
> ## Error in pctable(y, x, dat) (from #3) : object 'y' not found
>
>
> At one point yesterday, I was on the verge of comprehending the parse tree
> :)
>