gsubfn (version 0.7)

fn: Transform formula arguments to functions.

Description

When used in the form fn\$somefunction(...arguments...) it converts formulas among the arguments of somefunction to functions using as.function.formula. It uses a heuristic to decide which formulas to convert. If any of the following are true then that argument is converted from a formula to a function: (1) there is only one formula among the arguments, (2) the name of the formula argument is FUN or (3) the formula argument is not the first argument in the argument list.

It also removes any simplify argument whose value is not logical and after processing it in the same way just discussed in order to interpret it as a function it passes the output of the command through do.call(simplify, output).

It also performs quasi-perl style string interpolation on any character string arguments that begin with \1 removing the \1 character. A dollar sign followed by a variable name or R code within backticks are both evaluated.

Usage

# S3 method for fn
$(x, FUN)

Arguments

x

fn.

FUN

Name of a function.

Value

Returns a function.

See Also

as.function.formula

Examples

# NOT RUN {
   # use of formula to specify a function.
   # Note that LETTERS, letters and pi are automatically excluded from args
   fn$lapply(list(1:4, 1:3), ~ LETTERS[x])
   fn$sapply(1:3, ~ sin((n-1) * pi/180))

   # use of simplify = rbind instead of do.call(rbind, by(...)).
   # args to anonymous function are automatically determined.
   fn$by(BOD, 1:nrow(BOD), ~ c(mn = min(x), mx = max(x)), simplify = rbind)

   # calculate lm coefs of uptake vs conc for each Plant
   fn$by(CO2, CO2$Plant, d ~ coef(lm(uptake ~ conc, d)), simplify = rbind)

   # mid range of conc and uptake by Plant
   fn$aggregate(CO2[,4:5], CO2[1], ~ mean(range(x)))

   # string interpolation
   j <- fn$cat("pi = $pi, exp = `exp(1)`\n")

# }
# NOT RUN {
   # same but use cast/melt from reshape package
   library(reshape)
   fn$cast(Plant ~ variable, data = melt(CO2, id = 1:3), ~~ mean(range(x)))

   # same
   # uncomment when new version of doBy comes out (expected shortly)
   # library(doBy)
   # fn$summaryBy(.~Plant,CO2[-(2:3)],FUN= ~~mean(range(x)), pref='midrange')
# }
# NOT RUN {
   # generalized matrix product
   # can replace sum(x*y) with any other inner product of interest
   # this example just performs matrix multiplication of a times b
   a <- matrix(4:1, 2)
   b <- matrix(1:4, 2)
   fn$apply(b, 2, x ~ fn$apply(a, 1, y ~ sum(x*y)))

   # integration
   fn$integrate(~1/((x+1)*sqrt(x)), lower = 0, upper = Inf)

   # optimization
   fn$optimize(~ x^2, c(-1,1))

   # using fn with S4 definitions
   setClass('ooc', representation(a = 'numeric'))
   fn$setGeneric('incr', x + value ~ standardGeneric('incr'))
   fn$setMethod('incr', 'ooc', x + value ~ {x@a <- x@a+value; x})
   oo <- new('ooc',a=1)
   oo <- incr(oo,1)
   oo

# }
# NOT RUN {
   # plot quantile regression fits for various values of tau
   library(quantreg)
   data(engel)
   plot(engel$x, engel$y, xlab = 'income', ylab = 'food expenditure')
   junk <- fn$lapply(1:9/10, tau ~ abline(coef(rq(y ~ x, tau, engel))))

   # rolling mid-range
   library(zoo)
   fn$rollapply(LakeHuron, 12, ~ mean(range(x)))

   library(lattice)
   fn$xyplot(uptake ~ conc | Plant, CO2,
      panel = ... ~ { panel.xyplot(...); panel.text(200, 40, lab = 'X') })

   library(boot)
   set.seed(1)
   fn$boot(rivers, ~ median(x, d), R = 2000)
# }
# NOT RUN {
   x <- 0:50/50
   matplot(x, fn$outer(x, 1:8, ~ sin(x * k*pi)), type = 'blobcsSh')


# }