library(dplyr)

## 
## Attaching package: 'dplyr'
## 
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## 
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(magrittr)
library(rlist)
library(ggplot2)
library(stringr)

packageVersion("ggplot2")

## [1] '1.0.1.9000'

# 適当なデータをつっこんで、生成される変数名を返す関数
get_generated_variables <- function(stat_func){
  
  dummy_data <- data.frame(a = 1:25,
                           x_grid = rep(1:5, 5),
                           y_grid = rep(1:5, each = 5),
                           z_grid = 1:25 %% 3)
  
  stat_obj <- stat_func()
  required_aes <- stat_obj$stat$required_aes
  
  dummy_aes_args <- list()
  dummy_aes_args[required_aes] <- "a"
  if (stat_obj$stat$objname == "contour") {
    dummy_aes_args["x"] <- "x_grid"
    dummy_aes_args["y"] <- "y_grid"
    dummy_aes_args["z"] <- "z_grid"
  } else if (stat_obj$stat$objname == "ellipse")  {
    dummy_aes_args["y"] <- "y_grid"
  }
  
  dummy_aes <- do.call(aes_string, dummy_aes_args)
  
  nms <- 
    (ggplot(data = dummy_data, mapping = dummy_aes) + stat_obj) %>%
    ggplot_build %>%
    use_series(data) %>%
    extract2(1) %>%
    names
  
  setdiff(nms, c(required_aes, "PANEL"))
}

# main --------------------------------------------------------------------------------

# stat_XXXという名前の関数を取得
### stat_function, stat_vline, stat_hlineはそれぞれデフォルトで指定しなければいけない引数があるので取り除く
stat_funcs <- ls('package:ggplot2', pattern = "^stat_.+") %>%
  extract({is_in(., c("stat_function", "stat_hline", "stat_vline")) %>% `n'est pas`}) %>%
  list.map(get(., envir = asNamespace('ggplot2')))

# 必要なマッピング
required_aes <-
  stat_funcs %>%
  list.map(.()$stat$required_aes) %>%
  list.map(inset(list(), ., "★"))

# 計算される変数
generated_variables <-
  stat_funcs %>%
  list.map(get_generated_variables(.)) %>%
  list.map(inset(list(), ., "○"))

## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bindot: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## Smoothing formula not specified. Using: y ~ x
## geom_smooth: method="auto" and size of largest group is <1000, so using loess. Use 'method = x' to change the smoothing method.

# 結果をマージ
merged <- list.maps(
  plyr::defaults(..1, ..2),
  generated_variables,
  required_aes) %>%
  list.map(as_data_frame(.)) %>%
  bind_rows

# 見やすいようにNAには空文字を入れる
merged[is.na(merged)] <- ""

merged <- merged[,sort(colnames(merged))]
rownames(merged) <- str_replace(names(stat_funcs), "stat_", "")

library(xtable)
print(xtable(t(merged)), type = "html")

	abline	bin	bin2d	bindot	binhex	boxplot	contour	density	density2d	ecdf	ellipse	identity	qq	quantile	smooth	spoke	sum	summary	summary_hex	summary2d	unique	ydensity
angle																★
binwidth				○
count		○	○	○	○			○														○
countidx				○
density		○	○		○			○														○
fill			○		○														○	○
group	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○		○	○	○	○
intercept	○
level							○		○
lower						○
middle						○
n								○									○					○
ncount		○		○
ndensity		○
notchlower						○
notchupper						○
order							○
outliers						○
piece							○		○
prop																	○
quantile														○
radius																★
sample													★
scaled								○														○
se															○
size																	○
slope	○
stackpos				○
theoretical													○
upper						○
value																			○	○
violinwidth																						○
weight						○
width				○																		○
x		★	★	★	★	★	★	★	★	★	★		○	★	★	★	★	★	★	★		★
xbin			○																	○
xend																○
xint			○																	○
xmax		○	○	○		○		○												○		○
xmin		○	○	○		○		○												○		○
y		○	★	○	★	★	★	○	★	○	★		○	★	★	★	★	★	★	★		★
ybin			○																	○
yend																○
yint			○																	○
ymax		○	○	○		○		○							○					○		○
ymax_final						○
ymin		○	○	○		○		○							○					○		○
ymin_final						○
z							★												★	★