-
Notifications
You must be signed in to change notification settings - Fork 2k
/
stat-bin.r
166 lines (145 loc) · 5.38 KB
/
stat-bin.r
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
#' Bin data.
#'
#' Missing values are currently silently dropped.
#'
#' @section Aesthetics:
#' \Sexpr[results=rd,stage=build]{ggplot2:::rd_aesthetics("stat", "bin")}
#'
#' @inheritParams stat_identity
#' @param binwidth Bin width to use. Defaults to 1/30 of the range of the
#' data
#' @param breaks Actual breaks to use. Overrides bin width and origin
#' @param origin Origin of first bin
#' @param width Width of bars when used with categorical data
#' @param right If \code{TRUE}, right-closed, left-open, if \code{FALSE},
#" the default, right-open, left-closed.
#' @param drop If TRUE, remove all bins with zero counts
#' @return New data frame with additional columns:
#' \item{count}{number of points in bin}
#' \item{density}{density of points in bin, scaled to integrate to 1}
#' \item{ncount}{count, scaled to maximum of 1}
#' \item{ndensity}{density, scaled to maximum of 1}
#' @export
#' @examples
#' \donttest{
#' simple <- data.frame(x = rep(1:10, each = 2))
#' base <- ggplot(simple, aes(x))
#' # By default, right = TRUE, and intervals are of the form (a, b]
#' base + stat_bin(binwidth = 1, drop = FALSE, right = TRUE, col = "black")
#' # If right = FALSE intervals are of the form [a, b)
#' base + stat_bin(binwidth = 1, drop = FALSE, right = FALSE, col = "black")
#'
#' m <- ggplot(movies, aes(x=rating))
#' m + stat_bin()
#' m + stat_bin(binwidth=0.1)
#' m + stat_bin(breaks=seq(4,6, by=0.1))
#' # See geom_histogram for more histogram examples
#'
#' # To create a unit area histogram, use aes(y = ..density..)
#' (linehist <- m + stat_bin(aes(y = ..density..), binwidth=0.1,
#' geom="line", position="identity"))
#' linehist + stat_density(colour="blue", fill=NA)
#'
#' # Also works with categorical variables
#' ggplot(movies, aes(x=mpaa)) + stat_bin()
#' qplot(mpaa, data=movies, stat="bin")
#' }
stat_bin <- function (mapping = NULL, data = NULL, geom = "bar", position = "stack",
width = 0.9, drop = FALSE, right = FALSE, binwidth = NULL, origin = NULL, breaks = NULL, ...) {
StatBin$new(mapping = mapping, data = data, geom = geom, position = position,
width = width, drop = drop, right = right, binwidth = binwidth, origin = origin, breaks = breaks, ...)
}
StatBin <- proto(Stat, {
objname <- "bin"
informed <- FALSE
calculate_groups <- function(., data, ...) {
.$informed <- FALSE
.super$calculate_groups(., data, ...)
}
calculate <- function(., data, scales, binwidth=NULL, origin=NULL, breaks=NULL, width=0.9, drop = FALSE, right = FALSE, ...) {
range <- scale_dimension(scales$x, c(0, 0))
if (is.null(breaks) && is.null(binwidth) && !is.integer(data$x) && !.$informed) {
message("stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.")
.$informed <- TRUE
}
bin(data$x, data$weight, binwidth=binwidth, origin=origin, breaks=breaks, range=range, width=width, drop = drop, right = right)
}
default_aes <- function(.) aes(y = ..count..)
required_aes <- c("x")
default_geom <- function(.) GeomBar
})
bin <- function(x, weight=NULL, binwidth=NULL, origin=NULL, breaks=NULL, range=NULL, width=0.9, drop = FALSE, right = TRUE) {
if (length(na.omit(x)) == 0) return(data.frame())
if (is.null(weight)) weight <- rep(1, length(x))
weight[is.na(weight)] <- 0
if (is.null(range)) range <- range(x, na.rm = TRUE, finite=TRUE)
if (is.null(binwidth)) binwidth <- diff(range) / 30
if (is.integer(x)) {
bins <- x
x <- sort(unique(bins))
width <- width
} else if (diff(range) == 0) {
width <- width
bins <- x
} else { # if (is.numeric(x))
if (is.null(breaks)) {
if (is.null(origin)) {
breaks <- fullseq(range, binwidth, pad = TRUE)
} else {
breaks <- seq(origin, max(range) + binwidth, binwidth)
}
}
# Adapt break fuzziness from base::hist - this protects from floating
# point rounding errors
diddle <- 1e-07 * stats::median(diff(breaks))
if (right) {
fuzz <- c(-diddle, rep.int(diddle, length(breaks) - 1))
} else {
fuzz <- c(rep.int(-diddle, length(breaks) - 1), diddle)
}
fuzzybreaks <- sort(breaks) + fuzz
bins <- cut(x, fuzzybreaks, include.lowest=TRUE, right = right)
left <- breaks[-length(breaks)]
right <- breaks[-1]
x <- (left + right)/2
width <- diff(breaks)
}
results <- data.frame(
count = as.numeric(tapply(weight, bins, sum, na.rm=TRUE)),
x = x,
width = width
)
if (sum(results$count, na.rm = TRUE) == 0) {
return(results)
}
res <- within(results, {
count[is.na(count)] <- 0
density <- count / width / sum(abs(count), na.rm=TRUE)
ncount <- count / max(abs(count), na.rm=TRUE)
ndensity <- density / max(abs(density), na.rm=TRUE)
})
if (drop) res <- subset(res, count > 0)
res
}
# Generate sequence of fixed size intervals covering range
# All locations are multiples of size
#
# @param range
# @param interval size
# @keyword internal
# @seealso \code{\link{reshape}{round_any}}
fullseq <- function(range, size, pad = FALSE) {
if (diff(range) < 1e-6) return(c(range[1] - size / 2, range[1] + size / 2))
x <- seq(
round_any(range[1], size, floor),
round_any(range[2], size, ceiling),
by=size
)
if (pad) {
# Add extra bin on bottom and on top, to guarantee that we cover complete
# range of data, whether right = T or F
c(min(x) - size, x, max(x) + size)
} else {
x
}
}