hadley

month_seq <- seq(as.Date("2000-01-01"), as.Date("2000-12-31"), "month")
months <- factor(months(month_seq), levels = months(month_seq))
months_abbr <- factor(months(month_seq, TRUE), levels = months(month_seq, TRUE))

wday_seq <- seq(as.Date("2000-01-02"), as.Date("2000-01-08"), "day")
wdays <- factor(weekdays(wday_seq), levels = weekdays(wday_seq))
wdays_abbr <- factor(weekdays(wday_seq, TRUE), levels = weekdays(wday_seq, TRUE))

second <- function(x) as.POSIXlt(x)$sec
minute <- function(x) as.POSIXlt(x)$min

hadley

measure <- structure(list(age = structure(c(1L, 2L, 3L, 4L, 1L, 2L, 3L, 
4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L), .Label = c("7-yrs", "11-yrs", 
"15-yrs", "21-yrs"), class = "factor"), trt = structure(c(2L, 
2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L), .Label = c("alternation", 
"repetition"), class = "factor"), value = c(814, 500, 424, 394, 
967, 574, 472, 446, 10, 6, 6, 4, 13, 7, 6, 3), variable = c("rt", 
"rt", "rt", "rt", "rt", "rt", "rt", "rt", "error", "error", "error", 
"error", "error", "error", "error", "error")), .Names = c("age", 
"trt", "value", "variable"), row.names = c(NA, 16L), class = "data.frame")
plot <- ggplot(measure, aes(age, value, colour = trt, fill = trt)) + 

hadley

# dhist.
# An another algorithm for computing histogram breaks.  Produces irregular bins.
# Provided by Lorraine Denby
#
#
# @keyword internal
dhist <- function(x, a=5*diff(quantile(x, c(0.25,0.75))), nbins=10, rx = range(x)) {
  x <- sort(x)
  if(a == 0)
    a <- diff(range(x))/100000000

hadley

mid_range <- function(x) mean(range(x, na.rm = TRUE))
centres <- ddply(county_df, c("state", "county"), summarise, 
  lat = mid_range(lat), 
  long = mid_range(long)
)

bubbles <- merge(centres, unemp, by = c("state", "county"))
ggplot(bubbles, aes(long, lat)) +
  geom_polygon(aes(group = group), data = state_df, 
    colour = "white", fill = NA) +

hadley

How to write a reproducible example.

You are most likely to get good help with your R problem if you provide a reproducible example. A reproducible example allows someone else to recreate your problem by just copying and pasting R code.

There are four things you need to include to make your example reproducible: required packages, data, code, and a description of your R environment.

• Packages should be loaded at the top of the script, so it's easy to see which ones the example needs.

• The easiest way to include data in an email is to use dput() to generate

hadley

library(sinartra)

source("memoise.r")
source("help-parse-rd.r")
source("help-package.r")
source("help-topic.r")
source("help-index.r")

router <- Router$clone()

hadley

ks.default <- function(rows) seq(2, max(3, rows %/% 4))

many_kmeans <- function(x, ks = ks.default(nrow(x)), ...) {
  ldply(seq_along(ks), function(i) {
    cl <- kmeans(x, centers = ks[i], ...)
    data.frame(obs = seq_len(nrow(x)), i = i, k = ks[i], cluster = cl$cluster)
  })
}

all_hclust <- function(x, ks = ks.default(nrow(x)), point.dist = "euclidean", cluster.dist = "ward") {

hadley

#' For munching, only grobs are lines and polygons: everything else is 
#' transfomed into those special cases by the geom.
#' @examples
#' nz <- data.frame(map("nz", plot=FALSE)[c("x","y")])
#' munch_data(nz, segment_length = 0.1)
munch_data <- function(data, segment_length) {
  data <- add_group(data)
  n <- nrow(data)
  aes_df <- data[setdiff(names(data), c("x", "y"))]

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data <- iris

d <- dist(scale(iris[, 1:4]))
h <- hclust(d, "ward")

data$ORDER <- order(h$order)
data$HEIGHT <- 0
data$LEVEL <- 0
data$POINTS <- 1

hadley

all <- iconvlist()
names(all) <- all

tries <- llply(all[330:350], function(cur) iconv(song$artist, cur, "UTF-8"))
vapply(tries, function(x) sum(is.na(x)), integer(1))