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context("Stats")
test_that("plot succeeds even if some computation fails", {
p1 <- ggplot(mtcars, aes(disp, mpg)) +
geom_point() +
facet_grid(gear ~ carb)
p2 <- p1 + geom_smooth()
b1 <- ggplot_build(p1)
expect_equal(length(b1$data), 1)
expect_warning(b2 <- ggplot_build(p2))
expect_equal(length(b2$data), 2)
})
# helper function for stat calc tests.
test_stat <- function(stat) {
stat$data <- transform(stat$data, PANEL = 1)
dat <- stat$compute_aesthetics(stat$data, ggplot())
dat <- add_group(dat)
stat$calc_statistic(dat, NULL)
}
context("stat-bin")
test_that("stat_sum", {
dat <- data.frame(x = c("a", "b", "c"), y = c(1, 5, 10))
# Should get an error when mapping/setting y and also using stat_bin
# But errors caught by internal tryCatch :()
# expect_error(ggplot_build(ggplot(dat, aes(x=x, y=y)) + geom_bar()),
# "Mapping a variable to y and also using stat=\"bin\"")
# expect_error(p <- ggplot_build(ggplot(dat, aes(x=x, y=y)) + geom_bar(stat="bin")),
# "Mapping a variable to y and also using stat=\"bin\"")
#
# expect_error(p <- ggplot_build(ggplot(dat, aes(x=x)) + geom_bar(y=5)),
# "Mapping a variable to y and also using stat=\"bin\"")
# This gives an error (it would probably be OK if just one
# of these happened, but this test looks for both)
dat2 <- data.frame(x = c("a", "b", "c", "a", "b", "c"), y = c(1, 5, 10, 2, 3, 4))
# expect_error(
# p <- ggplot_build(ggplot(dat2, aes(x=x, y=y)) + geom_bar()))
})
context("stat-sum")
test_that("stat_sum", {
d <- diamonds[1:1000, ]
all_ones <- function(x) all.equal(mean(x), 1)
ret <- test_stat(stat_sum(aes(x = cut, y = clarity), data = d))
expect_equal(dim(ret), c(38, 5))
expect_equal(sum(ret$n), nrow(d))
expect_true(all_ones(ret$prop))
ret <- test_stat(stat_sum(aes(x = cut, y = clarity, group = 1), data = d))
expect_equal(dim(ret), c(38, 5))
expect_equal(sum(ret$n), nrow(d))
expect_equal(sum(ret$prop), 1)
ret <- test_stat(stat_sum(aes(x = cut, y = clarity, group = cut), data = d))
expect_equal(dim(ret), c(38, 5))
expect_equal(sum(ret$n), nrow(d))
expect_true(all_ones(tapply(ret$prop, ret$x, FUN = sum)))
ret <- test_stat(stat_sum(aes(x = cut, y = clarity, group = cut, colour = cut), data = d))
expect_equal(dim(ret), c(38, 6))
expect_equal(ret$x, ret$colour)
expect_equal(sum(ret$n), nrow(d))
expect_true(all_ones(tapply(ret$prop, ret$x, FUN = sum)))
ret <- test_stat(stat_sum(aes(x = cut, y = clarity, group = clarity), data = d))
expect_equal(dim(ret), c(38, 5))
expect_equal(sum(ret$n), nrow(d))
expect_true(all_ones(tapply(ret$prop, ret$y, FUN = sum)))
ret <- test_stat(stat_sum(aes(x = cut, y = clarity, group = clarity, colour = cut), data = d))
expect_equal(dim(ret), c(38, 6))
expect_equal(ret$x, ret$colour)
expect_equal(sum(ret$n), nrow(d))
expect_true(all_ones(tapply(ret$prop, ret$y, FUN = sum)))
ret <- test_stat(stat_sum(aes(x = cut, y = clarity, group = 1, weight = price), data = d))
expect_equal(dim(ret), c(38, 5))
expect_equal(sum(ret$n), sum(d$price))
expect_equal(sum(ret$prop), 1)
})
# helper function for stat calc tests.
test_stat_scale <- function(stat, scale) {
stat$data <- transform(stat$data, PANEL = 1)
dat <- stat$compute_aesthetics(stat$data, ggplot())
dat <- add_group(dat)
stat$calc_statistic(dat, scale)
}
context("stat-bin2d")
test_that("stat-bin2d", {
d <- diamonds[1:1000,]
full_scales <- list(x = scale_x_continuous(limits = range(d$carat, na.rm=TRUE)),
y = scale_y_continuous(limits = range(d$depth, na.rm=TRUE)))
ret <- test_stat_scale(stat_bin2d(aes(x = carat, y = depth), data=d), full_scales)
expect_equal(dim(ret), c(191,12))
d$carat[1] <- NA
d$depth[2] <- NA
full_scales <- list(x = scale_x_continuous(limits = range(d$carat, na.rm=TRUE)),
y = scale_y_continuous(limits = range(d$depth, na.rm=TRUE)))
ret <- test_stat_scale(stat_bin2d(aes(x = carat, y = depth), data=d), full_scales)
expect_equal(dim(ret), c(191,12))
})
context("stat-density2d")
test_that("stat-density2d", {
full_scales <- list(x = scale_x_continuous(limits=c(1,6)),
y = scale_y_continuous(limits=c(5,40)))
ret <- test_stat_scale(stat_density2d(aes(x = wt, y = mpg), data = mtcars), full_scales)
# Check that the contour data goes beyond data range.
# The specific values below are sort of arbitrary; but they go beyond the range
# of the data
expect_true(min(ret$x) < 1.2)
expect_true(max(ret$x) > 5.8)
expect_true(min(ret$y) < 8)
expect_true(max(ret$y) > 35)
})
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