tests/testthat/test-stat-bin.R

In TomeTiger/ggplot2: Create Elegant Data Visualisations Using the Grammar of Graphics

context("stat_bin/stat_count")

test_that("stat_bin throws error when y aesthetic is present", {
  dat <- data_frame(x = c("a", "b", "c"), y = c(1, 5, 10))

  expect_error(ggplot_build(ggplot(dat, aes(x, y)) + stat_bin()),
    "must not be used with a y aesthetic.")

  expect_error(
    ggplot_build(ggplot(dat, aes(x)) + stat_bin(y = 5)),
    "StatBin requires a continuous x"
  )
})

test_that("bins specifies the number of bins", {
  df <- data_frame(x = 1:10)
  out <- function(x, ...) {
    layer_data(ggplot(df, aes(x)) + geom_histogram(...))
  }

  expect_equal(nrow(out(bins = 2)), 2)
  expect_equal(nrow(out(bins = 100)), 100)
})

test_that("binwidth computes widths for function input", {
  df <- data_frame(x = 1:100)
  out <- layer_data(ggplot(df, aes(x)) + geom_histogram(binwidth = function(x) 5))

  expect_equal(nrow(out), 21)
})

test_that("geom_histogram defaults to pad = FALSE", {
  df <- data_frame(x = 1:3)
  out <- layer_data(ggplot(df, aes(x)) + geom_histogram(binwidth = 1))

  expect_equal(out$count, c(1, 1, 1))
})

test_that("geom_freqpoly defaults to pad = TRUE", {
  df <- data_frame(x = 1:3)
  out <- layer_data(ggplot(df, aes(x)) + geom_freqpoly(binwidth = 1))

  expect_equal(out$count, c(0, 1, 1, 1, 0))
})

test_that("can use breaks argument", {
  df <- data_frame(x = 1:3)
  out <- layer_data(ggplot(df, aes(x)) + geom_histogram(breaks = c(0, 1.5, 5)))

  expect_equal(out$count, c(1, 2))
})

test_that("fuzzy breaks are used when cutting", {
  df <- data_frame(x = c(-1, -0.5, -0.4, 0))
  p <- ggplot(df, aes(x)) +
    geom_histogram(binwidth = 0.1, boundary = 0.1, closed = "left")

  bins <- layer_data(p) %>% subset(count > 0) %>% .[1:5]
  expect_equal(bins$count, c(1, 1, 1, 1))
})

test_that("breaks are transformed by the scale", {
   df <- data_frame(x = rep(1:4, 1:4))
   base <- ggplot(df, aes(x)) + geom_histogram(breaks = c(1, 2.5, 4))

   out1 <- layer_data(base)
   out2 <- layer_data(base + scale_x_sqrt())
   expect_equal(out1$xmin, c(1, 2.5))
   expect_equal(out2$xmin, sqrt(c(1, 2.5)))
})

# Underlying binning algorithm --------------------------------------------

comp_bin <- function(df, ...) {
  plot <- ggplot(df, aes(x = x)) + stat_bin(...)
  layer_data(plot)
}

test_that("closed left or right", {
  dat <- data_frame(x = c(0, 10))

  res <- comp_bin(dat, binwidth = 10, pad = FALSE)
  expect_identical(res$count, c(1, 1))
  res <- comp_bin(dat, binwidth = 10, boundary = 5, pad = FALSE)
  expect_identical(res$count, c(1, 1))
  res <- comp_bin(dat, binwidth = 10, boundary = 0, pad = FALSE)
  expect_identical(res$count, 2)
  res <- comp_bin(dat, binwidth = 5, boundary = 0, pad = FALSE)
  expect_identical(res$count, c(1, 1))

  res <- comp_bin(dat, binwidth = 10, pad = FALSE, closed = "left")
  expect_identical(res$count, c(1, 1))
  res <- comp_bin(dat, binwidth = 10, boundary = 5, pad = FALSE, closed = "left")
  expect_identical(res$count, c(1, 1))
  res <- comp_bin(dat, binwidth = 10, boundary = 0, pad = FALSE, closed = "left")
  expect_identical(res$count, c(2))
  res <- comp_bin(dat, binwidth = 5, boundary = 0, pad = FALSE, closed = "left")
  expect_identical(res$count, c(1, 1))
})

test_that("setting boundary and center", {
  # numeric
  df <- data_frame(x = c(0, 30))

  # Error if both boundary and center are specified
  expect_error(comp_bin(df, boundary = 5, center = 0), "one of `boundary` and `center`")

  res <- comp_bin(df, binwidth = 10, boundary = 0, pad = FALSE)
  expect_identical(res$count, c(1, 0, 1))
  expect_identical(res$xmin[1], 0)
  expect_identical(res$xmax[3], 30)

  res <- comp_bin(df, binwidth = 10, center = 0, pad = FALSE)
  expect_identical(res$count, c(1, 0, 0, 1))
  expect_identical(res$xmin[1], df$x[1] - 5)
  expect_identical(res$xmax[4], df$x[2] + 5)
})

test_that("weights are added", {
  df <- data_frame(x = 1:10, y = 1:10)
  p <- ggplot(df, aes(x = x, weight = y)) + geom_histogram(binwidth = 1)
  out <- layer_data(p)

  expect_equal(out$count, df$y)
})

# stat_count --------------------------------------------------------------

test_that("stat_count throws error when y aesthetic present", {
  dat <- data_frame(x = c("a", "b", "c"), y = c(1, 5, 10))

  expect_error(
    ggplot_build(ggplot(dat, aes(x, y)) + stat_count()),
    "must not be used with a y aesthetic.")

  expect_error(
    ggplot_build(ggplot(dat, aes(x)) + stat_count(y = 5)),
    "must not be used with a y aesthetic."
  )
})

test_that("stat_count preserves x order for continuous and discrete", {
  # x is numeric
  b <- ggplot_build(ggplot(mtcars, aes(carb)) + geom_bar())
  expect_identical(b$data[[1]]$x, c(1,2,3,4,6,8))
  expect_identical(b$data[[1]]$y, c(7,10,3,10,1,1))

  # x is factor where levels match numeric order
  mtcars$carb2 <- factor(mtcars$carb)
  b <- ggplot_build(ggplot(mtcars, aes(carb2)) + geom_bar())
  expect_identical(b$data[[1]]$x, 1:6)
  expect_identical(b$data[[1]]$y, c(7,10,3,10,1,1))

  # x is factor levels differ from numeric order
  mtcars$carb3 <- factor(mtcars$carb, levels = c(4,1,2,3,6,8))
  b <- ggplot_build(ggplot(mtcars, aes(carb3)) + geom_bar())
  expect_identical(b$data[[1]]$x, 1:6)
  expect_identical(b$layout$panel_params[[1]]$x.labels, c("4","1","2","3","6","8"))
  expect_identical(b$data[[1]]$y, c(10,7,10,3,1,1))
})