```
library(testthat)
context("cut that keeps identical values together")
test_that("typical assignment of quartiles", {
observations <- c(10, 20, 30, 40, 50, 60, 70, 80)
quartile_breaks <- quantile(
observations,
probs = seq(0, 1, 0.25),
type = 6
)
expect_true(quartile_breaks[[2]] > 20)
expect_true(quartile_breaks[[2]] < 30)
expect_true(quartile_breaks[[3]] > 40)
expect_true(quartile_breaks[[3]] < 50)
# Expect cut into equal quantities per quartile.
level_codes <- cut(
observations,
breaks = quartile_breaks,
include.lowest = TRUE,
right = TRUE,
labels = FALSE
)
expect_equal(sum(level_codes == 1), sum(level_codes == 2))
expect_equal(sum(level_codes == 2), sum(level_codes == 3))
expect_equal(sum(level_codes == 3), sum(level_codes == 4))
})
test_that("cut fails when quantile breaks are not unique", {
observations <- c(23, 42, 42, 42, 42, 42, 84, 96)
quartile_breaks <- quantile(
observations,
probs = seq(0, 1, 0.25),
type = 6
)
# 25% and 50% breaks are identical
expect_equal(quartile_breaks[[2]], 42)
expect_equal(quartile_breaks[[3]], 42)
# cut expects that breaks are unique
expect_error(
cut(
observations,
breaks = quartile_breaks,
include.lowest = TRUE, # include an x[i] equal to upper bound
right = TRUE, # closed interval on the right
labels = FALSE
),
".*'breaks' are not unique")
})
# This is actual data from a customer.
# Two observations with value of 0.963884306 fall exactly
# on the upper bound of the first quartile. Per customer, these
# values should be included in the first quartile.
# The data is sorted here for convenience of developer review,
# but it is not necessary for the 'quantile' and 'cut' functions
# to work correctly.
getValues <- function() {
return(c(-0.882332254,
0.1338623,
0.215971655,
0.273843389,
0.288379391,
0.292232265,
0.379572845,
0.406928303,
0.432346897,
0.786682461,
0.892456064,
0.895333557,
0.895333557,
0.902776789,
0.938268317,
0.963884306,
0.963884306,
0.999116674,
1.038401724,
1.050939066,
1.216881199,
1.225567745,
1.229586293,
1.229586293,
1.237477609,
1.247182861,
1.267921673,
1.276053136,
1.317569133,
1.346648891,
1.346648891,
1.346648891,
1.346648891,
1.348288901,
1.388335998,
1.434631731,
1.434631731,
1.482184361,
1.486617112,
1.486617112,
1.489340391,
1.496462811,
1.50421368,
1.504312646,
1.523700358,
1.526535848,
1.574599952,
1.584580322,
1.593867641,
1.638701735,
1.663555395,
1.711811286,
1.751538235,
1.751538235,
1.751538235,
1.916191835,
1.927155017,
1.933473878,
2.003022218,
2.003022218,
2.003022218,
2.003022218,
2.167675818,
2.417554607,
2.505537447,
2.670191047))
}
# Every observation with a value equal to the upper bound of a
# quartile break should be assigned to that quartile, even if
# that quartile will hold more than 25% of the observations.
test_that("cut for observations equal to upper bound of a quartile", {
observations <- getValues()
quartile_breaks <- quantile(
observations,
probs = seq(0, 1, 0.25),
type = 6
)
# 2 observations fall exactly on 25% break point.
EXPECTED_BREAKPOINT <- 0.963884306
expect_equal(sum(observations == EXPECTED_BREAKPOINT), 2)
expect_equal(quartile_breaks[[2]], EXPECTED_BREAKPOINT)
# Verify that equal values are put in the same quartile.
level_codes <- cut(
observations,
breaks = quartile_breaks,
include.lowest = TRUE, # include an x[i] equal to upper bound
right = TRUE, # closed interval on the right
labels = FALSE
)
# All observations on upper bound of quartile are assigned to that quartile.
quartile_assignments <- data.frame(observations, level_codes)
expect_equal(
quartile_assignments[quartile_assignments$observations == EXPECTED_BREAKPOINT, "level_codes"],
c(1, 1))
# Expected number of values in 1st quartile.
expect_equal(sum(level_codes == 1), 17)
expect_true(sum(level_codes == 1) > length(level_codes) / 4)
})
```

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