tests/testthat/test-row-mean.R
In OuhscBbmc/OuhscMunge: Data Manipulation Operations
library(testthat)
test_that("mtcars -engine_mean", {
expected <- structure(
list(mpg = c(21, 21, 22.8, 21.4, 18.7, 18.1, 14.3,
24.4, 22.8, 19.2, 17.8, 16.4, 17.3, 15.2, 10.4, 10.4, 14.7, 32.4,
30.4, 33.9, 21.5, 15.5, 15.2, 13.3, 19.2, 27.3, 26, 30.4, 15.8,
19.7, 15, 21.4), cyl = c(6, 6, 4, 6, 8, 6, 8, 4, 4, 6, 6, 8,
8, 8, 8, 8, 8, 4, 4, 4, 4, 8, 8, 8, 8, 4, 4, 4, 8, 6, 8, 4),
disp = c(160, 160, 108, 258, 360, 225, 360, 146.7, 140.8,
167.6, 167.6, 275.8, 275.8, 275.8, 472, 460, 440, 78.7, 75.7,
71.1, 120.1, 318, 304, 350, 400, 79, 120.3, 95.1, 351, 145,
301, 121), hp = c(110, 110, 93, 110, 175, 105, 245, 62, 95,
123, 123, 180, 180, 180, 205, 215, 230, 66, 52, 65, 97, 150,
150, 245, 175, 66, 91, 113, 264, 175, 335, 109), drat = c(3.9,
3.9, 3.85, 3.08, 3.15, 2.76, 3.21, 3.69, 3.92, 3.92, 3.92,
3.07, 3.07, 3.07, 2.93, 3, 3.23, 4.08, 4.93, 4.22, 3.7, 2.76,
3.15, 3.73, 3.08, 4.08, 4.43, 3.77, 4.22, 3.62, 3.54, 4.11
), wt = c(2.62, 2.875, 2.32, 3.215, 3.44, 3.46, 3.57, 3.19,
3.15, 3.44, 3.44, 4.07, 3.73, 3.78, 5.25, 5.424, 5.345, 2.2,
1.615, 1.835, 2.465, 3.52, 3.435, 3.84, 3.845, 1.935, 2.14,
1.513, 3.17, 2.77, 3.57, 2.78), qsec = c(16.46, 17.02, 18.61,
19.44, 17.02, 20.22, 15.84, 20, 22.9, 18.3, 18.9, 17.4, 17.6,
18, 17.98, 17.82, 17.42, 19.47, 18.52, 19.9, 20.01, 16.87,
17.3, 15.41, 17.05, 18.9, 16.7, 16.9, 14.5, 15.5, 14.6, 18.6
), vs = c(0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0,
0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1), am = c(1,
1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,
0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1), gear = c(4, 4, 4, 3,
3, 3, 3, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 4, 4, 4, 3, 3, 3,
3, 3, 4, 5, 5, 5, 5, 5, 4), carb = c(4, 4, 1, 1, 2, 1, 4,
2, 2, 4, 4, 3, 3, 3, 4, 4, 4, 1, 2, 1, 1, 2, 2, 4, 2, 1,
2, 2, 4, 6, 8, 2), engine_mean = c(42.5, 42.5, 28.5, 66.5,
92.5, 58.25, 93, 38.425, 36.95, 44.65, 44.65, 71.7, 71.7,
71.7, 121, 118, 113, 21.175, 20.675, 19.275, 31.525, 82,
78.5, 90.5, 102.5, 21.25, 31.575, 25.525, 90.75, 39.25, 79.25,
32)), row.names = c("Mazda RX4", "Mazda RX4 Wag", "Datsun 710",
"Hornet 4 Drive", "Hornet Sportabout", "Valiant", "Duster 360",
"Merc 240D", "Merc 230", "Merc 280", "Merc 280C", "Merc 450SE",
"Merc 450SL", "Merc 450SLC", "Cadillac Fleetwood", "Lincoln Continental",
"Chrysler Imperial", "Fiat 128", "Honda Civic", "Toyota Corolla",
"Toyota Corona", "Dodge Challenger", "AMC Javelin", "Camaro Z28",
"Pontiac Firebird", "Fiat X1-9", "Porsche 914-2", "Lotus Europa",
"Ford Pantera L", "Ferrari Dino", "Maserati Bora", "Volvo 142E"
), class = "data.frame"
)
actual <-
mtcars |>
row_mean(
columns_to_process = c("cyl", "disp", "vs", "carb"),
new_column_name = "engine_mean"
)
# expected <- dput(actual)
testthat::expect_equal(actual, expected)
})
test_that("mtcars -engine_mean & nonmissing count", {
expected <-
structure(
list(mpg = c(21, 21, 22.8, 21.4, 18.7, 18.1, 14.3,
24.4, 22.8, 19.2, 17.8, 16.4, 17.3, 15.2, 10.4, 10.4, 14.7, 32.4,
30.4, 33.9, 21.5, 15.5, 15.2, 13.3, 19.2, 27.3, 26, 30.4, 15.8,
19.7, 15, 21.4), cyl = c(6, 6, 4, 6, 8, 6, 8, 4, 4, 6, 6, 8,
8, 8, 8, 8, 8, 4, 4, 4, 4, 8, 8, 8, 8, 4, 4, 4, 8, 6, 8, 4),
disp = c(160, 160, 108, 258, 360, 225, 360, 146.7, 140.8,
167.6, 167.6, 275.8, 275.8, 275.8, 472, 460, 440, 78.7, 75.7,
71.1, 120.1, 318, 304, 350, 400, 79, 120.3, 95.1, 351, 145,
301, 121), hp = c(110, 110, 93, 110, 175, 105, 245, 62, 95,
123, 123, 180, 180, 180, 205, 215, 230, 66, 52, 65, 97, 150,
150, 245, 175, 66, 91, 113, 264, 175, 335, 109), drat = c(3.9,
3.9, 3.85, 3.08, 3.15, 2.76, 3.21, 3.69, 3.92, 3.92, 3.92,
3.07, 3.07, 3.07, 2.93, 3, 3.23, 4.08, 4.93, 4.22, 3.7, 2.76,
3.15, 3.73, 3.08, 4.08, 4.43, 3.77, 4.22, 3.62, 3.54, 4.11
), wt = c(2.62, 2.875, 2.32, 3.215, 3.44, 3.46, 3.57, 3.19,
3.15, 3.44, 3.44, 4.07, 3.73, 3.78, 5.25, 5.424, 5.345, 2.2,
1.615, 1.835, 2.465, 3.52, 3.435, 3.84, 3.845, 1.935, 2.14,
1.513, 3.17, 2.77, 3.57, 2.78), qsec = c(16.46, 17.02, 18.61,
19.44, 17.02, 20.22, 15.84, 20, 22.9, 18.3, 18.9, 17.4, 17.6,
18, 17.98, 17.82, 17.42, 19.47, 18.52, 19.9, 20.01, 16.87,
17.3, 15.41, 17.05, 18.9, 16.7, 16.9, 14.5, 15.5, 14.6, 18.6
), vs = c(0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0,
0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1), am = c(1,
1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,
0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1), gear = c(4, 4, 4, 3,
3, 3, 3, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 4, 4, 4, 3, 3, 3,
3, 3, 4, 5, 5, 5, 5, 5, 4), carb = c(4, 4, 1, 1, 2, 1, 4,
2, 2, 4, 4, 3, 3, 3, 4, 4, 4, 1, 2, 1, 1, 2, 2, 4, 2, 1,
2, 2, 4, 6, 8, 2), engine_mean = c(42.5, 42.5, 28.5, 66.5,
92.5, 58.25, 93, 38.425, 36.95, 44.65, 44.65, 71.7, 71.7,
71.7, 121, 118, 113, 21.175, 20.675, 19.275, 31.525, 82,
78.5, 90.5, 102.5, 21.25, 31.575, 25.525, 90.75, 39.25, 79.25,
32), engine_nonmissing_count = c(4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4)), row.names = c("Mazda RX4",
"Mazda RX4 Wag", "Datsun 710", "Hornet 4 Drive", "Hornet Sportabout",
"Valiant", "Duster 360", "Merc 240D", "Merc 230", "Merc 280",
"Merc 280C", "Merc 450SE", "Merc 450SL", "Merc 450SLC", "Cadillac Fleetwood",
"Lincoln Continental", "Chrysler Imperial", "Fiat 128", "Honda Civic",
"Toyota Corolla", "Toyota Corona", "Dodge Challenger", "AMC Javelin",
"Camaro Z28", "Pontiac Firebird", "Fiat X1-9", "Porsche 914-2",
"Lotus Europa", "Ford Pantera L", "Ferrari Dino", "Maserati Bora",
"Volvo 142E"), class = "data.frame"
)
actual <-
mtcars |>
row_mean(
columns_to_process = c("cyl", "disp", "vs", "carb"),
new_column_name = "engine_mean",
nonmissing_count_name = "engine_nonmissing_count"
)
# expected <- dput(actual)
testthat::expect_equal(actual, expected)
})
test_that("billboard -all weeks", {
expected <-
c(
NA, NA, 26.4716981132075, 67.1, 56.2222222222222, 37.65, NA,
52.05, 16.65625, 67.75, 76.2727272727273, 19.952380952381, 24.1363636363636,
19.2916666666667, 51.25, NA, 80.2758620689655, NA, 82.05, 23.46875,
47.85, 32.35, 30.6451612903226, 28.05, 23.75, 60.2, 32.35, 62.5,
33.9523809523809, 72.4, 87.5555555555556, NA, 85.5333333333333,
62.9411764705882, 65, 21.5862068965517, 74, 85.3333333333333,
33.304347826087, 72.5833333333333, 51.6, 21, 57.15, NA, NA, 45.8,
76.8421052631579, NA, 84.375, 86, 90.4, NA, 37, 66.2666666666667,
NA, 73.8181818181818, 59.45, 69, 84.25, NA, 65.7368421052632,
47.3, 36.859649122807, 33.8297872340426, 88.6923076923077, NA,
47.4117647058824, 41.7619047619048, 74.6, 82, 75.6666666666667,
48.4, 68.55, NA, 14.8214285714286, 22.9375, 20.875, 77.4285714285714,
NA, 24.8571428571429, 75.3, 48.45, 50.6, 47.1, 43.25, 43.25,
89.3846153846154, 26.8928571428571, 71.1428571428571, NA, 30.7,
14.3333333333333, 68.0666666666667, 32.8421052631579, 69.8, NA,
NA, 54.7, NA, 60.875, 31.1904761904762, 62.7647058823529, 86.0833333333333,
34.15, 39.6666666666667, NA, NA, NA, 69.15, 64.5263157894737,
69.2666666666667, 86.5, 50, 21.6666666666667, 58.8, 49.9090909090909,
NA, 97.1666666666667, 71.3, 74.95, 96.625, 23.0188679245283,
69.5, 49.4285714285714, NA, 78.8461538461538, 72.2631578947368,
56, 17.8571428571429, 76.1111111111111, 49.1, 55.6666666666667,
65.6111111111111, 15.85, 61.1176470588235, 63.4117647058824,
31.15, 58.1, 67.5882352941177, 63.4, 34.9, 31.375, 19.4166666666667,
69.375, 31.3, 81.5555555555556, 87.8666666666667, 23.5238095238095,
60, 21.2045454545455, 77.7647058823529, 75.2, 45.65, 28.4375,
NA, 37.1666666666667, 69.6, 45.45, 67.8333333333333, NA, 39,
80.4444444444444, 76.5, NA, NA, NA, NA, 65.75, NA, 85.375, 45.75,
86.3636363636364, 86.6666666666667, 77.1, NA, 57.9230769230769,
86.2727272727273, 92.7222222222222, 78.9411764705882, 26.7272727272727,
60.3, 49.9, 70.2941176470588, 84.2142857142857, NA, 60.1052631578947,
38.8636363636364, 62.3333333333333, 79.6666666666667, 92.65,
43, 13.4583333333333, NA, 53.6111111111111, 37.1111111111111,
55.9, NA, 63.15, 70.8461538461538, 71.95, 56.05, 47.3333333333333,
44.2, 65, 88, 67.8461538461538, 43.2, 86.1, 84.4, NA, 54.5, 92.1111111111111,
64.1739130434783, 25.0909090909091, 50.65, 19.6333333333333,
71.4705882352941, 33.05, 14.2608695652174, 21.68, 19.6153846153846,
73.25, 30.8823529411765, 28.1904761904762, 26.4814814814815,
NA, 62.3846153846154, 94.4444444444444, 79.1111111111111, NA,
54.9, 65.25, NA, 21.7777777777778, 18.625, 75.625, NA, 80.15,
47.25, NA, NA, 67.5714285714286, 59, 61.8, 77.6842105263158,
36.1818181818182, 40.8, 82.95, 35.6, 63.9, 19.9230769230769,
52.85, 10.5, 46.55, 17.3636363636364, NA, 63.05, 68.2222222222222,
NA, 81.0666666666667, 44.875, NA, 62.05, 24.3076923076923, 19.1785714285714,
82.6, 28.7692307692308, NA, 49.5, 22.125, 41.5833333333333, 54.5,
48.1818181818182, 36, NA, 74, 38, 86.7692307692308, 73.8235294117647,
60.7, 44.7, 58.45, NA, NA, NA, 79.8333333333333, 25.8636363636364,
66.2, 36.4, 85.2727272727273, 66.95, 36.9259259259259, 90.0909090909091,
NA, 47.8181818181818, NA, 72, NA, 66.2631578947368, 57.15, 21.4390243902439,
37, 53.5833333333333, 68.5555555555556, 90.1818181818182, 63.5,
NA, 43.35, 75.4210526315789, 59.8333333333333, 85.5333333333333,
77.3, 83.125, NA, 88.8571428571429, NA, 18.6410256410256
)
actual <-
tidyr::billboard |>
row_mean(
pattern = "^wk\\d{1,2}$",
new_column_name = "week_mean",
threshold_proportion = .1,
verbose = TRUE
) |>
dplyr::pull(week_mean)
# expected <- dput(actual)
testthat::expect_equal(actual, expected)
})
test_that("billboard -all weeks nonmissing count", {
expected <-
c(
7, 3, 53, 20, 18, 20, 5, 20, 32, 20, 11, 21, 22, 24, 20, 5,
29, 3, 20, 32, 20, 20, 31, 20, 24, 15, 20, 20, 21, 15, 9, 3,
15, 17, 20, 29, 15, 9, 23, 12, 20, 37, 20, 3, 3, 20, 19, 6, 8,
11, 10, 7, 20, 15, 7, 11, 20, 17, 12, 6, 19, 20, 57, 47, 13,
5, 17, 21, 20, 11, 18, 20, 20, 3, 28, 32, 32, 14, 6, 28, 10,
20, 15, 20, 20, 20, 13, 28, 14, 2, 20, 21, 15, 19, 10, 4, 1,
20, 5, 16, 21, 17, 12, 20, 21, 1, 7, 1, 20, 19, 15, 12, 20, 27,
20, 11, 7, 12, 20, 20, 8, 53, 14, 14, 4, 13, 19, 11, 28, 9, 20,
12, 18, 20, 17, 17, 20, 20, 17, 15, 20, 24, 24, 8, 20, 9, 15,
21, 19, 44, 17, 15, 20, 32, 6, 24, 15, 20, 12, 5, 20, 9, 10,
5, 4, 2, 3, 20, 5, 8, 20, 11, 9, 10, 7, 13, 11, 18, 17, 55, 20,
20, 17, 14, 7, 19, 22, 12, 18, 20, 9, 24, 5, 18, 18, 20, 1, 20,
13, 20, 20, 21, 20, 14, 8, 13, 20, 20, 10, 6, 20, 9, 23, 22,
20, 30, 17, 20, 23, 25, 26, 16, 34, 21, 27, 5, 13, 9, 9, 4, 20,
20, 6, 27, 32, 8, 4, 20, 20, 5, 5, 14, 20, 20, 19, 22, 20, 20,
25, 20, 26, 20, 26, 20, 33, 2, 20, 9, 5, 15, 16, 6, 20, 26, 28,
20, 26, 4, 26, 24, 24, 20, 11, 20, 3, 12, 26, 13, 17, 20, 20,
20, 7, 6, 5, 12, 22, 20, 20, 11, 20, 27, 11, 4, 22, 2, 16, 7,
19, 20, 41, 21, 12, 9, 11, 20, 6, 20, 19, 18, 15, 10, 8, 6, 14,
2, 39
)
actual <-
tidyr::billboard |>
row_mean(
pattern = "^wk\\d{1,2}$",
new_column_name = "week_mean",
threshold_proportion = .1,
nonmissing_count_name = "nonmissing_count",
verbose = FALSE
) |>
dplyr::pull(nonmissing_count)
# expected <- dput(actual)
testthat::expect_equal(actual, expected)
})
test_that("billboard -subset", {
expected <-
c(
85.4285714285714, NA, 63, 66.7777777777778, 35.4444444444444,
22.4444444444444, NA, 46.8888888888889, 28.7777777777778, 67.3333333333333,
73.7777777777778, 27.5555555555556, 26.7777777777778, 23, 46.2222222222222,
NA, 96.5, NA, 77.4444444444444, 37.2222222222222, 42.3333333333333,
33.5555555555556, 48.4444444444444, 17.3333333333333, 31.1111111111111,
43.3333333333333, 38, 71.5555555555556, 49.2222222222222, 63.4444444444444,
87.5555555555556, NA, 81, 52.4444444444444, 59.6666666666667,
34.7777777777778, 73.2222222222222, 83.5, 57.1111111111111, 65,
46.6666666666667, 30.1111111111111, 45.2222222222222, NA, NA,
56, 82.8888888888889, NA, 84.375, 85.7777777777778, 90.2222222222222,
62, 44.2222222222222, 56.7777777777778, 90.5714285714286, 70,
56.2222222222222, 67.5555555555556, 83.7777777777778, NA, 52.5555555555556,
52, 64.1111111111111, 74.2222222222222, 89.8888888888889, NA,
36.7777777777778, 52.2222222222222, 80, 78.6666666666667, 64.7777777777778,
48.6666666666667, 82.7777777777778, NA, 30.4444444444444, 53.1111111111111,
36.1111111111111, 68.4444444444444, NA, 45.6666666666667, 73.5555555555556,
56.4444444444444, 26, 55.8888888888889, 40.6666666666667, 47.5555555555556,
85.7777777777778, 55.2222222222222, 64.2222222222222, NA, 19.3333333333333,
12.4444444444444, 58.2222222222222, 19, 68, NA, NA, 55.6666666666667,
NA, 54.3333333333333, 40.3333333333333, 60, 85.5555555555556,
49.2222222222222, 55.1111111111111, NA, 93.5714285714286, NA,
68.2222222222222, 66.8888888888889, 65.2222222222222, 87.4444444444444,
41.7777777777778, 31.8888888888889, 62.6666666666667, 42.2222222222222,
51.7142857142857, 97.8888888888889, 79.5555555555556, 67.5555555555556,
96.7142857142857, 47, 68.4444444444444, 40.2222222222222, NA,
72, 62.3333333333333, 51.2222222222222, 28, 76.1111111111111,
45.7777777777778, 50.2222222222222, 63.5555555555556, 24.3333333333333,
58.7777777777778, 66.7777777777778, 39.7777777777778, 56.5555555555556,
64.3333333333333, 54.1111111111111, 23, 45.8888888888889, 30,
69.375, 34.8888888888889, 81.5555555555556, 87.7777777777778,
27.4444444444444, 55.5555555555556, 51.2222222222222, 68.6666666666667,
62.7777777777778, 51.5555555555556, 64, NA, 49.6666666666667,
67.1111111111111, 51.3333333333333, 64.6666666666667, NA, 36,
80.4444444444444, 73.8888888888889, NA, NA, NA, NA, 56.7777777777778,
NA, 85.375, 25.8888888888889, 85.3333333333333, 84, 74.8888888888889,
98.1428571428571, 49.7777777777778, 84, 93.8888888888889, 70.2222222222222,
42.1111111111111, 62.8888888888889, 56.5555555555556, 61.3333333333333,
87.6666666666667, 86.8571428571429, 56.1111111111111, 52.6666666666667,
51.1111111111111, 79.4444444444444, 93.2222222222222, 43, 11.3333333333333,
NA, 49, 20, 51.8888888888889, NA, 57.8888888888889, 69, 69.4444444444444,
65.6666666666667, 60.3333333333333, 47, 63.5555555555556, 88,
66.1111111111111, 50.1111111111111, 81.7777777777778, 83.2222222222222,
NA, 51.6666666666667, 92.1111111111111, 45.1111111111111, 41.5555555555556,
59.6666666666667, 40, 61.5555555555556, 44.8888888888889, 14.5555555555556,
35.3333333333333, 18.8888888888889, 65.3333333333333, 60.1111111111111,
41.2222222222222, 44.7777777777778, NA, 48.8888888888889, 94.4444444444444,
79.1111111111111, NA, 48.5555555555556, 66.8888888888889, NA,
36.7777777777778, 12.6666666666667, 75.625, NA, 83.2222222222222,
23.8888888888889, NA, NA, 66, 67.4444444444444, 70.6666666666667,
77, 53.1111111111111, 47.8888888888889, 83.2222222222222, 61.7777777777778,
56.6666666666667, 22.8888888888889, 47.1111111111111, 4.88888888888889,
39.7777777777778, 27, NA, 59.3333333333333, 68.2222222222222,
NA, 77.6666666666667, 33.2222222222222, NA, 60, 42.7777777777778,
32.8888888888889, 70.8888888888889, 38.1111111111111, NA, 72.5555555555556,
28.2222222222222, 57.2222222222222, 37.8888888888889, 38.2222222222222,
23.1111111111111, NA, 67.1111111111111, 58.6666666666667, 83.5555555555556,
67.8888888888889, 54.8888888888889, 49.7777777777778, 42.6666666666667,
63.2857142857143, NA, NA, 74.7777777777778, 28.1111111111111,
57.1111111111111, 39.1111111111111, 83.2222222222222, 55.3333333333333,
49.6666666666667, 89.1111111111111, NA, 66.6666666666667, NA,
67.7777777777778, 69.4285714285714, 57.4444444444444, 57.8888888888889,
42.4444444444444, 39.7777777777778, 50.7777777777778, 68.5555555555556,
89.1111111111111, 53.1111111111111, NA, 58.3333333333333, 65.7777777777778,
47.6666666666667, 87.2222222222222, 75, 83.125, NA, 84.8888888888889,
NA, 26.6666666666667
)
expected_message <- "The following columns will be processed:\n- wk1\n- wk2\n- wk3\n- wk4\n- wk5\n- wk6\n- wk7\n- wk8\n- wk9"
expect_message(
regexp = expected_message,{
actual <-
tidyr::billboard |>
row_mean(
pattern = "^wk\\d$",
new_column_name = "week_mean",
nonmissing_count_name = "nonmissing_count",
verbose = TRUE
) |>
dplyr::pull(week_mean)
})
# expected <- dput(actual)
testthat::expect_equal(actual, expected)
})
OuhscBbmc/OuhscMunge documentation built on Dec. 5, 2024, 4:34 a.m.
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library(testthat)
test_that("mtcars -engine_mean", {
expected <- structure(
list(mpg = c(21, 21, 22.8, 21.4, 18.7, 18.1, 14.3,
24.4, 22.8, 19.2, 17.8, 16.4, 17.3, 15.2, 10.4, 10.4, 14.7, 32.4,
30.4, 33.9, 21.5, 15.5, 15.2, 13.3, 19.2, 27.3, 26, 30.4, 15.8,
19.7, 15, 21.4), cyl = c(6, 6, 4, 6, 8, 6, 8, 4, 4, 6, 6, 8,
8, 8, 8, 8, 8, 4, 4, 4, 4, 8, 8, 8, 8, 4, 4, 4, 8, 6, 8, 4),
disp = c(160, 160, 108, 258, 360, 225, 360, 146.7, 140.8,
167.6, 167.6, 275.8, 275.8, 275.8, 472, 460, 440, 78.7, 75.7,
71.1, 120.1, 318, 304, 350, 400, 79, 120.3, 95.1, 351, 145,
301, 121), hp = c(110, 110, 93, 110, 175, 105, 245, 62, 95,
123, 123, 180, 180, 180, 205, 215, 230, 66, 52, 65, 97, 150,
150, 245, 175, 66, 91, 113, 264, 175, 335, 109), drat = c(3.9,
3.9, 3.85, 3.08, 3.15, 2.76, 3.21, 3.69, 3.92, 3.92, 3.92,
3.07, 3.07, 3.07, 2.93, 3, 3.23, 4.08, 4.93, 4.22, 3.7, 2.76,
3.15, 3.73, 3.08, 4.08, 4.43, 3.77, 4.22, 3.62, 3.54, 4.11
), wt = c(2.62, 2.875, 2.32, 3.215, 3.44, 3.46, 3.57, 3.19,
3.15, 3.44, 3.44, 4.07, 3.73, 3.78, 5.25, 5.424, 5.345, 2.2,
1.615, 1.835, 2.465, 3.52, 3.435, 3.84, 3.845, 1.935, 2.14,
1.513, 3.17, 2.77, 3.57, 2.78), qsec = c(16.46, 17.02, 18.61,
19.44, 17.02, 20.22, 15.84, 20, 22.9, 18.3, 18.9, 17.4, 17.6,
18, 17.98, 17.82, 17.42, 19.47, 18.52, 19.9, 20.01, 16.87,
17.3, 15.41, 17.05, 18.9, 16.7, 16.9, 14.5, 15.5, 14.6, 18.6
), vs = c(0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0,
0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1), am = c(1,
1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,
0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1), gear = c(4, 4, 4, 3,
3, 3, 3, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 4, 4, 4, 3, 3, 3,
3, 3, 4, 5, 5, 5, 5, 5, 4), carb = c(4, 4, 1, 1, 2, 1, 4,
2, 2, 4, 4, 3, 3, 3, 4, 4, 4, 1, 2, 1, 1, 2, 2, 4, 2, 1,
2, 2, 4, 6, 8, 2), engine_mean = c(42.5, 42.5, 28.5, 66.5,
92.5, 58.25, 93, 38.425, 36.95, 44.65, 44.65, 71.7, 71.7,
71.7, 121, 118, 113, 21.175, 20.675, 19.275, 31.525, 82,
78.5, 90.5, 102.5, 21.25, 31.575, 25.525, 90.75, 39.25, 79.25,
32)), row.names = c("Mazda RX4", "Mazda RX4 Wag", "Datsun 710",
"Hornet 4 Drive", "Hornet Sportabout", "Valiant", "Duster 360",
"Merc 240D", "Merc 230", "Merc 280", "Merc 280C", "Merc 450SE",
"Merc 450SL", "Merc 450SLC", "Cadillac Fleetwood", "Lincoln Continental",
"Chrysler Imperial", "Fiat 128", "Honda Civic", "Toyota Corolla",
"Toyota Corona", "Dodge Challenger", "AMC Javelin", "Camaro Z28",
"Pontiac Firebird", "Fiat X1-9", "Porsche 914-2", "Lotus Europa",
"Ford Pantera L", "Ferrari Dino", "Maserati Bora", "Volvo 142E"
), class = "data.frame"
)
actual <-
mtcars |>
row_mean(
columns_to_process = c("cyl", "disp", "vs", "carb"),
new_column_name = "engine_mean"
)
# expected <- dput(actual)
testthat::expect_equal(actual, expected)
})
test_that("mtcars -engine_mean & nonmissing count", {
expected <-
structure(
list(mpg = c(21, 21, 22.8, 21.4, 18.7, 18.1, 14.3,
24.4, 22.8, 19.2, 17.8, 16.4, 17.3, 15.2, 10.4, 10.4, 14.7, 32.4,
30.4, 33.9, 21.5, 15.5, 15.2, 13.3, 19.2, 27.3, 26, 30.4, 15.8,
19.7, 15, 21.4), cyl = c(6, 6, 4, 6, 8, 6, 8, 4, 4, 6, 6, 8,
8, 8, 8, 8, 8, 4, 4, 4, 4, 8, 8, 8, 8, 4, 4, 4, 8, 6, 8, 4),
disp = c(160, 160, 108, 258, 360, 225, 360, 146.7, 140.8,
167.6, 167.6, 275.8, 275.8, 275.8, 472, 460, 440, 78.7, 75.7,
71.1, 120.1, 318, 304, 350, 400, 79, 120.3, 95.1, 351, 145,
301, 121), hp = c(110, 110, 93, 110, 175, 105, 245, 62, 95,
123, 123, 180, 180, 180, 205, 215, 230, 66, 52, 65, 97, 150,
150, 245, 175, 66, 91, 113, 264, 175, 335, 109), drat = c(3.9,
3.9, 3.85, 3.08, 3.15, 2.76, 3.21, 3.69, 3.92, 3.92, 3.92,
3.07, 3.07, 3.07, 2.93, 3, 3.23, 4.08, 4.93, 4.22, 3.7, 2.76,
3.15, 3.73, 3.08, 4.08, 4.43, 3.77, 4.22, 3.62, 3.54, 4.11
), wt = c(2.62, 2.875, 2.32, 3.215, 3.44, 3.46, 3.57, 3.19,
3.15, 3.44, 3.44, 4.07, 3.73, 3.78, 5.25, 5.424, 5.345, 2.2,
1.615, 1.835, 2.465, 3.52, 3.435, 3.84, 3.845, 1.935, 2.14,
1.513, 3.17, 2.77, 3.57, 2.78), qsec = c(16.46, 17.02, 18.61,
19.44, 17.02, 20.22, 15.84, 20, 22.9, 18.3, 18.9, 17.4, 17.6,
18, 17.98, 17.82, 17.42, 19.47, 18.52, 19.9, 20.01, 16.87,
17.3, 15.41, 17.05, 18.9, 16.7, 16.9, 14.5, 15.5, 14.6, 18.6
), vs = c(0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0,
0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1), am = c(1,
1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,
0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1), gear = c(4, 4, 4, 3,
3, 3, 3, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 4, 4, 4, 3, 3, 3,
3, 3, 4, 5, 5, 5, 5, 5, 4), carb = c(4, 4, 1, 1, 2, 1, 4,
2, 2, 4, 4, 3, 3, 3, 4, 4, 4, 1, 2, 1, 1, 2, 2, 4, 2, 1,
2, 2, 4, 6, 8, 2), engine_mean = c(42.5, 42.5, 28.5, 66.5,
92.5, 58.25, 93, 38.425, 36.95, 44.65, 44.65, 71.7, 71.7,
71.7, 121, 118, 113, 21.175, 20.675, 19.275, 31.525, 82,
78.5, 90.5, 102.5, 21.25, 31.575, 25.525, 90.75, 39.25, 79.25,
32), engine_nonmissing_count = c(4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4)), row.names = c("Mazda RX4",
"Mazda RX4 Wag", "Datsun 710", "Hornet 4 Drive", "Hornet Sportabout",
"Valiant", "Duster 360", "Merc 240D", "Merc 230", "Merc 280",
"Merc 280C", "Merc 450SE", "Merc 450SL", "Merc 450SLC", "Cadillac Fleetwood",
"Lincoln Continental", "Chrysler Imperial", "Fiat 128", "Honda Civic",
"Toyota Corolla", "Toyota Corona", "Dodge Challenger", "AMC Javelin",
"Camaro Z28", "Pontiac Firebird", "Fiat X1-9", "Porsche 914-2",
"Lotus Europa", "Ford Pantera L", "Ferrari Dino", "Maserati Bora",
"Volvo 142E"), class = "data.frame"
)
actual <-
mtcars |>
row_mean(
columns_to_process = c("cyl", "disp", "vs", "carb"),
new_column_name = "engine_mean",
nonmissing_count_name = "engine_nonmissing_count"
)
# expected <- dput(actual)
testthat::expect_equal(actual, expected)
})
test_that("billboard -all weeks", {
expected <-
c(
NA, NA, 26.4716981132075, 67.1, 56.2222222222222, 37.65, NA,
52.05, 16.65625, 67.75, 76.2727272727273, 19.952380952381, 24.1363636363636,
19.2916666666667, 51.25, NA, 80.2758620689655, NA, 82.05, 23.46875,
47.85, 32.35, 30.6451612903226, 28.05, 23.75, 60.2, 32.35, 62.5,
33.9523809523809, 72.4, 87.5555555555556, NA, 85.5333333333333,
62.9411764705882, 65, 21.5862068965517, 74, 85.3333333333333,
33.304347826087, 72.5833333333333, 51.6, 21, 57.15, NA, NA, 45.8,
76.8421052631579, NA, 84.375, 86, 90.4, NA, 37, 66.2666666666667,
NA, 73.8181818181818, 59.45, 69, 84.25, NA, 65.7368421052632,
47.3, 36.859649122807, 33.8297872340426, 88.6923076923077, NA,
47.4117647058824, 41.7619047619048, 74.6, 82, 75.6666666666667,
48.4, 68.55, NA, 14.8214285714286, 22.9375, 20.875, 77.4285714285714,
NA, 24.8571428571429, 75.3, 48.45, 50.6, 47.1, 43.25, 43.25,
89.3846153846154, 26.8928571428571, 71.1428571428571, NA, 30.7,
14.3333333333333, 68.0666666666667, 32.8421052631579, 69.8, NA,
NA, 54.7, NA, 60.875, 31.1904761904762, 62.7647058823529, 86.0833333333333,
34.15, 39.6666666666667, NA, NA, NA, 69.15, 64.5263157894737,
69.2666666666667, 86.5, 50, 21.6666666666667, 58.8, 49.9090909090909,
NA, 97.1666666666667, 71.3, 74.95, 96.625, 23.0188679245283,
69.5, 49.4285714285714, NA, 78.8461538461538, 72.2631578947368,
56, 17.8571428571429, 76.1111111111111, 49.1, 55.6666666666667,
65.6111111111111, 15.85, 61.1176470588235, 63.4117647058824,
31.15, 58.1, 67.5882352941177, 63.4, 34.9, 31.375, 19.4166666666667,
69.375, 31.3, 81.5555555555556, 87.8666666666667, 23.5238095238095,
60, 21.2045454545455, 77.7647058823529, 75.2, 45.65, 28.4375,
NA, 37.1666666666667, 69.6, 45.45, 67.8333333333333, NA, 39,
80.4444444444444, 76.5, NA, NA, NA, NA, 65.75, NA, 85.375, 45.75,
86.3636363636364, 86.6666666666667, 77.1, NA, 57.9230769230769,
86.2727272727273, 92.7222222222222, 78.9411764705882, 26.7272727272727,
60.3, 49.9, 70.2941176470588, 84.2142857142857, NA, 60.1052631578947,
38.8636363636364, 62.3333333333333, 79.6666666666667, 92.65,
43, 13.4583333333333, NA, 53.6111111111111, 37.1111111111111,
55.9, NA, 63.15, 70.8461538461538, 71.95, 56.05, 47.3333333333333,
44.2, 65, 88, 67.8461538461538, 43.2, 86.1, 84.4, NA, 54.5, 92.1111111111111,
64.1739130434783, 25.0909090909091, 50.65, 19.6333333333333,
71.4705882352941, 33.05, 14.2608695652174, 21.68, 19.6153846153846,
73.25, 30.8823529411765, 28.1904761904762, 26.4814814814815,
NA, 62.3846153846154, 94.4444444444444, 79.1111111111111, NA,
54.9, 65.25, NA, 21.7777777777778, 18.625, 75.625, NA, 80.15,
47.25, NA, NA, 67.5714285714286, 59, 61.8, 77.6842105263158,
36.1818181818182, 40.8, 82.95, 35.6, 63.9, 19.9230769230769,
52.85, 10.5, 46.55, 17.3636363636364, NA, 63.05, 68.2222222222222,
NA, 81.0666666666667, 44.875, NA, 62.05, 24.3076923076923, 19.1785714285714,
82.6, 28.7692307692308, NA, 49.5, 22.125, 41.5833333333333, 54.5,
48.1818181818182, 36, NA, 74, 38, 86.7692307692308, 73.8235294117647,
60.7, 44.7, 58.45, NA, NA, NA, 79.8333333333333, 25.8636363636364,
66.2, 36.4, 85.2727272727273, 66.95, 36.9259259259259, 90.0909090909091,
NA, 47.8181818181818, NA, 72, NA, 66.2631578947368, 57.15, 21.4390243902439,
37, 53.5833333333333, 68.5555555555556, 90.1818181818182, 63.5,
NA, 43.35, 75.4210526315789, 59.8333333333333, 85.5333333333333,
77.3, 83.125, NA, 88.8571428571429, NA, 18.6410256410256
)
actual <-
tidyr::billboard |>
row_mean(
pattern = "^wk\\d{1,2}$",
new_column_name = "week_mean",
threshold_proportion = .1,
verbose = TRUE
) |>
dplyr::pull(week_mean)
# expected <- dput(actual)
testthat::expect_equal(actual, expected)
})
test_that("billboard -all weeks nonmissing count", {
expected <-
c(
7, 3, 53, 20, 18, 20, 5, 20, 32, 20, 11, 21, 22, 24, 20, 5,
29, 3, 20, 32, 20, 20, 31, 20, 24, 15, 20, 20, 21, 15, 9, 3,
15, 17, 20, 29, 15, 9, 23, 12, 20, 37, 20, 3, 3, 20, 19, 6, 8,
11, 10, 7, 20, 15, 7, 11, 20, 17, 12, 6, 19, 20, 57, 47, 13,
5, 17, 21, 20, 11, 18, 20, 20, 3, 28, 32, 32, 14, 6, 28, 10,
20, 15, 20, 20, 20, 13, 28, 14, 2, 20, 21, 15, 19, 10, 4, 1,
20, 5, 16, 21, 17, 12, 20, 21, 1, 7, 1, 20, 19, 15, 12, 20, 27,
20, 11, 7, 12, 20, 20, 8, 53, 14, 14, 4, 13, 19, 11, 28, 9, 20,
12, 18, 20, 17, 17, 20, 20, 17, 15, 20, 24, 24, 8, 20, 9, 15,
21, 19, 44, 17, 15, 20, 32, 6, 24, 15, 20, 12, 5, 20, 9, 10,
5, 4, 2, 3, 20, 5, 8, 20, 11, 9, 10, 7, 13, 11, 18, 17, 55, 20,
20, 17, 14, 7, 19, 22, 12, 18, 20, 9, 24, 5, 18, 18, 20, 1, 20,
13, 20, 20, 21, 20, 14, 8, 13, 20, 20, 10, 6, 20, 9, 23, 22,
20, 30, 17, 20, 23, 25, 26, 16, 34, 21, 27, 5, 13, 9, 9, 4, 20,
20, 6, 27, 32, 8, 4, 20, 20, 5, 5, 14, 20, 20, 19, 22, 20, 20,
25, 20, 26, 20, 26, 20, 33, 2, 20, 9, 5, 15, 16, 6, 20, 26, 28,
20, 26, 4, 26, 24, 24, 20, 11, 20, 3, 12, 26, 13, 17, 20, 20,
20, 7, 6, 5, 12, 22, 20, 20, 11, 20, 27, 11, 4, 22, 2, 16, 7,
19, 20, 41, 21, 12, 9, 11, 20, 6, 20, 19, 18, 15, 10, 8, 6, 14,
2, 39
)
actual <-
tidyr::billboard |>
row_mean(
pattern = "^wk\\d{1,2}$",
new_column_name = "week_mean",
threshold_proportion = .1,
nonmissing_count_name = "nonmissing_count",
verbose = FALSE
) |>
dplyr::pull(nonmissing_count)
# expected <- dput(actual)
testthat::expect_equal(actual, expected)
})
test_that("billboard -subset", {
expected <-
c(
85.4285714285714, NA, 63, 66.7777777777778, 35.4444444444444,
22.4444444444444, NA, 46.8888888888889, 28.7777777777778, 67.3333333333333,
73.7777777777778, 27.5555555555556, 26.7777777777778, 23, 46.2222222222222,
NA, 96.5, NA, 77.4444444444444, 37.2222222222222, 42.3333333333333,
33.5555555555556, 48.4444444444444, 17.3333333333333, 31.1111111111111,
43.3333333333333, 38, 71.5555555555556, 49.2222222222222, 63.4444444444444,
87.5555555555556, NA, 81, 52.4444444444444, 59.6666666666667,
34.7777777777778, 73.2222222222222, 83.5, 57.1111111111111, 65,
46.6666666666667, 30.1111111111111, 45.2222222222222, NA, NA,
56, 82.8888888888889, NA, 84.375, 85.7777777777778, 90.2222222222222,
62, 44.2222222222222, 56.7777777777778, 90.5714285714286, 70,
56.2222222222222, 67.5555555555556, 83.7777777777778, NA, 52.5555555555556,
52, 64.1111111111111, 74.2222222222222, 89.8888888888889, NA,
36.7777777777778, 52.2222222222222, 80, 78.6666666666667, 64.7777777777778,
48.6666666666667, 82.7777777777778, NA, 30.4444444444444, 53.1111111111111,
36.1111111111111, 68.4444444444444, NA, 45.6666666666667, 73.5555555555556,
56.4444444444444, 26, 55.8888888888889, 40.6666666666667, 47.5555555555556,
85.7777777777778, 55.2222222222222, 64.2222222222222, NA, 19.3333333333333,
12.4444444444444, 58.2222222222222, 19, 68, NA, NA, 55.6666666666667,
NA, 54.3333333333333, 40.3333333333333, 60, 85.5555555555556,
49.2222222222222, 55.1111111111111, NA, 93.5714285714286, NA,
68.2222222222222, 66.8888888888889, 65.2222222222222, 87.4444444444444,
41.7777777777778, 31.8888888888889, 62.6666666666667, 42.2222222222222,
51.7142857142857, 97.8888888888889, 79.5555555555556, 67.5555555555556,
96.7142857142857, 47, 68.4444444444444, 40.2222222222222, NA,
72, 62.3333333333333, 51.2222222222222, 28, 76.1111111111111,
45.7777777777778, 50.2222222222222, 63.5555555555556, 24.3333333333333,
58.7777777777778, 66.7777777777778, 39.7777777777778, 56.5555555555556,
64.3333333333333, 54.1111111111111, 23, 45.8888888888889, 30,
69.375, 34.8888888888889, 81.5555555555556, 87.7777777777778,
27.4444444444444, 55.5555555555556, 51.2222222222222, 68.6666666666667,
62.7777777777778, 51.5555555555556, 64, NA, 49.6666666666667,
67.1111111111111, 51.3333333333333, 64.6666666666667, NA, 36,
80.4444444444444, 73.8888888888889, NA, NA, NA, NA, 56.7777777777778,
NA, 85.375, 25.8888888888889, 85.3333333333333, 84, 74.8888888888889,
98.1428571428571, 49.7777777777778, 84, 93.8888888888889, 70.2222222222222,
42.1111111111111, 62.8888888888889, 56.5555555555556, 61.3333333333333,
87.6666666666667, 86.8571428571429, 56.1111111111111, 52.6666666666667,
51.1111111111111, 79.4444444444444, 93.2222222222222, 43, 11.3333333333333,
NA, 49, 20, 51.8888888888889, NA, 57.8888888888889, 69, 69.4444444444444,
65.6666666666667, 60.3333333333333, 47, 63.5555555555556, 88,
66.1111111111111, 50.1111111111111, 81.7777777777778, 83.2222222222222,
NA, 51.6666666666667, 92.1111111111111, 45.1111111111111, 41.5555555555556,
59.6666666666667, 40, 61.5555555555556, 44.8888888888889, 14.5555555555556,
35.3333333333333, 18.8888888888889, 65.3333333333333, 60.1111111111111,
41.2222222222222, 44.7777777777778, NA, 48.8888888888889, 94.4444444444444,
79.1111111111111, NA, 48.5555555555556, 66.8888888888889, NA,
36.7777777777778, 12.6666666666667, 75.625, NA, 83.2222222222222,
23.8888888888889, NA, NA, 66, 67.4444444444444, 70.6666666666667,
77, 53.1111111111111, 47.8888888888889, 83.2222222222222, 61.7777777777778,
56.6666666666667, 22.8888888888889, 47.1111111111111, 4.88888888888889,
39.7777777777778, 27, NA, 59.3333333333333, 68.2222222222222,
NA, 77.6666666666667, 33.2222222222222, NA, 60, 42.7777777777778,
32.8888888888889, 70.8888888888889, 38.1111111111111, NA, 72.5555555555556,
28.2222222222222, 57.2222222222222, 37.8888888888889, 38.2222222222222,
23.1111111111111, NA, 67.1111111111111, 58.6666666666667, 83.5555555555556,
67.8888888888889, 54.8888888888889, 49.7777777777778, 42.6666666666667,
63.2857142857143, NA, NA, 74.7777777777778, 28.1111111111111,
57.1111111111111, 39.1111111111111, 83.2222222222222, 55.3333333333333,
49.6666666666667, 89.1111111111111, NA, 66.6666666666667, NA,
67.7777777777778, 69.4285714285714, 57.4444444444444, 57.8888888888889,
42.4444444444444, 39.7777777777778, 50.7777777777778, 68.5555555555556,
89.1111111111111, 53.1111111111111, NA, 58.3333333333333, 65.7777777777778,
47.6666666666667, 87.2222222222222, 75, 83.125, NA, 84.8888888888889,
NA, 26.6666666666667
)
expected_message <- "The following columns will be processed:\n- wk1\n- wk2\n- wk3\n- wk4\n- wk5\n- wk6\n- wk7\n- wk8\n- wk9"
expect_message(
regexp = expected_message,{
actual <-
tidyr::billboard |>
row_mean(
pattern = "^wk\\d$",
new_column_name = "week_mean",
nonmissing_count_name = "nonmissing_count",
verbose = TRUE
) |>
dplyr::pull(week_mean)
})
# expected <- dput(actual)
testthat::expect_equal(actual, expected)
})
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