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REDCapCAST/tests/testthat/test-csv-exports.R

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Writing unit tests for basic examples of data export for R.
2018-06-26 05:48:55 +02:00
# Set up the path and data -------------------------------------------------
metadata <- read.csv(
updating csv tests since R 4.0.0 uses stringsAsFactors = FALSE by default
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get_data_location("ExampleProject_DataDictionary_2018-06-07.csv"),
stringsAsFactors = TRUE
Writing unit tests for basic examples of data export for R.
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)
gp with CRAN in sight
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records <-
read.csv(get_data_location("ExampleProject_DATA_2018-06-07_1129.csv"),
linting
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stringsAsFactors = TRUE
)
Writing unit tests for basic examples of data export for R.
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Cleaning up code for #12 test now compares read_csv and read.csv output directly.
2019-07-29 18:30:52 +02:00
redcap_output_csv1 <- REDCap_split(records, metadata)
Writing unit tests for basic examples of data export for R.
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# Test that basic CSV export matches reference ------------------------------
test_that("CSV export matches reference", {
removed hash matching from tests to try to handle test errors on different systems
2024-09-05 15:38:38 +02:00
# expect_known_hash(redcap_output_csv1, "cb5074a06e1abcf659d60be1016965d2")
# dput(redcap_output_csv1)
expect_identical(
redcap_output_csv1,
list(
structure(list(
row = structure(1:32, levels = c(
"AMC Javelin",
"Cadillac Fleetwood", "Camaro Z28", "Chrysler Imperial", "Datsun 710",
"Dodge Challenger", "Duster 360", "Ferrari Dino", "Fiat 128",
"Fiat X1-9", "Ford Pantera L", "Honda Civic", "Hornet 4 Drive",
"Hornet Sportabout", "Lincoln Continental", "Lotus Europa", "Maserati Bora",
"Mazda RX4", "Mazda RX4 Wag", "Merc 230", "Merc 240D", "Merc 280",
"Merc 280C", "Merc 450SE", "Merc 450SL", "Merc 450SLC", "Pontiac Firebird",
"Porsche 914-2", "Toyota Corolla", "Toyota Corona", "Valiant",
"Volvo 142E"
), class = "factor"), mpg = c(
15.2, 10.4, 13.3, 14.7,
22.8, 15.5, 14.3, 19.7, 32.4, 27.3, 15.8, 30.4, 21.4, 18.7, 10.4,
30.4, 15, 21, 21, 22.8, 24.4, 19.2, 17.8, 16.4, 17.3, 15.2, 19.2,
26, 33.9, 21.5, 18.1, 21.4
), cyl = c(
8L, 8L, 8L, 8L, 4L, 8L,
8L, 6L, 4L, 4L, 8L, 4L, 6L, 8L, 8L, 4L, 8L, 6L, 6L, 4L, 4L, 6L,
6L, 8L, 8L, 8L, 8L, 4L, 4L, 4L, 6L, 4L
), disp = c(
304, 472, 350,
440, 108, 318, 360, 145, 78.7, 79, 351, 75.7, 258, 360, 460,
95.1, 301, 160, 160, 140.8, 146.7, 167.6, 167.6, 275.8, 275.8,
275.8, 400, 120.3, 71.1, 120.1, 225, 121
), hp = c(
150L, 205L,
245L, 230L, 93L, 150L, 245L, 175L, 66L, 66L, 264L, 52L, 110L,
175L, 215L, 113L, 335L, 110L, 110L, 95L, 62L, 123L, 123L, 180L,
180L, 180L, 175L, 91L, 65L, 97L, 105L, 109L
), drat = c(
3.15,
2.93, 3.73, 3.23, 3.85, 2.76, 3.21, 3.62, 4.08, 4.08, 4.22, 4.93,
3.08, 3.15, 3, 3.77, 3.54, 3.9, 3.9, 3.92, 3.69, 3.92, 3.92,
3.07, 3.07, 3.07, 3.08, 4.43, 4.22, 3.7, 2.76, 4.11
), wt = c(
3.435,
5.25, 3.84, 5.345, 2.32, 3.52, 3.57, 2.77, 2.2, 1.935, 3.17,
1.615, 3.215, 3.44, 5.424, 1.513, 3.57, 2.62, 2.875, 3.15, 3.19,
3.44, 3.44, 4.07, 3.73, 3.78, 3.845, 2.14, 1.835, 2.465, 3.46,
2.78
), qsec = c(
17.3, 17.98, 15.41, 17.42, 18.61, 16.87, 15.84,
15.5, 19.47, 18.9, 14.5, 18.52, 19.44, 17.02, 17.82, 16.9, 14.6,
16.46, 17.02, 22.9, 20, 18.3, 18.9, 17.4, 17.6, 18, 17.05, 16.7,
19.9, 20.01, 20.22, 18.6
), vs = c(
0L, 0L, 0L, 0L, 1L, 0L, 0L,
0L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 1L,
0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L
), am = c(
0L, 0L, 0L, 0L, 1L,
0L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L
), gear = c(
3L, 3L,
3L, 3L, 4L, 3L, 3L, 5L, 4L, 4L, 5L, 4L, 3L, 3L, 3L, 5L, 5L, 4L,
4L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 5L, 4L, 3L, 3L, 4L
), carb = c(
2L,
4L, 4L, 4L, 1L, 2L, 4L, 6L, 1L, 1L, 4L, 2L, 1L, 2L, 4L, 2L, 8L,
4L, 4L, 2L, 2L, 4L, 4L, 3L, 3L, 3L, 2L, 2L, 1L, 1L, 1L, 2L
),
color_available___red = c(
1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L
), color_available___green = c(
1L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L
), color_available___blue = c(
1L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L
), color_available___black = c(
0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L
), motor_trend_cars_complete = c(
1L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L
), letter_group___a = c(
1L,
0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L
), letter_group___b = c(
1L, 0L, 0L, 1L, 1L, 0L, 1L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L
), letter_group___c = c(
0L,
0L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L
), choice = structure(c(
3L, 1L, 2L, 2L, 1L, 1L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L
), levels = c(
"", "choice1",
"choice2"
), class = "factor"), grouping_complete = c(
2L,
0L, 2L, 2L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L
)
), row.names = c(
1L, 5L, 6L, 9L, 11L, 12L, 13L, 18L, 19L,
20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 34L, 35L,
36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L
), class = "data.frame"),
sale = structure(list(
row = structure(c(
1L, 1L, 1L, 3L, 3L,
4L, 7L, 7L, 7L, 7L, 20L, 20L, 20L
), levels = c(
"AMC Javelin",
"Cadillac Fleetwood", "Camaro Z28", "Chrysler Imperial",
"Datsun 710", "Dodge Challenger", "Duster 360", "Ferrari Dino",
"Fiat 128", "Fiat X1-9", "Ford Pantera L", "Honda Civic",
"Hornet 4 Drive", "Hornet Sportabout", "Lincoln Continental",
"Lotus Europa", "Maserati Bora", "Mazda RX4", "Mazda RX4 Wag",
"Merc 230", "Merc 240D", "Merc 280", "Merc 280C", "Merc 450SE",
"Merc 450SL", "Merc 450SLC", "Pontiac Firebird", "Porsche 914-2",
"Toyota Corolla", "Toyota Corona", "Valiant", "Volvo 142E"
), class = "factor"), redcap_repeat_instrument = c(
"sale",
"sale", "sale", "sale", "sale", "sale", "sale", "sale", "sale",
"sale", "sale", "sale", "sale"
), redcap_repeat_instance = c(
1L,
2L, 3L, 1L, 2L, 1L, 1L, 2L, 3L, 4L, 1L, 2L, 3L
), price = c(
12000.5,
13750.77, 15004.57, 7800, 8000, 7500, 8756.4, 6800.88, 8888.88,
970, 7800.98, 7954, 6800.55
), color = c(
1L, 3L, 2L, 2L, 3L,
1L, 4L, 2L, 1L, 4L, 2L, 1L, 3L
), customer = structure(c(
2L,
12L, 7L, 4L, 14L, 5L, 10L, 8L, 3L, 6L, 13L, 9L, 11L
), levels = c(
"",
"Bob", "Erica", "Janice", "Jim", "Juan", "Kim", "Pablo",
"Quentin", "Sarah", "Sharon", "Sue", "Ted", "Tim"
), class = "factor"),
sale_complete = c(
0L, 2L, 0L, 2L, 0L, 2L, 1L, 0L, 0L,
0L, 0L, 0L, 2L
)
), row.names = c(
2L, 3L, 4L, 7L, 8L, 10L,
14L, 15L, 16L, 17L, 31L, 32L, 33L
), class = "data.frame")
)
)
Writing unit tests for basic examples of data export for R.
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})
Major update. New functions and improvements. See NEWS.md.
2023-03-07 15:38:28 +01:00
# Test that REDCap_split can handle a focused dataset
linting
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records_red <- records[
!records$redcap_repeat_instrument == "sale",
!names(records) %in%
metadata$field_name[metadata$form_name == "sale"] &
!names(records) == "sale_complete"
]
gp with CRAN in sight
2023-04-13 10:57:04 +02:00
records_red$redcap_repeat_instrument <-
as.character(records_red$redcap_repeat_instrument)
Major update. New functions and improvements. See NEWS.md.
2023-03-07 15:38:28 +01:00
redcap_output_red <- REDCap_split(records_red, metadata)
linting
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test_that("REDCap_split handles subset dataset", {
testthat::expect_length(redcap_output_red, 1)
})
Major update. New functions and improvements. See NEWS.md.
2023-03-07 15:38:28 +01:00
Writing unit tests for basic examples of data export for R.
2018-06-26 05:48:55 +02:00
# Test that R code enhanced CSV export matches reference --------------------
Completed working test suite and added CI
2018-06-28 23:20:14 +02:00
if (requireNamespace("Hmisc", quietly = TRUE)) {
test_that("R code enhanced export matches reference", {
working to submit to cran
2023-01-16 09:49:17 +01:00
redcap_output_csv2 <-
REDCap_split(REDCap_process_csv(records), metadata)
Writing unit tests for basic examples of data export for R.
2018-06-26 05:48:55 +02:00
removed hash matching from tests to try to handle test errors on different systems
2024-09-05 15:38:38 +02:00
# expect_known_hash(redcap_output_csv2, "578dc054e59ec92a21e950042e08ee37")
# dput(redcap_output_csv2)
expect_identical(
redcap_output_csv2,
list(structure(list(
row = structure(1:32, levels = c(
"AMC Javelin",
"Cadillac Fleetwood", "Camaro Z28", "Chrysler Imperial", "Datsun 710",
"Dodge Challenger", "Duster 360", "Ferrari Dino", "Fiat 128",
"Fiat X1-9", "Ford Pantera L", "Honda Civic", "Hornet 4 Drive",
"Hornet Sportabout", "Lincoln Continental", "Lotus Europa", "Maserati Bora",
"Mazda RX4", "Mazda RX4 Wag", "Merc 230", "Merc 240D", "Merc 280",
"Merc 280C", "Merc 450SE", "Merc 450SL", "Merc 450SLC", "Pontiac Firebird",
"Porsche 914-2", "Toyota Corolla", "Toyota Corona", "Valiant",
"Volvo 142E"
), class = c("labelled", "factor"), label = "Name"),
mpg = structure(c(
15.2, 10.4, 13.3, 14.7, 22.8, 15.5, 14.3,
19.7, 32.4, 27.3, 15.8, 30.4, 21.4, 18.7, 10.4, 30.4, 15,
21, 21, 22.8, 24.4, 19.2, 17.8, 16.4, 17.3, 15.2, 19.2, 26,
33.9, 21.5, 18.1, 21.4
), label = "Miles/(US) gallon", class = c(
"labelled",
"numeric"
)), cyl = structure(c(
8L, 8L, 8L, 8L, 4L, 8L, 8L,
6L, 4L, 4L, 8L, 4L, 6L, 8L, 8L, 4L, 8L, 6L, 6L, 4L, 4L, 6L,
6L, 8L, 8L, 8L, 8L, 4L, 4L, 4L, 6L, 4L
), label = "Number of cylinders", class = c(
"labelled",
"integer"
)), disp = structure(c(
304, 472, 350, 440, 108,
318, 360, 145, 78.7, 79, 351, 75.7, 258, 360, 460, 95.1,
301, 160, 160, 140.8, 146.7, 167.6, 167.6, 275.8, 275.8,
275.8, 400, 120.3, 71.1, 120.1, 225, 121
), label = "Displacement", class = c(
"labelled",
"numeric"
)), hp = structure(c(
150L, 205L, 245L, 230L, 93L,
150L, 245L, 175L, 66L, 66L, 264L, 52L, 110L, 175L, 215L,
113L, 335L, 110L, 110L, 95L, 62L, 123L, 123L, 180L, 180L,
180L, 175L, 91L, 65L, 97L, 105L, 109L
), label = "Gross horsepower", class = c(
"labelled",
"integer"
)), drat = structure(c(
3.15, 2.93, 3.73, 3.23, 3.85,
2.76, 3.21, 3.62, 4.08, 4.08, 4.22, 4.93, 3.08, 3.15, 3,
3.77, 3.54, 3.9, 3.9, 3.92, 3.69, 3.92, 3.92, 3.07, 3.07,
3.07, 3.08, 4.43, 4.22, 3.7, 2.76, 4.11
), label = "Rear axle ratio", class = c(
"labelled",
"numeric"
)), wt = structure(c(
3.435, 5.25, 3.84, 5.345, 2.32,
3.52, 3.57, 2.77, 2.2, 1.935, 3.17, 1.615, 3.215, 3.44, 5.424,
1.513, 3.57, 2.62, 2.875, 3.15, 3.19, 3.44, 3.44, 4.07, 3.73,
3.78, 3.845, 2.14, 1.835, 2.465, 3.46, 2.78
), label = "Weight", class = c(
"labelled",
"numeric"
)), qsec = structure(c(
17.3, 17.98, 15.41, 17.42,
18.61, 16.87, 15.84, 15.5, 19.47, 18.9, 14.5, 18.52, 19.44,
17.02, 17.82, 16.9, 14.6, 16.46, 17.02, 22.9, 20, 18.3, 18.9,
17.4, 17.6, 18, 17.05, 16.7, 19.9, 20.01, 20.22, 18.6
), label = "1/4 mile time", class = c(
"labelled",
"numeric"
)), vs = structure(c(
0L, 0L, 0L, 0L, 1L, 0L, 0L,
0L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 1L,
1L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L
), label = "V engine?", class = c(
"labelled",
"integer"
)), am = structure(c(
0L, 0L, 0L, 0L, 1L, 0L, 0L,
1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L
), label = "Transmission", class = c(
"labelled",
"integer"
)), gear = structure(c(
3L, 3L, 3L, 3L, 4L, 3L, 3L,
5L, 4L, 4L, 5L, 4L, 3L, 3L, 3L, 5L, 5L, 4L, 4L, 4L, 4L, 4L,
4L, 3L, 3L, 3L, 3L, 5L, 4L, 3L, 3L, 4L
), label = "Number of forward gears", class = c(
"labelled",
"integer"
)), carb = structure(c(
2L, 4L, 4L, 4L, 1L, 2L, 4L,
6L, 1L, 1L, 4L, 2L, 1L, 2L, 4L, 2L, 8L, 4L, 4L, 2L, 2L, 4L,
4L, 3L, 3L, 3L, 2L, 2L, 1L, 1L, 1L, 2L
), label = "Number of carburetors", class = c(
"labelled",
"integer"
)), color_available___red = structure(c(
1L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L
), label = "Colors Available (choice<-Red)", class = c(
"labelled",
"integer"
)), color_available___green = structure(c(
1L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L
), label = "Colors Available (choice<-Green)", class = c(
"labelled",
"integer"
)), color_available___blue = structure(c(
1L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L
), label = "Colors Available (choice<-Blue)", class = c(
"labelled",
"integer"
)), color_available___black = structure(c(
0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L
), label = "Colors Available (choice<-Black)", class = c(
"labelled",
"integer"
)), motor_trend_cars_complete = structure(c(
1L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L
), label = "Complete?", class = c("labelled", "integer")), letter_group___a = structure(c(
1L, 0L, 1L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L
), label = "Which group? (choice<-A)", class = c(
"labelled",
"integer"
)), letter_group___b = structure(c(
1L, 0L, 0L, 1L,
1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L,
1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L
), label = "Which group? (choice<-B)", class = c(
"labelled",
"integer"
)), letter_group___c = structure(c(
0L, 0L, 1L, 1L,
1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L
), label = "Which group? (choice<-C)", class = c(
"labelled",
"integer"
)), choice = structure(c(
3L, 1L, 2L, 2L, 1L, 1L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L
), levels = c(
"",
"choice1", "choice2"
), class = c("labelled", "factor"), label = "Choose one"),
grouping_complete = structure(c(
2L, 0L, 2L, 2L, 0L, 0L, 1L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L
), label = "Complete?", class = c(
"labelled",
"integer"
)), cyl.factor = structure(c(
6L, 6L, 6L, 6L, 2L,
6L, 6L, 4L, 2L, 2L, 6L, 2L, 4L, 6L, 6L, 2L, 6L, 4L, 4L, 2L,
2L, 4L, 4L, 6L, 6L, 6L, 6L, 2L, 2L, 2L, 4L, 2L
), levels = c(
"3",
"4", "5", "6", "7", "8"
), class = "factor"), vs.factor = structure(c(
2L,
2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L,
2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L,
1L
), levels = c("Yes", "No"), class = "factor"), am.factor = structure(c(
1L,
1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L,
2L
), levels = c("Automatic", "Manual"), class = "factor"),
gear.factor = structure(c(
1L, 1L, 1L, 1L, 2L, 1L, 1L, 3L,
2L, 2L, 3L, 2L, 1L, 1L, 1L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L,
1L, 1L, 1L, 1L, 3L, 2L, 1L, 1L, 2L
), levels = c(
"3", "4",
"5"
), class = "factor"), carb.factor = structure(c(
2L, 4L,
4L, 4L, 1L, 2L, 4L, 6L, 1L, 1L, 4L, 2L, 1L, 2L, 4L, 2L, 8L,
4L, 4L, 2L, 2L, 4L, 4L, 3L, 3L, 3L, 2L, 2L, 1L, 1L, 1L, 2L
), levels = c("1", "2", "3", "4", "5", "6", "7", "8"), class = "factor"),
color_available___red.factor = structure(c(
2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L
), levels = c(
"Unchecked",
"Checked"
), class = "factor"), color_available___green.factor = structure(c(
2L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L
), levels = c("Unchecked", "Checked"), class = "factor"),
color_available___blue.factor = structure(c(
2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L
), levels = c(
"Unchecked",
"Checked"
), class = "factor"), color_available___black.factor = structure(c(
1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L
), levels = c("Unchecked", "Checked"), class = "factor"),
motor_trend_cars_complete.factor = structure(c(
2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L
), levels = c(
"Incomplete",
"Unverified", "Complete"
), class = "factor"), letter_group___a.factor = structure(c(
2L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L
), levels = c("Unchecked", "Checked"), class = "factor"),
letter_group___b.factor = structure(c(
2L, 1L, 1L, 2L, 2L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L
), levels = c(
"Unchecked",
"Checked"
), class = "factor"), letter_group___c.factor = structure(c(
1L,
1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L
), levels = c("Unchecked", "Checked"), class = "factor"),
choice.factor = structure(c(
2L, NA, 1L, 1L, NA, NA, 1L, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 2L, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA
), levels = c(
"Choice 1",
"Choice 2"
), class = "factor"), grouping_complete.factor = structure(c(
3L,
1L, 3L, 3L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L
), levels = c("Incomplete", "Unverified", "Complete"), class = "factor")
), row.names = c(
1L,
5L, 6L, 9L, 11L, 12L, 13L, 18L, 19L, 20L, 21L, 22L, 23L, 24L,
25L, 26L, 27L, 28L, 29L, 30L, 34L, 35L, 36L, 37L, 38L, 39L, 40L,
41L, 42L, 43L, 44L, 45L
), class = "data.frame"), sale = structure(list(
row = structure(c(
1L, 1L, 1L, 3L, 3L, 4L, 7L, 7L, 7L, 7L,
20L, 20L, 20L
), levels = c(
"AMC Javelin", "Cadillac Fleetwood",
"Camaro Z28", "Chrysler Imperial", "Datsun 710", "Dodge Challenger",
"Duster 360", "Ferrari Dino", "Fiat 128", "Fiat X1-9", "Ford Pantera L",
"Honda Civic", "Hornet 4 Drive", "Hornet Sportabout", "Lincoln Continental",
"Lotus Europa", "Maserati Bora", "Mazda RX4", "Mazda RX4 Wag",
"Merc 230", "Merc 240D", "Merc 280", "Merc 280C", "Merc 450SE",
"Merc 450SL", "Merc 450SLC", "Pontiac Firebird", "Porsche 914-2",
"Toyota Corolla", "Toyota Corona", "Valiant", "Volvo 142E"
), class = c("labelled", "factor"), label = "Name"), redcap_repeat_instrument = c(
"sale",
"sale", "sale", "sale", "sale", "sale", "sale", "sale", "sale",
"sale", "sale", "sale", "sale"
), redcap_repeat_instance = structure(c(
1L,
2L, 3L, 1L, 2L, 1L, 1L, 2L, 3L, 4L, 1L, 2L, 3L
), label = "Repeat Instance", class = c(
"labelled",
"integer"
)), price = structure(c(
12000.5, 13750.77, 15004.57,
7800, 8000, 7500, 8756.4, 6800.88, 8888.88, 970, 7800.98,
7954, 6800.55
), label = "Sale price", class = c(
"labelled",
"numeric"
)), color = structure(c(
1L, 3L, 2L, 2L, 3L, 1L,
4L, 2L, 1L, 4L, 2L, 1L, 3L
), label = "Color", class = c(
"labelled",
"integer"
)), customer = structure(c(
2L, 12L, 7L, 4L, 14L,
5L, 10L, 8L, 3L, 6L, 13L, 9L, 11L
), levels = c(
"", "Bob",
"Erica", "Janice", "Jim", "Juan", "Kim", "Pablo", "Quentin",
"Sarah", "Sharon", "Sue", "Ted", "Tim"
), class = c(
"labelled",
"factor"
), label = "Customer Name"), sale_complete = structure(c(
0L,
2L, 0L, 2L, 0L, 2L, 1L, 0L, 0L, 0L, 0L, 0L, 2L
), label = "Complete?", class = c(
"labelled",
"integer"
)), redcap_repeat_instrument.factor = structure(c(
1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L
), levels = "Sale", class = "factor"),
color.factor = structure(c(
1L, 3L, 2L, 2L, 3L, 1L, 4L, 2L,
1L, 4L, 2L, 1L, 3L
), levels = c("red", "green", "blue", "black"), class = "factor"), sale_complete.factor = structure(c(
1L,
3L, 1L, 3L, 1L, 3L, 2L, 1L, 1L, 1L, 1L, 1L, 3L
), levels = c(
"Incomplete",
"Unverified", "Complete"
), class = "factor")
), row.names = c(
2L,
3L, 4L, 7L, 8L, 10L, 14L, 15L, 16L, 17L, 31L, 32L, 33L
), class = "data.frame"))
)
Completed working test suite and added CI
2018-06-28 23:20:14 +02:00
})
}
Writing unit tests for basic examples of data export for R.
2018-06-26 05:48:55 +02:00
Cleaning up code for #12 test now compares read_csv and read.csv output directly.
2019-07-29 18:30:52 +02:00
if (requireNamespace("readr", quietly = TRUE)) {
gp with CRAN in sight
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metadata <-
readr::read_csv(get_data_location(
linting
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"ExampleProject_DataDictionary_2018-06-07.csv"
))
Cleaning up code for #12 test now compares read_csv and read.csv output directly.
2019-07-29 18:30:52 +02:00
gp with CRAN in sight
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records <-
readr::read_csv(get_data_location(
linting
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"ExampleProject_DATA_2018-06-07_1129.csv"
))
Cleaning up code for #12 test now compares read_csv and read.csv output directly.
2019-07-29 18:30:52 +02:00
redcap_output_readr <- REDCap_split(records, metadata)
expect_matching_elements <- function(FUN) {
FUN <- match.fun(FUN)
linting
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expect_identical(
lapply(redcap_output_readr, FUN),
lapply(redcap_output_csv1, FUN)
)
Cleaning up code for #12 test now compares read_csv and read.csv output directly.
2019-07-29 18:30:52 +02:00
}
gp with CRAN in sight
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test_that("Result of data read in with `readr` will
linting
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match result with `read.csv`", {
# The list itself
expect_identical(
length(redcap_output_readr),
length(redcap_output_csv1)
)
expect_identical(
names(redcap_output_readr),
names(redcap_output_csv1)
)
# Each element of the list
expect_matching_elements(names)
expect_matching_elements(dim)
})
Cleaning up code for #12 test now compares read_csv and read.csv output directly.
2019-07-29 18:30:52 +02:00
}
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