|
1 |
| -# load testing function and tools. |
2 |
| -# set up custom tests using: |
3 |
| -# custom_tests/regional-dataset-specific.R |
4 |
| -source("custom_tests/test-regional-dataset.R") |
| 1 | +if (identical(Sys.getenv("NOT_CRAN"), "true")) { |
| 2 | + # load testing function and tools. |
| 3 | + # set up custom tests using: |
| 4 | + # custom_tests/regional-dataset-specific.R |
| 5 | + source("custom_tests/test-regional-dataset.R") |
5 | 6 |
|
6 |
| -# should a single dataset be tested vs all datasets |
7 |
| -# set this when implementing a new dataset. |
8 |
| -# Can also be set using environment variables |
9 |
| -source_of_interest <- NULL |
10 |
| -if (!is.null(getOption("testSource"))) { |
11 |
| - source_of_interest <- getOption("testSource") |
12 |
| -} |
13 |
| -# should downloads be tested (defaults to FALSE) |
14 |
| -# set this to true when implementing a new data set |
15 |
| -# can also be controlled using an environment variable |
16 |
| -download <- FALSE |
17 |
| -if (!is.null(getOption("testDownload"))) { |
18 |
| - download <- getOption("testDownload") |
19 |
| -} |
| 7 | + # should a single dataset be tested vs all datasets |
| 8 | + # set this when implementing a new dataset. |
| 9 | + # Can also be set using environment variables |
| 10 | + source_of_interest <- NULL |
| 11 | + if (!is.null(getOption("testSource"))) { |
| 12 | + source_of_interest <- getOption("testSource") |
| 13 | + } |
| 14 | + # should downloads be tested (defaults to FALSE) |
| 15 | + # set this to true when implementing a new data set |
| 16 | + # can also be controlled using an environment variable |
| 17 | + download <- FALSE |
| 18 | + if (!is.null(getOption("testDownload"))) { |
| 19 | + download <- getOption("testDownload") |
| 20 | + } |
20 | 21 |
|
21 |
| -# get datasets for testing |
22 |
| -sources <- get_available_datasets() %>% |
23 |
| - dplyr::filter(.data$type %in% |
24 |
| - c("national", "regional")) %>% |
25 |
| - dplyr::select(source = class, level_1_region, level_2_region) %>% |
26 |
| - tidyr::pivot_longer( |
27 |
| - cols = -source, |
28 |
| - names_to = "level", |
29 |
| - values_to = "regions" |
30 |
| - ) %>% |
31 |
| - dplyr::mutate( |
32 |
| - level = stringr::str_split(level, "_"), |
33 |
| - level = purrr::map_chr(level, ~ .[2]) |
34 |
| - ) %>% |
35 |
| - tidyr::drop_na(regions) |
| 22 | + # get datasets for testing |
| 23 | + sources <- get_available_datasets() %>% |
| 24 | + dplyr::filter(.data$type %in% |
| 25 | + c("national", "regional")) %>% |
| 26 | + dplyr::select(source = class, level_1_region, level_2_region) %>% |
| 27 | + tidyr::pivot_longer( |
| 28 | + cols = -source, |
| 29 | + names_to = "level", |
| 30 | + values_to = "regions" |
| 31 | + ) %>% |
| 32 | + dplyr::mutate( |
| 33 | + level = stringr::str_split(level, "_"), |
| 34 | + level = purrr::map_chr(level, ~ .[2]) |
| 35 | + ) %>% |
| 36 | + tidyr::drop_na(regions) |
36 | 37 |
|
37 |
| -# filter out target datasets |
38 |
| -if (!is.null(source_of_interest)) { |
39 |
| - sources <- sources %>% |
40 |
| - dplyr::filter(source %in% source_of_interest) |
41 |
| -} |
| 38 | + # filter out target datasets |
| 39 | + if (!is.null(source_of_interest)) { |
| 40 | + sources <- sources %>% |
| 41 | + dplyr::filter(source %in% source_of_interest) |
| 42 | + } |
42 | 43 |
|
43 |
| -# apply tests to each data source in turn |
44 |
| -sources %>% |
45 |
| - dplyr::rowwise() %>% |
46 |
| - dplyr::group_split() %>% |
47 |
| - purrr::walk( |
48 |
| - ~ test_regional_dataset( |
49 |
| - source = .$source[[1]], |
50 |
| - level = .$level[[1]], |
51 |
| - download = download |
| 44 | + # apply tests to each data source in turn |
| 45 | + sources %>% |
| 46 | + dplyr::rowwise() %>% |
| 47 | + dplyr::group_split() %>% |
| 48 | + purrr::walk( |
| 49 | + ~ test_regional_dataset( |
| 50 | + source = .$source[[1]], |
| 51 | + level = .$level[[1]], |
| 52 | + download = download |
| 53 | + ) |
52 | 54 | )
|
53 |
| - ) |
| 55 | +} |
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