|
| 1 | +library(dplyr) |
| 2 | + |
| 3 | +test_that("first input must be a data.frame",{ |
| 4 | + expect_error(as_epi_archive(c(1,2,3),compactify=FALSE), |
| 5 | + regexp="`x` must be a data frame.") |
| 6 | +}) |
| 7 | + |
| 8 | +dt <- archive_cases_dv_subset$DT |
| 9 | + |
| 10 | +test_that("data.frame must contain geo_value, time_value and version columns",{ |
| 11 | + expect_error(as_epi_archive(select(dt,-geo_value), compactify=FALSE), |
| 12 | + regexp="`x` must contain a `geo_value` column.") |
| 13 | + expect_error(as_epi_archive(select(dt,-time_value), compactify=FALSE), |
| 14 | + regexp="`x` must contain a `time_value` column.") |
| 15 | + expect_error(as_epi_archive(select(dt,-version), compactify=FALSE), |
| 16 | + regexp="`x` must contain a `version` column.") |
| 17 | +}) |
| 18 | + |
| 19 | +test_that("other_keys can only contain names of the data.frame columns",{ |
| 20 | + expect_error(as_epi_archive(dt,other_keys = "xyz", compactify=FALSE), |
| 21 | + regexp="`other_keys` must be contained in the column names of `x`.") |
| 22 | + expect_error(as_epi_archive(dt,other_keys = "percent_cli", compactify=FALSE),NA) |
| 23 | +}) |
| 24 | + |
| 25 | +test_that("other_keys cannot contain names geo_value, time_value or version",{ |
| 26 | + expect_error(as_epi_archive(dt,other_keys = "geo_value", compactify=FALSE), |
| 27 | + regexp="`other_keys` cannot contain \"geo_value\", \"time_value\", or \"version\".") |
| 28 | + expect_error(as_epi_archive(dt,other_keys = "time_value", compactify=FALSE), |
| 29 | + regexp="`other_keys` cannot contain \"geo_value\", \"time_value\", or \"version\".") |
| 30 | + expect_error(as_epi_archive(dt,other_keys = "version", compactify=FALSE), |
| 31 | + regexp="`other_keys` cannot contain \"geo_value\", \"time_value\", or \"version\".") |
| 32 | +}) |
| 33 | + |
| 34 | +test_that("Warning thrown when other_metadata contains overlapping names with geo_type or time_type fields",{ |
| 35 | + expect_warning(as_epi_archive(dt,additional_metadata = list(geo_type = 1), compactify=FALSE), |
| 36 | + regexp="`additional_metadata` names overlap with existing metadata fields\n\"geo_type\", \"time_type\".") |
| 37 | + expect_warning(as_epi_archive(dt,additional_metadata = list(time_type = 1), compactify=FALSE), |
| 38 | + regexp="`additional_metadata` names overlap with existing metadata fields\n\"geo_type\", \"time_type\".") |
| 39 | +}) |
| 40 | + |
| 41 | +test_that("epi_archives are correctly instantiated with a variety of data types",{ |
| 42 | + # Data frame |
| 43 | + df <- data.frame(geo_value="ca", |
| 44 | + time_value=as.Date("2020-01-01"), |
| 45 | + version = as.Date("2020-01-01") + 0:19, |
| 46 | + value=1:20) |
| 47 | + |
| 48 | + ea1 <- as_epi_archive(df, compactify=FALSE) |
| 49 | + expect_equal(key(ea1$DT),c("geo_value","time_value","version")) |
| 50 | + expect_equal(ea1$additional_metadata,list()) |
| 51 | + |
| 52 | + ea2 <- as_epi_archive(df, other_keys="value", additional_metadata=list(value=df$value), compactify=FALSE) |
| 53 | + expect_equal(key(ea2$DT),c("geo_value","time_value","value","version")) |
| 54 | + expect_equal(ea2$additional_metadata,list(value=df$value)) |
| 55 | + |
| 56 | + # Tibble |
| 57 | + tib <- tibble::tibble(df, code="x") |
| 58 | + |
| 59 | + ea3 <- as_epi_archive(tib, compactify=FALSE) |
| 60 | + expect_equal(key(ea3$DT),c("geo_value","time_value","version")) |
| 61 | + expect_equal(ea3$additional_metadata,list()) |
| 62 | + |
| 63 | + ea4 <- as_epi_archive(tib, other_keys="code", additional_metadata=list(value=df$value), compactify=FALSE) |
| 64 | + expect_equal(key(ea4$DT),c("geo_value","time_value","code","version")) |
| 65 | + expect_equal(ea4$additional_metadata,list(value=df$value)) |
| 66 | + |
| 67 | + # Keyed data.table |
| 68 | + kdt <- data.table::data.table(geo_value="ca", |
| 69 | + time_value=as.Date("2020-01-01"), |
| 70 | + version = as.Date("2020-01-01") + 0:19, |
| 71 | + value = 1:20, |
| 72 | + code = "CA", |
| 73 | + key = "code") |
| 74 | + |
| 75 | + ea5 <- as_epi_archive(kdt, compactify=FALSE) |
| 76 | + # Key from data.table isn't absorbed when as_epi_archive is used |
| 77 | + expect_equal(key(ea5$DT),c("geo_value","time_value","version")) |
| 78 | + expect_equal(ea5$additional_metadata,list()) |
| 79 | + |
| 80 | + ea6 <- as_epi_archive(kdt,other_keys="value", additional_metadata=list(value=df$value), compactify=FALSE) |
| 81 | + # Mismatched keys, but the one from as_epi_archive overrides |
| 82 | + expect_equal(key(ea6$DT),c("geo_value","time_value","value","version")) |
| 83 | + expect_equal(ea6$additional_metadata,list(value=df$value)) |
| 84 | + |
| 85 | + # Unkeyed data.table |
| 86 | + udt <- data.table::data.table(geo_value="ca", |
| 87 | + time_value=as.Date("2020-01-01"), |
| 88 | + version = as.Date("2020-01-01") + 0:19, |
| 89 | + value=1:20, |
| 90 | + code = "CA") |
| 91 | + |
| 92 | + ea7 <- as_epi_archive(udt, compactify=FALSE) |
| 93 | + expect_equal(key(ea7$DT),c("geo_value","time_value","version")) |
| 94 | + expect_equal(ea7$additional_metadata,list()) |
| 95 | + |
| 96 | + ea8 <- as_epi_archive(udt,other_keys="code", additional_metadata=list(value=df$value), compactify=FALSE) |
| 97 | + expect_equal(key(ea8$DT),c("geo_value","time_value","code","version")) |
| 98 | + expect_equal(ea8$additional_metadata,list(value=df$value)) |
| 99 | + |
| 100 | + # epi_df |
| 101 | + edf1 <- jhu_csse_daily_subset %>% |
| 102 | + select(geo_value,time_value,cases) %>% |
| 103 | + mutate(version = max(time_value), code = "USA") |
| 104 | + |
| 105 | + ea9 <- as_epi_archive(edf1, compactify=FALSE) |
| 106 | + expect_equal(key(ea9$DT),c("geo_value","time_value","version")) |
| 107 | + expect_equal(ea9$additional_metadata,list()) |
| 108 | + |
| 109 | + ea10 <- as_epi_archive(edf1,other_keys="code", additional_metadata=list(value=df$value), compactify=FALSE) |
| 110 | + expect_equal(key(ea10$DT),c("geo_value","time_value","code","version")) |
| 111 | + expect_equal(ea10$additional_metadata,list(value=df$value)) |
| 112 | + |
| 113 | + # Keyed epi_df |
| 114 | + edf2 <- data.frame(geo_value = "al", |
| 115 | + time_value = rep(as.Date("2020-01-01") + 0:9,2), |
| 116 | + version = c(rep(as.Date("2020-01-25"),10), |
| 117 | + rep(as.Date("2020-01-26"),10)), |
| 118 | + cases = 1:20, |
| 119 | + misc = "USA") %>% |
| 120 | + as_epi_df(additional_metadata = list(other_keys = "misc")) |
| 121 | + |
| 122 | + ea11 <- as_epi_archive(edf2, compactify=FALSE) |
| 123 | + expect_equal(key(ea11$DT),c("geo_value","time_value","version")) |
| 124 | + expect_equal(ea11$additional_metadata,list()) |
| 125 | + |
| 126 | + ea12 <- as_epi_archive(edf2,other_keys="misc", additional_metadata=list(value=df$misc), compactify=FALSE) |
| 127 | + expect_equal(key(ea12$DT),c("geo_value","time_value","misc","version")) |
| 128 | + expect_equal(ea12$additional_metadata,list(value=df$misc)) |
| 129 | +}) |
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