dataset renamed

Author: dajmcdon

R/layer_population_scaling.R

@@ -48,7 +48,7 @@
 #' @export
 #' @examples
 #' library(dplyr)
-#' jhu <- jhu_csse_daily_subset %>%
+#' jhu <- cases_deaths_subset %>%
 #'   filter(time_value > "2021-11-01", geo_value %in% c("ca", "ny")) %>%
 #'   select(geo_value, time_value, cases)
 #'

R/step_population_scaling.R

@@ -45,7 +45,7 @@
 #' @export
 #' @examples
 #' library(dplyr)
-#' jhu <- jhu_csse_daily_subset %>%
+#' jhu <- cases_deaths_subset %>%
 #'   filter(time_value > "2021-11-01", geo_value %in% c("ca", "ny")) %>%
 #'   select(geo_value, time_value, cases)
 #'

man/layer_population_scaling.Rd

@@ -377,7 +377,7 @@
 # simple surface step test
 
     Code
-      epi_recipe(jhu_csse_daily_subset) %>% step_epi_lag(death_rate, lag = "hello")
+      epi_recipe(cases_deaths_subset) %>% step_epi_lag(death_rate, lag = "hello")
     Condition
       Error in `step_epi_lag()`:
       ! `lag` must be a non-negative integer.

man/step_population_scaling.Rd

@@ -149,6 +149,6 @@ test_that("coerce scalar to date", {
 test_that("simple surface step test", {
   expect_snapshot(
     error = TRUE,
-    epi_recipe(jhu_csse_daily_subset) %>% step_epi_lag(death_rate, lag = "hello")
+    epi_recipe(cases_deaths_subset) %>% step_epi_lag(death_rate, lag = "hello")
   )
 })

tests/testthat/_snaps/arg_is_.md

@@ -1,5 +1,5 @@
 test_that("training set validation works", {
-  template <- jhu_csse_daily_subset[1, ]
+  template <- cases_deaths_subset[1, ]
   rec <- list(template = template)
   t1 <- template
 

tests/testthat/test-arg_is_.R

@@ -90,7 +90,7 @@ test_that("Number of columns and column names returned correctly, Upper and lowe
 
 ## Postprocessing
 test_that("Postprocessing workflow works and values correct", {
-  jhu <- jhu_csse_daily_subset %>%
+  jhu <- cases_deaths_subset %>%
     dplyr::filter(time_value > "2021-11-01", geo_value %in% c("ca", "ny")) %>%
     dplyr::select(geo_value, time_value, cases)
 

tests/testthat/test-check-training-set.R

@@ -1,4 +1,4 @@
-train_data <- jhu_csse_daily_subset
+train_data <- cases_deaths_subset
 expect_snapshot_tibble <- function(x) {
   expect_snapshot_value(x, style = "deparse", cran = FALSE)
 }

tests/testthat/test-population_scaling.R

@@ -2,7 +2,7 @@
 # https://github.com/cmu-delphi/epipredict/issues/290
 
 library(dplyr)
-train <- jhu_csse_daily_subset |>
+train <- cases_deaths_subset |>
   filter(time_value >= as.Date("2021-10-01")) |>
   select(geo_value, time_value, cr = case_rate_7d_av, dr = death_rate_7d_av)
 ngeos <- n_distinct(train$geo_value)