replace all case_death_rate_subset references

Author: nmdefries

R/arx_classifier.R

@@ -27,7 +27,7 @@
 #'
 #' @examples
 #' library(dplyr)
-#' jhu <- case_death_rate_subset %>%
+#' jhu <- covid_case_death_rates %>%
 #'   filter(time_value >= as.Date("2021-11-01"))
 #'
 #' out <- arx_classifier(jhu, "death_rate", c("case_rate", "death_rate"))
@@ -97,7 +97,7 @@ arx_classifier <- function(
 #' @seealso [arx_classifier()]
 #' @examples
 #' library(dplyr)
-#' jhu <- case_death_rate_subset %>%
+#' jhu <- covid_case_death_rates %>%
 #'   filter(time_value >= as.Date("2021-11-01"))
 #'
 #' arx_class_epi_workflow(jhu, "death_rate", c("case_rate", "death_rate"))

R/arx_forecaster.R

@@ -25,7 +25,7 @@
 #' @seealso [arx_fcast_epi_workflow()], [arx_args_list()]
 #'
 #' @examples
-#' jhu <- case_death_rate_subset %>%
+#' jhu <- covid_case_death_rates %>%
 #'   dplyr::filter(time_value >= as.Date("2021-12-01"))
 #'
 #' out <- arx_forecaster(
@@ -92,7 +92,7 @@ arx_forecaster <- function(
 #'
 #' @examples
 #' library(dplyr)
-#' jhu <- case_death_rate_subset %>%
+#' jhu <- covid_case_death_rates %>%
 #'   filter(time_value >= as.Date("2021-12-01"))
 #'
 #' arx_fcast_epi_workflow(

R/autoplot.R

@@ -29,7 +29,7 @@ ggplot2::autoplot
 #' @name autoplot-epipred
 #' @examples
 #' library(dplyr)
-#' jhu <- case_death_rate_subset %>%
+#' jhu <- covid_case_death_rates %>%
 #'   filter(time_value >= as.Date("2021-11-01"))
 #'
 #' r <- epi_recipe(jhu) %>%
@@ -70,7 +70,7 @@ ggplot2::autoplot
 #'
 #' # ------- Plotting canned forecaster output
 #'
-#' jhu <- case_death_rate_subset %>%
+#' jhu <- covid_case_death_rates %>%
 #'   filter(time_value >= as.Date("2021-11-01"))
 #' flat <- flatline_forecaster(jhu, "death_rate")
 #' autoplot(flat, .max_facets = 4)

R/cdc_baseline_forecaster.R

@@ -23,7 +23,7 @@
 #'
 #' @examples
 #' library(dplyr)
-#' weekly_deaths <- case_death_rate_subset %>%
+#' weekly_deaths <- covid_case_death_rates %>%
 #'   select(geo_value, time_value, death_rate) %>%
 #'   left_join(state_census %>% select(pop, abbr), by = c("geo_value" = "abbr")) %>%
 #'   mutate(deaths = pmax(death_rate / 1e5 * pop * 7, 0)) %>%

R/epi_recipe.R

@@ -48,7 +48,7 @@ epi_recipe.default <- function(x, ...) {
 #' @examples
 #' library(dplyr)
 #' library(recipes)
-#' jhu <- case_death_rate_subset %>%
+#' jhu <- covid_case_death_rates %>%
 #'   filter(time_value > "2021-08-01") %>%
 #'   arrange(geo_value, time_value)
 #'
@@ -270,7 +270,7 @@ is_epi_recipe <- function(x) {
 #' library(dplyr)
 #' library(recipes)
 #'
-#' jhu <- case_death_rate_subset %>%
+#' jhu <- covid_case_death_rates %>%
 #'   filter(time_value > "2021-08-01") %>%
 #'   arrange(geo_value, time_value)
 #'
@@ -354,7 +354,7 @@ update_epi_recipe <- function(x, recipe, ..., blueprint = default_epi_recipe_blu
 #' library(dplyr)
 #' library(workflows)
 #'
-#' jhu <- case_death_rate_subset %>%
+#' jhu <- covid_case_death_rates %>%
 #'   filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
 #' r <- epi_recipe(jhu) %>%
 #'   step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%

R/epi_workflow.R

@@ -20,7 +20,7 @@
 #' @importFrom generics augment
 #' @export
 #' @examples
-#' jhu <- case_death_rate_subset
+#' jhu <- covid_case_death_rates
 #'
 #' r <- epi_recipe(jhu) %>%
 #'   step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%
@@ -84,7 +84,7 @@ is_epi_workflow <- function(x) {
 #' @name fit-epi_workflow
 #' @export
 #' @examples
-#' jhu <- case_death_rate_subset %>%
+#' jhu <- covid_case_death_rates %>%
 #'   filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
 #'
 #' r <- epi_recipe(jhu) %>%
@@ -139,7 +139,7 @@ fit.epi_workflow <- function(object, data, ..., control = workflows::control_wor
 #' @name predict-epi_workflow
 #' @export
 #' @examples
-#' jhu <- case_death_rate_subset
+#' jhu <- covid_case_death_rates
 #'
 #' r <- epi_recipe(jhu) %>%
 #'   step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%

R/flatline_forecaster.R

@@ -24,7 +24,7 @@
 #' @export
 #'
 #' @examples
-#' jhu <- case_death_rate_subset %>%
+#' jhu <- covid_case_death_rates %>%
 #'   dplyr::filter(time_value >= as.Date("2021-12-01"))
 #'
 #' out <- flatline_forecaster(jhu, "death_rate")

R/flusight_hub_formatter.R

@@ -57,7 +57,7 @@ abbr_to_location <- function(abbr) {
 #'
 #' @examples
 #' library(dplyr)
-#' weekly_deaths <- case_death_rate_subset %>%
+#' weekly_deaths <- covid_case_death_rates %>%
 #'   filter(
 #'     time_value >= as.Date("2021-09-01"),
 #'     geo_value %in% c("ca", "ny", "dc", "ga", "vt")

R/frosting.R

@@ -9,7 +9,7 @@
 #'
 #' @examples
 #' library(dplyr)
-#' jhu <- case_death_rate_subset %>%
+#' jhu <- covid_case_death_rates %>%
 #'   filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
 #' r <- epi_recipe(jhu) %>%
 #'   step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%
@@ -128,7 +128,7 @@ update_frosting <- function(x, frosting, ...) {
 #' @export
 #' @examples
 #' library(dplyr)
-#' jhu <- case_death_rate_subset %>%
+#' jhu <- covid_case_death_rates %>%
 #'   filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
 #' r <- epi_recipe(jhu) %>%
 #'   step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%
@@ -268,7 +268,7 @@ new_frosting <- function() {
 #' wf <- epi_workflow() %>% add_frosting(f)
 #'
 #' # A more realistic example
-#' jhu <- case_death_rate_subset %>%
+#' jhu <- covid_case_death_rates %>%
 #'   filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
 #'
 #' r <- epi_recipe(jhu) %>%

R/get_test_data.R

@@ -35,11 +35,11 @@
 #'   keys, as well other variables in the original dataset.
 #' @examples
 #' # create recipe
-#' rec <- epi_recipe(case_death_rate_subset) %>%
+#' rec <- epi_recipe(covid_case_death_rates) %>%
 #'   step_epi_ahead(death_rate, ahead = 7) %>%
 #'   step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%
 #'   step_epi_lag(case_rate, lag = c(0, 7, 14))
-#' get_test_data(recipe = rec, x = case_death_rate_subset)
+#' get_test_data(recipe = rec, x = covid_case_death_rates)
 #' @importFrom rlang %@%
 #' @export
 get_test_data <- function(

R/layer_add_forecast_date.R

@@ -20,7 +20,7 @@
 #' @export
 #' @examples
 #' library(dplyr)
-#' jhu <- case_death_rate_subset %>%
+#' jhu <- covid_case_death_rates %>%
 #'   filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
 #' r <- epi_recipe(jhu) %>%
 #'   step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%

R/layer_add_target_date.R

@@ -21,7 +21,7 @@
 #' @export
 #' @examples
 #' library(dplyr)
-#' jhu <- case_death_rate_subset %>%
+#' jhu <- covid_case_death_rates %>%
 #'   filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
 #' r <- epi_recipe(jhu) %>%
 #'   step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%

R/layer_cdc_flatline_quantiles.R

@@ -56,22 +56,22 @@
 #'
 #' @examples
 #' library(dplyr)
-#' r <- epi_recipe(case_death_rate_subset) %>%
+#' r <- epi_recipe(covid_case_death_rates) %>%
 #'   # data is "daily", so we fit this to 1 ahead, the result will contain
 #'   # 1 day ahead residuals
 #'   step_epi_ahead(death_rate, ahead = 1L, skip = TRUE) %>%
 #'   recipes::update_role(death_rate, new_role = "predictor") %>%
 #'   recipes::add_role(time_value, geo_value, new_role = "predictor")
 #'
-#' forecast_date <- max(case_death_rate_subset$time_value)
+#' forecast_date <- max(covid_case_death_rates$time_value)
 #'
 #' f <- frosting() %>%
 #'   layer_predict() %>%
 #'   layer_cdc_flatline_quantiles(aheads = c(7, 14, 21, 28), symmetrize = TRUE)
 #'
 #' eng <- linear_reg(engine = "flatline")
 #'
-#' wf <- epi_workflow(r, eng, f) %>% fit(case_death_rate_subset)
+#' wf <- epi_workflow(r, eng, f) %>% fit(covid_case_death_rates)
 #' preds <- forecast(wf) %>%
 #'   select(-time_value) %>%
 #'   mutate(forecast_date = forecast_date)
@@ -91,7 +91,7 @@
 #'     geom_ribbon(aes(ymin = `0.25`, ymax = `0.75`), fill = blues9[6]) +
 #'     geom_line(aes(y = .pred), color = "orange") +
 #'     geom_line(
-#'       data = case_death_rate_subset %>% filter(geo_value %in% four_states),
+#'       data = covid_case_death_rates %>% filter(geo_value %in% four_states),
 #'       aes(x = time_value, y = death_rate)
 #'     ) +
 #'     scale_x_date(limits = c(forecast_date - 90, forecast_date + 30)) +

R/layer_naomit.R

@@ -12,7 +12,7 @@
 #' @export
 #' @examples
 #' library(dplyr)
-#' jhu <- case_death_rate_subset %>%
+#' jhu <- covid_case_death_rates %>%
 #'   filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
 #'
 #' r <- epi_recipe(jhu) %>%

R/layer_point_from_distn.R

@@ -17,7 +17,7 @@
 #'
 #' @examples
 #' library(dplyr)
-#' jhu <- case_death_rate_subset %>%
+#' jhu <- covid_case_death_rates %>%
 #'   filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
 #'
 #' r <- epi_recipe(jhu) %>%

R/layer_predict.R

@@ -17,7 +17,7 @@
 #'
 #' @examples
 #' library(dplyr)
-#' jhu <- case_death_rate_subset %>%
+#' jhu <- covid_case_death_rates %>%
 #'   filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
 #'
 #' r <- epi_recipe(jhu) %>%

R/layer_predictive_distn.R

@@ -21,7 +21,7 @@
 #'
 #' @examples
 #' library(dplyr)
-#' jhu <- case_death_rate_subset %>%
+#' jhu <- covid_case_death_rates %>%
 #'   filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
 #'
 #' r <- epi_recipe(jhu) %>%

R/layer_quantile_distn.R

@@ -23,7 +23,7 @@
 #'
 #' @examples
 #' library(dplyr)
-#' jhu <- case_death_rate_subset %>%
+#' jhu <- covid_case_death_rates %>%
 #'   filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
 #'
 #' r <- epi_recipe(jhu) %>%

R/layer_residual_quantiles.R

@@ -15,7 +15,7 @@
 #' @export
 #' @examples
 #' library(dplyr)
-#' jhu <- case_death_rate_subset %>%
+#' jhu <- covid_case_death_rates %>%
 #'   filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
 #'
 #' r <- epi_recipe(jhu) %>%

R/layer_threshold_preds.R

@@ -23,7 +23,7 @@
 #' @export
 #' @examples
 #' library(dplyr)
-#' jhu <- case_death_rate_subset %>%
+#' jhu <- covid_case_death_rates %>%
 #'   filter(time_value < "2021-03-08", geo_value %in% c("ak", "ca", "ar"))
 #' r <- epi_recipe(jhu) %>%
 #'   step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%

R/layers.R

@@ -42,7 +42,7 @@ layer <- function(subclass, ..., .prefix = "layer_") {
 #'
 #' @examples
 #' library(dplyr)
-#' jhu <- case_death_rate_subset %>%
+#' jhu <- covid_case_death_rates %>%
 #'   filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
 #' r <- epi_recipe(jhu) %>%
 #'   step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%

R/make_grf_quantiles.R

@@ -61,7 +61,7 @@
 #' # -- a more complicated task
 #'
 #' library(dplyr)
-#' dat <- case_death_rate_subset %>%
+#' dat <- covid_case_death_rates %>%
 #'   filter(time_value > as.Date("2021-10-01"))
 #' rec <- epi_recipe(dat) %>%
 #'   step_epi_lag(case_rate, death_rate, lag = c(0, 7, 14)) %>%

R/model-methods.R

@@ -33,7 +33,7 @@
 #' @export
 #' @examples
 #' library(dplyr)
-#' jhu <- case_death_rate_subset %>%
+#' jhu <- covid_case_death_rates %>%
 #'   filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
 #'
 #' r <- epi_recipe(jhu) %>%

R/pivot_quantiles.R

@@ -8,7 +8,7 @@
 #' @examples
 #' library(dplyr)
 #' library(tidyr)
-#' edf <- case_death_rate_subset[1:3, ]
+#' edf <- covid_case_death_rates[1:3, ]
 #' edf$q <- dist_quantiles(list(1:5, 2:4, 3:10), list(1:5 / 6, 2:4 / 5, 3:10 / 11))
 #'
 #' edf_nested <- edf %>% mutate(q = nested_quantiles(q))

R/step_epi_naomit.R

@@ -8,7 +8,7 @@
 #'   of data loss.
 #' @export
 #' @examples
-#' case_death_rate_subset %>%
+#' covid_case_death_rates %>%
 #'   epi_recipe() %>%
 #'   step_epi_naomit()
 step_epi_naomit <- function(recipe) {

R/step_epi_shift.R

@@ -42,7 +42,7 @@
 #' @rdname step_epi_shift
 #' @export
 #' @examples
-#' r <- epi_recipe(case_death_rate_subset) %>%
+#' r <- epi_recipe(covid_case_death_rates) %>%
 #'   step_epi_ahead(death_rate, ahead = 7) %>%
 #'   step_epi_lag(death_rate, lag = c(0, 7, 14))
 #' r

R/step_epi_slide.R

@@ -32,7 +32,7 @@
 #' @export
 #' @examples
 #' library(dplyr)
-#' jhu <- case_death_rate_subset %>%
+#' jhu <- covid_case_death_rates %>%
 #'   filter(time_value >= as.Date("2021-01-01"), geo_value %in% c("ca", "ny"))
 #' rec <- epi_recipe(jhu) %>%
 #'   step_epi_slide(case_rate, death_rate,

R/step_growth_rate.R

@@ -32,13 +32,13 @@
 #' @importFrom epiprocess growth_rate
 #' @export
 #' @examples
-#' r <- epi_recipe(case_death_rate_subset) %>%
+#' r <- epi_recipe(covid_case_death_rates) %>%
 #'   step_growth_rate(case_rate, death_rate)
 #' r
 #'
 #' r %>%
-#'   prep(case_death_rate_subset) %>%
-#'   bake(case_death_rate_subset)
+#'   prep(covid_case_death_rates) %>%
+#'   bake(covid_case_death_rates)
 step_growth_rate <-
   function(recipe,
            ...,

R/step_lag_difference.R

@@ -15,14 +15,14 @@
 #' @family row operation steps
 #' @export
 #' @examples
-#' r <- epi_recipe(case_death_rate_subset) %>%
+#' r <- epi_recipe(covid_case_death_rates) %>%
 #'   step_lag_difference(case_rate, death_rate, horizon = c(7, 14)) %>%
 #'   step_epi_naomit()
 #' r
 #'
 #' r %>%
-#'   prep(case_death_rate_subset) %>%
-#'   bake(case_death_rate_subset)
+#'   prep(covid_case_death_rates) %>%
+#'   bake(covid_case_death_rates)
 step_lag_difference <-
   function(recipe,
            ...,

R/tidy.R

@@ -27,7 +27,7 @@
 #'
 #' @examples
 #' library(dplyr)
-#' jhu <- case_death_rate_subset %>%
+#' jhu <- covid_case_death_rates %>%
 #'   filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
 #'
 #' r <- epi_recipe(jhu) %>%