Merge pull request #444 from cmu-delphi/440-epiprocess-imports

440 epiprocess imports

Author: dajmcdon

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: epipredict
 Title: Basic epidemiology forecasting methods
-Version: 0.1.10
+Version: 0.1.11
 Authors@R: c(
     person("Daniel J.", "McDonald", , "[email protected]", role = c("aut", "cre")),
     person("Ryan", "Tibshirani", , "[email protected]", role = "aut"),
@@ -25,14 +25,14 @@ URL: https://github.com/cmu-delphi/epipredict/,
 BugReports: https://github.com/cmu-delphi/epipredict/issues/
 Depends:
     epidatasets,
-    epiprocess (>= 0.10.4),
     parsnip (>= 1.0.0),
     R (>= 3.5.0)
 Imports:
     checkmate,
     cli,
     distributional,
     dplyr,
+    epiprocess (>= 0.10.4),
     generics,
     ggplot2,
     glue,

NAMESPACE

@@ -144,6 +144,8 @@ export(arx_class_epi_workflow)
 export(arx_classifier)
 export(arx_fcast_epi_workflow)
 export(arx_forecaster)
+export(as_epi_df)
+export(as_tibble)
 export(autoplot)
 export(bake)
 export(cdc_baseline_args_list)
@@ -160,6 +162,7 @@ export(extract_argument)
 export(extract_frosting)
 export(extract_layers)
 export(extrapolate_quantiles)
+export(filter)
 export(fit)
 export(flatline)
 export(flatline_args_list)
@@ -171,6 +174,7 @@ export(get_test_data)
 export(is_epi_recipe)
 export(is_epi_workflow)
 export(is_layer)
+export(key_colnames)
 export(layer)
 export(layer_add_forecast_date)
 export(layer_add_target_date)
@@ -184,17 +188,22 @@ export(layer_quantile_distn)
 export(layer_residual_quantiles)
 export(layer_threshold)
 export(layer_unnest)
+export(mutate)
 export(nested_quantiles)
 export(new_default_epi_recipe_blueprint)
 export(new_epi_recipe_blueprint)
+export(pivot_longer)
 export(pivot_quantiles_longer)
 export(pivot_quantiles_wider)
+export(pivot_wider)
 export(prep)
 export(quantile_reg)
 export(rand_id)
 export(remove_epi_recipe)
 export(remove_frosting)
 export(remove_model)
+export(rename)
+export(select)
 export(slather)
 export(smooth_quantile_reg)
 export(step_adjust_latency)
@@ -208,6 +217,7 @@ export(step_population_scaling)
 export(step_training_window)
 export(tibble)
 export(tidy)
+export(unnest)
 export(update_epi_recipe)
 export(update_frosting)
 export(update_model)
@@ -256,8 +266,12 @@ importFrom(dplyr,summarize)
 importFrom(dplyr,tibble)
 importFrom(dplyr,tribble)
 importFrom(dplyr,ungroup)
+importFrom(epiprocess,as_epi_df)
 importFrom(epiprocess,epi_slide)
 importFrom(epiprocess,growth_rate)
+importFrom(epiprocess,growth_rate_params)
+importFrom(epiprocess,is_epi_df)
+importFrom(epiprocess,key_colnames)
 importFrom(generics,augment)
 importFrom(generics,fit)
 importFrom(generics,forecast)
@@ -316,6 +330,8 @@ importFrom(tidyr,crossing)
 importFrom(tidyr,drop_na)
 importFrom(tidyr,expand_grid)
 importFrom(tidyr,fill)
+importFrom(tidyr,pivot_longer)
+importFrom(tidyr,pivot_wider)
 importFrom(tidyr,unnest)
 importFrom(tidyselect,all_of)
 importFrom(utils,capture.output)

NEWS.md

@@ -14,6 +14,8 @@ Pre-1.0.0 numbering scheme: 0.x will indicate releases, while 0.0.x will indicat
 - Addresses upstream breaking changes from cmu-delphi/epiprocess#595 (`growth_rate()`). 
   `step_growth_rate()` has lost its `additional_gr_args_list` argument and now
   has an `na_rm` argument.
+- Moves `epiprocess` out of depends (#440). No internals have changed, but downstream
+  users may need to add `library(epiprocess)` to existing code.
 
 ## Improvements
 

R/arx_classifier.R

@@ -26,7 +26,6 @@
 #' @seealso [arx_class_epi_workflow()], [arx_class_args_list()]
 #'
 #' @examples
-#' library(dplyr)
 #' tiny_geos <- c("as", "mp", "vi", "gu", "pr")
 #' jhu <- covid_case_death_rates %>%
 #'   filter(time_value >= as.Date("2021-11-01"), !(geo_value %in% tiny_geos))
@@ -107,7 +106,6 @@ arx_classifier <- function(
 #' @export
 #' @seealso [arx_classifier()] [arx_class_args_list()]
 #' @examples
-#' library(dplyr)
 #' jhu <- covid_case_death_rates %>%
 #'   filter(time_value >= as.Date("2021-11-01"))
 #'

R/arx_forecaster.R

@@ -95,7 +95,6 @@ arx_forecaster <- function(
 #' @seealso [arx_forecaster()]
 #'
 #' @examples
-#' library(dplyr)
 #' jhu <- covid_case_death_rates %>%
 #'   filter(time_value >= as.Date("2021-12-01"))
 #'

R/autoplot.R

@@ -28,7 +28,6 @@ ggplot2::autoplot
 #'
 #' @name autoplot-epipred
 #' @examples
-#' library(dplyr)
 #' jhu <- covid_case_death_rates %>%
 #'   filter(time_value >= as.Date("2021-11-01"))
 #'

R/cdc_baseline_forecaster.R

@@ -23,6 +23,7 @@
 #'
 #' @examples
 #' library(dplyr)
+#' library(epiprocess)
 #' 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")) %>%
@@ -36,25 +37,24 @@
 #' cdc <- cdc_baseline_forecaster(weekly_deaths, "deaths_7dsum")
 #' preds <- pivot_quantiles_wider(cdc$predictions, .pred_distn)
 #'
-#' if (require(ggplot2)) {
-#'   forecast_date <- unique(preds$forecast_date)
-#'   four_states <- c("ca", "pa", "wa", "ny")
-#'   preds %>%
-#'     filter(geo_value %in% four_states) %>%
-#'     ggplot(aes(target_date)) +
-#'     geom_ribbon(aes(ymin = `0.1`, ymax = `0.9`), fill = blues9[3]) +
-#'     geom_ribbon(aes(ymin = `0.25`, ymax = `0.75`), fill = blues9[6]) +
-#'     geom_line(aes(y = .pred), color = "orange") +
-#'     geom_line(
-#'       data = weekly_deaths %>% filter(geo_value %in% four_states),
-#'       aes(x = time_value, y = deaths_7dsum)
-#'     ) +
-#'     scale_x_date(limits = c(forecast_date - 90, forecast_date + 30)) +
-#'     labs(x = "Date", y = "Weekly deaths") +
-#'     facet_wrap(~geo_value, scales = "free_y") +
-#'     theme_bw() +
-#'     geom_vline(xintercept = forecast_date)
-#' }
+#' library(ggplot2)
+#' forecast_date <- unique(preds$forecast_date)
+#' four_states <- c("ca", "pa", "wa", "ny")
+#' preds %>%
+#'   filter(geo_value %in% four_states) %>%
+#'   ggplot(aes(target_date)) +
+#'   geom_ribbon(aes(ymin = `0.1`, ymax = `0.9`), fill = blues9[3]) +
+#'   geom_ribbon(aes(ymin = `0.25`, ymax = `0.75`), fill = blues9[6]) +
+#'   geom_line(aes(y = .pred), color = "orange") +
+#'   geom_line(
+#'     data = weekly_deaths %>% filter(geo_value %in% four_states),
+#'     aes(x = time_value, y = deaths_7dsum)
+#'   ) +
+#'   scale_x_date(limits = c(forecast_date - 90, forecast_date + 30)) +
+#'   labs(x = "Date", y = "Weekly deaths") +
+#'   facet_wrap(~geo_value, scales = "free_y") +
+#'   theme_bw() +
+#'   geom_vline(xintercept = forecast_date)
 cdc_baseline_forecaster <- function(
     epi_data,
     outcome,

R/create-layer.R

@@ -45,19 +45,14 @@ epi_recipe <- function(x, ...) {
 #'
 #' @export
 #' @examples
-#' library(dplyr)
-#' library(recipes)
 #' jhu <- covid_case_death_rates %>%
-#'   filter(time_value > "2021-08-01") %>%
-#'   arrange(geo_value, time_value)
+#'   filter(time_value > "2021-08-01")
 #'
 #' r <- epi_recipe(jhu) %>%
 #'   step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%
 #'   step_epi_ahead(death_rate, ahead = 7) %>%
 #'   step_epi_lag(case_rate, lag = c(0, 7, 14)) %>%
-#'   step_naomit(all_predictors()) %>%
-#'   # below, `skip` means we don't do this at predict time
-#'   step_naomit(all_outcomes(), skip = TRUE)
+#'   step_epi_naomit()
 #'
 #' r
 epi_recipe.epi_df <- function(x,
@@ -271,19 +266,14 @@ is_epi_recipe <- function(x) {
 #'
 #' @export
 #' @examples
-#' library(dplyr)
-#' library(recipes)
-#'
 #' jhu <- covid_case_death_rates %>%
-#'   filter(time_value > "2021-08-01") %>%
-#'   arrange(geo_value, time_value)
+#'   filter(time_value > "2021-08-01")
 #'
 #' r <- epi_recipe(jhu) %>%
 #'   step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%
 #'   step_epi_ahead(death_rate, ahead = 7) %>%
 #'   step_epi_lag(case_rate, lag = c(0, 7, 14)) %>%
-#'   step_naomit(all_predictors()) %>%
-#'   step_naomit(all_outcomes(), skip = TRUE)
+#'   step_epi_naomit()
 #'
 #' workflow <- epi_workflow() %>%
 #'   add_epi_recipe(r)
@@ -355,7 +345,6 @@ update_epi_recipe <- function(x, recipe, ..., blueprint = default_epi_recipe_blu
 #'
 #' @export
 #' @examples
-#' library(dplyr)
 #' library(workflows)
 #'
 #' jhu <- covid_case_death_rates %>%

R/epi_recipe.R

@@ -9,11 +9,11 @@
 #' @importFrom dplyr full_join relocate summarise everything
 #' @importFrom dplyr inner_join
 #' @importFrom dplyr summarize filter mutate select left_join rename ungroup
+#' @importFrom epiprocess growth_rate growth_rate_params is_epi_df
 #' @importFrom magrittr extract2
 #' @importFrom rlang := !! %||% as_function global_env set_names !!! caller_arg
 #' @importFrom rlang is_logical is_true inject enquo enquos expr sym arg_match
 #' @importFrom stats poly predict lm residuals quantile
 #' @importFrom tibble as_tibble
-na_chr <- NA_character_
 ## usethis namespace: end
 NULL

R/epipredict-package.R

@@ -55,6 +55,7 @@ abbr_to_location <- function(abbr) {
 #'
 #' @examples
 #' library(dplyr)
+#' library(epiprocess)
 #' weekly_deaths <- covid_case_death_rates %>%
 #'   filter(
 #'     time_value >= as.Date("2021-09-01"),

R/flusight_hub_formatter.R

@@ -8,7 +8,6 @@
 #' @export
 #'
 #' @examples
-#' library(dplyr)
 #' jhu <- covid_case_death_rates %>%
 #'   filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
 #' r <- epi_recipe(jhu) %>%
@@ -127,7 +126,6 @@ update_frosting <- function(x, frosting, ...) {
 #'
 #' @export
 #' @examples
-#' library(dplyr)
 #' jhu <- covid_case_death_rates %>%
 #'   filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
 #' r <- epi_recipe(jhu) %>%
@@ -262,7 +260,6 @@ new_frosting <- function() {
 #' @export
 #'
 #' @examples
-#' library(dplyr)
 #' # Toy example to show that frosting can be created and added for postprocessing
 #' f <- frosting()
 #' wf <- epi_workflow() %>% add_frosting(f)

R/frosting.R

@@ -21,7 +21,6 @@
 #'
 #' @export
 #' @examples
-#' library(dplyr)
 #' jhu <- covid_case_death_rates %>%
 #'   filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
 #' r <- epi_recipe(jhu) %>%

R/layer_add_forecast_date.R

@@ -24,7 +24,6 @@
 #'
 #' @export
 #' @examples
-#' library(dplyr)
 #' jhu <- covid_case_death_rates %>%
 #'   filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))
 #' r <- epi_recipe(jhu) %>%