Load libraries in data-generating scripts

DESCRIPTION

@@ -6,7 +6,7 @@ Authors@R:
     person(c("Daniel", "J."), "McDonald", , "[email protected]", role = c("cre", "aut"))
 Description: This package contains data sets used to compile vignettes and
     other documentation in Delphi R Packages. The goal is to avoid calls
-    to the Delphi Epidata API, and deposit some examples here for easy
+    to the Delphi Epidata API, and to deposit some examples here for easy
     offline use.
 License: MIT + file LICENSE
 Depends:
@@ -16,6 +16,7 @@ Suggests:
     dplyr,
     epidatr,
     epipredict,
+    epiprocess,
     here,
     lubridate,
     magrittr,

R/epipredict-data.R

@@ -80,3 +80,10 @@
 #' by the Johns Hopkins University on behalf of its Center for Systems Science in Engineering.
 #' Copyright Johns Hopkins University 2020.
 "counts_subset"
+
+#' COVID Cases for Canada 
+#'
+#' THIS IS A STUB This dataset covers the period from March 2020 to March 2023 and is
+#' used in the [epipredict] slide vignette.
+"cancovid"
+

data-raw/archive_cases_dv_subset.R → data-raw/archive_cases_dv_subset_dt.R

@@ -1,3 +1,7 @@
+library(dplyr)
+library(epidatr)
+library(epiprocess)
+
 dv_subset <- covidcast(
   data_source = "doctor-visits",
   signals = "smoothed_adj_cli",

data-raw/cancovid.R

@@ -1,5 +1,10 @@
 ## code to prepare `cancovid` dataset goes here
 
+library(dplyr)
+library(epiprocess)
+library(readr)
+library(purrr)
+
 path_to_csvs <- here::here("../../COVID-BC/Covid19Canada/updates.nosync/")
 files <- list.files(path_to_csvs)
 ca_as_ofs <- as.Date(substr(files, 1, 10)) %>%

data-raw/cases_deaths_subset.R

@@ -1,3 +1,7 @@
+library(dplyr)
+library(epidatr)
+library(epiprocess)
+
 confirmed_7dav_incidence_prop <- covidcast(
   data_source = "jhu-csse",
   signals = "confirmed_7dav_incidence_prop",

data-raw/counts_subset.R

@@ -1,5 +1,9 @@
 ## code to prepare jhu_incidence_num dataset goes here
 
+library(dplyr)
+library(epidatr)
+library(epiprocess)
+
 x <- covidcast(
   data_source = "jhu-csse",
   signals = "confirmed_incidence_num",

data-raw/covid_case_death_rates.R

@@ -1,3 +1,7 @@
+library(dplyr)
+library(epidatr)
+library(epiprocess)
+
 x <- covidcast(
   data_source = "jhu-csse",
   signals = "confirmed_7dav_incidence_prop",
@@ -20,8 +24,8 @@ y <- covidcast(
   fetch() %>%
   select(geo_value, time_value, death_rate = value)
 
-case_death_rate_subset <- x %>%
+covid_case_death_rates <- x %>%
   full_join(y, by = c("geo_value", "time_value")) %>%
   as_epi_df()
 
-usethis::use_data(case_death_rate_subset, overwrite = TRUE)
+usethis::use_data(covid_case_death_rates, overwrite = TRUE)

data-raw/covid_incidence_county_subset.R

@@ -1,4 +1,9 @@
 # Use covidcast::county_census to get the county and state names
+library(dplyr)
+library(covidcast)
+library(epidatr)
+library(epiprocess)
+
 y <- covidcast::county_census %>%
   filter(STNAME %in% c("Massachusetts", "Vermont"), STNAME != CTYNAME) %>%
   select(geo_value = FIPS, county_name = CTYNAME, state_name = STNAME)

data-raw/covid_incidence_outliers.R

@@ -1,4 +1,8 @@
-incidence_num_outlier_example <- covidcast(
+library(dplyr)
+library(epidatr)
+library(epiprocess)
+
+covid_incidence_outliers <- covidcast(
   data_source = "jhu-csse",
   signals = "confirmed_incidence_num",
   time_type = "day",

data-raw/ctis_covid_behaviours.R

@@ -1,5 +1,8 @@
 ## code to prepare `ctis_covid_behaviours` dataset goes here
 
+library(dplyr)
+library(epidatr)
+
 behav_ind_mask <- covidcast(
   data_source = "fb-survey",
   signals = "smoothed_wwearing_mask_7d",
@@ -20,8 +23,6 @@ behav_ind_distancing <- covidcast(
   fetch() %>%
   select(geo_value, time_value, distancing = value)
 
-pop_dat <- state_census %>% select(abbr, pop)
-
 ctis_covid_behaviours <- behav_ind_mask %>%
   full_join(behav_ind_distancing, by = c("geo_value", "time_value"))
 

data-raw/jhu_confirmed_cumulative_num.R

@@ -1,4 +1,5 @@
-## code to prepare `jhu_confirmed_cumulative_num` dataset goes here
+library(dplyr)
+library(epidatr)
 
 jhu_confirmed_cumulative_num <- covidcast(
   data_source = "jhu-csse",

data-raw/state_census.R

@@ -1,3 +1,6 @@
+library(dplyr)
+library(covidcast)
+
 state_census <- covidcast::state_census %>%
   select(STATE, NAME, POPESTIMATE2019, ABBR) %>%
   rename(abbr = ABBR, name = NAME, pop = POPESTIMATE2019, fips = STATE) %>%