refactor+style: update for epidatr 1.0.0

data-raw/archive_cases_dv_subset.R

@@ -3,41 +3,40 @@ library(epiprocess)
 library(data.table)
 library(dplyr)
 
-dv_subset <- covidcast(
-  data_source = "doctor-visits",
+dv_subset <- pub_covidcast(
+  source = "doctor-visits",
   signals = "smoothed_adj_cli",
-  time_type = "day",
   geo_type = "state",
-  time_values = epirange(20200601, 20211201),
+  time_type = "day",
   geo_values = "ca,fl,ny,tx",
+  time_values = epirange(20200601, 20211201),
   issues = epirange(20200601, 20211201)
 ) %>%
-  fetch() %>%
   select(geo_value, time_value, version = issue, percent_cli = value) %>%
   # We're using compactify=FALSE here and below to avoid some testthat test
   # failures on tests that were based on a non-compactified version.
-  as_epi_archive(compactify=FALSE)
+  as_epi_archive(compactify = FALSE)
 
-case_rate_subset <- covidcast(
-  data_source = "jhu-csse",
+case_rate_subset <- pub_covidcast(
+  source = "jhu-csse",
   signals = "confirmed_7dav_incidence_prop",
-  time_type = "day",
   geo_type = "state",
-  time_values = epirange(20200601, 20211201),
+  time_type = "day",
   geo_values = "ca,fl,ny,tx",
+  time_values = epirange(20200601, 20211201),
   issues = epirange(20200601, 20211201)
 ) %>%
-  fetch() %>%
   select(geo_value, time_value, version = issue, case_rate_7d_av = value) %>%
-  as_epi_archive(compactify=FALSE)
+  as_epi_archive(compactify = FALSE)
 
-archive_cases_dv_subset = epix_merge(dv_subset, case_rate_subset,
-                                     sync="locf",
-                                     compactify=FALSE)
+archive_cases_dv_subset <- epix_merge(dv_subset, case_rate_subset,
+  sync = "locf",
+  compactify = FALSE
+)
 
 # If we directly store an epi_archive R6 object as data, it will store its class
 # implementation there as well. To prevent mismatches between these stored
 # implementations and the latest class definition, don't store them as R6
 # objects; store the DT and construct the R6 object on request.
-archive_cases_dv_subset_dt = archive_cases_dv_subset$DT
+archive_cases_dv_subset_dt <- archive_cases_dv_subset$DT
 usethis::use_data(archive_cases_dv_subset_dt, overwrite = TRUE, internal = TRUE)

data-raw/incidence_num_outlier_example.R

@@ -3,16 +3,15 @@ library(epiprocess)
 library(dplyr)
 library(tidyr)
 
-incidence_num_outlier_example <- covidcast(
-  data_source = "jhu-csse",
+incidence_num_outlier_example <- pub_covidcast(
+  source = "jhu-csse",
   signals = "confirmed_incidence_num",
-  time_type = "day",
   geo_type = "state",
-  time_values = epirange(20200601, 20210531),
+  time_type = "day",
   geo_values = "fl,nj",
+  time_values = epirange(20200601, 20210531),
   as_of = 20211028
 ) %>%
-  fetch() %>%
   select(geo_value, time_value, cases = value) %>%
   as_epi_df()
 

data-raw/jhu_csse_county_level_subset.R

@@ -9,15 +9,14 @@ y <- covidcast::county_census %>%
   select(geo_value = FIPS, county_name = CTYNAME, state_name = STNAME)
 
 # Fetch only counties from Massachusetts and Vermont, then append names columns as well
-jhu_csse_county_level_subset <- covidcast(
-  data_source = "jhu-csse",
+jhu_csse_county_level_subset <- pub_covidcast(
+  source = "jhu-csse",
   signals = "confirmed_incidence_num",
-  time_type = "day",
   geo_type = "county",
+  time_type = "day",
+  geo_values = paste(y$geo_value, collapse = ","),
   time_values = epirange(20200601, 20211231),
-  geo_values = paste(y$geo_value, collapse = ",")
 ) %>%
-  fetch() %>%
   select(geo_value, time_value, cases = value) %>%
   full_join(y, by = "geo_value") %>%
   as_epi_df()

data-raw/jhu_csse_daily_subset.R

@@ -2,61 +2,60 @@ library(epidatr)
 library(epiprocess)
 library(dplyr)
 
-confirmed_7dav_incidence_prop <- covidcast(
-  data_source = "jhu-csse",
+confirmed_7dav_incidence_prop <- pub_covidcast(
+  source = "jhu-csse",
   signals = "confirmed_7dav_incidence_prop",
-  time_type = "day",
   geo_type = "state",
+  time_type = "day",
+  geo_values = "ca,fl,ny,tx,ga,pa",
   time_values = epirange(20200301, 20211231),
-  geo_values = "ca,fl,ny,tx,ga,pa"
 ) %>%
-  fetch() %>%
   select(geo_value, time_value, case_rate_7d_av = value) %>%
-  arrange(geo_value, time_value) 
+  arrange(geo_value, time_value)
 
-deaths_7dav_incidence_prop <- covidcast(
-  data_source = "jhu-csse",
+deaths_7dav_incidence_prop <- pub_covidcast(
+  source = "jhu-csse",
   signals = "deaths_7dav_incidence_prop",
-  time_type = "day",
   geo_type = "state",
+  time_type = "day",
+  geo_values = "ca,fl,ny,tx,ga,pa",
   time_values = epirange(20200301, 20211231),
-  geo_values = "ca,fl,ny,tx,ga,pa"
 ) %>%
-  fetch() %>%
   select(geo_value, time_value, death_rate_7d_av = value) %>%
-  arrange(geo_value, time_value) 
+  arrange(geo_value, time_value)
 
-confirmed_incidence_num <- covidcast(
-  data_source = "jhu-csse",
+confirmed_incidence_num <- pub_covidcast(
+  source = "jhu-csse",
   signals = "confirmed_incidence_num",
-  time_type = "day",
   geo_type = "state",
+  time_type = "day",
+  geo_values = "ca,fl,ny,tx,ga,pa",
   time_values = epirange(20200301, 20211231),
-  geo_values = "ca,fl,ny,tx,ga,pa"
 ) %>%
-  fetch() %>%
   select(geo_value, time_value, cases = value) %>%
-  arrange(geo_value, time_value) 
+  arrange(geo_value, time_value)
 
-confirmed_7dav_incidence_num <- covidcast(
-  data_source = "jhu-csse",
+confirmed_7dav_incidence_num <- pub_covidcast(
+  source = "jhu-csse",
   signals = "confirmed_7dav_incidence_num",
-  time_type = "day",
   geo_type = "state",
+  time_type = "day",
+  geo_values = "ca,fl,ny,tx,ga,pa",
   time_values = epirange(20200301, 20211231),
-  geo_values = "ca,fl,ny,tx,ga,pa"
 ) %>%
-  fetch() %>%
   select(geo_value, time_value, cases_7d_av = value) %>%
   arrange(geo_value, time_value)
 
 jhu_csse_daily_subset <- confirmed_7dav_incidence_prop %>%
-   full_join(deaths_7dav_incidence_prop, 
-             by = c("geo_value", "time_value")) %>%
-  full_join(confirmed_incidence_num, 
-            by = c("geo_value", "time_value")) %>%
-  full_join(confirmed_7dav_incidence_num, 
-            by = c("geo_value", "time_value")) %>%
-   as_epi_df()
+  full_join(deaths_7dav_incidence_prop,
+    by = c("geo_value", "time_value")
+  ) %>%
+  full_join(confirmed_incidence_num,
+    by = c("geo_value", "time_value")
+  ) %>%
+  full_join(confirmed_7dav_incidence_num,
+    by = c("geo_value", "time_value")
+  ) %>%
+  as_epi_df()
 
 usethis::use_data(jhu_csse_daily_subset, overwrite = TRUE)