use new epidatr::pub_covidcast fn

data_source arg changed to source

Author: nmdefries

data-raw/archive_cases_dv_subset_dt.R

@@ -2,31 +2,29 @@ library(dplyr)
 library(epidatr)
 library(epiprocess)
 
-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),
   geo_values = "ca,fl,ny,tx",
   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)
 
-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),
   geo_values = "ca,fl,ny,tx",
   issues = epirange(20200601, 20211201)
 ) %>%
-  fetch() %>%
   select(geo_value, time_value, version = issue, case_rate_7d_av = value) %>%
   as_epi_archive(compactify = FALSE)
 

data-raw/cases_deaths_subset.R

@@ -2,51 +2,47 @@ library(dplyr)
 library(epidatr)
 library(epiprocess)
 
-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_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)
 
-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_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)
 
-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_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)
 
-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_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)
 

data-raw/counts_subset.R

@@ -4,24 +4,22 @@ library(dplyr)
 library(epidatr)
 library(epiprocess)
 
-x <- covidcast(
-  data_source = "jhu-csse",
+x <- pub_covidcast(
+  source = "jhu-csse",
   signals = "confirmed_incidence_num",
   time_type = "day",
   geo_type = "state",
   time_values = epirange(20210604, 20211231),
   geo_values = "ca,fl,tx,ny,nj") %>%
-  fetch() %>%
   select(geo_value, time_value, cases = value)
 
-y <- covidcast(
-  data_source = "jhu-csse",
+y <- pub_covidcast(
+  source = "jhu-csse",
   signals = "deaths_incidence_num",
   time_type = "day",
   geo_type = "state",
   time_values = epirange(20210604, 20211231),
   geo_values = "ca,fl,tx,ny,nj") %>%
-  fetch() %>%
   select(geo_value, time_value, deaths = value)
 
 counts_subset <- full_join(x, y, by = c("geo_value", "time_value")) %>%

data-raw/covid_case_death_rates.R

@@ -2,26 +2,24 @@ library(dplyr)
 library(epidatr)
 library(epiprocess)
 
-x <- covidcast(
-  data_source = "jhu-csse",
+x <- pub_covidcast(
+  source = "jhu-csse",
   signals = "confirmed_7dav_incidence_prop",
   time_type = "day",
   geo_type = "state",
   time_values = epirange(20200301, 20211231),
   geo_values = "*"
 ) %>%
-  fetch() %>%
   select(geo_value, time_value, case_rate = value)
 
-y <- covidcast(
-  data_source = "jhu-csse",
+y <- pub_covidcast(
+  source = "jhu-csse",
   signals = "deaths_7dav_incidence_prop",
   time_type = "day",
   geo_type = "state",
   time_values = epirange(20200301, 20211231),
   geo_values = "*"
 ) %>%
-  fetch() %>%
   select(geo_value, time_value, death_rate = value)
 
 covid_case_death_rates <- x %>%

data-raw/covid_incidence_county_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
-covid_incidence_county_subset <- covidcast(
-  data_source = "jhu-csse",
+covid_incidence_county_subset <- pub_covidcast(
+  source = "jhu-csse",
   signals = "confirmed_incidence_num",
   time_type = "day",
   geo_type = "county",
   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/covid_incidence_outliers.R

@@ -2,16 +2,15 @@ library(dplyr)
 library(epidatr)
 library(epiprocess)
 
-covid_incidence_outliers <- covidcast(
-  data_source = "jhu-csse",
+covid_incidence_outliers <- pub_covidcast(
+  source = "jhu-csse",
   signals = "confirmed_incidence_num",
   time_type = "day",
   geo_type = "state",
   time_values = epirange(20200601, 20210531),
   geo_values = "fl,nj",
   as_of = 20211028
 ) %>%
-  fetch() %>%
   select(geo_value, time_value, cases = value) %>%
   as_epi_df()
 

data-raw/ctis_covid_behaviours.R

@@ -3,24 +3,22 @@
 library(dplyr)
 library(epidatr)
 
-behav_ind_mask <- covidcast(
-  data_source = "fb-survey",
+behav_ind_mask <- pub_covidcast(
+  source = "fb-survey",
   signals = "smoothed_wwearing_mask_7d",
   time_type = "day",
   geo_type = "state",
   time_values = epirange(20210604, 20211231),
   geo_values = "ca,fl,tx,ny,nj")  %>%
-  fetch() %>%
   select(geo_value, time_value, masking = value)
 
-behav_ind_distancing <- covidcast(
-  data_source = "fb-survey",
+behav_ind_distancing <- pub_covidcast(
+  source = "fb-survey",
   signals = "smoothed_wothers_distanced_public",
   time_type = "day",
   geo_type = "state",
   time_values = epirange(20210604, 20211231),
   geo_values = "ca,fl,tx,ny,nj")  %>%
-  fetch() %>%
   select(geo_value, time_value, distancing = value)
 
 ctis_covid_behaviours <- behav_ind_mask %>%

data-raw/jhu_confirmed_cumulative_num.R

@@ -1,14 +1,13 @@
 library(dplyr)
 library(epidatr)
 
-jhu_confirmed_cumulative_num <- covidcast(
-  data_source = "jhu-csse",
+jhu_confirmed_cumulative_num <- pub_covidcast(
+  source = "jhu-csse",
   signals = "confirmed_cumulative_num",
   time_type = "day",
   geo_type = "state",
   time_values = epirange(20200301, 20220131),
   geo_values = "ca,fl,ny,tx"
-) %>% fetch()
-
+)
 
 usethis::use_data(jhu_confirmed_cumulative_num, overwrite = TRUE)

data-raw/state_census.R

@@ -7,5 +7,4 @@ state_census <- covidcast::state_census %>%
   mutate(abbr = tolower(abbr)) %>%
   as_tibble()
 
-
 usethis::use_data(state_census, overwrite = TRUE)