note as_of in data docs; use as_of in vignette data fetch code

Author: nmdefries

man/cases_deaths_subset.Rd

@@ -20,11 +20,13 @@ library(covidcast)
 library(epiprocess)
 library(dplyr)
 
-x <- epiprocess::covid_incidence_county_subset
+x <- covid_incidence_county_subset
 ```
 
 The data can also be fetched from the Delphi API with the following query:
 ```{r, message = FALSE, eval = FALSE, warning = FALSE}
+d <- as.Date("2024-03-20")
+
 # Use covidcast::county_census to get the county and state names
 y <- covidcast::county_census %>%
   filter(STNAME %in% c("Massachusetts", "Vermont"), STNAME != CTYNAME) %>%
@@ -38,10 +40,11 @@ x <- pub_covidcast(
   time_type = "day",
   geo_values = paste(y$geo_value, collapse = ","),
   time_values = epirange(20200601, 20211231),
+  as_of = d
 ) %>%
   select(geo_value, time_value, cases = value) %>%
   full_join(y, by = "geo_value") %>%
-  as_epi_df()
+  as_epi_df(as_of = d)
 ```
 
 The data contains 16,212 rows and 5 columns.

man/covid_incidence_county_subset.Rd

@@ -23,37 +23,40 @@ library(dplyr)
 
 The data is included in this package (via the [`epidatasets` package](https://cmu-delphi.github.io/epidatasets/)) and can be loaded with:
 ```{r}
-x <- cases_deaths_subset %>%
-  select(geo_value, time_value, case_rate = case_rate_7d_av, death_rate = death_rate_7d_av) %>%
-  arrange(geo_value, time_value) %>%
-  as_epi_df()
+x <- covid_case_death_rates %>%
+  select(geo_value, time_value, case_rate, death_rate) %>%
+  arrange(geo_value, time_value)
 ```
 
 The data can also be fetched from the Delphi API with the following query:
 ```{r, eval = FALSE}
+d <- as.Date("2024-03-20")
+
 x <- pub_covidcast(
   source = "jhu-csse",
   signals = "confirmed_7dav_incidence_prop",
-  geo_type = "state",
   time_type = "day",
-  geo_values = "*",
+  geo_type = "state",
   time_values = epirange(20200301, 20211231),
+  geo_values = "*",
+  as_of = d
 ) %>%
   select(geo_value, time_value, case_rate = value)
 
 y <- pub_covidcast(
   source = "jhu-csse",
   signals = "deaths_7dav_incidence_prop",
-  geo_type = "state",
   time_type = "day",
-  geo_values = "*",
+  geo_type = "state",
   time_values = epirange(20200301, 20211231),
+  geo_values = "*",
+  as_of = d
 ) %>%
   select(geo_value, time_value, death_rate = value)
 
 x <- x %>%
   full_join(y, by = c("geo_value", "time_value")) %>%
-  as_epi_df()
+  as_epi_df(as_of = d)
 ```
 
 ## Correlations grouped by time

man/covid_incidence_outliers.Rd

@@ -110,7 +110,7 @@ library(dplyr)
 library(tidyr)
 library(withr)
 
-cases <- epiprocess::jhu_confirmed_cumulative_num
+cases <- jhu_confirmed_cumulative_num
 
 colnames(cases)
 ```

man/jhu_confirmed_cumulative_num.Rd

@@ -24,26 +24,27 @@ library(tidyr)
 The data is included in this package (via the [`epidatasets` package](https://cmu-delphi.github.io/epidatasets/)) and can be loaded with:
 
 ```{r}
-x <- epiprocess::cases_deaths_subset %>%
+x <- cases_deaths_subset %>%
   select(geo_value, time_value, cases = cases_7d_av) %>%
   filter(geo_value %in% c("pa", "ga") & time_value >= "2020-06-01") %>%
-  arrange(geo_value, time_value) %>%
-  as_epi_df()
+  arrange(geo_value, time_value)
 ```
 
 The data can also be fetched from the Delphi API with the following query:
 ```{r, message = FALSE, eval = FALSE}
+d <- as.Date("2024-03-20")
 x <- pub_covidcast(
   source = "jhu-csse",
   signals = "confirmed_7dav_incidence_num",
   geo_type = "state",
   time_type = "day",
   geo_values = "ga,pa",
   time_values = epirange(20200601, 20211231),
+  as_of = d
 ) %>%
   select(geo_value, time_value, cases = value) %>%
   arrange(geo_value, time_value) %>%
-  as_epi_df()
+  as_epi_df(as_of = d)
 ```
 
 The data has 1,158 rows and 3 columns.

vignettes/aggregation.Rmd

@@ -22,7 +22,7 @@ library(epiprocess)
 library(dplyr)
 library(tidyr)
 
-x <- epiprocess::covid_incidence_outliers
+x <- covid_incidence_outliers
 ```
 
 ```{r, fig.width = 8, fig.height = 7, warning=FALSE,message=FALSE}

vignettes/correlation.Rmd

@@ -37,25 +37,26 @@ library(dplyr)
 The data is included in this package (via the [`epidatasets` package](https://cmu-delphi.github.io/epidatasets/)) and can be loaded with:
 
 ```{r}
-x <- epiprocess::cases_deaths_subset %>%
+x <- cases_deaths_subset %>%
   select(geo_value, time_value, cases) %>%
-  arrange(geo_value, time_value) %>%
-  as_epi_df()
+  arrange(geo_value, time_value)
 ```
 
 The data can also be fetched from the Delphi API with the following query:
 ```{r, message = FALSE, eval = FALSE}
+d <- as.Date("2024-03-20")
 x <- pub_covidcast(
   source = "jhu-csse",
   signals = "confirmed_incidence_num",
   geo_type = "state",
   time_type = "day",
   geo_values = "ca,fl,ny,tx,ga,pa",
   time_values = epirange(20200301, 20211231),
+  as_of = d
 ) %>%
   select(geo_value, time_value, cases = value) %>%
   arrange(geo_value, time_value) %>%
-  as_epi_df()
+  as_epi_df(as_of = d)
 ```
 
 The data has 2,684 rows and 3 columns.