Rebuild documentation for 0.5.2

R-packages/covidcast/NEWS.md

@@ -3,7 +3,10 @@
 - `covidcast_meta()` now caches the server's response for a length of time
   specified by the COVIDcast API server, based on how frequently the metadata is
   recomputed. Because `covidcast_meta()` is called by `covidcast_signal()`, this
-  saves one API call per call to `covidcast_signal()`.
+  saves one API call per call to `covidcast_signal()`. (@krivard, #645)
+
+- `covidcast_meta()` now more clearly reports errors when the API usage limit
+  has been reached.
 
 # covidcast 0.5.1
 

R-packages/covidcast/vignettes/correlation-utils.Rmd.orig

@@ -11,7 +11,7 @@ vignette: >
 ```{r, setup, include=FALSE}
 knitr::opts_chunk$set(
   comment = "", fig.width = 6, fig.height = 6, fig.path = "figures/corr-",
-  fig.cap = ""
+  fig.cap = "", dev = "ragg_png"
 )
 ```
 

R-packages/covidcast/vignettes/external-data.Rmd

@@ -8,6 +8,8 @@ vignette: >
   %\VignetteEncoding{UTF-8}
 ---
 
+
+
 While the COVIDcast Epidata API provides numerous useful COVID data streams, you
 may sometimes find yourself with relevant data from a different source. This
 package provides the tools you need to load such data and use it alongside
@@ -25,7 +27,8 @@ results.
 We've saved a sample of the data in this package to use as an example. Let's
 load it and examine a few columns using `knitr::kable()` to print them nicely:
 
-```{r, message=FALSE}
+
+```r
 library(dplyr)
 
 data <- read.csv(system.file("extdata", "covid-tracking-project-oct-2020.csv",
@@ -38,6 +41,17 @@ data %>%
     knitr::kable()
 ```
 
+
+
+|date       |state | death| deathIncrease| hospitalizedCurrently| hospitalizedIncrease|
+|:----------|:-----|-----:|-------------:|---------------------:|--------------------:|
+|2020-10-31 |AK    |    82|             1|                    94|                    7|
+|2020-10-31 |AL    |  2967|            35|                   960|                    0|
+|2020-10-31 |AR    |  1925|            25|                   652|                   35|
+|2020-10-31 |AS    |     0|             0|                    NA|                    0|
+|2020-10-31 |AZ    |  5979|            45|                   889|                   68|
+|2020-10-31 |CA    | 17626|            55|                  3212|                    0|
+
 This is in a convenient format: Each day's observations for each state are in
 one row. Suppose we would like to extract `hospitalizedIncrease` as a signal we
 want to map and analyze alongside other data fetched with `covidcast_signal()`.
@@ -59,7 +73,8 @@ your data source includes multiple revisions of each observation.)
 and source/signal name to apply to the data. With a bit of
 [dplyr](https://dplyr.tidyverse.org/) data wrangling, we can do this easily:
 
-```{r, message=FALSE}
+
+```r
 library(covidcast)
 
 hospitalized <- data %>%
@@ -76,21 +91,41 @@ head(hospitalized) %>%
     knitr::kable()
 ```
 
+
+
+|data_source    |signal                |geo_value |time_value | value|issue      |
+|:--------------|:---------------------|:---------|:----------|-----:|:----------|
+|covid-tracking |hospitalized_increase |ak        |2020-10-31 |     7|2023-07-12 |
+|covid-tracking |hospitalized_increase |al        |2020-10-31 |     0|2023-07-12 |
+|covid-tracking |hospitalized_increase |ar        |2020-10-31 |    35|2023-07-12 |
+|covid-tracking |hospitalized_increase |as        |2020-10-31 |     0|2023-07-12 |
+|covid-tracking |hospitalized_increase |az        |2020-10-31 |    68|2023-07-12 |
+|covid-tracking |hospitalized_increase |ca        |2020-10-31 |     0|2023-07-12 |
+
 This allows us to make maps using the same functions used for other COVIDcast
 data:
 
-```{r}
+
+```r
 plot(hospitalized, plot_type = "choro")
 ```
 
+```
+Warning: Metadata for signal mean and standard deviation not available;
+defaulting to observed mean and standard deviation to set plot range.
+```
+
+![](figures/ext-unnamed-chunk-3-1.png)
+
 ## Analysis alongside other signals
 
 Now that our data is loaded as a `covidcast_signal` object, we can use it
 alongside other signals from the API. For example, let's examine how new COVID
 hospitalizations correlate with outpatient doctor visits with deaths during
 October 2020, where we use death data as reported by the API.
 
-```{r, message=FALSE}
+
+```r
 deaths <- covidcast_signal("jhu-csse", "deaths_7dav_incidence_prop",
                            start_day = "2020-10-01",
                            end_day = "2020-10-31",
@@ -99,17 +134,46 @@ deaths <- covidcast_signal("jhu-csse", "deaths_7dav_incidence_prop",
 covidcast_cor(deaths, hospitalized, by = "time_value")
 ```
 
+```
+# A tibble: 31 × 2
+   time_value  value
+   <date>      <dbl>
+ 1 2020-10-01 0.218 
+ 2 2020-10-02 0.120 
+ 3 2020-10-03 0.147 
+ 4 2020-10-04 0.148 
+ 5 2020-10-05 0.0436
+ 6 2020-10-06 0.0706
+ 7 2020-10-07 0.123 
+ 8 2020-10-08 0.0300
+ 9 2020-10-09 0.0955
+10 2020-10-10 0.178 
+# ℹ 21 more rows
+```
+
 We can also use the tools provided by this package to place both signals into a
 single data frame for analysis. For example, to build a model that uses
 hospitalizations and other data to predict deaths, it may be convenient to
 produce a data frame where each row represents one state on one day, and each
 column is a variable (such as hospitalization or death). Using
 `aggregate_signals()`, this is easy:
 
-```{r}
+
+```r
 death_hosp <- aggregate_signals(list(deaths, hospitalized),
                                 format = "wide")
 
 head(death_hosp) %>%
     knitr::kable()
 ```
+
+
+
+|geo_value |time_value | value+0:jhu-csse_deaths_7dav_incidence_prop| value+0:covid-tracking_hospitalized_increase|
+|:---------|:----------|-------------------------------------------:|--------------------------------------------:|
+|ca        |2020-10-01 |                                   0.2286116|                                            0|
+|ca        |2020-10-02 |                                   0.2137337|                                            0|
+|ca        |2020-10-03 |                                   0.2122822|                                            0|
+|ca        |2020-10-04 |                                   0.2122822|                                            0|
+|ca        |2020-10-05 |                                   0.2177254|                                            0|
+|ca        |2020-10-06 |                                   0.1788977|                                            0|