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tags/v0.9.0
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R/get-flu-data.r View File

@ -84,7 +84,7 @@ get_flu_data <- function(region="hhs", sub_region=1:10,
"New York City",
"Los Angeles")),
num = 1:57,
stringsAsFactors = F)
stringsAsFactors = FALSE)
sub_region_inpt <- state_match$num[state_match$state %in% sub_region]


+ 10
- 11
README.Rmd View File

@ -54,7 +54,6 @@ Deprecated functions:
- `get_flu_data`: Retrieves state, regional or national influenza statistics from the CDC (deprecated)
- `get_hosp_data`: Retrieves influenza hospitalization statistics from the CDC (deprecated)
- `get_mortality_surveillance_data`: Mortality Surveillance Data from the National Center for Health Statistics (deprecated)
- `get_state_data`: Retrieves state/territory-level influenza statistics from the CDC (deprecated)
@ -72,7 +71,7 @@ devtools::install_github("hrbrmstr/cdcfluview")
## Usage
```{r}
```{r libs}
library(cdcfluview)
library(hrbrthemes)
library(tidyverse)
@ -83,13 +82,13 @@ packageVersion("cdcfluview")
### Age Group Distribution of Influenza Positive Tests Reported by Public Health Laboratories
```{r}
```{r age-group-dist}
glimpse(age_group_distribution(years=2015))
```
### Retrieve CDC U.S. Coverage Map
```{r}
```{r cdc-basemaps}
plot(cdc_basemap("national"))
plot(cdc_basemap("hhs"))
plot(cdc_basemap("census"))
@ -100,13 +99,13 @@ plot(cdc_basemap("surv"))
### State and Territorial Epidemiologists Reports of Geographic Spread of Influenza
```{r message=FALSE, warning=FALSE}
```{r geographic-spread, message=FALSE, warning=FALSE}
glimpse(geographic_spread())
```
### Laboratory-Confirmed Influenza Hospitalizations
```{r fig.width=10, fig.height=7.5}
```{r surveillance-areas, fig.width=10, fig.height=7.5}
surveillance_areas()
glimpse(fs_nat <- hospitalizations("flusurv"))
@ -130,7 +129,7 @@ glimpse(hospitalizations("ihsp", "Oklahoma", years=2015))
### Retrieve ILINet Surveillance Data
```{r}
```{r ili-df}
walk(c("national", "hhs", "census", "state"), ~{
ili_df <- ilinet(region = .x)
@ -151,7 +150,7 @@ walk(c("national", "hhs", "census", "state"), ~{
### Retrieve weekly state-level ILI indicators per-state for a given season
```{r fig.width=10, fig.height=5}
```{r ili-weekly-activity, fig.width=10, fig.height=5}
ili_weekly_activity_indicators(2017)
xdf <- map_df(2008:2017, ili_weekly_activity_indicators)
@ -170,7 +169,7 @@ count(xdf, weekend, activity_level_label) %>%
### Pneumonia and Influenza Mortality Surveillance
```{r}
```{r nat-pi-mortality}
(nat_pi <- pi_mortality("national"))
select(nat_pi, wk_end, percent_pni, baseline, threshold) %>%
@ -191,13 +190,13 @@ select(nat_pi, wk_end, percent_pni, baseline, threshold) %>%
### Retrieve metadata about U.S. State CDC Provider Data
```{r}
```{r state-data-providers}
state_data_providers()
```
### Retrieve WHO/NREVSS Surveillance Data
```{r}
```{r who-vrevss}
glimpse(xdat <- who_nrevss("national"))
mutate(xdat$combined_prior_to_2015_16,


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README.md View File

@ -71,8 +71,6 @@ Deprecated functions:
statistics from the CDC (deprecated)
- `get_hosp_data`: Retrieves influenza hospitalization statistics from
the CDC (deprecated)
- `get_mortality_surveillance_data`: Mortality Surveillance Data from
the National Center for Health Statistics (deprecated)
- `get_state_data`: Retrieves state/territory-level influenza
statistics from the CDC (deprecated)
@ -102,7 +100,7 @@ library(tidyverse)
packageVersion("cdcfluview")
```
## [1] '0.7.0'
## [1] '0.8.0'
### Age Group Distribution of Influenza Positive Tests Reported by Public Health Laboratories
@ -113,12 +111,12 @@ glimpse(age_group_distribution(years=2015))
## Observations: 1,872
## Variables: 16
## $ sea_label <chr> "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "...
## $ age_label <fctr> 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, ...
## $ vir_label <fctr> A (Subtyping not Performed), A (Subtyping not Performed), A (Subtyping not Performed), A...
## $ age_label <fct> 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0...
## $ vir_label <fct> A (Subtyping not Performed), A (Subtyping not Performed), A (Subtyping not Performed), A ...
## $ count <int> 0, 1, 0, 1, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 3, 2, 2, 3, 3, 3, 0, 0, 2, 0, 1, 1, 0,...
## $ mmwrid <int> 2806, 2807, 2808, 2809, 2810, 2811, 2812, 2813, 2814, 2815, 2816, 2817, 2818, 2819, 2820,...
## $ seasonid <int> 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 5...
## $ publishyearweekid <int> 2914, 2914, 2914, 2914, 2914, 2914, 2914, 2914, 2914, 2914, 2914, 2914, 2914, 2914, 2914,...
## $ publishyearweekid <int> 2956, 2956, 2956, 2956, 2956, 2956, 2956, 2956, 2956, 2956, 2956, 2956, 2956, 2956, 2956,...
## $ sea_description <chr> "Season 2015-16", "Season 2015-16", "Season 2015-16", "Season 2015-16", "Season 2015-16",...
## $ sea_startweek <int> 2806, 2806, 2806, 2806, 2806, 2806, 2806, 2806, 2806, 2806, 2806, 2806, 2806, 2806, 2806,...
## $ sea_endweek <int> 2857, 2857, 2857, 2857, 2857, 2857, 2857, 2857, 2857, 2857, 2857, 2857, 2857, 2857, 2857,...
@ -135,37 +133,37 @@ glimpse(age_group_distribution(years=2015))
plot(cdc_basemap("national"))
```
![](README.gfm-ascii_identifiers_files/figure-gfm/unnamed-chunk-5-1.png)<!-- -->
<img src="README_files/figure-gfm/cdc-basemaps-1.png" width="672" />
``` r
plot(cdc_basemap("hhs"))
```
![](README.gfm-ascii_identifiers_files/figure-gfm/unnamed-chunk-5-2.png)<!-- -->
<img src="README_files/figure-gfm/cdc-basemaps-2.png" width="672" />
``` r
plot(cdc_basemap("census"))
```
![](README.gfm-ascii_identifiers_files/figure-gfm/unnamed-chunk-5-3.png)<!-- -->
<img src="README_files/figure-gfm/cdc-basemaps-3.png" width="672" />
``` r
plot(cdc_basemap("states"))
```
![](README.gfm-ascii_identifiers_files/figure-gfm/unnamed-chunk-5-4.png)<!-- -->
<img src="README_files/figure-gfm/cdc-basemaps-4.png" width="672" />
``` r
plot(cdc_basemap("spread"))
```
![](README.gfm-ascii_identifiers_files/figure-gfm/unnamed-chunk-5-5.png)<!-- -->
<img src="README_files/figure-gfm/cdc-basemaps-5.png" width="672" />
``` r
plot(cdc_basemap("surv"))
```
![](README.gfm-ascii_identifiers_files/figure-gfm/unnamed-chunk-5-6.png)<!-- -->
<img src="README_files/figure-gfm/cdc-basemaps-6.png" width="672" />
### State and Territorial Epidemiologists Reports of Geographic Spread of Influenza
@ -173,7 +171,7 @@ plot(cdc_basemap("surv"))
glimpse(geographic_spread())
```
## Observations: 25,848
## Observations: 27,351
## Variables: 7
## $ statename <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "...
## $ url <chr> "http://adph.org/influenza/", "http://adph.org/influenza/", "http://adph.org/influenza/",...
@ -217,7 +215,7 @@ surveillance_areas()
glimpse(fs_nat <- hospitalizations("flusurv"))
```
## Observations: 1,476
## Observations: 1,656
## Variables: 14
## $ surveillance_area <chr> "FluSurv-NET", "FluSurv-NET", "FluSurv-NET", "FluSurv-NET", "FluSurv-NET", "FluSurv-NET",...
## $ region <chr> "Entire Network", "Entire Network", "Entire Network", "Entire Network", "Entire Network",...
@ -229,7 +227,7 @@ glimpse(fs_nat <- hospitalizations("flusurv"))
## $ rate <dbl> 0.5, 2.5, 4.6, 6.7, 10.9, 18.1, 28.3, 39.1, 47.3, 53.3, 57.5, 60.1, 61.6, 62.9, 64.1, 65....
## $ weeklyrate <dbl> 0.5, 2.0, 2.0, 2.1, 4.3, 7.2, 10.2, 10.8, 8.2, 6.0, 4.2, 2.6, 1.5, 1.3, 1.3, 1.0, 1.2, 1....
## $ age <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ age_label <fctr> 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, ...
## $ age_label <fct> 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0...
## $ sea_label <chr> "2009-10", "2009-10", "2009-10", "2009-10", "2009-10", "2009-10", "2009-10", "2009-10", "...
## $ sea_description <chr> "Season 2009-10", "Season 2009-10", "Season 2009-10", "Season 2009-10", "Season 2009-10",...
## $ mmwrid <int> 2488, 2489, 2490, 2491, 2492, 2493, 2494, 2495, 2496, 2497, 2498, 2499, 2500, 2501, 2502,...
@ -244,7 +242,7 @@ ggplot(fs_nat, aes(wk_end, rate)) +
theme_ipsum_rc()
```
![](README.gfm-ascii_identifiers_files/figure-gfm/unnamed-chunk-7-1.png)<!-- -->
<img src="README_files/figure-gfm/surveillance-areas-1.png" width="960" />
``` r
glimpse(hospitalizations("eip", years=2015))
@ -262,7 +260,7 @@ glimpse(hospitalizations("eip", years=2015))
## $ rate <dbl> 0.1, 0.3, 0.4, 0.5, 0.8, 0.8, 1.1, 1.4, 1.6, 1.7, 1.8, 2.1, 2.4, 2.9, 3.2, 3.5, 4.2, 5.3,...
## $ weeklyrate <dbl> 0.1, 0.3, 0.1, 0.1, 0.3, 0.0, 0.3, 0.3, 0.2, 0.1, 0.1, 0.3, 0.3, 0.5, 0.3, 0.3, 0.6, 1.2,...
## $ age <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ age_label <fctr> 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, ...
## $ age_label <fct> 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0...
## $ sea_label <chr> "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "...
## $ sea_description <chr> "Season 2015-16", "Season 2015-16", "Season 2015-16", "Season 2015-16", "Season 2015-16",...
## $ mmwrid <int> 2806, 2807, 2808, 2809, 2810, 2811, 2812, 2813, 2814, 2815, 2816, 2817, 2818, 2819, 2820,...
@ -280,10 +278,10 @@ glimpse(hospitalizations("eip", "Colorado", years=2015))
## $ wk_start <date> 2015-10-04, 2015-10-11, 2015-10-18, 2015-10-25, 2015-11-01, 2015-11-08, 2015-11-15, 2015...
## $ wk_end <date> 2015-10-10, 2015-10-17, 2015-10-24, 2015-10-31, 2015-11-07, 2015-11-14, 2015-11-21, 2015...
## $ year_wk_num <int> 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12...
## $ rate <dbl> 0.0, 0.0, 0.6, 0.6, 0.6, 0.6, 1.2, 1.7, 1.7, 1.7, 1.7, 1.7, 2.9, 3.5, 3.5, 3.5, 4.1, 6.4,...
## $ rate <dbl> 0.0, 0.0, 0.6, 0.6, 0.6, 0.6, 1.2, 1.8, 1.8, 1.8, 1.8, 1.8, 2.9, 3.5, 3.5, 3.5, 4.1, 6.4,...
## $ weeklyrate <dbl> 0.0, 0.0, 0.6, 0.0, 0.0, 0.0, 0.6, 0.6, 0.0, 0.0, 0.0, 0.0, 1.2, 0.6, 0.0, 0.0, 0.6, 2.3,...
## $ age <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ age_label <fctr> 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, ...
## $ age_label <fct> 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0...
## $ sea_label <chr> "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "...
## $ sea_description <chr> "Season 2015-16", "Season 2015-16", "Season 2015-16", "Season 2015-16", "Season 2015-16",...
## $ mmwrid <int> 2806, 2807, 2808, 2809, 2810, 2811, 2812, 2813, 2814, 2815, 2816, 2817, 2818, 2819, 2820,...
@ -304,7 +302,7 @@ glimpse(hospitalizations("ihsp", years=2015))
## $ rate <dbl> 0.0, 0.0, 0.4, 0.4, 0.4, 1.1, 1.1, 1.1, 1.1, 1.5, 1.8, 2.2, 2.2, 2.5, 2.5, 2.5, 2.9, 4.0,...
## $ weeklyrate <dbl> 0.0, 0.0, 0.4, 0.0, 0.0, 0.7, 0.0, 0.0, 0.0, 0.4, 0.4, 0.4, 0.0, 0.4, 0.0, 0.0, 0.4, 1.1,...
## $ age <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ age_label <fctr> 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, ...
## $ age_label <fct> 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0...
## $ sea_label <chr> "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "2015-16", "...
## $ sea_description <chr> "Season 2015-16", "Season 2015-16", "Season 2015-16", "Season 2015-16", "Season 2015-16",...
## $ mmwrid <int> 2806, 2807, 2808, 2809, 2810, 2811, 2812, 2813, 2814, 2815, 2816, 2817, 2818, 2819, 2820,...
@ -322,10 +320,10 @@ glimpse(hospitalizations("ihsp", "Oklahoma", years=2015))
## $ wk_start <date> 2010-10-03, 2010-10-10, 2010-10-17, 2010-10-24, 2010-10-31, 2010-11-07, 2010-11-14, 2010...
## $ wk_end <date> 2010-10-09, 2010-10-16, 2010-10-23, 2010-10-30, 2010-11-06, 2010-11-13, 2010-11-20, 2010...
## $ year_wk_num <int> 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12...
## $ rate <dbl> 0.0, 0.0, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 2.6, 2.6, 6.6, 15.9, 18.5, 35.7, 54.2, 83.3,...
## $ rate <dbl> 0.0, 0.0, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 2.6, 2.6, 6.6, 15.9, 18.5, 35.7, 54.2, 83.4,...
## $ weeklyrate <dbl> 0.0, 0.0, 1.3, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.3, 0.0, 4.0, 9.3, 2.6, 17.2, 18.5, 29.1, 2...
## $ age <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ age_label <fctr> 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, ...
## $ age_label <fct> 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0-4 yr, 0...
## $ sea_label <chr> "2010-11", "2010-11", "2010-11", "2010-11", "2010-11", "2010-11", "2010-11", "2010-11", "...
## $ sea_description <chr> "Season 2010-11", "Season 2010-11", "Season 2010-11", "Season 2010-11", "Season 2010-11",...
## $ mmwrid <int> 2545, 2546, 2547, 2548, 2549, 2550, 2551, 2552, 2553, 2554, 2555, 2556, 2557, 2558, 2559,...
@ -351,7 +349,7 @@ walk(c("national", "hhs", "census", "state"), ~{
})
```
## Observations: 1,049
## Observations: 1,093
## Variables: 16
## $ region_type <chr> "National", "National", "National", "National", "National", "National", "National", "Natio...
## $ region <chr> "National", "National", "National", "National", "National", "National", "National", "Natio...
@ -369,28 +367,28 @@ walk(c("national", "hhs", "census", "state"), ~{
## $ num_of_providers <dbl> 192, 191, 219, 213, 213, 195, 248, 256, 252, 253, 242, 190, 251, 250, 254, 255, 245, 245, ...
## $ total_patients <dbl> 46842, 48023, 54961, 57044, 55506, 51062, 64463, 66749, 52890, 67887, 61314, 47719, 48429,...
## $ week_start <date> 1997-10-06, 1997-10-13, 1997-10-20, 1997-10-27, 1997-11-03, 1997-11-10, 1997-11-17, 1997-...
## # A tibble: 1,049 x 16
## region_type region year week weighted_ili unweighted_ili age_0_4 age_25_49 age_25_64 age_5_24 age_50_64 age_65
## <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 National National 1997 40 1.10148 1.21686 179 NA 157 205 NA 29
## 2 National National 1997 41 1.20007 1.28064 199 NA 151 242 NA 23
## 3 National National 1997 42 1.37876 1.23906 228 NA 153 266 NA 34
## 4 National National 1997 43 1.19920 1.14473 188 NA 193 236 NA 36
## 5 National National 1997 44 1.65618 1.26112 217 NA 162 280 NA 41
## 6 National National 1997 45 1.41326 1.28275 178 NA 148 281 NA 48
## 7 National National 1997 46 1.98680 1.44579 294 NA 240 328 NA 70
## 8 National National 1997 47 2.44749 1.64796 288 NA 293 456 NA 63
## 9 National National 1997 48 1.73901 1.67517 268 NA 206 343 NA 69
## 10 National National 1997 49 1.93919 1.61739 299 NA 282 415 NA 102
## # ... with 1,039 more rows, and 4 more variables: ilitotal <dbl>, num_of_providers <dbl>, total_patients <dbl>,
## # A tibble: 1,093 x 16
## region_type region year week weighted_ili unweighted_ili age_0_4 age_25_49 age_25_64 age_5_24 age_50_64 age_65
## <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 National National 1997 40 1.10 1.22 179 NA 157 205 NA 29
## 2 National National 1997 41 1.20 1.28 199 NA 151 242 NA 23
## 3 National National 1997 42 1.38 1.24 228 NA 153 266 NA 34
## 4 National National 1997 43 1.20 1.14 188 NA 193 236 NA 36
## 5 National National 1997 44 1.66 1.26 217 NA 162 280 NA 41
## 6 National National 1997 45 1.41 1.28 178 NA 148 281 NA 48
## 7 National National 1997 46 1.99 1.45 294 NA 240 328 NA 70
## 8 National National 1997 47 2.45 1.65 288 NA 293 456 NA 63
## 9 National National 1997 48 1.74 1.68 268 NA 206 343 NA 69
## 10 National National 1997 49 1.94 1.62 299 NA 282 415 NA 102
## # ... with 1,083 more rows, and 4 more variables: ilitotal <dbl>, num_of_providers <dbl>, total_patients <dbl>,
## # week_start <date>
![](README.gfm-ascii_identifiers_files/figure-gfm/unnamed-chunk-8-1.png)<!-- -->
<img src="README_files/figure-gfm/ili-df-1.png" width="672" />
## Observations: 10,490
## Observations: 10,930
## Variables: 16
## $ region_type <chr> "HHS Regions", "HHS Regions", "HHS Regions", "HHS Regions", "HHS Regions", "HHS Regions", ...
## $ region <fctr> Region 1, Region 2, Region 3, Region 4, Region 5, Region 6, Region 7, Region 8, Region 9,...
## $ region <fct> Region 1, Region 2, Region 3, Region 4, Region 5, Region 6, Region 7, Region 8, Region 9, ...
## $ year <int> 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, ...
## $ week <int> 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 42, 42, 42...
## $ weighted_ili <dbl> 0.498535, 0.374963, 1.354280, 0.400338, 1.229260, 1.018980, 0.871791, 0.516017, 1.807610, ...
@ -405,25 +403,25 @@ walk(c("national", "hhs", "census", "state"), ~{
## $ num_of_providers <dbl> 32, 7, 16, 29, 49, 4, 14, 5, 23, 13, 29, 7, 17, 31, 48, 4, 14, 6, 23, 12, 40, 7, 15, 33, 6...
## $ total_patients <dbl> 7053, 780, 2385, 10222, 9875, 669, 2342, 1183, 10758, 1575, 6987, 872, 2740, 11310, 9618, ...
## $ week_start <date> 1997-10-06, 1997-10-06, 1997-10-06, 1997-10-06, 1997-10-06, 1997-10-06, 1997-10-06, 1997-...
## # A tibble: 10,490 x 16
## region_type region year week weighted_ili unweighted_ili age_0_4 age_25_49 age_25_64 age_5_24 age_50_64 age_65
## <chr> <fctr> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 HHS Regions Region 1 1997 40 0.498535 0.623848 15 NA 7 22 NA 0
## 2 HHS Regions Region 2 1997 40 0.374963 0.384615 0 NA 3 0 NA 0
## 3 HHS Regions Region 3 1997 40 1.354280 1.341720 6 NA 7 15 NA 4
## 4 HHS Regions Region 4 1997 40 0.400338 0.450010 12 NA 23 11 NA 0
## 5 HHS Regions Region 5 1997 40 1.229260 0.901266 31 NA 24 30 NA 4
## 6 HHS Regions Region 6 1997 40 1.018980 0.747384 2 NA 1 2 NA 0
## 7 HHS Regions Region 7 1997 40 0.871791 1.152860 0 NA 4 18 NA 5
## 8 HHS Regions Region 8 1997 40 0.516017 0.422654 2 NA 0 3 NA 0
## 9 HHS Regions Region 9 1997 40 1.807610 2.258780 80 NA 76 74 NA 13
## 10 HHS Regions Region 10 1997 40 4.743520 4.825400 31 NA 12 30 NA 3
## # ... with 10,480 more rows, and 4 more variables: ilitotal <dbl>, num_of_providers <dbl>, total_patients <dbl>,
## # A tibble: 10,930 x 16
## region_type region year week weighted_ili unweighted_ili age_0_4 age_25_49 age_25_64 age_5_24 age_50_64 age_65
## <chr> <fct> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 HHS Regions Region 1 1997 40 0.499 0.624 15 NA 7 22 NA 0
## 2 HHS Regions Region 2 1997 40 0.375 0.385 0 NA 3 0 NA 0
## 3 HHS Regions Region 3 1997 40 1.35 1.34 6 NA 7 15 NA 4
## 4 HHS Regions Region 4 1997 40 0.400 0.450 12 NA 23 11 NA 0
## 5 HHS Regions Region 5 1997 40 1.23 0.901 31 NA 24 30 NA 4
## 6 HHS Regions Region 6 1997 40 1.02 0.747 2 NA 1 2 NA 0
## 7 HHS Regions Region 7 1997 40 0.872 1.15 0 NA 4 18 NA 5
## 8 HHS Regions Region 8 1997 40 0.516 0.423 2 NA 0 3 NA 0
## 9 HHS Regions Region 9 1997 40 1.81 2.26 80 NA 76 74 NA 13
## 10 HHS Regions Region 10 1997 40 4.74 4.83 31 NA 12 30 NA 3
## # ... with 10,920 more rows, and 4 more variables: ilitotal <dbl>, num_of_providers <dbl>, total_patients <dbl>,
## # week_start <date>
![](README.gfm-ascii_identifiers_files/figure-gfm/unnamed-chunk-8-2.png)<!-- -->
<img src="README_files/figure-gfm/ili-df-2.png" width="672" />
## Observations: 9,441
## Observations: 9,837
## Variables: 16
## $ region_type <chr> "Census Regions", "Census Regions", "Census Regions", "Census Regions", "Census Regions", ...
## $ region <chr> "New England", "Mid-Atlantic", "East North Central", "West North Central", "South Atlantic...
@ -441,25 +439,25 @@ walk(c("national", "hhs", "census", "state"), ~{
## $ num_of_providers <dbl> 32, 13, 47, 17, 30, 9, 4, 16, 24, 29, 13, 46, 17, 32, 10, 4, 17, 23, 40, 12, 62, 16, 33, 1...
## $ total_patients <dbl> 7053, 2119, 9649, 2892, 6912, 4356, 669, 10719, 2473, 6987, 2384, 9427, 2823, 7591, 4947, ...
## $ week_start <date> 1997-10-06, 1997-10-06, 1997-10-06, 1997-10-06, 1997-10-06, 1997-10-06, 1997-10-06, 1997-...
## # A tibble: 9,441 x 16
## region_type region year week weighted_ili unweighted_ili age_0_4 age_25_49 age_25_64 age_5_24
## <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Census Regions New England 1997 40 0.4985350 0.6238480 15 NA 7 22
## 2 Census Regions Mid-Atlantic 1997 40 0.8441440 1.3213800 4 NA 8 12
## 3 Census Regions East North Central 1997 40 0.7924860 0.8187380 28 NA 20 28
## 4 Census Regions West North Central 1997 40 1.7640500 1.2793900 3 NA 8 20
## 5 Census Regions South Atlantic 1997 40 0.5026620 0.7233800 14 NA 22 14
## 6 Census Regions East South Central 1997 40 0.0542283 0.0688705 0 NA 3 0
## 7 Census Regions West South Central 1997 40 1.0189800 0.7473840 2 NA 1 2
## 8 Census Regions Mountain 1997 40 2.2587800 2.2763300 87 NA 71 71
## 9 Census Regions Pacific 1997 40 2.0488300 3.2349400 26 NA 17 36
## 10 Census Regions New England 1997 41 0.6426690 0.8158010 14 NA 14 29
## # ... with 9,431 more rows, and 6 more variables: age_50_64 <dbl>, age_65 <dbl>, ilitotal <dbl>,
## # num_of_providers <dbl>, total_patients <dbl>, week_start <date>
![](README.gfm-ascii_identifiers_files/figure-gfm/unnamed-chunk-8-3.png)<!-- -->
## Observations: 19,772
## # A tibble: 9,837 x 16
## region_type region year week weighted_ili unweighted_ili age_0_4 age_25_49 age_25_64 age_5_24 age_50_64 age_65
## <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Census Regi… New Engl… 1997 40 0.499 0.624 15 NA 7 22 NA 0
## 2 Census Regi… Mid-Atla… 1997 40 0.844 1.32 4 NA 8 12 NA 4
## 3 Census Regi… East Nor… 1997 40 0.792 0.819 28 NA 20 28 NA 3
## 4 Census Regi… West Nor… 1997 40 1.76 1.28 3 NA 8 20 NA 6
## 5 Census Regi… South At… 1997 40 0.503 0.723 14 NA 22 14 NA 0
## 6 Census Regi… East Sou… 1997 40 0.0542 0.0689 0 NA 3 0 NA 0
## 7 Census Regi… West Sou… 1997 40 1.02 0.747 2 NA 1 2 NA 0
## 8 Census Regi… Mountain 1997 40 2.26 2.28 87 NA 71 71 NA 15
## 9 Census Regi… Pacific 1997 40 2.05 3.23 26 NA 17 36 NA 1
## 10 Census Regi… New Engl… 1997 41 0.643 0.816 14 NA 14 29 NA 0
## # ... with 9,827 more rows, and 4 more variables: ilitotal <dbl>, num_of_providers <dbl>, total_patients <dbl>,
## # week_start <date>
<img src="README_files/figure-gfm/ili-df-3.png" width="672" />
## Observations: 22,148
## Variables: 16
## $ region_type <chr> "States", "States", "States", "States", "States", "States", "States", "States", "States", ...
## $ region <chr> "Alabama", "Alaska", "Arizona", "Arkansas", "California", "Colorado", "Connecticut", "Dela...
@ -473,27 +471,27 @@ walk(c("national", "hhs", "census", "state"), ~{
## $ age_5_24 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ age_50_64 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ age_65 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ ilitotal <dbl> 249, 15, 172, 18, 632, 134, 3, 4, 73, NA, 647, 20, 19, 505, 65, 10, 39, 19, NA, 22, 117, 1...
## $ num_of_providers <dbl> 35, 7, 49, 15, 112, 14, 12, 13, 4, NA, 62, 18, 12, 74, 44, 6, 40, 14, NA, 30, 17, 56, 47, ...
## $ ilitotal <dbl> 249, 15, 172, 18, 632, 134, 3, 4, 73, NA, 647, 20, 19, 505, 65, 10, 39, 19, 391, 22, 117, ...
## $ num_of_providers <dbl> 35, 7, 49, 15, 112, 14, 12, 13, 4, NA, 62, 18, 12, 74, 44, 6, 40, 14, 41, 30, 17, 56, 47, ...
## $ total_patients <dbl> 11664, 1714, 25492, 2586, 32342, 20282, 3831, 3995, 2599, NA, 40314, 1943, 4579, 39390, 12...
## $ week_start <date> 2010-10-04, 2010-10-04, 2010-10-04, 2010-10-04, 2010-10-04, 2010-10-04, 2010-10-04, 2010-...
## # A tibble: 19,772 x 16
## region_type region year week weighted_ili unweighted_ili age_0_4 age_25_49 age_25_64 age_5_24
## <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 States Alabama 2010 40 NA 2.1347700 NA NA NA NA
## 2 States Alaska 2010 40 NA 0.8751460 NA NA NA NA
## 3 States Arizona 2010 40 NA 0.6747210 NA NA NA NA
## 4 States Arkansas 2010 40 NA 0.6960560 NA NA NA NA
## 5 States California 2010 40 NA 1.9541200 NA NA NA NA
## 6 States Colorado 2010 40 NA 0.6606840 NA NA NA NA
## 7 States Connecticut 2010 40 NA 0.0783085 NA NA NA NA
## 8 States Delaware 2010 40 NA 0.1001250 NA NA NA NA
## 9 States District of Columbia 2010 40 NA 2.8087700 NA NA NA NA
## 10 States Florida 2010 40 NA NA NA NA NA NA
## # ... with 19,762 more rows, and 6 more variables: age_50_64 <dbl>, age_65 <dbl>, ilitotal <dbl>,
## # num_of_providers <dbl>, total_patients <dbl>, week_start <date>
![](README.gfm-ascii_identifiers_files/figure-gfm/unnamed-chunk-8-4.png)<!-- -->
## # A tibble: 22,148 x 16
## region_type region year week weighted_ili unweighted_ili age_0_4 age_25_49 age_25_64 age_5_24 age_50_64 age_65
## <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 States Alabama 2010 40 NA 2.13 NA NA NA NA NA NA
## 2 States Alaska 2010 40 NA 0.875 NA NA NA NA NA NA
## 3 States Arizona 2010 40 NA 0.675 NA NA NA NA NA NA
## 4 States Arkansas 2010 40 NA 0.696 NA NA NA NA NA NA
## 5 States California 2010 40 NA 1.95 NA NA NA NA NA NA
## 6 States Colorado 2010 40 NA 0.661 NA NA NA NA NA NA
## 7 States Connectic… 2010 40 NA 0.0783 NA NA NA NA NA NA
## 8 States Delaware 2010 40 NA 0.100 NA NA NA NA NA NA
## 9 States District … 2010 40 NA 2.81 NA NA NA NA NA NA
## 10 States Florida 2010 40 NA NA NA NA NA NA NA NA
## # ... with 22,138 more rows, and 4 more variables: ilitotal <dbl>, num_of_providers <dbl>, total_patients <dbl>,
## # week_start <date>
<img src="README_files/figure-gfm/ili-df-4.png" width="672" />
### Retrieve weekly state-level ILI indicators per-state for a given season
@ -501,21 +499,20 @@ walk(c("national", "hhs", "census", "state"), ~{
ili_weekly_activity_indicators(2017)
```
## # A tibble: 270 x 8
## statename url
## * <chr> <chr>
## 1 Virgin Islands http://doh.vi.gov/
## 2 Virgin Islands http://doh.vi.gov/
## 3 Virgin Islands http://doh.vi.gov/
## 4 Puerto Rico http://www.salud.gov.pr/Estadisticas-Registros-y-Publicaciones/Pages/Influenza.aspx
## 5 Virgin Islands http://doh.vi.gov/
## 6 Puerto Rico http://www.salud.gov.pr/Estadisticas-Registros-y-Publicaciones/Pages/Influenza.aspx
## 7 Virgin Islands http://doh.vi.gov/
## 8 Indiana http://www.in.gov/isdh/22104.htm
## 9 Iowa http://idph.iowa.gov/influenza/surveillance
## 10 Kansas http://www.kdheks.gov/flu/surveillance.htm
## # ... with 260 more rows, and 6 more variables: website <chr>, activity_level <dbl>, activity_level_label <chr>,
## # weekend <date>, season <chr>, weeknumber <dbl>
## # A tibble: 1,782 x 8
## statename url website activity_level activity_level_label weekend season weeknumber
## * <chr> <chr> <chr> <dbl> <chr> <date> <chr> <dbl>
## 1 Virgin Islands http://doh.vi.gov/ Influenza 0 Insufficient Data 2017-10-07 2017-18 40
## 2 Virgin Islands http://doh.vi.gov/ Influenza 0 Insufficient Data 2017-10-14 2017-18 41
## 3 Virgin Islands http://doh.vi.gov/ Influenza 0 Insufficient Data 2017-10-21 2017-18 42
## 4 Virgin Islands http://doh.vi.gov/ Influenza 0 Insufficient Data 2017-10-28 2017-18 43
## 5 Virgin Islands http://doh.vi.gov/ Influenza 0 Insufficient Data 2017-11-04 2017-18 44
## 6 Virgin Islands http://doh.vi.gov/ Influenza 0 Insufficient Data 2017-11-11 2017-18 45
## 7 Virgin Islands http://doh.vi.gov/ Influenza 0 Insufficient Data 2017-12-02 2017-18 48
## 8 Virgin Islands http://doh.vi.gov/ Influenza 0 Insufficient Data 2017-12-09 2017-18 49
## 9 Virgin Islands http://doh.vi.gov/ Influenza 0 Insufficient Data 2017-12-23 2017-18 51
## 10 Virgin Islands http://doh.vi.gov/ Influenza 0 Insufficient Data 2017-12-30 2017-18 52
## # ... with 1,772 more rows
``` r
xdf <- map_df(2008:2017, ili_weekly_activity_indicators)
@ -532,7 +529,7 @@ count(xdf, weekend, activity_level_label) %>%
theme(legend.position="bottom")
```
![](README.gfm-ascii_identifiers_files/figure-gfm/unnamed-chunk-9-1.png)<!-- -->
<img src="README_files/figure-gfm/ili-weekly-activity-1.png" width="960" />
### Pneumonia and Influenza Mortality Surveillance
@ -540,20 +537,20 @@ count(xdf, weekend, activity_level_label) %>%
(nat_pi <- pi_mortality("national"))
```
## # A tibble: 420 x 19
## # A tibble: 464 x 19
## seasonid baseline threshold percent_pni percent_complete number_influenza number_pneumonia all_deaths total_pni
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 57 0.058 0.061 0.056 0.858 12 2276 40834 2288
## 2 57 0.058 0.062 0.056 0.764 14 2025 36328 2039
## 3 57 0.059 0.063 0.058 0.678 17 1844 32279 1861
## 4 56 0.059 0.063 0.059 1.000 18 3022 51404 3040
## 5 56 0.060 0.063 0.061 1.000 11 3193 52130 3204
## 6 56 0.061 0.064 0.062 1.000 7 3178 51443 3185
## 7 56 0.062 0.065 0.061 1.000 17 3129 51865 3146
## 8 56 0.063 0.066 0.060 1.000 16 3099 51753 3115
## 9 56 0.064 0.067 0.061 1.000 19 3208 52541 3227
## 10 56 0.065 0.068 0.060 1.000 7 3192 53460 3199
## # ... with 410 more rows, and 10 more variables: weeknumber <chr>, geo_description <chr>, age_label <chr>,
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 57 0.057 0.06 0.0580 1 16 3020 52110 3036
## 2 57 0.0580 0.061 0.059 1 18 3000 51572 3018
## 3 57 0.059 0.062 0.061 1 28 3154 52222 3182
## 4 57 0.06 0.063 0.063 1 23 3279 52548 3302
## 5 57 0.06 0.063 0.061 1 36 3214 53679 3250
## 6 57 0.061 0.064 0.06 1 45 3177 53258 3222
## 7 57 0.062 0.065 0.063 1 50 3315 53771 3365
## 8 57 0.063 0.066 0.06 1 48 3200 54120 3248
## 9 57 0.064 0.067 0.065 1 83 3491 54760 3574
## 10 57 0.065 0.068 0.066 1 118 3526 55595 3644
## # ... with 454 more rows, and 10 more variables: weeknumber <chr>, geo_description <chr>, age_label <chr>,
## # wk_start <date>, wk_end <date>, year_wk_num <int>, mmwrid <chr>, coverage_area <chr>, region_name <chr>,
## # callout <chr>
@ -570,7 +567,7 @@ select(nat_pi, wk_end, percent_pni, baseline, threshold) %>%
theme(legend.position="bottom")
```
![](README.gfm-ascii_identifiers_files/figure-gfm/unnamed-chunk-10-1.png)<!-- -->
<img src="README_files/figure-gfm/nat-pi-mortality-1.png" width="672" />
``` r
(st_pi <- pi_mortality("state", years=2015))
@ -578,17 +575,17 @@ select(nat_pi, wk_end, percent_pni, baseline, threshold) %>%
## # A tibble: 2,704 x 19
## seasonid baseline threshold percent_pni percent_complete number_influenza number_pneumonia all_deaths total_pni
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 55 NA NA 0.047 1.000 0 46 979 46
## 2 55 NA NA 0.038 0.963 0 34 889 34
## 3 55 NA NA 0.053 1.000 0 52 977 52
## 4 55 NA NA 0.070 1.000 0 68 968 68
## 5 55 NA NA 0.053 0.981 0 48 906 48
## 6 55 NA NA 0.058 1.000 0 56 968 56
## 7 55 NA NA 0.051 1.000 0 53 1041 53
## 8 55 NA NA 0.062 1.000 1 63 1031 64
## 9 55 NA NA 0.056 1.000 0 55 976 55
## 10 55 NA NA 0.054 1.000 0 56 1045 56
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 55 NA NA 0.047 1 0 46 979 46
## 2 55 NA NA 0.038 0.963 0 34 889 34
## 3 55 NA NA 0.053 1 0 52 977 52
## 4 55 NA NA 0.07 1 0 68 968 68
## 5 55 NA NA 0.053 0.981 0 48 906 48
## 6 55 NA NA 0.0580 1 0 56 968 56
## 7 55 NA NA 0.051 1 0 53 1041 53
## 8 55 NA NA 0.062 1 1 63 1031 64
## 9 55 NA NA 0.0560 1 0 55 976 55
## 10 55 NA NA 0.054 1 0 56 1045 56
## # ... with 2,694 more rows, and 10 more variables: weeknumber <chr>, geo_description <chr>, age_label <chr>,
## # wk_start <date>, wk_end <date>, year_wk_num <int>, mmwrid <chr>, coverage_area <chr>, region_name <chr>,
## # callout <chr>
@ -599,17 +596,17 @@ select(nat_pi, wk_end, percent_pni, baseline, threshold) %>%
## # A tibble: 520 x 19
## seasonid baseline threshold percent_pni percent_complete number_influenza number_pneumonia all_deaths total_pni
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 55 0.065 0.073 0.071 1 0 178 2520 178
## 2 55 0.066 0.073 0.063 1 0 159 2505 159
## 3 55 0.067 0.074 0.058 1 1 141 2452 142
## 4 55 0.068 0.075 0.071 1 0 171 2422 171
## 5 55 0.069 0.076 0.066 1 2 166 2554 168
## 6 55 0.070 0.077 0.067 1 1 160 2404 161
## 7 55 0.071 0.079 0.079 1 0 195 2478 195
## 8 55 0.073 0.080 0.072 1 1 176 2463 177
## 9 55 0.074 0.081 0.067 1 3 154 2347 157
## 10 55 0.075 0.082 0.062 1 0 151 2437 151
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 55 0.066 0.073 0.071 1 0 178 2520 178
## 2 55 0.067 0.074 0.063 1 0 159 2505 159
## 3 55 0.067 0.075 0.0580 1 1 141 2452 142
## 4 55 0.068 0.076 0.071 1 0 171 2422 171
## 5 55 0.07 0.077 0.066 1 2 166 2554 168
## 6 55 0.071 0.078 0.067 1 1 160 2404 161
## 7 55 0.072 0.079 0.079 1 0 195 2478 195
## 8 55 0.073 0.081 0.072 1 1 176 2463 177
## 9 55 0.074 0.0820 0.067 1 3 154 2347 157
## 10 55 0.076 0.083 0.062 1 0 151 2437 151
## # ... with 510 more rows, and 10 more variables: weeknumber <chr>, geo_description <chr>, age_label <chr>,
## # wk_start <date>, wk_end <date>, year_wk_num <int>, mmwrid <chr>, coverage_area <chr>, region_name <chr>,
## # callout <chr>
@ -621,19 +618,19 @@ state_data_providers()
```
## # A tibble: 59 x 5
## statename statehealthdeptname
## * <chr> <chr>
## 1 Alabama Alabama Department of Public Health
## 2 Alaska State of Alaska Health and Social Services
## 3 Arizona Arizona Department of Health Services
## 4 Arkansas Arkansas Department of Health
## 5 California California Department of Public Health
## 6 Colorado Colorado Department of Public Health and Environment
## 7 Connecticut Connecticut Department of Public Health
## 8 Delaware Delaware Health and Social Services
## 9 District of Columbia District of Columbia Department of Health
## 10 Florida Florida Department of Health
## # ... with 49 more rows, and 3 more variables: url <chr>, statewebsitename <chr>, statefluphonenum <chr>
## statename statehealthdeptname url statewebsitename statefluphonenum
## * <chr> <chr> <chr> <chr> <chr>
## 1 Alabama Alabama Department of Public Health http://… Influenza Surve… 334-206-5300
## 2 Alaska State of Alaska Health and Social Services "http:/… Influenza Surve… 907-269-8000
## 3 Arizona Arizona Department of Health Services http://… Influenza & RSV… 602-542-1025
## 4 Arkansas Arkansas Department of Health http://… Communicable Di… 501-661-2000
## 5 California California Department of Public Health https:/… Influenza (Flu) 916-558-1784
## 6 Colorado Colorado Department of Public Health and Environment https:/… Influenza Surve… 303-692-2000
## 7 Connecticut Connecticut Department of Public Health http://… Flu Statistics 860-509-8000
## 8 Delaware Delaware Health and Social Services http://… Weekly Influenz… 302-744-4700
## 9 District of Columbia District of Columbia Department of Health http://… Influenza Infor… 202-442-5955
## 10 Florida Florida Department of Health "http:/… Weekly Influenz… 850-245-4300
## # ... with 49 more rows
### Retrieve WHO/NREVSS Surveillance Data
@ -657,32 +654,32 @@ glimpse(xdat <- who_nrevss("national"))
## ..$ b : int [1:940] 0 0 1 0 0 0 1 1 1 1 ...
## ..$ h3n2v : int [1:940] 0 0 0 0 0 0 0 0 0 0 ...
## ..$ wk_date : Date[1:940], format: "1997-09-28" "1997-10-05" "1997-10-12" "1997-10-19" ...
## $ public_health_labs :Classes 'tbl_df', 'tbl' and 'data.frame': 109 obs. of 13 variables:
## ..$ region_type : chr [1:109] "National" "National" "National" "National" ...
## ..$ region : chr [1:109] "National" "National" "National" "National" ...
## ..$ year : int [1:109] 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 ...
## ..$ week : int [1:109] 40 41 42 43 44 45 46 47 48 49 ...
## ..$ total_specimens : int [1:109] 1139 1152 1198 1244 1465 1393 1458 1157 1550 1518 ...
## ..$ a_2009_h1n1 : int [1:109] 4 5 10 9 4 11 17 17 27 38 ...
## ..$ a_h3 : int [1:109] 65 41 50 31 23 34 42 24 36 37 ...
## ..$ a_subtyping_not_performed: int [1:109] 2 2 1 4 4 1 1 0 3 3 ...
## ..$ b : int [1:109] 10 7 8 9 9 10 4 4 9 11 ...
## ..$ bvic : int [1:109] 0 3 3 1 1 4 0 3 3 2 ...
## ..$ byam : int [1:109] 1 0 2 4 4 2 4 9 12 11 ...
## ..$ h3n2v : int [1:109] 0 0 0 0 0 0 0 0 0 0 ...
## ..$ wk_date : Date[1:109], format: "2015-10-04" "2015-10-11" "2015-10-18" "2015-10-25" ...
## $ clinical_labs :Classes 'tbl_df', 'tbl' and 'data.frame': 109 obs. of 11 variables:
## ..$ region_type : chr [1:109] "National" "National" "National" "National" ...
## ..$ region : chr [1:109] "National" "National" "National" "National" ...
## ..$ year : int [1:109] 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 ...
## ..$ week : int [1:109] 40 41 42 43 44 45 46 47 48 49 ...
## ..$ total_specimens : int [1:109] 12029 13111 13441 13537 14687 15048 15250 15234 16201 16673 ...
## ..$ total_a : int [1:109] 84 116 97 98 97 122 84 119 145 140 ...
## ..$ total_b : int [1:109] 43 54 52 52 68 86 98 92 81 106 ...
## ..$ percent_positive: num [1:109] 1.06 1.3 1.11 1.11 1.12 ...
## ..$ percent_a : num [1:109] 0.698 0.885 0.722 0.724 0.66 ...
## ..$ percent_b : num [1:109] 0.357 0.412 0.387 0.384 0.463 ...
## ..$ wk_date : Date[1:109], format: "2015-10-04" "2015-10-11" "2015-10-18" "2015-10-25" ...
## $ public_health_labs :Classes 'tbl_df', 'tbl' and 'data.frame': 153 obs. of 13 variables:
## ..$ region_type : chr [1:153] "National" "National" "National" "National" ...
## ..$ region : chr [1:153] "National" "National" "National" "National" ...
## ..$ year : int [1:153] 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 ...
## ..$ week : int [1:153] 40 41 42 43 44 45 46 47 48 49 ...
## ..$ total_specimens : int [1:153] 1139 1152 1198 1244 1465 1393 1458 1157 1550 1518 ...
## ..$ a_2009_h1n1 : int [1:153] 4 5 10 9 4 11 17 17 27 38 ...
## ..$ a_h3 : int [1:153] 65 41 50 31 23 34 42 24 36 37 ...
## ..$ a_subtyping_not_performed: int [1:153] 2 2 1 4 4 1 1 0 3 3 ...
## ..$ b : int [1:153] 10 7 8 9 9 10 4 4 9 11 ...
## ..$ bvic : int [1:153] 0 3 3 1 1 4 0 3 3 2 ...
## ..$ byam : int [1:153] 1 0 2 4 4 2 4 9 12 11 ...
## ..$ h3n2v : int [1:153] 0 0 0 0 0 0 0 0 0 0 ...
## ..$ wk_date : Date[1:153], format: "2015-10-04" "2015-10-11" "2015-10-18" "2015-10-25" ...
## $ clinical_labs :Classes 'tbl_df', 'tbl' and 'data.frame': 153 obs. of 11 variables:
## ..$ region_type : chr [1:153] "National" "National" "National" "National" ...
## ..$ region : chr [1:153] "National" "National" "National" "National" ...
## ..$ year : int [1:153] 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 ...
## ..$ week : int [1:153] 40 41 42 43 44 45 46 47 48 49 ...
## ..$ total_specimens : int [1:153] 12029 13111 13441 13537 14687 15048 15250 15234 16201 16673 ...
## ..$ total_a : int [1:153] 84 116 97 98 97 122 84 119 145 140 ...
## ..$ total_b : int [1:153] 43 54 52 52 68 86 98 92 81 106 ...
## ..$ percent_positive: num [1:153] 1.06 1.3 1.11 1.11 1.12 ...
## ..$ percent_a : num [1:153] 0.698 0.885 0.722 0.724 0.66 ...
## ..$ percent_b : num [1:153] 0.357 0.412 0.387 0.384 0.463 ...
## ..$ wk_date : Date[1:153], format: "2015-10-04" "2015-10-11" "2015-10-18" "2015-10-25" ...
``` r
mutate(xdat$combined_prior_to_2015_16,
@ -694,7 +691,7 @@ mutate(xdat$combined_prior_to_2015_16,
theme_ipsum_rc(grid="XY")
```
![](README.gfm-ascii_identifiers_files/figure-gfm/unnamed-chunk-12-1.png)<!-- -->
<img src="README_files/figure-gfm/who-vrevss-1.png" width="672" />
``` r
who_nrevss("hhs", years=2016)
@ -702,34 +699,34 @@ who_nrevss("hhs", years=2016)
## $public_health_labs
## # A tibble: 520 x 13
## region_type region year week total_specimens a_2009_h1n1 a_h3 a_subtyping_not_performed b bvic byam
## <chr> <chr> <int> <chr> <int> <int> <int> <int> <int> <int> <int>
## 1 HHS Regions Region 1 2016 <NA> 31 0 6 0 0 0 0
## 2 HHS Regions Region 2 2016 <NA> 31 0 6 0 0 2 0
## 3 HHS Regions Region 3 2016 <NA> 112 2 2 0 0 0 0
## 4 HHS Regions Region 4 2016 <NA> 112 1 11 0 1 2 0
## 5 HHS Regions Region 5 2016 <NA> 204 0 7 0 0 0 1
## 6 HHS Regions Region 6 2016 <NA> 39 1 1 0 0 0 0
## 7 HHS Regions Region 7 2016 <NA> 24 0 2 0 0 1 0
## 8 HHS Regions Region 8 2016 <NA> 46 2 8 0 0 0 0
## 9 HHS Regions Region 9 2016 <NA> 186 3 27 0 0 0 3
## 10 HHS Regions Region 10 2016 <NA> 113 0 17 0 0 0 0
## # ... with 510 more rows, and 2 more variables: h3n2v <int>, wk_date <date>
## region_type region year week total_specimens a_2009_h1n1 a_h3 a_subtyping_not… b bvic byam h3n2v wk_date
## <chr> <chr> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <date>
## 1 HHS Regions Regio… 2016 40 31 0 6 0 0 0 0 0 2016-10-02
## 2 HHS Regions Regio… 2016 40 31 0 6 0 0 2 0 0 2016-10-02
## 3 HHS Regions Regio… 2016 40 112 2 2 0 0 0 0 0 2016-10-02
## 4 HHS Regions Regio… 2016 40 112 1 11 0 1 2 0 0 2016-10-02
## 5 HHS Regions Regio… 2016 40 204 0 7 0 0 0 1 0 2016-10-02
## 6 HHS Regions Regio… 2016 40 39 1 1 0 0 0 0 0 2016-10-02
## 7 HHS Regions Regio… 2016 40 24 0 2 0 0 1 0 0 2016-10-02
## 8 HHS Regions Regio… 2016 40 46 2 8 0 0 0 0 0 2016-10-02
## 9 HHS Regions Regio… 2016 40 186 3 27 0 0 0 3 0 2016-10-02
## 10 HHS Regions Regio… 2016 40 113 0 17 0 0 0 0 0 2016-10-02
## # ... with 510 more rows
##
## $clinical_labs
## # A tibble: 520 x 11
## region_type region year week total_specimens total_a total_b percent_positive percent_a percent_b wk_date
## <chr> <chr> <int> <int> <int> <int> <int> <dbl> <dbl> <dbl> <date>
## 1 HHS Regions Region 1 2016 40 654 5 1 0.917431 0.764526 0.1529050 2016-10-02
## 2 HHS Regions Region 2 2016 40 1307 10 3 0.994644 0.765111 0.2295330 2016-10-02
## 3 HHS Regions Region 3 2016 40 941 1 4 0.531350 0.106270 0.4250800 2016-10-02
## 4 HHS Regions Region 4 2016 40 2758 46 62 3.915880 1.667880 2.2480100 2016-10-02
## 5 HHS Regions Region 5 2016 40 2386 8 5 0.544845 0.335289 0.2095560 2016-10-02
## 6 HHS Regions Region 6 2016 40 1914 22 13 1.828630 1.149430 0.6792060 2016-10-02
## 7 HHS Regions Region 7 2016 40 723 0 0 0.000000 0.000000 0.0000000 2016-10-02
## 8 HHS Regions Region 8 2016 40 913 8 0 0.876232 0.876232 0.0000000 2016-10-02
## 9 HHS Regions Region 9 2016 40 1123 7 1 0.712378 0.623330 0.0890472 2016-10-02
## 10 HHS Regions Region 10 2016 40 590 14 0 2.372880 2.372880 0.0000000 2016-10-02
## region_type region year week total_specimens total_a total_b percent_positive percent_a percent_b wk_date
## <chr> <chr> <int> <int> <int> <int> <int> <dbl> <dbl> <dbl> <date>
## 1 HHS Regions Region 1 2016 40 654 5 1 0.917 0.765 0.153 2016-10-02
## 2 HHS Regions Region 2 2016 40 1307 10 3 0.995 0.765 0.230 2016-10-02
## 3 HHS Regions Region 3 2016 40 941 1 4 0.531 0.106 0.425 2016-10-02
## 4 HHS Regions Region 4 2016 40 2960 46 63 3.68 1.55 2.13 2016-10-02
## 5 HHS Regions Region 5 2016 40 2386 8 5 0.545 0.335 0.210 2016-10-02
## 6 HHS Regions Region 6 2016 40 1914 22 13 1.83 1.15 0.679 2016-10-02
## 7 HHS Regions Region 7 2016 40 723 0 0 0 0 0 2016-10-02
## 8 HHS Regions Region 8 2016 40 913 8 0 0.876 0.876 0 2016-10-02
## 9 HHS Regions Region 9 2016 40 992 6 1 0.706 0.605 0.101 2016-10-02
## 10 HHS Regions Region 10 2016 40 590 14 0 2.37 2.37 0 2016-10-02
## # ... with 510 more rows
``` r
@ -738,35 +735,35 @@ who_nrevss("census", years=2016)
## $public_health_labs
## # A tibble: 468 x 13
## region_type region year week total_specimens a_2009_h1n1 a_h3 a_subtyping_not_performed b
## <chr> <chr> <int> <chr> <int> <int> <int> <int> <int>
## 1 Census Regions New England 2016 <NA> 31 0 6 0 0
## 2 Census Regions Mid-Atlantic 2016 <NA> 50 0 8 0 0
## 3 Census Regions East North Central 2016 <NA> 139 0 4 0 0
## 4 Census Regions West North Central 2016 <NA> 103 0 6 0 0
## 5 Census Regions South Atlantic 2016 <NA> 181 3 11 0 1
## 6 Census Regions East South Central 2016 <NA> 24 0 0 0 0
## 7 Census Regions West South Central 2016 <NA> 27 0 1 0 0
## 8 Census Regions Mountain 2016 <NA> 54 3 10 0 0
## 9 Census Regions Pacific 2016 <NA> 289 3 41 0 0
## 10 Census Regions New England 2016 <NA> 14 0 2 0 0
## # ... with 458 more rows, and 4 more variables: bvic <int>, byam <int>, h3n2v <int>, wk_date <date>
## region_type region year week total_specimens a_2009_h1n1 a_h3 a_subtyping_not… b bvic byam h3n2v wk_date
## <chr> <chr> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <date>
## 1 Census Reg… New E… 2016 40 31 0 6 0 0 0 0 0 2016-10-02
## 2 Census Reg… Mid-A… 2016 40 50 0 8 0 0 2 0 0 2016-10-02
## 3 Census Reg… East … 2016 40 139 0 4 0 0 0 1 0 2016-10-02
## 4 Census Reg… West … 2016 40 103 0 6 0 0 1 0 0 2016-10-02
## 5 Census Reg… South… 2016 40 181 3 11 0 1 2 0 0 2016-10-02
## 6 Census Reg… East … 2016 40 24 0 0 0 0 0 0 0 2016-10-02
## 7 Census Reg… West … 2016 40 27 0 1 0 0 0 0 0 2016-10-02
## 8 Census Reg… Mount… 2016 40 54 3 10 0 0 0 1 0 2016-10-02
## 9 Census Reg… Pacif… 2016 40 289 3 41 0 0 0 2 0 2016-10-02
## 10 Census Reg… New E… 2016 41 14 0 2 0 0 0 0 0 2016-10-09
## # ... with 458 more rows
##
## $clinical_labs
## # A tibble: 468 x 11
## region_type region year week total_specimens total_a total_b percent_positive percent_a percent_b
## <chr> <chr> <int> <int> <int> <int> <int> <dbl> <dbl> <dbl>
## 1 Census Regions New England 2016 40 654 5 1 0.917431 0.764526 0.1529050
## 2 Census Regions Mid-Atlantic 2016 40 1579 10 4 0.886637 0.633312 0.2533250
## 3 Census Regions East North Central 2016 40 2176 6 5 0.505515 0.275735 0.2297790
## 4 Census Regions West North Central 2016 40 1104 3 0 0.271739 0.271739 0.0000000
## 5 Census Regions South Atlantic 2016 40 2610 43 62 4.022990 1.647510 2.3754800
## 6 Census Regions East South Central 2016 40 817 4 3 0.856793 0.489596 0.3671970
## 7 Census Regions West South Central 2016 40 1738 21 13 1.956270 1.208290 0.7479860
## 8 Census Regions Mountain 2016 40 1067 8 0 0.749766 0.749766 0.0000000
## 9 Census Regions Pacific 2016 40 1564 21 1 1.406650 1.342710 0.0639386
## 10 Census Regions New England 2016 41 810 5 1 0.740741 0.617284 0.1234570
## # ... with 458 more rows, and 1 more variables: wk_date <date>
## region_type region year week total_specimens total_a total_b percent_positive percent_a percent_b wk_date
## <chr> <chr> <int> <int> <int> <int> <int> <dbl> <dbl> <dbl> <date>
## 1 Census Regions New Engl… 2016 40 654 5 1 0.917 0.765 0.153 2016-10-02
## 2 Census Regions Mid-Atla… 2016 40 1579 10 4 0.887 0.633 0.253 2016-10-02
## 3 Census Regions East Nor… 2016 40 2176 6 5 0.506 0.276 0.230 2016-10-02
## 4 Census Regions West Nor… 2016 40 1104 3 0 0.272 0.272 0 2016-10-02
## 5 Census Regions South At… 2016 40 2785 43 62 3.77 1.54 2.23 2016-10-02
## 6 Census Regions East Sou… 2016 40 844 4 4 0.948 0.474 0.474 2016-10-02
## 7 Census Regions West Sou… 2016 40 1738 21 13 1.96 1.21 0.748 2016-10-02
## 8 Census Regions Mountain 2016 40 1067 8 0 0.750 0.750 0 2016-10-02
## 9 Census Regions Pacific 2016 40 1433 20 1 1.47 1.40 0.0698 2016-10-02
## 10 Census Regions New Engl… 2016 41 810 5 1 0.741 0.617 0.123 2016-10-09
## # ... with 458 more rows
``` r
who_nrevss("state", years=2016)
@ -774,35 +771,35 @@ who_nrevss("state", years=2016)
## $public_health_labs
## # A tibble: 54 x 12
## region_type region season_description total_specimens a_2009_h1n1 a_h3 a_subtyping_not_performed
## <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 States Alabama Season 2016-17 549 3 222 0
## 2 States Alaska Season 2016-17 5226 14 905 3
## 3 States Arizona Season 2016-17 2974 63 1630 0
## 4 States Arkansas Season 2016-17 121 0 51 0
## 5 States California Season 2016-17 14033 184 4694 120
## 6 States Colorado Season 2016-17 715 3 267 2
## 7 States Connecticut Season 2016-17 1347 19 968 0
## 8 States Delaware Season 2016-17 3090 5 659 4
## 9 States District of Columbia Season 2016-17 69 1 32 0
## 10 States Florida Season 2016-17 <NA> <NA> <NA> <NA>
## # ... with 44 more rows, and 5 more variables: b <chr>, bvic <chr>, byam <chr>, h3n2v <chr>, wk_date <date>
## region_type region season_descript… total_specimens a_2009_h1n1 a_h3 a_subtyping_not_p… b bvic byam h3n2v
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 States Alabama Season 2016-17 570 3 227 1 2 15 14 0
## 2 States Alaska Season 2016-17 5222 14 905 3 252 2 11 0
## 3 States Arizona Season 2016-17 2975 63 1630 0 5 227 578 0
## 4 States Arkansas Season 2016-17 121 0 51 0 0 4 0 0
## 5 States California Season 2016-17 14074 184 4696 120 116 28 152 0
## 6 States Colorado Season 2016-17 714 3 267 2 4 31 219 0
## 7 States Connectic… Season 2016-17 1348 19 968 0 0 62 263 0
## 8 States Delaware Season 2016-17 3090 5 659 4 11 27 127 1
## 9 States District … Season 2016-17 73 1 34 0 3 0 4 0
## 10 States Florida Season 2016-17 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## # ... with 44 more rows, and 1 more variable: wk_date <date>
##
## $clinical_labs
## # A tibble: 2,808 x 11
## region_type region year week total_specimens total_a total_b percent_positive percent_a percent_b
## <chr> <chr> <int> <int> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 States Alabama 2016 40 379 4 0 1.06 1.06 0
## 2 States Alaska 2016 40 <NA> <NA> <NA> <NA> <NA> <NA>
## 3 States Arizona 2016 40 133 0 0 0 0 0
## 4 States Arkansas 2016 40 47 0 0 0 0 0
## 5 States California 2016 40 799 3 0 0.38 0.38 0
## 6 States Colorado 2016 40 260 0 0 0 0 0
## 7 States Connecticut 2016 40 199 3 0 1.51 1.51 0
## 8 States Delaware 2016 40 40 0 0 0 0 0
## 9 States District of Columbia 2016 40 <NA> <NA> <NA> <NA> <NA> <NA>
## 10 States Florida 2016 40 <NA> <NA> <NA> <NA> <NA> <NA>
## # ... with 2,798 more rows, and 1 more variables: wk_date <date>
## region_type region year week total_specimens total_a total_b percent_positive percent_a percent_b wk_date
## <chr> <chr> <int> <int> <chr> <chr> <chr> <chr> <chr> <chr> <date>
## 1 States Alabama 2016 40 406 4 1 1.23 0.99 0.25 2016-10-02
## 2 States Alaska 2016 40 <NA> <NA> <NA> <NA> <NA> <NA> 2016-10-02
## 3 States Arizona 2016 40 133 0 0 0 0 0 2016-10-02
## 4 States Arkansas 2016 40 47 0 0 0 0 0 2016-10-02
## 5 States California 2016 40 668 2 0 0.3 0.3 0 2016-10-02
## 6 States Colorado 2016 40 260 0 0 0 0 0 2016-10-02
## 7 States Connecticut 2016 40 199 3 0 1.51 1.51 0 2016-10-02
## 8 States Delaware 2016 40 40 0 0 0 0 0 2016-10-02
## 9 States District of… 2016 40 <NA> <NA> <NA> <NA> <NA> <NA> 2016-10-02
## 10 States Florida 2016 40 <NA> <NA> <NA> <NA> <NA> <NA> 2016-10-02
## # ... with 2,798 more rows
## Code of Conduct


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- 0
cran-comments.md View File

@ -21,6 +21,8 @@ The CDC changed their endpoints again and remove support for one of them. The
function had been deprecated in the previous version submitted to CRAN and
had been removed in this version due to the removal from the CDC.
Support for new endpoints has been added along with tests for these new endpoints.
Only some examples run on CRAN due to their time consuming nature and the need
to make external calls. Weekly tests are performed on Travis-CI
<https://travis-ci.org/hrbrmstr/cdcfluview> and the package itself has 91%


+ 4
- 4
tests/testthat/test-cdcfluview.R View File

@ -2,8 +2,6 @@ context("new API functionality")
test_that("New API works", {
skip_on_cran()
expect_that(age_group_distribution(years=2017), is_a("data.frame"))
expect_that(geographic_spread(years=2017), is_a("data.frame"))
@ -16,6 +14,8 @@ test_that("New API works", {
expect_that(hospitalizations("ihsp", years=2017), is_a("data.frame"))
expect_that(hospitalizations("ihsp", "Oklahoma", years=2017), is_a("data.frame"))
skip_on_cran()
expect_that(ilinet("national", years=2017), is_a("data.frame"))
expect_that(ilinet("hhs", years=2017), is_a("data.frame"))
expect_that(ilinet("census", years=2017), is_a("data.frame"))
@ -60,10 +60,10 @@ context("old API functionality")
test_that("Old API works", {
skip_on_cran()
expect_that(dim(get_flu_data("hhs", years=2015)), equals(c(520L, 15L)))
skip_on_cran()
expect_that(dim(get_state_data(2008)), equals(c(2494L, 8L)))
invisible(get_flu_data())


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