README.md
In hrbrmstr/cdccovidview: Weekly Surveillance Summary of U.S. COVID-19 Activity
cdccovidview
Weekly Surveillance Summary of U.S. COVID-19 Activity
Description
The U.S. Centers for Disease Control provides weekly summary and
interpretation of key indicators that have been adapted to track the
COVID-19 pandemic in the United States. Tools are provided to retrive
data from both COVIDView
(https://www.cdc.gov/coronavirus/2019-ncov/covid-data/covidview/index.html)
and COVID-NET (https://gis.cdc.gov/grasp/COVIDNet/COVID19_3.html).
What’s Inside The Tin
The following functions are implemented:
about: Display information about the data sourceage_groups: Return age groups used in the surveillanceavailable_seasons: Show available seasonsclinical_labs: Retrieve U.S. Clinical Laboratories Reporting
SARS-CoV-2 Test Results to CDClaboratory_confirmed_hospitalizations: Retrieve
Laboratory-Confirmed COVID-19-Associated Hospitalizationsmmwr_week_to_date: Convert an MMWR year+week or year+week+day to a
Date objectmmwr_week: Convert a Date to an MMWR day+week+yearmmwr_weekday: Convert a Date to an MMWR weekdaymmwrid_map: MMWR ID to Calendar Mappingsmortality_surveillance_data: Retrieve NCHS Mortality Surveillance
Datanssp_er_visits_national: Retrieve National Syndromic Surveillance
Program (NSSP): Emergency Department Visits Percentage of Visits for
COVID-19-Like Illness (CLI) or Influenza-like Illness (ILI)nssp_er_visits_regional: Retrieve Regional Syndromic Surveillance
Program (NSSP): Emergency Department Visits Percentage of Visits for
COVID-19-Like Illness (CLI) or Influenza-like Illness (ILI)provisional_death_counts: Retrieve Provisional Death Counts for
Coronavirus Disease (COVID-19)public_health_labs_national: Retrieve National Surveillance of
U.S. State and Local Public Health Laboratories Reporting to CDCpublic_health_labs_regional: Retrieve Regional Surveillance of
U.S. State and Local Public Health Laboratories Reporting to CDCsurveillance_areas: Show network & network catchments
Installation
install.packages("cdccovidview", repos = c("https://cinc.rud.is", "https://cloud.r-project.org/"))
# or
remotes::install_git("https://git.rud.is/hrbrmstr/cdccovidview.git")
# or
remotes::install_git("https://git.sr.ht/~hrbrmstr/cdccovidview")
# or
remotes::install_gitlab("hrbrmstr/cdccovidview")
# or
remotes::install_bitbucket("hrbrmstr/cdccovidview")
# or
remotes::install_github("hrbrmstr/cdccovidview")
NOTE: To use the ‘remotes’ install options you will need to have the
{remotes} package installed.
Usage
library(cdccovidview)
# current version
packageVersion("cdccovidview")
## [1] '0.1.1'
library(cdccovidview)
library(hrbrthemes)
library(tidyverse)
hosp <- laboratory_confirmed_hospitalizations()
hosp
## # A tibble: 4,590 x 8
## catchment network year mmwr_year mmwr_week age_category cumulative_rate weekly_rate
## <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
## 1 Entire Network COVID-NET 2020 2020 10 0-4 yr 0 0
## 2 Entire Network COVID-NET 2020 2020 11 0-4 yr 0 0
## 3 Entire Network COVID-NET 2020 2020 12 0-4 yr 0 0
## 4 Entire Network COVID-NET 2020 2020 13 0-4 yr 0.3 0.3
## 5 Entire Network COVID-NET 2020 2020 14 0-4 yr 0.6 0.3
## 6 Entire Network COVID-NET 2020 2020 15 0-4 yr NA NA
## 7 Entire Network COVID-NET 2020 2020 16 0-4 yr NA NA
## 8 Entire Network COVID-NET 2020 2020 17 0-4 yr NA NA
## 9 Entire Network COVID-NET 2020 2020 18 0-4 yr NA NA
## 10 Entire Network COVID-NET 2020 2020 19 0-4 yr NA NA
## # … with 4,580 more rows
c(
"0-4 yr", "5-17 yr", "18-49 yr", "50-64 yr", "65+ yr", "65-74 yr", "75-84 yr", "85+"
) -> age_f
mutate(hosp, start = mmwr_week_to_date(mmwr_year, mmwr_week)) %>%
filter(!is.na(weekly_rate)) %>%
filter(catchment == "Entire Network") %>%
select(start, network, age_category, weekly_rate) %>%
filter(age_category != "Overall") %>%
mutate(age_category = factor(age_category, levels = age_f)) %>%
ggplot() +
geom_line(
aes(start, weekly_rate)
) +
scale_x_date(
date_breaks = "2 weeks", date_labels = "%b\n%d"
) +
facet_grid(network~age_category) +
labs(
x = NULL, y = "Rates per 100,000 pop",
title = "COVID-NET Weekly Rates by Network and Age Group",
caption = sprintf("Source: COVID-NET: COVID-19-Associated Hospitalization Surveillance Network, Centers for Disease Control and Prevention.\n<https://gis.cdc.gov/grasp/COVIDNet/COVID19_3.html>; Accessed on %s", Sys.Date())
) +
theme_ipsum_es(grid="XY")
Clinical Labs
head(clinical_labs())
## week num_labs tested tested_pos pct_pos region source
## 1 202011 26 2785 182 0.065 National Clinical Labs
## 2 202012 41 18494 1149 0.062 National Clinical Labs
## 3 202013 50 37390 2966 0.079 National Clinical Labs
## 4 202014 37 36468 2798 0.077 National Clinical Labs
Public Health Labs
head(public_health_labs_national())
## week num_labs tested tested_pos pct_pos age_group region source
## 1 202010 73 8049 945 0.117 Overall National Public Health Labs
## 2 202011 79 32072 3292 0.103 Overall National Public Health Labs
## 3 202012 80 63369 6494 0.103 Overall National Public Health Labs
## 4 202013 79 56443 9529 0.169 Overall National Public Health Labs
## 5 202014 75 65917 12177 0.185 Overall National Public Health Labs
## 6 202010 73 212 9 0.043 0-4 yr National Public Health Labs
head(public_health_labs_regional())
## week num_labs tested tested_pos pct_pos region source
## 1 202010 8 619 46 0.074 Region 1 Public Health Labs
## 2 202011 17 3208 194 0.061 Region 1 Public Health Labs
## 3 202012 18 9608 732 0.076 Region 1 Public Health Labs
## 4 202013 16 4625 700 0.151 Region 1 Public Health Labs
## 5 202014 15 6123 1611 0.263 Region 1 Public Health Labs
## 6 202010 5 1381 193 0.140 Region 2 Public Health Labs
Emergency Department Visits
head(nssp_er_visits_national())
## week num_fac total_ed_visits visits pct_visits visit_type region source year
## 1 40 3255 2146776 19503 0.009 ili National Emergency Departments 2019
## 2 41 3249 2106999 20457 0.010 ili National Emergency Departments 2019
## 3 42 3256 2101358 22515 0.011 ili National Emergency Departments 2019
## 4 43 3254 2122427 23776 0.011 ili National Emergency Departments 2019
## 5 44 3295 2087335 25466 0.012 ili National Emergency Departments 2019
## 6 45 3315 2137854 29948 0.014 ili National Emergency Departments 2019
head(nssp_er_visits_regional())
## week num_fac total_ed_visits visits pct_visits visit_type region source year
## 1 41 202 130377 814 0.006 ili Region 1 Emergency Departments 2019
## 2 42 202 132385 912 0.007 ili Region 1 Emergency Departments 2019
## 3 43 202 131866 883 0.007 ili Region 1 Emergency Departments 2019
## 4 44 203 128256 888 0.007 ili Region 1 Emergency Departments 2019
## 5 45 203 127466 979 0.008 ili Region 1 Emergency Departments 2019
## 6 46 202 125306 1188 0.009 ili Region 1 Emergency Departments 2019
Mortality
head(mortality_surveillance_data())
## year week total_deaths deaths pct_deaths cause region source
## 1 2019 40 52452 0 0 COVID-19 National NCHS
## 2 2019 41 52860 0 0 COVID-19 National NCHS
## 3 2019 42 54129 0 0 COVID-19 National NCHS
## 4 2019 43 53914 0 0 COVID-19 National NCHS
## 5 2019 44 53980 0 0 COVID-19 National NCHS
## 6 2019 45 55468 0 0 COVID-19 National NCHS
pd <- provisional_death_counts()
head(pd$by_week)
## week covid_deaths total_deaths percent_expected_deaths pneumonia_deaths pneumonia_and_covid_deaths
## 2 2020-02-01 0 56402 0.95 3618 0
## 3 2020-02-08 0 56737 0.95 3601 0
## 4 2020-02-15 0 55273 0.94 3580 0
## 5 2020-02-22 0 54859 0.94 3427 0
## 6 2020-02-29 5 54513 0.95 3464 3
## 7 2020-03-07 18 53801 0.93 3552 11
## all_influenza_deaths_j09_j11
## 2 452
## 3 483
## 4 489
## 5 502
## 6 573
## 7 555
head(pd$by_age)
## age_group covid_deaths total_deaths percent_expected_deaths pneumonia_deaths pneumonia_and_covid_deaths
## 12 All ages 4984 511424 0.89 36423 2341
## 13 Under 1 yr 0 2727 0.65 19 0
## 14 1-4 yr 1 552 0.76 27 1
## 15 5-14 yr 1 809 0.73 26 0
## 16 15-24 yr 6 4638 0.81 87 2
## 17 25-34 yr 46 9624 0.86 257 21
## all_influenza_deaths_j09_j11
## 12 4541
## 13 9
## 14 26
## 15 34
## 16 35
## 17 106
head(pd$by_state)
## state covid_deaths total_deaths percent_expected_deaths pneumonia_deaths pneumonia_and_covid_deaths
## 25 Alabama 14 9220 0.87 539 4
## 26 Alaska 1 627 0.75 31 1
## 27 Arizona 26 11862 0.97 748 13
## 28 Arkansas 3 5938 0.92 372 2
## 29 California 175 52505 0.94 4170 96
## 30 Colorado 62 7787 0.98 493 33
## all_influenza_deaths_j09_j11
## 25 75
## 26 3
## 27 95
## 28 62
## 29 511
## 30 77
head(pd$by_sex)
## sex covid_deaths total_deaths percent_expected_deaths pneumonia_deaths pneumonia_and_covid_deaths
## 79 Male 2993 262727 0.90 19129 1374
## 80 Female 1991 248679 0.89 17294 967
## 81 Unknown 0 18 0.82 0 0
## all_influenza_deaths_j09_j11
## 79 2262
## 80 2279
## 81 0
cdccovidview Metrics
| Lang | # Files | (%) | LoC | (%) | Blank lines | (%) | # Lines | (%) |
| :--- | -------: | ---: | --: | --: | ----------: | --: | -------: | --: |
| R | 18 | 0.95 | 433 | 0.9 | 145 | 0.8 | 173 | 0.8 |
| Rmd | 1 | 0.05 | 47 | 0.1 | 36 | 0.2 | 44 | 0.2 |
Code of Conduct
Please note that this project is released with a Contributor Code of
Conduct. By participating in this project you agree to abide by its
terms.
hrbrmstr/cdccovidview documentation built on April 23, 2020, 11:40 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
cdccovidview
Weekly Surveillance Summary of U.S. COVID-19 Activity
Description
The U.S. Centers for Disease Control provides weekly summary and interpretation of key indicators that have been adapted to track the COVID-19 pandemic in the United States. Tools are provided to retrive data from both COVIDView (https://www.cdc.gov/coronavirus/2019-ncov/covid-data/covidview/index.html) and COVID-NET (https://gis.cdc.gov/grasp/COVIDNet/COVID19_3.html).
What’s Inside The Tin
The following functions are implemented:
about: Display information about the data sourceage_groups: Return age groups used in the surveillanceavailable_seasons: Show available seasonsclinical_labs: Retrieve U.S. Clinical Laboratories Reporting SARS-CoV-2 Test Results to CDClaboratory_confirmed_hospitalizations: Retrieve Laboratory-Confirmed COVID-19-Associated Hospitalizationsmmwr_week_to_date: Convert an MMWR year+week or year+week+day to a Date objectmmwr_week: Convert a Date to an MMWR day+week+yearmmwr_weekday: Convert a Date to an MMWR weekdaymmwrid_map: MMWR ID to Calendar Mappingsmortality_surveillance_data: Retrieve NCHS Mortality Surveillance Datanssp_er_visits_national: Retrieve National Syndromic Surveillance Program (NSSP): Emergency Department Visits Percentage of Visits for COVID-19-Like Illness (CLI) or Influenza-like Illness (ILI)nssp_er_visits_regional: Retrieve Regional Syndromic Surveillance Program (NSSP): Emergency Department Visits Percentage of Visits for COVID-19-Like Illness (CLI) or Influenza-like Illness (ILI)provisional_death_counts: Retrieve Provisional Death Counts for Coronavirus Disease (COVID-19)public_health_labs_national: Retrieve National Surveillance of U.S. State and Local Public Health Laboratories Reporting to CDCpublic_health_labs_regional: Retrieve Regional Surveillance of U.S. State and Local Public Health Laboratories Reporting to CDCsurveillance_areas: Show network & network catchments
Installation
install.packages("cdccovidview", repos = c("https://cinc.rud.is", "https://cloud.r-project.org/"))
# or
remotes::install_git("https://git.rud.is/hrbrmstr/cdccovidview.git")
# or
remotes::install_git("https://git.sr.ht/~hrbrmstr/cdccovidview")
# or
remotes::install_gitlab("hrbrmstr/cdccovidview")
# or
remotes::install_bitbucket("hrbrmstr/cdccovidview")
# or
remotes::install_github("hrbrmstr/cdccovidview")
NOTE: To use the ‘remotes’ install options you will need to have the {remotes} package installed.
Usage
library(cdccovidview)
# current version
packageVersion("cdccovidview")
## [1] '0.1.1'
library(cdccovidview)
library(hrbrthemes)
library(tidyverse)
hosp <- laboratory_confirmed_hospitalizations()
hosp
## # A tibble: 4,590 x 8
## catchment network year mmwr_year mmwr_week age_category cumulative_rate weekly_rate
## <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
## 1 Entire Network COVID-NET 2020 2020 10 0-4 yr 0 0
## 2 Entire Network COVID-NET 2020 2020 11 0-4 yr 0 0
## 3 Entire Network COVID-NET 2020 2020 12 0-4 yr 0 0
## 4 Entire Network COVID-NET 2020 2020 13 0-4 yr 0.3 0.3
## 5 Entire Network COVID-NET 2020 2020 14 0-4 yr 0.6 0.3
## 6 Entire Network COVID-NET 2020 2020 15 0-4 yr NA NA
## 7 Entire Network COVID-NET 2020 2020 16 0-4 yr NA NA
## 8 Entire Network COVID-NET 2020 2020 17 0-4 yr NA NA
## 9 Entire Network COVID-NET 2020 2020 18 0-4 yr NA NA
## 10 Entire Network COVID-NET 2020 2020 19 0-4 yr NA NA
## # … with 4,580 more rows
c(
"0-4 yr", "5-17 yr", "18-49 yr", "50-64 yr", "65+ yr", "65-74 yr", "75-84 yr", "85+"
) -> age_f
mutate(hosp, start = mmwr_week_to_date(mmwr_year, mmwr_week)) %>%
filter(!is.na(weekly_rate)) %>%
filter(catchment == "Entire Network") %>%
select(start, network, age_category, weekly_rate) %>%
filter(age_category != "Overall") %>%
mutate(age_category = factor(age_category, levels = age_f)) %>%
ggplot() +
geom_line(
aes(start, weekly_rate)
) +
scale_x_date(
date_breaks = "2 weeks", date_labels = "%b\n%d"
) +
facet_grid(network~age_category) +
labs(
x = NULL, y = "Rates per 100,000 pop",
title = "COVID-NET Weekly Rates by Network and Age Group",
caption = sprintf("Source: COVID-NET: COVID-19-Associated Hospitalization Surveillance Network, Centers for Disease Control and Prevention.\n<https://gis.cdc.gov/grasp/COVIDNet/COVID19_3.html>; Accessed on %s", Sys.Date())
) +
theme_ipsum_es(grid="XY")
Clinical Labs
head(clinical_labs())
## week num_labs tested tested_pos pct_pos region source
## 1 202011 26 2785 182 0.065 National Clinical Labs
## 2 202012 41 18494 1149 0.062 National Clinical Labs
## 3 202013 50 37390 2966 0.079 National Clinical Labs
## 4 202014 37 36468 2798 0.077 National Clinical Labs
Public Health Labs
head(public_health_labs_national())
## week num_labs tested tested_pos pct_pos age_group region source
## 1 202010 73 8049 945 0.117 Overall National Public Health Labs
## 2 202011 79 32072 3292 0.103 Overall National Public Health Labs
## 3 202012 80 63369 6494 0.103 Overall National Public Health Labs
## 4 202013 79 56443 9529 0.169 Overall National Public Health Labs
## 5 202014 75 65917 12177 0.185 Overall National Public Health Labs
## 6 202010 73 212 9 0.043 0-4 yr National Public Health Labs
head(public_health_labs_regional())
## week num_labs tested tested_pos pct_pos region source
## 1 202010 8 619 46 0.074 Region 1 Public Health Labs
## 2 202011 17 3208 194 0.061 Region 1 Public Health Labs
## 3 202012 18 9608 732 0.076 Region 1 Public Health Labs
## 4 202013 16 4625 700 0.151 Region 1 Public Health Labs
## 5 202014 15 6123 1611 0.263 Region 1 Public Health Labs
## 6 202010 5 1381 193 0.140 Region 2 Public Health Labs
Emergency Department Visits
head(nssp_er_visits_national())
## week num_fac total_ed_visits visits pct_visits visit_type region source year
## 1 40 3255 2146776 19503 0.009 ili National Emergency Departments 2019
## 2 41 3249 2106999 20457 0.010 ili National Emergency Departments 2019
## 3 42 3256 2101358 22515 0.011 ili National Emergency Departments 2019
## 4 43 3254 2122427 23776 0.011 ili National Emergency Departments 2019
## 5 44 3295 2087335 25466 0.012 ili National Emergency Departments 2019
## 6 45 3315 2137854 29948 0.014 ili National Emergency Departments 2019
head(nssp_er_visits_regional())
## week num_fac total_ed_visits visits pct_visits visit_type region source year
## 1 41 202 130377 814 0.006 ili Region 1 Emergency Departments 2019
## 2 42 202 132385 912 0.007 ili Region 1 Emergency Departments 2019
## 3 43 202 131866 883 0.007 ili Region 1 Emergency Departments 2019
## 4 44 203 128256 888 0.007 ili Region 1 Emergency Departments 2019
## 5 45 203 127466 979 0.008 ili Region 1 Emergency Departments 2019
## 6 46 202 125306 1188 0.009 ili Region 1 Emergency Departments 2019
Mortality
head(mortality_surveillance_data())
## year week total_deaths deaths pct_deaths cause region source
## 1 2019 40 52452 0 0 COVID-19 National NCHS
## 2 2019 41 52860 0 0 COVID-19 National NCHS
## 3 2019 42 54129 0 0 COVID-19 National NCHS
## 4 2019 43 53914 0 0 COVID-19 National NCHS
## 5 2019 44 53980 0 0 COVID-19 National NCHS
## 6 2019 45 55468 0 0 COVID-19 National NCHS
pd <- provisional_death_counts()
head(pd$by_week)
## week covid_deaths total_deaths percent_expected_deaths pneumonia_deaths pneumonia_and_covid_deaths
## 2 2020-02-01 0 56402 0.95 3618 0
## 3 2020-02-08 0 56737 0.95 3601 0
## 4 2020-02-15 0 55273 0.94 3580 0
## 5 2020-02-22 0 54859 0.94 3427 0
## 6 2020-02-29 5 54513 0.95 3464 3
## 7 2020-03-07 18 53801 0.93 3552 11
## all_influenza_deaths_j09_j11
## 2 452
## 3 483
## 4 489
## 5 502
## 6 573
## 7 555
head(pd$by_age)
## age_group covid_deaths total_deaths percent_expected_deaths pneumonia_deaths pneumonia_and_covid_deaths
## 12 All ages 4984 511424 0.89 36423 2341
## 13 Under 1 yr 0 2727 0.65 19 0
## 14 1-4 yr 1 552 0.76 27 1
## 15 5-14 yr 1 809 0.73 26 0
## 16 15-24 yr 6 4638 0.81 87 2
## 17 25-34 yr 46 9624 0.86 257 21
## all_influenza_deaths_j09_j11
## 12 4541
## 13 9
## 14 26
## 15 34
## 16 35
## 17 106
head(pd$by_state)
## state covid_deaths total_deaths percent_expected_deaths pneumonia_deaths pneumonia_and_covid_deaths
## 25 Alabama 14 9220 0.87 539 4
## 26 Alaska 1 627 0.75 31 1
## 27 Arizona 26 11862 0.97 748 13
## 28 Arkansas 3 5938 0.92 372 2
## 29 California 175 52505 0.94 4170 96
## 30 Colorado 62 7787 0.98 493 33
## all_influenza_deaths_j09_j11
## 25 75
## 26 3
## 27 95
## 28 62
## 29 511
## 30 77
head(pd$by_sex)
## sex covid_deaths total_deaths percent_expected_deaths pneumonia_deaths pneumonia_and_covid_deaths
## 79 Male 2993 262727 0.90 19129 1374
## 80 Female 1991 248679 0.89 17294 967
## 81 Unknown 0 18 0.82 0 0
## all_influenza_deaths_j09_j11
## 79 2262
## 80 2279
## 81 0
cdccovidview Metrics
| Lang | # Files | (%) | LoC | (%) | Blank lines | (%) | # Lines | (%) | | :--- | -------: | ---: | --: | --: | ----------: | --: | -------: | --: | | R | 18 | 0.95 | 433 | 0.9 | 145 | 0.8 | 173 | 0.8 | | Rmd | 1 | 0.05 | 47 | 0.1 | 36 | 0.2 | 44 | 0.2 |
Code of Conduct
Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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