vignettes/covidcountydataR.md
In CovidCountyData/covidcountydataR: Client library to access COVID-19 Modeling Data Collaborative (CMDC) API
title: "covidcountydataR"
output:
github_document: null
rmarkdown::html_vignette: null
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%\VignetteIndexEntry{covidcountydataR}
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Welcome to the R client library for accessing the COVID County Data
database.
Links:
As of right now, this library is a wrapper around the
covidcountydata.py Python client. For
more examples and documentation, please see that library. If you are an
R programmer and are willing to contribute to making a native library,
please reach out at our
repository!
Also, please see the project website for
more information.
COVID County Data
Covid County Data (CCD) is a project funded by
Schmidt Futures and seeks to simplify the
data ingestion process for researchers and policy makers who are working
to enact and understand COVID-19 related policies. We accomplish this
goal in several ways:
- Collect unique, hard-to-acquire, datasets that are not widely
distributed
- Aggregate data collected by other related organizations into a
centralized database
- Work with other related organizations to expand and improve their
data collection processes
- Build tools, such as this library (and
Python and
Julia equivalents), to
simplify the data ingestion process
More information about our project and what data is collected can be
found on our website.
We are always looking to hear from both those who would like to help us
build CCD and those who would like use CCD. Please reach out to
us!
Installation
Please install this package using devtools::install_github as follows
devtools::install_github("CovidCountyData/covidcountydataR")
After installing the package, you need to make sure that the underlying
python package is installed.
To do this, use
covidcountydataR::install_ccdPY()
During the installation process, R will check if you have an existing
Python installation that can be used
You may be prompted to accept the installation of a dedicated Python
(via miniconda) for R to use
We recommend that you accept this request, but if you are comfortable
managing your own Python installation you can say no
Creating a Client
Once the package is installed, the first step is to create an API
client:
library(covidcountydataR)
cl <- client()
Datasets
You can see a list of currently available datasets using:
datasets(cl)
#> [1] "covid_global" "covid_historical" "covid_us" "covidtrackingproject"
#> [5] "demographics" "economic_snapshots" "economics" "mobility_devices"
#> [9] "mobility_locations" "npi_us" "nytimes_covid" "swagger.json"
#> [13] "us_counties" "us_states" "usafacts_covid"
Each dataset has an associated function
You can get detailed information on a specific dataset using the info
method. For example
info(cl)
#> Covid County Data Client
#> [1] "Datasets are:"
#>
#> - covid_global
#> - covid_historical
#> - covid_us
#> - covidtrackingproject
#> - demographics
#> - economic_snapshots
#> - economics
#> - mobility_devices
#> - mobility_locations
#> - npi_us
#> - nytimes_covid
#> - swagger.json
#> - us_counties
#> - us_states
#> - usafacts_covid
#> Covid County Data Client
info(cl, "demographics")
#> Request builder for demographics endpoint
#> Valid filters are location, variable, value, select, order, Range, Range-Unit, offset, limit, Prefer
#>
#>
#> For the United States, this data comes from the American Community Survey that is administered by the US Census. Currently, the following variables are collected in the database
#>
#> * Total population
#> * Median age
#> * Fraction of the population over 65
#> * Fraction of the population who identify as various races or as Hispanic/Latino
#> * Fraction of the population with various degrees of education
#> * Fraction of the population that commutes in various ways
#> * Mean travel time to work (minutes)
#> * Median household income
#> * Mean household income
#> * Fraction of the (civilian) population with/without health insurance
#> * Fraction of families who had an income less than poverty level in the last year
#>
#> Please note that we are willing (and easily able!) to add other years or variables if there is interest --- The variables that we do include are because people have asked about them.
#>
#> Source(s):
#>
#> US Census American Community Survey (https://www.census.gov/programs-surveys/acs)
#> Covid County Data Client
info(cl, "covid_historical")
#> Request builder for covid_historical endpoint
#> Valid filters are vintage, dt, fips, variable, value, select, order, Range, Range-Unit, offset, limit, Prefer
#>
#>
#> This table returns all vintages (data from different collection dates) of data in our system.
#>
#> For only the most recent data, please use the `covid` endpoint.
#>
#> Currently, the following variables are collected in the database
#>
#> * `cases_suspected`: Total number of suspected cases
#> * `cases_confirmed`: Total number of confirmed cases
#> * `cases_total`: The number of suspected or confirmed cases
#> * `deaths_suspected`: The number of deaths that are suspected to have been caused by COVID-19
#> * `deaths_confirmed`: The number of deaths that are confirmed to have been caused by COVID-19
#> * `deaths_total`: The number of deaths that are either suspected or confirmed to have been caused by COVID-19
#> * `positive_tests_total`: The total number of tests that have been positive
#> * `negative_tests_total`: The total number of tests that have been negative
#> * `icu_beds_capacity_count`: The number of ICU beds available in the geography
#> * `icu_beds_in_use_any`: The number of ICU beds currently in use
#> * `icu_beds_in_use_covid_suspected`: The number of ICU beds currently in use by a patient suspected of COVID-19
#> * `icu_beds_in_use_covid_confirmed`: The number of ICU beds currently in use by a patient confirmed to have COVID-19
#> * `icu_beds_in_use_covid_total`: The number of ICU beds currently in use by a patient who is suspected of having or confirmed to have COVID-19
#> * `icu_beds_in_use_covid_new`: The number of ICU beds occupied by an individual suspected or confirmed of having COVID-19 that have been admitted today
#> * `hospital_beds_capacity_count`: The number of hospital beds available in the geography
#> * `hospital_beds_in_use_any`: The number of hospital beds currently in use
#> * `hospital_beds_in_use_covid_suspected`: The number of hospital beds currently in use by a patient suspected of COVID-19
#> * `hospital_beds_in_use_covid_confirmed`: The number of hospital beds currently in use by a patient confirmed to have COVID-19
#> * `hospital_beds_in_use_covid_total`: The number of hospital beds currently in use by a patient who is suspected of having or confirmed to have COVID-19
#> * `hospital_beds_in_use_covid_new`: The number of hospital beds occupied by an individual suspected or confirmed of having COVID-19 that have been admitted today
#> * `ventilators_capacity_count`: The number of individuals who can be supported by a ventilator
#> * `ventilators_in_use_any`: The number of individuals who are currently on a ventilator
#> * `ventilators_in_use_covid_suspected`: The number of individuals who are suspected of having COVID-19 that are currently on a ventilator
#> * `ventilators_in_use_covid_confirmed`: The number of individuals who are confirmed to have COVID-19 that are currently on a ventilator
#> * `ventilators_in_use_covid_total`: The number of individuals who are either suspected of having or confirmed to have COVID-19 that are on a ventilator
#> * `ventilators_in_use_covid_new`: The number of ventilators that are currently on a ventilator that are suspected of having or confirmed to have COVID-19 that started the ventilator today
#> * `recovered_total`: The number of individuals who tested positive for COVID-19 and no longer test positive
#> * `active_total`: The number of currently active COVID-19 cases
#>
#> These variables are only collected from official US federal/state/county government sources
#> Covid County Data Client
Requesting Data
Requesting a dataset has three parts:
- Create a client
- Build a request with desired datasets
fetch the datasets
1. Create a client
To create a client, use the client function as shown above
cl <- client()
You can optionally pass in an API key if you have one (see section on
API keys below)
cl <- client("my api key")
If you have previously registered for an API key (again, see below) on
your current machine, it will be loaded and used automatically for you
In practice you should rarely need to pass the apikey by hand unless you
are loading the key from an environment variable or another source
2. Build a request
Each of the datasets in the API have an associated function
To add datasets to the current request, datasetName(client) function:
covid_us(cl, state="CA")
#> Covid County Data Client. Current request:
#> -covid_us: {'location': [6001, 6003, 6005, 6007, 6009, 6011, 6013, 6015, 6017, 6019, 6021, 6023, 6025, 6027, 6029, 6031, 6033, 6035, 6037, 6039, 6041, 6043, 6045, 6047, 6049, 6051, 6053, 6055, 6057, 6059, 6061, 6063, 6065, 6067, 6069, 6071, 6073, 6075, 6077, 6079, 6081, 6083, 6085, 6087, 6089, 6091, 6093, 6095, 6097, 6099, 6101, 6103, 6105, 6107, 6109, 6111, 6113, 6115, 6]}
demographics(cl)
#> Covid County Data Client. Current request:
#> -covid_us: {'location': [6001, 6003, 6005, 6007, 6009, 6011, 6013, 6015, 6017, 6019, 6021, 6023, 6025, 6027, 6029, 6031, 6033, 6035, 6037, 6039, 6041, 6043, 6045, 6047, 6049, 6051, 6053, 6055, 6057, 6059, 6061, 6063, 6065, 6067, 6069, 6071, 6073, 6075, 6077, 6079, 6081, 6083, 6085, 6087, 6089, 6091, 6093, 6095, 6097, 6099, 6101, 6103, 6105, 6107, 6109, 6111, 6113, 6115, 6]}
#> -demographics: {}
cl
#> Covid County Data Client. Current request:
#> -covid_us: {'location': [6001, 6003, 6005, 6007, 6009, 6011, 6013, 6015, 6017, 6019, 6021, 6023, 6025, 6027, 6029, 6031, 6033, 6035, 6037, 6039, 6041, 6043, 6045, 6047, 6049, 6051, 6053, 6055, 6057, 6059, 6061, 6063, 6065, 6067, 6069, 6071, 6073, 6075, 6077, 6079, 6081, 6083, 6085, 6087, 6089, 6091, 6093, 6095, 6097, 6099, 6101, 6103, 6105, 6107, 6109, 6111, 6113, 6115, 6]}
#> -demographics: {}
You can see that the printed form of the client is updated to show you
what the current request looks like
To clear the current request, use reset(cl):
reset(cl)
#> Covid County Data Client
#> CCD Client
Each dataset function will build up a request for the client and will
return the client itself
This allows us to use the pipe operator (%>%) to do the above as:
cl %>% covid_us(state="CA") %>% demographics()
#> Covid County Data Client. Current request:
#> -covid_us: {'location': [6001, 6003, 6005, 6007, 6009, 6011, 6013, 6015, 6017, 6019, 6021, 6023, 6025, 6027, 6029, 6031, 6033, 6035, 6037, 6039, 6041, 6043, 6045, 6047, 6049, 6051, 6053, 6055, 6057, 6059, 6061, 6063, 6065, 6067, 6069, 6071, 6073, 6075, 6077, 6079, 6081, 6083, 6085, 6087, 6089, 6091, 6093, 6095, 6097, 6099, 6101, 6103, 6105, 6107, 6109, 6111, 6113, 6115, 6]}
#> -demographics: {}
Filtering data
Each of the dataset functions has a number of filters that can be
applied
This allows you to select certain rows and/or columns
For example, in the above example we had covid_us(state="CA"). This
instructs the client to only fetch data for counties in the state of
California
Refer to the info for each dataset’s function for more information on
which filters can be passed
Also, check out the examples section at the end for more examples
NOTE: If a filter is passed to one dataset in the request but is
applicable to other datasets in the request, it will be applied to all
datasets
For example in cl %>% covid_us(state="CA") %>% demographics() we only
specify a state filter on the covid_us dataset
However, when the data is collected it will also be applied to
demographics
We do this because we end up doing an inner join on all requested
datasets, so when we filter the state in covid_us they also get filtered
in demographics
3. Fetch the data
Now for the easy part!
When you are ready with your current
To fetch the data, call the fetch function on the client:
df <- fetch(cl)
df
#> # A tibble: 11,271 x 52
#> dt location cases_total deaths_total hospital_beds_c… hospital_beds_i… hospital_beds_i…
#> <dttm> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2020-01-21 00:00:00 6 0 0 NaN NaN NaN
#> 2 2020-01-22 00:00:00 6 0 0 NaN NaN NaN
#> 3 2020-01-23 00:00:00 6 0 0 NaN NaN NaN
#> 4 2020-01-24 00:00:00 6 0 0 NaN NaN NaN
#> 5 2020-01-25 00:00:00 6 0 0 NaN NaN NaN
#> 6 2020-01-26 00:00:00 6 2 0 NaN NaN NaN
#> 7 2020-01-27 00:00:00 6 3 0 NaN NaN NaN
#> 8 2020-01-28 00:00:00 6 3 0 NaN NaN NaN
#> 9 2020-01-29 00:00:00 6 4 0 NaN NaN NaN
#> 10 2020-01-30 00:00:00 6 4 0 NaN NaN NaN
#> # … with 11,261 more rows, and 45 more variables: hospital_beds_in_use_covid_total <dbl>,
#> # icu_beds_capacity_count <dbl>, icu_beds_in_use_any <dbl>, icu_beds_in_use_covid_confirmed <dbl>,
#> # icu_beds_in_use_covid_suspected <dbl>, icu_beds_in_use_covid_total <dbl>, negative_tests_total <dbl>,
#> # positive_tests_total <dbl>, tests_total <dbl>, ventilators_capacity_count <dbl>,
#> # ventilators_in_use_any <dbl>, ventilators_in_use_covid_confirmed <dbl>,
#> # ventilators_in_use_covid_suspected <dbl>, `Fraction of population over 65` <dbl>, `Mean household
#> # income` <dbl>, `Mean travel time to work (minutes)` <dbl>, `Median age` <dbl>, `Median household
#> # income` <dbl>, `Percent Asian` <dbl>, `Percent Hispanic/Latino (any race)` <dbl>, `Percent Native American
#> # or Alaska Native` <dbl>, `Percent Native Hawaiian or other Pacific Islander` <dbl>, `Percent black` <dbl>,
#> # `Percent of 16+ commute carpool` <dbl>, `Percent of 16+ commute driving alone` <dbl>, `Percent of 16+
#> # commute other way` <dbl>, `Percent of 16+ commute public transit` <dbl>, `Percent of 16+ commute
#> # walk` <dbl>, `Percent of 16+ commute work at home` <dbl>, `Percent of 25+ with Associate degree` <dbl>,
#> # `Percent of 25+ with Associate's degree` <dbl>, `Percent of 25+ with Bachelor degree` <dbl>, `Percent of 25+
#> # with Bachelor's degree` <dbl>, `Percent of 25+ with HS degree but no college` <dbl>, `Percent of 25+ with
#> # less than 9th grade education` <dbl>, `Percent of 25+ with professional degree` <dbl>, `Percent of 25+ with
#> # some HS but no HS degree` <dbl>, `Percent of 25+ with some college but no degree` <dbl>, `Percent of
#> # civilian population with health insurance` <dbl>, `Percent of civilian population with no health
#> # insurance` <dbl>, `Percent of families w/ income < poverty lvl in last year` <dbl>, `Percent other
#> # race` <dbl>, `Percent two or more races` <dbl>, `Percent white` <dbl>, `Total population` <dbl>
names(df)
#> [1] "dt"
#> [2] "location"
#> [3] "cases_total"
#> [4] "deaths_total"
#> [5] "hospital_beds_capacity_count"
#> [6] "hospital_beds_in_use_covid_confirmed"
#> [7] "hospital_beds_in_use_covid_suspected"
#> [8] "hospital_beds_in_use_covid_total"
#> [9] "icu_beds_capacity_count"
#> [10] "icu_beds_in_use_any"
#> [11] "icu_beds_in_use_covid_confirmed"
#> [12] "icu_beds_in_use_covid_suspected"
#> [13] "icu_beds_in_use_covid_total"
#> [14] "negative_tests_total"
#> [15] "positive_tests_total"
#> [16] "tests_total"
#> [17] "ventilators_capacity_count"
#> [18] "ventilators_in_use_any"
#> [19] "ventilators_in_use_covid_confirmed"
#> [20] "ventilators_in_use_covid_suspected"
#> [21] "Fraction of population over 65"
#> [22] "Mean household income"
#> [23] "Mean travel time to work (minutes)"
#> [24] "Median age"
#> [25] "Median household income"
#> [26] "Percent Asian"
#> [27] "Percent Hispanic/Latino (any race)"
#> [28] "Percent Native American or Alaska Native"
#> [29] "Percent Native Hawaiian or other Pacific Islander"
#> [30] "Percent black"
#> [31] "Percent of 16+ commute carpool"
#> [32] "Percent of 16+ commute driving alone"
#> [33] "Percent of 16+ commute other way"
#> [34] "Percent of 16+ commute public transit"
#> [35] "Percent of 16+ commute walk"
#> [36] "Percent of 16+ commute work at home"
#> [37] "Percent of 25+ with Associate degree"
#> [38] "Percent of 25+ with Associate's degree"
#> [39] "Percent of 25+ with Bachelor degree"
#> [40] "Percent of 25+ with Bachelor's degree"
#> [41] "Percent of 25+ with HS degree but no college"
#> [42] "Percent of 25+ with less than 9th grade education"
#> [43] "Percent of 25+ with professional degree"
#> [44] "Percent of 25+ with some HS but no HS degree"
#> [45] "Percent of 25+ with some college but no degree"
#> [46] "Percent of civilian population with health insurance"
#> [47] "Percent of civilian population with no health insurance"
#> [48] "Percent of families w/ income < poverty lvl in last year"
#> [49] "Percent other race"
#> [50] "Percent two or more races"
#> [51] "Percent white"
#> [52] "Total population"
Notice that after each successful request, the client is reset so there
are no “built-up” requests:
cl
#> Covid County Data Client
API keys
Our API is and always will be free for unlimited public use
However, we have an API key system in place to help us understand the
needs of our users
We kindly request that you register for an API key so we can understand
how to prioritize future work
In order to do so, you can use the register function
register(cl)
By default, function will prompt you to input an email address
You can also pass the email address as the second argument for
non-interactive use
register(cl, "me@me.com")
After you register for an API key it will be added to the client. All
future requests with this client will use the API key
We also save the key to a file at ~/.covidcountydata/apikey
If this file exists, each time you call client and do not explicitly
pass an apikey we will read the key from ~/.covidcountydata/apikey and
automatically apply it for you
Thus, to use the key in future sessions you just need to do cl <-
client() and we’ll handle the key for you!
Final thoughts
Due to the urgency of the COVID-19 crisis and the need for researchers,
modelers, and policy makers to have accurate data quickly, this project
moves fast!
We have created this library so that as we add new datasets to our
backend, they automatically appear here and are accessible via this
library
Please check back often and see what has been updated
Examples
# Single dataset all
cl %>% mobility_devices() %>% fetch()
#> # A tibble: 386,903 x 6
#> dt location dex dex_a num_devices num_devices_a
#> <dttm> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2020-01-20 00:00:00 1 169. 157. 499012 537243
#> 2 2020-01-20 00:00:00 2 138. 129. 30999 33130
#> 3 2020-01-20 00:00:00 4 158. 146. 443186 478038
#> 4 2020-01-20 00:00:00 5 159. 147. 266296 287199
#> 5 2020-01-20 00:00:00 6 184. 171. 1758372 1889100
#> 6 2020-01-20 00:00:00 8 237. 218. 349252 381059
#> 7 2020-01-20 00:00:00 9 139. 129. 204366 221176
#> 8 2020-01-20 00:00:00 10 167. 151. 61097 67674
#> 9 2020-01-20 00:00:00 11 166. 120. 22766 31604
#> 10 2020-01-20 00:00:00 12 294. 270. 1694376 1851355
#> # … with 386,893 more rows
# Single dataset filter on deaths
cl %>% covid_us(location="<100", variable="deaths_total", value=">100") %>% fetch()
#> # A tibble: 4,751 x 3
#> dt location deaths_total
#> <dttm> <dbl> <dbl>
#> 1 2020-03-20 00:00:00 53 104
#> 2 2020-03-21 00:00:00 53 111
#> 3 2020-03-22 00:00:00 36 114
#> 4 2020-03-22 00:00:00 53 126
#> 5 2020-03-23 00:00:00 36 114
#> 6 2020-03-23 00:00:00 53 137
#> 7 2020-03-24 00:00:00 22 108
#> 8 2020-03-24 00:00:00 36 210
#> 9 2020-03-24 00:00:00 53 146
#> 10 2020-03-25 00:00:00 22 130
#> # … with 4,741 more rows
# Single dataset single states with all counties
# OR: `cl %>% mobility_devices(state=as.integer(48)) %>% fetch()`
# OR: `cl %>% mobility_devices(state="TX") %>% fetch()`
cl %>% mobility_devices(state="48") %>% fetch()
#> # A tibble: 27,676 x 6
#> dt location dex dex_a num_devices num_devices_a
#> <dttm> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2020-01-20 00:00:00 48 380. 350. 2286293 2484430
#> 2 2020-01-20 00:00:00 48001 119. 110. 4542 4895
#> 3 2020-01-20 00:00:00 48003 81.4 74.7 1807 1967
#> 4 2020-01-20 00:00:00 48005 209. 195. 7748 8291
#> 5 2020-01-20 00:00:00 48007 95.5 84.0 2718 3090
#> 6 2020-01-20 00:00:00 48013 112. 102. 4754 5197
#> 7 2020-01-20 00:00:00 48015 156. 144. 3024 3286
#> 8 2020-01-20 00:00:00 48019 136. 125. 2266 2473
#> 9 2020-01-20 00:00:00 48021 185. 166. 7492 8375
#> 10 2020-01-20 00:00:00 48025 91.6 83.4 1999 2194
#> # … with 27,666 more rows
# Single dataset multiple states with all counties
cl %>% mobility_devices(state=c("CA", "TX")) %>% fetch()
#> # A tibble: 37,213 x 6
#> dt location dex dex_a num_devices num_devices_a
#> <dttm> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2020-01-20 00:00:00 6 184. 171. 1758372 1889100
#> 2 2020-01-20 00:00:00 48 380. 350. 2286293 2484430
#> 3 2020-01-20 00:00:00 6001 176. 159. 59993 66390
#> 4 2020-01-20 00:00:00 6005 59.9 56.3 2073 2207
#> 5 2020-01-20 00:00:00 6007 113. 104. 10931 11892
#> 6 2020-01-20 00:00:00 6009 46.8 45.0 3100 3227
#> 7 2020-01-20 00:00:00 6013 158. 150. 50593 53338
#> 8 2020-01-20 00:00:00 6015 41.7 40.4 1334 1379
#> 9 2020-01-20 00:00:00 6017 139. 134. 11561 12033
#> 10 2020-01-20 00:00:00 6019 120. 111. 45808 49425
#> # … with 37,203 more rows
# Single dataset variable select
cl %>% demographics(variable = c("Total population", "Fraction of population over 65", "Median age")) %>% fetch()
#> # A tibble: 3,272 x 4
#> location `Fraction of population over 65` `Median age` `Total population`
#> <dbl> <dbl> <dbl> <dbl>
#> 1 1 17 39 4887871
#> 2 2 11 34 737438
#> 3 4 17 38 7171646
#> 4 5 16 38 3013825
#> 5 6 14 36 39557044
#> 6 8 14 36 5695564
#> 7 9 17 41 3572665
#> 8 10 18 41 967171
#> 9 11 12 33 702455
#> 10 12 20 42 21299324
#> # … with 3,262 more rows
# Multiple datasets all data
cl %>% demographics() %>% covid_us(dt=">2020-07-20") %>% fetch()
#> # A tibble: 34,834 x 59
#> location `Fraction of po… `Mean household… `Mean travel ti… `Median age` `Median househo… `Percent Asian`
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 17 69091 25 39 49861 1
#> 2 1 17 69091 25 39 49861 1
#> 3 1 17 69091 25 39 49861 1
#> 4 1 17 69091 25 39 49861 1
#> 5 1 17 69091 25 39 49861 1
#> 6 1 17 69091 25 39 49861 1
#> 7 1 17 69091 25 39 49861 1
#> 8 1 17 69091 25 39 49861 1
#> 9 1 17 69091 25 39 49861 1
#> 10 1 17 69091 25 39 49861 1
#> # … with 34,824 more rows, and 52 more variables: `Percent Hispanic/Latino (any race)` <dbl>, `Percent Native
#> # American or Alaska Native` <dbl>, `Percent Native Hawaiian or other Pacific Islander` <dbl>, `Percent
#> # black` <dbl>, `Percent of 16+ commute carpool` <dbl>, `Percent of 16+ commute driving alone` <dbl>, `Percent
#> # of 16+ commute other way` <dbl>, `Percent of 16+ commute public transit` <dbl>, `Percent of 16+ commute
#> # walk` <dbl>, `Percent of 16+ commute work at home` <dbl>, `Percent of 25+ with Associate degree` <dbl>,
#> # `Percent of 25+ with Associate's degree` <dbl>, `Percent of 25+ with Bachelor degree` <dbl>, `Percent of 25+
#> # with Bachelor's degree` <dbl>, `Percent of 25+ with HS degree but no college` <dbl>, `Percent of 25+ with
#> # less than 9th grade education` <dbl>, `Percent of 25+ with professional degree` <dbl>, `Percent of 25+ with
#> # some HS but no HS degree` <dbl>, `Percent of 25+ with some college but no degree` <dbl>, `Percent of
#> # civilian population with health insurance` <dbl>, `Percent of civilian population with no health
#> # insurance` <dbl>, `Percent of families w/ income < poverty lvl in last year` <dbl>, `Percent other
#> # race` <dbl>, `Percent two or more races` <dbl>, `Percent white` <dbl>, `Total population` <dbl>, dt <dttm>,
#> # active_total <dbl>, cases_confirmed <dbl>, cases_suspected <dbl>, cases_total <dbl>, deaths_confirmed <dbl>,
#> # deaths_suspected <dbl>, deaths_total <dbl>, hospital_beds_capacity_count <dbl>,
#> # hospital_beds_in_use_any <dbl>, hospital_beds_in_use_covid_confirmed <dbl>,
#> # hospital_beds_in_use_covid_new <dbl>, hospital_beds_in_use_covid_suspected <dbl>,
#> # hospital_beds_in_use_covid_total <dbl>, icu_beds_capacity_count <dbl>, icu_beds_in_use_any <dbl>,
#> # icu_beds_in_use_covid_confirmed <dbl>, icu_beds_in_use_covid_suspected <dbl>,
#> # icu_beds_in_use_covid_total <dbl>, negative_tests_total <dbl>, positive_tests_total <dbl>,
#> # recovered_total <dbl>, tests_total <dbl>, ventilators_capacity_count <dbl>, ventilators_in_use_any <dbl>,
#> # ventilators_in_use_covid_total <dbl>
# Multiple datasets states only
cl %>% demographics() %>% covid_us(location="<100", dt=">2020-07-20") %>% fetch()
#> # A tibble: 577 x 54
#> location `Fraction of po… `Mean household… `Mean travel ti… `Median age` `Median househo… `Percent Asian`
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 17 69091 25 39 49861 1
#> 2 1 17 69091 25 39 49861 1
#> 3 1 17 69091 25 39 49861 1
#> 4 1 17 69091 25 39 49861 1
#> 5 1 17 69091 25 39 49861 1
#> 6 1 17 69091 25 39 49861 1
#> 7 1 17 69091 25 39 49861 1
#> 8 1 17 69091 25 39 49861 1
#> 9 1 17 69091 25 39 49861 1
#> 10 1 17 69091 25 39 49861 1
#> # … with 567 more rows, and 47 more variables: `Percent Hispanic/Latino (any race)` <dbl>, `Percent Native
#> # American or Alaska Native` <dbl>, `Percent Native Hawaiian or other Pacific Islander` <dbl>, `Percent
#> # black` <dbl>, `Percent of 16+ commute carpool` <dbl>, `Percent of 16+ commute driving alone` <dbl>, `Percent
#> # of 16+ commute other way` <dbl>, `Percent of 16+ commute public transit` <dbl>, `Percent of 16+ commute
#> # walk` <dbl>, `Percent of 16+ commute work at home` <dbl>, `Percent of 25+ with Associate degree` <dbl>,
#> # `Percent of 25+ with Bachelor degree` <dbl>, `Percent of 25+ with HS degree but no college` <dbl>, `Percent
#> # of 25+ with less than 9th grade education` <dbl>, `Percent of 25+ with professional degree` <dbl>, `Percent
#> # of 25+ with some HS but no HS degree` <dbl>, `Percent of 25+ with some college but no degree` <dbl>,
#> # `Percent of civilian population with health insurance` <dbl>, `Percent of civilian population with no health
#> # insurance` <dbl>, `Percent of families w/ income < poverty lvl in last year` <dbl>, `Percent other
#> # race` <dbl>, `Percent two or more races` <dbl>, `Percent white` <dbl>, `Total population` <dbl>, dt <dttm>,
#> # active_total <dbl>, cases_confirmed <dbl>, cases_suspected <dbl>, cases_total <dbl>, deaths_confirmed <dbl>,
#> # deaths_suspected <dbl>, deaths_total <dbl>, hospital_beds_capacity_count <dbl>,
#> # hospital_beds_in_use_any <dbl>, hospital_beds_in_use_covid_confirmed <dbl>,
#> # hospital_beds_in_use_covid_suspected <dbl>, hospital_beds_in_use_covid_total <dbl>,
#> # icu_beds_capacity_count <dbl>, icu_beds_in_use_any <dbl>, icu_beds_in_use_covid_total <dbl>,
#> # negative_tests_total <dbl>, positive_tests_total <dbl>, recovered_total <dbl>, tests_total <dbl>,
#> # ventilators_capacity_count <dbl>, ventilators_in_use_any <dbl>, ventilators_in_use_covid_total <dbl>
# Multiple datasets counties only
cl %>% demographics() %>% covid_us(location=">1000", dt=">2020-07-20") %>% fetch()
#> # A tibble: 34,257 x 59
#> location `Fraction of po… `Mean household… `Mean travel ti… `Median age` `Median househo… `Percent Asian`
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1001 14 75515 25 37 58786 1
#> 2 1001 14 75515 25 37 58786 1
#> 3 1001 14 75515 25 37 58786 1
#> 4 1001 14 75515 25 37 58786 1
#> 5 1001 14 75515 25 37 58786 1
#> 6 1001 14 75515 25 37 58786 1
#> 7 1001 14 75515 25 37 58786 1
#> 8 1001 14 75515 25 37 58786 1
#> 9 1001 14 75515 25 37 58786 1
#> 10 1001 14 75515 25 37 58786 1
#> # … with 34,247 more rows, and 52 more variables: `Percent Hispanic/Latino (any race)` <dbl>, `Percent Native
#> # American or Alaska Native` <dbl>, `Percent Native Hawaiian or other Pacific Islander` <dbl>, `Percent
#> # black` <dbl>, `Percent of 16+ commute carpool` <dbl>, `Percent of 16+ commute driving alone` <dbl>, `Percent
#> # of 16+ commute other way` <dbl>, `Percent of 16+ commute public transit` <dbl>, `Percent of 16+ commute
#> # walk` <dbl>, `Percent of 16+ commute work at home` <dbl>, `Percent of 25+ with Associate degree` <dbl>,
#> # `Percent of 25+ with Associate's degree` <dbl>, `Percent of 25+ with Bachelor degree` <dbl>, `Percent of 25+
#> # with Bachelor's degree` <dbl>, `Percent of 25+ with HS degree but no college` <dbl>, `Percent of 25+ with
#> # less than 9th grade education` <dbl>, `Percent of 25+ with professional degree` <dbl>, `Percent of 25+ with
#> # some HS but no HS degree` <dbl>, `Percent of 25+ with some college but no degree` <dbl>, `Percent of
#> # civilian population with health insurance` <dbl>, `Percent of civilian population with no health
#> # insurance` <dbl>, `Percent of families w/ income < poverty lvl in last year` <dbl>, `Percent other
#> # race` <dbl>, `Percent two or more races` <dbl>, `Percent white` <dbl>, `Total population` <dbl>, dt <dttm>,
#> # active_total <dbl>, cases_confirmed <dbl>, cases_suspected <dbl>, cases_total <dbl>, deaths_confirmed <dbl>,
#> # deaths_suspected <dbl>, deaths_total <dbl>, hospital_beds_capacity_count <dbl>,
#> # hospital_beds_in_use_any <dbl>, hospital_beds_in_use_covid_confirmed <dbl>,
#> # hospital_beds_in_use_covid_new <dbl>, hospital_beds_in_use_covid_suspected <dbl>,
#> # hospital_beds_in_use_covid_total <dbl>, icu_beds_capacity_count <dbl>, icu_beds_in_use_any <dbl>,
#> # icu_beds_in_use_covid_confirmed <dbl>, icu_beds_in_use_covid_suspected <dbl>,
#> # icu_beds_in_use_covid_total <dbl>, negative_tests_total <dbl>, positive_tests_total <dbl>,
#> # recovered_total <dbl>, tests_total <dbl>, ventilators_capacity_count <dbl>, ventilators_in_use_any <dbl>,
#> # ventilators_in_use_covid_total <dbl>
CovidCountyData/covidcountydataR documentation built on March 6, 2021, 10:53 a.m.
R Package Documentation
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title: "covidcountydataR" output: github_document: null rmarkdown::html_vignette: null vignette: > %\VignetteIndexEntry{covidcountydataR} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8}
Welcome to the R client library for accessing the COVID County Data database.
Links:
As of right now, this library is a wrapper around the covidcountydata.py Python client. For more examples and documentation, please see that library. If you are an R programmer and are willing to contribute to making a native library, please reach out at our repository!
Also, please see the project website for more information.
COVID County Data
Covid County Data (CCD) is a project funded by Schmidt Futures and seeks to simplify the data ingestion process for researchers and policy makers who are working to enact and understand COVID-19 related policies. We accomplish this goal in several ways:
- Collect unique, hard-to-acquire, datasets that are not widely distributed
- Aggregate data collected by other related organizations into a centralized database
- Work with other related organizations to expand and improve their data collection processes
- Build tools, such as this library (and Python and Julia equivalents), to simplify the data ingestion process
More information about our project and what data is collected can be found on our website.
We are always looking to hear from both those who would like to help us build CCD and those who would like use CCD. Please reach out to us!
Installation
Please install this package using devtools::install_github as follows
devtools::install_github("CovidCountyData/covidcountydataR")
After installing the package, you need to make sure that the underlying python package is installed.
To do this, use
covidcountydataR::install_ccdPY()
During the installation process, R will check if you have an existing Python installation that can be used
You may be prompted to accept the installation of a dedicated Python (via miniconda) for R to use
We recommend that you accept this request, but if you are comfortable managing your own Python installation you can say no
Creating a Client
Once the package is installed, the first step is to create an API client:
library(covidcountydataR)
cl <- client()
Datasets
You can see a list of currently available datasets using:
datasets(cl)
#> [1] "covid_global" "covid_historical" "covid_us" "covidtrackingproject"
#> [5] "demographics" "economic_snapshots" "economics" "mobility_devices"
#> [9] "mobility_locations" "npi_us" "nytimes_covid" "swagger.json"
#> [13] "us_counties" "us_states" "usafacts_covid"
Each dataset has an associated function
You can get detailed information on a specific dataset using the info
method. For example
info(cl)
#> Covid County Data Client
#> [1] "Datasets are:"
#>
#> - covid_global
#> - covid_historical
#> - covid_us
#> - covidtrackingproject
#> - demographics
#> - economic_snapshots
#> - economics
#> - mobility_devices
#> - mobility_locations
#> - npi_us
#> - nytimes_covid
#> - swagger.json
#> - us_counties
#> - us_states
#> - usafacts_covid
#> Covid County Data Client
info(cl, "demographics")
#> Request builder for demographics endpoint
#> Valid filters are location, variable, value, select, order, Range, Range-Unit, offset, limit, Prefer
#>
#>
#> For the United States, this data comes from the American Community Survey that is administered by the US Census. Currently, the following variables are collected in the database
#>
#> * Total population
#> * Median age
#> * Fraction of the population over 65
#> * Fraction of the population who identify as various races or as Hispanic/Latino
#> * Fraction of the population with various degrees of education
#> * Fraction of the population that commutes in various ways
#> * Mean travel time to work (minutes)
#> * Median household income
#> * Mean household income
#> * Fraction of the (civilian) population with/without health insurance
#> * Fraction of families who had an income less than poverty level in the last year
#>
#> Please note that we are willing (and easily able!) to add other years or variables if there is interest --- The variables that we do include are because people have asked about them.
#>
#> Source(s):
#>
#> US Census American Community Survey (https://www.census.gov/programs-surveys/acs)
#> Covid County Data Client
info(cl, "covid_historical")
#> Request builder for covid_historical endpoint
#> Valid filters are vintage, dt, fips, variable, value, select, order, Range, Range-Unit, offset, limit, Prefer
#>
#>
#> This table returns all vintages (data from different collection dates) of data in our system.
#>
#> For only the most recent data, please use the `covid` endpoint.
#>
#> Currently, the following variables are collected in the database
#>
#> * `cases_suspected`: Total number of suspected cases
#> * `cases_confirmed`: Total number of confirmed cases
#> * `cases_total`: The number of suspected or confirmed cases
#> * `deaths_suspected`: The number of deaths that are suspected to have been caused by COVID-19
#> * `deaths_confirmed`: The number of deaths that are confirmed to have been caused by COVID-19
#> * `deaths_total`: The number of deaths that are either suspected or confirmed to have been caused by COVID-19
#> * `positive_tests_total`: The total number of tests that have been positive
#> * `negative_tests_total`: The total number of tests that have been negative
#> * `icu_beds_capacity_count`: The number of ICU beds available in the geography
#> * `icu_beds_in_use_any`: The number of ICU beds currently in use
#> * `icu_beds_in_use_covid_suspected`: The number of ICU beds currently in use by a patient suspected of COVID-19
#> * `icu_beds_in_use_covid_confirmed`: The number of ICU beds currently in use by a patient confirmed to have COVID-19
#> * `icu_beds_in_use_covid_total`: The number of ICU beds currently in use by a patient who is suspected of having or confirmed to have COVID-19
#> * `icu_beds_in_use_covid_new`: The number of ICU beds occupied by an individual suspected or confirmed of having COVID-19 that have been admitted today
#> * `hospital_beds_capacity_count`: The number of hospital beds available in the geography
#> * `hospital_beds_in_use_any`: The number of hospital beds currently in use
#> * `hospital_beds_in_use_covid_suspected`: The number of hospital beds currently in use by a patient suspected of COVID-19
#> * `hospital_beds_in_use_covid_confirmed`: The number of hospital beds currently in use by a patient confirmed to have COVID-19
#> * `hospital_beds_in_use_covid_total`: The number of hospital beds currently in use by a patient who is suspected of having or confirmed to have COVID-19
#> * `hospital_beds_in_use_covid_new`: The number of hospital beds occupied by an individual suspected or confirmed of having COVID-19 that have been admitted today
#> * `ventilators_capacity_count`: The number of individuals who can be supported by a ventilator
#> * `ventilators_in_use_any`: The number of individuals who are currently on a ventilator
#> * `ventilators_in_use_covid_suspected`: The number of individuals who are suspected of having COVID-19 that are currently on a ventilator
#> * `ventilators_in_use_covid_confirmed`: The number of individuals who are confirmed to have COVID-19 that are currently on a ventilator
#> * `ventilators_in_use_covid_total`: The number of individuals who are either suspected of having or confirmed to have COVID-19 that are on a ventilator
#> * `ventilators_in_use_covid_new`: The number of ventilators that are currently on a ventilator that are suspected of having or confirmed to have COVID-19 that started the ventilator today
#> * `recovered_total`: The number of individuals who tested positive for COVID-19 and no longer test positive
#> * `active_total`: The number of currently active COVID-19 cases
#>
#> These variables are only collected from official US federal/state/county government sources
#> Covid County Data Client
Requesting Data
Requesting a dataset has three parts:
- Create a client
- Build a request with desired datasets
fetchthe datasets
1. Create a client
To create a client, use the client function as shown above
cl <- client()
You can optionally pass in an API key if you have one (see section on API keys below)
cl <- client("my api key")
If you have previously registered for an API key (again, see below) on your current machine, it will be loaded and used automatically for you
In practice you should rarely need to pass the apikey by hand unless you are loading the key from an environment variable or another source
2. Build a request
Each of the datasets in the API have an associated function
To add datasets to the current request, datasetName(client) function:
covid_us(cl, state="CA")
#> Covid County Data Client. Current request:
#> -covid_us: {'location': [6001, 6003, 6005, 6007, 6009, 6011, 6013, 6015, 6017, 6019, 6021, 6023, 6025, 6027, 6029, 6031, 6033, 6035, 6037, 6039, 6041, 6043, 6045, 6047, 6049, 6051, 6053, 6055, 6057, 6059, 6061, 6063, 6065, 6067, 6069, 6071, 6073, 6075, 6077, 6079, 6081, 6083, 6085, 6087, 6089, 6091, 6093, 6095, 6097, 6099, 6101, 6103, 6105, 6107, 6109, 6111, 6113, 6115, 6]}
demographics(cl)
#> Covid County Data Client. Current request:
#> -covid_us: {'location': [6001, 6003, 6005, 6007, 6009, 6011, 6013, 6015, 6017, 6019, 6021, 6023, 6025, 6027, 6029, 6031, 6033, 6035, 6037, 6039, 6041, 6043, 6045, 6047, 6049, 6051, 6053, 6055, 6057, 6059, 6061, 6063, 6065, 6067, 6069, 6071, 6073, 6075, 6077, 6079, 6081, 6083, 6085, 6087, 6089, 6091, 6093, 6095, 6097, 6099, 6101, 6103, 6105, 6107, 6109, 6111, 6113, 6115, 6]}
#> -demographics: {}
cl
#> Covid County Data Client. Current request:
#> -covid_us: {'location': [6001, 6003, 6005, 6007, 6009, 6011, 6013, 6015, 6017, 6019, 6021, 6023, 6025, 6027, 6029, 6031, 6033, 6035, 6037, 6039, 6041, 6043, 6045, 6047, 6049, 6051, 6053, 6055, 6057, 6059, 6061, 6063, 6065, 6067, 6069, 6071, 6073, 6075, 6077, 6079, 6081, 6083, 6085, 6087, 6089, 6091, 6093, 6095, 6097, 6099, 6101, 6103, 6105, 6107, 6109, 6111, 6113, 6115, 6]}
#> -demographics: {}
You can see that the printed form of the client is updated to show you what the current request looks like
To clear the current request, use reset(cl):
reset(cl)
#> Covid County Data Client
#> CCD Client
Each dataset function will build up a request for the client and will return the client itself
This allows us to use the pipe operator (%>%) to do the above as:
cl %>% covid_us(state="CA") %>% demographics()
#> Covid County Data Client. Current request:
#> -covid_us: {'location': [6001, 6003, 6005, 6007, 6009, 6011, 6013, 6015, 6017, 6019, 6021, 6023, 6025, 6027, 6029, 6031, 6033, 6035, 6037, 6039, 6041, 6043, 6045, 6047, 6049, 6051, 6053, 6055, 6057, 6059, 6061, 6063, 6065, 6067, 6069, 6071, 6073, 6075, 6077, 6079, 6081, 6083, 6085, 6087, 6089, 6091, 6093, 6095, 6097, 6099, 6101, 6103, 6105, 6107, 6109, 6111, 6113, 6115, 6]}
#> -demographics: {}
Filtering data
Each of the dataset functions has a number of filters that can be applied
This allows you to select certain rows and/or columns
For example, in the above example we had covid_us(state="CA"). This
instructs the client to only fetch data for counties in the state of
California
Refer to the info for each dataset’s function for more information on
which filters can be passed
Also, check out the examples section at the end for more examples
NOTE: If a filter is passed to one dataset in the request but is applicable to other datasets in the request, it will be applied to all datasets
For example in cl %>% covid_us(state="CA") %>% demographics() we only
specify a state filter on the covid_us dataset
However, when the data is collected it will also be applied to
demographics
We do this because we end up doing an inner join on all requested
datasets, so when we filter the state in covid_us they also get filtered
in demographics
3. Fetch the data
Now for the easy part!
When you are ready with your current
To fetch the data, call the fetch function on the client:
df <- fetch(cl)
df
#> # A tibble: 11,271 x 52
#> dt location cases_total deaths_total hospital_beds_c… hospital_beds_i… hospital_beds_i…
#> <dttm> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2020-01-21 00:00:00 6 0 0 NaN NaN NaN
#> 2 2020-01-22 00:00:00 6 0 0 NaN NaN NaN
#> 3 2020-01-23 00:00:00 6 0 0 NaN NaN NaN
#> 4 2020-01-24 00:00:00 6 0 0 NaN NaN NaN
#> 5 2020-01-25 00:00:00 6 0 0 NaN NaN NaN
#> 6 2020-01-26 00:00:00 6 2 0 NaN NaN NaN
#> 7 2020-01-27 00:00:00 6 3 0 NaN NaN NaN
#> 8 2020-01-28 00:00:00 6 3 0 NaN NaN NaN
#> 9 2020-01-29 00:00:00 6 4 0 NaN NaN NaN
#> 10 2020-01-30 00:00:00 6 4 0 NaN NaN NaN
#> # … with 11,261 more rows, and 45 more variables: hospital_beds_in_use_covid_total <dbl>,
#> # icu_beds_capacity_count <dbl>, icu_beds_in_use_any <dbl>, icu_beds_in_use_covid_confirmed <dbl>,
#> # icu_beds_in_use_covid_suspected <dbl>, icu_beds_in_use_covid_total <dbl>, negative_tests_total <dbl>,
#> # positive_tests_total <dbl>, tests_total <dbl>, ventilators_capacity_count <dbl>,
#> # ventilators_in_use_any <dbl>, ventilators_in_use_covid_confirmed <dbl>,
#> # ventilators_in_use_covid_suspected <dbl>, `Fraction of population over 65` <dbl>, `Mean household
#> # income` <dbl>, `Mean travel time to work (minutes)` <dbl>, `Median age` <dbl>, `Median household
#> # income` <dbl>, `Percent Asian` <dbl>, `Percent Hispanic/Latino (any race)` <dbl>, `Percent Native American
#> # or Alaska Native` <dbl>, `Percent Native Hawaiian or other Pacific Islander` <dbl>, `Percent black` <dbl>,
#> # `Percent of 16+ commute carpool` <dbl>, `Percent of 16+ commute driving alone` <dbl>, `Percent of 16+
#> # commute other way` <dbl>, `Percent of 16+ commute public transit` <dbl>, `Percent of 16+ commute
#> # walk` <dbl>, `Percent of 16+ commute work at home` <dbl>, `Percent of 25+ with Associate degree` <dbl>,
#> # `Percent of 25+ with Associate's degree` <dbl>, `Percent of 25+ with Bachelor degree` <dbl>, `Percent of 25+
#> # with Bachelor's degree` <dbl>, `Percent of 25+ with HS degree but no college` <dbl>, `Percent of 25+ with
#> # less than 9th grade education` <dbl>, `Percent of 25+ with professional degree` <dbl>, `Percent of 25+ with
#> # some HS but no HS degree` <dbl>, `Percent of 25+ with some college but no degree` <dbl>, `Percent of
#> # civilian population with health insurance` <dbl>, `Percent of civilian population with no health
#> # insurance` <dbl>, `Percent of families w/ income < poverty lvl in last year` <dbl>, `Percent other
#> # race` <dbl>, `Percent two or more races` <dbl>, `Percent white` <dbl>, `Total population` <dbl>
names(df)
#> [1] "dt"
#> [2] "location"
#> [3] "cases_total"
#> [4] "deaths_total"
#> [5] "hospital_beds_capacity_count"
#> [6] "hospital_beds_in_use_covid_confirmed"
#> [7] "hospital_beds_in_use_covid_suspected"
#> [8] "hospital_beds_in_use_covid_total"
#> [9] "icu_beds_capacity_count"
#> [10] "icu_beds_in_use_any"
#> [11] "icu_beds_in_use_covid_confirmed"
#> [12] "icu_beds_in_use_covid_suspected"
#> [13] "icu_beds_in_use_covid_total"
#> [14] "negative_tests_total"
#> [15] "positive_tests_total"
#> [16] "tests_total"
#> [17] "ventilators_capacity_count"
#> [18] "ventilators_in_use_any"
#> [19] "ventilators_in_use_covid_confirmed"
#> [20] "ventilators_in_use_covid_suspected"
#> [21] "Fraction of population over 65"
#> [22] "Mean household income"
#> [23] "Mean travel time to work (minutes)"
#> [24] "Median age"
#> [25] "Median household income"
#> [26] "Percent Asian"
#> [27] "Percent Hispanic/Latino (any race)"
#> [28] "Percent Native American or Alaska Native"
#> [29] "Percent Native Hawaiian or other Pacific Islander"
#> [30] "Percent black"
#> [31] "Percent of 16+ commute carpool"
#> [32] "Percent of 16+ commute driving alone"
#> [33] "Percent of 16+ commute other way"
#> [34] "Percent of 16+ commute public transit"
#> [35] "Percent of 16+ commute walk"
#> [36] "Percent of 16+ commute work at home"
#> [37] "Percent of 25+ with Associate degree"
#> [38] "Percent of 25+ with Associate's degree"
#> [39] "Percent of 25+ with Bachelor degree"
#> [40] "Percent of 25+ with Bachelor's degree"
#> [41] "Percent of 25+ with HS degree but no college"
#> [42] "Percent of 25+ with less than 9th grade education"
#> [43] "Percent of 25+ with professional degree"
#> [44] "Percent of 25+ with some HS but no HS degree"
#> [45] "Percent of 25+ with some college but no degree"
#> [46] "Percent of civilian population with health insurance"
#> [47] "Percent of civilian population with no health insurance"
#> [48] "Percent of families w/ income < poverty lvl in last year"
#> [49] "Percent other race"
#> [50] "Percent two or more races"
#> [51] "Percent white"
#> [52] "Total population"
Notice that after each successful request, the client is reset so there are no “built-up” requests:
cl
#> Covid County Data Client
API keys
Our API is and always will be free for unlimited public use
However, we have an API key system in place to help us understand the needs of our users
We kindly request that you register for an API key so we can understand how to prioritize future work
In order to do so, you can use the register function
register(cl)
By default, function will prompt you to input an email address
You can also pass the email address as the second argument for non-interactive use
register(cl, "me@me.com")
After you register for an API key it will be added to the client. All
future requests with this client will use the API key
We also save the key to a file at ~/.covidcountydata/apikey
If this file exists, each time you call client and do not explicitly
pass an apikey we will read the key from ~/.covidcountydata/apikey and
automatically apply it for you
Thus, to use the key in future sessions you just need to do cl <-
client() and we’ll handle the key for you!
Final thoughts
Due to the urgency of the COVID-19 crisis and the need for researchers, modelers, and policy makers to have accurate data quickly, this project moves fast!
We have created this library so that as we add new datasets to our backend, they automatically appear here and are accessible via this library
Please check back often and see what has been updated
Examples
# Single dataset all
cl %>% mobility_devices() %>% fetch()
#> # A tibble: 386,903 x 6
#> dt location dex dex_a num_devices num_devices_a
#> <dttm> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2020-01-20 00:00:00 1 169. 157. 499012 537243
#> 2 2020-01-20 00:00:00 2 138. 129. 30999 33130
#> 3 2020-01-20 00:00:00 4 158. 146. 443186 478038
#> 4 2020-01-20 00:00:00 5 159. 147. 266296 287199
#> 5 2020-01-20 00:00:00 6 184. 171. 1758372 1889100
#> 6 2020-01-20 00:00:00 8 237. 218. 349252 381059
#> 7 2020-01-20 00:00:00 9 139. 129. 204366 221176
#> 8 2020-01-20 00:00:00 10 167. 151. 61097 67674
#> 9 2020-01-20 00:00:00 11 166. 120. 22766 31604
#> 10 2020-01-20 00:00:00 12 294. 270. 1694376 1851355
#> # … with 386,893 more rows
# Single dataset filter on deaths
cl %>% covid_us(location="<100", variable="deaths_total", value=">100") %>% fetch()
#> # A tibble: 4,751 x 3
#> dt location deaths_total
#> <dttm> <dbl> <dbl>
#> 1 2020-03-20 00:00:00 53 104
#> 2 2020-03-21 00:00:00 53 111
#> 3 2020-03-22 00:00:00 36 114
#> 4 2020-03-22 00:00:00 53 126
#> 5 2020-03-23 00:00:00 36 114
#> 6 2020-03-23 00:00:00 53 137
#> 7 2020-03-24 00:00:00 22 108
#> 8 2020-03-24 00:00:00 36 210
#> 9 2020-03-24 00:00:00 53 146
#> 10 2020-03-25 00:00:00 22 130
#> # … with 4,741 more rows
# Single dataset single states with all counties
# OR: `cl %>% mobility_devices(state=as.integer(48)) %>% fetch()`
# OR: `cl %>% mobility_devices(state="TX") %>% fetch()`
cl %>% mobility_devices(state="48") %>% fetch()
#> # A tibble: 27,676 x 6
#> dt location dex dex_a num_devices num_devices_a
#> <dttm> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2020-01-20 00:00:00 48 380. 350. 2286293 2484430
#> 2 2020-01-20 00:00:00 48001 119. 110. 4542 4895
#> 3 2020-01-20 00:00:00 48003 81.4 74.7 1807 1967
#> 4 2020-01-20 00:00:00 48005 209. 195. 7748 8291
#> 5 2020-01-20 00:00:00 48007 95.5 84.0 2718 3090
#> 6 2020-01-20 00:00:00 48013 112. 102. 4754 5197
#> 7 2020-01-20 00:00:00 48015 156. 144. 3024 3286
#> 8 2020-01-20 00:00:00 48019 136. 125. 2266 2473
#> 9 2020-01-20 00:00:00 48021 185. 166. 7492 8375
#> 10 2020-01-20 00:00:00 48025 91.6 83.4 1999 2194
#> # … with 27,666 more rows
# Single dataset multiple states with all counties
cl %>% mobility_devices(state=c("CA", "TX")) %>% fetch()
#> # A tibble: 37,213 x 6
#> dt location dex dex_a num_devices num_devices_a
#> <dttm> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2020-01-20 00:00:00 6 184. 171. 1758372 1889100
#> 2 2020-01-20 00:00:00 48 380. 350. 2286293 2484430
#> 3 2020-01-20 00:00:00 6001 176. 159. 59993 66390
#> 4 2020-01-20 00:00:00 6005 59.9 56.3 2073 2207
#> 5 2020-01-20 00:00:00 6007 113. 104. 10931 11892
#> 6 2020-01-20 00:00:00 6009 46.8 45.0 3100 3227
#> 7 2020-01-20 00:00:00 6013 158. 150. 50593 53338
#> 8 2020-01-20 00:00:00 6015 41.7 40.4 1334 1379
#> 9 2020-01-20 00:00:00 6017 139. 134. 11561 12033
#> 10 2020-01-20 00:00:00 6019 120. 111. 45808 49425
#> # … with 37,203 more rows
# Single dataset variable select
cl %>% demographics(variable = c("Total population", "Fraction of population over 65", "Median age")) %>% fetch()
#> # A tibble: 3,272 x 4
#> location `Fraction of population over 65` `Median age` `Total population`
#> <dbl> <dbl> <dbl> <dbl>
#> 1 1 17 39 4887871
#> 2 2 11 34 737438
#> 3 4 17 38 7171646
#> 4 5 16 38 3013825
#> 5 6 14 36 39557044
#> 6 8 14 36 5695564
#> 7 9 17 41 3572665
#> 8 10 18 41 967171
#> 9 11 12 33 702455
#> 10 12 20 42 21299324
#> # … with 3,262 more rows
# Multiple datasets all data
cl %>% demographics() %>% covid_us(dt=">2020-07-20") %>% fetch()
#> # A tibble: 34,834 x 59
#> location `Fraction of po… `Mean household… `Mean travel ti… `Median age` `Median househo… `Percent Asian`
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 17 69091 25 39 49861 1
#> 2 1 17 69091 25 39 49861 1
#> 3 1 17 69091 25 39 49861 1
#> 4 1 17 69091 25 39 49861 1
#> 5 1 17 69091 25 39 49861 1
#> 6 1 17 69091 25 39 49861 1
#> 7 1 17 69091 25 39 49861 1
#> 8 1 17 69091 25 39 49861 1
#> 9 1 17 69091 25 39 49861 1
#> 10 1 17 69091 25 39 49861 1
#> # … with 34,824 more rows, and 52 more variables: `Percent Hispanic/Latino (any race)` <dbl>, `Percent Native
#> # American or Alaska Native` <dbl>, `Percent Native Hawaiian or other Pacific Islander` <dbl>, `Percent
#> # black` <dbl>, `Percent of 16+ commute carpool` <dbl>, `Percent of 16+ commute driving alone` <dbl>, `Percent
#> # of 16+ commute other way` <dbl>, `Percent of 16+ commute public transit` <dbl>, `Percent of 16+ commute
#> # walk` <dbl>, `Percent of 16+ commute work at home` <dbl>, `Percent of 25+ with Associate degree` <dbl>,
#> # `Percent of 25+ with Associate's degree` <dbl>, `Percent of 25+ with Bachelor degree` <dbl>, `Percent of 25+
#> # with Bachelor's degree` <dbl>, `Percent of 25+ with HS degree but no college` <dbl>, `Percent of 25+ with
#> # less than 9th grade education` <dbl>, `Percent of 25+ with professional degree` <dbl>, `Percent of 25+ with
#> # some HS but no HS degree` <dbl>, `Percent of 25+ with some college but no degree` <dbl>, `Percent of
#> # civilian population with health insurance` <dbl>, `Percent of civilian population with no health
#> # insurance` <dbl>, `Percent of families w/ income < poverty lvl in last year` <dbl>, `Percent other
#> # race` <dbl>, `Percent two or more races` <dbl>, `Percent white` <dbl>, `Total population` <dbl>, dt <dttm>,
#> # active_total <dbl>, cases_confirmed <dbl>, cases_suspected <dbl>, cases_total <dbl>, deaths_confirmed <dbl>,
#> # deaths_suspected <dbl>, deaths_total <dbl>, hospital_beds_capacity_count <dbl>,
#> # hospital_beds_in_use_any <dbl>, hospital_beds_in_use_covid_confirmed <dbl>,
#> # hospital_beds_in_use_covid_new <dbl>, hospital_beds_in_use_covid_suspected <dbl>,
#> # hospital_beds_in_use_covid_total <dbl>, icu_beds_capacity_count <dbl>, icu_beds_in_use_any <dbl>,
#> # icu_beds_in_use_covid_confirmed <dbl>, icu_beds_in_use_covid_suspected <dbl>,
#> # icu_beds_in_use_covid_total <dbl>, negative_tests_total <dbl>, positive_tests_total <dbl>,
#> # recovered_total <dbl>, tests_total <dbl>, ventilators_capacity_count <dbl>, ventilators_in_use_any <dbl>,
#> # ventilators_in_use_covid_total <dbl>
# Multiple datasets states only
cl %>% demographics() %>% covid_us(location="<100", dt=">2020-07-20") %>% fetch()
#> # A tibble: 577 x 54
#> location `Fraction of po… `Mean household… `Mean travel ti… `Median age` `Median househo… `Percent Asian`
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 17 69091 25 39 49861 1
#> 2 1 17 69091 25 39 49861 1
#> 3 1 17 69091 25 39 49861 1
#> 4 1 17 69091 25 39 49861 1
#> 5 1 17 69091 25 39 49861 1
#> 6 1 17 69091 25 39 49861 1
#> 7 1 17 69091 25 39 49861 1
#> 8 1 17 69091 25 39 49861 1
#> 9 1 17 69091 25 39 49861 1
#> 10 1 17 69091 25 39 49861 1
#> # … with 567 more rows, and 47 more variables: `Percent Hispanic/Latino (any race)` <dbl>, `Percent Native
#> # American or Alaska Native` <dbl>, `Percent Native Hawaiian or other Pacific Islander` <dbl>, `Percent
#> # black` <dbl>, `Percent of 16+ commute carpool` <dbl>, `Percent of 16+ commute driving alone` <dbl>, `Percent
#> # of 16+ commute other way` <dbl>, `Percent of 16+ commute public transit` <dbl>, `Percent of 16+ commute
#> # walk` <dbl>, `Percent of 16+ commute work at home` <dbl>, `Percent of 25+ with Associate degree` <dbl>,
#> # `Percent of 25+ with Bachelor degree` <dbl>, `Percent of 25+ with HS degree but no college` <dbl>, `Percent
#> # of 25+ with less than 9th grade education` <dbl>, `Percent of 25+ with professional degree` <dbl>, `Percent
#> # of 25+ with some HS but no HS degree` <dbl>, `Percent of 25+ with some college but no degree` <dbl>,
#> # `Percent of civilian population with health insurance` <dbl>, `Percent of civilian population with no health
#> # insurance` <dbl>, `Percent of families w/ income < poverty lvl in last year` <dbl>, `Percent other
#> # race` <dbl>, `Percent two or more races` <dbl>, `Percent white` <dbl>, `Total population` <dbl>, dt <dttm>,
#> # active_total <dbl>, cases_confirmed <dbl>, cases_suspected <dbl>, cases_total <dbl>, deaths_confirmed <dbl>,
#> # deaths_suspected <dbl>, deaths_total <dbl>, hospital_beds_capacity_count <dbl>,
#> # hospital_beds_in_use_any <dbl>, hospital_beds_in_use_covid_confirmed <dbl>,
#> # hospital_beds_in_use_covid_suspected <dbl>, hospital_beds_in_use_covid_total <dbl>,
#> # icu_beds_capacity_count <dbl>, icu_beds_in_use_any <dbl>, icu_beds_in_use_covid_total <dbl>,
#> # negative_tests_total <dbl>, positive_tests_total <dbl>, recovered_total <dbl>, tests_total <dbl>,
#> # ventilators_capacity_count <dbl>, ventilators_in_use_any <dbl>, ventilators_in_use_covid_total <dbl>
# Multiple datasets counties only
cl %>% demographics() %>% covid_us(location=">1000", dt=">2020-07-20") %>% fetch()
#> # A tibble: 34,257 x 59
#> location `Fraction of po… `Mean household… `Mean travel ti… `Median age` `Median househo… `Percent Asian`
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1001 14 75515 25 37 58786 1
#> 2 1001 14 75515 25 37 58786 1
#> 3 1001 14 75515 25 37 58786 1
#> 4 1001 14 75515 25 37 58786 1
#> 5 1001 14 75515 25 37 58786 1
#> 6 1001 14 75515 25 37 58786 1
#> 7 1001 14 75515 25 37 58786 1
#> 8 1001 14 75515 25 37 58786 1
#> 9 1001 14 75515 25 37 58786 1
#> 10 1001 14 75515 25 37 58786 1
#> # … with 34,247 more rows, and 52 more variables: `Percent Hispanic/Latino (any race)` <dbl>, `Percent Native
#> # American or Alaska Native` <dbl>, `Percent Native Hawaiian or other Pacific Islander` <dbl>, `Percent
#> # black` <dbl>, `Percent of 16+ commute carpool` <dbl>, `Percent of 16+ commute driving alone` <dbl>, `Percent
#> # of 16+ commute other way` <dbl>, `Percent of 16+ commute public transit` <dbl>, `Percent of 16+ commute
#> # walk` <dbl>, `Percent of 16+ commute work at home` <dbl>, `Percent of 25+ with Associate degree` <dbl>,
#> # `Percent of 25+ with Associate's degree` <dbl>, `Percent of 25+ with Bachelor degree` <dbl>, `Percent of 25+
#> # with Bachelor's degree` <dbl>, `Percent of 25+ with HS degree but no college` <dbl>, `Percent of 25+ with
#> # less than 9th grade education` <dbl>, `Percent of 25+ with professional degree` <dbl>, `Percent of 25+ with
#> # some HS but no HS degree` <dbl>, `Percent of 25+ with some college but no degree` <dbl>, `Percent of
#> # civilian population with health insurance` <dbl>, `Percent of civilian population with no health
#> # insurance` <dbl>, `Percent of families w/ income < poverty lvl in last year` <dbl>, `Percent other
#> # race` <dbl>, `Percent two or more races` <dbl>, `Percent white` <dbl>, `Total population` <dbl>, dt <dttm>,
#> # active_total <dbl>, cases_confirmed <dbl>, cases_suspected <dbl>, cases_total <dbl>, deaths_confirmed <dbl>,
#> # deaths_suspected <dbl>, deaths_total <dbl>, hospital_beds_capacity_count <dbl>,
#> # hospital_beds_in_use_any <dbl>, hospital_beds_in_use_covid_confirmed <dbl>,
#> # hospital_beds_in_use_covid_new <dbl>, hospital_beds_in_use_covid_suspected <dbl>,
#> # hospital_beds_in_use_covid_total <dbl>, icu_beds_capacity_count <dbl>, icu_beds_in_use_any <dbl>,
#> # icu_beds_in_use_covid_confirmed <dbl>, icu_beds_in_use_covid_suspected <dbl>,
#> # icu_beds_in_use_covid_total <dbl>, negative_tests_total <dbl>, positive_tests_total <dbl>,
#> # recovered_total <dbl>, tests_total <dbl>, ventilators_capacity_count <dbl>, ventilators_in_use_any <dbl>,
#> # ventilators_in_use_covid_total <dbl>
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