Notice: The cmdcR library has been renamed to covidcountydataR
Please use that package instead of this one. See the covidcountydataR package on GitHub for more information
This package will remain active for historical users of cmdcR, but we strongly encourage all users to upgrade to the new package to continue receiving new features and bug fixes.
Welcome to the R client library for accessing the COVID Modeling Data Collaborative (CMDC) database.
Links:
- Repository
- Website
- Python and Julia clients
- Raw REST API
- GraphQL API
As of right now, this library is a wrapper around the cmdc.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.
The COVID Modeling Data Collaborative (CMDC) 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 CMDC and those who would like use CMDC. Please reach out to us!
Please install this package using devtools::install_github as follows
devtools::install_github("valorumdata/cmdcR")After installing the package, you need to make sure that the underlying python package is installed.
To do this, use
cmdcR::install_cmdcPY()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
Once the package is installed, the first step is to create an API client:
library(cmdcR)cl <- client()You can see a list of currently available datasets using:
datasets(cl)
#> [1] "covid" "covid_historical" "covid_us"
#> [4] "demographics" "economic_snapshots" "economics"
#> [7] "jhu_covid" "mobility_devices" "mobility_locations"
#> [10] "nytimes_covid" "swagger.json" "us_counties"
#> [13] "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)
#> CMDC Client
#> [1] "Datasets are:"
#>
#> - covid
#> - covid_historical
#> - covid_us
#> - demographics
#> - economic_snapshots
#> - economics
#> - jhu_covid
#> - mobility_devices
#> - mobility_locations
#> - nytimes_covid
#> - swagger.json
#> - us_counties
#> - us_states
#> - usafacts_covid
#> CMDC 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)
#> CMDC 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
#> CMDC ClientRequesting a dataset has three parts:
- Create a client
- Build a request with desired datasets
fetchthe datasets
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
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")
#> CMDC 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)
#> CMDC 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
#> CMDC 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)
#> CMDC Client
#> CMDC ClientEach 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()
#> CMDC 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: {}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
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: 3,232 x 49
#> location dt cases_total deaths_total hospital_beds_c…
#> <dbl> <dttm> <dbl> <dbl> <dbl>
#> 1 6 2020-03-03 19:00:00 NaN NaN NaN
#> 2 6 2020-03-04 19:00:00 NaN NaN NaN
#> 3 6 2020-03-05 19:00:00 NaN NaN NaN
#> 4 6 2020-03-06 19:00:00 NaN NaN NaN
#> 5 6 2020-03-07 19:00:00 NaN NaN NaN
#> 6 6 2020-03-08 20:00:00 NaN NaN NaN
#> 7 6 2020-03-09 20:00:00 NaN NaN NaN
#> 8 6 2020-03-10 20:00:00 NaN NaN NaN
#> 9 6 2020-03-11 20:00:00 NaN 4 NaN
#> 10 6 2020-03-12 20:00:00 NaN 4 NaN
#> # … with 3,222 more rows, and 44 more variables:
#> # 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_confirmed <dbl>,
#> # icu_beds_in_use_covid_suspected <dbl>, icu_beds_in_use_covid_total <dbl>,
#> # negative_tests_total <dbl>, positive_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 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>
names(df)
#> [1] "location"
#> [2] "dt"
#> [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] "ventilators_capacity_count"
#> [17] "ventilators_in_use_any"
#> [18] "ventilators_in_use_covid_confirmed"
#> [19] "ventilators_in_use_covid_suspected"
#> [20] "Fraction of population over 65"
#> [21] "Mean household income"
#> [22] "Mean travel time to work (minutes)"
#> [23] "Median age"
#> [24] "Median household income"
#> [25] "Percent Asian"
#> [26] "Percent Hispanic/Latino (any race)"
#> [27] "Percent Native American or Alaska Native"
#> [28] "Percent Native Hawaiian or other Pacific Islander"
#> [29] "Percent black"
#> [30] "Percent of 16+ commute carpool"
#> [31] "Percent of 16+ commute driving alone"
#> [32] "Percent of 16+ commute other way"
#> [33] "Percent of 16+ commute public transit"
#> [34] "Percent of 16+ commute walk"
#> [35] "Percent of 16+ commute work at home"
#> [36] "Percent of 25+ with Associate degree"
#> [37] "Percent of 25+ with Bachelor degree"
#> [38] "Percent of 25+ with HS degree but no college"
#> [39] "Percent of 25+ with less than 9th grade education"
#> [40] "Percent of 25+ with professional degree"
#> [41] "Percent of 25+ with some HS but no HS degree"
#> [42] "Percent of 25+ with some college but no degree"
#> [43] "Percent of civilian population with health insurance"
#> [44] "Percent of civilian population with no health insurance"
#> [45] "Percent of families w/ income < poverty lvl in last year"
#> [46] "Percent other race"
#> [47] "Percent two or more races"
#> [48] "Percent white"
#> [49] "Total population"Notice that after each successful request, the client is reset so there are no “built-up” requests:
cl
#> CMDC ClientOur 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, "[email protected]")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 ~/.cmdc/apikey
If this file exists, each time you call client and do not explicitly
pass an apikey we will read the key from ~/.cmdc/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!
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
# Single dataset all
cl %>% mobility_devices() %>% fetch()
#> # A tibble: 293,798 x 6
#> location dt dex dex_a num_devices num_devices_a
#> <dbl> <dttm> <dbl> <dbl> <dbl> <dbl>
#> 1 1 2020-01-19 19:00:00 169. 157. 499012 537243
#> 2 1 2020-01-20 19:00:00 126. 120. 510377 537243
#> 3 1 2020-01-21 19:00:00 129. 123. 512102 537243
#> 4 1 2020-01-22 19:00:00 133. 129. 518147 537243
#> 5 1 2020-01-23 19:00:00 201. 193. 515504 537243
#> 6 1 2020-01-24 19:00:00 215. 201. 501874 537243
#> 7 1 2020-01-25 19:00:00 134. 125. 504931 537243
#> 8 1 2020-01-26 19:00:00 128. 124. 518708 537243
#> 9 1 2020-01-27 19:00:00 124. 120. 519096 537243
#> 10 1 2020-01-28 19:00:00 125. 122. 522119 537243
#> # … with 293,788 more rows# Single dataset filter on deaths
cl %>% covid_us(location="<100", variable="deaths_total", value=">100") %>% fetch()
#> # A tibble: 2,699 x 3
#> location dt deaths_total
#> <dbl> <dttm> <dbl>
#> 1 1 2020-04-13 20:00:00 110
#> 2 1 2020-04-14 20:00:00 121
#> 3 1 2020-04-15 20:00:00 133
#> 4 1 2020-04-16 20:00:00 144
#> 5 1 2020-04-17 20:00:00 146
#> 6 1 2020-04-18 20:00:00 154
#> 7 1 2020-04-19 20:00:00 167
#> 8 1 2020-04-20 20:00:00 177
#> 9 1 2020-04-21 20:00:00 194
#> 10 1 2020-04-22 20:00:00 197
#> # … with 2,689 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: 21,016 x 6
#> location dt dex dex_a num_devices num_devices_a
#> <dbl> <dttm> <dbl> <dbl> <dbl> <dbl>
#> 1 48 2020-01-19 19:00:00 380. 350. 2286293 2484430
#> 2 48 2020-01-20 19:00:00 316. 299. 2353190 2484430
#> 3 48 2020-01-21 19:00:00 291. 277. 2364256 2484430
#> 4 48 2020-01-22 19:00:00 359. 343. 2373529 2484430
#> 5 48 2020-01-23 19:00:00 413. 392. 2357238 2484430
#> 6 48 2020-01-24 19:00:00 387. 359. 2308221 2484430
#> 7 48 2020-01-25 19:00:00 310. 287. 2297910 2484430
#> 8 48 2020-01-26 19:00:00 324. 312. 2391577 2484430
#> 9 48 2020-01-27 19:00:00 288. 279. 2405361 2484430
#> 10 48 2020-01-28 19:00:00 310. 298. 2390516 2484430
#> # … with 21,006 more rows# Single dataset multiple states with all counties
cl %>% mobility_devices(state=c("CA", "TX")) %>% fetch()
#> # A tibble: 28,258 x 6
#> location dt dex dex_a num_devices num_devices_a
#> <dbl> <dttm> <dbl> <dbl> <dbl> <dbl>
#> 1 6 2020-01-19 19:00:00 184. 171. 1758372 1889100
#> 2 6 2020-01-20 19:00:00 140. 135. 1817536 1889100
#> 3 6 2020-01-21 19:00:00 138. 133. 1819199 1889100
#> 4 6 2020-01-22 19:00:00 156. 151. 1828709 1889100
#> 5 6 2020-01-23 19:00:00 183. 176. 1818155 1889100
#> 6 6 2020-01-24 19:00:00 181. 169. 1769486 1889100
#> 7 6 2020-01-25 19:00:00 164. 152. 1757118 1889100
#> 8 6 2020-01-26 19:00:00 146. 142. 1838461 1889100
#> 9 6 2020-01-27 19:00:00 129. 125. 1829651 1889100
#> 10 6 2020-01-28 19:00:00 136. 132. 1838290 1889100
#> # … with 28,248 more rows# Single dataset variable select
cl %>% demographics(variable = c("Total population", "Fraction of population over 65", "Median age")) %>% fetch()
#> # A tibble: 878 x 4
#> location `Fraction of population over 65` `Median age` `Total population`
#> <dbl> <dbl> <dbl> <dbl>
#> 1 1 17 39.3 4887871
#> 2 2 11.9 34.9 737438
#> 3 4 17.6 38 7171646
#> 4 5 16.8 38.1 3013825
#> 5 6 14.3 36.7 39557044
#> 6 8 14.2 36.9 5695564
#> 7 9 17.2 41.1 3572665
#> 8 10 18.7 41.1 967171
#> 9 11 12.2 33.9 702455
#> 10 12 20.5 42.2 21299324
#> # … with 868 more rows# Multiple datasets all data
cl %>% demographics() %>% covid_us() %>% fetch()
#> # A tibble: 13,414 x 57
#> location `Fraction of po… `Mean household… `Mean travel ti… `Median age`
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 17 69091 25.2 39.3
#> 2 1 17 69091 25.2 39.3
#> 3 1 17 69091 25.2 39.3
#> 4 1 17 69091 25.2 39.3
#> 5 1 17 69091 25.2 39.3
#> 6 1 17 69091 25.2 39.3
#> 7 1 17 69091 25.2 39.3
#> 8 1 17 69091 25.2 39.3
#> 9 1 17 69091 25.2 39.3
#> 10 1 17 69091 25.2 39.3
#> # … with 13,404 more rows, and 52 more variables: `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 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_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>, ventilators_capacity_count <dbl>,
#> # ventilators_in_use_any <dbl>, ventilators_in_use_covid_confirmed <dbl>,
#> # ventilators_in_use_covid_suspected <dbl>,
#> # ventilators_in_use_covid_total <dbl># Multiple datasets states only
cl %>% demographics() %>% covid_us(location="<100") %>% fetch()
#> # A tibble: 5,322 x 48
#> location `Fraction of po… `Mean household… `Mean travel ti… `Median age`
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 17 69091 25.2 39.3
#> 2 1 17 69091 25.2 39.3
#> 3 1 17 69091 25.2 39.3
#> 4 1 17 69091 25.2 39.3
#> 5 1 17 69091 25.2 39.3
#> 6 1 17 69091 25.2 39.3
#> 7 1 17 69091 25.2 39.3
#> 8 1 17 69091 25.2 39.3
#> 9 1 17 69091 25.2 39.3
#> 10 1 17 69091 25.2 39.3
#> # … with 5,312 more rows, and 43 more variables: `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 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>,
#> # 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_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>,
#> # 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") %>% fetch()
#> # A tibble: 8,092 x 56
#> location `Fraction of po… `Mean household… `Mean travel ti… `Median age`
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2020 11.3 106660 19.6 34.3
#> 2 2020 11.3 106660 19.6 34.3
#> 3 2020 11.3 106660 19.6 34.3
#> 4 2020 11.3 106660 19.6 34.3
#> 5 2020 11.3 106660 19.6 34.3
#> 6 2020 11.3 106660 19.6 34.3
#> 7 2020 11.3 106660 19.6 34.3
#> 8 2020 11.3 106660 19.6 34.3
#> 9 2020 11.3 106660 19.6 34.3
#> 10 2020 11.3 106660 19.6 34.3
#> # … with 8,082 more rows, and 51 more variables: `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 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_total <dbl>,
#> # deaths_confirmed <dbl>, deaths_suspected <dbl>, deaths_total <dbl>,
#> # hospital_beds_capacity_count <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>, ventilators_capacity_count <dbl>,
#> # ventilators_in_use_any <dbl>, ventilators_in_use_covid_confirmed <dbl>,
#> # ventilators_in_use_covid_suspected <dbl>,
#> # ventilators_in_use_covid_total <dbl>