The EPHTrackR package provides an R interface to access and download
publicly available data stored on the CDC National Environmental Public
Health Tracking Network
(Tracking Network) via a connection with the Tracking Network Data
API. A detailed user guide
describing available API calls and associated outputs can be found at
that link. Associated metadata for downloaded measure data can be found
on the Tracking Network Indicators and
Data page. Users might find
it easier to view the online Data
Explorer to get a sense of
the available datasets before or while using this package.
The purpose of the Tracking Network is to deliver information and data to protect the nation from health issues arising from or directly related to environmental factors. At the local, state, and national levels, the Tracking Network relies on a variety of people and information systems to deliver a core set of health, exposure, and hazards data; information summaries; and tools to enable analysis, visualization, and reporting of insights drawn from data.
Measures are the core data product of the Tracking Network. Measures
are organized into indicators, groups of highly related measures,
and content areas, the highest level of categorization containing
all the indicators related to a broad topic of interest. Using Tracking
Network data, users can create customized maps, tables, and graphs of
local, state, and national data. The Tracking Network contains data
covering several focal areas:
— Health effects of exposures, such
as asthma
— Hazards in the environment, such as air pollution
— Climate, such as extreme heat events
— Community design, such as
access to parks
— Lifestyle risk factors, such as smoking
—
Population characteristics, such as age and income
#install development version from Github
#install devtools first if you haven't previously - install.packages("devtools")
devtools::install_github("CDCgov/EPHTrackR",
dependencies = TRUE)library(EPHTrackR)
#> Welcome to the CDC Environmental Public Health Tracking Network! This package provides an R interface to the Tracking Network Data API. To easily visualize our data products, please visit https://ephtracking.cdc.gov/DataExplorer/.The tracking_api_token() function adds a Tracking API token to your
.Renviron file so it can be called securely without being stored in your
code. After you have run this function, the token will be called
automatically by all the other functions in this package. Leaving the
default argument install=T, ensures that the token will be saved in
future R sessions. A token can be acquired by emailing,
trackingsupport(AT)cdc.gov. A token is not required to use this package,
but you will experience less throttling and better API support if you
have one. Further information is available at
https://ephtracking.cdc.gov/apihelp.
tracking_api_token("XXXXXXXXXXXXXXXXX",
install=T)
#After you run this function, reload your environment so you can use the token without restarting R.
readRenviron("~/.Renviron")
#Token can be viewed by running:
Sys.getenv("TRACKING_API_TOKEN")Running the list_measures() function without any additional inputs
retrieves the latest inventory of content areas, indicators, and
measures from the Tracking Network Data API.
measures_inventory <- list_measures()
View(measures_inventory)Each content area, indicator, and measure has a full name and a unique identifier. You can use this information to determine what data are available on the Tracking Network and make appropriate calls to download the data (see below).
ca_df <- list_content_areas()
head(ca_df)
#> contentAreaId contentAreaName
#> 1 1 Drinking Water
#> 2 2 Unintentional Carbon Monoxide (CO) Poisoning
#> 3 3 Asthma
#> 4 4 Heart Disease & Stroke
#> 5 5 Birth Defects
#> 6 6 Childhood Lead Poisoningind_df <- list_indicators(content_area = "Heat & Heat-related Illness (HRI)")
head(ind_df)
#> contentAreaId contentAreaName indicatorId
#> 1 35 Heat & Heat-related Illness (HRI) 67
#> 2 35 Heat & Heat-related Illness (HRI) 88
#> 3 35 Heat & Heat-related Illness (HRI) 89
#> 4 35 Heat & Heat-related Illness (HRI) 97
#> 5 35 Heat & Heat-related Illness (HRI) 172
#> 6 35 Heat & Heat-related Illness (HRI) 173
#> indicatorName
#> 1 Mortality from HRI
#> 2 Hospitalizations for HRI
#> 3 Emergency Department Visits for HRI
#> 4 Projected Temperature & Heat
#> 5 Vulnerability & Preparedness: Heat
#> 6 Historical Temperature & Heat Indexmeas_df <- list_measures(content_area = 36)
head(meas_df)
#> contentAreaId contentAreaName indicatorId
#> 1 36 Precipitation & Flooding 106
#> 2 36 Precipitation & Flooding 106
#> 3 36 Precipitation & Flooding 106
#> 4 36 Precipitation & Flooding 106
#> 5 36 Precipitation & Flooding 106
#> 6 36 Precipitation & Flooding 106
#> indicatorName measureId
#> 1 Vulnerability & Preparedness: Precipitation & Flooding 581
#> 2 Vulnerability & Preparedness: Precipitation & Flooding 582
#> 3 Vulnerability & Preparedness: Precipitation & Flooding 583
#> 4 Vulnerability & Preparedness: Precipitation & Flooding 584
#> 5 Vulnerability & Preparedness: Precipitation & Flooding 1133
#> 6 Vulnerability & Preparedness: Precipitation & Flooding 1134
#> measureName
#> 1 Number of Square Miles within FEMA Designated Flood Hazard Area
#> 2 Percent Area (Square Miles) within FEMA Designated Flood Hazard Area
#> 3 Number of People within FEMA Designated Flood Hazard Area
#> 4 Number of Housing Units within FEMA Designated Flood Hazard Area
#> 5 Number of Square Miles within EPA Designated Flood Hazard Area
#> 6 Percent Area (Square Miles) within EPA Designated Flood Hazard Area
#> indicatorStatusId contentAreaStatusId
#> 1 3 3
#> 2 3 3
#> 3 3 3
#> 4 3 3
#> 5 3 3
#> 6 3 3
#> keywords
#> 1 flood, hazard, area, flash, weather, wet, zone, climate, change
#> 2 flood, hazard, area, flash, weather, wet, zone, climate, change
#> 3 flood, hazard, area, flash, weather, wet, zone, climate, change, population, people
#> 4 flood, hazard, area, flash, weather, wet, zone, climate, change, housing, houses, units
#> 5 flood, flood hazard, flooding
#> 6 flood, flood hazard, floodingMeasures on the Tracking Network vary in their geographic resolution (e.g., state, county), geographic extent (e.g., Massachusetts, Michigan, Pennsylvania, California, Georgia), temporal resolution (e.g., year, month) and temporal extent (e.g., 2000-2010, 2010-2020). By becoming familiar with the geographies and temporal periods for which data are available using this function, you can make more targeted data downloads.
Measures are typically available at the state, county, or census tract level.
geog_type_df <- list_GeographicTypes(measure= "Number of Square Miles within FEMA Designated Flood Hazard Area")
head(geog_type_df[[1]])
#> geographicType geographicTypeId measureId
#> 1 County 2 581
#> measureName
#> 1 Number of Square Miles within FEMA Designated Flood Hazard Area
#> smoothingLevel
#> 1 No Smoothing AvailableThis function identifies the particular geographic items (e.g., Alabama) that are available for a specified measure. It will reveal both the lowest level geographic items available (e.g., a county or census tract) and an overarching items, like states (i.e., parent geographic item) that contains the lowest level geographic items you would like returned in the data.
geog_item_df <- list_GeographicItems(measure= "Annual Number of Extreme Heat Days from May to September",
geo_type="County")
head(geog_item_df[[1]])
#> parentGeographicId parentName childGeographicId childName measureId
#> 1 1 Alabama 1001 Autauga 423
#> 2 1 Alabama 1003 Baldwin 423
#> 3 1 Alabama 1005 Barbour 423
#> 4 1 Alabama 1007 Bibb 423
#> 5 1 Alabama 1009 Blount 423
#> 6 1 Alabama 1011 Bullock 423
#> measureName geo_type geo_typeID
#> 1 Annual Number of Extreme Heat Days from May to September County 2
#> 2 Annual Number of Extreme Heat Days from May to September County 2
#> 3 Annual Number of Extreme Heat Days from May to September County 2
#> 4 Annual Number of Extreme Heat Days from May to September County 2
#> 5 Annual Number of Extreme Heat Days from May to September County 2
#> 6 Annual Number of Extreme Heat Days from May to September County 2Measures are typically available at an annual scale, but also can be daily, monthly, or weekly. This function identifies the particular years, months, days etc. that are available for the specified measure (e.g., 2001, Aug 2020, etc.)
temp_df <- list_TemporalItems(measure= "Annual Number of Extreme Heat Days from May to September")
head(temp_df[[1]])
#> id parentTemporalId parentTemporal parentMinimumTemporalId
#> 1 1979 NA NA NA
#> 2 1980 NA NA NA
#> 3 1981 NA NA NA
#> 4 1982 NA NA NA
#> 5 1983 NA NA NA
#> 6 1984 NA NA NA
#> parentTemporalTypeId parentTemporalType temporalId minimumTemporalId
#> 1 NA NA 1979 NA
#> 2 NA NA 1980 NA
#> 3 NA NA 1981 NA
#> 4 NA NA 1982 NA
#> 5 NA NA 1983 NA
#> 6 NA NA 1984 NA
#> minimumTemporal temporal temporalTypeId temporalType temporalTextOverride
#> 1 NA 1979 1 Year NA
#> 2 NA 1980 1 Year NA
#> 3 NA 1981 1 Year NA
#> 4 NA 1982 1 Year NA
#> 5 NA 1983 1 Year NA
#> 6 NA 1984 1 Year NA
#> parentTemporalDisplay
#> 1
#> 2
#> 3
#> 4
#> 5
#> 6
#> measureName measureId geo_type
#> 1 Annual Number of Extreme Heat Days from May to September 423 County
#> 2 Annual Number of Extreme Heat Days from May to September 423 County
#> 3 Annual Number of Extreme Heat Days from May to September 423 County
#> 4 Annual Number of Extreme Heat Days from May to September 423 County
#> 5 Annual Number of Extreme Heat Days from May to September 423 County
#> 6 Annual Number of Extreme Heat Days from May to September 423 County
#> geo_typeID
#> 1 2
#> 2 2
#> 3 2
#> 4 2
#> 5 2
#> 6 2In addition to geographic and temporal specifications, some measures on the Tracking Network have a set of Advanced Options that allow users to access data stratified by other variables. For instance, data on asthma hospitalizations can be broken down by age and/or gender.
Advanced Options might only be available at a particular geographic scale (e.g., age-breakdown of asthma hospitalizations is only available at the state level). Therefore, results showing available stratification levels always include the geography type. The output of this function is a list with a separate element for each geography type available (e.g., state, county) for the specified measure. Each row in the data frame elements of the list shows a stratification available for the measure.
strat_df <- list_StratificationLevels(measure=99)
head(strat_df[[1]])
#> stratificationLevelId stratificationLevelName stratificationLevelAbbreviation
#> 1 1 State ST
#> 2 3 State x Age ST_AG
#> 3 4 State x Gender ST_GN
#> 4 37 State x Age x Gender ST_AG_GN
#> 5 2 State x County ST_CT
#> geographicTypeId stratificationType
#> 1 1 NULL
#> 2 1 3, Age Group, AG, AgeBandId
#> 3 1 4, Gender, GN, GenderId
#> 4 1 3, 4, Age Group, Gender, AG, GN, AgeBandId, GenderId
#> 5 2 NULL
#> measureId measureName Geo_Type geo_typeID
#> 1 99 Annual Number of Hospitalizations for Asthma State 1
#> 2 99 Annual Number of Hospitalizations for Asthma State 1
#> 3 99 Annual Number of Hospitalizations for Asthma State 1
#> 4 99 Annual Number of Hospitalizations for Asthma State 1
#> 5 99 Annual Number of Hospitalizations for Asthma County 2If you’d like to query Tracking data by a specific stratification level
(e.g., you’d like data for just males), then you need to run the
list_StratificationTypes() function to identify the internal name for
the stratification and the appropriate code for the stratification level
of interest (e.g., 1 for male, 2 for female). The internal name can be
found in the in the ColumnName column of the function output and codes
can be found in the nested list found in the stratificationItem column
of the function output. Refer back to these when constructing queries
with with the get_data() function.
strat_df <- list_StratificationTypes(measure=99,
geo_type="State")
strat_df[[1]]
#> displayName isDisplayed isRequired isGrouped displayAllValues selectOneItem
#> 1 Age Group TRUE FALSE FALSE FALSE FALSE
#> 2 Gender TRUE FALSE FALSE FALSE FALSE
#> stratificationItem
#> 1 0 TO 4, 5 TO 14, 15 TO 34, 35 TO 64, >= 65, 0 TO 4, 5 TO 14, 15 TO 34, 35 TO 64, >= 65, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 1, 2, 3, 4, 5
#> 2 Male, Female, Male, Female, FALSE, FALSE, FALSE, FALSE, 1, 2
#> columnName stratificationTypeId measureId
#> 1 AgeBandId 3 99
#> 2 GenderId 4 99
#> measureName Geo_Type geo_typeID
#> 1 Annual Number of Hospitalizations for Asthma State 1
#> 2 Annual Number of Hospitalizations for Asthma State 1
#viewing the nested list in the stratificationItem column of the function output to identify stratification level codes
strat_df[[1]]$stratificationItem
#> [[1]]
#> name longName isDefault useLongName localId
#> 1 0 TO 4 0 TO 4 FALSE FALSE 1
#> 2 5 TO 14 5 TO 14 FALSE FALSE 2
#> 3 15 TO 34 15 TO 34 FALSE FALSE 3
#> 4 35 TO 64 35 TO 64 FALSE FALSE 4
#> 5 >= 65 >= 65 FALSE FALSE 5
#>
#> [[2]]
#> name longName isDefault useLongName localId
#> 1 Male Male FALSE FALSE 1
#> 2 Female Female FALSE FALSE 2You can use the information from the functions listed above to request specific data from the Tracking Network Data API. Be careful when making queries. If you request data that include many years and/or data at a fine geographic scale, the dataset could be very large and take a long time to download (if it doesn’t crash your session and bog down the entire API).
We recommend that you include only one measure and one stratification level in a data query. Many geographies and temporal periods may be submitted. The function will likely still work if vectors of multiple measures or stratification levels are submitted, but the resulting object will be a multi-element list and distinguishing the element that applies to a particular measure or stratification might be difficult. The output of function calls with a single measure and stratification level is a list with one element containing the relevant data frame.
data_st<-get_data(measure=99,
strat_level = "ST")
#> Building API call for measure: 99 with stratification: State.
#> Retrieving data...
#> Done
head(data_st[[1]])
#> geo geoId geoAbbreviation parentGeographicTypeId parentGeo parentGeoId
#> 1 Arizona 04 StateAbbreviation NA NA NA
#> 2 Arizona 04 StateAbbreviation NA NA NA
#> 3 Arizona 04 StateAbbreviation NA NA NA
#> 4 Arizona 04 StateAbbreviation NA NA NA
#> 5 Arizona 04 StateAbbreviation NA NA NA
#> 6 Arizona 04 StateAbbreviation NA NA NA
#> parentGeoAbbreviation temporalTypeId temporal temporalDescription
#> 1 NA 1 2005 Single Year
#> 2 NA 1 2006 Single Year
#> 3 NA 1 2007 Single Year
#> 4 NA 1 2008 Single Year
#> 5 NA 1 2009 Single Year
#> 6 NA 1 2010 Single Year
#> temporalColumnName temporalRollingColumnName temporalId minimumTemporal
#> 1 ReportYear RollingYearCount 2005 NA
#> 2 ReportYear RollingYearCount 2006 NA
#> 3 ReportYear RollingYearCount 2007 NA
#> 4 ReportYear RollingYearCount 2008 NA
#> 5 ReportYear RollingYearCount 2009 NA
#> 6 ReportYear RollingYearCount 2010 NA
#> minimumTemporalId parentTemporalTypeId parentTemporalType parentTemporal
#> 1 NA NA NA NA
#> 2 NA NA NA NA
#> 3 NA NA NA NA
#> 4 NA NA NA NA
#> 5 NA NA NA NA
#> 6 NA NA NA NA
#> parentTemporalId date dataValue suppressionFlag confidenceIntervalLow
#> 1 NA 2005 7115 0 NA
#> 2 NA 2006 6712 0 NA
#> 3 NA 2007 6735 0 NA
#> 4 NA 2008 7511 0 NA
#> 5 NA 2009 8265 0 NA
#> 6 NA 2010 8050 0 NA
#> confidenceIntervalHigh confidenceIntervalName standardError standardErrorName
#> 1 NA NA NA NA
#> 2 NA NA NA NA
#> 3 NA NA NA NA
#> 4 NA NA NA NA
#> 5 NA NA NA NA
#> 6 NA NA NA NA
#> secondaryValue secondaryValueName descriptiveValue descriptiveValueName
#> 1 NA NA NA NA
#> 2 NA NA NA NA
#> 3 NA NA NA NA
#> 4 NA NA NA NA
#> 5 NA NA NA NA
#> 6 NA NA NA NA
#> includeDescriptiveValueName category categoryName title
#> 1 NA NA NA Arizona
#> 2 NA NA NA Arizona
#> 3 NA NA NA Arizona
#> 4 NA NA NA Arizona
#> 5 NA NA NA Arizona
#> 6 NA NA NA Arizona
#> confidenceIntervalLowName parentMinimumTemporal parentMinimumTemporalId
#> 1 NA NA
#> 2 NA NA
#> 3 NA NA
#> 4 NA NA
#> 5 NA NA
#> 6 NA NA
#> measureId measureName geo_typeID Geo_Type
#> 1 99 Annual Number of Hospitalizations for Asthma 1 State
#> 2 99 Annual Number of Hospitalizations for Asthma 1 State
#> 3 99 Annual Number of Hospitalizations for Asthma 1 State
#> 4 99 Annual Number of Hospitalizations for Asthma 1 State
#> 5 99 Annual Number of Hospitalizations for Asthma 1 State
#> 6 99 Annual Number of Hospitalizations for Asthma 1 StateThe advanced stratification options are submitted via the strat_level
argument and the subset of stratification levels derived from the
list_StratificationTypes() function can be submitted with the
stratItems argument.
data_strat.item <- get_data(measure=99,
strat_level = "ST_AG_GN",
temporalItems = c(2005),
geoItems = "Arizona",
stratItems = c("GenderId=1","AgeBandId=3"))
#> Building API call for measure: 99 with stratification: State x Age x Gender.
#> Retrieving data...
#> Done
head(data_strat.item[[1]])
#> geo geoId geoAbbreviation parentGeographicTypeId parentGeo parentGeoId
#> 1 Arizona 04 StateAbbreviation NA NA NA
#> parentGeoAbbreviation temporalTypeId temporal temporalDescription
#> 1 NA 1 2005 Single Year
#> temporalColumnName temporalRollingColumnName temporalId minimumTemporal
#> 1 ReportYear RollingYearCount 2005 NA
#> minimumTemporalId parentTemporalTypeId parentTemporalType parentTemporal
#> 1 NA NA NA NA
#> parentTemporalId date dataValue suppressionFlag confidenceIntervalLow
#> 1 NA 2005 304 0 NA
#> confidenceIntervalHigh confidenceIntervalName standardError standardErrorName
#> 1 NA NA NA NA
#> secondaryValue secondaryValueName descriptiveValue descriptiveValueName
#> 1 NA NA NA NA
#> includeDescriptiveValueName category categoryName title
#> 1 NA NA NA Arizona
#> confidenceIntervalLowName parentMinimumTemporal parentMinimumTemporalId
#> 1 NA NA
#> full_stratification Gender Age Group measureId
#> 1 Male, 15 TO 34 Male 15 TO 34 99
#> measureName geo_typeID Geo_Type
#> 1 Annual Number of Hospitalizations for Asthma 1 StateYou can submit state names, abbreviations, or state FIPS codes with the
geoItems argument. This will return data for either the specified
state(s) or all the sub-state geographies within the state, depending on
the geography type of the data (e.g., state, county). Individual
sub-state geographies can also be submitted by FIPS code or name with
this argument. For measures with sub-state geographies, a mix of state
and sub-state entries can be submitted.
data_mo.geo<-get_data(measure=99,
strat_level = "State x County", #this can be written by name or ID (i.e., "ST_CT)
geoItems = c("Massachusetts",
"Alameda, CA", #county name should not include word 'county' and must have state
1001))
#> Building API call for measure: 99 with stratification: State x County.
#> Retrieving data...
#> Done
head(data_mo.geo[[1]])
#> geo geoId geoAbbreviation parentGeographicTypeId parentGeo
#> 1 Alameda 06001 CountyAbbreviation 1 California
#> 2 Alameda 06001 CountyAbbreviation 1 California
#> 3 Alameda 06001 CountyAbbreviation 1 California
#> 4 Alameda 06001 CountyAbbreviation 1 California
#> 5 Alameda 06001 CountyAbbreviation 1 California
#> 6 Alameda 06001 CountyAbbreviation 1 California
#> parentGeoId parentGeoAbbreviation temporalTypeId temporal temporalDescription
#> 1 06 CA 1 2000 Single Year
#> 2 06 CA 1 2001 Single Year
#> 3 06 CA 1 2002 Single Year
#> 4 06 CA 1 2003 Single Year
#> 5 06 CA 1 2004 Single Year
#> 6 06 CA 1 2005 Single Year
#> temporalColumnName temporalRollingColumnName temporalId minimumTemporal
#> 1 ReportYear RollingYearCount 2000 NA
#> 2 ReportYear RollingYearCount 2001 NA
#> 3 ReportYear RollingYearCount 2002 NA
#> 4 ReportYear RollingYearCount 2003 NA
#> 5 ReportYear RollingYearCount 2004 NA
#> 6 ReportYear RollingYearCount 2005 NA
#> minimumTemporalId parentTemporalTypeId parentTemporalType parentTemporal
#> 1 NA NA NA NA
#> 2 NA NA NA NA
#> 3 NA NA NA NA
#> 4 NA NA NA NA
#> 5 NA NA NA NA
#> 6 NA NA NA NA
#> parentTemporalId date dataValue suppressionFlag confidenceIntervalLow
#> 1 NA 2000 2389 0 NA
#> 2 NA 2001 2243 0 NA
#> 3 NA 2002 2260 0 NA
#> 4 NA 2003 2383 0 NA
#> 5 NA 2004 2059 0 NA
#> 6 NA 2005 2348 0 NA
#> confidenceIntervalHigh confidenceIntervalName standardError standardErrorName
#> 1 NA NA NA NA
#> 2 NA NA NA NA
#> 3 NA NA NA NA
#> 4 NA NA NA NA
#> 5 NA NA NA NA
#> 6 NA NA NA NA
#> secondaryValue secondaryValueName descriptiveValue descriptiveValueName
#> 1 NA NA NA NA
#> 2 NA NA NA NA
#> 3 NA NA NA NA
#> 4 NA NA NA NA
#> 5 NA NA NA NA
#> 6 NA NA NA NA
#> includeDescriptiveValueName category categoryName title
#> 1 NA NA NA Alameda, CA
#> 2 NA NA NA Alameda, CA
#> 3 NA NA NA Alameda, CA
#> 4 NA NA NA Alameda, CA
#> 5 NA NA NA Alameda, CA
#> 6 NA NA NA Alameda, CA
#> confidenceIntervalLowName parentMinimumTemporal parentMinimumTemporalId
#> 1 NA NA
#> 2 NA NA
#> 3 NA NA
#> 4 NA NA
#> 5 NA NA
#> 6 NA NA
#> measureId measureName geo_typeID Geo_Type
#> 1 99 Annual Number of Hospitalizations for Asthma 2 County
#> 2 99 Annual Number of Hospitalizations for Asthma 2 County
#> 3 99 Annual Number of Hospitalizations for Asthma 2 County
#> 4 99 Annual Number of Hospitalizations for Asthma 2 County
#> 5 99 Annual Number of Hospitalizations for Asthma 2 County
#> 6 99 Annual Number of Hospitalizations for Asthma 2 Countydata_tpm.geo <- get_data(measure=99,
strat_level = "ST",
geoItems = c("CO", "ME", "FL"),
temporalItems = c(2014:2018))
#> Building API call for measure: 99 with stratification: State.
#> Retrieving data...
#> Done
head(data_tpm.geo[[1]])
#> geo geoId geoAbbreviation parentGeographicTypeId parentGeo parentGeoId
#> 1 Colorado 08 StateAbbreviation NA NA NA
#> 2 Colorado 08 StateAbbreviation NA NA NA
#> 3 Colorado 08 StateAbbreviation NA NA NA
#> 4 Colorado 08 StateAbbreviation NA NA NA
#> 5 Colorado 08 StateAbbreviation NA NA NA
#> 6 Florida 12 StateAbbreviation NA NA NA
#> parentGeoAbbreviation temporalTypeId temporal temporalDescription
#> 1 NA 1 2014 Single Year
#> 2 NA 1 2015 Single Year
#> 3 NA 1 2016 Single Year
#> 4 NA 1 2017 Single Year
#> 5 NA 1 2018 Single Year
#> 6 NA 1 2014 Single Year
#> temporalColumnName temporalRollingColumnName temporalId minimumTemporal
#> 1 ReportYear RollingYearCount 2014 NA
#> 2 ReportYear RollingYearCount 2015 NA
#> 3 ReportYear RollingYearCount 2016 NA
#> 4 ReportYear RollingYearCount 2017 NA
#> 5 ReportYear RollingYearCount 2018 NA
#> 6 ReportYear RollingYearCount 2014 NA
#> minimumTemporalId parentTemporalTypeId parentTemporalType parentTemporal
#> 1 NA NA NA NA
#> 2 NA NA NA NA
#> 3 NA NA NA NA
#> 4 NA NA NA NA
#> 5 NA NA NA NA
#> 6 NA NA NA NA
#> parentTemporalId date dataValue suppressionFlag confidenceIntervalLow
#> 1 NA 2014 3979 0 NA
#> 2 NA 2015 3170 0 NA
#> 3 NA 2016 2484 0 NA
#> 4 NA 2017 2367 0 NA
#> 5 NA 2018 2236 0 NA
#> 6 NA 2014 28014 0 NA
#> confidenceIntervalHigh confidenceIntervalName standardError standardErrorName
#> 1 NA NA NA NA
#> 2 NA NA NA NA
#> 3 NA NA NA NA
#> 4 NA NA NA NA
#> 5 NA NA NA NA
#> 6 NA NA NA NA
#> secondaryValue secondaryValueName descriptiveValue descriptiveValueName
#> 1 NA NA NA NA
#> 2 NA NA NA NA
#> 3 NA NA NA NA
#> 4 NA NA NA NA
#> 5 NA NA NA NA
#> 6 NA NA NA NA
#> includeDescriptiveValueName category categoryName title
#> 1 NA NA NA Colorado
#> 2 NA NA NA Colorado
#> 3 NA NA NA Colorado
#> 4 NA NA NA Colorado
#> 5 NA NA NA Colorado
#> 6 NA NA NA Florida
#> confidenceIntervalLowName parentMinimumTemporal parentMinimumTemporalId
#> 1 NA NA
#> 2 NA NA
#> 3 NA NA
#> 4 NA NA
#> 5 NA NA
#> 6 NA NA
#> measureId measureName geo_typeID Geo_Type
#> 1 99 Annual Number of Hospitalizations for Asthma 1 State
#> 2 99 Annual Number of Hospitalizations for Asthma 1 State
#> 3 99 Annual Number of Hospitalizations for Asthma 1 State
#> 4 99 Annual Number of Hospitalizations for Asthma 1 State
#> 5 99 Annual Number of Hospitalizations for Asthma 1 State
#> 6 99 Annual Number of Hospitalizations for Asthma 1 StateThis repository constitutes a work of the United States Government and is not subject to domestic copyright protection under 17 USC § 105. This repository is in the public domain within the United States, and copyright and related rights in the work worldwide are waived through the CC0 1.0 Universal public domain dedication. All contributions to this repository will be released under the CC0 dedication. By submitting a pull request you are agreeing to comply with this waiver of copyright interest.
The repository utilizes code licensed under the terms of the Apache Software License and therefore is licensed under ASL v2 or later.
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