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Project Status: Active - The project has reached a stable, usable state and is being actively developed. R build status CRAN status Lifecycle: stable

EPHTrackR

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

Installation

#install development version from Github
#install devtools first if you haven't previously - install.packages("devtools")
devtools::install_github("CDCgov/EPHTrackR", 
                         dependencies = TRUE)

Load package

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/.

Saving a Tracking API token

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")

Full measure inventory

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)

Viewing content area, indicator, and measure names

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).

List content areas available

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 Poisoning

List indicators in specified content area(s)

ind_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 Index

List measures in specified indicator(s) and/or content area(s)

meas_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, flooding

Viewing available geographic and temporal types and items for specified measures

Measures 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.

List geographic types available for specified measures

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 Available

List geographic items available for specified measures

This 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          2

List temporal items available for specified measures

Measures 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          2

Viewing available Advanced Options for data stratification

In 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.

List available stratification levels for a measure

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          2

List available stratification types for a measure

If 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       2

Accessing Tracking Network data

You 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.

Downloading state-level measure data

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    State

Downloading measure data with advanced options

The 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    State

Downloading measure data with advanced options and specific geographies

You 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   County

Downloading measure data with specific geographies and temporal periods selected

data_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    State

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