The goal of cdssr is to
provide functions that help R users to navigate, explore, and make
requests to the CDSS REST API web
service.
The Coloradoās Decision Support Systems (CDSS) is a water management system created and developed by the Colorado Water Conservation Board (CWCB) and the Colorado Division of Water Resources (DWR).
Thank you to those at CWCB and DWR for providing an accessible and well documented REST API!
See
cdsspy, for the Python version of this package
You can install the development version of cdssr from
GitHub with:
# install.packages("devtools")
devtools::install_github("anguswg-ucsb/cdssr")# Load package
library(cdssr)Below is a table of all of the CDSS API endpoints that cdssr
provides functions for.
| - | Function | Description | Endpoint |
|---|---|---|---|
| 1 | get_admin_calls() | Returns list of active/historic administrative calls | administrativecalls/active |
| 2 | get_structures() | Returns list of administrative structures | structures |
| 3 | get_structures_divrec_ts() | Returns list of diversion/release records based on WDID | structures/divrec/divrec |
| 4 | get_structures_stage_ts() | Returns list of stage/volume records based on WDID | structures/divrec/stagevolume |
| 5 | get_climate_stations() | Returns Climate Stations | climatedata/climatestations |
| 6 | get_climate_ts() | Returns Climate Station Time Series (day, month, year) | climatedata/climatestationts |
| 7 | get_climate_frostdates() | Returns Climate Station Frost Dates | climatedata/climatestationfrostdates |
| 8 | get_gw_gplogs_wells() | Returns Groundwater GeophysicalLogsWell from filters | groundwater/geophysicallogs/ |
| 9 | get_gw_gplogs_geologpicks() | Returns Groundwater Geophysical Log picks by well ID | groundwater/geophysicallogs/ |
| 10 | get_gw_wl_wells() | Returns WaterLevelsWell from filters | groundwater/waterlevels/wells |
| 11 | get_gw_wl_wellmeasures() | Returns Groundwater Measurements | groundwater/waterlevels/wellmeasurements |
| 12 | get_reference_tbl() | Returns reference tables list | referencetables/ |
| 13 | get_sw_stations() | Returns Surface Water Station info | surfacewater/surfacewaterstations |
| 14 | get_sw_ts() | Returns Surface Water Time Series | surfacewater/surfacewaterts |
| 15 | get_telemetry_stations() | Returns telemetry stations and their most recent parameter reading | telemetrystations/telemetrystation |
| 16 | get_telemetry_ts() | Returns telemetry time series data (raw, hour, day) | telemetrystations/telemetrytimeseries |
| 17 | get_water_rights_netamount() | Returns current status of a water right based on all of its court decreed actions | waterrights/netamount |
| 18 | get_water_rights_trans() | Returns court decreed actions that affect amount and use(s) that can be used by each water right | waterrights/transaction |
| 19 | get_call_analysis_wdid() | Performs a call analysis that returns a time series showing the percentage of each day that the specified WDID and priority was out of priority and the downstream call in priority | analysisservices/callanalysisbywdid |
| 20 | get_source_route_framework() | Returns the DWR source route framework reference table for the criteria specified | analysisservices/watersourcerouteframework |
| 21 | get_parceluse_ts() | Returns list of Parcel Use Time Series | structures/parcelusets |
To check out the various CDSS API endpoint, cdssr comes packaged
with an api_endpoint table which details endpoint names,
descriptions, and relevant URLs.
dplyr::tibble(cdssr::api_endpoints)
#> # A tibble: 61 Ć 5
#> resource descripā¦Ā¹ endpoā¦Ā² url endpoā¦Ā³
#> <chr> <chr> <chr> <chr> <chr>
#> 1 Active Administrative Calls Generator Returns ā¦ api/v2ā¦ httpā¦ https:ā¦
#> 2 Historical Administrative Calls Generator Returns ā¦ api/v2ā¦ httpā¦ https:ā¦
#> 3 Water Source Route Analysis Info Generator Returns ā¦ api/v2ā¦ httpā¦ https:ā¦
#> 4 Water Source Route Framework Info Generator Returns ā¦ api/v2ā¦ httpā¦ https:ā¦
#> 5 Call Analysis Structure Info Generator Performsā¦ api/v2ā¦ httpā¦ https:ā¦
#> 6 Call Analysis Stream Mile Info Generator Performsā¦ api/v2ā¦ httpā¦ https:ā¦
#> 7 Climate Stations Generator Returns ā¦ api/v2ā¦ httpā¦ https:ā¦
#> 8 Climate Station Data Types Generator Returns ā¦ api/v2ā¦ httpā¦ https:ā¦
#> 9 Climate Station Time Series - Day Generator Returns ā¦ api/v2ā¦ httpā¦ https:ā¦
#> 10 Climate Station Time Series - Month Generator Returns ā¦ api/v2ā¦ httpā¦ https:ā¦
#> # ā¦ with 51 more rows, and abbreviated variable names Ā¹ādescription, Ā²āendpoint,
#> # Ā³āendpoint_urlcdssr also comes packaged with a resource_meta dataset which
provides meta data for the data retrieved by cdssr (via the CDSS
REST API)
dplyr::tibble(cdssr::resource_meta)
#> # A tibble: 1,031 Ć 5
#> endpoint name descrā¦Ā¹ type endpoā¦Ā²
#> <chr> <chr> <chr> <chr> <chr>
#> 1 api/v2/administrativecalls/active boundingStructureLatā¦ Latituā¦ deciā¦ https:ā¦
#> 2 api/v2/administrativecalls/active boundingStructureLonā¦ Longitā¦ deciā¦ https:ā¦
#> 3 api/v2/administrativecalls/active boundingStructureName Structā¦ striā¦ https:ā¦
#> 4 api/v2/administrativecalls/active boundingWdid WDID oā¦ striā¦ https:ā¦
#> 5 api/v2/administrativecalls/active callNumber Uniqueā¦ inteā¦ https:ā¦
#> 6 api/v2/administrativecalls/active callType The tyā¦ striā¦ https:ā¦
#> 7 api/v2/administrativecalls/active dateTimeReleased Date aā¦ date https:ā¦
#> 8 api/v2/administrativecalls/active dateTimeSet Date aā¦ date https:ā¦
#> 9 api/v2/administrativecalls/active division DWR Waā¦ inteā¦ https:ā¦
#> 10 api/v2/administrativecalls/active locationStructureLatā¦ Latituā¦ deciā¦ https:ā¦
#> # ā¦ with 1,021 more rows, and abbreviated variable names Ā¹ādescription,
#> # Ā²āendpoint_url
The get_reference_tbl() function will return tables that makes it
easier to know what information should be supplied to the data retrieval
functions in cdssr. For more information on the exact reference
tables click
here.
Letās locate the parameters available at telemetry stations.
# available parameters for telemetry stations
telemetry_params <- cdssr::get_reference_tbl(
table_name = "telemetryparams"
)
#> Retrieving telemetry parameter reference table#> # A tibble: 46 Ć 1
#> parameter
#> <chr>
#> 1 AIRTEMP
#> 2 BAR_P
#> 3 BATTERY
#> 4 COND
#> 5 D1
#> 6 D2
#> 7 DISCHRG
#> 8 DISCHRG1
#> 9 DISCHRG2
#> 10 DISCHRG3
#> # ā¦ with 36 more rows
cdssr provides functions for locating
structures/stations/wells/sites by providing a spatial extent, water
district, division, county, designated basin, or management district to
the functions in the table below. Site data can also be retrieved by
providing the site specific abbreviations, GNIS IDs, USGS IDs, WDIDs, or
Well IDs.
| - | Function | Description | Endpoint |
|---|---|---|---|
| 1 | get_structures() | Returns list of administrative structures | structures |
| 2 | get_climate_stations() | Returns Climate Stations | climatedata/climatestations |
| 3 | get_gw_gplogs_wells() | Returns Groundwater GeophysicalLogsWell from filters | groundwater/geophysicallogs/ |
| 4 | get_gw_wl_wells() | Returns WaterLevelsWell from filters | groundwater/waterlevels/wells |
| 5 | get_sw_stations() | Returns Surface Water Station info | surfacewater/surfacewaterstations |
| 6 | get_telemetry_stations() | Returns telemetry stations and their most recent parameter reading | telemetrystations/telemetrystation |
# identify telemetry stations in Boulder county
stations <- cdssr::get_telemetry_stations(
county = "Boulder"
)
#> Retrieving telemetry station data#> # A tibble: 110 Ć 35
#> divisā¦Ā¹ waterā¦Ā² county statiā¦Ā³ data_ā¦ā“ data_ā¦āµ waterā¦ā¶ gnis_id streaā¦ā· abbrev
#> <int> <int> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <chr>
#> 1 1 5 BOULDā¦ "CLOUGā¦ NCWCD Northeā¦ SAINT ā¦ 002050ā¦ NA 05005ā¦
#> 2 1 6 BOULDā¦ "ANDERā¦ DWR Co. Diā¦ BOULDEā¦ 001783ā¦ 23.6 ANDDIā¦
#> 3 1 6 BOULDā¦ "ANDREā¦ DWR Co. Diā¦ SOUTH ā¦ 001809ā¦ 1.5 ANFDIā¦
#> 4 1 6 BOULDā¦ "BASELā¦ DWR Co. Diā¦ BOULDEā¦ 001783ā¦ 19.2 BASOUā¦
#> 5 1 6 BOULDā¦ "BOULDā¦ NCWCD Northeā¦ BOULDEā¦ 001783ā¦ 15.5 BCSCBā¦
#> 6 1 5 BOULDā¦ "BOULDā¦ NCWCD Northeā¦ LEFT Hā¦ 001782ā¦ 8.66 BFCINā¦
#> 7 1 5 BOULDā¦ "BOULDā¦ NCWCD Northeā¦ SAINT ā¦ 002050ā¦ 31.7 BFCLYā¦
#> 8 1 5 BOULDā¦ "BOULDā¦ NCWCD Northeā¦ SAINT ā¦ 002050ā¦ 31.7 BFCLYā¦
#> 9 1 5 BOULDā¦ "LYONSā¦ DWR Co. Diā¦ SOUTH ā¦ 001782ā¦ 0.03 BHNPRā¦
#> 10 1 6 BOULDā¦ "BOULDā¦ DWR Co. Diā¦ BOULDEā¦ 001783ā¦ 22.3 BLDLHā¦
#> # ā¦ with 100 more rows, 25 more variables: usgs_station_id <chr>,
#> # station_status <chr>, station_type <chr>, structure_type <chr>,
#> # meas_date_time <dttm>, parameter <chr>, stage <dbl>, meas_value <dbl>,
#> # units <chr>, flag_a <chr>, flag_b <chr>, contr_area <dbl>,
#> # drain_area <dbl>, huc10 <chr>, utm_x <dbl>, utm_y <dbl>, latitude <dbl>,
#> # longitude <dbl>, location_accuracy <chr>, wdid <chr>, modified <chr>,
#> # more_information <chr>, station_por_start <dttm>, station_por_end <dttm>, ā¦
# identify telemetry stations 10 miles around a point
stations <- cdssr::get_telemetry_stations(
aoi = c(-105.358164, 40.092608),
radius = 10
)#> # A tibble: 110 Ć 35
#> divisā¦Ā¹ waterā¦Ā² county statiā¦Ā³ data_ā¦ā“ data_ā¦āµ waterā¦ā¶ gnis_id streaā¦ā· abbrev
#> <int> <int> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <chr>
#> 1 1 5 BOULDā¦ "CLOUGā¦ NCWCD Northeā¦ SAINT ā¦ 002050ā¦ NA 05005ā¦
#> 2 1 6 BOULDā¦ "ANDERā¦ DWR Co. Diā¦ BOULDEā¦ 001783ā¦ 23.6 ANDDIā¦
#> 3 1 6 BOULDā¦ "ANDREā¦ DWR Co. Diā¦ SOUTH ā¦ 001809ā¦ 1.5 ANFDIā¦
#> 4 1 6 BOULDā¦ "BASELā¦ DWR Co. Diā¦ BOULDEā¦ 001783ā¦ 19.2 BASOUā¦
#> 5 1 6 BOULDā¦ "BOULDā¦ NCWCD Northeā¦ BOULDEā¦ 001783ā¦ 15.5 BCSCBā¦
#> 6 1 5 BOULDā¦ "BOULDā¦ NCWCD Northeā¦ LEFT Hā¦ 001782ā¦ 8.66 BFCINā¦
#> 7 1 5 BOULDā¦ "BOULDā¦ NCWCD Northeā¦ SAINT ā¦ 002050ā¦ 31.7 BFCLYā¦
#> 8 1 5 BOULDā¦ "BOULDā¦ NCWCD Northeā¦ SAINT ā¦ 002050ā¦ 31.7 BFCLYā¦
#> 9 1 5 BOULDā¦ "LYONSā¦ DWR Co. Diā¦ SOUTH ā¦ 001782ā¦ 0.03 BHNPRā¦
#> 10 1 6 BOULDā¦ "BOULDā¦ DWR Co. Diā¦ BOULDEā¦ 001783ā¦ 22.3 BLDLHā¦
#> # ā¦ with 100 more rows, 25 more variables: usgs_station_id <chr>,
#> # station_status <chr>, station_type <chr>, structure_type <chr>,
#> # meas_date_time <dttm>, parameter <chr>, stage <dbl>, meas_value <dbl>,
#> # units <chr>, flag_a <chr>, flag_b <chr>, contr_area <dbl>,
#> # drain_area <dbl>, huc10 <chr>, utm_x <dbl>, utm_y <dbl>, latitude <dbl>,
#> # longitude <dbl>, location_accuracy <chr>, wdid <chr>, modified <chr>,
#> # more_information <chr>, station_por_start <dttm>, station_por_end <dttm>, ā¦
A masking operation is performed when a location search is done using a polygon. This ensures that the function only returns points that are within the given polygon.
# load AOI to retrieve county polygons
library(AOI)
# identify telemetry stations 15 miles around the centroid of a polygon
stations <- cdssr::get_telemetry_stations(
aoi = AOI::aoi_get(county = "Boulder", state = "CO"),
radius = 15
)
#> Retrieving telemetry station data
#> Location search:
#> Latitude: 40.0843975
#> Longitude: -105.345242774525
#> Radius (miles): 15#> # A tibble: 108 Ć 35
#> divisā¦Ā¹ waterā¦Ā² county statiā¦Ā³ data_ā¦ā“ data_ā¦āµ waterā¦ā¶ gnis_id streaā¦ā· abbrev
#> <int> <int> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <chr>
#> 1 1 5 BOULDā¦ "CLOUGā¦ NCWCD Northeā¦ SAINT ā¦ 002050ā¦ NA 05005ā¦
#> 2 1 6 BOULDā¦ "ANDERā¦ DWR Co. Diā¦ BOULDEā¦ 001783ā¦ 23.6 ANDDIā¦
#> 3 1 6 BOULDā¦ "ANDREā¦ DWR Co. Diā¦ SOUTH ā¦ 001809ā¦ 1.5 ANFDIā¦
#> 4 1 6 BOULDā¦ "BASELā¦ DWR Co. Diā¦ BOULDEā¦ 001783ā¦ 19.2 BASOUā¦
#> 5 1 6 BOULDā¦ "BOULDā¦ NCWCD Northeā¦ BOULDEā¦ 001783ā¦ 15.5 BCSCBā¦
#> 6 1 5 BOULDā¦ "BOULDā¦ NCWCD Northeā¦ LEFT Hā¦ 001782ā¦ 8.66 BFCINā¦
#> 7 1 5 BOULDā¦ "BOULDā¦ NCWCD Northeā¦ SAINT ā¦ 002050ā¦ 31.7 BFCLYā¦
#> 8 1 5 BOULDā¦ "BOULDā¦ NCWCD Northeā¦ SAINT ā¦ 002050ā¦ 31.7 BFCLYā¦
#> 9 1 5 BOULDā¦ "LYONSā¦ DWR Co. Diā¦ SOUTH ā¦ 001782ā¦ 0.03 BHNPRā¦
#> 10 1 6 BOULDā¦ "BOULDā¦ DWR Co. Diā¦ BOULDEā¦ 001783ā¦ 22.3 BLDLHā¦
#> # ā¦ with 98 more rows, 25 more variables: usgs_station_id <chr>,
#> # station_status <chr>, station_type <chr>, structure_type <chr>,
#> # meas_date_time <dttm>, parameter <chr>, stage <dbl>, meas_value <dbl>,
#> # units <chr>, flag_a <chr>, flag_b <chr>, contr_area <dbl>,
#> # drain_area <dbl>, huc10 <chr>, utm_x <dbl>, utm_y <dbl>, latitude <dbl>,
#> # longitude <dbl>, location_accuracy <chr>, wdid <chr>, modified <chr>,
#> # more_information <chr>, station_por_start <dttm>, station_por_end <dttm>, ā¦
This gif highlights the masking process that happens when the aoi
argument is given a polygon
The functions in the table below retrieve time series data from the various time series related CDSS API endpoints.
| - | Function | Description | Endpoint |
|---|---|---|---|
| 1 | get_structures_divrec_ts() | Returns list of diversion/release records based on WDID | structures/divrec/divrec |
| 2 | get_structures_stage_ts() | Returns list of stage/volume records based on WDID | structures/divrec/stagevolume |
| 3 | get_climate_ts() | Returns Climate Station Time Series (day, month, year) | climatedata/climatestationts |
| 4 | get_gw_wl_wellmeasures() | Returns Groundwater Measurements | groundwater/waterlevels/wellmeasurements |
| 5 | get_sw_ts() | Returns Surface Water Time Series | surfacewater/surfacewaterts |
| 6 | get_telemetry_ts() | Returns telemetry time series data (raw, hour, day) | telemetrystations/telemetrytimeseries |
| 7 | get_parceluse_ts() | Returns list of Parcel Use Time Series | structures/parcelusets |
We can then take a station abbreviations from the
get_telemetry_stations() call, a parameter from the
get_reference_tbl() call, and use this information as inputs into
the get_telemetry_ts() function.
The function below returns a dataframe of daily discharge for the āANDDITCOā site between 2015-2022.
# Daily discharge at "ANDDITCO" telemetry station
discharge_ts <- cdssr::get_telemetry_ts(
abbrev = "ANDDITCO", # Site abbreviation
parameter = "DISCHRG", # Desired parameter
start_date = "2015-01-01", # Starting date
end_date = "2022-01-01", # Ending date
timescale = "day" # select daily timescale
)
#> Retrieving telemetry station time series data (day - DISCHRG)#> # A tibble: 572 Ć 11
#> abbrev parameter meas_date meas_ā¦Ā¹ meas_ā¦Ā² flag_a flag_b meas_ā¦Ā³ modifā¦ā“
#> <chr> <chr> <chr> <dbl> <chr> <chr> <chr> <int> <chr>
#> 1 ANDDITCO DISCHRG 2020-05-07 ā¦ 3.05 cfs O <NA> 33 2020-0ā¦
#> 2 ANDDITCO DISCHRG 2020-05-08 ā¦ 3.04 cfs O <NA> 96 2020-0ā¦
#> 3 ANDDITCO DISCHRG 2020-05-09 ā¦ 2.98 cfs O <NA> 96 2020-0ā¦
#> 4 ANDDITCO DISCHRG 2020-05-10 ā¦ 2.95 cfs O <NA> 96 2020-0ā¦
#> 5 ANDDITCO DISCHRG 2020-05-11 ā¦ 2.95 cfs O <NA> 96 2020-0ā¦
#> 6 ANDDITCO DISCHRG 2020-05-12 ā¦ 2.95 cfs O <NA> 96 2020-0ā¦
#> 7 ANDDITCO DISCHRG 2020-05-13 ā¦ 2.95 cfs O <NA> 96 2020-0ā¦
#> 8 ANDDITCO DISCHRG 2020-05-14 ā¦ 2.95 cfs O <NA> 9 2020-0ā¦
#> 9 ANDDITCO DISCHRG 2020-05-15 ā¦ 2.95 cfs O <NA> 63 2020-0ā¦
#> 10 ANDDITCO DISCHRG 2020-05-16 ā¦ 2.95 cfs O <NA> 96 2020-0ā¦
#> # ā¦ with 562 more rows, 2 more variables: datetime <dttm>, timescale <chr>, and
#> # abbreviated variable names Ā¹āmeas_value, Ā²āmeas_unit, Ā³āmeas_count, ā“āmodified
Some of the CDSS API endpoints allow users to request data from multiple structures if you provide a list of IDs. If we want to get diversion data from multiple structure locations, weāll need to get a list of WDIDs. We can get a list WDIDs within a given area by:
- Executing a spatial search using
get_structures() - Selecting the WDIDs of interest from the search results
- Providing the WDIDs as a vector to
get_structures_divrec_ts()
Note: Data availability can vary between structures (i.e.Ā Missing data, not all structures have every data type/temporal resolution available, etc.)
# 1. Executing a spatial search
structures <- cdssr::get_structures(
aoi = c(-105.3578, 40.09244),
radius = 5
)
#> Retreiving administrative structures
#> Location search:
#> Latitude: 40.09244
#> Longitude: -105.3578
#> Radius (miles): 5
# 2. Selecting the WDID's of interest from our search results
ditch_wdids <-
structures %>%
dplyr::filter(ciu_code == "A", structure_type == "DITCH") %>%
.$wdid
# 3. Providing the WDID's as a vector to get_structures_divrec_ts()
diversion_rec <-
cdssr::get_structures_divrec_ts(
wdid = ditch_wdids,
wc_identifier = "diversion",
start_date = "1990-01-01",
end_date = "2022-01-01",
timescale = "month"
)
#> Retrieving monthly diversion data#> # A tibble: 495 Ć 12
#> wdid waterā¦Ā¹ wc_idā¦Ā² meas_ā¦Ā³ meas_ā¦ā“ data_ā¦āµ data_ā¦ā¶ meas_ā¦ā· obs_cā¦āø approā¦ā¹
#> <chr> <int> <chr> <chr> <int> <chr> <dbl> <chr> <chr> <chr>
#> 1 0500ā¦ 1.05e7 050056ā¦ Daily 18 1992-04 165. ACFT U Approvā¦
#> 2 0500ā¦ 1.05e7 050056ā¦ Daily 31 1992-05 745. ACFT U Approvā¦
#> 3 0500ā¦ 1.05e7 050056ā¦ Daily 30 1992-06 430. ACFT U Approvā¦
#> 4 0500ā¦ 1.05e7 050056ā¦ Daily 31 1992-07 372. ACFT U Approvā¦
#> 5 0500ā¦ 1.05e7 050056ā¦ Daily 31 1992-08 611. ACFT U Approvā¦
#> 6 0500ā¦ 1.05e7 050056ā¦ Daily 29 1992-09 315. ACFT U Approvā¦
#> 7 0500ā¦ 1.05e7 050056ā¦ Daily 29 1992-10 120. ACFT U Approvā¦
#> 8 0500ā¦ 1.05e7 050056ā¦ Daily 25 2004-04 244. ACFT U Approvā¦
#> 9 0500ā¦ 1.05e7 050056ā¦ Daily 40 2004-05 699. ACFT U Approvā¦
#> 10 0500ā¦ 1.05e7 050056ā¦ Daily 25 2004-06 294. ACFT U Approvā¦
#> # ā¦ with 485 more rows, 2 more variables: modified <chr>, datetime <date>, and
#> # abbreviated variable names Ā¹āwater_class_num, Ā²āwc_identifier,
#> # Ā³āmeas_interval, ā“āmeas_count, āµādata_meas_date, ā¶ādata_value, ā·āmeas_units,
#> # āøāobs_code, ā¹āapproval_status
The get_gw_() set of functions lets users make get requests to the
various CDSS API groundwater endpoints shown in the table below:
| - | Function | Endpoint |
|---|---|---|
| 1 | get_gw_wl_wellmeasures() | api/v2/groundwater/waterlevels/wellmeasurements |
| 2 | get_gw_wl_wells() | api/v2/groundwater/waterlevels/wells |
| 3 | get_gw_gplogs_wells() | api/v2/groundwater/geophysicallogs/wells |
| 4 | get_gw_gplogs_geologpicks() | api/v2/groundwater/geophysicallogs/geoplogpicks |
Here we will retrieve groundwater well measurement data for Well ID 1274 between 1990-2022.
# Request wellmeasurements endpoint (api/v2/groundwater/waterlevels/wellmeasurements)
well_measure <- cdssr::get_gw_wl_wellmeasures(
wellid = 1274,
start_date = "1990-01-01",
end_date = "2022-01-01"
)
#> Retrieving groundwater well measurements#> # A tibble: 1,469 Ć 18
#> well_id well_name divisā¦Ā¹ waterā¦Ā² county managā¦Ā³ desigā¦ā“ publiā¦āµ measuā¦ā¶
#> <int> <chr> <int> <int> <chr> <lgl> <lgl> <chr> <chr>
#> 1 1274 LSP-020 03N6ā¦ 1 2 WELD NA NA LOWER ā¦ 1990-0ā¦
#> 2 1274 LSP-020 03N6ā¦ 1 2 WELD NA NA LOWER ā¦ 1990-1ā¦
#> 3 1274 LSP-020 03N6ā¦ 1 2 WELD NA NA LOWER ā¦ 1991-0ā¦
#> 4 1274 LSP-020 03N6ā¦ 1 2 WELD NA NA LOWER ā¦ 1991-1ā¦
#> 5 1274 LSP-020 03N6ā¦ 1 2 WELD NA NA LOWER ā¦ 1992-0ā¦
#> 6 1274 LSP-020 03N6ā¦ 1 2 WELD NA NA LOWER ā¦ 1992-0ā¦
#> 7 1274 LSP-020 03N6ā¦ 1 2 WELD NA NA LOWER ā¦ 1992-1ā¦
#> 8 1274 LSP-020 03N6ā¦ 1 2 WELD NA NA LOWER ā¦ 1993-0ā¦
#> 9 1274 LSP-020 03N6ā¦ 1 2 WELD NA NA LOWER ā¦ 1993-1ā¦
#> 10 1274 LSP-020 03N6ā¦ 1 2 WELD NA NA LOWER ā¦ 1994-0ā¦
#> # ā¦ with 1,459 more rows, 9 more variables: depth_to_water <dbl>,
#> # measuring_point_above_land_surface <dbl>,
#> # depth_water_below_land_surface <dbl>, elevation_of_water <dbl>,
#> # delta <dbl>, data_source <chr>, published <chr>, modified <chr>,
#> # datetime <dttm>, and abbreviated variable names Ā¹ādivision, Ā²āwater_district,
#> # Ā³āmanagement_district, ā“ādesignated_basin, āµāpublication, ā¶āmeasurement_date
