This document gets you up and running with cimir, an R interface to the California Irrigiation Management Information System (CIMIS) Web API. In order to use this package, you will need to create a CIMIS account and request a web services AppKey.
First, simply load the cimir library:
If you want cimir to automatically use an existing AppKey when you attach the library, you can either set the option cimir.appkey in your .Rprofile or create an environment variable CIMIS_APPKEY. To set an AppKey for a single session, use set_key():
There are two types of CIMIS queries: station queries and data queries. Station queries return metadata on stations, while data queries return environmental monitoring data. For a list of possible data items, use the function data_items():
data_items()
#> Item Name Class Provider
#> 1 day-air-tmp-avg Average Air Temperature Daily WSN
#> 2 day-air-tmp-avg Average Air Temperature Daily WSN
#> 3 day-air-tmp-min Minimum Air Temperature Daily WSN
#> 4 day-dew-pnt Dew Point Daily WSN
#> 5 day-eto CIMIS ETo Daily WSN
#> 6 day-asce-eto ASCE ETo Daily WSN & SCS
#> 7 day-asce-etr ASCE ETr Daily WSN
#> 8 day-precip Precipitation Daily WSN
#> 9 day-rel-hum-avg Average Relative Humidity Daily WSN
#> 10 day-rel-hum-max Maximum Relative Humidity Daily WSN
#> 11 day-rel-hum-min Minimum Relative Humidity Daily WSN
#> 12 day-soil-tmp-avg Average Soil Temperature Daily WSN
#> 13 day-soil-tmp-max Maximum Soil Temperature Daily WSN
#> 14 day-soil-tmp-min Minimum Soil Temperature Daily WSN
#> 15 day-sol-rad-avg Average Solar Radiation Daily WSN & SCS
#> 16 day-sol-rad-net Net Solar Radiation Daily WSN
#> 17 day-vap-pres-max Maximum Vapor Pressure Daily WSN
#> 18 day-vap-pres-avg Average Vapor Pressure Daily WSN
#> 19 day-wind-ene Wind East-North-East Daily WSN
#> 20 day-wind-ese Wind East-South-East Daily WSN
#> 21 day-wind-nne Wind North-North-East Daily WSN
#> 22 day-wind-nnw Wind North-North-West Daily WSN
#> 23 day-wind-run Wind Run Daily WSN
#> 24 day-wind-spd-avg Average Wind Speed Daily WSN
#> 25 day-wind-ssw Wind South-South-West Daily WSN
#> 26 day-wind-wnw Wind West-North-West Daily WSN
#> 27 day-wind-wsw Wind West-South-West Daily WSN
#> 28 hly-air-tmp Air Temperature Hourly WSN
#> 29 hly-dew-pnt Dew Point Hourly WSN
#> 30 hly-eto CIMIS ETo Hourly WSN
#> 31 hly-net-rad Net Radiation Hourly WSN
#> 32 hly-asce-eto ASCE ETo Hourly WSN
#> 33 hly-asce-etr ASCE ETr Hourly WSN
#> 34 hly-precip Precipitation Hourly WSN
#> 35 hly-rel-hum Relative Humidity Hourly WSN
#> 36 hly-res-wind Resultant Wind Hourly WSN
#> 37 hly-soil-tmp Soil Temperature Hourly WSN
#> 38 hly-sol-rad Solar Radiation Hourly WSN
#> 39 hly-vap-pres Vapor Pressure Hourly WSN
#> 40 hly-wind-dir Wind Direction Hourly WSN
#> 41 hly-wind-spd Wind Speed Hourly WSNTo get a list of station IDs, use get_station():
get_station()
#> # A tibble: 1,391 x 15
#> StationNbr Name City RegionalOffice County ConnectDate DisconnectDate
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 1 Fres~ Fres~ South Central~ Fresno 6/7/1982 9/25/1988
#> 2 1 Fres~ Fres~ South Central~ Fresno 6/7/1982 9/25/1988
#> 3 1 Fres~ Fres~ South Central~ Fresno 6/7/1982 9/25/1988
#> 4 1 Fres~ Fres~ South Central~ Fresno 6/7/1982 9/25/1988
#> 5 1 Fres~ Fres~ South Central~ Fresno 6/7/1982 9/25/1988
#> 6 1 Fres~ Fres~ South Central~ Fresno 6/7/1982 9/25/1988
#> 7 1 Fres~ Fres~ South Central~ Fresno 6/7/1982 9/25/1988
#> 8 1 Fres~ Fres~ South Central~ Fresno 6/7/1982 9/25/1988
#> 9 1 Fres~ Fres~ South Central~ Fresno 6/7/1982 9/25/1988
#> 10 1 Fres~ Fres~ South Central~ Fresno 6/7/1982 9/25/1988
#> # ... with 1,381 more rows, and 8 more variables: IsActive <chr>,
#> # IsEtoStation <chr>, Elevation <chr>, GroundCover <chr>,
#> # HmsLatitude <chr>, HmsLongitude <chr>, ZipCodes <chr>,
#> # SitingDesc <chr>To get metadata for a subset of stations, simply pass a vector of station IDs. You can also query metadata for stations associated with a particular zipcode (or spatial zipcode) get_station_zipcode() and get_station_spatial_zipcode():
get_station_zipcode()
#> # A tibble: 1,391 x 5
#> StationNbr ZipCode ConnectDate DisconnectDate IsActive
#> <int> <chr> <chr> <chr> <chr>
#> 1 99 90401 12/11/1992 5/8/2050 True
#> 2 99 90402 12/11/1992 5/8/2050 True
#> 3 99 90403 12/11/1992 5/8/2050 True
#> 4 99 90404 12/11/1992 5/8/2050 True
#> 5 99 90405 12/11/1992 5/8/2050 True
#> 6 99 90406 12/11/1992 5/8/2050 True
#> 7 99 90407 12/11/1992 5/8/2050 True
#> 8 99 90408 12/11/1992 5/8/2050 True
#> 9 99 90409 12/11/1992 5/8/2050 True
#> 10 99 90410 12/11/1992 5/8/2050 True
#> # ... with 1,381 more rows
get_station_spatial_zipcode()
#> # A tibble: 1,715 x 4
#> ZipCode ConnectDate DisconnectDate IsActive
#> <chr> <chr> <chr> <chr>
#> 1 85328 2/20/2003 12/31/2030 True
#> 2 85334 2/20/2003 12/31/2030 True
#> 3 85344 2/20/2003 12/31/2030 True
#> 4 85364 2/20/2003 12/31/2030 True
#> 5 85365 2/20/2003 12/31/2030 True
#> 6 86440 2/20/2003 12/31/2030 True
#> 7 89003 2/20/2003 12/31/2030 True
#> 8 89010 2/20/2003 12/31/2030 True
#> 9 89013 2/20/2003 12/31/2030 True
#> 10 89019 2/20/2003 12/31/2030 True
#> # ... with 1,705 more rowsData queries also support zipcodes, spatial coordinates (latitude and longitude, in decimal degrees) and street addresses, as well as options for data units and data providers. See ?get_data and the CIMIS Web API documentation for more information. Once the stations or regions of interest are identified, you can retrieve data using get_data():
get_data(targets = 170, start.date = "2018-09-25", end.date = "2018-10-01")
#> # A tibble: 98 x 13
#> Name Type Owner Date Julian Station Standard ZipCodes Scope
#> <chr> <chr> <chr> <date> <int> <chr> <chr> <chr> <chr>
#> 1 cimis stat~ wate~ 2018-09-25 268 170 english 94529, ~ daily
#> 2 cimis stat~ wate~ 2018-09-25 268 170 english 94529, ~ daily
#> 3 cimis stat~ wate~ 2018-09-25 268 170 english 94529, ~ daily
#> 4 cimis stat~ wate~ 2018-09-25 268 170 english 94529, ~ daily
#> 5 cimis stat~ wate~ 2018-09-25 268 170 english 94529, ~ daily
#> 6 cimis stat~ wate~ 2018-09-25 268 170 english 94529, ~ daily
#> 7 cimis stat~ wate~ 2018-09-25 268 170 english 94529, ~ daily
#> 8 cimis stat~ wate~ 2018-09-25 268 170 english 94529, ~ daily
#> 9 cimis stat~ wate~ 2018-09-25 268 170 english 94529, ~ daily
#> 10 cimis stat~ wate~ 2018-09-25 268 170 english 94529, ~ daily
#> # ... with 88 more rows, and 4 more variables: Item <chr>, Value <dbl>,
#> # Qc <chr>, Unit <chr>The data are returned in tidy format, i.e. the "Value" column displays the recorded value for the data item specified in the "Item"column. The CIMIS Web API returns a predefined set of data items by default, but custom item sets can also be passed to get_data().
When hourly data is retrieved, an additional column "Hour" is returned. The to_datetime() function provides a quick way to convert these two columns to a single "Datetime" column. Note that when mixed daily and hourly data is present in the dataframe, the daily values are assumed to correspond to midnight (00:00).