The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
This document gets illustrates some of the helper functions in cimir.
First, simply load the cimir library:
library(cimir)In this vignette, we’ll use some example data from the Markleeville station (#246). The station metadata can be retrieved with cimis_station():
station.meta = cimis_station(246)
print(station.meta)| StationNbr | Name | City | RegionalOffice | County | ConnectDate | DisconnectDate | IsActive | IsEtoStation | Elevation | GroundCover | HmsLatitude | HmsLongitude | ZipCodes | SitingDesc |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 246 | Markleeville | Markleeville | North Central Region Office | Alpine | 6/13/2014 | 12/31/2050 | True | True | 5517 | Grass | 38º46’24N / 38.773409 | -119º47’31W / -119.791930 | 96120 | |
| 246 | Markleeville | Markleeville | North Central Region Office | Alpine | 6/13/2014 | 12/31/2050 | True | True | 5517 | Grass | 38º46’24N / 38.773409 | -119º47’31W / -119.791930 | 96133 |
Notice that the station latitude and longitude is provided as a text string, in both Hour Minute Second (HMMS) and Decimal Degree (DD) format. We can extract one or the other of these formats using cimis_format_location():
station.meta = cimis_format_location(station.meta, "DD")
head(station.meta)| StationNbr | Name | City | RegionalOffice | County | ConnectDate | DisconnectDate | IsActive | IsEtoStation | Elevation | GroundCover | Latitude | Longitude | ZipCodes | SitingDesc |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 246 | Markleeville | Markleeville | North Central Region Office | Alpine | 6/13/2014 | 12/31/2050 | True | True | 5517 | Grass | 38.77341 | -119.7919 | 96120 | |
| 246 | Markleeville | Markleeville | North Central Region Office | Alpine | 6/13/2014 | 12/31/2050 | True | True | 5517 | Grass | 38.77341 | -119.7919 | 96133 |
Now let’s retrieve some data with cimis_data():
station.data = cimis_data(246, "2017-04-01", "2017-04-30",
c("day-air-tmp-avg", "hly-air-tmp"))
head(station.data)| Name | Type | Owner | Date | Julian | Station | Standard | ZipCodes | Scope | Item | Value | Qc | Unit | Hour |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| cimis | station | water.ca.gov | 2017-04-01 | 91 | 246 | english | 96120, 96133 | daily | DayAirTmpAvg | 42.8 | (F) | NA | |
| cimis | station | water.ca.gov | 2017-04-02 | 92 | 246 | english | 96120, 96133 | daily | DayAirTmpAvg | 45.7 | (F) | NA | |
| cimis | station | water.ca.gov | 2017-04-03 | 93 | 246 | english | 96120, 96133 | daily | DayAirTmpAvg | 41.1 | (F) | NA | |
| cimis | station | water.ca.gov | 2017-04-04 | 94 | 246 | english | 96120, 96133 | daily | DayAirTmpAvg | 47.0 | (F) | NA | |
| cimis | station | water.ca.gov | 2017-04-05 | 95 | 246 | english | 96120, 96133 | daily | DayAirTmpAvg | 52.4 | (F) | NA | |
| cimis | station | water.ca.gov | 2017-04-06 | 96 | 246 | english | 96120, 96133 | daily | DayAirTmpAvg | 48.9 | (F) | NA |
Notice that hourly data returns timestamps in two columns “Date” and “Hour”. Furthermore, since we requested both a daily item and an hourly item, the daily item records have NA values for the “Hour” column. We can collapse these columns into a single datetime column using cimis_to_datetime():
station.data = cimis_to_datetime(station.data)
head(station.data)| Name | Type | Owner | Datetime | Julian | Station | Standard | ZipCodes | Scope | Item | Value | Qc | Unit |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| cimis | station | water.ca.gov | 2017-04-01 00:00:00 | 91 | 246 | english | 96120, 96133 | daily | DayAirTmpAvg | 42.8 | (F) | |
| cimis | station | water.ca.gov | 2017-04-02 00:00:00 | 92 | 246 | english | 96120, 96133 | daily | DayAirTmpAvg | 45.7 | (F) | |
| cimis | station | water.ca.gov | 2017-04-03 00:00:00 | 93 | 246 | english | 96120, 96133 | daily | DayAirTmpAvg | 41.1 | (F) | |
| cimis | station | water.ca.gov | 2017-04-04 00:00:00 | 94 | 246 | english | 96120, 96133 | daily | DayAirTmpAvg | 47.0 | (F) | |
| cimis | station | water.ca.gov | 2017-04-05 00:00:00 | 95 | 246 | english | 96120, 96133 | daily | DayAirTmpAvg | 52.4 | (F) | |
| cimis | station | water.ca.gov | 2017-04-06 00:00:00 | 96 | 246 | english | 96120, 96133 | daily | DayAirTmpAvg | 48.9 | (F) |
Note that a time of 00:00:00 is used for daily records.
The CIMIS Web API has fairly conservative limitations on the number of records you can query at once. Large queries can be split automatically into a series of smaller queries using cimis_split_queries:
queries = cimis_split_query(247, "2017-04-01", "2018-04-30",
c("day-air-tmp-avg", "hly-air-tmp"))
queries
#> # A tibble: 7 x 4
#> start.date end.date items targets
#> <date> <date> <list> <list>
#> 1 2017-04-01 2018-04-30 <chr [1]> <dbl [1]>
#> 2 2017-04-01 2017-06-04 <chr [1]> <dbl [1]>
#> 3 2017-06-05 2017-08-09 <chr [1]> <dbl [1]>
#> 4 2017-08-10 2017-10-14 <chr [1]> <dbl [1]>
#> 5 2017-10-15 2017-12-18 <chr [1]> <dbl [1]>
#> 6 2017-12-19 2018-02-22 <chr [1]> <dbl [1]>
#> 7 2018-02-23 2018-04-30 <chr [1]> <dbl [1]>The queries can then be run in sequence using e.g. mapply() or purrr::pmap():
purrr::pmap_dfr(queries, cimis_data)Note that the CIMIS API may reject your requests if you submit too many queries in a short period of time.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.