library(azmetr)
library(lubridate)
#>
#> Attaching package: 'lubridate'
#> The following objects are masked from 'package:base':
#>
#> date, intersect, setdiff, unionRetrieving the most recent day
To retrieve the the most recent day of data for all stations simply
by calling az_daily() or az_hourly() without
any arguments. az_daily() retrieves daily summary data and
az_hourly() retrieves hourly data.
daily <- az_daily()
#> Querying data from 2024-07-22
#> Returning data from 2024-07-22
hourly <- az_hourly()
#> Querying most recent hour of data ...
#> Returning data from 2024-07-23 15:00
head(daily)
#> # A tibble: 6 × 75
#> meta_bat_volt_max meta_bat_volt_mean meta_bat_volt_min meta_needs_review
#> <dbl> <dbl> <dbl> <dbl>
#> 1 13.9 13.2 12.8 0
#> 2 13.7 12.9 12.6 0
#> 3 14.4 13.0 12.6 0
#> 4 13.7 12.9 12.5 0
#> 5 13.9 13.0 12.6 0
#> 6 13.7 13.0 12.6 0
#> # ℹ 71 more variables: meta_station_id <chr>, meta_station_name <chr>,
#> # meta_version <dbl>, chill_hours_0C <dbl>, chill_hours_20C <dbl>,
#> # chill_hours_32F <dbl>, chill_hours_45F <dbl>, chill_hours_68F <dbl>,
#> # chill_hours_7C <dbl>, date_doy <dbl>, date_year <dbl>, datetime <date>,
#> # dwpt_mean <dbl>, dwpt_meanF <dbl>, eto_azmet <dbl>, eto_azmet_in <dbl>,
#> # eto_pen_mon <dbl>, eto_pen_mon_in <dbl>, heat_units_10C <dbl>,
#> # heat_units_13C <dbl>, heat_units_3413C <dbl>, heat_units_45F <dbl>, …
head(hourly)
#> # A tibble: 6 × 42
#> meta_bat_volt meta_needs_review meta_station_id meta_station_name meta_version
#> <dbl> <dbl> <chr> <chr> <dbl>
#> 1 13.2 0 az01 Tucson 1
#> 2 13.0 0 az02 Yuma Valley 1
#> 3 13.4 0 az04 Safford 1
#> 4 13.0 0 az05 Coolidge 1
#> 5 13.2 0 az06 Maricopa 1
#> 6 13.0 0 az07 Aguila 1
#> # ℹ 37 more variables: date_datetime <dttm>, date_doy <dbl>, date_hour <chr>,
#> # date_year <dbl>, dwpt <dbl>, dwptF <dbl>, eto_azmet <dbl>,
#> # eto_azmet_in <dbl>, heatstress_cottonC <dbl>, heatstress_cottonF <dbl>,
#> # precip_total <dbl>, precip_total_in <dbl>, relative_humidity <dbl>,
#> # sol_rad_total <dbl>, sol_rad_total_ly <dbl>, temp_airC <dbl>,
#> # temp_airF <dbl>, temp_soil_10cmC <dbl>, temp_soil_10cmF <dbl>,
#> # temp_soil_50cmC <dbl>, temp_soil_50cmF <dbl>, vp_actual <dbl>, …Specifying date ranges
By supplying start_date to az_daily() or
start_date_time to az_hourly() you can
retrieve data going back further in time.
last_date <- max(daily$datetime)
last_date
#> [1] "2024-07-22"
last_week <- last_date - lubridate::weeks(1)
wk <- az_daily(start_date = last_week)
#> Querying data from 2024-07-15 through 2024-07-22
#> Returning data from 2024-07-15 through 2024-07-22
range(wk$datetime)
#> [1] "2024-07-15" "2024-07-22"
last_datetime <- max(hourly$date_datetime)
last_datetime
#> [1] "2024-07-23 15:00:00 MST"
last_48h <- last_datetime - hours(48)
hr <- az_hourly(start_date_time = last_48h)
#> Querying data from 2024-07-21 15:00 through 2024-07-23 15:00
#> Returning data from 2024-07-21 15:00 through 2024-07-23 15:00
range(hr$date_datetime)
#> [1] "2024-07-21 15:00:00 MST" "2024-07-23 15:00:00 MST"To specify an end date, use end_date or
end_date_time. You must also supply a start date if you
supply an end date.
daily_range <- az_daily(start_date = "2022-01-01", end_date = "2022-01-05")
#> Querying data from 2022-01-01 through 2022-01-05
#> Returning data from 2022-01-01 through 2022-01-05
range(daily_range$datetime)
#> [1] "2022-01-01" "2022-01-05"Note that the dates and datetimes can be supplied as character values
in year, month, day order or they can be supplied as Date or POSIXct
vectors. If the supplied date is more precise than the data, it will be
rounded down. For az_daily() datetimes will be rounded down
to the nearest day and for az_hourly() datetimes will be
rounded down to the nearest hour.
char_daily <- az_daily(start_date = "2023-01-10 12:43:22", end_date = "2023-01-11 15:00:01")
#> Querying data from 2023-01-10 through 2023-01-11
#> Returning data from 2023-01-10 through 2023-01-11
range(char_daily$datetime)
#> [1] "2023-01-10" "2023-01-11"
char_hourly <- az_hourly(start_date = "2023-01-10 12:43:22", end_date = "2023-01-11 15:00:01")
#> Querying data from 2023-01-10 12:43 through 2023-01-11 15:00
#> Warning in az_hourly(start_date = "2023-01-10 12:43:22", end_date = "2023-01-11
#> 15:00:01"): You requested data through 2023-01-11 15:00:00 but only data
#> through 2023-01-11 14:00:00 were available
#> Returning data from 2023-01-10 13:00 through 2023-01-11 14:00
range(char_hourly$date_datetime)
#> [1] "2023-01-10 13:00:00 MST" "2023-01-11 14:00:00 MST"Filtering by station
Information on the stations available is contained in the
station_info dataset including station name, station ID,
and location.
station_info
#> # A tibble: 31 × 5
#> meta_station_name meta_station_id latitude longitude elev_m
#> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 Tucson az01 32.3 -111. 717
#> 2 Yuma Valley az02 32.7 -115. 36
#> 3 Safford az04 32.8 -110. 903
#> 4 Coolidge az05 33.0 -112. 423
#> 5 Maricopa az06 33.1 -112. 362
#> 6 Aguila az07 33.9 -113. 657
#> 7 Parker az08 34.0 -114. 98
#> 8 Bonita az09 32.5 -110. 1349
#> 9 Phoenix Greenway az12 33.6 -112. 403
#> 10 Yuma N.Gila az14 32.8 -115. 43
#> # ℹ 21 more rowsIf you only need data for a subset of stations, you can supply
station_id. However, note that this will query the API once
per station due to limitations of how the API works. It may be faster to
just get data for all stations and subset it after since that only
queries the web API once and results in an identical dataset.
system.time(
sub_wk <- az_daily(station_id = c(1, 2, 8), start_date = "2022-01-01", end_date = "2022-01-15")
)
#> Querying data from 2022-01-01 through 2022-01-15
#> Returning data from 2022-01-01 through 2022-01-15
#> user system elapsed
#> 0.098 0.004 0.634
system.time(
sub_wk2 <- subset(
az_daily(start_date = "2022-01-01", end_date = "2022-01-15"),
meta_station_id %in% c("az01", "az02", "az08")
)
)
#> Querying data from 2022-01-01 through 2022-01-15
#> Returning data from 2022-01-01 through 2022-01-15
#> user system elapsed
#> 0.383 0.000 0.887
all(sub_wk2 == sub_wk)
#> [1] NA