This package is maintained by the Knowledge Management Branch of the British Columbia Ministry of Environment and Climate Change Strategy.
- Provides functions (
hy_*) that access hydrometric data from the HYDAT database, a national archive of Canadian hydrometric data and return tidy data.
- Provides functions (
realtime_*) that access Environment and Climate Change Canada’s real-time hydrometric data source.
- Provides functions (
search_*) that can search through the approximately 7000 stations in the database and aid in generating station vectors
- Keep functions as simple as possible. For example, for daily flows,
hy_daily_flows()function queries the database, tidies the data and returns a tibble of daily flows.
You can install
tidyhydat from CRAN:
To install the development version of the
tidyhydat package, you need
to install the
remotes package then the
if(!requireNamespace("remotes")) install.packages("remotes") remotes::install_github("ropensci/tidyhydat")
A more thorough vignette can be found on the
When you install
tidyhydat, several other packages will be installed
as well. One of those packages,
dplyr, is useful for data
manipulations and is used regularly here. To use
dplyr, it is required
to be loaded by itself. A helpful
dplyr tutorial can be found
To use many of the functions in the
tidyhydat package you will need to
download a version of the HYDAT database, Environment and Climate Change
Canada’s database of historical hydrometric data then tell R where to
find the database. Conveniently
tidyhydat does all this for you via:
This downloads (with your permission) the most recent version of HYDAT
and then saves it in a location on your computer where
function will look for it. Do be patient though as this takes a long
time! To see where HYDAT was saved you can run
hy_dir(). Now that you
have HYDAT downloaded and ready to go, you are all set to begin looking
at Canadian hydrometric data.
Most functions in
tidyhydat follow a common argument structure. We
will use the
hy_daily_flows() function for the following examples
though the same approach applies to most functions in the package (See
help(package = "tidyhydat") for a list of exported objects). Much of
the functionality of
tidyhydat originates with the choice of
hydrometric stations that you are interested in. A user will often find
themselves creating vectors of station numbers. There are several ways
to do this.
The simplest case is if you would like to extract only station. You can
supply this directly to the
hy_daily_flows(station_number = "08LA001") #> Queried from version of HYDAT released on 2019-07-17 #> Observations: 29,890 #> Measurement flags: 5,922 #> Parameter(s): Flow #> Date range: 1914-01-01 to 2017-12-31 #> Station(s) returned: 1 #> Stations requested but not returned: #> All stations returned. #> # A tibble: 29,890 x 5 #> STATION_NUMBER Date Parameter Value Symbol #> <chr> <date> <chr> <dbl> <chr> #> 1 08LA001 1914-01-01 Flow 144 <NA> #> 2 08LA001 1914-01-02 Flow 144 <NA> #> 3 08LA001 1914-01-03 Flow 144 <NA> #> 4 08LA001 1914-01-04 Flow 140 <NA> #> 5 08LA001 1914-01-05 Flow 140 <NA> #> 6 08LA001 1914-01-06 Flow 136 <NA> #> 7 08LA001 1914-01-07 Flow 136 <NA> #> 8 08LA001 1914-01-08 Flow 140 <NA> #> 9 08LA001 1914-01-09 Flow 140 <NA> #> 10 08LA001 1914-01-10 Flow 140 <NA> #> # ... with 29,880 more rows
Another method is to use
hy_stations() to generate your vector which
is then given the
station_number argument. For example, we could take
a subset for only those active stations within Prince Edward Island
PE) and then create vector which is passed to the
hy_daily(). This function queries the flow,
level, sediment load and suspended sediment concentration tables and
combines them (if present) into one dataframe:
PEI_stns <- hy_stations() %>% filter(HYD_STATUS == "ACTIVE") %>% filter(PROV_TERR_STATE_LOC == "PE") %>% pull_station_number() PEI_stns #>  "01CA003" "01CB002" "01CB004" "01CC002" "01CC005" "01CC010" "01CC011" #>  "01CD005" hy_daily(station_number = PEI_stns) #> Queried from version of HYDAT released on 2019-07-17 #> Observations: 138,085 #> Measurement flags: 20,521 #> Parameter(s): Flow/Level/Load/Suscon #> Date range: 1961-08-01 to 2017-12-31 #> Station(s) returned: 8 #> Stations requested but not returned: #> All stations returned. #> # A tibble: 138,085 x 5 #> STATION_NUMBER Date Parameter Value Symbol #> <chr> <date> <chr> <dbl> <chr> #> 1 01CA003 1961-08-01 Flow NA <NA> #> 2 01CA003 1961-08-02 Flow NA <NA> #> 3 01CA003 1961-08-03 Flow NA <NA> #> 4 01CA003 1961-08-04 Flow NA <NA> #> 5 01CA003 1961-08-05 Flow NA <NA> #> 6 01CA003 1961-08-06 Flow NA <NA> #> 7 01CA003 1961-08-07 Flow NA <NA> #> 8 01CA003 1961-08-08 Flow NA <NA> #> 9 01CA003 1961-08-09 Flow NA <NA> #> 10 01CA003 1961-08-10 Flow NA <NA> #> # ... with 138,075 more rows
We can also merge our station choice and data extraction into one unified pipe which accomplishes a single goal. For example, if for some reason we wanted all the stations in Canada that had the name “Canada” in them we could unify those selection and data extraction processes into a single pipe:
search_stn_name("canada") %>% pull_station_number() %>% hy_daily_flows() #> Queried from version of HYDAT released on 2019-07-17 #> Observations: 80,455 #> Measurement flags: 24,036 #> Parameter(s): Flow #> Date range: 1918-08-01 to 2019-05-31 #> Station(s) returned: 7 #> Stations requested but not returned: #> All stations returned. #> # A tibble: 80,455 x 5 #> STATION_NUMBER Date Parameter Value Symbol #> <chr> <date> <chr> <dbl> <chr> #> 1 01AK001 1918-08-01 Flow NA <NA> #> 2 01AK001 1918-08-02 Flow NA <NA> #> 3 01AK001 1918-08-03 Flow NA <NA> #> 4 01AK001 1918-08-04 Flow NA <NA> #> 5 01AK001 1918-08-05 Flow NA <NA> #> 6 01AK001 1918-08-06 Flow NA <NA> #> 7 01AK001 1918-08-07 Flow 1.78 <NA> #> 8 01AK001 1918-08-08 Flow 1.78 <NA> #> 9 01AK001 1918-08-09 Flow 1.5 <NA> #> 10 01AK001 1918-08-10 Flow 1.78 <NA> #> # ... with 80,445 more rows
These example illustrate a few ways that an vector can be generated and
supplied to functions within
To download real-time data using the datamart we can use approximately
the same conventions discussed above. Using
realtime_dd() we can
easily select specific stations by supplying a station of interest:
realtime_dd(station_number = "08LG006") #> Queried on: 2019-09-03 16:51:45 (UTC) #> Date range: 2019-08-04 to 2019-09-03 #> # A tibble: 17,412 x 8 #> STATION_NUMBER PROV_TERR_STATE~ Date Parameter Value #> <chr> <chr> <dttm> <chr> <dbl> #> 1 08LG006 BC 2019-08-04 08:00:00 Flow 9.19 #> 2 08LG006 BC 2019-08-04 08:05:00 Flow 9.19 #> 3 08LG006 BC 2019-08-04 08:10:00 Flow 9.19 #> 4 08LG006 BC 2019-08-04 08:15:00 Flow 9.19 #> 5 08LG006 BC 2019-08-04 08:20:00 Flow 9.19 #> 6 08LG006 BC 2019-08-04 08:25:00 Flow 9.19 #> 7 08LG006 BC 2019-08-04 08:30:00 Flow 9.19 #> 8 08LG006 BC 2019-08-04 08:35:00 Flow 9.19 #> 9 08LG006 BC 2019-08-04 08:40:00 Flow 9.19 #> 10 08LG006 BC 2019-08-04 08:45:00 Flow 9.19 #> # ... with 17,402 more rows, and 3 more variables: Grade <chr>, #> # Symbol <chr>, Code <chr>
Another option is to provide simply the province as an argument and download all stations from that province:
realtime_dd(prov_terr_state_loc = "PE")
Plot methods are also provided to quickly visualize realtime data:
realtime_ex <- realtime_dd(station_number = "08LG006") plot(realtime_ex)
and also historical data:
hy_ex <- hy_daily_flows(station_number = "08LA001", start_date = "2013-01-01") plot(hy_ex)
Getting Help or Reporting an Issue
To report bugs/issues/feature requests, please file an issue.
These are very welcome!
How to Contribute
If you would like to contribute to the package, please see our CONTRIBUTING guidelines.
Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.
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Copyright 2017 Province of British Columbia
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