Tidyhydat
An R package to import Water Survey of Canada hydrometric data and make it tidy
Install / Use
/learn @ropensci/TidyhydatREADME
tidyhydat <img src="man/figures/logo.png" align="right" />
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What does tidyhydat do?
- Provides functions (
available_*) that combine validated historical data with provisional real-time data. - Provides functions (
hy_*) that access hydrometric data from the HYDAT database or web service, 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,
the
hy_daily_flows()function queries the database, tidies the data and returns a tibble of daily flows.
Installation
You can install tidyhydat from CRAN:
install.packages("tidyhydat")
To install the development version of the tidyhydat package, you can
install directly from the rOpenSci development server:
install.packages("tidyhydat", repos = "https://dev.ropensci.org")
Usage
More documentation on tidyhydat can found at the rOpenSci doc page:
https://docs.ropensci.org/tidyhydat/
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 actually use dplyr in
a session you must explicitly load it. A helpful dplyr tutorial can be
found
here.
library(tidyhydat)
library(dplyr)
HYDAT download
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:
download_hydat()
This downloads (with your permission) the most recent version of HYDAT
and then saves it in a location on your computer where tidyhydat’s
function will look for it. Do be patient though as this can take a long
time! To see where HYDAT was saved you can run hy_default_db(). Now
that you have HYDAT downloaded and ready to go, you are all set to begin
looking at Canadian hydrometric data.
Combining validated and provisional data
For a complete record combining validated historical data with recent
provisional data use the available_flows and available_levels
functions.
available_flows(
station_number = "08MF005",
start_date = "2020-01-01",
end_date = Sys.Date()
)
#> Queried on: 2026-01-26 23:00:31.03418 (UTC)
#> Historical data source: HYDAT
#> Overall date range: 2020-01-01 to 2026-01-26
#> Flow records by approval status:
#> final: 1,827
#> provisional: 391
#> Station(s) returned: 1
#> All stations successfully retrieved.
#> Use summary() for per-station date ranges.
#> # A tibble: 2,218 × 6
#> STATION_NUMBER Date Parameter Value Symbol Approval
#> <chr> <date> <chr> <dbl> <chr> <chr>
#> 1 08MF005 2020-01-01 Flow 1340 <NA> final
#> 2 08MF005 2020-01-02 Flow 1330 <NA> final
#> 3 08MF005 2020-01-03 Flow 1310 <NA> final
#> 4 08MF005 2020-01-04 Flow 1420 <NA> final
#> 5 08MF005 2020-01-05 Flow 1350 <NA> final
#> 6 08MF005 2020-01-06 Flow 1310 <NA> final
#> 7 08MF005 2020-01-07 Flow 1280 <NA> final
#> 8 08MF005 2020-01-08 Flow 1320 <NA> final
#> 9 08MF005 2020-01-09 Flow 1230 <NA> final
#> 10 08MF005 2020-01-10 Flow 1210 <NA> final
#> # ℹ 2,208 more rows
Use summary() to see date ranges and record counts by station:
flows <- available_flows(
station_number = c("08MF005", "08NM116"),
start_date = "2020-01-01"
)
summary(flows)
#> # A tibble: 2 × 7
#> STATION_NUMBER final_start final_end final_n provisional_start
#> <chr> <date> <date> <int> <date>
#> 1 08MF005 2020-01-01 2024-12-31 1827 2025-01-01
#> 2 08NM116 2020-01-01 2023-12-31 1461 2025-01-01
#> # ℹ 2 more variables: provisional_end <date>, provisional_n <int>
Note that provisional data is aggregated to daily means to match the
daily format of HYDAT data. For non-aggregated real-time data at
sub-daily intervals, use realtime_ws() directly.
Real-time
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 = "08MF005")
#> Queried on: 2026-01-26 23:00:37.058285 (UTC)
#> Date range: 2025-12-27 to 2026-01-26
#> # A tibble: 17,622 × 8
#> STATION_NUMBER PROV_TERR_STATE_LOC Date Parameter Value Grade
#> <chr> <chr> <dttm> <chr> <dbl> <chr>
#> 1 08MF005 BC 2025-12-27 08:00:00 Flow 1030 <NA>
#> 2 08MF005 BC 2025-12-27 08:05:00 Flow 1030 <NA>
#> 3 08MF005 BC 2025-12-27 08:10:00 Flow 1030 <NA>
#> 4 08MF005 BC 2025-12-27 08:15:00 Flow 1030 <NA>
#> 5 08MF005 BC 2025-12-27 08:20:00 Flow 1030 <NA>
#> 6 08MF005 BC 2025-12-27 08:25:00 Flow 1030 <NA>
#> 7 08MF005 BC 2025-12-27 08:30:00 Flow 1030 <NA>
#> 8 08MF005 BC 2025-12-27 08:35:00 Flow 1030 <NA>
#> 9 08MF005 BC 2025-12-27 08:40:00 Flow 1030 <NA>
#> 10 08MF005 BC 2025-12-27 08:45:00 Flow 1030 <NA>
#> # ℹ 17,612 more rows
#> # ℹ 2 more variables: Symbol <chr>, Code <chr>
Or we can use realtime_ws:
realtime_ws(
station_number = "08MF005",
parameters = c(46, 5), ## see param_id for a list of codes
start_date = Sys.Date() - 14,
end_date = Sys.Date()
)
#> Queried on: 2026-01-26 23:00:38.302716 (UTC)
#> Date range: 2026-01-12 to 2026-01-26
#> Station(s) returned: 1
#> All stations successfully retrieved.
#> All parameters successfully retrieved.
#> # A tibble: 4,658 × 12
#> STATION_NUMBER Date Name_En Value Unit Grade Symbol Approval
#> <chr> <dttm> <chr> <dbl> <chr> <lgl> <chr> <chr>
#> 1 08MF005 2026-01-12 00:00:00 Water t… 5.1 °C NA <NA> Provisi…
#> 2 08MF005 2026-01-12 01:00:00 Water t… 5.11 °C NA <NA> Provisi…
#> 3 08MF005 2026-01-12 02:00:00 Water t… 5.09 °C NA <NA> Provisi…
#> 4 08MF005 2026-01-12 03:00:00 Water t… 5.09 °C NA <NA> Provisi…
#> 5 08MF005 2026-01-12 04:00:00 Water t… 5.1 °C NA <NA> Provisi…
#> 6 08MF005 2026-01-12 05:00:00 Water t… 5.1 °C NA <NA> Provisi…
#> 7 08MF005 2026-01-12 06:00:00 Water t… 5.1 °C NA <NA> Provisi…
#> 8 08MF005 2026-01-12 07:00:00 Water t… 5.1 °C NA <NA> Provisi…
#> 9 08MF005 2026-01-12 08:00:00 Water t… 5.1 °C NA <NA> Provisi…
#> 10 08MF005 2026-01-12 09:00:00 Water t… 5.11 °C NA <NA> Provisi…
#> # ℹ 4,648 more rows
#> # ℹ 4 more variables: Parameter <dbl>, Code <chr>, Qualifier <chr>,
#> # Qualifiers <lgl>
Using only HYDAT
If you wish to use only the final approved data in HYDAT database you can use:
hy_daily_flows(
station_number = "08MF005",
start_date = "2020-01-01",
end_date = "2020-12-31"
)
#> Queried from version of HYDAT released on 2025-10-14
#> Observations: 366
#> Measurement flags: 0
#> Parameter(s): Flow
#> Date range: 2020-01-01 to 2020-12-31
#> Station(s) returned: 1
#> Stations requested but not returned:
#> All stations returned.
#> # A tibble: 366 × 5
#> STATION_NUMBER Date Parameter Value Symbol
#> <chr> <date> <chr> <dbl> <chr>
#> 1 08MF005 2020-01-01 Flow 1340 <NA>
#> 2 08MF005 2020-01-02 Flow 1330 <NA>
#> 3 08
