This package is maintained by the Knowledge Management Branch of the British Columbia Ministry of Environment and Climate Change Strategy.

Here is a summary of what tidyhydat does:

• 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, the hy_daily_flows() function queries the database, tidies the data and returns a tibble of daily flows.

You can install tidyhydat from CRAN:

install.packages("tidyhydat")

To install the development version of the tidyhydat package, you need to install the remotes package then the tidyhydat package

install.packages("remotes")
remotes::install_github("ropensci/tidyhydat")

A more thorough vignette can be found on the tidyhydat CRAN page.

To load the package you need to use the library() function. 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. Even though dplyr is installed alongside tidyhydat, it is helpful to load it by itself as there are many useful functions contained within dplyr. A helpful dplyr tutorial can be found here.

library(tidyhydat)
library(dplyr)

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 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 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 some hydrologic analysis.

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 ls("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 station_number argument:

hy_daily_flows(station_number = "08LA001")
#> No start and end dates specified. All dates available will be returned.
#> All station successfully retrieved
#> # A tibble: 29,159 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 29,149 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 (Province code: PE) and then create vector which is passed to the multi-parameter function 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)
#> All station successfully retrieved

PEI_stns
#> [1] "01CA003" "01CB002" "01CB004" "01CC002" "01CC005" "01CC010" "01CD005"

hy_daily(station_number = PEI_stns)
#> # A tibble: 123,225 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 123,215 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()
#> No start and end dates specified. All dates available will be returned.
#> The following station(s) were not retrieved: 07DB006
#> Check station number typos or if it is a valid station in the network
#> # A tibble: 76,679 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       1.50 <NA>  
#> 10 01AK001        1918-08-10 Flow       1.78 <NA>  
#> # ... with 76,669 more rows

These example illustrate a few ways that an vector can be generated and supplied to functions within tidyhydat.

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")

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")

A simple plotting tool is also provided to quickly visualize realtime data:

realtime_plot("08LG006")

To report bugs/issues/feature requests, please file an issue.

These are very welcome!

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.

Get citation information for tidyhydat in R by running:

citation("tidyhydat")

Copyright 2017 Province of British Columbia

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.