canwqdata

An R 📦 to download open water quality data from Environment and Climate Change Canada’s National Long-term Water Quality Monitoring Data.

Features

This package is designed to get Canadian Water Quality Monitoring data into R quickly and easily. You can get data from a single monitoring station, multiple stations, or from an entire basin.

Installation

remotes::install_github("bcgov/canwqdata")

Usage

First load the package:

library(canwqdata)

The first thing you will probably want to do is get a list of the available sites and associated metadata:

sites <- wq_sites()

sites
#> # A tibble: 339 × 16
#>    SITE_NO SITE_NAME       SITE_NOM_FR SITE_TYPE SITE_DESC SITE_DESC_FR LATITUDE
#>    <chr>   <chr>           <chr>       <chr>     <chr>     <chr>           <dbl>
#>  1 72      BEAUHARNOIS CA… CANAL DE B… RIVER/RI… <NA>      <NA>             45.2
#>  2 75      ST.LAWRENCE RI… FLEUVE SAI… RIVER/RI… <NA>      <NA>             45.9
#>  3 78      ST.LAWRENCE RI… FLEUVE SAI… RIVER/RI… <NA>      <NA>             45.4
#>  4 2330001 ETCHEMIN RIVER… RIVIÈRE ET… RIVER/RI… <NA>      <NA>             46.8
#>  5 2340033 CHAUDIÈRE RIVE… RIVIÈRE CH… RIVER/RI… <NA>      <NA>             46.7
#>  6 2400004 BÉCANCOUR RIVE… RIVIÈRE BÉ… RIVER/RI… <NA>      <NA>             46.4
#>  7 3020073 MAGOG RIVER AT… RIVIÈRE MA… RIVER/RI… <NA>      <NA>             45.3
#>  8 3020333 COATICOOK RIVE… RIVIÈRE CO… RIVER/RI… <NA>      <NA>             45.3
#>  9 3040010 RICHELIEU RIVE… RIVIÈRE RI… RIVER/RI… <NA>      <NA>             45.4
#> 10 3040012 RICHELIEU RIVE… RIVIÈRE RI… RIVER/RI… <NA>      <NA>             45.1
#> # … with 329 more rows, and 9 more variables: LONGITUDE <dbl>, DATUM <chr>,
#> #   PROV_TERR <chr>, PEARSEDA <chr>, PEARSEDA_FR <chr>, OCEANDA <chr>,
#> #   OCEANDA_FR <chr>, DATA_URL <chr>, DATA_URL_FR <chr>

Then get some data from a particular station:

AL07AA0015 is a site in Alberta called Athabasca River above Athabasca Falls

athabasca_falls <- wq_site_data("AL07AA0015")

athabasca_falls
#> # A tibble: 10,538 × 11
#>    SITE_NO    DATE_TIME_HEURE     FLAG_MARQUEUR VALUE_VALEUR SDL_LDE MDL_LDM
#>    <chr>      <dttm>              <chr>                <dbl>   <dbl>   <dbl>
#>  1 AL07AA0015 2000-01-11 13:05:00 <NA>               93.2         NA      NA
#>  2 AL07AA0015 2000-01-11 13:05:00 <                   0.02        NA      NA
#>  3 AL07AA0015 2000-01-11 13:05:00 <                   0.005       NA      NA
#>  4 AL07AA0015 2000-01-11 13:05:00 <NA>                0           NA      NA
#>  5 AL07AA0015 2000-01-11 13:05:00 <                   0.0001      NA      NA
#>  6 AL07AA0015 2000-01-11 13:05:00 <NA>                0.065       NA      NA
#>  7 AL07AA0015 2000-01-11 13:05:00 <                   0.5         NA      NA
#>  8 AL07AA0015 2000-01-11 13:05:00 <NA>              114.          NA      NA
#>  9 AL07AA0015 2000-01-11 13:05:00 <                   0.002       NA      NA
#> 10 AL07AA0015 2000-01-11 13:05:00 <                   0.001       NA      NA
#> # … with 10,528 more rows, and 5 more variables: VMV_CODE <chr>,
#> #   UNIT_UNITE <chr>, VARIABLE <chr>, VARIABLE_FR <chr>, STATUS_STATUT <chr>

We can also get data from more than one station:

wq_site_data(c("YT09FC0002", "SA05JM0014"))
#> # A tibble: 23,932 × 11
#>    SITE_NO    DATE_TIME_HEURE     FLAG_MARQUEUR VALUE_VALEUR SDL_LDE MDL_LDM
#>    <chr>      <dttm>              <chr>                <dbl>   <dbl>   <dbl>
#>  1 SA05JM0014 2000-03-07 12:45:00 <NA>                0           NA      NA
#>  2 SA05JM0014 2000-03-07 12:45:00 <NA>              253           NA      NA
#>  3 SA05JM0014 2000-03-07 12:45:00 <NA>                0.047       NA      NA
#>  4 SA05JM0014 2000-03-07 12:45:00 <NA>                0.607       NA      NA
#>  5 SA05JM0014 2000-03-07 12:45:00 <NA>                0.079       NA      NA
#>  6 SA05JM0014 2000-03-07 12:45:00 <NA>                0.001       NA      NA
#>  7 SA05JM0014 2000-03-07 12:45:00 <NA>                0.039       NA      NA
#>  8 SA05JM0014 2000-03-07 12:45:00 <NA>                0.0569      NA      NA
#>  9 SA05JM0014 2000-03-07 12:45:00 <                   0.5         NA      NA
#> 10 SA05JM0014 2000-03-07 12:45:00 <                   0.05        NA      NA
#> # … with 23,922 more rows, and 5 more variables: VMV_CODE <chr>,
#> #   UNIT_UNITE <chr>, VARIABLE <chr>, VARIABLE_FR <chr>, STATUS_STATUT <chr>

Or an entire basin:

The basins are in the PEARSEDA column of the data.frame returned by wq_sites():

basins <- sort(unique(sites$PEARSEDA))
basins
#>  [1] "ARCTIC COAST-ISLANDS"      "ASSINIBOINE-RED"          
#>  [3] "CHURCHILL"                 "COLUMBIA"                 
#>  [5] "FRASER-LOWER MAINLAND"     "GREAT LAKES"              
#>  [7] "KEEWATIN-SOUTHERN BAFFIN"  "LOWER MACKENZIE"          
#>  [9] "LOWER SASKATCHEWAN-NELSON" "MARITIME COASTAL"         
#> [11] "MISSOURI"                  "NEWFOUNDLAND-LABRADOR"    
#> [13] "NORTH SASKATCHEWAN"        "NORTH SHORE-GASPÉ"        
#> [15] "OKANAGAN-SIMILKAMEEN"      "OTTAWA"                   
#> [17] "PACIFIC COASTAL"           "PEACE-ATHABASCA"          
#> [19] "SAINT JOHN-ST. CROIX"      "SOUTH SASKATCHEWAN"       
#> [21] "ST. LAWRENCE"              "WINNIPEG"                 
#> [23] "YUKON"

fraser <- wq_basin_data("FRASER-LOWER MAINLAND")

Do some quick summary stats of the fraser dataset:

library(dplyr)

fraser %>% 
  group_by(SITE_NO) %>% 
  summarise(first_date = min(DATE_TIME_HEURE), 
            latest_date = max(DATE_TIME_HEURE), 
            n_params = length(unique(VARIABLE)), 
            total_samples = n())
#> # A tibble: 15 × 5
#>    SITE_NO    first_date          latest_date         n_params total_samples
#>    <chr>      <dttm>              <dttm>                 <int>         <int>
#>  1 BC08KA0007 2000-01-12 07:45:00 2019-09-12 08:58:00      108         24941
#>  2 BC08KE0010 2000-01-05 00:00:00 2019-09-16 10:00:00       76         23477
#>  3 BC08KH0012 2006-05-11 13:07:00 2019-09-29 08:30:00      140         19511
#>  4 BC08KH0013 2014-06-16 12:45:00 2019-09-23 09:45:00      107         10375
#>  5 BC08KH0014 2014-09-23 14:00:00 2019-09-09 06:55:00      110          9397
#>  6 BC08LC0005 2011-02-24 09:45:00 2019-09-18 11:20:00       69         11866
#>  7 BC08LE0004 2000-01-04 10:00:00 2019-10-02 11:30:00      112         23469
#>  8 BC08LF0001 2000-01-05 12:00:00 2014-12-15 10:20:00       89         18410
#>  9 BC08LG0001 2003-06-24 10:45:00 2019-09-18 14:30:00       71         10366
#> 10 BC08MB0007 2004-11-15 12:00:00 2019-10-01 12:21:00      105         21297
#> 11 BC08MC0001 2000-04-18 16:30:00 2019-09-30 08:37:00      107         21775
#> 12 BC08MF0001 2000-01-04 14:10:00 2019-09-12 12:00:00      129         21475
#> 13 BC08MH0027 2000-01-07 12:16:00 2019-09-24 12:02:00      115         34775
#> 14 BC08MH0269 2004-03-03 14:40:00 2019-09-24 13:45:00      137         25932
#> 15 BC08MH0453 2008-09-02 16:25:00 2019-09-30 12:00:00      107         13389

We can also look at metadata that helps us understand what is in the different columns.

wq_params() returns a list of water quality parameters (variables), and related data - units, methods, codes, etc:

params <- wq_params()
glimpse(params)
#> Rows: 1,964
#> Columns: 12
#> $ VMV_CODE                <chr> "77", "78", "79", "80", "157", "160", "201", "…
#> $ NATIONAL_VARIABLE_CODE  <chr> "635", "365", "4541", "414", "864", "1073", "8…
#> $ VARIABLE_COMMON_NAME    <chr> "Nitrogen total", "Alkalinity total HCO3", "Ch…
#> $ VARIABLE_COMMON_NAME_FR <chr> "Azote total", "Alcalinité totale HCO3", "Chlo…
#> $ VARIABLE_TYPE           <chr> "Nitrogen", "Physical", "Chlorophyll", "Chloro…
#> $ VARIABLE_TYPE_FR        <chr> "Azote", "Physique", "Chlorophylle", "Chloroph…
#> $ MEASUREMENT_UNIT        <chr> "mg/L", "mg/L", "µg/L", "µg/L", "NTU", "mg/L",…
#> $ DESCRIPTION             <chr> "milligram per liter", "milligram per liter", …
#> $ DESCRIPTION_FR          <chr> "milligramme par litre", "milligramme par litr…
#> $ NATIONAL_METHOD_CODE    <chr> "23", "30", "35", "41", "188", "189", "8", "9"…
#> $ METHOD_TITLE            <chr> "Total nitrogen measurement by persulfate oxid…
#> $ METHOD_TITLE_FR         <chr> "Azote total par la méthode d'oxydation au per…

# wq_param_desc shows the column headings (in all other tables) and what they mean
wq_data_desc() %>% 
  glimpse()
#> Rows: 39
#> Columns: 5
#> $ COL_TITLE_TITRE    <chr> "COL_DESCRIPTION", "COL_DESCRIPTION_FR", "COL_TITLE…
#> $ COL_TITLE_FULL     <chr> "COLUMN HEADER DESCRIPTION", "COLUMN HEADER DESCRIP…
#> $ COL_TITRE_COMPLET  <chr> "DESCRIPTION DE L'EN-TÊTE DE COLONNE", "DESCRIPTION…
#> $ COL_DESCRIPTION    <chr> "COLUMN HEADER DESCRIPTION", "COLUMN HEADER DESCRIP…
#> $ COL_DESCRIPTION_FR <chr> "DESCRIPTION DE L'EN-TÊTE DE COLONNE", "DESCRIPTION…

Let’s look at Total Nitrogen in the Fraser basin:

fraser_n_total <- fraser %>% filter(VARIABLE == "NITROGEN TOTAL")

Now lets do some plotting - plot Total Nitrogen over time at all the sites, (plot it on a log scale so that they all fit)

library(ggplot2)

ggplot(fraser_n_total, aes(x = DATE_TIME_HEURE, y = VALUE_VALEUR)) + 
  geom_point(size = 0.4, alpha = 0.4, colour = "purple") + 
  facet_wrap(~ SITE_NO) + 
  scale_y_log10()

It’s also possible to download data from an entire province:

bc_sites <- sites %>% 
  filter(PROV_TERR == "BC") %>% 
  pull(SITE_NO)

all_bc_data <- wq_site_data(bc_sites)

glimpse(all_bc_data)
#> Rows: 925,542
#> Columns: 11
#> $ SITE_NO         <chr> "BC07FB0005", "BC07FB0005", "BC07FB0005", "BC07FB0005"…
#> $ DATE_TIME_HEURE <dttm> 2017-01-25 09:35:00, 2017-01-25 09:35:00, 2017-01-25 …
#> $ FLAG_MARQUEUR   <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "<", NA, N…
#> $ VALUE_VALEUR    <dbl> 163.000, 4.100, 31.900, 0.060, 0.061, 0.130, 0.150, 10…
#> $ SDL_LDE         <dbl> 1.000, 0.500, 0.500, 0.001, 0.001, 0.010, 0.010, 0.050…
#> $ MDL_LDM         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
#> $ VMV_CODE        <chr> "9134", "107941", "107905", "107965", "107929", "10794…
#> $ UNIT_UNITE      <chr> "MG/L", "UG/L", "UG/L", "UG/L", "UG/L", "UG/L", "UG/L"…
#> $ VARIABLE        <chr> "ALKALINITY TOTAL CACO3", "ALUMINUM DISSOLVED", "ALUMI…
#> $ VARIABLE_FR     <chr> "ALCALINITÉ TOTALE CACO3", "ALUMINIUM DISSOUS", "ALUMI…
#> $ STATUS_STATUT   <chr> "P", "P", "P", "P", "P", "P", "P", "P", "P", "P", "P",…

Project Status

Under development, but ready for use and testing.

Getting Help or Reporting an Issue

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

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.

License

Copyright 2018 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.

This repository is maintained by Environmental Reporting BC. Click here for a complete list of our repositories on GitHub.

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man		man
tests		tests
tools/readme		tools/readme
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.gitignore		.gitignore
CODE_OF_CONDUCT.md		CODE_OF_CONDUCT.md
COMPLIANCE.yaml		COMPLIANCE.yaml
CONTRIBUTING.md		CONTRIBUTING.md
DESCRIPTION		DESCRIPTION
LICENSE		LICENSE
NAMESPACE		NAMESPACE
NEWS.md		NEWS.md
README.Rmd		README.Rmd
README.md		README.md
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canwqdata

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