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weathercan

:name status badge weathercan status badge R-CMD-check codecov

DOI DOI

This package makes it easier to search for and download multiple months/years of historical weather data from Environment and Climate Change Canada (ECCC) website.

Bear in mind that these downloads can be fairly large and performing multiple downloads may use up ECCC’s bandwidth unnecessarily. Try to stick to what you need.

For more details and tutorials checkout the weathercan website

Check out the Demo weathercan shiny dashboard (html; source)

Installation

You can install weathercan from the rOpenSci r-Universe:

install.packages("weathercan", 
                 repos = c("https://ropensci.r-universe.dev", 
                           "https://cloud.r-project.org"))

View the available vignettes with vignette(package = "weathercan")

View a particular vignette with, for example, vignette("weathercan", package = "weathercan")

General usage

To download data, you first need to know the station_id associated with the station you’re interested in.

Stations

weathercan includes the function stations() which returns a list of stations and their details (including station_id).

head(stations())
## # A tibble: 6 × 16
##   prov  station_name        station_id climate_id WMO_id TC_id   lat   lon  elev tz        interval start   end normals normals_1981_2010 normals_1971_2000
##   <chr> <chr>                    <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr>     <chr>    <dbl> <dbl> <lgl>   <lgl>             <lgl>            
## 1 AB    DAYSLAND                  1795 301AR54        NA <NA>   52.9 -112.  689. Etc/GMT+7 day       1908  1922 FALSE   FALSE             FALSE            
## 2 AB    DAYSLAND                  1795 301AR54        NA <NA>   52.9 -112.  689. Etc/GMT+7 hour        NA    NA FALSE   FALSE             FALSE            
## 3 AB    DAYSLAND                  1795 301AR54        NA <NA>   52.9 -112.  689. Etc/GMT+7 month     1908  1922 FALSE   FALSE             FALSE            
## 4 AB    EDMONTON CORONATION       1796 301BK03        NA <NA>   53.6 -114.  671. Etc/GMT+7 day       1978  1979 FALSE   FALSE             FALSE            
## 5 AB    EDMONTON CORONATION       1796 301BK03        NA <NA>   53.6 -114.  671. Etc/GMT+7 hour        NA    NA FALSE   FALSE             FALSE            
## 6 AB    EDMONTON CORONATION       1796 301BK03        NA <NA>   53.6 -114.  671. Etc/GMT+7 month     1978  1979 FALSE   FALSE             FALSE
glimpse(stations())
## Rows: 26,343
## Columns: 16
## $ prov              <chr> "AB", "AB", "AB", "AB", "AB", "AB", "AB", "AB", "AB", "AB", "AB", "AB", "AB", "AB", "AB", "AB", "AB", "AB", "AB", "AB", "AB", "AB", …
## $ station_name      <chr> "DAYSLAND", "DAYSLAND", "DAYSLAND", "EDMONTON CORONATION", "EDMONTON CORONATION", "EDMONTON CORONATION", "FLEET", "FLEET", "FLEET", …
## $ station_id        <dbl> 1795, 1795, 1795, 1796, 1796, 1796, 1797, 1797, 1797, 1798, 1798, 1798, 1799, 1799, 1799, 1800, 1800, 1800, 1801, 1801, 1801, 1802, …
## $ climate_id        <chr> "301AR54", "301AR54", "301AR54", "301BK03", "301BK03", "301BK03", "301B6L0", "301B6L0", "301B6L0", "301B8LR", "301B8LR", "301B8LR", …
## $ WMO_id            <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ TC_id             <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ lat               <dbl> 52.87, 52.87, 52.87, 53.57, 53.57, 53.57, 52.15, 52.15, 52.15, 53.20, 53.20, 53.20, 52.40, 52.40, 52.40, 54.08, 54.08, 54.08, 53.52,…
## $ lon               <dbl> -112.28, -112.28, -112.28, -113.57, -113.57, -113.57, -111.73, -111.73, -111.73, -110.15, -110.15, -110.15, -115.20, -115.20, -115.2…
## $ elev              <dbl> 688.8, 688.8, 688.8, 670.6, 670.6, 670.6, 838.2, 838.2, 838.2, 640.0, 640.0, 640.0, 1036.0, 1036.0, 1036.0, 585.2, 585.2, 585.2, 668…
## $ tz                <chr> "Etc/GMT+7", "Etc/GMT+7", "Etc/GMT+7", "Etc/GMT+7", "Etc/GMT+7", "Etc/GMT+7", "Etc/GMT+7", "Etc/GMT+7", "Etc/GMT+7", "Etc/GMT+7", "E…
## $ interval          <chr> "day", "hour", "month", "day", "hour", "month", "day", "hour", "month", "day", "hour", "month", "day", "hour", "month", "day", "hour…
## $ start             <dbl> 1908, NA, 1908, 1978, NA, 1978, 1987, NA, 1987, 1987, NA, 1987, 1980, NA, 1980, 1980, NA, 1980, 1986, NA, 1986, 1987, NA, 1987, 1986…
## $ end               <dbl> 1922, NA, 1922, 1979, NA, 1979, 1990, NA, 1990, 1998, NA, 1998, 2009, NA, 2007, 1981, NA, 1981, 2019, NA, 2007, 1991, NA, 1991, 1995…
## $ normals           <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, TRUE, TRUE, FALSE, FALSE, FALSE, TRUE, TRU…
## $ normals_1981_2010 <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, TRUE, TRUE, FALSE, FALSE, FALSE, TRUE, TRU…
## $ normals_1971_2000 <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,…

You can look through this data frame directly, or you can use the stations_search function:

stations_search("Kamloops", interval = "hour")
## # A tibble: 3 × 16
##   prov  station_name station_id climate_id WMO_id TC_id   lat   lon  elev tz        interval start   end normals normals_1981_2010 normals_1971_2000
##   <chr> <chr>             <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr>     <chr>    <dbl> <dbl> <lgl>   <lgl>             <lgl>            
## 1 BC    KAMLOOPS A         1275 1163780     71887 YKA    50.7 -120.  345. Etc/GMT+8 hour      1953  2013 TRUE    TRUE              TRUE             
## 2 BC    KAMLOOPS A        51423 1163781     71887 YKA    50.7 -120.  345. Etc/GMT+8 hour      2013  2021 FALSE   FALSE             FALSE            
## 3 BC    KAMLOOPS AUT      42203 1163842     71741 ZKA    50.7 -120.  345  Etc/GMT+8 hour      2006  2021 FALSE   FALSE             FALSE

Time frame must be one of “hour”, “day”, or “month”.

You can also search by proximity:

stations_search(coords = c(50.667492, -120.329049), dist = 20, interval = "hour")
## # A tibble: 3 × 17
##   prov  station_name station_id climate_id WMO_id TC_id   lat   lon  elev tz        interval start   end normals normals_1981_2010 normals_1971_2000 distance
##   <chr> <chr>             <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr>     <chr>    <dbl> <dbl> <lgl>   <lgl>             <lgl>                <dbl>
## 1 BC    KAMLOOPS A         1275 1163780     71887 YKA    50.7 -120.  345. Etc/GMT+8 hour      1953  2013 TRUE    TRUE              TRUE                  8.64
## 2 BC    KAMLOOPS AUT      42203 1163842     71741 ZKA    50.7 -120.  345  Etc/GMT+8 hour      2006  2021 FALSE   FALSE             FALSE                 8.64
## 3 BC    KAMLOOPS A        51423 1163781     71887 YKA    50.7 -120.  345. Etc/GMT+8 hour      2013  2021 FALSE   FALSE             FALSE                 9.28

You can update this list of stations with

stations_dl()
## According to Environment Canada, Modified Date: 2021-11-30 23:34 UTC

## Stations data saved...
## Use `stations()` to access most recent version and `stations_meta()` to see when this was last updated

And check when it was last updated with

stations_meta()
## $ECCC_modified
## [1] "2021-11-30 23:34:00 UTC"
## 
## $weathercan_modified
## [1] "2022-01-31"

Note: For reproducibility, if you are using the stations list to gather your data, it can be a good idea to take note of the ECCC date of modification and include it in your reports/manuscripts.

Weather

Once you have your station_id(s) you can download weather data:

kam <- weather_dl(station_ids = 51423, start = "2018-02-01", end = "2018-04-15")
## As of weathercan v0.3.0 time display is either local time or UTC
## See Details under ?weather_dl for more information.
## This message is shown once per session
kam
## # A tibble: 1,776 × 37
##    station_name station_id station_operator prov    lat   lon  elev climate_id WMO_id TC_id date       time                year  month day   hour  weather  hmdx
##    <chr>             <dbl> <lgl>            <chr> <dbl> <dbl> <dbl> <chr>      <chr>  <chr> <date>     <dttm>              <chr> <chr> <chr> <chr> <chr>   <dbl>
##  1 KAMLOOPS A        51423 NA               BC     50.7 -120.  345. 1163781    71887  YKA   2018-02-01 2018-02-01 00:00:00 2018  02    01    00:00 <NA>       NA
##  2 KAMLOOPS A        51423 NA               BC     50.7 -120.  345. 1163781    71887  YKA   2018-02-01 2018-02-01 01:00:00 2018  02    01    01:00 Snow       NA
##  3 KAMLOOPS A        51423 NA               BC     50.7 -120.  345. 1163781    71887  YKA   2018-02-01 2018-02-01 02:00:00 2018  02    01    02:00 <NA>       NA
##  4 KAMLOOPS A        51423 NA               BC     50.7 -120.  345. 1163781    71887  YKA   2018-02-01 2018-02-01 03:00:00 2018  02    01    03:00 <NA>       NA
##  5 KAMLOOPS A        51423 NA               BC     50.7 -120.  345. 1163781    71887  YKA   2018-02-01 2018-02-01 04:00:00 2018  02    01    04:00 Cloudy     NA
##  6 KAMLOOPS A        51423 NA               BC     50.7 -120.  345. 1163781    71887  YKA   2018-02-01 2018-02-01 05:00:00 2018  02    01    05:00 <NA>       NA
##  7 KAMLOOPS A        51423 NA               BC     50.7 -120.  345. 1163781    71887  YKA   2018-02-01 2018-02-01 06:00:00 2018  02    01    06:00 <NA>       NA
##  8 KAMLOOPS A        51423 NA               BC     50.7 -120.  345. 1163781    71887  YKA   2018-02-01 2018-02-01 07:00:00 2018  02    01    07:00 Cloudy     NA
##  9 KAMLOOPS A        51423 NA               BC     50.7 -120.  345. 1163781    71887  YKA   2018-02-01 2018-02-01 08:00:00 2018  02    01    08:00 <NA>       NA
## 10 KAMLOOPS A        51423 NA               BC     50.7 -120.  345. 1163781    71887  YKA   2018-02-01 2018-02-01 09:00:00 2018  02    01    09:00 <NA>       NA
## # … with 1,766 more rows

You can also download data from multiple stations at once:

kam_pg <- weather_dl(station_ids = c(48248, 51423), start = "2018-02-01", end = "2018-04-15")

Climate Normals

To access climate normals, you first need to know the climate_id associated with the station you’re interested in.

stations_search("Winnipeg", normals_years = "current")
## # A tibble: 1 × 13
##   prov  station_name                station_id climate_id WMO_id TC_id   lat   lon  elev tz        normals normals_1981_2010 normals_1971_2000
##   <chr> <chr>                            <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr>     <lgl>   <lgl>             <lgl>            
## 1 MB    WINNIPEG RICHARDSON INT'L A       3698 5023222     71852 YWG    49.9 -97.2  239. Etc/GMT+6 TRUE    TRUE              TRUE

Then you can download the climate normals with the normals_dl() function.

n <- normals_dl("5023222")

See the Getting Started vignette for more details.

Citation

citation("weathercan")
## 
## To cite 'weathercan' in publications, please use:
## 
##   LaZerte, Stefanie E and Sam Albers (2018). weathercan: Download and format weather data from Environment and Climate Change Canada. The
##   Journal of Open Source Software 3(22):571. doi:10.21105/joss.00571.
## 
## A BibTeX entry for LaTeX users is
## 
##   @Article{,
##     title = {{weathercan}: {D}ownload and format weather data from Environment and Climate Change Canada},
##     author = {Stefanie E LaZerte and Sam Albers},
##     journal = {The Journal of Open Source Software},
##     volume = {3},
##     number = {22},
##     pages = {571},
##     year = {2018},
##     url = {https://joss.theoj.org/papers/10.21105/joss.00571},
##   }

License

The data and the code in this repository are licensed under multiple licences. All code is licensed GPL-3. All weather data is licensed under the (Open Government License - Canada).

weathercan in the wild!

  • Browse weathercan use cases on rOpenSci.org
  • Checkout the weathercan Shiny App by Nick Rong (@nickyrong) and Nathan Smith (@WraySmith)
  • R package RavenR has functions for converting ECCC data downloaded by weathercan to the .rvt format for Raven.
  • R package meteoland has functions for converting ECCC data downloaded by weathercan to the format required for use in meteoland.

Similar packages

rclimateca

weathercan and rclimateca were developed at roughly the same time and as a result, both present up-to-date methods for accessing and downloading data from ECCC. The largest differences between the two packages are: a) weathercan includes functions for interpolating weather data and directly integrating it into other data sources. b) weathercan actively seeks to apply tidy data principles in R and integrates well with the tidyverse including using tibbles and nested listcols. c) rclimateca contains arguments for specifying short vs. long data formats. d) rclimateca has the option of formatting data in the MUData format using the mudata2 package by the same author.

CHCN

CHCN is an older package last updated in 2012. Unfortunately, ECCC updated their services within the last couple of years which caused a great many of the previous web scrapers to fail. CHCN relies on a decommissioned older web-scraper and so is currently broken.

Contributions

We welcome any and all contributions! To make the process as painless as possible for all involved, please see our guide to contributing

Code of Conduct

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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