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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 unecessarily. Try to stick to what you need.

For more details and tutorials checkout the weathercan website

Installation

You can install weathercan directly from CRAN:

Use the devtools package to install the developmental package from GitHub:

To build the developmental vignettes (tutorials) locally, use:

devtools::install_github("ropensci/weathercan", build_vignettes = TRUE) 

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 a data frame called stations which includes a list of stations and their details (including station_id.

## # A tibble: 6 x 13
##   prov  station_name station_id climate_id WMO_id TC_id   lat   lon  elev tz   interval start   end
##   <fct> <chr>        <fct>      <fct>      <fct>  <fct> <dbl> <dbl> <dbl> <ch> <chr>    <int> <int>
## 1 AB    DAYSLAND     1795       301AR54    <NA>   <NA>   52.9 -112.  689. Etc… day       1908  1922
## 2 AB    DAYSLAND     1795       301AR54    <NA>   <NA>   52.9 -112.  689. Etc… hour        NA    NA
## 3 AB    DAYSLAND     1795       301AR54    <NA>   <NA>   52.9 -112.  689. Etc… month     1908  1922
## 4 AB    EDMONTON CO… 1796       301BK03    <NA>   <NA>   53.6 -114.  671. Etc… day       1978  1979
## 5 AB    EDMONTON CO… 1796       301BK03    <NA>   <NA>   53.6 -114.  671. Etc… hour        NA    NA
## 6 AB    EDMONTON CO… 1796       301BK03    <NA>   <NA>   53.6 -114.  671. Etc… month     1978  1979
## Observations: 26,217
## Variables: 13
## $ prov         <fct> 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 CORON...
## $ station_id   <fct> 1795, 1795, 1795, 1796, 1796, 1796, 1797, 1797, 1797, 1798, 1798, 1798, 17...
## $ climate_id   <fct> 301AR54, 301AR54, 301AR54, 301BK03, 301BK03, 301BK03, 301B6L0, 301B6L0, 30...
## $ WMO_id       <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA...
## $ TC_id        <fct> 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, 53.2...
## $ lon          <dbl> -112.28, -112.28, -112.28, -113.57, -113.57, -113.57, -111.73, -111.73, -1...
## $ elev         <dbl> 688.8, 688.8, 688.8, 670.6, 670.6, 670.6, 838.2, 838.2, 838.2, 640.0, 640....
## $ tz           <chr> "Etc/GMT+7", "Etc/GMT+7", "Etc/GMT+7", "Etc/GMT+7", "Etc/GMT+7", "Etc/GMT+...
## $ interval     <chr> "day", "hour", "month", "day", "hour", "month", "day", "hour", "month", "d...
## $ start        <int> 1908, NA, 1908, 1978, NA, 1978, 1987, NA, 1987, 1987, NA, 1987, 1980, NA, ...
## $ end          <int> 1922, NA, 1922, 1979, NA, 1979, 1990, NA, 1990, 1998, NA, 1998, 2009, NA, ...

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

stations_search("Kamloops", interval = "hour")
## # A tibble: 3 x 13
##   prov  station_name station_id climate_id WMO_id TC_id   lat   lon  elev tz   interval start   end
##   <fct> <chr>        <fct>      <fct>      <fct>  <fct> <dbl> <dbl> <dbl> <ch> <chr>    <int> <int>
## 1 BC    KAMLOOPS A   1275       1163780    71887  YKA    50.7 -120.  345. Etc… hour      1953  2013
## 2 BC    KAMLOOPS A   51423      1163781    71887  YKA    50.7 -120.  345. Etc… hour      2013  2018
## 3 BC    KAMLOOPS AUT 42203      1163842    71741  ZKA    50.7 -120.  345  Etc… hour      2006  2018

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 x 14
##   prov  station_name station_id climate_id WMO_id TC_id   lat   lon  elev tz    interval start   end
##   <fct> <chr>        <fct>      <fct>      <fct>  <fct> <dbl> <dbl> <dbl> <chr> <chr>    <int> <int>
## 1 BC    KAMLOOPS A   1275       1163780    71887  YKA    50.7 -120.  345. Etc/… hour      1953  2013
## 2 BC    KAMLOOPS AUT 42203      1163842    71741  ZKA    50.7 -120.  345  Etc/… hour      2006  2018
## 3 BC    KAMLOOPS A   51423      1163781    71887  YKA    50.7 -120.  345. Etc/… hour      2013  2018
## # ... with 1 more variable: distance <dbl>

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")
kam
## # A tibble: 1,776 x 35
##    station_name station_id station_operator prov    lat   lon  elev climate_id WMO_id TC_id
##  * <chr>             <dbl> <chr>            <fct> <dbl> <dbl> <dbl> <chr>      <chr>  <chr>
##  1 KAMLOOPS A        51423 NAV Canada       BC     50.7 -120.  345. 1163781    71887  YKA  
##  2 KAMLOOPS A        51423 NAV Canada       BC     50.7 -120.  345. 1163781    71887  YKA  
##  3 KAMLOOPS A        51423 NAV Canada       BC     50.7 -120.  345. 1163781    71887  YKA  
##  4 KAMLOOPS A        51423 NAV Canada       BC     50.7 -120.  345. 1163781    71887  YKA  
##  5 KAMLOOPS A        51423 NAV Canada       BC     50.7 -120.  345. 1163781    71887  YKA  
##  6 KAMLOOPS A        51423 NAV Canada       BC     50.7 -120.  345. 1163781    71887  YKA  
##  7 KAMLOOPS A        51423 NAV Canada       BC     50.7 -120.  345. 1163781    71887  YKA  
##  8 KAMLOOPS A        51423 NAV Canada       BC     50.7 -120.  345. 1163781    71887  YKA  
##  9 KAMLOOPS A        51423 NAV Canada       BC     50.7 -120.  345. 1163781    71887  YKA  
## 10 KAMLOOPS A        51423 NAV Canada       BC     50.7 -120.  345. 1163781    71887  YKA  
## # ... with 1,766 more rows, and 25 more variables

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")

And plot it:

library(ggplot2)

ggplot(data = kam_pg, aes(x = time, y = temp, group = station_name, colour = station_name)) +
  theme_minimal() + 
  geom_line()

Citation

## 
## 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 = {http://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).

Similar packages

  1. 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.

  1. 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 one of these older web-scrapers 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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