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R package for downloading weather data from Environment and Climate Change Canada
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README.md

weathercan

Build Status AppVeyor Build status codecov

DOI DOI

CRAN_Status_Badge CRAN Downloads

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

Installation

You can install weathercan directly from CRAN:

install.packages("weathercan")

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

install.packages("devtools") # If not already installed
devtools::install_github("ropensci/weathercan") 

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.

head(stations)
## # A tibble: 6 x 14
##   prov  station_name station_id climate_id WMO_id TC_id   lat   lon  elev tz    interval start   end
##   <chr> <chr>             <int> <fct>       <int> <fct> <dbl> <dbl> <dbl> <chr> <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
## # … with 1 more variable: normals <lgl>
glimpse(stations)
## Observations: 26,286
## Variables: 14
## $ prov         <chr> "AB", "AB", "AB", "AB", "AB", "AB", "AB", "AB", "AB", "AB", "AB", "AB", "AB"…
## $ station_name <chr> "DAYSLAND", "DAYSLAND", "DAYSLAND", "EDMONTON CORONATION", "EDMONTON CORONAT…
## $ station_id   <int> 1795, 1795, 1795, 1796, 1796, 1796, 1797, 1797, 1797, 1798, 1798, 1798, 1799…
## $ climate_id   <fct> 301AR54, 301AR54, 301AR54, 301BK03, 301BK03, 301BK03, 301B6L0, 301B6L0, 301B…
## $ WMO_id       <int> 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, 53.20,…
## $ lon          <dbl> -112.28, -112.28, -112.28, -113.57, -113.57, -113.57, -111.73, -111.73, -111…
## $ elev         <dbl> 688.8, 688.8, 688.8, 670.6, 670.6, 670.6, 838.2, 838.2, 838.2, 640.0, 640.0,…
## $ tz           <chr> "Etc/GMT+7", "Etc/GMT+7", "Etc/GMT+7", "Etc/GMT+7", "Etc/GMT+7", "Etc/GMT+7"…
## $ interval     <chr> "day", "hour", "month", "day", "hour", "month", "day", "hour", "month", "day…
## $ start        <int> 1908, NA, 1908, 1978, NA, 1978, 1987, NA, 1987, 1987, NA, 1987, 1980, NA, 19…
## $ end          <int> 1922, NA, 1922, 1979, NA, 1979, 1990, NA, 1990, 1998, NA, 1998, 2009, NA, 20…
## $ normals      <lgl> 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 x 14
##   prov  station_name station_id climate_id WMO_id TC_id   lat   lon  elev tz    interval start   end
##   <chr> <chr>             <int> <fct>       <int> <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 A        51423 1163781     71887 YKA    50.7 -120.  345. Etc/… hour      2013  2019
## 3 BC    KAMLOOPS AUT      42203 1163842     71741 ZKA    50.7 -120.  345  Etc/… hour      2006  2019
## # … with 1 more variable: normals <lgl>

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 15
##   prov  station_name station_id climate_id WMO_id TC_id   lat   lon  elev tz    interval start   end
##   <chr> <chr>             <int> <fct>       <int> <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  2019
## 3 BC    KAMLOOPS A        51423 1163781     71887 YKA    50.7 -120.  345. Etc/… hour      2013  2019
## # … with 2 more variables: normals <lgl>, 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")
## 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 x 35
##    station_name station_id station_operator prov    lat   lon  elev climate_id WMO_id TC_id
##    <chr>             <dbl> <lgl>            <chr> <dbl> <dbl> <dbl> <chr>      <chr>  <chr>
##  1 KAMLOOPS A        51423 NA               BC     50.7 -120.  345. 1163781    71887  YKA  
##  2 KAMLOOPS A        51423 NA               BC     50.7 -120.  345. 1163781    71887  YKA  
##  3 KAMLOOPS A        51423 NA               BC     50.7 -120.  345. 1163781    71887  YKA  
##  4 KAMLOOPS A        51423 NA               BC     50.7 -120.  345. 1163781    71887  YKA  
##  5 KAMLOOPS A        51423 NA               BC     50.7 -120.  345. 1163781    71887  YKA  
##  6 KAMLOOPS A        51423 NA               BC     50.7 -120.  345. 1163781    71887  YKA  
##  7 KAMLOOPS A        51423 NA               BC     50.7 -120.  345. 1163781    71887  YKA  
##  8 KAMLOOPS A        51423 NA               BC     50.7 -120.  345. 1163781    71887  YKA  
##  9 KAMLOOPS A        51423 NA               BC     50.7 -120.  345. 1163781    71887  YKA  
## 10 KAMLOOPS A        51423 NA               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()

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_only = TRUE)
## # A tibble: 3 x 11
##   prov  station_name            station_id climate_id WMO_id TC_id   lat   lon  elev tz      normals
##   <chr> <chr>                        <int> <fct>       <int> <fct> <dbl> <dbl> <dbl> <chr>   <lgl>  
## 1 MB    WINNIPEG A CS                27174 502S001     71849 XWG    49.9 -97.2  239. Etc/GM… TRUE   
## 2 MB    WINNIPEG RICHARDSON IN…       3698 5023222     71852 YWG    49.9 -97.2  239. Etc/GM… TRUE   
## 3 MB    WINNIPEG THE FORKS           28051 5023262     71579 XWN    49.9 -97.1  230  Etc/GM… TRUE

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

n <- normals_dl("5023222")

There are two parts to the normals data, average weather measurements and average frost dates.

library(tidyr)
unnest(n, normals)
## # A tibble: 13 x 202
##    prov  station_name climate_id meets_wmo period temp_daily_aver… temp_daily_aver… temp_sd
##    <chr> <chr>        <chr>      <lgl>     <fct>             <dbl> <chr>              <dbl>
##  1 MB    WINNIPEG RI… 5023222    TRUE      Jan               -16.4 A                    4.1
##  2 MB    WINNIPEG RI… 5023222    TRUE      Feb               -13.2 A                    4.2
##  3 MB    WINNIPEG RI… 5023222    TRUE      Mar                -5.8 A                    3.1
##  4 MB    WINNIPEG RI… 5023222    TRUE      Apr                 4.4 A                    2.7
##  5 MB    WINNIPEG RI… 5023222    TRUE      May                11.6 A                    2.1
##  6 MB    WINNIPEG RI… 5023222    TRUE      Jun                17   A                    2  
##  7 MB    WINNIPEG RI… 5023222    TRUE      Jul                19.7 A                    1.4
##  8 MB    WINNIPEG RI… 5023222    TRUE      Aug                18.8 A                    1.9
##  9 MB    WINNIPEG RI… 5023222    TRUE      Sep                12.7 A                    1.3
## 10 MB    WINNIPEG RI… 5023222    TRUE      Oct                 5   A                    1.8
## 11 MB    WINNIPEG RI… 5023222    TRUE      Nov                -4.9 A                    3.6
## 12 MB    WINNIPEG RI… 5023222    TRUE      Dec               -13.2 A                    4.4
## 13 MB    WINNIPEG RI… 5023222    TRUE      Year                3   A                    1.2
## # … with 194 more variables: temp_sd_code <chr>, temp_daily_max <dbl>, temp_daily_max_code <chr>,
## #   temp_daily_min <dbl>, temp_daily_min_code <chr>, temp_extreme_max <dbl>,
## #   temp_extreme_max_code <chr>, temp_extreme_max_date <date>, temp_extreme_max_date_code <chr>,
## #   temp_extreme_min <dbl>, temp_extreme_min_code <chr>, temp_extreme_min_date <date>,
## #   temp_extreme_min_date_code <chr>, rain <dbl>, rain_code <chr>, snow <dbl>, snow_code <chr>,
## #   precip <dbl>, precip_code <chr>, snow_mean_depth <dbl>, snow_mean_depth_code <chr>,
## #   snow_median_depth <dbl>, snow_median_depth_code <chr>, snow_depth_month_end <dbl>,
## #   snow_depth_month_end_code <chr>, rain_extreme_daily <dbl>, rain_extreme_daily_code <chr>,
## #   rain_extreme_daily_date <date>, rain_extreme_daily_date_code <chr>, snow_extreme_daily <dbl>,
## #   snow_extreme_daily_code <chr>, snow_extreme_daily_date <date>,
## #   snow_extreme_daily_date_code <chr>, precip_extreme_daily <dbl>,
## #   precip_extreme_daily_code <chr>, precip_extreme_daily_date <date>,
## #   precip_extreme_daily_date_code <chr>, snow_extreme_depth <dbl>, snow_extreme_depth_code <chr>,
## #   snow_extreme_depth_date <date>, snow_extreme_depth_date_code <chr>, `temp_max_days_<=0` <dbl>,
## #   `temp_max_days_<=0_code` <chr>, `temp_max_days_>0` <dbl>, `temp_max_days_>0_code` <chr>,
## #   `temp_max_days_>10` <dbl>, `temp_max_days_>10_code` <chr>, `temp_max_days_>20` <dbl>,
## #   `temp_max_days_>20_code` <chr>, `temp_max_days_>30` <dbl>, `temp_max_days_>30_code` <chr>,
## #   `temp_max_days_>35` <dbl>, `temp_max_days_>35_code` <chr>, `temp_min_days_>0` <dbl>,
## #   `temp_min_days_>0_code` <chr>, `temp_min_days_<=2` <dbl>, `temp_min_days_<=2_code` <chr>,
## #   `temp_min_days_<=0` <dbl>, `temp_min_days_<=0_code` <chr>, `temp_min_days_<-2` <dbl>,
## #   `temp_min_days_<-2_code` <chr>, `temp_min_days_<-10` <dbl>, `temp_min_days_<-10_code` <chr>,
## #   `temp_min_days_<-20` <dbl>, `temp_min_days_<-20_code` <chr>, `temp_min_days_<-30` <dbl>,
## #   `temp_min_days_<-30_code` <chr>, `rain_days_>=0.2` <dbl>, `rain_days_>=0.2_code` <chr>,
## #   `rain_days_>=5` <dbl>, `rain_days_>=5_code` <chr>, `rain_days_>=10` <dbl>,
## #   `rain_days_>=10_code` <chr>, `rain_days_>=25` <dbl>, `rain_days_>=25_code` <chr>,
## #   `snow_days_>=0.2` <dbl>, `snow_days_>=0.2_code` <chr>, `snow_days_>=5` <dbl>,
## #   `snow_days_>=5_code` <chr>, `snow_days_>=10` <dbl>, `snow_days_>=10_code` <chr>,
## #   `snow_days_>=25` <dbl>, `snow_days_>=25_code` <chr>, `precip_days_>=0.2` <dbl>,
## #   `precip_days_>=0.2_code` <chr>, `precip_days_>=5` <dbl>, `precip_days_>=5_code` <chr>,
## #   `precip_days_>=10` <dbl>, `precip_days_>=10_code` <chr>, `precip_days_>=25` <dbl>,
## #   `precip_days_>=25_code` <chr>, `snow_depth_days_>=1` <dbl>, `snow_depth_days_>=1_code` <chr>,
## #   `snow_depth_days_>=5` <dbl>, `snow_depth_days_>=5_code` <chr>, `snow_depth_days_>=10` <dbl>,
## #   `snow_depth_days_>=10_code` <chr>, `snow_depth_days_>=20` <dbl>,
## #   `snow_depth_days_>=20_code` <chr>, wind_speed <dbl>, …
unnest(n, frost)
## # A tibble: 7 x 13
##   prov  station_name climate_id meets_wmo normals frost_code date_first_fall… date_last_sprin…
##   <chr> <chr>        <chr>      <lgl>     <list>  <chr>                 <dbl>            <dbl>
## 1 MB    WINNIPEG RI… 5023222    TRUE      <tibbl… A                       265              143
## 2 MB    WINNIPEG RI… 5023222    TRUE      <tibbl… A                       265              143
## 3 MB    WINNIPEG RI… 5023222    TRUE      <tibbl… A                       265              143
## 4 MB    WINNIPEG RI… 5023222    TRUE      <tibbl… A                       265              143
## 5 MB    WINNIPEG RI… 5023222    TRUE      <tibbl… A                       265              143
## 6 MB    WINNIPEG RI… 5023222    TRUE      <tibbl… A                       265              143
## 7 MB    WINNIPEG RI… 5023222    TRUE      <tibbl… A                       265              143
## # … with 5 more variables: length_frost_free <dbl>, prob <fct>,
## #   prob_first_fall_temp_below_0_on_date <dbl>, prob_length_frost_free <dbl>,
## #   prob_last_spring_temp_below_0_on_date <dbl>

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 = {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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