Introduction to weathercan

Québec-Océan Students 2025

steffilazerte
@steffilazerte@fosstodon.org
steffilazerte.ca

Compiled: 2025-10-27

Preamble

Online workshops can be challenging

Consider keeping your video on (if possible)

Interrupt me!

Generally keep yourself muted but un-mute anytime to ask questions

Ask Questions!

Group trouble-shooting is really valuable
If you have a problem, others may also (or may have it in the future)

I may ask you to share your screen with the group (feel free to decline)
For privacy, close your email etc. Or just share your RStudio window

Introductions

Me and my creatures

My garden

What about you?

Name
Background (Role, Area of study, etc.)
Familiarity with R or Programming
Creatures (furry, feathery, scaley, green or otherwise)?

About this Workshop

Format

I will provide you tools and workflow to get started with weathercan in R
We’ll have hands-on activities, lectures, and demonstrations

R is hard: But have no fear!

Don’t expect to remember everything!
Copy/Paste is your friend (never apologize for using it!)
Consider this workshop a resource to return to

A frustrated little monster sits on the ground with his hat next to him, saying "I just need a minute." Looking on empathetically is the R logo, with the word "Error" in many different styles behind it.

Artwork by @allison_horst

Getting Started

R is a Programming language

A programming language is a way to give instructions in order to get a computer to do something

You need to know the language (i.e., the code)
Computers don’t know what you mean, only what you type (unfortunately)
Spelling, punctuation, and capitalization all matter!

For example

R, what is 56 times 5.8?

56 * 5.8

[1] 324.8

Use code to tell R what to do

R, what is the average of numbers 1, 2, 3, 4?

mean(c(1, 2, 3, 4))

[1] 2.5

R, save this value for later

steffis_mean <- mean(c(1, 2, 3, 4))

R, multiply this value by 6

steffis_mean * 6

[1] 15

Some Terminology

Code, Output, Scripts

Code

The actual commands

Output

The result of running code or a script

Script

A text file full of code that you want to run
You should always keep your code in a script

For example:

mean(c(1, 2, 3, 4))

[1] 2.5

Code Output

A screenshot of a script in the RStudio window: many lines of code in a file called '4_analysis.R' Script

weathercan vs. WeatherCAN

weathercan is not WeatherCAN
weathercan is an R package created by Steffi to access ECCC historical weather data
WeatherCAN is an App created by ECCC for accessing current weather on your photo

We’re using weathercan today

RStudio vs. R

RStudio is not R
RStudio is a User Interface or IDE (integrated development environment)
- (i.e., Makes coding simpler)

We’re using both RStudio and R today

Open RStudio

ImpostR Syndrome

Text reading "Impost R Syndrome" with the Blue R logo for the 'R'

ImpostR Syndrome

Text reading "Impost R Syndrome" with the Blue R logo for the 'R'

Two yellow circles. The one on the left has 'Imposter Syndrome' written above. Inside this yellow circle it says 'What I think others know', inside a small blue circle reads 'What I know'. The yellow circle on the right has 'Reality' written above. This yellow circle is made up of many small yellow circles with the label 'What others know', surrounding a small blue circle which reads 'What I know' — David Whittaker

Moral of the story?

Make friends, code in groups, learn together and don’t beat yourself up

The Goal

An R-logo with a scary face, and a small scared little fuzzy monster holding up a white flag in surrender while under a dark storm cloud. The text above says "at first I was like…"

Artwork by @allison_horst

A friendly, smiling R-logo jumping up to give a happy fuzzy monster a high-five under a smiling sun and next to colorful flowers. The text above reads "but now it’s like…"

weathercan

What is weathercan?

Peer-reviewed R package 🎉
Bundle of functions for downloading ECCC historical weather data and normals

Using weathercan in a nutshell

1library(weathercan)
2stations()
3stations_search("Brandon")
4w <- weather_dl("49909", start = "2025-09-01")
5n <- normals_dl("5010480")

1: Access weathercan functions with the library() function
2: Use the stations() function to get a list of stations
3: OR use the stations_search() function to search for a station
4: Use weather_dl() to download recent data by station_id
5: Use normals_dl() to download climate normals by climate_id

That’s it! Workshop over 😁

Using weathercan

Understanding the functions - Documentation

Online: https://docs.ropensci.org/weathercan
In RStudio: ?weather_dl

Citing weathercan

Cite R and all R packages
Cite data as ECCC historical weather data or Climate normals
Cite weathercan as access to data

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},
  }

Using weathercan

Understanding the data - Glossaries of terms

Three glossaries with datasets in weathercan:
- Flags, flags
- Weather Terms and Units, glossary
- Climate Normal Terms and Units, glossary_normals
All from ECCC, sometimes best to go to the source!

flags

# A tibble: 16 × 2
   code    meaning                                                            
   <chr>   <chr>                                                              
 1 A       Accumulated                                                        
 2 B       More than one occurrence and estimated                             
 3 C       Precipitation occurred, amount uncertain                           
 4 D       Data subject to further quality control procedure                  
 5 E       Estimated                                                          
 6 F       Accumulated and estimated                                          
 7 L       Precipitation may or may not have occurred                         
 8 M       Missing                                                            
 9 N       Temperature missing but known to be > 0                            
10 S       More than one occurrence                                           
11 T       Trace                                                              
12 Y       Temperature missing but known to be < 0                            
13 [empty] Indicates an unobserved value                                      
14 ^       The value displayed is based on incomplete data                    
15 †       Data that is not subject to review by the National Climate Archives
16 <NA>    Not Available

Stations

Where?

`stations()`

Included Data frame

library(weathercan)
stations()

# A tibble: 26,451 × 17
   prov  station_name        station_id climate_id WMO_id TC_id   lat   lon  elev tz        interval start   end normals
   <chr> <chr>                    <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr>     <chr>    <dbl> <dbl> <lgl>  
 1 AB    DAYSLAND                  1795 301AR54        NA <NA>   52.9 -112.  689. Etc/GMT+7 day       1908  1922 FALSE  
 2 AB    DAYSLAND                  1795 301AR54        NA <NA>   52.9 -112.  689. Etc/GMT+7 hour        NA    NA FALSE  
 3 AB    DAYSLAND                  1795 301AR54        NA <NA>   52.9 -112.  689. Etc/GMT+7 month     1908  1922 FALSE  
 4 AB    EDMONTON CORONATION       1796 301BK03        NA <NA>   53.6 -114.  671. Etc/GMT+7 day       1978  1979 FALSE  
 5 AB    EDMONTON CORONATION       1796 301BK03        NA <NA>   53.6 -114.  671. Etc/GMT+7 hour        NA    NA FALSE  
 6 AB    EDMONTON CORONATION       1796 301BK03        NA <NA>   53.6 -114.  671. Etc/GMT+7 month     1978  1979 FALSE  
 7 AB    FLEET                     1797 301B6L0        NA <NA>   52.2 -112.  838. Etc/GMT+7 day       1987  1990 FALSE  
 8 AB    FLEET                     1797 301B6L0        NA <NA>   52.2 -112.  838. Etc/GMT+7 hour        NA    NA FALSE  
 9 AB    FLEET                     1797 301B6L0        NA <NA>   52.2 -112.  838. Etc/GMT+7 month     1987  1990 FALSE  
10 AB    GOLDEN VALLEY             1798 301B8LR        NA <NA>   53.2 -110.  640  Etc/GMT+7 day       1987  1998 FALSE  
# ℹ 26,441 more rows
# ℹ 3 more variables: normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

`stations_dl()`

Make sure it’s up-to-date

stations_dl()

According to Environment Canada, Modified Date: 2025-10-08 23:30 UTC

Environment Canada Disclaimers:
"Station Inventory Disclaimer: Please note that this inventory list is a snapshot of stations on our website as of the modified date, and may be subject to change without notice."
"Station ID Disclaimer: Station IDs are an internal index numbering system and may be subject to change without notice."

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

stations_meta()

$ECCC_modified
[1] "2025-10-08 23:30:00 UTC"

$weathercan_modified
[1] "2025-10-27"

`stations_search()`

By name

stations_search(name = "Brandon")

# A tibble: 17 × 17
   prov  station_name          station_id climate_id WMO_id TC_id   lat    lon  elev tz     interval start   end normals
   <chr> <chr>                      <dbl> <chr>       <dbl> <chr> <dbl>  <dbl> <dbl> <chr>  <chr>    <dbl> <dbl> <lgl>  
 1 MB    BRANDON #1 WINTER BAY       3474 5010498        NA <NA>   49.8 -100.0  396  Etc/G… day       1987  2002 FALSE  
 2 MB    BRANDON #1 WINTER BAY       3474 5010498        NA <NA>   49.8 -100.0  396  Etc/G… month     1987  2002 FALSE  
 3 MB    BRANDON A                   3471 5010480     71140 YBR    49.9 -100.0  409. Etc/G… day       1941  2012 TRUE   
 4 MB    BRANDON A                   3471 5010480     71140 YBR    49.9 -100.0  409. Etc/G… hour      1958  2012 TRUE   
 5 MB    BRANDON A                   3471 5010480     71140 YBR    49.9 -100.0  409. Etc/G… month     1941  2012 TRUE   
 6 MB    BRANDON CDA                 3472 5010485        NA <NA>   49.9 -100.0  363. Etc/G… day       1890  2010 TRUE   
 7 MB    BRANDON CDA                 3472 5010485        NA <NA>   49.9 -100.0  363. Etc/G… month     1890  2007 TRUE   
 8 MB    BRANDON MUNI A             50821 5010481     71140 YBR    49.9 -100.0  409. Etc/G… day       2012  2025 TRUE   
 9 MB    BRANDON MUNI A             50821 5010481     71140 YBR    49.9 -100.0  409. Etc/G… hour      2012  2025 TRUE   
10 MB    BRANDON MUNI A             55738 5010482     71140 YBR    49.9 -100.0  409. Etc/G… day       2025  2025 FALSE  
11 MB    BRANDON MUNI A             55738 5010482     71140 YBR    49.9 -100.0  409. Etc/G… hour      2025  2025 FALSE  
12 MB    BRANDON RCS                49909 5010490     71136 PBO    49.9 -100.0  409. Etc/G… day       2012  2025 FALSE  
13 MB    BRANDON RCS                49909 5010490     71136 PBO    49.9 -100.0  409. Etc/G… hour      2012  2025 FALSE  
14 MB    BRANDON SOUTH               3473 5010494        NA <NA>   49.8 -100.0  396. Etc/G… day       1972  1975 FALSE  
15 MB    BRANDON SOUTH               3473 5010494        NA <NA>   49.8 -100.0  396. Etc/G… month     1972  1975 FALSE  
16 QC    ST GABRIEL DE BRANDON       5273 7017270        NA <NA>   46.3  -73.4  198. Etc/G… day       1919  1985 FALSE  
17 QC    ST GABRIEL DE BRANDON       5273 7017270        NA <NA>   46.3  -73.4  198. Etc/G… month     1919  1985 FALSE  
# ℹ 3 more variables: normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

`stations_search()`

By date and interval

stations_search(
  name = "Brandon",
  interval = "day",
  starts_latest = 2020,
  ends_earliest = 2025
)

# A tibble: 2 × 17
  prov  station_name   station_id climate_id WMO_id TC_id   lat    lon  elev tz        interval start   end normals
  <chr> <chr>               <dbl> <chr>       <dbl> <chr> <dbl>  <dbl> <dbl> <chr>     <chr>    <dbl> <dbl> <lgl>  
1 MB    BRANDON MUNI A      50821 5010481     71140 YBR    49.9 -100.0  409. Etc/GMT+6 day       2012  2025 TRUE   
2 MB    BRANDON RCS         49909 5010490     71136 PBO    49.9 -100.0  409. Etc/GMT+6 day       2012  2025 FALSE  
# ℹ 3 more variables: normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

Hmmm, that’s a bit tough to read

Looking at tables

Use glimpse() from the dplyr package

stations_search(
  name = "Brandon",
  interval = "day",
  starts_latest = 2020,
  ends_earliest = 2025
) |> dplyr::glimpse()

Rows: 2
Columns: 17
$ prov              <chr> "MB", "MB"
$ station_name      <chr> "BRANDON MUNI A", "BRANDON RCS"
$ station_id        <dbl> 50821, 49909
$ climate_id        <chr> "5010481", "5010490"
$ WMO_id            <dbl> 71140, 71136
$ TC_id             <chr> "YBR", "PBO"
$ lat               <dbl> 49.91, 49.90
$ lon               <dbl> -99.95, -99.95
$ elev              <dbl> 409.3, 409.4
$ tz                <chr> "Etc/GMT+6", "Etc/GMT+6"
$ interval          <chr> "day", "day"
$ start             <dbl> 2012, 2012
$ end               <dbl> 2025, 2025
$ normals           <lgl> TRUE, FALSE
$ normals_1991_2020 <lgl> TRUE, FALSE
$ normals_1981_2010 <lgl> FALSE, FALSE
$ normals_1971_2000 <lgl> FALSE, FALSE

Looking at tables

Save as an object and use RStudio’s viewer

s <- stations_search(
  name = "Brandon",
  interval = "day",
  starts_latest = 2020,
  ends_earliest = 2025
)

After running this code, click on ‘s’ in the Environment Pane

`stations_search()`

By distance

stations_search(coords = c(49.85, -99.91))

# A tibble: 15 × 18
   prov  station_name          station_id climate_id WMO_id TC_id   lat    lon  elev tz     interval start   end normals
   <chr> <chr>                      <dbl> <chr>       <dbl> <chr> <dbl>  <dbl> <dbl> <chr>  <chr>    <dbl> <dbl> <lgl>  
 1 MB    BRANDON SOUTH               3473 5010494        NA <NA>   49.8 -100.0  396. Etc/G… day       1972  1975 FALSE  
 2 MB    BRANDON SOUTH               3473 5010494        NA <NA>   49.8 -100.0  396. Etc/G… month     1972  1975 FALSE  
 3 MB    BRANDON CDA                 3472 5010485        NA <NA>   49.9 -100.0  363. Etc/G… day       1890  2010 TRUE   
 4 MB    BRANDON CDA                 3472 5010485        NA <NA>   49.9 -100.0  363. Etc/G… month     1890  2007 TRUE   
 5 MB    BRANDON #1 WINTER BAY       3474 5010498        NA <NA>   49.8 -100.0  396  Etc/G… day       1987  2002 FALSE  
 6 MB    BRANDON #1 WINTER BAY       3474 5010498        NA <NA>   49.8 -100.0  396  Etc/G… month     1987  2002 FALSE  
 7 MB    BRANDON RCS                49909 5010490     71136 PBO    49.9 -100.0  409. Etc/G… day       2012  2025 FALSE  
 8 MB    BRANDON RCS                49909 5010490     71136 PBO    49.9 -100.0  409. Etc/G… hour      2012  2025 FALSE  
 9 MB    BRANDON A                   3471 5010480     71140 YBR    49.9 -100.0  409. Etc/G… day       1941  2012 TRUE   
10 MB    BRANDON A                   3471 5010480     71140 YBR    49.9 -100.0  409. Etc/G… hour      1958  2012 TRUE   
11 MB    BRANDON A                   3471 5010480     71140 YBR    49.9 -100.0  409. Etc/G… month     1941  2012 TRUE   
12 MB    BRANDON MUNI A             50821 5010481     71140 YBR    49.9 -100.0  409. Etc/G… day       2012  2025 TRUE   
13 MB    BRANDON MUNI A             50821 5010481     71140 YBR    49.9 -100.0  409. Etc/G… hour      2012  2025 TRUE   
14 MB    BRANDON MUNI A             55738 5010482     71140 YBR    49.9 -100.0  409. Etc/G… day       2025  2025 FALSE  
15 MB    BRANDON MUNI A             55738 5010482     71140 YBR    49.9 -100.0  409. Etc/G… hour      2025  2025 FALSE  
# ℹ 4 more variables: normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>, distance <dbl>

`stations_search()`

Using dplyr package for advanced searching

library(dplyr)
library(stringr)

stations() |>
  filter(
    str_detect(station_name, "BRANDON"),
    interval == "day",
    start <= 2020,
    end >= 2025
  )

# A tibble: 2 × 17
  prov  station_name   station_id climate_id WMO_id TC_id   lat    lon  elev tz        interval start   end normals
  <chr> <chr>               <dbl> <chr>       <dbl> <chr> <dbl>  <dbl> <dbl> <chr>     <chr>    <dbl> <dbl> <lgl>  
1 MB    BRANDON RCS         49909 5010490     71136 PBO    49.9 -100.0  409. Etc/GMT+6 day       2012  2025 FALSE  
2 MB    BRANDON MUNI A      50821 5010481     71140 YBR    49.9 -100.0  409. Etc/GMT+6 day       2012  2025 TRUE   
# ℹ 3 more variables: normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

We’re not actually using stations_search() at all here

Your turn!

Locate a station of interest, take note of it’s Station ID

(Feel free to locate several stations)

Weather

Historical hourly, daily,
or monthly weather

`weather_dl()`

Find a station

stations_search("brandon", interval = "day")

# A tibble: 8 × 17
  prov  station_name          station_id climate_id WMO_id TC_id   lat    lon  elev tz      interval start   end normals
  <chr> <chr>                      <dbl> <chr>       <dbl> <chr> <dbl>  <dbl> <dbl> <chr>   <chr>    <dbl> <dbl> <lgl>  
1 MB    BRANDON #1 WINTER BAY       3474 5010498        NA <NA>   49.8 -100.0  396  Etc/GM… day       1987  2002 FALSE  
2 MB    BRANDON A                   3471 5010480     71140 YBR    49.9 -100.0  409. Etc/GM… day       1941  2012 TRUE   
3 MB    BRANDON CDA                 3472 5010485        NA <NA>   49.9 -100.0  363. Etc/GM… day       1890  2010 TRUE   
4 MB    BRANDON MUNI A             50821 5010481     71140 YBR    49.9 -100.0  409. Etc/GM… day       2012  2025 TRUE   
5 MB    BRANDON MUNI A             55738 5010482     71140 YBR    49.9 -100.0  409. Etc/GM… day       2025  2025 FALSE  
6 MB    BRANDON RCS                49909 5010490     71136 PBO    49.9 -100.0  409. Etc/GM… day       2012  2025 FALSE  
7 MB    BRANDON SOUTH               3473 5010494        NA <NA>   49.8 -100.0  396. Etc/GM… day       1972  1975 FALSE  
8 QC    ST GABRIEL DE BRANDON       5273 7017270        NA <NA>   46.3  -73.4  198. Etc/GM… day       1919  1985 FALSE  
# ℹ 3 more variables: normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

Now download the data

w <- weather_dl(station_id = 49909, interval = "day", start = "2025-01-01", end = "2025-08-31")

weathercan uses ‘caching’ and will only download this data once per session

What do we have?

# A tibble: 243 × 37
   station_name station_id station_operator prov    lat    lon  elev climate_id WMO_id TC_id date       year  month
   <chr>             <dbl> <lgl>            <chr> <dbl>  <dbl> <dbl> <chr>      <chr>  <chr> <date>     <chr> <chr>
 1 BRANDON RCS       49909 NA               MB     49.9 -100.0  409. 5010490    71136  PBO   2025-01-01 2025  01   
 2 BRANDON RCS       49909 NA               MB     49.9 -100.0  409. 5010490    71136  PBO   2025-01-02 2025  01   
 3 BRANDON RCS       49909 NA               MB     49.9 -100.0  409. 5010490    71136  PBO   2025-01-03 2025  01   
 4 BRANDON RCS       49909 NA               MB     49.9 -100.0  409. 5010490    71136  PBO   2025-01-04 2025  01   
 5 BRANDON RCS       49909 NA               MB     49.9 -100.0  409. 5010490    71136  PBO   2025-01-05 2025  01   
 6 BRANDON RCS       49909 NA               MB     49.9 -100.0  409. 5010490    71136  PBO   2025-01-06 2025  01   
 7 BRANDON RCS       49909 NA               MB     49.9 -100.0  409. 5010490    71136  PBO   2025-01-07 2025  01   
 8 BRANDON RCS       49909 NA               MB     49.9 -100.0  409. 5010490    71136  PBO   2025-01-08 2025  01   
 9 BRANDON RCS       49909 NA               MB     49.9 -100.0  409. 5010490    71136  PBO   2025-01-09 2025  01   
10 BRANDON RCS       49909 NA               MB     49.9 -100.0  409. 5010490    71136  PBO   2025-01-10 2025  01   
# ℹ 233 more rows
# ℹ 24 more variables: day <chr>, qual <chr>, cool_deg_days <dbl>, cool_deg_days_flag <chr>, dir_max_gust <dbl>,
#   dir_max_gust_flag <chr>, heat_deg_days <dbl>, heat_deg_days_flag <chr>, max_temp <dbl>, max_temp_flag <chr>,
#   mean_temp <dbl>, mean_temp_flag <chr>, min_temp <dbl>, min_temp_flag <chr>, snow_grnd <dbl>, snow_grnd_flag <chr>,
#   spd_max_gust <dbl>, spd_max_gust_flag <chr>, total_precip <dbl>, total_precip_flag <chr>, total_rain <dbl>,
#   total_rain_flag <chr>, total_snow <dbl>, total_snow_flag <chr>

A lot of stuff, apparently…

What do we have?

skimr::skim(w)

── Data Summary ────────────────────────
                           Values
Name                       w     
Number of rows             243   
Number of columns          37    
_______________________          
Column type frequency:           
  character                20    
  Date                     1     
  logical                  1     
  numeric                  15    
________________________         
Group variables            None  

── Variable type: character ────────────────────────────────────────────────────────────────────────────────────────────
   skim_variable      n_missing complete_rate min max empty n_unique whitespace
 1 station_name               0        1       11  11     0        1          0
 2 prov                       0        1        2   2     0        1          0
 3 climate_id                 0        1        7   7     0        1          0
 4 WMO_id                     0        1        5   5     0        1          0
 5 TC_id                      0        1        3   3     0        1          0
 6 year                       0        1        4   4     0        1          0
 7 month                      0        1        2   2     0        8          0
 8 day                        0        1        2   2     0       31          0
 9 qual                     243        0       NA  NA     0        0          0
10 cool_deg_days_flag       236        0.0288   1   1     0        1          0
11 dir_max_gust_flag        236        0.0288   1   1     0        1          0
12 heat_deg_days_flag       236        0.0288   1   1     0        1          0
13 max_temp_flag            236        0.0288   1   1     0        1          0
14 mean_temp_flag           236        0.0288   1   1     0        1          0
15 min_temp_flag            236        0.0288   1   1     0        1          0
16 snow_grnd_flag           243        0       NA  NA     0        0          0
17 spd_max_gust_flag        236        0.0288   1   1     0        1          0
18 total_precip_flag        236        0.0288   1   1     0        1          0
19 total_rain_flag          243        0       NA  NA     0        0          0
20 total_snow_flag          243        0       NA  NA     0        0          0

── Variable type: Date ─────────────────────────────────────────────────────────────────────────────────────────────────
  skim_variable n_missing complete_rate min        max        median     n_unique
1 date                  0             1 2025-01-01 2025-08-31 2025-05-02      243

── Variable type: logical ──────────────────────────────────────────────────────────────────────────────────────────────
  skim_variable    n_missing complete_rate mean count
1 station_operator       243             0  NaN ": " 

── Variable type: numeric ──────────────────────────────────────────────────────────────────────────────────────────────
   skim_variable n_missing complete_rate      mean    sd      p0      p25      p50       p75    p100 hist   
 1 station_id            0         1     49909      0    49909   49909    49909    49909     49909   "▁▁▇▁▁"
 2 lat                   0         1        49.9    0       49.9    49.9     49.9     49.9      49.9 "▁▁▇▁▁"
 3 lon                   0         1      -100.0    0     -100.0  -100.0   -100.0   -100.0    -100.0 "▁▁▇▁▁"
 4 elev                  0         1       409.     0      409.    409.     409.     409.      409.  "▁▁▇▁▁"
 5 cool_deg_days         7         0.971     0.444  1.10     0       0        0        0         5.4 "▇▁▁▁▁"
 6 dir_max_gust         89         0.634    19.2   10.9      1       7       23       29        35   "▇▂▃▆▇"
 7 heat_deg_days         7         0.971    14.8   14.9      0       1.17     9.85    24.9      49.8 "▇▂▃▂▂"
 8 max_temp              7         0.971    10.1   16.2    -27.9    -1.65    15.6     24.1      32.9 "▂▂▅▅▇"
 9 mean_temp             7         0.971     3.64  15.3    -31.8    -6.9      8.15    16.8      23.4 "▂▂▃▃▇"
10 min_temp              7         0.971    -2.84  14.9    -37.1   -12.2      1.05     9.83     18.3 "▂▃▅▅▇"
11 snow_grnd           123         0.494    16.9   10.0      1       5       19       27        31   "▆▂▂▂▇"
12 spd_max_gust         89         0.634    42.8   10.1     31      35       40.5     48        76   "▇▅▂▁▁"
13 total_precip          7         0.971     0.936  3.57     0       0        0        0.125    43.5 "▇▁▁▁▁"
14 total_rain          243         0       NaN     NA       NA      NA       NA       NA        NA   " "    
15 total_snow          243         0       NaN     NA       NA      NA       NA       NA        NA   " "

What do we have?

library(ggplot2)
ggplot(data = w, aes(x = date, colour = mean_temp)) +
  scale_color_viridis_c() +
  geom_errorbar(aes(ymin = min_temp, ymax = max_temp)) +
  geom_point(aes(y = mean_temp))

Scatterplot of mean temperature by date showing daily min/max ranges

`weather_dl()`

Multiple stations at once

s <- stations_search("Brandon", interval = "day")
w <- weather_dl(station_id = s$station_id, interval = "day", start = "2025-01-01", end = "2025-08-31")

There are no data for some stations (3474, 3471, 3472, 3473, 5273), in this time range (2025-01-01 to 2025-08-31), for this interval (day)
Available Station Data:
# A tibble: 11 × 17
   prov  station_name          station_id climate_id WMO_id TC_id   lat    lon  elev tz     interval start   end normals
   <chr> <chr>                      <dbl> <chr>       <dbl> <chr> <dbl>  <dbl> <dbl> <chr>  <chr>    <dbl> <dbl> <lgl>  
 1 MB    BRANDON A                   3471 5010480     71140 YBR    49.9 -100.0  409. Etc/G… day       1941  2012 TRUE   
 2 MB    BRANDON A                   3471 5010480     71140 YBR    49.9 -100.0  409. Etc/G… hour      1958  2012 TRUE   
 3 MB    BRANDON A                   3471 5010480     71140 YBR    49.9 -100.0  409. Etc/G… month     1941  2012 TRUE   
 4 MB    BRANDON CDA                 3472 5010485        NA <NA>   49.9 -100.0  363. Etc/G… day       1890  2010 TRUE   
 5 MB    BRANDON CDA                 3472 5010485        NA <NA>   49.9 -100.0  363. Etc/G… month     1890  2007 TRUE   
 6 MB    BRANDON SOUTH               3473 5010494        NA <NA>   49.8 -100.0  396. Etc/G… day       1972  1975 FALSE  
 7 MB    BRANDON SOUTH               3473 5010494        NA <NA>   49.8 -100.0  396. Etc/G… month     1972  1975 FALSE  
 8 MB    BRANDON #1 WINTER BAY       3474 5010498        NA <NA>   49.8 -100.0  396  Etc/G… day       1987  2002 FALSE  
 9 MB    BRANDON #1 WINTER BAY       3474 5010498        NA <NA>   49.8 -100.0  396  Etc/G… month     1987  2002 FALSE  
10 QC    ST GABRIEL DE BRANDON       5273 7017270        NA <NA>   46.3  -73.4  198. Etc/G… day       1919  1985 FALSE  
11 QC    ST GABRIEL DE BRANDON       5273 7017270        NA <NA>   46.3  -73.4  198. Etc/G… month     1919  1985 FALSE  
# ℹ 3 more variables: normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

Some stations don’t have data in this time range (makes sense if you look at their start/end ranges)

What do we have?

ggplot(data = w, aes(x = date, colour = mean_temp)) +
  scale_color_viridis_c() +
  geom_errorbar(aes(ymin = min_temp, ymax = max_temp)) +
  geom_point(aes(y = mean_temp))  +
  facet_wrap(~ station_name)

Scatterplot of mean temperature by date showing daily min/max ranges for two stations

Your turn!

Download some data for your station(s).

Take a look at them!

Normals

Climate normals and averages
calculated by ECCC for 30-year periods

`normals_dl()`

Find station

stations_search("brandon", normals_years = "current")

The most current normals available for download by weathercan are '1981-2010'

# A tibble: 5 × 17
  prov  station_name station_id climate_id WMO_id TC_id   lat    lon  elev tz        interval start   end normals
  <chr> <chr>             <dbl> <chr>       <dbl> <chr> <dbl>  <dbl> <dbl> <chr>     <chr>    <dbl> <dbl> <lgl>  
1 MB    BRANDON A          3471 5010480     71140 YBR    49.9 -100.0  409. Etc/GMT+6 day       1941  2012 TRUE   
2 MB    BRANDON A          3471 5010480     71140 YBR    49.9 -100.0  409. Etc/GMT+6 hour      1958  2012 TRUE   
3 MB    BRANDON A          3471 5010480     71140 YBR    49.9 -100.0  409. Etc/GMT+6 month     1941  2012 TRUE   
4 MB    BRANDON CDA        3472 5010485        NA <NA>   49.9 -100.0  363. Etc/GMT+6 day       1890  2010 TRUE   
5 MB    BRANDON CDA        3472 5010485        NA <NA>   49.9 -100.0  363. Etc/GMT+6 month     1890  2007 TRUE   
# ℹ 3 more variables: normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

Now download the data

n <- normals_dl(climate_id = "5010480")  # Note: climate_id!!

‘current’ may not be what you think it is…
Run ?stations_search or ?normals_dl and look at the details of normals_years…

What do we have?

# A tibble: 1 × 7
  prov  station_name climate_id normals_years meets_wmo normals             frost           
  <chr> <chr>        <chr>      <chr>         <lgl>     <list>              <list>          
1 MB    BRANDON A    5010480    1981-2010     TRUE      <tibble [13 × 197]> <tibble [7 × 8]>

Oh weird! ’tibble’s in the columns?

What do we have?

Because weather normals data are so different from frost normals data, they are separate data frames.

normals <- tidyr::unnest(n, "normals")
frost <- tidyr::unnest(n, "frost")
normals

# A tibble: 13 × 203
   prov  station_name climate_id normals_years meets_wmo period temp_daily_average temp_daily_average_code temp_sd
   <chr> <chr>        <chr>      <chr>         <lgl>     <fct>               <dbl> <chr>                     <dbl>
 1 MB    BRANDON A    5010480    1981-2010     TRUE      Jan                 -16.6 A                           4.2
 2 MB    BRANDON A    5010480    1981-2010     TRUE      Feb                 -13.6 A                           4  
 3 MB    BRANDON A    5010480    1981-2010     TRUE      Mar                  -6.2 A                           3.2
 4 MB    BRANDON A    5010480    1981-2010     TRUE      Apr                   4   A                           2.4
 5 MB    BRANDON A    5010480    1981-2010     TRUE      May                  10.6 A                           1.8
 6 MB    BRANDON A    5010480    1981-2010     TRUE      Jun                  15.9 A                           1.8
 7 MB    BRANDON A    5010480    1981-2010     TRUE      Jul                  18.5 A                           1.4
 8 MB    BRANDON A    5010480    1981-2010     TRUE      Aug                  17.7 A                           1.8
 9 MB    BRANDON A    5010480    1981-2010     TRUE      Sep                  11.8 A                           1.6
10 MB    BRANDON A    5010480    1981-2010     TRUE      Oct                   4.1 A                           1.8
11 MB    BRANDON A    5010480    1981-2010     TRUE      Nov                  -5.6 A                           3.6
12 MB    BRANDON A    5010480    1981-2010     TRUE      Dec                 -14   A                           4.2
13 MB    BRANDON A    5010480    1981-2010     TRUE      Year                  2.2 A                           1.1
# ℹ 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>, …

What do we have?

ggplot(data = normals, aes(x = period, colour = temp_daily_average)) +
  geom_errorbar(aes(ymin = temp_extreme_min, ymax = temp_extreme_max), width = 0.2) +
  geom_point(aes(y = temp_daily_average)) +
  scale_colour_viridis_c() +
  scale_y_continuous(breaks = seq(-50, 35, 5)) +
  labs(title = normals$normals_years[1])

Scatterplot of daily max temperature by month

In Conclusion…

Check weathercan Documentation
Cite all R packages
ECCC is your source of truth
- Climate Normals Technical documentation (EN|FR)
- ECCC Glossary (EN|FR)

Thank you!

steffilazerte.ca
sel@steffilazerte.ca

Slides created with Quarto Updated 2025-10-27

Troubleshooting

No Data!

w <- weather_dl(station_id = 5256, interval = "day", start = "1950-07-01", end = "1951-08-31")

There are no data for station 5256, in this time range (1950-07-01 to 1951-08-31), for this interval (day), 
Available Station Data:
# A tibble: 2 × 17
  prov  station_name        station_id climate_id WMO_id TC_id   lat   lon  elev tz        interval start   end normals
  <chr> <chr>                    <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr>     <chr>    <dbl> <dbl> <lgl>  
1 QC    ST ALEXIS DES MONTS       5256 7016816        NA <NA>   46.5 -73.2   183 Etc/GMT+5 day       1963  2025 TRUE   
2 QC    ST ALEXIS DES MONTS       5256 7016816        NA <NA>   46.5 -73.2   183 Etc/GMT+5 month     1963  2018 TRUE   
# ℹ 3 more variables: normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>

Check the date range
Check the interval
Check for ‘replacement’ stations
- Stations near by that start up after your station

# A tibble: 6 × 18
  prov  station_name            station_id climate_id WMO_id TC_id   lat   lon  elev tz     interval start   end normals
  <chr> <chr>                        <dbl> <chr>       <dbl> <chr> <dbl> <dbl> <dbl> <chr>  <chr>    <dbl> <dbl> <lgl>  
1 QC    ST ALEXIS DES MONTS           5256 7016816        NA <NA>   46.5 -73.2  183  Etc/G… day       1963  2025 TRUE   
2 QC    ST ALEXIS DES MONTS           5256 7016816        NA <NA>   46.5 -73.2  183  Etc/G… month     1963  2018 TRUE   
3 QC    ST PAULIN                     5282 7017640        NA <NA>   46.4 -73.0  167  Etc/G… day       1950  1991 TRUE   
4 QC    ST PAULIN                     5282 7017640        NA <NA>   46.4 -73.0  167  Etc/G… month     1951  1991 TRUE   
5 QC    ST CHARLES MANDEVILLE 2       5263 7016981        NA <NA>   46.4 -73.4  174. Etc/G… day       1968  1970 FALSE  
6 QC    ST CHARLES MANDEVILLE 2       5263 7016981        NA <NA>   46.4 -73.4  174. Etc/G… month     1968  1970 FALSE  
# ℹ 4 more variables: normals_1991_2020 <lgl>, normals_1981_2010 <lgl>, normals_1971_2000 <lgl>, distance <dbl>

Map

library(mapview)
library(sf)

# Our point of interest
# lat, lon = 49.85, -99.91

# Get local stations
s <- stations_search(
  coords = c(49.85, -99.91), interval = "day", 
  starts_latest = 2020, 
  ends_earliest = 2020) |>  # lat, lon
  st_as_sf(coords = c("lon", "lat"), crs = 4326)

p <- st_sfc(st_point(c(-99.91, 49.85)), crs = 4326) # lon, lat

# Interactive map of the stations with reference to our point of interest
mapview(s, zcol = "distance") + mapview(p, col.regions = "black", cex = 20)

Introduction to weathercan

Québec-Océan Students 2025

Preamble

Online workshops can be challenging

Consider keeping your video on (if possible)

Interrupt me!

Ask Questions!

Screen-sharing

Introductions

Me and my creatures

My garden

What about you?

About this Workshop

Format

R is hard: But have no fear!

Getting Started

R is a Programming language

For example

Use code to tell R what to do

R, what is the average of numbers 1, 2, 3, 4?

R, save this value for later

R, multiply this value by 6

Some Terminology

Code, Output, Scripts

Code

Output

Script

For example:

weathercan vs. WeatherCAN

RStudio vs. R

Open RStudio

ImpostR Syndrome

ImpostR Syndrome

The Goal

weathercan

weathercan

What is weathercan?

Using weathercan in a nutshell

Using weathercan

Understanding the functions - Documentation

Citing weathercan

Using weathercan

Understanding the data - Glossaries of terms

Stations

stations()

Included Data frame

stations_dl()

Make sure it’s up-to-date

stations_search()

By name

stations_search()

By date and interval

Looking at tables

Looking at tables

stations_search()

By distance

stations_search()

Using dplyr package for advanced searching

Your turn!

Weather

weather_dl()

Find a station

Now download the data

What do we have?

What do we have?

What do we have?

weather_dl()

Multiple stations at once

What do we have?

Your turn!

Normals

normals_dl()

Find station

Now download the data

What do we have?

What do we have?

What do we have?

In Conclusion…

In Conclusion…

Thank you!

`stations()`

`stations_dl()`

`stations_search()`

`stations_search()`

`stations_search()`

`stations_search()`

`weather_dl()`

`weather_dl()`

`normals_dl()`