Steffi LaZerte
This package is 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.
Use the devtools package to directly install R packages from github:
install.packages("devtools") # If not already installed
devtools::install_github("steffilazerte/weathercan") To download data, you first need to know the station_id associated with the station you're interested in.
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 12
## prov station_name station_id climate_id WMO_id TC_id lat
## <fctr> <chr> <fctr> <fctr> <fctr> <fctr> <dbl>
## 1 BC ACTIVE PASS 14 1010066 NA NA 48.87
## 2 BC ALBERT HEAD 15 1010235 NA NA 48.40
## 3 BC BAMBERTON OCEAN CEMENT 16 1010595 NA NA 48.58
## 4 BC BEAR CREEK 17 1010720 NA NA 48.50
## 5 BC BEAVER LAKE 18 1010774 NA NA 48.50
## 6 BC BECHER BAY 19 1010780 NA NA 48.33
## # ... with 5 more variables: lon <dbl>, elev <dbl>, interval <chr>,
## # start <int>, end <int>
glimpse(stations)## Observations: 26,211
## Variables: 12
## $ prov <fctr> BC, BC, BC, BC, BC, BC, BC, BC, BC, BC, BC, BC, ...
## $ station_name <chr> "ACTIVE PASS", "ALBERT HEAD", "BAMBERTON OCEAN CE...
## $ station_id <fctr> 14, 15, 16, 17, 18, 19, 20, 21, 22, 25, 24, 23, ...
## $ climate_id <fctr> 1010066, 1010235, 1010595, 1010720, 1010774, 101...
## $ WMO_id <fctr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, ...
## $ TC_id <fctr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, ...
## $ lat <dbl> 48.87, 48.40, 48.58, 48.50, 48.50, 48.33, 48.60, ...
## $ lon <dbl> -123.28, -123.48, -123.52, -124.00, -123.35, -123...
## $ elev <dbl> 4.0, 17.0, 85.3, 350.5, 61.0, 12.2, 38.0, 30.5, 9...
## $ interval <chr> "hour", "hour", "hour", "hour", "hour", "hour", "...
## $ start <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N...
## $ end <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N...
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 12
## prov station_name station_id climate_id WMO_id TC_id lat lon
## <fctr> <chr> <fctr> <fctr> <fctr> <fctr> <dbl> <dbl>
## 1 BC KAMLOOPS A 1275 1163780 71887 YKA 50.7 -120.44
## 2 BC KAMLOOPS A 51423 1163781 71887 YKA 50.7 -120.45
## 3 BC KAMLOOPS AUT 42203 1163842 71741 ZKA 50.7 -120.44
## # ... with 4 more variables: elev <dbl>, interval <chr>, start <int>,
## # end <int>
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 13
## prov station_name station_id climate_id WMO_id TC_id lat lon
## <fctr> <chr> <fctr> <fctr> <fctr> <fctr> <dbl> <dbl>
## 1 BC KAMLOOPS A 1275 1163780 71887 YKA 50.7 -120.44
## 2 BC KAMLOOPS AUT 42203 1163842 71741 ZKA 50.7 -120.44
## 3 BC KAMLOOPS A 51423 1163781 71887 YKA 50.7 -120.45
## # ... with 5 more variables: elev <dbl>, interval <chr>, start <int>,
## # end <int>, distance <dbl>
Once you have your station_id(s) you can download weather data:
kam <- weather(station_ids = 51423, start = "2016-01-01", end = "2016-02-15")
kam## # A tibble: 1,104 x 35
## station_name station_id prov lat lon date
## * <chr> <dbl> <fctr> <dbl> <dbl> <date>
## 1 KAMLOOPS A 51423 BC 50.7 -120.45 2016-01-01
## 2 KAMLOOPS A 51423 BC 50.7 -120.45 2016-01-01
## 3 KAMLOOPS A 51423 BC 50.7 -120.45 2016-01-01
## 4 KAMLOOPS A 51423 BC 50.7 -120.45 2016-01-01
## 5 KAMLOOPS A 51423 BC 50.7 -120.45 2016-01-01
## 6 KAMLOOPS A 51423 BC 50.7 -120.45 2016-01-01
## 7 KAMLOOPS A 51423 BC 50.7 -120.45 2016-01-01
## 8 KAMLOOPS A 51423 BC 50.7 -120.45 2016-01-01
## 9 KAMLOOPS A 51423 BC 50.7 -120.45 2016-01-01
## 10 KAMLOOPS A 51423 BC 50.7 -120.45 2016-01-01
## # ... with 1,094 more rows, and 29 more variables: time <dttm>,
## # year <chr>, month <chr>, day <chr>, hour <chr>, qual <chr>,
## # weather <chr>, hmdx <dbl>, hmdx_flag <chr>, pressure <dbl>,
## # pressure_flag <chr>, rel_hum <dbl>, rel_hum_flag <chr>, temp <dbl>,
## # temp_dew <dbl>, temp_dew_flag <chr>, temp_flag <chr>, visib <dbl>,
## # visib_flag <chr>, wind_chill <dbl>, wind_chill_flag <chr>,
## # wind_dir <dbl>, wind_dir_flag <chr>, wind_spd <dbl>,
## # wind_spd_flag <chr>, elev <dbl>, climat_id <chr>, WMO_id <chr>,
## # TC_id <chr>
You can also download data from multiple stations at once:
kam_pg <- weather(station_ids = c(48248, 51423), start = "2016-01-01", end = "2016-02-15")
kam_pg## # A tibble: 2,208 x 35
## station_name station_id prov lat lon date
## * <chr> <dbl> <fctr> <dbl> <dbl> <date>
## 1 PRINCE GEORGE AIRPORT AUTO 48248 BC 53.89 -122.67 2016-01-01
## 2 PRINCE GEORGE AIRPORT AUTO 48248 BC 53.89 -122.67 2016-01-01
## 3 PRINCE GEORGE AIRPORT AUTO 48248 BC 53.89 -122.67 2016-01-01
## 4 PRINCE GEORGE AIRPORT AUTO 48248 BC 53.89 -122.67 2016-01-01
## 5 PRINCE GEORGE AIRPORT AUTO 48248 BC 53.89 -122.67 2016-01-01
## 6 PRINCE GEORGE AIRPORT AUTO 48248 BC 53.89 -122.67 2016-01-01
## 7 PRINCE GEORGE AIRPORT AUTO 48248 BC 53.89 -122.67 2016-01-01
## 8 PRINCE GEORGE AIRPORT AUTO 48248 BC 53.89 -122.67 2016-01-01
## 9 PRINCE GEORGE AIRPORT AUTO 48248 BC 53.89 -122.67 2016-01-01
## 10 PRINCE GEORGE AIRPORT AUTO 48248 BC 53.89 -122.67 2016-01-01
## # ... with 2,198 more rows, and 29 more variables: time <dttm>,
## # year <chr>, month <chr>, day <chr>, hour <chr>, qual <chr>,
## # weather <chr>, hmdx <dbl>, hmdx_flag <chr>, pressure <dbl>,
## # pressure_flag <chr>, rel_hum <dbl>, rel_hum_flag <chr>, temp <dbl>,
## # temp_dew <dbl>, temp_dew_flag <chr>, temp_flag <chr>, visib <dbl>,
## # visib_flag <chr>, wind_chill <dbl>, wind_chill_flag <chr>,
## # wind_dir <dbl>, wind_dir_flag <chr>, wind_spd <dbl>,
## # wind_spd_flag <chr>, elev <dbl>, climat_id <chr>, WMO_id <chr>,
## # TC_id <chr>
And plot it:
library(ggplot2)
ggplot(data = kam_pg, aes(x = time, y = temp, group = station_name, colour = station_name)) +
theme(legend.position = "top") +
geom_line() +
theme_minimal()
The data and the code in this repository are licensed under multiple licences. All code is licensed GPL-3. All data is licensed under the (Open Government License - Canada).