Nothing
Interpolating
In weathercan: Download Weather Data from Environment and Climate Change Canada
Packages
You'll need several packages from the tidyverse in addition to weathercan to complete the following analysis.
library(weathercan)
library(ggplot2)
library(dplyr)
General usage
You can merge weather data with other data frames by linearly interpolating between points.
For example, here we have a dataset of weather data from Kamloops
glimpse(kamloops)
## Rows: 4,368
## Columns: 37
## $ station_name <chr> "KAMLOOPS A", "KAMLOOPS A", "KAMLOOPS A", "KAMLOOPS A", "KAMLOOPS A", "KAMLOOPS A", "KAMLO…
## $ station_id <dbl> 51423, 51423, 51423, 51423, 51423, 51423, 51423, 51423, 51423, 51423, 51423, 51423, 51423,…
## $ station_operator <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ prov <chr> "BC", "BC", "BC", "BC", "BC", "BC", "BC", "BC", "BC", "BC", "BC", "BC", "BC", "BC", "BC", …
## $ lat <dbl> 50.7, 50.7, 50.7, 50.7, 50.7, 50.7, 50.7, 50.7, 50.7, 50.7, 50.7, 50.7, 50.7, 50.7, 50.7, …
## $ lon <dbl> -120.45, -120.45, -120.45, -120.45, -120.45, -120.45, -120.45, -120.45, -120.45, -120.45, …
## $ elev <dbl> 345.3, 345.3, 345.3, 345.3, 345.3, 345.3, 345.3, 345.3, 345.3, 345.3, 345.3, 345.3, 345.3,…
## $ climate_id <chr> "1163781", "1163781", "1163781", "1163781", "1163781", "1163781", "1163781", "1163781", "1…
## $ WMO_id <chr> "71887", "71887", "71887", "71887", "71887", "71887", "71887", "71887", "71887", "71887", …
## $ TC_id <chr> "YKA", "YKA", "YKA", "YKA", "YKA", "YKA", "YKA", "YKA", "YKA", "YKA", "YKA", "YKA", "YKA",…
## $ date <date> 2016-01-01, 2016-01-01, 2016-01-01, 2016-01-01, 2016-01-01, 2016-01-01, 2016-01-01, 2016-…
## $ time <dttm> 2016-01-01 00:00:00, 2016-01-01 01:00:00, 2016-01-01 02:00:00, 2016-01-01 03:00:00, 2016-…
## $ year <chr> "2016", "2016", "2016", "2016", "2016", "2016", "2016", "2016", "2016", "2016", "2016", "2…
## $ month <chr> "01", "01", "01", "01", "01", "01", "01", "01", "01", "01", "01", "01", "01", "01", "01", …
## $ day <chr> "01", "01", "01", "01", "01", "01", "01", "01", "01", "01", "01", "01", "01", "01", "01", …
## $ hour <chr> "00:00", "01:00", "02:00", "03:00", "04:00", "05:00", "06:00", "07:00", "08:00", "09:00", …
## $ weather <chr> NA, "Mostly Cloudy", NA, NA, "Cloudy", NA, NA, "Cloudy", NA, "Snow", "Snow", "Snow", "Snow…
## $ hmdx <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hmdx_flag <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ precip_amt <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ precip_amt_flag <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ pressure <dbl> 99.95, 99.93, 99.92, 99.90, 99.86, 99.82, 99.80, 99.78, 99.77, 99.78, 99.79, 99.74, 99.69,…
## $ pressure_flag <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ rel_hum <dbl> 74, 76, 74, 73, 70, 71, 69, 69, 71, 71, 71, 70, 69, 70, 68, 68, 70, 74, 73, 74, 74, 74, 77…
## $ rel_hum_flag <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ temp <dbl> -9.1, -9.6, -9.9, -9.5, -9.4, -9.8, -10.0, -10.2, -10.1, -9.7, -9.4, -9.0, -8.6, -8.2, -8.…
## $ temp_dew <dbl> -12.9, -13.1, -13.7, -13.5, -13.9, -14.1, -14.7, -14.9, -14.4, -14.0, -13.7, -13.5, -13.3,…
## $ temp_dew_flag <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ temp_flag <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ visib <dbl> 64.4, 64.4, 64.4, 64.4, 64.4, 64.4, 64.4, 64.4, 48.3, 48.3, 48.3, 48.3, 48.3, 48.3, 48.3, …
## $ visib_flag <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ wind_chill <dbl> -17, -17, -18, -17, -17, -17, -18, -17, -17, -16, -15, -14, -14, -13, -13, -13, -13, -14, …
## $ wind_chill_flag <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ wind_dir <dbl> 13, 11, 11, 11, 11, 10, 9, 7, 7, 10, 11, 10, 10, 13, 11, 10, 10, 9, 12, 10, 13, 12, 10, 12…
## $ wind_dir_flag <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ wind_spd <dbl> 19, 20, 20, 18, 18, 16, 23, 15, 14, 15, 12, 11, 12, 9, 10, 12, 11, 12, 10, 11, 11, 6, 6, 4…
## $ wind_spd_flag <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
As well as a data set of finch visits to an RFID feeder
glimpse(finches)
## Rows: 16,886
## Columns: 10
## $ animal_id <fct> 041868FF93, 041868FF93, 041868FF93, 06200003BB, 06200003BB, 06200003BB, 06200003BB, 06200003BB, 0…
## $ date <date> 2016-03-01, 2016-03-01, 2016-03-01, 2016-03-01, 2016-03-01, 2016-03-01, 2016-03-01, 2016-03-01, …
## $ time <dttm> 2016-03-01 06:57:42, 2016-03-01 06:58:41, 2016-03-01 07:07:21, 2016-03-01 07:32:34, 2016-03-01 0…
## $ logger_id <fct> 2300, 2300, 2300, 2400, 2400, 2400, 2400, 2400, 2300, 2300, 2300, 2300, 2300, 2400, 2300, 2400, 2…
## $ species <chr> "Mountain Chickadee", "Mountain Chickadee", "Mountain Chickadee", "House Finch", "House Finch", "…
## $ age <chr> "AHY", "AHY", "AHY", "SY", "SY", "SY", "SY", "SY", "AHY", "AHY", "AHY", "AHY", "AHY", "SY", "AHY"…
## $ sex <chr> "U", "U", "U", "M", "M", "M", "M", "M", "F", "F", "F", "F", "F", "M", "F", "M", "M", "M", "M", "M…
## $ site_name <chr> "Kamloops, BC", "Kamloops, BC", "Kamloops, BC", "Kamloops, BC", "Kamloops, BC", "Kamloops, BC", "…
## $ lon <dbl> -120.3622, -120.3622, -120.3622, -120.3635, -120.3635, -120.3635, -120.3635, -120.3635, -120.3622…
## $ lat <dbl> 50.66967, 50.66967, 50.66967, 50.66938, 50.66938, 50.66938, 50.66938, 50.66938, 50.66967, 50.6696…
Although the times in the weather data do not exactly match those in the finch data, we can merge them together through linear interpolation. This function uses the approx function from the stats package under the hood.
Here we specify that we only want the temperature (temp) column:
finches_temperature <- weather_interp(data = finches, weather = kamloops, cols = "temp")
## temp is missing 4 out of 4368 data, interpolation may be less accurate as a result.
summary(finches_temperature)
## animal_id date time logger_id species
## 0620000513:7624 Min. :2016-03-01 Min. :2016-03-01 06:57:42 1500:6370 Length:16886
## 041868D861:2767 1st Qu.:2016-03-05 1st Qu.:2016-03-05 13:54:13 2100: 968 Class :character
## 0620000514:1844 Median :2016-03-09 Median :2016-03-09 16:54:47 2200:2266 Mode :character
## 06200004F8:1386 Mean :2016-03-08 Mean :2016-03-09 07:45:58 2300:3531
## 041868BED6: 944 3rd Qu.:2016-03-13 3rd Qu.:2016-03-13 08:24:58 2400:1477
## 06200003BB: 708 Max. :2016-03-16 Max. :2016-03-16 16:39:30 2700:2274
## (Other) :1613
## age sex site_name lon lat temp
## Length:16886 Length:16886 Length:16886 Min. :-120.4 Min. :50.67 Min. :-0.2317
## Class :character Class :character Class :character 1st Qu.:-120.4 1st Qu.:50.67 1st Qu.: 5.0561
## Mode :character Mode :character Mode :character Median :-120.4 Median :50.67 Median : 7.1651
## Mean :-120.4 Mean :50.67 Mean : 7.4349
## 3rd Qu.:-120.4 3rd Qu.:50.67 3rd Qu.: 9.3319
## Max. :-120.4 Max. :50.67 Max. :16.3712
##
glimpse(finches_temperature)
## Rows: 16,886
## Columns: 11
## $ animal_id <fct> 041868FF93, 041868FF93, 041868FF93, 06200003BB, 06200003BB, 06200003BB, 06200003BB, 06200003BB, 0…
## $ date <date> 2016-03-01, 2016-03-01, 2016-03-01, 2016-03-01, 2016-03-01, 2016-03-01, 2016-03-01, 2016-03-01, …
## $ time <dttm> 2016-03-01 06:57:42, 2016-03-01 06:58:41, 2016-03-01 07:07:21, 2016-03-01 07:32:34, 2016-03-01 0…
## $ logger_id <fct> 2300, 2300, 2300, 2400, 2400, 2400, 2400, 2400, 2300, 2300, 2300, 2300, 2300, 2400, 2300, 2400, 2…
## $ species <chr> "Mountain Chickadee", "Mountain Chickadee", "Mountain Chickadee", "House Finch", "House Finch", "…
## $ age <chr> "AHY", "AHY", "AHY", "SY", "SY", "SY", "SY", "SY", "AHY", "AHY", "AHY", "AHY", "AHY", "SY", "AHY"…
## $ sex <chr> "U", "U", "U", "M", "M", "M", "M", "M", "F", "F", "F", "F", "F", "M", "F", "M", "M", "M", "M", "M…
## $ site_name <chr> "Kamloops, BC", "Kamloops, BC", "Kamloops, BC", "Kamloops, BC", "Kamloops, BC", "Kamloops, BC", "…
## $ lon <dbl> -120.3622, -120.3622, -120.3622, -120.3635, -120.3635, -120.3635, -120.3635, -120.3635, -120.3622…
## $ lat <dbl> 50.66967, 50.66967, 50.66967, 50.66938, 50.66938, 50.66938, 50.66938, 50.66938, 50.66967, 50.6696…
## $ temp <dbl> 3.984667, 3.991222, 4.036750, 4.162833, 4.162917, 4.163000, 4.163083, 4.163167, 4.180417, 4.18075…
ggplot(data = finches_temperature, aes(x = temp, fill = animal_id)) +
theme_bw() +
theme(legend.position = "none") +
geom_histogram(binwidth = 1) +
labs(x = "Temperature (C)", y = "Activity Count", fill = "Finch ID")
Or summarized:
finches_temperature <- finches_temperature %>%
group_by(date) %>%
summarize(n = length(time),
temp = mean(temp))
ggplot(data = finches_temperature, aes(x = date, y = n)) +
theme_bw() +
theme(legend.position = "top") +
geom_point(aes(shape = "Activity")) +
geom_line(aes(y = temp * 100, colour = "Temperature")) +
scale_colour_discrete(name = "") +
scale_shape_discrete(name = "") +
scale_y_continuous(name = "Activity", sec.axis = sec_axis(~. / 100, name = "Temperature (C)"))
Data gaps
By default, gaps of 2 hours (or 2 days, with a daily scale) will be interpolated over (i.e. they will be filled with values interpolated from either side of the gap), but longer gaps will be skipped and filled with NAs. You can adjust this behaviour with na_gap. Note that as Environment and Climate Change Canada data is downloaded on an hourly scale, it makes no sense to apply na_gap values of less than 1.
In this example, note the larger number of NAs in temp and how it corresponds to the missing variables in the weather dataset:
finches_temperature <- weather_interp(data = finches, weather = kamloops,
cols = "temp", na_gap = 1)
## temp is missing 4 out of 4368 data, interpolation may be less accurate as a result.
summary(finches_temperature)
## animal_id date time logger_id species
## 0620000513:7624 Min. :2016-03-01 Min. :2016-03-01 06:57:42 1500:6370 Length:16886
## 041868D861:2767 1st Qu.:2016-03-05 1st Qu.:2016-03-05 13:54:13 2100: 968 Class :character
## 0620000514:1844 Median :2016-03-09 Median :2016-03-09 16:54:47 2200:2266 Mode :character
## 06200004F8:1386 Mean :2016-03-08 Mean :2016-03-09 07:45:58 2300:3531
## 041868BED6: 944 3rd Qu.:2016-03-13 3rd Qu.:2016-03-13 08:24:58 2400:1477
## 06200003BB: 708 Max. :2016-03-16 Max. :2016-03-16 16:39:30 2700:2274
## (Other) :1613
## age sex site_name lon lat temp
## Length:16886 Length:16886 Length:16886 Min. :-120.4 Min. :50.67 Min. :-0.2317
## Class :character Class :character Class :character 1st Qu.:-120.4 1st Qu.:50.67 1st Qu.: 5.0746
## Mode :character Mode :character Mode :character Median :-120.4 Median :50.67 Median : 7.1668
## Mean :-120.4 Mean :50.67 Mean : 7.4433
## 3rd Qu.:-120.4 3rd Qu.:50.67 3rd Qu.: 9.3458
## Max. :-120.4 Max. :50.67 Max. :16.3712
## NA's :84
finches_temperature %>%
select(date, time, temp) %>%
filter(is.na(temp))
## # A tibble: 84 × 3
## date time temp
## <date> <dttm> <dbl>
## 1 2016-03-10 2016-03-10 16:00:12 NA
## 2 2016-03-10 2016-03-10 16:00:33 NA
## 3 2016-03-10 2016-03-10 16:00:36 NA
## 4 2016-03-10 2016-03-10 16:00:39 NA
## 5 2016-03-10 2016-03-10 16:00:42 NA
## 6 2016-03-10 2016-03-10 16:00:45 NA
## 7 2016-03-10 2016-03-10 16:00:48 NA
## 8 2016-03-10 2016-03-10 16:00:51 NA
## 9 2016-03-10 2016-03-10 16:00:54 NA
## 10 2016-03-10 2016-03-10 16:00:57 NA
## # … with 74 more rows
kamloops %>%
select(time, temp) %>%
filter(is.na(temp))
## # A tibble: 4 × 2
## time temp
## <dttm> <dbl>
## 1 2016-02-11 19:00:00 NA
## 2 2016-03-08 13:00:00 NA
## 3 2016-03-11 01:00:00 NA
## 4 2016-04-09 00:00:00 NA
Multiple weather columns
We could also add in more than one column at a time:
finches_weather <- weather_interp(data = finches, weather = kamloops,
cols = c("temp", "wind_spd"))
## temp is missing 4 out of 4368 data, interpolation may be less accurate as a result.
## wind_spd is missing 4 out of 4368 data, interpolation may be less accurate as a result.
summary(finches_weather)
## animal_id date time logger_id species
## 0620000513:7624 Min. :2016-03-01 Min. :2016-03-01 06:57:42 1500:6370 Length:16886
## 041868D861:2767 1st Qu.:2016-03-05 1st Qu.:2016-03-05 13:54:13 2100: 968 Class :character
## 0620000514:1844 Median :2016-03-09 Median :2016-03-09 16:54:47 2200:2266 Mode :character
## 06200004F8:1386 Mean :2016-03-08 Mean :2016-03-09 07:45:58 2300:3531
## 041868BED6: 944 3rd Qu.:2016-03-13 3rd Qu.:2016-03-13 08:24:58 2400:1477
## 06200003BB: 708 Max. :2016-03-16 Max. :2016-03-16 16:39:30 2700:2274
## (Other) :1613
## age sex site_name lon lat temp
## Length:16886 Length:16886 Length:16886 Min. :-120.4 Min. :50.67 Min. :-0.2317
## Class :character Class :character Class :character 1st Qu.:-120.4 1st Qu.:50.67 1st Qu.: 5.0561
## Mode :character Mode :character Mode :character Median :-120.4 Median :50.67 Median : 7.1651
## Mean :-120.4 Mean :50.67 Mean : 7.4349
## 3rd Qu.:-120.4 3rd Qu.:50.67 3rd Qu.: 9.3319
## Max. :-120.4 Max. :50.67 Max. :16.3712
##
## wind_spd
## Min. : 1.000
## 1st Qu.: 7.634
## Median :13.738
## Mean :14.443
## 3rd Qu.:19.907
## Max. :44.939
##
glimpse(finches_weather)
## Rows: 16,886
## Columns: 12
## $ animal_id <fct> 041868FF93, 041868FF93, 041868FF93, 06200003BB, 06200003BB, 06200003BB, 06200003BB, 06200003BB, 0…
## $ date <date> 2016-03-01, 2016-03-01, 2016-03-01, 2016-03-01, 2016-03-01, 2016-03-01, 2016-03-01, 2016-03-01, …
## $ time <dttm> 2016-03-01 06:57:42, 2016-03-01 06:58:41, 2016-03-01 07:07:21, 2016-03-01 07:32:34, 2016-03-01 0…
## $ logger_id <fct> 2300, 2300, 2300, 2400, 2400, 2400, 2400, 2400, 2300, 2300, 2300, 2300, 2300, 2400, 2300, 2400, 2…
## $ species <chr> "Mountain Chickadee", "Mountain Chickadee", "Mountain Chickadee", "House Finch", "House Finch", "…
## $ age <chr> "AHY", "AHY", "AHY", "SY", "SY", "SY", "SY", "SY", "AHY", "AHY", "AHY", "AHY", "AHY", "SY", "AHY"…
## $ sex <chr> "U", "U", "U", "M", "M", "M", "M", "M", "F", "F", "F", "F", "F", "M", "F", "M", "M", "M", "M", "M…
## $ site_name <chr> "Kamloops, BC", "Kamloops, BC", "Kamloops, BC", "Kamloops, BC", "Kamloops, BC", "Kamloops, BC", "…
## $ lon <dbl> -120.3622, -120.3622, -120.3622, -120.3635, -120.3635, -120.3635, -120.3635, -120.3635, -120.3622…
## $ lat <dbl> 50.66967, 50.66967, 50.66967, 50.66938, 50.66938, 50.66938, 50.66938, 50.66938, 50.66967, 50.6696…
## $ temp <dbl> 3.984667, 3.991222, 4.036750, 4.162833, 4.162917, 4.163000, 4.163083, 4.163167, 4.180417, 4.18075…
## $ wind_spd <dbl> 22.88500, 22.93417, 22.26500, 19.74333, 19.74167, 19.74000, 19.73833, 19.73667, 19.39167, 19.3850…
finches_weather <- finches_weather %>%
group_by(date) %>%
summarize(n = length(time),
temp = mean(temp),
wind_spd = mean(wind_spd))
ggplot(data = finches_weather, aes(x = date, y = n)) +
theme_bw() +
theme(legend.position = "top") +
geom_bar(stat = "identity") +
geom_line(aes(y = temp * 50, colour = "Temperature"), size = 2) +
geom_line(aes(y = wind_spd * 50, colour = "Wind Speed"), size = 2) +
scale_colour_discrete(name = "") +
scale_y_continuous(
name = "Activity Counts",
sec.axis = sec_axis(~. / 50, name = "Temperature (C) / Wind Speed (km/h)"))
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weathercan documentation built on Dec. 11, 2021, 10 a.m.
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Packages
You'll need several packages from the tidyverse in addition to weathercan to complete the following analysis.
library(weathercan) library(ggplot2) library(dplyr)
General usage
You can merge weather data with other data frames by linearly interpolating between points.
For example, here we have a dataset of weather data from Kamloops
glimpse(kamloops)
## Rows: 4,368 ## Columns: 37 ## $ station_name <chr> "KAMLOOPS A", "KAMLOOPS A", "KAMLOOPS A", "KAMLOOPS A", "KAMLOOPS A", "KAMLOOPS A", "KAMLO… ## $ station_id <dbl> 51423, 51423, 51423, 51423, 51423, 51423, 51423, 51423, 51423, 51423, 51423, 51423, 51423,… ## $ station_operator <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… ## $ prov <chr> "BC", "BC", "BC", "BC", "BC", "BC", "BC", "BC", "BC", "BC", "BC", "BC", "BC", "BC", "BC", … ## $ lat <dbl> 50.7, 50.7, 50.7, 50.7, 50.7, 50.7, 50.7, 50.7, 50.7, 50.7, 50.7, 50.7, 50.7, 50.7, 50.7, … ## $ lon <dbl> -120.45, -120.45, -120.45, -120.45, -120.45, -120.45, -120.45, -120.45, -120.45, -120.45, … ## $ elev <dbl> 345.3, 345.3, 345.3, 345.3, 345.3, 345.3, 345.3, 345.3, 345.3, 345.3, 345.3, 345.3, 345.3,… ## $ climate_id <chr> "1163781", "1163781", "1163781", "1163781", "1163781", "1163781", "1163781", "1163781", "1… ## $ WMO_id <chr> "71887", "71887", "71887", "71887", "71887", "71887", "71887", "71887", "71887", "71887", … ## $ TC_id <chr> "YKA", "YKA", "YKA", "YKA", "YKA", "YKA", "YKA", "YKA", "YKA", "YKA", "YKA", "YKA", "YKA",… ## $ date <date> 2016-01-01, 2016-01-01, 2016-01-01, 2016-01-01, 2016-01-01, 2016-01-01, 2016-01-01, 2016-… ## $ time <dttm> 2016-01-01 00:00:00, 2016-01-01 01:00:00, 2016-01-01 02:00:00, 2016-01-01 03:00:00, 2016-… ## $ year <chr> "2016", "2016", "2016", "2016", "2016", "2016", "2016", "2016", "2016", "2016", "2016", "2… ## $ month <chr> "01", "01", "01", "01", "01", "01", "01", "01", "01", "01", "01", "01", "01", "01", "01", … ## $ day <chr> "01", "01", "01", "01", "01", "01", "01", "01", "01", "01", "01", "01", "01", "01", "01", … ## $ hour <chr> "00:00", "01:00", "02:00", "03:00", "04:00", "05:00", "06:00", "07:00", "08:00", "09:00", … ## $ weather <chr> NA, "Mostly Cloudy", NA, NA, "Cloudy", NA, NA, "Cloudy", NA, "Snow", "Snow", "Snow", "Snow… ## $ hmdx <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… ## $ hmdx_flag <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… ## $ precip_amt <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… ## $ precip_amt_flag <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… ## $ pressure <dbl> 99.95, 99.93, 99.92, 99.90, 99.86, 99.82, 99.80, 99.78, 99.77, 99.78, 99.79, 99.74, 99.69,… ## $ pressure_flag <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… ## $ rel_hum <dbl> 74, 76, 74, 73, 70, 71, 69, 69, 71, 71, 71, 70, 69, 70, 68, 68, 70, 74, 73, 74, 74, 74, 77… ## $ rel_hum_flag <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… ## $ temp <dbl> -9.1, -9.6, -9.9, -9.5, -9.4, -9.8, -10.0, -10.2, -10.1, -9.7, -9.4, -9.0, -8.6, -8.2, -8.… ## $ temp_dew <dbl> -12.9, -13.1, -13.7, -13.5, -13.9, -14.1, -14.7, -14.9, -14.4, -14.0, -13.7, -13.5, -13.3,… ## $ temp_dew_flag <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… ## $ temp_flag <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… ## $ visib <dbl> 64.4, 64.4, 64.4, 64.4, 64.4, 64.4, 64.4, 64.4, 48.3, 48.3, 48.3, 48.3, 48.3, 48.3, 48.3, … ## $ visib_flag <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… ## $ wind_chill <dbl> -17, -17, -18, -17, -17, -17, -18, -17, -17, -16, -15, -14, -14, -13, -13, -13, -13, -14, … ## $ wind_chill_flag <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… ## $ wind_dir <dbl> 13, 11, 11, 11, 11, 10, 9, 7, 7, 10, 11, 10, 10, 13, 11, 10, 10, 9, 12, 10, 13, 12, 10, 12… ## $ wind_dir_flag <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… ## $ wind_spd <dbl> 19, 20, 20, 18, 18, 16, 23, 15, 14, 15, 12, 11, 12, 9, 10, 12, 11, 12, 10, 11, 11, 6, 6, 4… ## $ wind_spd_flag <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
As well as a data set of finch visits to an RFID feeder
glimpse(finches)
## Rows: 16,886 ## Columns: 10 ## $ animal_id <fct> 041868FF93, 041868FF93, 041868FF93, 06200003BB, 06200003BB, 06200003BB, 06200003BB, 06200003BB, 0… ## $ date <date> 2016-03-01, 2016-03-01, 2016-03-01, 2016-03-01, 2016-03-01, 2016-03-01, 2016-03-01, 2016-03-01, … ## $ time <dttm> 2016-03-01 06:57:42, 2016-03-01 06:58:41, 2016-03-01 07:07:21, 2016-03-01 07:32:34, 2016-03-01 0… ## $ logger_id <fct> 2300, 2300, 2300, 2400, 2400, 2400, 2400, 2400, 2300, 2300, 2300, 2300, 2300, 2400, 2300, 2400, 2… ## $ species <chr> "Mountain Chickadee", "Mountain Chickadee", "Mountain Chickadee", "House Finch", "House Finch", "… ## $ age <chr> "AHY", "AHY", "AHY", "SY", "SY", "SY", "SY", "SY", "AHY", "AHY", "AHY", "AHY", "AHY", "SY", "AHY"… ## $ sex <chr> "U", "U", "U", "M", "M", "M", "M", "M", "F", "F", "F", "F", "F", "M", "F", "M", "M", "M", "M", "M… ## $ site_name <chr> "Kamloops, BC", "Kamloops, BC", "Kamloops, BC", "Kamloops, BC", "Kamloops, BC", "Kamloops, BC", "… ## $ lon <dbl> -120.3622, -120.3622, -120.3622, -120.3635, -120.3635, -120.3635, -120.3635, -120.3635, -120.3622… ## $ lat <dbl> 50.66967, 50.66967, 50.66967, 50.66938, 50.66938, 50.66938, 50.66938, 50.66938, 50.66967, 50.6696…
Although the times in the weather data do not exactly match those in the finch data, we can merge them together through linear interpolation. This function uses the approx function from the stats package under the hood.
Here we specify that we only want the temperature (temp) column:
finches_temperature <- weather_interp(data = finches, weather = kamloops, cols = "temp")
## temp is missing 4 out of 4368 data, interpolation may be less accurate as a result.
summary(finches_temperature)
## animal_id date time logger_id species ## 0620000513:7624 Min. :2016-03-01 Min. :2016-03-01 06:57:42 1500:6370 Length:16886 ## 041868D861:2767 1st Qu.:2016-03-05 1st Qu.:2016-03-05 13:54:13 2100: 968 Class :character ## 0620000514:1844 Median :2016-03-09 Median :2016-03-09 16:54:47 2200:2266 Mode :character ## 06200004F8:1386 Mean :2016-03-08 Mean :2016-03-09 07:45:58 2300:3531 ## 041868BED6: 944 3rd Qu.:2016-03-13 3rd Qu.:2016-03-13 08:24:58 2400:1477 ## 06200003BB: 708 Max. :2016-03-16 Max. :2016-03-16 16:39:30 2700:2274 ## (Other) :1613 ## age sex site_name lon lat temp ## Length:16886 Length:16886 Length:16886 Min. :-120.4 Min. :50.67 Min. :-0.2317 ## Class :character Class :character Class :character 1st Qu.:-120.4 1st Qu.:50.67 1st Qu.: 5.0561 ## Mode :character Mode :character Mode :character Median :-120.4 Median :50.67 Median : 7.1651 ## Mean :-120.4 Mean :50.67 Mean : 7.4349 ## 3rd Qu.:-120.4 3rd Qu.:50.67 3rd Qu.: 9.3319 ## Max. :-120.4 Max. :50.67 Max. :16.3712 ##
glimpse(finches_temperature)
## Rows: 16,886 ## Columns: 11 ## $ animal_id <fct> 041868FF93, 041868FF93, 041868FF93, 06200003BB, 06200003BB, 06200003BB, 06200003BB, 06200003BB, 0… ## $ date <date> 2016-03-01, 2016-03-01, 2016-03-01, 2016-03-01, 2016-03-01, 2016-03-01, 2016-03-01, 2016-03-01, … ## $ time <dttm> 2016-03-01 06:57:42, 2016-03-01 06:58:41, 2016-03-01 07:07:21, 2016-03-01 07:32:34, 2016-03-01 0… ## $ logger_id <fct> 2300, 2300, 2300, 2400, 2400, 2400, 2400, 2400, 2300, 2300, 2300, 2300, 2300, 2400, 2300, 2400, 2… ## $ species <chr> "Mountain Chickadee", "Mountain Chickadee", "Mountain Chickadee", "House Finch", "House Finch", "… ## $ age <chr> "AHY", "AHY", "AHY", "SY", "SY", "SY", "SY", "SY", "AHY", "AHY", "AHY", "AHY", "AHY", "SY", "AHY"… ## $ sex <chr> "U", "U", "U", "M", "M", "M", "M", "M", "F", "F", "F", "F", "F", "M", "F", "M", "M", "M", "M", "M… ## $ site_name <chr> "Kamloops, BC", "Kamloops, BC", "Kamloops, BC", "Kamloops, BC", "Kamloops, BC", "Kamloops, BC", "… ## $ lon <dbl> -120.3622, -120.3622, -120.3622, -120.3635, -120.3635, -120.3635, -120.3635, -120.3635, -120.3622… ## $ lat <dbl> 50.66967, 50.66967, 50.66967, 50.66938, 50.66938, 50.66938, 50.66938, 50.66938, 50.66967, 50.6696… ## $ temp <dbl> 3.984667, 3.991222, 4.036750, 4.162833, 4.162917, 4.163000, 4.163083, 4.163167, 4.180417, 4.18075…
ggplot(data = finches_temperature, aes(x = temp, fill = animal_id)) + theme_bw() + theme(legend.position = "none") + geom_histogram(binwidth = 1) + labs(x = "Temperature (C)", y = "Activity Count", fill = "Finch ID")
Or summarized:
finches_temperature <- finches_temperature %>% group_by(date) %>% summarize(n = length(time), temp = mean(temp)) ggplot(data = finches_temperature, aes(x = date, y = n)) + theme_bw() + theme(legend.position = "top") + geom_point(aes(shape = "Activity")) + geom_line(aes(y = temp * 100, colour = "Temperature")) + scale_colour_discrete(name = "") + scale_shape_discrete(name = "") + scale_y_continuous(name = "Activity", sec.axis = sec_axis(~. / 100, name = "Temperature (C)"))
Data gaps
By default, gaps of 2 hours (or 2 days, with a daily scale) will be interpolated over (i.e. they will be filled with values interpolated from either side of the gap), but longer gaps will be skipped and filled with NAs. You can adjust this behaviour with na_gap. Note that as Environment and Climate Change Canada data is downloaded on an hourly scale, it makes no sense to apply na_gap values of less than 1.
In this example, note the larger number of NAs in temp and how it corresponds to the missing variables in the weather dataset:
finches_temperature <- weather_interp(data = finches, weather = kamloops, cols = "temp", na_gap = 1)
## temp is missing 4 out of 4368 data, interpolation may be less accurate as a result.
summary(finches_temperature)
## animal_id date time logger_id species ## 0620000513:7624 Min. :2016-03-01 Min. :2016-03-01 06:57:42 1500:6370 Length:16886 ## 041868D861:2767 1st Qu.:2016-03-05 1st Qu.:2016-03-05 13:54:13 2100: 968 Class :character ## 0620000514:1844 Median :2016-03-09 Median :2016-03-09 16:54:47 2200:2266 Mode :character ## 06200004F8:1386 Mean :2016-03-08 Mean :2016-03-09 07:45:58 2300:3531 ## 041868BED6: 944 3rd Qu.:2016-03-13 3rd Qu.:2016-03-13 08:24:58 2400:1477 ## 06200003BB: 708 Max. :2016-03-16 Max. :2016-03-16 16:39:30 2700:2274 ## (Other) :1613 ## age sex site_name lon lat temp ## Length:16886 Length:16886 Length:16886 Min. :-120.4 Min. :50.67 Min. :-0.2317 ## Class :character Class :character Class :character 1st Qu.:-120.4 1st Qu.:50.67 1st Qu.: 5.0746 ## Mode :character Mode :character Mode :character Median :-120.4 Median :50.67 Median : 7.1668 ## Mean :-120.4 Mean :50.67 Mean : 7.4433 ## 3rd Qu.:-120.4 3rd Qu.:50.67 3rd Qu.: 9.3458 ## Max. :-120.4 Max. :50.67 Max. :16.3712 ## NA's :84
finches_temperature %>% select(date, time, temp) %>% filter(is.na(temp))
## # A tibble: 84 × 3 ## date time temp ## <date> <dttm> <dbl> ## 1 2016-03-10 2016-03-10 16:00:12 NA ## 2 2016-03-10 2016-03-10 16:00:33 NA ## 3 2016-03-10 2016-03-10 16:00:36 NA ## 4 2016-03-10 2016-03-10 16:00:39 NA ## 5 2016-03-10 2016-03-10 16:00:42 NA ## 6 2016-03-10 2016-03-10 16:00:45 NA ## 7 2016-03-10 2016-03-10 16:00:48 NA ## 8 2016-03-10 2016-03-10 16:00:51 NA ## 9 2016-03-10 2016-03-10 16:00:54 NA ## 10 2016-03-10 2016-03-10 16:00:57 NA ## # … with 74 more rows
kamloops %>% select(time, temp) %>% filter(is.na(temp))
## # A tibble: 4 × 2 ## time temp ## <dttm> <dbl> ## 1 2016-02-11 19:00:00 NA ## 2 2016-03-08 13:00:00 NA ## 3 2016-03-11 01:00:00 NA ## 4 2016-04-09 00:00:00 NA
Multiple weather columns
We could also add in more than one column at a time:
finches_weather <- weather_interp(data = finches, weather = kamloops, cols = c("temp", "wind_spd"))
## temp is missing 4 out of 4368 data, interpolation may be less accurate as a result.
## wind_spd is missing 4 out of 4368 data, interpolation may be less accurate as a result.
summary(finches_weather)
## animal_id date time logger_id species ## 0620000513:7624 Min. :2016-03-01 Min. :2016-03-01 06:57:42 1500:6370 Length:16886 ## 041868D861:2767 1st Qu.:2016-03-05 1st Qu.:2016-03-05 13:54:13 2100: 968 Class :character ## 0620000514:1844 Median :2016-03-09 Median :2016-03-09 16:54:47 2200:2266 Mode :character ## 06200004F8:1386 Mean :2016-03-08 Mean :2016-03-09 07:45:58 2300:3531 ## 041868BED6: 944 3rd Qu.:2016-03-13 3rd Qu.:2016-03-13 08:24:58 2400:1477 ## 06200003BB: 708 Max. :2016-03-16 Max. :2016-03-16 16:39:30 2700:2274 ## (Other) :1613 ## age sex site_name lon lat temp ## Length:16886 Length:16886 Length:16886 Min. :-120.4 Min. :50.67 Min. :-0.2317 ## Class :character Class :character Class :character 1st Qu.:-120.4 1st Qu.:50.67 1st Qu.: 5.0561 ## Mode :character Mode :character Mode :character Median :-120.4 Median :50.67 Median : 7.1651 ## Mean :-120.4 Mean :50.67 Mean : 7.4349 ## 3rd Qu.:-120.4 3rd Qu.:50.67 3rd Qu.: 9.3319 ## Max. :-120.4 Max. :50.67 Max. :16.3712 ## ## wind_spd ## Min. : 1.000 ## 1st Qu.: 7.634 ## Median :13.738 ## Mean :14.443 ## 3rd Qu.:19.907 ## Max. :44.939 ##
glimpse(finches_weather)
## Rows: 16,886 ## Columns: 12 ## $ animal_id <fct> 041868FF93, 041868FF93, 041868FF93, 06200003BB, 06200003BB, 06200003BB, 06200003BB, 06200003BB, 0… ## $ date <date> 2016-03-01, 2016-03-01, 2016-03-01, 2016-03-01, 2016-03-01, 2016-03-01, 2016-03-01, 2016-03-01, … ## $ time <dttm> 2016-03-01 06:57:42, 2016-03-01 06:58:41, 2016-03-01 07:07:21, 2016-03-01 07:32:34, 2016-03-01 0… ## $ logger_id <fct> 2300, 2300, 2300, 2400, 2400, 2400, 2400, 2400, 2300, 2300, 2300, 2300, 2300, 2400, 2300, 2400, 2… ## $ species <chr> "Mountain Chickadee", "Mountain Chickadee", "Mountain Chickadee", "House Finch", "House Finch", "… ## $ age <chr> "AHY", "AHY", "AHY", "SY", "SY", "SY", "SY", "SY", "AHY", "AHY", "AHY", "AHY", "AHY", "SY", "AHY"… ## $ sex <chr> "U", "U", "U", "M", "M", "M", "M", "M", "F", "F", "F", "F", "F", "M", "F", "M", "M", "M", "M", "M… ## $ site_name <chr> "Kamloops, BC", "Kamloops, BC", "Kamloops, BC", "Kamloops, BC", "Kamloops, BC", "Kamloops, BC", "… ## $ lon <dbl> -120.3622, -120.3622, -120.3622, -120.3635, -120.3635, -120.3635, -120.3635, -120.3635, -120.3622… ## $ lat <dbl> 50.66967, 50.66967, 50.66967, 50.66938, 50.66938, 50.66938, 50.66938, 50.66938, 50.66967, 50.6696… ## $ temp <dbl> 3.984667, 3.991222, 4.036750, 4.162833, 4.162917, 4.163000, 4.163083, 4.163167, 4.180417, 4.18075… ## $ wind_spd <dbl> 22.88500, 22.93417, 22.26500, 19.74333, 19.74167, 19.74000, 19.73833, 19.73667, 19.39167, 19.3850…
finches_weather <- finches_weather %>% group_by(date) %>% summarize(n = length(time), temp = mean(temp), wind_spd = mean(wind_spd)) ggplot(data = finches_weather, aes(x = date, y = n)) + theme_bw() + theme(legend.position = "top") + geom_bar(stat = "identity") + geom_line(aes(y = temp * 50, colour = "Temperature"), size = 2) + geom_line(aes(y = wind_spd * 50, colour = "Wind Speed"), size = 2) + scale_colour_discrete(name = "") + scale_y_continuous( name = "Activity Counts", sec.axis = sec_axis(~. / 50, name = "Temperature (C) / Wind Speed (km/h)"))
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