Retrieving and Processing Weather Data with R

weather data
API
data wrangling
daymetr
Author

Dr. Adrian Correndo

Published

February 13, 2026

Description

This lesson provides a step-by-step guide on retrieving and processing daily-weather data using R. It covers downloading data from DAYMET and processing it to generate weather summaries useful for agricultural research. We will put several of our previous lessons in action!

This tutorial focuses on how to:

  1. Download daily weather data from DAYMET.
  2. Process the retrieved data to calculate agronomic variables.
  3. Summarize weather data for different time intervals.

This is a reproducible workflow that can be adapted to different locations, crops, and time periods. The code is organized into functions to facilitate its use and adaptation. For a full version, please see Correndo et al. (2021)

Required packages 

library(pacman)
p_load(dplyr, tidyr, stringr) # Data wrangling
p_load(purrr) # Iteration
p_load(lubridate) # Date operations
p_load(kableExtra) # Table formatting to better display
p_load(daymetr) # Weather data from Daymet
p_load(skimr) # Summarizing weather data
p_load(vegan) # Shannon Diversity Index
p_load(writexl) # save excel files
p_load(ggplot2) # Plotting

1 Locations & Dates Data

# Locations
df_sites <- data.frame(
  ID = c('1', '2', '3'),
  Crop = c('Corn', 'Corn', 'Soybean'),
  Site = c('Elora', 'Ridgetown', 'Winchester'),
  latitude = c(43.6489, 42.4464, 45.0833),
  longitude = c(-80.4037, -81.8835, -75.3500)
)

# Dates
df_time <- data.frame(
  ID = c('1', '2', '3'), # column in common to match and merge
  Start = c('2021-04-25', '2022-05-01', '2023-05-20'), # date to start collecting weather
  End = c('2021-09-30', '2022-09-30', '2023-10-10'), # date to stop collecting weather
  # Add key crop phenology dates
  Flo = c('2021-07-20', '2022-07-21', '2023-07-15'), # Flowering dates for processing
  SeFi = c('2021-08-08', '2022-08-10', '2023-08-15') # Seed filling dates for processing
) %>% 
  # Express start and end as dates using "across"
  dplyr::mutate(across(Start:SeFi, ~as.Date(., format = '%Y-%m-%d')))

# Merge
df_input <- left_join(df_sites, df_time, by = "ID")

2 Retrieving Weather Data from DAYMET

daymetr is a programmatic interface to the Daymet web services. Allows for easy downloads of Daymet climate data directly to your R workspace or your computer. Spatial resolution: ~1 km2. Temporal resolution: daily. Historical data since year 1980.

Official Website: http://bluegreen-labs.github.io/daymetr/

2.1 How to cite the daymetr package

Hufkens K., Basler J. D., Milliman T. Melaas E., Richardson A.D. 2018 An integrated phenology modelling framework in R: Phenology modelling with phenor. Methods in Ecology & Evolution, 9: 1-10. https://doi.org/10.1111/2041-210X.12970

2.2 Evapotranspiration

Daymet does not provide data on reference evapotranspiration (\(\text{ET}_0\)). However, it is possible to estimate \(\text{ET}_0\) using the Hargreaves and Samani approach, which only requires temperature information (Hargreaves and Samani, 1985; Raziei and Pereira, 2013). Nonetheless, the \(\text{ET}_{0-HS}\) equation is reported to give unreliable estimates for daily \(ET0\) and therefore it should be used for 10-day periods at the shortest (Cobaner et al., 2017).

2.2.1 Constants

# Constants for ET0 (Cobaner et al., 2017)
# Solar constant:
Gsc <- 0.0820 # (MJ m-2 min-1)
# Radiation adjustment coefficient (Samani, 2004)
kRs <- 0.17

2.3 Function to retrieve

We can retrieve a function from a separate file. This is useful for keeping the code organized and to avoid having a long script. 

# Name of functions using dots (.) instead of underscore (_)
# We keep underscore for other objects
source("functions/weather_daymet.R")

2.4 Run retrieving function

# Specify input = dataframe containing sites-data 
# Specify Days prior planting. Default is dpp = 0. Here we use dpp = 30.

df_weather_daymet <- weather.daymet(input = df_input, dpp = 30)
Downloading DAYMET data for: 1 at 43.6489/-80.4037 latitude/longitude !
Done !
Downloading DAYMET data for: 2 at 42.4464/-81.8835 latitude/longitude !
Done !
Downloading DAYMET data for: 3 at 45.0833/-75.35 latitude/longitude !
Done !
# Overview of the variables (useful checking for missing values):
skimr::skim(df_weather_daymet)
Data summary
Name df_weather_daymet
Number of rows 546
Number of columns 25
_______________________
Column type frequency:
character 3
Date 5
numeric 17
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
ID 0 1 1 1 0 3 0
Crop 0 1 4 7 0 2 0
Site 0 1 5 10 0 3 0

Variable type: Date

skim_variable n_missing complete_rate min max median n_unique
Start 0 1 2021-04-25 2023-05-20 2022-05-01 3
End 0 1 2021-09-30 2023-10-10 2022-09-30 3
Flo 0 1 2021-07-20 2023-07-15 2022-07-21 3
SeFi 0 1 2021-08-08 2023-08-15 2022-08-10 3
Date 0 1 2021-03-26 2023-10-10 2022-06-23 546

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
latitude 0 1 43.70 1.07 42.45 42.45 43.65 45.08 45.08 ▇▁▇▁▇
longitude 0 1 -79.29 2.76 -81.88 -81.88 -80.40 -75.35 -75.35 ▇▇▁▁▇
DOY 0 1 185.58 53.22 85.00 140.25 186.00 231.00 283.00 ▆▇▇▇▇
Year 0 1 2021.97 0.82 2021.00 2021.00 2022.00 2023.00 2023.00 ▇▁▇▁▇
Month 0 1 6.62 1.74 3.00 5.00 7.00 8.00 10.00 ▃▅▇▅▅
Day 0 1 15.91 8.93 1.00 8.00 16.00 24.00 31.00 ▇▇▆▇▇
DL 0 1 13.95 1.14 10.96 13.10 14.21 14.96 15.44 ▁▂▃▅▇
PP 0 1 2.72 5.66 0.00 0.00 0.00 3.29 44.25 ▇▁▁▁▁
Rad 0 1 18.32 5.60 3.52 14.19 17.99 22.82 30.23 ▁▅▇▆▃
SWE 0 1 0.26 2.06 0.00 0.00 0.00 0.00 27.78 ▇▁▁▁▁
Tmax 0 1 22.31 6.52 -0.30 18.77 23.88 26.99 33.85 ▁▂▃▇▅
Tmin 0 1 10.87 6.06 -6.59 6.74 11.77 15.51 22.75 ▁▃▅▇▃
VP 0 1 1.36 0.51 0.33 0.96 1.35 1.73 2.77 ▅▇▇▅▁
Tmean 0 1 16.59 6.06 -2.68 12.83 17.92 21.16 27.40 ▁▂▃▇▅
ET0_HS 0 1 4.08 1.27 0.93 3.18 4.25 5.01 7.44 ▂▆▇▆▁
EPE_i 0 1 0.01 0.12 0.00 0.00 0.00 0.00 1.00 ▇▁▁▁▁
ETE_i 0 1 0.06 0.24 0.00 0.00 0.00 0.00 1.00 ▇▁▁▁▁ 
# Exporting data as a .csv file
# write.csv(df.weather.daymet, row.names = FALSE, na = '', file = paste0(path, 'Output_daymet.csv'))

3 Processing Weather Data

3.1 Define time intervals

In this section we create time intervals during the cropping season using pre-specified dates as columns at the initial data table with site information.

The user can apply: i) a unique seasonal interval (season), ii) even intervals (even), or iii) customized intervals (custom).

3.1.1 Full-season interval

# Defining season-intervals
season_interval <- 
  df_input %>%
  dplyr::mutate(Interval = "Season") %>%
  dplyr::rename(Start.in = Start, End.in = End) %>%
  dplyr::select(ID, Site, Interval, Start.in, End.in)

# Creating a table to visualize results
kable(season_interval) %>% 
  kable_styling(latex_options = c("striped"),
                position = "center", font_size = 10)
ID Site Interval Start.in End.in
1 Elora Season 2021-04-25 2021-09-30
2 Ridgetown Season 2022-05-01 2022-09-30
3 Winchester Season 2023-05-20 2023-10-10

3.1.2 Even-intervals

# Number of intervals:
n <- 4 
# Days prior planting:
dpp <- 30 

# Defining even-intervals:
even_intervals <- 
  df_input %>% 
  # Create new data:
  dplyr::mutate(Intervals = 
          purrr::map2(.x = Start, .y = End,
                      .f = ~ data.frame(
                    Interval = c("Prev", # Prior to start date 
                                 LETTERS[0:n]), # Each interval from start date
                      # Start
                      Start.in = c(.x - dpp, seq.Date(.x, .y + 1, length.out = n + 1)[1:n]),                              # End
                      End.in = c(.x - 1, seq.Date(.x, .y + 1, length.out = n + 1)[2:(n + 1)]))) ) %>% 
  # Selecting columns:
  dplyr::select(ID, Site, Intervals) %>% 
  tidyr::unnest(cols = c(Intervals))

# Creating a table to visualize results
kable(even_intervals) %>% 
  kable_styling(latex_options = c("striped"), 
                position = "center", font_size = 10)
ID Site Interval Start.in End.in
1 Elora Prev 2021-03-26 2021-04-24
1 Elora A 2021-04-25 2021-06-03
1 Elora B 2021-06-03 2021-07-13
1 Elora C 2021-07-13 2021-08-22
1 Elora D 2021-08-22 2021-10-01
2 Ridgetown Prev 2022-04-01 2022-04-30
2 Ridgetown A 2022-05-01 2022-06-08
2 Ridgetown B 2022-06-08 2022-07-16
2 Ridgetown C 2022-07-16 2022-08-23
2 Ridgetown D 2022-08-23 2022-10-01
3 Winchester Prev 2023-04-20 2023-05-19
3 Winchester A 2023-05-20 2023-06-25
3 Winchester B 2023-06-25 2023-07-31
3 Winchester C 2023-07-31 2023-09-05
3 Winchester D 2023-09-05 2023-10-11

3.1.3 Custom-intervals

# Count the number of interval columns (assuming intervals start at column "Start")
i <- df_input %>% dplyr::select(Start:last_col()) %>% ncol()

# Defining custom-intervals
custom_intervals <- 
  df_input %>% 
  dplyr::mutate(Intervals = # Create
                  purrr::pmap(# List of object to iterate over
                              .l = list(x = Start - dpp,
                                        y = Start,
                                        z = Flo,
                                        m = SeFi,
                                        k = End),
                              # The function to run
                              .f = function(x, y, z, m, k) {
                      data.frame(# New data
                        Interval = c(LETTERS[1:i]),
                        Name = c("Prev", "Plant-Flo", "Flo-SeFi", "SeFi-End"),
                        Start.in = c(x, y, z, m),
                        End.in = c(y-1, z-1, m-1, k))}
                    )) %>% 
  # Selecting columns:
  dplyr::select(ID,, Site, Intervals) %>% 
  tidyr::unnest(cols = c(Intervals))

# Creating a table to visualize results:
kable(custom_intervals) %>% 
  kable_styling(latex_options = c("striped"), position = "center", font_size = 10)
ID Site Interval Name Start.in End.in
1 Elora A Prev 2021-03-26 2021-04-24
1 Elora B Plant-Flo 2021-04-25 2021-07-19
1 Elora C Flo-SeFi 2021-07-20 2021-08-07
1 Elora D SeFi-End 2021-08-08 2021-09-30
2 Ridgetown A Prev 2022-04-01 2022-04-30
2 Ridgetown B Plant-Flo 2022-05-01 2022-07-20
2 Ridgetown C Flo-SeFi 2022-07-21 2022-08-09
2 Ridgetown D SeFi-End 2022-08-10 2022-09-30
3 Winchester A Prev 2023-04-20 2023-05-19
3 Winchester B Plant-Flo 2023-05-20 2023-07-14
3 Winchester C Flo-SeFi 2023-07-15 2023-08-14
3 Winchester D SeFi-End 2023-08-15 2023-10-10

3.2 Summary function

For each of the period or interval of interest a variety of variables can be created. Here, we present a set of variables that can capture environmental variations that might be missing by analyzing standard weather data (precipitations, temperature, radiation). These variables represent an example that was used for studying influence of weather in corn yields by Correndo et al. (2021).

3.2.1 Load function

source("functions/summary_daymet.R")

3.3 Run summaries

3.3.1 Seasonal

# Run the summary
# input = dataframe containing the data (from daymet).
# intervals = type of intervals (season, custom or even)

season_summary_daymet <-
  summary.daymet(input = df_weather_daymet,
                           intervals = season_interval)

# Skim data
skimr::skim(season_summary_daymet)
Data summary
Name season_summary_daymet
Number of rows 3
Number of columns 20
_______________________
Column type frequency:
character 4
Date 2
numeric 14
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
ID 0 1 1 1 0 3 0
Interval 0 1 6 6 0 1 0
Crop 0 1 4 7 0 2 0
Site 0 1 5 10 0 3 0

Variable type: Date

skim_variable n_missing complete_rate min max median n_unique
Start.in 0 1 2021-04-25 2023-05-20 2022-05-01 3
End.in 0 1 2021-09-30 2023-10-10 2022-09-30 3

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
Dur 0 1 151.00 7.55 143.00 147.50 152.00 155.00 158.00 ▇▁▇▁▇
PP 0 1 415.30 73.58 331.50 388.27 445.05 457.20 469.35 ▃▁▁▁▇
Tmean 0 1 18.42 1.12 17.18 17.94 18.71 19.04 19.37 ▇▁▁▇▇
Rad 0 1 2778.49 279.01 2461.52 2674.26 2887.00 2936.97 2986.94 ▃▁▁▁▇
VP 0 1 224.75 13.71 216.63 216.83 217.04 228.81 240.58 ▇▁▁▁▃
ET0_HS 0 1 657.79 35.73 616.55 646.95 677.36 678.41 679.45 ▃▁▁▁▇
ETE 0 1 11.33 4.51 7.00 9.00 11.00 13.50 16.00 ▇▁▇▁▇
EPE 0 1 2.33 1.53 1.00 1.50 2.00 3.00 4.00 ▇▇▁▁▇
CHU 0 1 3097.65 131.86 3000.08 3022.65 3045.22 3146.44 3247.67 ▇▁▁▁▃
SDI 0 1 0.73 0.04 0.69 0.71 0.73 0.75 0.78 ▇▁▇▁▇
GDD 0 1 1339.05 99.58 1273.50 1281.76 1290.02 1371.82 1453.63 ▇▁▁▁▃
Q_chu 0 1 0.90 0.08 0.82 0.85 0.89 0.93 0.98 ▇▁▇▁▇
Q_gdd 0 1 2.08 0.23 1.91 1.95 1.99 2.17 2.35 ▇▁▁▁▃
AWDR 0 1 305.30 66.55 228.50 285.01 341.51 343.71 345.90 ▃▁▁▁▇ 
# Creating a table to visualize results
kbl(season_summary_daymet) %>%
  kable_styling(font_size = 7, position = "center", 
                latex_options = c("scale_down"))
ID Interval Start.in End.in Crop Site Dur PP Tmean Rad VP ET0_HS ETE EPE CHU SDI GDD Q_chu Q_gdd AWDR
1 Season 2021-04-25 2021-09-30 Corn Elora 158 469.35 17.17851 2986.936 216.6294 677.3595 7 4 3045.216 0.7276272 1273.500 0.9808618 2.345455 341.5118
2 Season 2022-05-01 2022-09-30 Corn Ridgetown 152 331.50 19.36694 2887.004 240.5812 679.4511 16 2 3247.665 0.6892949 1453.635 0.8889474 1.986058 228.5013
3 Season 2023-05-20 2023-10-10 Soybean Winchester 143 445.05 18.70930 2461.524 217.0387 616.5490 11 1 3000.080 0.7772147 1290.015 0.8204861 1.908136 345.8994

3.3.2 Even

# Run the summary
# input = dataframe containing the data (from daymet).
# intervals = type of intervals (season, custom or even)

even_summary_daymet <-
  summary.daymet(input = df_weather_daymet,
                           intervals = even_intervals)

# Skim data
skimr::skim(even_summary_daymet)
Data summary
Name even_summary_daymet
Number of rows 15
Number of columns 20
_______________________
Column type frequency:
character 4
Date 2
numeric 14
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
ID 0 1 1 1 0 3 0
Interval 0 1 1 4 0 5 0
Crop 0 1 4 7 0 2 0
Site 0 1 5 10 0 3 0

Variable type: Date

skim_variable n_missing complete_rate min max median n_unique
Start.in 0 1 2021-03-26 2023-09-05 2022-06-08 15
End.in 0 1 2021-04-24 2023-10-11 2022-07-16 15

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
Dur 0 1 36.20 4.00 29.00 36.00 38.00 39.00 40.00 ▃▁▁▃▇
PP 0 1 98.00 46.14 20.54 68.25 90.72 124.79 190.88 ▆▇▇▃▃
Tmean 0 1 16.23 5.27 6.20 13.63 17.15 19.96 22.24 ▂▂▁▅▇
Rad 0 1 661.92 145.37 450.04 543.88 649.96 776.50 892.91 ▇▆▃▇▃
VP 0 1 49.40 17.63 18.88 39.66 53.65 62.80 72.28 ▆▂▆▇▇
ET0_HS 0 1 147.72 46.27 68.05 114.39 160.16 184.08 206.72 ▂▂▃▂▇
ETE 0 1 2.27 2.52 0.00 0.00 2.00 3.00 8.00 ▇▆▁▁▂
EPE 0 1 0.53 0.83 0.00 0.00 0.00 1.00 3.00 ▇▅▁▁▁
CHU 0 1 656.35 284.42 111.78 535.39 729.39 876.21 944.22 ▃▁▁▅▇
SDI 0 1 0.64 0.07 0.56 0.59 0.64 0.67 0.81 ▇▆▇▁▂
GDD 0 1 280.44 137.50 36.71 215.41 296.47 400.84 461.30 ▅▂▅▅▇
Q_chu 0 1 1.41 1.10 0.63 0.76 0.84 1.59 4.54 ▇▁▁▁▁
Q_gdd 0 1 3.75 3.53 1.51 1.81 1.96 4.13 13.83 ▇▂▁▁▁
AWDR 0 1 64.09 33.76 11.46 45.54 56.92 78.87 139.77 ▅▇▆▂▃ 
# Creating a table to visualize results
kbl(even_summary_daymet) %>%
  kable_styling(font_size = 7, position = "center", 
                latex_options = c("scale_down"))
ID Interval Start.in End.in Crop Site Dur PP Tmean Rad VP ET0_HS ETE EPE CHU SDI GDD Q_chu Q_gdd AWDR
1 Prev 2021-03-26 2021-04-24 Corn Elora 29 79.48 6.267931 460.4183 18.87947 68.32607 0 0 154.6380 0.6003913 51.060 2.9773933 9.017201 47.71910
1 A 2021-04-25 2021-06-03 Corn Elora 40 51.97 11.462000 892.9099 32.99382 160.15896 0 0 449.2242 0.5655066 174.070 1.9876708 5.129603 29.38938
1 B 2021-06-03 2021-07-13 Corn Elora 40 130.87 19.800875 756.3732 65.72674 199.67338 2 1 931.2573 0.7020210 399.490 0.8122064 1.893347 91.87349
1 C 2021-07-13 2021-08-22 Corn Elora 40 95.63 20.127000 796.6236 65.08540 189.50500 2 0 944.2226 0.6839553 406.645 0.8436820 1.959015 65.40664
1 D 2021-08-22 2021-10-01 Corn Elora 39 190.88 17.150641 554.8840 53.64645 130.26551 3 3 729.3916 0.6219867 296.470 0.7607492 1.871636 118.72483
2 Prev 2022-04-01 2022-04-30 Corn Ridgetown 29 53.63 6.195862 507.7087 20.10403 68.04829 0 0 111.7844 0.6669155 36.715 4.5418546 13.828371 35.76668
2 A 2022-05-01 2022-06-08 Corn Ridgetown 39 104.36 15.800513 803.9339 48.57693 167.84915 1 1 677.3768 0.6405580 256.750 1.1868342 3.131193 66.84863
2 B 2022-06-08 2022-07-16 Corn Ridgetown 38 20.54 20.658553 869.7095 60.50965 206.72186 8 0 812.2194 0.5580330 402.185 1.0707814 2.162461 11.46200
2 C 2022-07-16 2022-08-23 Corn Ridgetown 38 138.96 22.235789 696.3248 72.27967 181.42772 7 1 920.7932 0.5736522 461.300 0.7562228 1.509484 79.71471
2 D 2022-08-23 2022-10-01 Corn Ridgetown 38 67.64 18.656316 532.8785 60.06231 125.97368 0 0 848.1910 0.6719260 337.475 0.6282529 1.579016 45.44907
3 Prev 2023-04-20 2023-05-19 Soybean Winchester 29 85.34 10.473793 591.9513 25.05817 100.19840 0 1 261.9933 0.5697489 93.780 2.2594144 6.312128 48.62237
3 A 2023-05-20 2023-06-25 Soybean Winchester 36 68.85 17.041806 744.2820 46.33343 182.88118 3 0 654.8623 0.6628592 274.325 1.1365473 2.713140 45.63785
3 B 2023-06-25 2023-07-31 Soybean Winchester 36 172.37 21.990278 649.9607 68.07814 185.28684 4 0 904.2380 0.8108563 428.210 0.7187938 1.517855 139.76731
3 C 2023-07-31 2023-09-05 Soybean Winchester 36 118.71 19.080000 620.7637 56.26818 146.69390 1 1 823.5029 0.6573549 329.955 0.7538087 1.881359 78.03460
3 D 2023-09-05 2023-10-11 Soybean Winchester 36 90.72 16.455694 450.0399 47.34861 102.79934 3 0 621.5492 0.6273709 258.120 0.7240615 1.743530 56.91508

3.3.3 Custom

# Run the summary
# input = dataframe containing the data (from daymet).
# intervals = type of intervals (season, custom or even)

custom_summary_daymet <-
  summary.daymet(input = df_weather_daymet,
                           intervals = custom_intervals)

# Skim data
skimr::skim(custom_summary_daymet)
Data summary
Name custom_summary_daymet
Number of rows 12
Number of columns 21
_______________________
Column type frequency:
character 5
Date 2
numeric 14
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
ID 0 1 1 1 0 3 0
Interval 0 1 1 1 0 4 0
Name 0 1 4 9 0 4 0
Crop 0 1 4 7 0 2 0
Site 0 1 5 10 0 3 0

Variable type: Date

skim_variable n_missing complete_rate min max median n_unique
Start.in 0 1 2021-03-26 2023-08-15 2022-06-10 12
End.in 0 1 2021-04-24 2023-10-10 2022-07-30 12

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
Dur 0 1 44.50 22.44 18.00 29.00 40.50 55.25 85.00 ▇▁▅▁▂
PP 0 1 121.30 57.33 50.63 74.48 118.77 167.75 215.00 ▇▃▃▂▆
Tmean 0 1 16.16 5.52 6.20 14.57 18.46 19.04 23.40 ▂▁▁▇▂
Rad 0 1 814.98 478.95 343.20 495.89 688.73 881.31 1743.26 ▇▃▁▁▂
VP 0 1 60.63 34.82 18.88 25.44 65.07 82.40 115.34 ▇▃▁▇▃
ET0_HS 0 1 181.73 115.00 68.05 92.79 161.76 217.97 390.34 ▇▆▁▂▃
ETE 0 1 2.75 3.41 0.00 0.00 1.00 5.00 10.00 ▇▁▂▁▁
EPE 0 1 0.67 0.98 0.00 0.00 0.00 1.00 3.00 ▇▃▁▁▁
CHU 0 1 806.71 517.34 111.78 367.24 914.93 1133.25 1559.58 ▆▃▂▇▃
SDI 0 1 0.64 0.09 0.45 0.60 0.64 0.68 0.79 ▂▂▇▅▃
GDD 0 1 344.22 226.64 36.71 141.07 379.82 485.38 698.82 ▇▂▂▇▃
Q_chu 0 1 1.47 1.20 0.66 0.74 0.97 1.43 4.54 ▇▁▂▁▁
Q_gdd 0 1 3.94 3.87 1.36 1.73 2.30 3.66 13.83 ▇▁▁▁▁
AWDR 0 1 80.61 43.45 23.00 44.99 78.01 110.13 148.53 ▇▂▆▂▆ 
# Creating a table to visualize results
kbl(custom_summary_daymet) %>%
  kable_styling(font_size = 7, position = "center", 
                latex_options = c("scale_down"))
ID Interval Name Start.in End.in Crop Site Dur PP Tmean Rad VP ET0_HS ETE EPE CHU SDI GDD Q_chu Q_gdd AWDR
1 A Prev 2021-03-26 2021-04-24 Corn Elora 29 79.48 6.267931 460.4183 18.87947 68.32607 0 0 154.6380 0.6003913 51.060 2.9773933 9.017201 47.71910
1 B Plant-Flo 2021-04-25 2021-07-19 Corn Elora 85 190.47 15.934588 1743.2608 107.61957 383.46725 2 1 1510.0443 0.7061373 627.485 1.1544434 2.778171 134.49798
1 C Flo-SeFi 2021-07-20 2021-08-07 Corn Elora 18 59.50 18.644167 389.5612 25.56795 87.42324 0 0 402.3209 0.6187163 156.840 0.9682848 2.483813 36.81362
1 D SeFi-End 2021-08-08 2021-09-30 Corn Elora 53 215.00 18.513396 813.1071 80.11335 196.08039 5 3 1080.6921 0.6349615 466.215 0.7523948 1.744060 136.51673
2 A Prev 2022-04-01 2022-04-30 Corn Ridgetown 29 53.63 6.195862 507.7087 20.10403 68.04829 0 0 111.7844 0.6669155 36.715 4.5418546 13.828371 35.76668
2 B Plant-Flo 2022-05-01 2022-07-20 Corn Ridgetown 80 161.38 18.402500 1726.6087 115.33982 390.34473 10 2 1559.5834 0.6061851 698.825 1.1070961 2.470731 97.82615
2 C Flo-SeFi 2022-07-21 2022-08-09 Corn Ridgetown 19 50.63 23.402368 343.2025 38.99942 94.58351 5 0 462.4571 0.4542949 252.150 0.7421284 1.361105 23.00095
2 D SeFi-End 2022-08-10 2022-09-30 Corn Ridgetown 51 115.13 19.234902 785.4997 81.95346 185.19135 0 0 1185.1321 0.6618711 476.810 0.6627951 1.647406 76.20122
3 A Prev 2023-04-20 2023-05-19 Soybean Winchester 29 85.34 10.473793 591.9513 25.05817 100.19840 0 1 261.9933 0.5697489 93.780 2.2594144 6.312128 48.62237
3 B Plant-Flo 2023-05-20 2023-07-14 Soybean Winchester 55 135.77 18.980818 1085.9110 83.74737 283.62792 7 0 1115.9606 0.7513595 511.085 0.9730729 2.124717 102.01208
3 C Flo-SeFi 2023-07-15 2023-08-14 Soybean Winchester 30 186.87 20.319667 527.4011 51.32045 140.03873 0 1 749.1665 0.7948514 311.280 0.7039839 1.694298 148.53388
3 D SeFi-End 2023-08-15 2023-10-10 Soybean Winchester 56 122.41 17.546429 805.1760 78.82447 183.47259 4 0 1086.7277 0.6520579 448.355 0.7409179 1.795845 79.81841

4 Historical weather

4.1 Locations and Dates Data

# For historical data (from Jan-01-2000 to Dec-31-2022)
df_historical <- data.frame(ID = c('1', '2', '3'),
                      # Dates as YYYY_MM_DD, using "_" to separate
                      Start = c('2000-01-01', '2000-01-01', '2000-01-01'),
                      End = c('2024-12-31', '2024-12-31', '2024-12-31')) %>% 
  # Express start and end as dates using "across"
  dplyr::mutate(across(Start:End, ~as.Date(., format = '%Y-%m-%d')))

# Merge for historical weather data
df_historical <- df_sites %>% dplyr::left_join(df_historical, by = "ID")

4.2 Retrieve from DAYMET

# Specify input = dataframe containing historical dates from sites 
hist_weather_daymet <- weather.daymet(input = df_historical)
Downloading DAYMET data for: 1 at 43.6489/-80.4037 latitude/longitude !
Done !
Downloading DAYMET data for: 2 at 42.4464/-81.8835 latitude/longitude !
Done !
Downloading DAYMET data for: 3 at 45.0833/-75.35 latitude/longitude !
Done !
# This is a large data frame (21900 obs), so good to have an overview
# Skim data
skimr::skim(hist_weather_daymet)
Data summary
Name hist_weather_daymet
Number of rows 27375
Number of columns 23
_______________________
Column type frequency:
character 3
Date 3
numeric 17
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
ID 0 1 1 1 0 3 0
Crop 0 1 4 7 0 2 0
Site 0 1 5 10 0 3 0

Variable type: Date

skim_variable n_missing complete_rate min max median n_unique
Start 0 1 2000-01-01 2000-01-01 2000-01-01 1
End 0 1 2024-12-31 2024-12-31 2024-12-31 1
Date 0 1 2000-01-01 2024-12-30 2012-07-01 9125

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
latitude 0 1 43.73 1.08 42.45 42.45 43.65 45.08 45.08 ▇▁▇▁▇
longitude 0 1 -79.21 2.80 -81.88 -81.88 -80.40 -75.35 -75.35 ▇▇▁▁▇
DOY 0 1 183.00 105.37 1.00 92.00 183.00 274.00 365.00 ▇▇▇▇▇
Year 0 1 2012.00 7.21 2000.00 2006.00 2012.00 2018.00 2024.00 ▇▇▇▇▇
Month 0 1 6.52 3.45 1.00 4.00 7.00 10.00 12.00 ▇▅▅▅▇
Day 0 1 15.72 8.79 1.00 8.00 16.00 23.00 31.00 ▇▇▇▇▆
DL 0 1 12.00 2.26 8.56 9.80 12.00 14.20 15.44 ▇▅▅▅▇
PP 0 1 2.59 5.28 0.00 0.00 0.00 2.93 85.53 ▇▁▁▁▁
Rad 0 1 13.38 7.36 0.74 6.85 12.58 19.29 32.59 ▇▇▇▆▂
SWE 0 1 15.37 32.02 0.00 0.00 0.00 10.20 211.59 ▇▁▁▁▁
Tmax 0 1 12.85 11.47 -22.58 3.12 13.60 23.23 36.23 ▁▅▇▇▅
Tmin 0 1 3.13 10.39 -32.52 -3.66 3.49 11.75 25.02 ▁▂▇▇▅
VP 0 1 0.93 0.59 0.04 0.46 0.77 1.35 3.17 ▇▆▃▁▁
Tmean 0 1 7.99 10.76 -26.25 -0.17 8.54 17.48 29.88 ▁▃▇▇▅
ET0_HS 0 1 2.42 1.82 -0.45 0.73 1.98 4.01 7.44 ▇▅▅▅▁
EPE_i 0 1 0.01 0.10 0.00 0.00 0.00 0.00 1.00 ▇▁▁▁▁
ETE_i 0 1 0.03 0.17 0.00 0.00 0.00 0.00 1.00 ▇▁▁▁▁

4.3 Summary functions

4.3.1 By year

# Defining function to summarize historical weather (years)

# Revised function:
historical.years <- function(hist.data) {
  # Creates an input tibble with the start and end date for each year a summary is desired
  # Args:
  #  hist.data = data frame containing the historical weather data to summarize (must be complete years)
  # Returns:
  #  a tibble of monthly summaries for each ID
  #
  # By year:
  hist.data %>% 
    dplyr::group_by(ID, Crop, Site) %>%
    tidyr::nest() %>%
    dplyr::mutate(the_Dates = purrr::map(data, function(.data) {.data %>% dplyr::group_by(Year) %>% 
        dplyr::summarise(Start.in = min(Date), End.in = max(Date), .groups = "drop")})) %>%
    dplyr::ungroup() %>%
    dplyr::select(ID, Site, the_Dates) %>%
    tidyr::unnest(cols = c(the_Dates))
}

4.3.2 By year-month

# Defining function to summarize historical weather (years & months)
# Revised function:
historical.yearmonths <- function(hist.data) {
  # Creates an input tibble with the start and end date for each year & month a summary is desired (monthly summaries)
  # Args:
  #  hist.data = data frame containing the historical weather data to summarize (must be complete years)
  # Returns:
  #  a tibble of monthly summaries for each ID
  #
  # By month in year:
  hist.data %>% 
    dplyr::group_by(ID, Crop, Site) %>%
    tidyr::nest() %>%
    dplyr::mutate(the_Dates = purrr::map(data, function(.data) {.data %>% 
        dplyr::group_by(Year, Month) %>% 
        dplyr::summarise(Start.in = min(Date), End.in = max(Date), .groups = "drop")})) %>%
    dplyr::ungroup() %>%
    dplyr::select(ID, Site, the_Dates) %>%
    tidyr::unnest(cols = c(the_Dates))
}

4.4 Run Historical Summaries

Summary can be obtained by years or by years.months. User must specify this option at the “intervals” argument of the summary function.

4.4.1 By year

# Specify hist.data = dataframe containing the historical weather data to summarize
year_intervals <- historical.years(hist.data = hist_weather_daymet)

# input = dataframe containing the historical weather data.
# intervals = type of historical intervals (years, years.months)

# Summarizing historical weather
year_summary_daymet <-
  summary.daymet(input = hist_weather_daymet,
                           intervals = year_intervals)

# Creating a table to visualize data
kbl(head(year_summary_daymet)) %>%
  kable_styling(font_size = 7, position = "center", latex_options = c("scale_down"))
ID Year Start.in End.in Crop Site Dur PP Tmean Rad VP ET0_HS ETE EPE CHU SDI GDD Q_chu Q_gdd AWDR
1 2000 2000-01-01 2000-12-30 Corn Elora 364 1035.79 6.784354 4882.077 311.9404 834.4626 1 5 3363.850 0.7951588 1226.465 1.451336 3.980609 823.6175
1 2001 2001-01-01 2001-12-31 Corn Elora 364 909.30 7.786786 4956.964 321.2663 874.0620 11 2 3266.985 0.7815342 1304.725 1.517290 3.799241 710.6491
1 2002 2002-01-01 2002-12-31 Corn Elora 364 795.34 7.495824 4869.331 327.4677 869.5653 16 2 3140.274 0.7811108 1363.225 1.550607 3.571921 621.2487
1 2003 2003-01-01 2003-12-31 Corn Elora 364 948.80 6.212733 4976.636 300.1867 841.3591 4 2 3158.797 0.7825177 1214.135 1.575484 4.098915 742.4528
1 2004 2004-01-01 2004-12-30 Corn Elora 364 952.48 6.537857 4883.104 308.5060 827.3507 0 3 3248.058 0.8086498 1183.855 1.503392 4.124748 770.2228
1 2005 2005-01-01 2005-12-31 Corn Elora 364 842.09 7.185206 5117.729 300.0098 921.0563 24 3 3242.154 0.7836771 1466.130 1.578497 3.490638 659.9266

4.4.2 By year-month

# Specify hist.data = dataframe containing the historical weather data to summarize
yearmonth_intervals <- historical.yearmonths(hist.data = hist_weather_daymet)

# input = dataframe containing the historical weather data.
# intervals = type of historical intervals (years, years.months)

# Summarizing historical weather
yearmonth_summary_daymet <-
  summary.daymet(input = hist_weather_daymet,
                           intervals = yearmonth_intervals)

# Creating a table to visualize data
kbl(head(yearmonth_summary_daymet)) %>%
  kable_styling(font_size = 7, position = "center", latex_options = c("scale_down"))
ID Year Month Start.in End.in Crop Site Dur PP Tmean Rad VP ET0_HS ETE EPE CHU SDI GDD Q_chu Q_gdd AWDR
1 2000 1 2000-01-01 2000-01-31 Corn Elora 30 44.22 -7.187333 191.5335 8.62155 12.22926 0 0 0.9515712 0.7172157 0.290 201.2813416 660.460441 31.71528
1 2000 2 2000-02-01 2000-02-29 Corn Elora 28 57.08 -4.463036 269.1460 9.91130 19.01688 0 0 6.6954018 0.7480953 2.105 40.1986311 127.860326 42.70128
1 2000 3 2000-03-01 2000-03-31 Corn Elora 30 44.97 2.868500 441.0486 16.34147 47.73356 0 0 70.8484896 0.6801023 26.220 6.2252371 16.821077 30.58420
1 2000 4 2000-04-01 2000-04-30 Corn Elora 29 64.87 5.135345 552.8653 18.09003 65.36530 0 1 100.1956452 0.5036470 34.100 5.5178573 16.213058 32.67158
1 2000 5 2000-05-01 2000-05-31 Corn Elora 30 147.43 12.970667 584.3621 31.84376 114.94758 0 2 398.9491488 0.6533741 140.385 1.4647534 4.162568 96.32695
1 2000 6 2000-06-01 2000-06-30 Corn Elora 29 199.88 17.281207 560.0957 41.55868 133.48935 1 1 576.7064388 0.7668216 217.885 0.9711973 2.570603 153.27230

5 Exporting Results

# *.csv
#write.csv(my_summary_dataframe, "my_summary_dataframe.csv", row.names = FALSE)
# *.xlsx
#writexl::write_xlsx(x = my_summary_dataframe, path = "my_summary_Daymet.csv")

6 Plot results

6.1 prepare data

# Get overall mean of PP, Tmean, and Rad by month
monthly_means <- 
  yearmonth_summary_daymet %>% 
  # Create a date column for plotting NOT INCLUDING Year
  dplyr::mutate(plot_date = as.Date(paste0("2000-", Month, "-01"), format = "%Y-%m-%d")) %>%
  group_by(Site, Month, plot_date) %>% 
  summarise(across(c(PP, Tmean, Rad), mean, na.rm = TRUE)) %>% 
  ungroup() %>% 
  group_by(Site) %>% 
  arrange(Month) %>%
      mutate(PP_ac = cumsum(PP)) %>% 
  ungroup()
Warning: There was 1 warning in `summarise()`.
ℹ In argument: `across(c(PP, Tmean, Rad), mean, na.rm = TRUE)`.
ℹ In group 1: `Site = "Elora"`, `Month = 1`, `plot_date = 2000-01-01`.
Caused by warning:
! The `...` argument of `across()` is deprecated as of dplyr 1.1.0.
Supply arguments directly to `.fns` through an anonymous function instead.

  # Previously
  across(a:b, mean, na.rm = TRUE)

  # Now
  across(a:b, \(x) mean(x, na.rm = TRUE))
`summarise()` has grouped output by 'Site', 'Month'. You can override using the
`.groups` argument.
# Get year-moth
yearmonth_means <- 
  yearmonth_summary_daymet %>% 
  # Create a date column for plotting NOT INCLUDING Year
  dplyr::mutate(plot_date = as.Date(paste0("2000-", Month, "-01"), format = "%Y-%m-%d")) %>%
  group_by(Site, Year) %>%
      arrange(Month) %>%
      mutate(PP_ac = cumsum(PP)) %>% 
  ungroup() %>%
  group_by(Site, Year, Month, plot_date) %>% 
  summarise(across(c(PP, PP_ac, Tmean, Rad), mean, na.rm = TRUE))
`summarise()` has grouped output by 'Site', 'Year', 'Month'. You can override
using the `.groups` argument.

6.2 Precipitation

# Plotting PP as boxplot by month and jitter for years
yearmonth_means %>% 
  ggplot(aes(x = plot_date, y = PP)) +
  geom_boxplot(aes(group = Month, fill = Site)) +
  geom_jitter(size = 0.05, alpha = 0.5) +
  scale_x_date(date_labels = "%b", # Show abbreviated month names
               limits = as.Date(c("2000-01-01", "2000-12-31")),
               date_breaks = "1 month",  # Break at every month
               ) +
  labs(title = "Historical weather",
       subtitle = "Seasons: 2000-2024",
       x = "Month",
       y = "Mean Precipitation (mm)") +
  facet_wrap(~Site)+
  theme_classic()+
  theme(legend.position = "none",
        axis.text.x = element_text(angle = 90, vjust = 0.5))
Warning: Removed 1 row containing missing values or values outside the scale range
(`geom_segment()`).
Removed 1 row containing missing values or values outside the scale range
(`geom_segment()`).
Removed 1 row containing missing values or values outside the scale range
(`geom_segment()`).
Warning: Removed 39 rows containing missing values or values outside the scale range
(`geom_point()`).

6.3 Cummulative PP

# Plotting mean temperature by month with a general line and for each year
monthly_means %>% 
  ggplot(aes(x = plot_date)) +
  geom_smooth(data = yearmonth_means, aes(y = PP_ac, group = Year), 
              color = "grey55", se = F, linewidth = 0.1) +
  geom_smooth(aes(y = PP_ac), se=F) +
  scale_x_date(date_labels = "%b",# Show abbreviated month names
               limits = as.Date(c("2000-01-01", "2000-12-31")),
               date_breaks = "1 month",  # Break at every month
               ) +
  scale_y_continuous(limits = c(0, 1200), 
                     breaks = seq(0,1200,by=200)) +
  labs(title = "Historical weather",
       subtitle = "Seasons: 2000-2024",
       x = "Month",
       y = "Cumulative PP") +
  facet_wrap(~Site)+
  theme_classic()+
  theme(legend.position = "none",
        axis.text.x = element_text(angle = 90, vjust = 0.5))
`geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Warning: Removed 3 rows containing non-finite outside the scale range
(`stat_smooth()`).
`geom_smooth()` using method = 'loess' and formula = 'y ~ x'

6.4 Temperature

# Plotting mean temperature by month with a general line and for each year
monthly_means %>% 
  ggplot(aes(x = plot_date)) +
  geom_smooth(data = yearmonth_means, aes(y = Tmean, group = Year), 
              color = "grey55", se = F, linewidth = 0.1) +
  geom_smooth(aes(y = Tmean), se=F, color = "#a4133c") +
  scale_x_date(date_labels = "%b",# Show abbreviated month names
               limits = as.Date(c("2000-01-01", "2000-12-31")),
               date_breaks = "1 month",  # Break at every month
               ) +
  scale_y_continuous(limits = c(-12, 22), breaks = seq(-12,22,by=2)) +
  geom_hline(yintercept = 0, linetype = "dashed", color = "grey55") + 
  labs(title = "Historical weather",
       subtitle = "Seasons: 2000-2024",
       x = "Month",
       y = "Mean Temperature (oC)") +
  facet_wrap(~Site)+
  theme_classic()+
  theme(legend.position = "none",
        axis.text.x = element_text(angle = 90, vjust = 0.5))
`geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Warning: Removed 34 rows containing non-finite outside the scale range
(`stat_smooth()`).
`geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Warning: Removed 20 rows containing missing values or values outside the scale range
(`geom_smooth()`).

6.5 Radiation

# Plotting mean radiation by month with a general line and for each year
monthly_means %>%
  ggplot(aes(x = plot_date)) +
  geom_col(aes(y = Rad), fill = "#fff3b0", color = "grey15") +
  geom_smooth(data = yearmonth_means, aes(y = Rad, group = Year), 
              color = "grey55", se = F, linewidth = 0.1) +
  geom_smooth(aes(y = Rad), se=F, color = "#e09f3e") +
  scale_x_date(date_labels = "%b",# Show abbreviated month names
               # add limits Jan to Dec
               limits = as.Date(c("2000-01-01", "2000-12-31")),
               date_breaks = "1 month",  # Break at every month
               ) +
  scale_y_continuous(limits = c(0, 700), breaks = seq(0,700,by=100)) +
  labs(title = "Historical weather",
       subtitle = "Seasons: 2000-2024",
       x = "Month",
       y = "Mean Radiation (MJ/m2)") +
  facet_wrap(~Site)+
  theme_classic()+
  theme(legend.position = "none",
        axis.text.x = element_text(angle = 90, vjust = 0.5))
`geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Warning: Removed 5 rows containing non-finite outside the scale range
(`stat_smooth()`).
`geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Warning: Removed 3 rows containing missing values or values outside the scale range
(`geom_col()`).

7 References