I would like to share some R codes that I wrote for creating weather predictors which I used in predictive modeling of sugarcane rusts. I have also created several complex weather variables during my research work but I am going to share some with averages/sums of hourly, weekly, monthly, and also specific time in a day. I will also share the R codes to calculate more complex weather variables in separate posts.
library(readxl)
## Warning: package 'readxl' was built under R version 4.3.3
library(lubridate)
##
## Attaching package: 'lubridate'
## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, union
library(plyr)
## Warning: package 'plyr' was built under R version 4.3.3
Wdata <- read_excel("weatherdata.xlsx")
head (Wdata, 10)
## # A tibble: 10 × 11
## Period `2m T avg (F)` `2m T min (F)` `2m T max (F)`
## <dttm> <dbl> <dbl> <dbl>
## 1 2013-11-15 00:00:00 57.9 57.5 58.1
## 2 2013-11-15 01:00:00 57.9 57.2 58.6
## 3 2013-11-15 02:00:00 58.8 58.2 59.4
## 4 2013-11-15 03:00:00 59.0 58.8 59.2
## 5 2013-11-15 04:00:00 58.8 58.7 58.9
## 6 2013-11-15 05:00:00 59.6 59.2 59.8
## 7 2013-11-15 06:00:00 59.8 59.4 60.1
## 8 2013-11-15 07:00:00 61.4 60.3 62.6
## 9 2013-11-15 08:00:00 66.6 64.3 68.8
## 10 2013-11-15 09:00:00 72.9 70.6 74.7
## # ℹ 7 more variables: `RelHum avg 2m (pct)` <dbl>, `2m Rain tot (in)` <dbl>,
## # `2m Rain max over 15min(in)` <dbl>, `10m Wind avg (mph)` <dbl>,
## # `10m Wind min (mph)` <dbl>, `10m Wind max (mph)` <dbl>, `N (# obs)` <dbl>
str(Wdata)
## tibble [9,888 × 11] (S3: tbl_df/tbl/data.frame)
## $ Period : POSIXct[1:9888], format: "2013-11-15 00:00:00" "2013-11-15 01:00:00" ...
## $ 2m T avg (F) : num [1:9888] 57.9 57.9 58.8 59 58.8 ...
## $ 2m T min (F) : num [1:9888] 57.5 57.2 58.1 58.8 58.7 ...
## $ 2m T max (F) : num [1:9888] 58.1 58.6 59.4 59.2 58.9 ...
## $ RelHum avg 2m (pct) : num [1:9888] 93 93 92 92 93 94 94 95 92 82 ...
## $ 2m Rain tot (in) : num [1:9888] 0 0 0 0 0 0 0 0 0 0 ...
## $ 2m Rain max over 15min(in): num [1:9888] 0 0 0 0 0 0 0 0 0 0 ...
## $ 10m Wind avg (mph) : num [1:9888] 4.49 4.29 4.78 4.77 4.43 4.98 4.46 3.62 4.23 9.29 ...
## $ 10m Wind min (mph) : num [1:9888] 2.43 1.8 2.57 2.8 2.57 3.07 2.53 1.87 1.57 3.33 ...
## $ 10m Wind max (mph) : num [1:9888] 6.63 6.73 6.9 7.23 6.5 7.57 7.9 5.97 6.6 22.8 ...
## $ N (# obs) : num [1:9888] 4 4 4 4 4 4 4 4 4 4 ...
Wdata$Period <- as.POSIXct(Wdata$Period, format = "%m/%d/%Y %H: %M: %S")
str(Wdata)
## tibble [9,888 × 11] (S3: tbl_df/tbl/data.frame)
## $ Period : POSIXct[1:9888], format: "2013-11-15 00:00:00" "2013-11-15 01:00:00" ...
## $ 2m T avg (F) : num [1:9888] 57.9 57.9 58.8 59 58.8 ...
## $ 2m T min (F) : num [1:9888] 57.5 57.2 58.1 58.8 58.7 ...
## $ 2m T max (F) : num [1:9888] 58.1 58.6 59.4 59.2 58.9 ...
## $ RelHum avg 2m (pct) : num [1:9888] 93 93 92 92 93 94 94 95 92 82 ...
## $ 2m Rain tot (in) : num [1:9888] 0 0 0 0 0 0 0 0 0 0 ...
## $ 2m Rain max over 15min(in): num [1:9888] 0 0 0 0 0 0 0 0 0 0 ...
## $ 10m Wind avg (mph) : num [1:9888] 4.49 4.29 4.78 4.77 4.43 4.98 4.46 3.62 4.23 9.29 ...
## $ 10m Wind min (mph) : num [1:9888] 2.43 1.8 2.57 2.8 2.57 3.07 2.53 1.87 1.57 3.33 ...
## $ 10m Wind max (mph) : num [1:9888] 6.63 6.73 6.9 7.23 6.5 7.57 7.9 5.97 6.6 22.8 ...
## $ N (# obs) : num [1:9888] 4 4 4 4 4 4 4 4 4 4 ...
#Create Multiple groups to split the data into Days, Weeks and Month
Wdata$days <- floor_date(Wdata$Period, "day")
Wdata$weeks <- floor_date(Wdata$Period, 'week')
Wdata$month <- floor_date(Wdata$Period, 'month')
Wdata$hours <- floor_date(Wdata$Period, 'hours')Lets calculate the weekly average of the temperature. You can use same process for other weather variables just by changing the column name and sums by changing the function name.
Weekly average of temperature!
WeeklyAvg_temp <- aggregate(Wdata$`2m T avg (F)`, by = list(Wdata$weeks), FUN = mean)
WeeklyAvg_temp
## Group.1 x
## 1 2013-11-10 69.47396
## 2 2013-11-17 73.15399
## 3 2013-11-24 67.51911
## 4 2013-12-01 67.79571
## 5 2013-12-08 69.79107
## 6 2013-12-15 65.81786
## 7 2013-12-22 70.21244
## 8 2013-12-29 67.44357
## 9 2014-01-05 65.80673
## 10 2014-01-12 58.73036
## 11 2014-01-19 55.52095
## 12 2014-01-26 65.00202
## 13 2014-02-02 71.07030
## 14 2014-02-09 63.95810
## 15 2014-02-16 66.72917
## 16 2014-02-23 68.80214
## 17 2014-03-02 67.49810
## 18 2014-03-09 66.84065
## 19 2014-03-16 71.14958
## 20 2014-03-23 67.81946
## 21 2014-03-30 68.41476
## 22 2014-04-06 70.89054
## 23 2014-04-13 74.03732
## 24 2014-04-20 71.31018
## 25 2014-04-27 76.57851
## 26 2014-05-04 74.32536
## 27 2014-05-11 74.98089
## 28 2014-05-18 74.17655
## 29 2014-05-25 76.97202
## 30 2014-06-01 76.78679
## 31 2014-06-08 76.08429
## 32 2014-06-15 75.66089
## 33 2014-06-22 80.04083
## 34 2014-06-29 79.54649
## 35 2014-07-06 77.83143
## 36 2014-07-13 78.92655
## 37 2014-07-20 80.19125
## 38 2014-07-27 81.04583
## 39 2014-08-03 79.61185
## 40 2014-08-10 79.95744
## 41 2014-08-17 81.61702
## 42 2014-08-24 80.66464
## 43 2014-08-31 78.17417
## 44 2014-09-07 77.50595
## 45 2014-09-14 77.10208
## 46 2014-09-21 76.86661
## 47 2014-09-28 79.28756
## 48 2014-10-05 75.39542
## 49 2014-10-12 74.09589
## 50 2014-10-19 72.61161
## 51 2014-10-26 69.34804
## 52 2014-11-02 66.65696
## 53 2014-11-09 66.46226
## 54 2014-11-16 67.26988
## 55 2014-11-23 67.53137
## 56 2014-11-30 69.39036
## 57 2014-12-07 58.09333
## 58 2014-12-14 60.08720
## 59 2014-12-21 70.62952
## 60 2014-12-28 71.70760Lets calculate the weekly average of the temperature from(6 AM-9 PM) of each day.
Use lubridate package for subsetting the data which contains the values from 6 AM to 9 PM.
T6AMTo9PM <- subset(Wdata, hour(hours) >= 6 & hour(hours) <= 21 & days >= '2014-01-01' & days <= '2014-12-31')
After subsetting, lets calculate for temperature variable!
T6AMTo9PM_WeeklyAvg_temp <- aggregate(T6AMTo9PM$`2m T avg (F)`, by = list(T6AMTo9PM$weeks), FUN = mean)
T6AMTo9PM_WeeklyAvg_temp
## Group.1 x
## 1 2013-12-29 64.83667
## 2 2014-01-05 67.94321
## 3 2014-01-12 60.91509
## 4 2014-01-19 59.00554
## 5 2014-01-26 67.92598
## 6 2014-02-02 73.42321
## 7 2014-02-09 67.48607
## 8 2014-02-16 71.45955
## 9 2014-02-23 71.54554
## 10 2014-03-02 70.96393
## 11 2014-03-09 70.59089
## 12 2014-03-16 74.68250
## 13 2014-03-23 70.24589
## 14 2014-03-30 72.48982
## 15 2014-04-06 74.66982
## 16 2014-04-13 76.28152
## 17 2014-04-20 74.86929
## 18 2014-04-27 79.91045
## 19 2014-05-04 78.54688
## 20 2014-05-11 77.67696
## 21 2014-05-18 78.65795
## 22 2014-05-25 80.31634
## 23 2014-06-01 80.14795
## 24 2014-06-08 79.31384
## 25 2014-06-15 78.04286
## 26 2014-06-22 83.38902
## 27 2014-06-29 82.04607
## 28 2014-07-06 80.34714
## 29 2014-07-13 81.29125
## 30 2014-07-20 82.94768
## 31 2014-07-27 83.83795
## 32 2014-08-03 81.99170
## 33 2014-08-10 82.77527
## 34 2014-08-17 84.94571
## 35 2014-08-24 83.89491
## 36 2014-08-31 80.63357
## 37 2014-09-07 79.72955
## 38 2014-09-14 79.52652
## 39 2014-09-21 78.80696
## 40 2014-09-28 82.26545
## 41 2014-10-05 78.44259
## 42 2014-10-12 77.62929
## 43 2014-10-19 75.41018
## 44 2014-10-26 73.29625
## 45 2014-11-02 70.26107
## 46 2014-11-09 69.15714
## 47 2014-11-16 69.24893
## 48 2014-11-23 69.61714
## 49 2014-11-30 72.46089
## 50 2014-12-07 60.26027
## 51 2014-12-14 64.50598
## 52 2014-12-21 73.04393
## 53 2014-12-28 74.35500