SpecialtyCropAndGddCode.RmdDownloading Libraries to Load, Clean and Format Data
library(dplyr)
library(tidyr)
library(tidyverse)
library(tidycensus)
library(dplyr)
library(ggplot2)For the purpose of calculations of the Growing Degree Days, data about the average temperature of days is required. For this reason, the first task is to load the data. It was retrieved from Iowa Mesonet Reanalysis Tool (IEMRE)
Information about the Specialty Crops, and their Base Temperature is required to measue the GDD. Thus the dataset is loaded as a csv. The information and data used to create the specialty crop dataset is collected from the 2022-Midwest-Veg-Guide and the Donald N. Maynard, George J. Hochmuth -Knott’s Handbook forVegetable Growers-Wiley.
knitr::include_graphics("../../man/figures/crop_ss.png")Dropped certain columns that didn’t have information required for the GDD calculations. Additionally removed rows with no/null data, for easy and clear visualization
Calculated the average temperatures of every day from 2015-2021.This was done by adding the lowest Temperature of that day to the Highest Temperature of that day, and then diving the sum by 2.
mydata$Average_temp = (mydata$daily_high_f + mydata$daily_low_f)/2
View(mydata)
mydata$OnionGDD <- mydata$Average_temp - 40
mydata$PeasGDD <- mydata$Average_temp - 39
mydata$AsparagusGDD <- mydata$Average_temp - 50
mydata$PotatoGDD <- mydata$Average_temp - 32
mydata$CabbageGDD <- mydata$Average_temp - 40
mydata$Sweet_PotatoGDD <- mydata$Average_temp - 65
mydata$SquashGDD <- mydata$Average_temp - 60
mydata$CucumberGDD <- mydata$Average_temp - 60
mydata$CauliflowerGDD <- mydata$Average_temp - 40
mydata$PumpkinGDD <- mydata$Average_temp - 60
mydata$TomatoGDD <- mydata$Average_temp - 50
mydata$WatermelonGDD <- mydata$Average_temp - 50
mydata$CarrotGDD <- mydata$Average_temp - 40
mydata$Sweet_cornGDD <- mydata$Average_temp - 50
mydata$LettuceGDD <- mydata$Average_temp - 40
mydata$EggplantGDD <- mydata$Average_temp - 50
mydata$RadishGDD <- mydata$Average_temp - 40
mydata$SpinachGDD <- mydata$Average_temp - 35
mydata$CantaloupeGDD <- mydata$Average_temp - 65
View(mydata)If Average Temperature of the day is lesser than the Base Temperature, then the GDD = 0. The code helps to search GDD values that have negative value, and replace those values with 0
mydata$OnionGDD= ifelse(mydata$OnionGDD < 0, "0", mydata$OnionGDD)
mydata$PeasGDD= ifelse(mydata$PeasGDD < 0, "0",mydata$PeasGDD )
mydata$AsparagusGDD= ifelse(mydata$AsparagusGDD < 0, "0", mydata$AsparagusGDD )
mydata$PotatoGDD= ifelse(mydata$PotatoGDD < 0, "0", mydata$PotatoGDD )
mydata$CabbageGDD= ifelse(mydata$CabbageGDD < 0, "0",mydata$CabbageGDD )
mydata$Sweet_PotatoGDD= ifelse(mydata$Sweet_PotatoGDD < 0, "0",mydata$Sweet_PotatoGDD )
mydata$SquashGDD= ifelse(mydata$SquashGDD < 0, "0",mydata$SquashGDD )
mydata$CucumberGDD= ifelse( mydata$CucumberGDD< 0, "0",mydata$CucumberGDD )
mydata$CauliflowerGDD= ifelse(mydata$CauliflowerGDD < 0, "0", mydata$CauliflowerGDD)
mydata$PumpkinGDD= ifelse(mydata$PumpkinGDD < 0, "0",mydata$PumpkinGDD )
mydata$TomatoGDD= ifelse( mydata$TomatoGDD< 0, "0",mydata$TomatoGDD )
mydata$WatermelonGDD= ifelse(mydata$WatermelonGDD < 0, "0",mydata$WatermelonGDD )
mydata$CarrotGDD= ifelse( mydata$CarrotGDD< 0, "0",mydata$CarrotGDD )
mydata$Sweet_cornGDD= ifelse(mydata$Sweet_cornGDD < 0, "0",mydata$Sweet_cornGDD )
mydata$LettuceGDD= ifelse(mydata$LettuceGDD < 0, "0",mydata$LettuceGDD )
mydata$EggplantGDD= ifelse(mydata$EggplantGDD < 0, "0",mydata$EggplantGDD )
mydata$RadishGDD= ifelse(mydata$RadishGDD < 0, "0",mydata$RadishGDD )
mydata$SpinachGDD= ifelse(mydata$SpinachGDD < 0, "0",mydata$SpinachGDD )
mydata$CantaloupeGDD= ifelse(mydata$CantaloupeGDD < 0, "0",mydata$CantaloupeGDD )
View(mydata)
glimpse(mydata)Separating the Months and Years, in order to group data by the Year
mydata2 <- mydata %>% mutate(Month = substr(date,0,1), year= substr(date, (nchar(date)-3), nchar(date))) %>% mutate(Month = ifelse(Month ==1,10,Month))%>% group_by(year, Month) %>% summarise(acc_gdd_onion= sum(as.numeric(OnionGDD)),acc_gdd_peas = sum(as.numeric(PeasGDD)), acc_gdd_asparaguss = sum(as.numeric(AsparagusGDD)), acc_gdd_potato = sum(as.numeric(PotatoGDD)), acc_gdd_cabbage = sum(as.numeric(CabbageGDD)), acc_gdd_sweet_potato = sum(as.numeric(Sweet_PotatoGDD)), acc_gdd_Squash = sum(as.numeric(SquashGDD)), acc_gdd_cucumber = sum(as.numeric(CucumberGDD)), acc_gdd_Cauliflower = sum(as.numeric(CauliflowerGDD)), acc_gdd_Pumpkin = sum(as.numeric(PumpkinGDD)), acc_gdd_Tomato = sum(as.numeric(TomatoGDD)), acc_gdd_Watermelon = sum(as.numeric(WatermelonGDD)), acc_gdd_carrot = sum(as.numeric(CarrotGDD)), acc_gdd_sweet_corn = sum(as.numeric(Sweet_cornGDD)), acc_gdd_lettuce = sum(as.numeric(LettuceGDD)), acc_gdd_Eggplant = sum(as.numeric(EggplantGDD)), acc_gdd_radish = sum(as.numeric(RadishGDD)), acc_gdd_spinach = sum(as.numeric(SpinachGDD)), acc_gdd_cantaloupe = sum(as.numeric(CantaloupeGDD)))Specifying the target months, and formatting the dates clearly. The target months include April, May, June, July, August, September and October. Other months have lower average temperatures, thus not possible to grow crops then.
mydata2<- mydata2 %>%
mutate(Date = paste(Month,year,sep="/"))
mydata2$Month = ifelse(mydata2$Month == 4,"April",
ifelse(mydata2$Month == 5, "May",
ifelse(mydata2$Month == 6, "June",
ifelse(mydata2$Month == 7, "July",
ifelse(mydata2$Month == 8, "August",
ifelse(mydata2$Month == 9, "September",
ifelse(mydata2$Month == 10,"October", mydata2$Month)))))))
View(mydata2)Saving the data as a csv file to the local directory
write.csv(mydata2, "S:/DSPG2022/ProjectB/Speciality_Crops_GDD_cleaned_2015-2021.csv", row.names = FALSE)