In raefuhrman/262_Final_Package: Various climate impacts on plant growth

knitr::opts_chunk$set(echo = TRUE)
library(ClimateImpacts)
library(tidyverse)

This example illustrates the use of the function plant_growth using the imaginary sunshine_hours data

#Get sunshine_hours data.  This represents (imaginary) average daily hours of sunshine for a given month between 2001 and 2006, for three different locations.  It is a multidimension array (12 months as columns, 6 years as rows, and 3 locations in the 3rd dimension)
data(sunshine_hours)

#If you want to use the standard data in multidimensional array format, you can simply run the function.  This gives the (imaginary) growth in mm of a plant in that month.

plant_growth_result <- plant_growth()
plant_growth_result

#This should give the same results as plant_growth_mda, which was created by applying the plant_growth function to the sunshine_hours multidimensional array.
data(plant_growth_mda)
plant_growth_mda

#You could also run the function with other data in other formats, such as a single vector.  For instance:

madeup_vector <- c(3,4,8,23) # This vector represents 3 hours of sun on Monday, 4 hours of sun on Tuesday, 8 on Wednesday, and 23 on Thursday.
madeup_example <- plant_growth(madeup_vector)
madeup_example

This example illustrates the use of the plant_revenue function.

# Get growth data.  This was created by applying the plant_growth function to the sunshine_hours multidimensional array, and is also a multidimensional array.
data(plant_growth_mda)

# To apply the revenue function to the default plant_growth_mda, simply run the function:

plant_growth_revenue <- plant_revenue()
plant_growth_revenue

#Assuming each mm of growth is worth $3.75 (imaginary plants are worth a lot of money), this function will provide the total revenue per timestep per location of the input data - in this example, it shows the revenue for each month between 2001 and 2006 of the three locations.

#Other data can also be used as an input.

madeup_growth_vector <- c(2,18,15,4,9) # This plant grew 2mm on Monday, 18 on Tuesday, and so on.
madeup_growth_example <- plant_revenue(madeup_growth_vector)
madeup_growth_example

This example illustrates the use of the total_precipitation function.

# Get climate data.  This was accessed from Climate Data Online from  https://www.ncdc.noaa.gov, and represents climate data from Santa Barbara in 2018.  It is a dataframe format with several different stations and types of weather data (listed in different columns): in this example, we will only be using the Station data and precipitation information.
data(climate_data)

# Applying the function to the data will return total precipitation for Station "USC00047902" in 2018

total_station_precip <- Total_yearly_precipitation(climate_data)
total_station_precip

This example illustrates the use of the average_monthly_temperature function

# Get climate data.  This was accessed from Climate Data Online from  https://www.ncdc.noaa.gov, and represents climate data from Santa Barbara in 2018.  It is a dataframe format with several different stations and types of weather data (listed in different columns): in this example, we will only be using the Station data and temperature information.
data(climate_data)

# Applying the function to the data will return the average temperature for each month of Station "USC00047902" in 2018, in degrees F.  Month 1 represents January and so on.

avg_month_temp <- Average_Monthly_Temperature(climate_data)
avg_month_temp

raefuhrman/262_Final_Package documentation built on June 15, 2019, 2:44 p.m.