Homework_09

library(ggplot2)
###### Organizing code with structured programming

myfun <- function(nSize,nMean,nSD) {
     ID <- 1:(sum(nSize))
     nGroup <- c("Legume", "Maize","Mix")
    Bird_count <- c(rnorm(n=nSize[1],mean = nMean[1],sd=nSD[1]), 
          rnorm(n=nSize[2],mean = nMean[2],sd=nSD[2]),
          rnorm(n=nSize[3],mean = nMean[3],sd=nSD[3]))
     TGroup <- rep(nGroup,nSize)
    data1 <- data.frame(ID,TGroup,Bird_count)
    m2 <- aov(Bird_count ~ TGroup, data = data1)
    m3<- summary(m2)
    Anoplot <- ggplot(data1, aes(x=TGroup,y=Bird_count)) +
    geom_boxplot(fill="#0099f8")
    return(Anoplot)
}

nSize <- c(425,389,672) # number of observations in each group
nMean <- c(1.8,2.0,1.9) # mean of each group
nSD <- c(1.9,3.4,1.9) # standard deviations for each ground

myfun(nSize=nSize,nMean=nMean,nSD=nSD)

Using different means and sample size

# Performing another test using different simulation

myfun2 <- function(nSize,nMean,nSD) {
  ID <- 1:(sum(nSize))
  nGroup <- c("Legume", "Maize","Mix")
  Bird_count <- c(rnorm(n=nSize[1],mean = nMean[1],sd=nSD[1]), 
                  rnorm(n=nSize[2],mean = nMean[2],sd=nSD[2]),
                  rnorm(n=nSize[3],mean = nMean[3],sd=nSD[3]))
  TGroup <- rep(nGroup,nSize)
  data1 <- data.frame(ID,TGroup,Bird_count)
  m2 <- aov(Bird_count ~ TGroup, data = data1)
  m3<- summary(m2)
  Anoplot <- ggplot(data1, aes(x=TGroup,y=Bird_count)) +
    geom_boxplot(fill="#2ecc71")
  return(Anoplot)
}

nSize <- c(340, 380, 490) # number of observations in each group
nMean <- c(2.1,2.1,1.8) # mean of each group
nSD <- c(07,0.6,0.6) # standard deviations for each ground

myfun2(nSize=nSize,nMean=nMean,nSD=nSD)