#Description-------------------
# This is a re-work of the eye size data I created in homework 8 using structural programming
# 23 Mar 2022
#JAG
#-----------------------------
#Intialize------------------
library(tidyverse)
## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.6 v dplyr 1.0.8
## v tidyr 1.2.0 v stringr 1.4.0
## v readr 2.1.2 v forcats 0.5.1
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
library(wesanderson)
## Warning: package 'wesanderson' was built under R version 4.1.3
library(ggthemes)
## Warning: package 'ggthemes' was built under R version 4.1.3
library(patchwork)
## Warning: package 'patchwork' was built under R version 4.1.3
#set.seed(100)
#Load Functions--------------
###############################
#FUNCTION:createDataframe
# purpose:To take eyesize data and set them to adjust the size,mean, and SD affects the output
# input: variables of eye size
# output: a long dataframe that can be manipulated
#------------------------------
createDataframe <- function(size=30, meanWT=290 , meanN= 240, sd=50 )
{
WTeyesize <- rnorm(n=size, mean=meanWT, sd=sd)
Nulleyesize <- rnorm(n=size, mean=meanN, sd=sd)
eye_size_df <- data.frame (WTeyesize,Nulleyesize)
long_df <- eye_size_df %>%
pivot_longer(cols=WTeyesize:Nulleyesize, names_to= "Genotype" , values_to= "EyeSize")
}
data<-createDataframe()
###############################
#FUNCTION:t- test
# purpose: To perform a statistical analysis on the data
# input: an x variable and y variable
# output : the summary of the t-test
#------------------------------
test <- function(x_var=eye_size_df$WTeyesize,y_var=eye_size_df$Nulleyesize) {
WTeyesize <- rnorm(n=100, mean=290 , sd=50)
Nulleyesize <-rnorm( n=100, mean=220 , sd=50)
.<-t.test(WTeyesize,Nulleyesize, paired=TRUE)
.<-print(.)
return(.)
}
###############################
#FUNCTION: graphData
# purpose: To visualize the differences in eyesize
# input:long eyesize dataframe
# output: a box plot of the two catergories of eye
#------------------------------
graphData <- function(data) {
p1 <- ggplot(data=data, aes(x=Genotype, y=EyeSize)) + geom_point(color="white" , fill="purple", size=0.2) +
stat_boxplot()
return(print(p1))
}
graphData(data)
#Second function here!
###############################
#FUNCTION: graphData2
# purpose: To visualize the differences in eyesize
# input:long eyesize dataframe
# output: a colored violin plot of the two categories of eyesize, with the t-test value on graph
#------------------------------
graphData2 <- function(data) {
my_cols<-wes_palettes$IsleofDogs1[1:2]
p2 <- ggplot(data=data, aes(x=Genotype, y=EyeSize, fill=Genotype))+geom_violin()+scale_fill_manual(values=my_cols)+ annotate("text", x = 1.19, y = 400, label = "p-value should be less the 0.05", size=2)
return(print(p2))
}
graphData2(data)
#Global Variables------------
#Program Body----------------
data<- createDataframe()
print(data)
## # A tibble: 60 x 2
## Genotype EyeSize
## <chr> <dbl>
## 1 WTeyesize 250.
## 2 Nulleyesize 185.
## 3 WTeyesize 290.
## 4 Nulleyesize 168.
## 5 WTeyesize 286.
## 6 Nulleyesize 268.
## 7 WTeyesize 224.
## 8 Nulleyesize 302.
## 9 WTeyesize 337.
## 10 Nulleyesize 220.
## # ... with 50 more rows
test(x_var, y_var)
##
## Paired t-test
##
## data: WTeyesize and Nulleyesize
## t = 8.6605, df = 99, p-value = 9.044e-14
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 49.59785 79.07898
## sample estimates:
## mean of the differences
## 64.33842
##
## Paired t-test
##
## data: WTeyesize and Nulleyesize
## t = 8.6605, df = 99, p-value = 9.044e-14
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 49.59785 79.07898
## sample estimates:
## mean of the differences
## 64.33842
g1<-graphData(data)
g2<-graphData2(data)
g1+g2
