Module 9: Multivariate Analysis

 ##Module 9: Multivariate Analysis: Tobacco Statistics from the CDC

#Libraries

library(ggplot2)

library(dplyr)

library(tidyverse)

library(plyr)

library(stats)

#Read CSV file

smokedata <- read.csv("C:\\Users\\tyler\\Desktop\\Spring 2023\\Visual Analytics\\SmokeBan.csv")

#Omit numbering column

smokedata <- smokedata[,-1]

head(smokedata)

#Clean Column Names

colnames(smokedata) <- c("Smoker","Ban","Age","Education","African American","Hispanic","Gender")

#factor statement to differentiate every education level

smokedata$Education <- factor(smokedata$Education, labels = c("hs dropout","hs","somecollege","college","master"))

#Multivariate Visual

ggplot(smokedata, aes(Education, Age, color = Smoker)) +

  geom_boxplot() +

  labs(

    x = "Level Of Education",

    y = "Age"

  ) + ggtitle("Age versus Education in Smokers versus Non-Smokers")

#As we can see from the visual provided in the assignment, there are clear signs that smokers are more of a polarized character in academia.

## While results do place them at lower success rates in lower levels of education, they are neck and neck at the highest level.

# This group of people are more prone to being disciplined with smoking, or not, and choose to drink, smoke, party etc. causin your priorities to be neglected.

#The 5 elements of design are Alignment, Repetition, Contrast, Proximity, and Balance.

#By creating a clearly and contrast labeled plot, with color differences and clear axial notation, this demonstrates useage of the 5 design principles because I did end up remedying

#Some of the issues I encountered, by using these principles as a guiding hand.