#Amilka Diaz # Introduction, ## I chose to go with an article on obesity.I think data like this is important to see how the health of our society is progressing ###https://archive.ics.uci.edu/dataset/544/estimation+of+obesity+levels+based+on+eating+habits+and+physical+condition ## first i’d like to see if there is a correlation with having a family history of overweightness and weight

library(tidyverse) library(openintro) library(readr) obesity <- read_csv(“https://raw.githubusercontent.com/AmilkaD/hw1/refs/heads/main/obesity.csv”)

ggplot( data = obesity, mapping = aes(x = family_history_with_overweight , y = Weight) ) + geom_boxplot() + labs( title = “Family History of Overweightness and Weight” ) #next I wanted to check how many participants of each gender there were

ggplot(obesity, aes(x = Gender)) + geom_bar() + labs( title = “Gender of Study Participants” )

#Next I wanted to filter everyone who answers yes to a family history of obesity but wanted to make it easier for me to type out

library(dplyr) obesity <- obesity%>% rename(familyhistory = family_history_with_overweight)

obesity |> filter(familyhistory == ‘yes’) #next I wanted to filter by family history and male gender

obesity |> filter(familyhistory == ‘yes’ & Gender == ‘Male’)

#I then wanted to see if there was a correlation between family history of overweightness and gender

ggplot( data = obesity, aes(x = familyhistory, color=Gender)) + geom_bar() #conclusion #It seems to me thqt there is a correlation with weight & familyhistory of obesity. I’d like to sharpen my skills and be able to clearly see how the data adds up with smokers, modes of transportation and by gender. #the original data will be on github