library(ggplot2)
poll_award_sex <- read.csv("C:\\Users\\berak\\OneDrive\\Desktop\\EPSY 1261\\Data sets EPSY 1261 Fall 2025\\poll_award_sex.csv")
ggplot(data = poll_award_sex, aes(x=award, fill = sex))+
  geom_bar(position = "dodge", color = "black")

Female award winners tend to have a higher count overall competing with men. Their highest category is the Nobel prize, whereas their lowest, which is Olympic, still surpasses that of the highest scores from the males.

ggplot(data = poll_award_sex, aes(x=award, fill = sex))+
  geom_bar(position = "stack")

1. which variable is easier to compare with the stacked bar chat? - the sex 2. which variable is easier to compare with the side-by-side bar chart? - the count of awards

library(ggplot2)
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
phone_by_sex <-read.csv("C:\\Users\\berak\\OneDrive\\Desktop\\EPSY 1261\\Data sets EPSY 1261 Fall 2025\\phone-by-sex.csv")
head(phone_by_sex)
##      Sex   Phone Count
## 1 Female  iPhone     8
## 2 Female Android     2
## 3   Male  iPhone     3
## 4   Male Android     7
ggplot(data=phone_by_sex, aes(x=Sex,y=Count, fill = Phone))+
  geom_bar(position = "dodge", stat = "identity")

ggplot(phone_by_sex, aes(x=Sex, y = Count, fill = Phone))+
  geom_bar(stat="identity", position = "dodge")+
  labs(y= "Number of People",
       title = "Relationship between phone model and sex?",
       fill= ("Phone Operating System"))+
  scale_fill_manual(values = c("red", "blue"))