ggplot(data = fitness)+
geom_bar(mapping = aes(age, fill= gender),
stat = "count", position = "dodge")+
labs(title= "Demographic of participants", x="Age", y = "Total")
ggplot(data = fitness)+
geom_bar(mapping = aes(time_of_day, fill = gender) ,
stat = "count", position = "dodge")+
facet_wrap(~age,1)+
labs(title= "Time of Day", x="Age", y = "Total")+
theme(axis.text = element_text(angle = 30))
ggplot(data = fitness, aes(x= importance_of_fitness, fill = gender, color = gender))+
geom_line(stat= "count")+
labs(title = "Importance of Fitness", x = "Rating", y= "Total")
ggplot(data = fitness) +
geom_bar(mapping = aes(current_fit_level, fill= gender))+
facet_wrap(~gender)
ggplot(data = fitness)+
geom_point(mapping = aes(frenquency ,fill = gender, color = gender),
stat = "count", position= position_dodge(width = 1))+
theme(axis.text.x = element_text(angle = 90, vjust = 1))+
labs(title= "Frequency", x="Frequency", y = "Total")
avg_health_level<-aggregate.data.frame(fitness$health_level,list(fitness$gender), FUN=mean)
colnames(avg_health_level) <- c("Gender", "Average Health Level")
kable((avg_health_level), align = rep('c',2))
|
Gender
|
Average Health Level
|
|
Female
|
3.165563
|
|
Male
|
3.374486
|
avg_importance_level <-aggregate.data.frame(fitness$importance_of_fitness,list(fitness$gender), FUN=mean)
colnames(avg_importance_level) <- c("Gender", "Average Importance Level")
kable((avg_importance_level), align = rep('c',2))
|
Gender
|
Average Importance Level
|
|
Female
|
3.751656
|
|
Male
|
4.049383
|
(agebrkdwn<- fitness %>%
group_by(age, gender) %>%
summarise(count = n())) %>%
arrange(gender)
## # A tibble: 10 × 3
## # Groups: age [5]
## age gender count
## <chr> <chr> <int>
## 1 15 to 18 Female 111
## 2 19 to 25 Female 118
## 3 26 to 30 Female 12
## 4 30 to 40 Female 22
## 5 40 and above Female 39
## 6 15 to 18 Male 56
## 7 19 to 25 Male 118
## 8 26 to 30 Male 8
## 9 30 to 40 Male 15
## 10 40 and above Male 46
cur_level <- fitness %>%
group_by(current_fit_level, gender) %>%
summarise(count= n())
cur_level %>% arrange(gender)
## # A tibble: 10 × 3
## # Groups: current_fit_level [5]
## current_fit_level gender count
## <chr> <chr> <int>
## 1 Average Female 117
## 2 Good Female 122
## 3 Perfect Female 9
## 4 Unfit Female 28
## 5 Very good Female 26
## 6 Average Male 78
## 7 Good Male 98
## 8 Perfect Male 14
## 9 Unfit Male 26
## 10 Very good Male 27