titanic case study.txt - options(digits = 3 report 3 significant digits library(tidyverse library(titanic titanic titanic < titanic_train%>

titanic case study.txt - options(digits = 3 report 3...

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options(digits = 3) # report 3 significant digits library(tidyverse) library(titanic) titanic titanic <- titanic_train %>% select(Survived, Pclass, Sex, Age, SibSp, Parch, Fare) %>% mutate(Survived = factor(Survived), Pclass = factor(Pclass), Sex = factor(Sex)) tit<-titanic%>%mutate(filter (Age>=18,Age<=35)%>%ggplot(aes(Age)) +geom_histogram(binwidth = 5,color="red") tit titanic %>% ggplot(aes(Age, fill = Sex)) + geom_density(alpha = 0.2) + facet_grid(Sex ~ .) titanic %>% ggplot(aes(Age, y = ..count.., fill = Sex)) + geom_density(alpha = 0.2, position = "stack") titanic %>% ggplot(aes(Age, fill = Sex)) + geom_density(alpha = 0.5) params <- titanic %>% filter(!is.na(Age)) %>% summarize(mean = mean(Age), sd = sd(Age)) titanic %>% filter(!is.na(Age)) %>% ggplot(aes(sample = Age)) + geom_qq(dparams = params) + geom_abline() #plot 1 - survival filled by sex titanic %>% ggplot(aes(Survived, fill = Sex)) + geom_bar() # plot 2 - survival filled by sex with position_dodge titanic %>% ggplot(aes(Survived, fill = Sex)) +
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  • Winter '18
  • Rafeal Izzary
  • Madonna

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