Titanic

titanic <- read.csv(“C:/Users/ALICE/Desktop/Titanic Data.csv”) View(titanic) dim(titanic) [1] 889 8 nrow(subset(titanic,Survived == 1)) [1] 340 titanic\(Survived <- as.factor(titanic\)Survived) survivedTable <- table(titanic$Survived) survivedTable

0 1 549 340 > prop <- prop.table(survivedTable) > proPer <- prop*100 > proPer

   0        1 

61.75478 38.24522 > proPer[2] 1 38.24522 > nrow(subset(titanic,Survived==1 & Pclass==1)) [1] 134 > surviversByclass <- xtabs(~ Survived+Pclass, data = titanic) > prop.table(surviversByclass,2) Pclass Survived 1 2 3 0 0.3738318 0.5271739 0.7576375 1 0.6261682 0.4728261 0.2423625 > 100*prop.table(surviversByclass,2)[2,1][1] 62.61682 > mytable2 <- xtabs(~ Survived + Pclass + Sex, data=titanic) > addmargins(mytable2) , , Sex = female

    Pclass

Survived 1 2 3 Sum 0 3 6 72 81 1 89 70 72 231 Sum 92 76 144 312

, , Sex = male

    Pclass

Survived 1 2 3 Sum 0 77 91 300 468 1 45 17 47 109 Sum 122 108 347 577

, , Sex = Sum

    Pclass

Survived 1 2 3 Sum 0 80 97 372 549 1 134 87 119 340 Sum 214 184 491 889

ftable(mytable2) Sex female male Survived Pclass
0 1 3 77 2 6 91 3 72 300 1 1 89 45 2 70 17 3 72 47 surviversBySex <- xtabs(~ Survived + Sex, data=titanic) surviversBySex Sex Survived female male 0 81 468 1 231 109 propSur <- prop.table(surviversBySex,1) propSurPer <- propSur100 propSurPer[2,1][1] 67.94118 propSur2 <- prop.table(surviversBySex,2) propSur2Per <- propSur2100 propSur2Per Sex Survived female male 0 25.96154 81.10919 1 74.03846 18.89081 chisq.test(surviversBySex)

Pearson's Chi-squared test with Yates' continuity correction

data: surviversBySex X-squared = 258.43, df = 1, p-value < 2.2e-16