## Loading required package: ggvis
## Loading required package: 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
##
## Loading required package: magrittr
## Loading required package: titanic1
## Warning in library(package, lib.loc = lib.loc, character.only = TRUE,
## logical.return = TRUE, : there is no package called 'titanic1'
2.Total Number of Passengers in the Dataset
summarise(titanic1, count=n())
## count
## 1 2201
3. Calculate the total proportion of passengers surviving
titanic1 %>% group_by(Survive) %>% summarise (n=n()) %>% mutate (freq= n/sum(n))
## Source: local data frame [2 x 3]
##
## Survive n freq
## 1 0 1490 0.676965
## 2 1 711 0.323035
4. Calculate the total proportion of passengers surviving for each class of passenger
titanic1 %>% group_by(Survive, Class) %>% summarise (n=n()) %>% mutate (freq= n/sum(n))
## Source: local data frame [8 x 4]
## Groups: Survive
##
## Survive Class n freq
## 1 0 0 673 0.45167785
## 2 0 1 122 0.08187919
## 3 0 2 167 0.11208054
## 4 0 3 528 0.35436242
## 5 1 0 212 0.29817159
## 6 1 1 203 0.28551336
## 7 1 2 118 0.16596343
## 8 1 3 178 0.25035162
5. Calculate the proportion of passengers surviving for each sex category
titanic1 %>% group_by(Survive, Sex) %>% summarise (n=n()) %>% mutate (freq= n/sum(n))
## Source: local data frame [4 x 4]
## Groups: Survive
##
## Survive Sex n freq
## 1 0 0 126 0.08456376
## 2 0 1 1364 0.91543624
## 3 1 0 344 0.48382560
## 4 1 1 367 0.51617440
6. Calculate the proportion of passengers surviving for each age category
titanic1 %>% group_by(Survive, Age) %>% summarise (n=n()) %>% mutate (freq= n/sum(n))
## Source: local data frame [4 x 4]
## Groups: Survive
##
## Survive Age n freq
## 1 0 0 52 0.03489933
## 2 0 1 1438 0.96510067
## 3 1 0 57 0.08016878
## 4 1 1 654 0.91983122
7. Calculate the proportion of passengers surviving for each age/sex category
titanic1 %>% group_by(Survive, Age, Sex) %>% summarise (n=n()) %>% mutate (freq= n/sum(n))
## Source: local data frame [8 x 5]
## Groups: Survive, Age
##
## Survive Age Sex n freq
## 1 0 0 0 17 0.32692308
## 2 0 0 1 35 0.67307692
## 3 0 1 0 109 0.07579972
## 4 0 1 1 1329 0.92420028
## 5 1 0 0 28 0.49122807
## 6 1 0 1 29 0.50877193
## 7 1 1 0 316 0.48318043
## 8 1 1 1 338 0.51681957
8 Calculate the Proportion for each age/sex/class category
titanic1 %>% group_by(Survive, Age, Sex, Class) %>% summarise (n=n()) %>% mutate (freq= n/sum(n))
## Source: local data frame [24 x 6]
## Groups: Survive, Age, Sex
##
## Survive Age Sex Class n freq
## 1 0 0 0 3 17 1.00000000
## 2 0 0 1 3 35 1.00000000
## 3 0 1 0 0 3 0.02752294
## 4 0 1 0 1 4 0.03669725
## 5 0 1 0 2 13 0.11926606
## 6 0 1 0 3 89 0.81651376
## 7 0 1 1 0 670 0.50413845
## 8 0 1 1 1 118 0.08878856
## 9 0 1 1 2 154 0.11587660
## 10 0 1 1 3 387 0.29119639
## .. ... ... ... ... ... ...