Now that we have our data sets set up let’s take a look at them and start to analyze what happened. (And learn some R basics along the way.)
Let’s focus on the Titanic.
Start by loading the dataset that you previously saved.
load(paste("~","data/MSEstonia.RData", sep="/"))The data we downloaded contains 1 row per person on the ship for a given ship, in this case MS Estonia.
Find some basic information about the RMS_Titanic using the str() function and the summary() function on the MS_Estonia data set. That means, put the name of the data set inside the parentheses.
str(`MS Estonia`)## Classes 'tbl_df', 'tbl' and 'data.frame': 989 obs. of 20 variables:
## $ Id_16 : num 1 2 3 4 5 6 7 8 9 10 ...
## $ Ship Id : num 16 16 16 16 16 16 16 16 16 16 ...
## $ Year : num 1994 1994 1994 1994 1994 ...
## $ Nationality of the Ship : chr "Estonia" "Estonia" "Estonia" "Estonia" ...
## $ Women and children first : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Quick : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Cause : chr "Technical" "Technical" "Technical" "Technical" ...
## $ No. of passengers : num 796 796 796 796 796 796 796 796 796 796 ...
## $ No. of women passengers : num 377 377 377 377 377 377 377 377 377 377 ...
## $ Women passengers/passengers: num 0.474 0.474 0.474 0.474 0.474 0.474 0.474 0.474 0.474 0.474 ...
## $ Ship size : num 989 989 989 989 989 989 989 989 989 989 ...
## $ Length of voyage : num 2 2 2 2 2 2 2 2 2 2 ...
## $ Gender : num 0 1 1 0 1 1 0 1 0 1 ...
## $ Age : num 62 22 21 53 56 71 60 18 31 63 ...
## $ Child : num NA NA NA NA NA NA NA NA NA NA ...
## $ Crew : num 0 1 1 1 0 0 0 0 1 0 ...
## $ Passenger Class : num NA NA NA NA NA NA NA NA NA NA ...
## $ Nationality of Passenger : chr "Swedish" "Estonian" "Estonian" "Swedish" ...
## $ Companionship : num NA NA NA NA NA NA NA NA NA NA ...
## $ Survival : num 0 0 0 0 0 0 0 0 0 0 ...summary(`MS Estonia`) ## Id_16 Ship Id Year Nationality of the Ship
## Min. : 1 Min. :16 Min. :1994 Length:989
## 1st Qu.:248 1st Qu.:16 1st Qu.:1994 Class :character
## Median :495 Median :16 Median :1994 Mode :character
## Mean :495 Mean :16 Mean :1994
## 3rd Qu.:742 3rd Qu.:16 3rd Qu.:1994
## Max. :989 Max. :16 Max. :1994
##
## Women and children first Quick Cause No. of passengers
## Min. :0 Min. :0 Length:989 Min. :796
## 1st Qu.:0 1st Qu.:0 Class :character 1st Qu.:796
## Median :0 Median :0 Mode :character Median :796
## Mean :0 Mean :0 Mean :796
## 3rd Qu.:0 3rd Qu.:0 3rd Qu.:796
## Max. :0 Max. :0 Max. :796
##
## No. of women passengers Women passengers/passengers Ship size
## Min. :377 Min. :0.474 Min. :989
## 1st Qu.:377 1st Qu.:0.474 1st Qu.:989
## Median :377 Median :0.474 Median :989
## Mean :377 Mean :0.474 Mean :989
## 3rd Qu.:377 3rd Qu.:0.474 3rd Qu.:989
## Max. :377 Max. :0.474 Max. :989
##
## Length of voyage Gender Age Child
## Min. :2 Min. :0.0000 Min. : 0.00 Min. : NA
## 1st Qu.:2 1st Qu.:0.0000 1st Qu.:30.00 1st Qu.: NA
## Median :2 Median :0.0000 Median :44.00 Median : NA
## Mean :2 Mean :0.4904 Mean :44.73 Mean :NaN
## 3rd Qu.:2 3rd Qu.:1.0000 3rd Qu.:59.00 3rd Qu.: NA
## Max. :2 Max. :1.0000 Max. :87.00 Max. : NA
## NA's :989
## Crew Passenger Class Nationality of Passenger Companionship
## Min. :0.0000 Min. : NA Length:989 Min. : NA
## 1st Qu.:0.0000 1st Qu.: NA Class :character 1st Qu.: NA
## Median :0.0000 Median : NA Mode :character Median : NA
## Mean :0.1951 Mean :NaN Mean :NaN
## 3rd Qu.:0.0000 3rd Qu.: NA 3rd Qu.: NA
## Max. :1.0000 Max. : NA Max. : NA
## NA's :989 NA's :989
## Survival
## Min. :0.0000
## 1st Qu.:0.0000
## Median :0.0000
## Mean :0.1385
## 3rd Qu.:0.0000
## Max. :1.0000
## You should notice some odd things.
Child,Passenger Class,Companionship
We need to pay attention to the fact that there are some variables not available for all of the ships. When a variable is not available, all values will be missing.
The characteristics is the same because they were all in the same ship.
They have the same characteristics because everyone in the ship saw the same thing.Which means the characteristics were the same because they were in the same ship.
Passenger Class not avaliable.
Now let’s get some descrptive information about the people on the ship using the crosstab function. The variables we are interested in in the MS_Estonia data set are: Gender, Passenger Class, Crew, Survival.
#Here is the first. You add the others.
crosstab(`MS Estonia`, row.vars = "Gender")##
## Gender Count Total %
## 0 504.00 50.96
## 1 485.00 49.04
## Sum 989.00 100.00#crosstab(`MS Estonia`, row.vars = "Passenger Class")
crosstab(`MS Estonia`, row.vars = "Crew")##
## Crew Count Total %
## 0 796.00 80.49
## 1 193.00 19.51
## Sum 989.00 100.00crosstab(`MS Estonia`, row.vars = "Survival")##
## Survival Count Total %
## 0 852.00 86.15
## 1 137.00 13.85
## Sum 989.00 100.00We would also like to know about the proportion who are 15 and under. The problem is that the Child variable is all missing and age is given in years. Let’s update the Child variable with information from the Age variable and then run the crosstab. (For your other ship you need to see if this is necessary and possible. You may need to do other similar things with other variables.)
MS_Estonia$Child <- as.numeric(MS_Estonia$Age) <= 15
# Add the crosstab below
crosstab(MS_Estonia, row.vars = "Survival")##
## Survival Count Total %
## 0 852.00 86.15
## 1 137.00 13.85
## Sum 989.00 100.00MS_Estonia$Survival <- as.numeric(MS_Estonia$Age) <= 15 does.This code reflects that most of the people in the ship died.It was about 86.15%.Very little survivors in this ship.
Write a paragraph describing the people who were on board the Estonia and what happened to them. A car and passenger ferry, MS Estonia, has sunk in the Baltic Sea with 950 people on board.She was carrying 989 people: 803 passengers and 186 crew. Most of the passengers were Scandinavian, while most of the crew members were Estonian. 138 were rescued alive, but one died later in hospital.[1] Ships rescued 34 and helicopters 104; the ferries played a much smaller part than the planners had intended because it was too dangerous.Most of the survivors were from the Crew.Most of the passengers die.