Homework assignment at the course Applied Data Analysis in Business with R

Descriptive statistics of dataset Titanic

1 Description of data set used in the analysis

1.1 Source

Kaggle: Titanic - Machine Learning from Disaster
https://www.kaggle.com/competitions/titanic/data?select=train.csv

mydata <- read.table("./train.csv", header = TRUE, sep = ",", dec = ".")

head(mydata)

##   PassengerId Survived Pclass
## 1           1        0      3
## 2           2        1      1
## 3           3        1      3
## 4           4        1      1
## 5           5        0      3
## 6           6        0      3
##                                                  Name    Sex Age SibSp Parch
## 1                             Braund, Mr. Owen Harris   male  22     1     0
## 2 Cumings, Mrs. John Bradley (Florence Briggs Thayer) female  38     1     0
## 3                              Heikkinen, Miss. Laina female  26     0     0
## 4        Futrelle, Mrs. Jacques Heath (Lily May Peel) female  35     1     0
## 5                            Allen, Mr. William Henry   male  35     0     0
## 6                                    Moran, Mr. James   male  NA     0     0
##             Ticket    Fare Cabin Embarked
## 1        A/5 21171  7.2500              S
## 2         PC 17599 71.2833   C85        C
## 3 STON/O2. 3101282  7.9250              S
## 4           113803 53.1000  C123        S
## 5           373450  8.0500              S
## 6           330877  8.4583              Q

1.2 Explanation of the dataset

Unit of Observation: Each row in the dataset represents a passenger who was aboard the Titanic.
Sample Size: The sample size is 891, which is the total number of rows (passengers) in the dataset.
Definition of All Variables
- PassengerId: A unique identifier assigned to each passenger.
- Survived: Indicates whether the passenger survived the disaster (0 = No, 1 = Yes).
- Pclass: Ticket class of the passenger (1st, 2nd, or 3rd class).
- Name: Name of the passenger.
- Sex: Gender of the passenger (male or female).
- Age: Age of the passenger in years.
- SibSp: Number of siblings/spouses aboard the Titanic.
- Parch: Number of parents/children aboard the Titanic.
- Ticket: Ticket number of the passenger.
- Fare: Fare paid by the passenger.
- Cabin: Cabin number of the passenger.
- Embarked: Port of embarkation (C = Cherbourg, Q = Queenstown, S = Southampton).
Units of Measurement
- PassengerId: Numeric (sequential integer values).
- Survived: Categorical (0 or 1).
- Pclass: Categorical (1st, 2nd, or 3rd class).
- Name: Text.
- Sex: Categorical (male or female).
- Age: Numeric (years).
- SibSp: Numeric (count of siblings/spouses).
- Parch: Numeric (count of parents/children).
- Ticket: Text or alphanumeric.
- Fare: Numeric (currency).
- Cabin: Text or alphanumeric.
- Embarked: Categorical (Cherbourg, Queenstown, Southampton).

1.3 Data manipulation

Check the structure of the dataset
- There are 891 observations and 12 variables.

str(mydata)

## 'data.frame':    891 obs. of  12 variables:
##  $ PassengerId: int  1 2 3 4 5 6 7 8 9 10 ...
##  $ Survived   : int  0 1 1 1 0 0 0 0 1 1 ...
##  $ Pclass     : int  3 1 3 1 3 3 1 3 3 2 ...
##  $ Name       : chr  "Braund, Mr. Owen Harris" "Cumings, Mrs. John Bradley (Florence Briggs Thayer)" "Heikkinen, Miss. Laina" "Futrelle, Mrs. Jacques Heath (Lily May Peel)" ...
##  $ Sex        : chr  "male" "female" "female" "female" ...
##  $ Age        : num  22 38 26 35 35 NA 54 2 27 14 ...
##  $ SibSp      : int  1 1 0 1 0 0 0 3 0 1 ...
##  $ Parch      : int  0 0 0 0 0 0 0 1 2 0 ...
##  $ Ticket     : chr  "A/5 21171" "PC 17599" "STON/O2. 3101282" "113803" ...
##  $ Fare       : num  7.25 71.28 7.92 53.1 8.05 ...
##  $ Cabin      : chr  "" "C85" "" "C123" ...
##  $ Embarked   : chr  "S" "C" "S" "S" ...

Summary of the dataset

summary(mydata)

##   PassengerId       Survived          Pclass          Name          
##  Min.   :  1.0   Min.   :0.0000   Min.   :1.000   Length:891        
##  1st Qu.:223.5   1st Qu.:0.0000   1st Qu.:2.000   Class :character  
##  Median :446.0   Median :0.0000   Median :3.000   Mode  :character  
##  Mean   :446.0   Mean   :0.3838   Mean   :2.309                     
##  3rd Qu.:668.5   3rd Qu.:1.0000   3rd Qu.:3.000                     
##  Max.   :891.0   Max.   :1.0000   Max.   :3.000                     
##                                                                     
##      Sex                 Age            SibSp           Parch       
##  Length:891         Min.   : 0.42   Min.   :0.000   Min.   :0.0000  
##  Class :character   1st Qu.:20.12   1st Qu.:0.000   1st Qu.:0.0000  
##  Mode  :character   Median :28.00   Median :0.000   Median :0.0000  
##                     Mean   :29.70   Mean   :0.523   Mean   :0.3816  
##                     3rd Qu.:38.00   3rd Qu.:1.000   3rd Qu.:0.0000  
##                     Max.   :80.00   Max.   :8.000   Max.   :6.0000  
##                     NA's   :177                                     
##     Ticket               Fare           Cabin             Embarked        
##  Length:891         Min.   :  0.00   Length:891         Length:891        
##  Class :character   1st Qu.:  7.91   Class :character   Class :character  
##  Mode  :character   Median : 14.45   Mode  :character   Mode  :character  
##                     Mean   : 32.20                                        
##                     3rd Qu.: 31.00                                        
##                     Max.   :512.33                                        
##

Check for missing value
- The variable “Age” has 177 missing value (NA).

colSums(is.na(mydata))

## PassengerId    Survived      Pclass        Name         Sex         Age 
##           0           0           0           0           0         177 
##       SibSp       Parch      Ticket        Fare       Cabin    Embarked 
##           0           0           0           0           0           0

Convert categorical variables to factors

mydata$Survived <- factor(mydata$Survived)
mydata$Pclass <- factor(mydata$Pclass)
mydata$Sex <- factor(mydata$Sex)
mydata$Embarked <- factor(mydata$Embarked)

Check the structure of the dataset after conversion

str(mydata)

## 'data.frame':    891 obs. of  12 variables:
##  $ PassengerId: int  1 2 3 4 5 6 7 8 9 10 ...
##  $ Survived   : Factor w/ 2 levels "0","1": 1 2 2 2 1 1 1 1 2 2 ...
##  $ Pclass     : Factor w/ 3 levels "1","2","3": 3 1 3 1 3 3 1 3 3 2 ...
##  $ Name       : chr  "Braund, Mr. Owen Harris" "Cumings, Mrs. John Bradley (Florence Briggs Thayer)" "Heikkinen, Miss. Laina" "Futrelle, Mrs. Jacques Heath (Lily May Peel)" ...
##  $ Sex        : Factor w/ 2 levels "female","male": 2 1 1 1 2 2 2 2 1 1 ...
##  $ Age        : num  22 38 26 35 35 NA 54 2 27 14 ...
##  $ SibSp      : int  1 1 0 1 0 0 0 3 0 1 ...
##  $ Parch      : int  0 0 0 0 0 0 0 1 2 0 ...
##  $ Ticket     : chr  "A/5 21171" "PC 17599" "STON/O2. 3101282" "113803" ...
##  $ Fare       : num  7.25 71.28 7.92 53.1 8.05 ...
##  $ Cabin      : chr  "" "C85" "" "C123" ...
##  $ Embarked   : Factor w/ 4 levels "","C","Q","S": 4 2 4 4 4 3 4 4 4 2 ...

Summary of the dataset after conversion

summary(mydata)

##   PassengerId    Survived Pclass      Name               Sex     
##  Min.   :  1.0   0:549    1:216   Length:891         female:314  
##  1st Qu.:223.5   1:342    2:184   Class :character   male  :577  
##  Median :446.0            3:491   Mode  :character               
##  Mean   :446.0                                                   
##  3rd Qu.:668.5                                                   
##  Max.   :891.0                                                   
##                                                                  
##       Age            SibSp           Parch           Ticket         
##  Min.   : 0.42   Min.   :0.000   Min.   :0.0000   Length:891        
##  1st Qu.:20.12   1st Qu.:0.000   1st Qu.:0.0000   Class :character  
##  Median :28.00   Median :0.000   Median :0.0000   Mode  :character  
##  Mean   :29.70   Mean   :0.523   Mean   :0.3816                     
##  3rd Qu.:38.00   3rd Qu.:1.000   3rd Qu.:0.0000                     
##  Max.   :80.00   Max.   :8.000   Max.   :6.0000                     
##  NA's   :177                                                        
##       Fare           Cabin           Embarked
##  Min.   :  0.00   Length:891          :  2   
##  1st Qu.:  7.91   Class :character   C:168   
##  Median : 14.45   Mode  :character   Q: 77   
##  Mean   : 32.20                      S:644   
##  3rd Qu.: 31.00                              
##  Max.   :512.33                              
##

2 Descriptive statistics

Select variables
- Select four variables “Age”, “Fare”, “SibSp”, “Parch”.

selected_vars <- c("Age", "Fare", "SibSp", "Parch")
selected_data <- mydata[, selected_vars]

Parameter estimation
- The mean of Age is 29.70.
- The median of Fare is 14.45.
- The standard deviation of SibSp is 1.10.
- The standard deviation of Parch is 0.81.

#install.packages("psych")
library(psych)
describe(selected_data)

##       vars   n  mean    sd median trimmed   mad  min    max  range skew
## Age      1 714 29.70 14.53  28.00   29.27 13.34 0.42  80.00  79.58 0.39
## Fare     2 891 32.20 49.69  14.45   21.38 10.24 0.00 512.33 512.33 4.77
## SibSp    3 891  0.52  1.10   0.00    0.27  0.00 0.00   8.00   8.00 3.68
## Parch    4 891  0.38  0.81   0.00    0.18  0.00 0.00   6.00   6.00 2.74
##       kurtosis   se
## Age       0.16 0.54
## Fare     33.12 1.66
## SibSp    17.73 0.04
## Parch     9.69 0.03

3 Graphical representations

Load necessary libraries for plotting

library(ggplot2)

## 
## Attaching package: 'ggplot2'

## The following objects are masked from 'package:psych':
## 
##     %+%, alpha

Histogram of Age

The histogram of Age shows the distribution of ages among passengers.
It visualize the age distribution, indicating it’s skewed to right.

ggplot(mydata, aes(x = Age)) +
  geom_histogram(binwidth = 5, fill = "lightblue",color = "gray") +
  labs(title = "Distribution of Age",
       x = "Age",
       y = "Frequency")

## Warning: Removed 177 rows containing non-finite outside the scale range
## (`stat_bin()`).

Histogram of Fare

The histogram of Fare displays the distribution of fares paid by passengers, providing insights into the fare structure aboard the Titanic.
It indicates it’s skewed to right.

ggplot(mydata, aes(x = Fare)) +
  geom_histogram(binwidth = 10, fill = "lightgreen", color = "gray") +
  labs(title = "Distribution of Fare",
       x = "Fare",
       y = "Frequency")

Scatterplot of Age vs. Fare

The scatterplot of Age vs. Fare shows the relationship between age and fare paid by passengers.
It identify patterns or correlations between the two variables.

ggplot(mydata, aes(x = Age, y = Fare)) +
  geom_point(color = "blue") +
  labs(title = "Scatterplot of Age vs. Fare",
       x = "Age",
       y = "Fare")

## Warning: Removed 177 rows containing missing values or values outside the scale range
## (`geom_point()`).

Homework assignment at the course Applied Data Analysis in Business with R

2024-04-07

Descriptive statistics of dataset Titanic

1 Description of data set used in the analysis

1.1 Source

1.2 Explanation of the dataset

1.3 Data manipulation

2 Descriptive statistics

3 Graphical representations

Histogram of Age

Histogram of Fare

Scatterplot of Age vs. Fare