tidyverse

1. Introduction

This is the dataset of titanic, I have chosen from Kaggle. This data set has below columns.

2. Load library

#install.packages("tidyverse")
#install.packages("ggplot2")
library(ggplot2)
library(tidyverse)

## ── Attaching packages ─────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──

## ✓ tibble  2.1.3     ✓ dplyr   0.8.5
## ✓ tidyr   1.0.2     ✓ stringr 1.4.0
## ✓ readr   1.3.1     ✓ forcats 0.5.0
## ✓ purrr   0.3.3

## ── Conflicts ────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

3. Load data to R

# get the data from Git repository
url <- "https://raw.githubusercontent.com/SubhalaxmiRout002/tidyverse/master/titanic.csv"
# read the csv file
titanic_data <- read.csv(url, stringsAsFactors = FALSE)
# view fisrt 6 rows of data
head(titanic_data)

##   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

4. Clean data

# remove unwanted column
titanic_data <- titanic_data %>% select(-SibSp,-Ticket,-Fare,-Cabin,-Embarked,-Parch)

# remove where name in NA
titanic_data <- titanic_data %>% filter(!is.na(Name))

# remove duplicates from data, if present any
titanic_data <- unique(titanic_data)

# rename column
titanic_data <- titanic_data %>% rename(Class_Type = Pclass)

# view data
head(titanic_data,5)

##   PassengerId Survived Class_Type
## 1           1        0          3
## 2           2        1          1
## 3           3        1          3
## 4           4        1          1
## 5           5        0          3
##                                                  Name    Sex Age
## 1                             Braund, Mr. Owen Harris   male  22
## 2 Cumings, Mrs. John Bradley (Florence Briggs Thayer) female  38
## 3                              Heikkinen, Miss. Laina female  26
## 4        Futrelle, Mrs. Jacques Heath (Lily May Peel) female  35
## 5                            Allen, Mr. William Henry   male  35

5. Analysis

# find number of survivor ratio
row <- nrow(titanic_data)

# add new colum Survived_Ration in to dataset
titanic_data <- titanic_data %>% mutate(Survived_Ratio = Survived/row)

5.1 survivor group by sex

# number of survivor group by sex
survivor_sex <- titanic_data %>%  filter(Survived == 1) %>% group_by(Sex) %>% count(Survived)


# draw graph
ggplot(survivor_sex) + geom_bar(aes(x = survivor_sex$Sex, y = survivor_sex$n), stat = "identity", fill = "steelblue") + xlab("Sex") + ylab("Number of survivor(#)") + ggtitle("Number of suvivor by sex") + theme(plot.title = element_text(hjust = 0.5))+ geom_text(aes(x = survivor_sex$Sex,y = survivor_sex$n,label=survivor_sex$n), vjust=1.6, color="white", size=4.5)

5.2 survivor group class type

# number of survivor group by class type
survivor_class <- titanic_data %>%  filter(Survived == 1) %>% group_by(Class_Type,Sex) %>% count(Survived)

# draw graph
ggplot(data = survivor_class, aes(x = survivor_class$Class_Type, y = survivor_class$n, fill = survivor_class$Sex)) + geom_bar(stat = "identity", position=position_stack())+ xlab("Class Type") + ylab("Number of survivor(#)") + ggtitle("Number of suvivor by Class") + theme(plot.title = element_text(hjust = 0.5))+ geom_text(aes(x = survivor_class$Class_Type,y = survivor_class$n,label=survivor_class$n), hjust=1.2,vjust = 2.6, color="white", size=4.5)+scale_fill_manual(values=c("#1f5f76", "#92acb8"))+labs(fill = "Sex")

6. Conclusion

From Plot 4.1 and 4.2 we found:

• The number of Surivior is high in female.
• Highest survivor is in female and Class 1 type.

I have used, select(), filter(), mutate(), rename() functions of tidyverse package to clean and manupulate data.