Tugas VDE 17 Mei

setwd("C:/Users/Mahasiswa/Documents")
netflix = read.csv("C:/Users/Mahasiswa/Documents/netflix_titles.csv")
library(ggplot2)

## Warning: package 'ggplot2' was built under R version 4.3.2

custom_col <- c("red", "green", "orange")
ggplot(netflix, aes(x = "", fill = factor(type))) +
  geom_bar(width = 1) +
  theme(axis.line = element_blank(),
        plot.title = element_text(hjust = 0.5 , size = 22)) +
  labs(fill= "class",
       x=NULL,
       y=NULL,
       title= "Pie Chart of Netflix shows")+
  coord_polar(theta = "y", start = 0)+
  scale_fill_manual(values = custom_col)

library("dplyr")

## Warning: package 'dplyr' was built under R version 4.3.2

## 
## 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

library("tidyr")

## Warning: package 'tidyr' was built under R version 4.3.3

library("readr")

## Warning: package 'readr' was built under R version 4.3.3

library("stringr")

## Warning: package 'stringr' was built under R version 4.3.2

library("forcats")

## Warning: package 'forcats' was built under R version 4.3.3

library("modelr")

## Warning: package 'modelr' was built under R version 4.3.3

library("ggplot2")

setwd("C:/Users/Mahasiswa/Documents")
train = read.csv("C:/Users/Mahasiswa/Downloads/train.csv")
head(train)

##   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

library(ggplot2)
sex_counts <- table(train$Sex)

pie_chart <- ggplot(data = NULL, aes(x = "", y = sex_counts, fill = names(sex_counts))) +
  geom_bar(stat = "identity", width = 1) +
  coord_polar("y", start = 0) +
  labs(title = "Distribution of Sex") +
  theme_void() +
  scale_fill_manual(values = c("male" = "blue", "female" = "pink")) 
print(pie_chart)

## Don't know how to automatically pick scale for object of type <table>.
## Defaulting to continuous.

p_age = ggplot(train) +
  geom_freqpoly(mapping = aes(x = Age, color = Survived), binwidth = 1) +
  theme(legend.position = "right")

p_sex = ggplot(train, mapping = aes(x = Sex, fill = Survived)) +
  geom_bar(stat='count', position='fill') +
  labs(x = 'Sex') +
  scale_fill_discrete(name="Surv")

p_class = ggplot(
  train, mapping = aes(x = Pclass, fill = Survived, colour = Survived)) +
  geom_bar(stat='count', position='fill') +
  labs(x = 'Pclass') +
  theme(legend.position = "none")

p_emb = ggplot(train, aes(Embarked, fill = Survived)) +
  geom_bar(stat='count', position='fill') +
  labs(x = 'Embarked') +
  theme(legend.position = "none")

p_sib = ggplot(train, aes(SibSp, fill = Survived)) +
  geom_bar(stat='count', position='fill') +
  labs(x = 'SibSp') +
  theme(legend.position = "none")

p_par = ggplot(train, aes(Parch, fill = Survived)) +
  geom_bar(stat='count', position='fill') +
  labs(x = 'Parch') +
  theme(legend.position = "none")

p_fare = ggplot(train) +
  geom_freqpoly(mapping = aes(Fare, color = Survived), binwidth = 0.05) +
  scale_x_log10() +
  theme(legend.position = "none")

p_age

Fig. 2

p_sex

Fig. 2

p_fare

Fig. 2

p_class

Fig. 2

p_emb

Fig. 2

p_sib

Fig. 2

p_par

Fig. 2