library (tidyr)
library (ggplot2)
data("Titanic")
head (Titanic)
## , , Age = Child, Survived = No
## 
##       Sex
## Class  Male Female
##   1st     0      0
##   2nd     0      0
##   3rd    35     17
##   Crew    0      0
## 
## , , Age = Adult, Survived = No
## 
##       Sex
## Class  Male Female
##   1st   118      4
##   2nd   154     13
##   3rd   387     89
##   Crew  670      3
## 
## , , Age = Child, Survived = Yes
## 
##       Sex
## Class  Male Female
##   1st     5      1
##   2nd    11     13
##   3rd    13     14
##   Crew    0      0
## 
## , , Age = Adult, Survived = Yes
## 
##       Sex
## Class  Male Female
##   1st    57    140
##   2nd    14     80
##   3rd    75     76
##   Crew  192     20
#mengecek missing value (nomer 6)
data1 <- data.frame(Titanic)
colSums(is.na(data1))
##    Class      Sex      Age Survived     Freq 
##        0        0        0        0        0
# menghitung jumlah outliers pada kolom Freq
freq_data <- data1$Freq
Q1 <- quantile(freq_data, 0.25)
Q3 <- quantile(freq_data, 0.75)
IQR <- Q3 - Q1
lower_bound <- Q1 - 1.5 * IQR
upper_bound <- Q3 + 1.5 * IQR
outliers <- freq_data[freq_data < lower_bound | freq_data > upper_bound]
length(outliers)
## [1] 3
sum(duplicated(data1))
## [1] 0

#mencari rata-rata, median dan std

nilai <- c(70, 75, 80, 85, 85, 90, 95, 100, 60, 75, 77, 85, 90, 98, 68, 92, 85, 66, 75, 80, 72, 84, 50, 69, 76, 80, 90, 95, 88, 77)
mean(nilai)
## [1] 80.4
median (nilai)
## [1] 80
sd(nilai)
## [1] 11.48792

#membagi dataset

library(mlbench)
## Warning: package 'mlbench' was built under R version 4.4.3
data("BreastCancer")
library(caTools)
## Warning: package 'caTools' was built under R version 4.4.3
set.seed(110)
split=sample.split(BreastCancer, SplitRatio = 0.8) 
training_set=subset(BreastCancer,split==TRUE)       
test_set=subset(BreastCancer,split==FALSE)
dim(training_set)
## [1] 509  11
dim(test_set)
## [1] 190  11