Practice Open Source
Yendra Wijayanto
2024-04-15
#Number 1
t_test <- function(xbar, miu,stadev, n, alfa, pilihan_uji="upper" ){
df <- n-1
t_value <- (xbar - miu)/ (stadev/sqrt(n))
if (pilihan_uji == "two_sided") {
p_value <- 2*pt(abs(t_value), df)
} else if (pilihan_uji == "lower") {
p_value <- pt(t_value, df)
} else if (pilihan_uji == "upper") {
p_value <- (1-pt(t_value, df))
} else {
warning("pilihan Uji yang anda masukkan tidak sesuai")
}
return(list(p_value = p_value))
}#Number 2
xbar = 16
miu = 16.43
stadev = 0.8
n = 15
alfa = 0.05
lower <- t_test(xbar,miu,stadev,n,alfa, pilihan_uji="lower")
paste("Uji Lower Tail:\n")## [1] "Uji Lower Tail:\n"paste("P-Value:", lower$p_value, "\n")## [1] "P-Value: 0.0280993701831434 \n"if(lower$p_value<alfa){
print("H0 Ditolak")
}else{
print("H0 Gagal Ditolak")
}## [1] "H0 Ditolak"upper <- t_test(xbar,miu,stadev,n,alfa, pilihan_uji="upper")
paste("Uji Upper Tail:\n")## [1] "Uji Upper Tail:\n"paste("P-Value:", upper$p_value, "\n")## [1] "P-Value: 0.971900629816857 \n"if(upper$p_value<alfa){
print("H0 Ditolak")
}else{
print("H0 Gagal Ditolak")
}## [1] "H0 Gagal Ditolak"two_sided <- t_test(xbar,miu,stadev,n,alfa, pilihan_uji="two_sided")
paste("Uji Two Sided:\n")## [1] "Uji Two Sided:\n"paste("P-Value:", two_sided$p_value, "\n")## [1] "P-Value: 1.94380125963371 \n"if(two_sided$p_value<alfa){
print("H0 Ditolak")
}else{
print("H0 Gagal Ditolak")
}## [1] "H0 Gagal Ditolak"#Number 3
cek_integer<- function(vector, int) {
if(round(int)!=int || is.numeric(vector)!=TRUE){
warning("angka yang anda masukkan bukan bilangan bulat")
}
if(int %in% vector){
return(TRUE)
}else{
return(FALSE)
}
}
cek_integer(c(1:100),100.5)## Warning in cek_integer(c(1:100), 100.5): angka yang anda masukkan bukan
## bilangan bulat## [1] FALSEcek_integer(c(1:100),100)## [1] TRUEcek_integer(c(1:100),101)## [1] FALSE#Number 4
library(numbers)## Warning: package 'numbers' was built under R version 4.3.3cek_prima <- function(a){
if(isPrime(a)){
print("TRUE")
}else{
print("FALSE")
}
}
cek_prima(2)## [1] "TRUE"#Number 5
Faktorial <- function(n){
if(n==0){
return(1)
} else{
z = 1
for(i in 1:n){
z = z * i
}
return(z)
}
}
Faktorial(3)## [1] 6Faktorial(0)## [1] 1#Number 6
data("iris")
p <- iris
mean_max_scalling <- function(z){
xnew <- (z-min(z))/(max(z)-min(z))
return(xnew)
}
Hasil <- mean_max_scalling(p$Sepal.Length)
Hasil## [1] 0.22222222 0.16666667 0.11111111 0.08333333 0.19444444 0.30555556
## [7] 0.08333333 0.19444444 0.02777778 0.16666667 0.30555556 0.13888889
## [13] 0.13888889 0.00000000 0.41666667 0.38888889 0.30555556 0.22222222
## [19] 0.38888889 0.22222222 0.30555556 0.22222222 0.08333333 0.22222222
## [25] 0.13888889 0.19444444 0.19444444 0.25000000 0.25000000 0.11111111
## [31] 0.13888889 0.30555556 0.25000000 0.33333333 0.16666667 0.19444444
## [37] 0.33333333 0.16666667 0.02777778 0.22222222 0.19444444 0.05555556
## [43] 0.02777778 0.19444444 0.22222222 0.13888889 0.22222222 0.08333333
## [49] 0.27777778 0.19444444 0.75000000 0.58333333 0.72222222 0.33333333
## [55] 0.61111111 0.38888889 0.55555556 0.16666667 0.63888889 0.25000000
## [61] 0.19444444 0.44444444 0.47222222 0.50000000 0.36111111 0.66666667
## [67] 0.36111111 0.41666667 0.52777778 0.36111111 0.44444444 0.50000000
## [73] 0.55555556 0.50000000 0.58333333 0.63888889 0.69444444 0.66666667
## [79] 0.47222222 0.38888889 0.33333333 0.33333333 0.41666667 0.47222222
## [85] 0.30555556 0.47222222 0.66666667 0.55555556 0.36111111 0.33333333
## [91] 0.33333333 0.50000000 0.41666667 0.19444444 0.36111111 0.38888889
## [97] 0.38888889 0.52777778 0.22222222 0.38888889 0.55555556 0.41666667
## [103] 0.77777778 0.55555556 0.61111111 0.91666667 0.16666667 0.83333333
## [109] 0.66666667 0.80555556 0.61111111 0.58333333 0.69444444 0.38888889
## [115] 0.41666667 0.58333333 0.61111111 0.94444444 0.94444444 0.47222222
## [121] 0.72222222 0.36111111 0.94444444 0.55555556 0.66666667 0.80555556
## [127] 0.52777778 0.50000000 0.58333333 0.80555556 0.86111111 1.00000000
## [133] 0.58333333 0.55555556 0.50000000 0.94444444 0.55555556 0.58333333
## [139] 0.47222222 0.72222222 0.66666667 0.72222222 0.41666667 0.69444444
## [145] 0.66666667 0.66666667 0.55555556 0.61111111 0.52777778 0.44444444library(ggplot2)## Warning: package 'ggplot2' was built under R version 4.3.3library(ggpubr)## Warning: package 'ggpubr' was built under R version 4.3.3qqnorm(Hasil)
ggdensity(Hasil, main="Density Plot")
Dari plot diatas dapat diketahui bahwa data hasil scaling berdistribusi normal
#Number 7
p <- iris
ggplot(p, aes(Species, Sepal.Length, fill = Species)) +
geom_boxplot() +
labs(title = "Boxplot")
#Number 8
data("swiss")
k <- swiss
k <- transform(k, IPM = (log(swiss$Education)+log(swiss$Fertility)+log(swiss$Agriculture))^(1/3)*10)
k## Fertility Agriculture Examination Education Catholic
## Courtelary 80.2 17.0 15 12 9.96
## Delemont 83.1 45.1 6 9 84.84
## Franches-Mnt 92.5 39.7 5 5 93.40
## Moutier 85.8 36.5 12 7 33.77
## Neuveville 76.9 43.5 17 15 5.16
## Porrentruy 76.1 35.3 9 7 90.57
## Broye 83.8 70.2 16 7 92.85
## Glane 92.4 67.8 14 8 97.16
## Gruyere 82.4 53.3 12 7 97.67
## Sarine 82.9 45.2 16 13 91.38
## Veveyse 87.1 64.5 14 6 98.61
## Aigle 64.1 62.0 21 12 8.52
## Aubonne 66.9 67.5 14 7 2.27
## Avenches 68.9 60.7 19 12 4.43
## Cossonay 61.7 69.3 22 5 2.82
## Echallens 68.3 72.6 18 2 24.20
## Grandson 71.7 34.0 17 8 3.30
## Lausanne 55.7 19.4 26 28 12.11
## La Vallee 54.3 15.2 31 20 2.15
## Lavaux 65.1 73.0 19 9 2.84
## Morges 65.5 59.8 22 10 5.23
## Moudon 65.0 55.1 14 3 4.52
## Nyone 56.6 50.9 22 12 15.14
## Orbe 57.4 54.1 20 6 4.20
## Oron 72.5 71.2 12 1 2.40
## Payerne 74.2 58.1 14 8 5.23
## Paysd'enhaut 72.0 63.5 6 3 2.56
## Rolle 60.5 60.8 16 10 7.72
## Vevey 58.3 26.8 25 19 18.46
## Yverdon 65.4 49.5 15 8 6.10
## Conthey 75.5 85.9 3 2 99.71
## Entremont 69.3 84.9 7 6 99.68
## Herens 77.3 89.7 5 2 100.00
## Martigwy 70.5 78.2 12 6 98.96
## Monthey 79.4 64.9 7 3 98.22
## St Maurice 65.0 75.9 9 9 99.06
## Sierre 92.2 84.6 3 3 99.46
## Sion 79.3 63.1 13 13 96.83
## Boudry 70.4 38.4 26 12 5.62
## La Chauxdfnd 65.7 7.7 29 11 13.79
## Le Locle 72.7 16.7 22 13 11.22
## Neuchatel 64.4 17.6 35 32 16.92
## Val de Ruz 77.6 37.6 15 7 4.97
## ValdeTravers 67.6 18.7 25 7 8.65
## V. De Geneve 35.0 1.2 37 53 42.34
## Rive Droite 44.7 46.6 16 29 50.43
## Rive Gauche 42.8 27.7 22 29 58.33
## Infant.Mortality IPM
## Courtelary 22.2 21.32865
## Delemont 22.2 21.84614
## Franches-Mnt 20.2 21.41284
## Moutier 20.3 21.54093
## Neuveville 20.6 22.12009
## Porrentruy 26.6 21.43016
## Broye 23.6 21.98463
## Glane 24.9 22.11928
## Gruyere 21.0 21.78119
## Sarine 24.4 22.09988
## Veveyse 24.5 21.84567
## Aigle 16.5 22.08540
## Aubonne 19.1 21.80070
## Avenches 22.7 22.12021
## Cossonay 18.7 21.52290
## Echallens 21.2 20.95533
## Grandson 20.0 21.45659
## Lausanne 20.2 21.76996
## La Vallee 10.8 21.33518
## Lavaux 20.0 22.01074
## Morges 18.0 21.95005
## Moudon 22.4 21.01597
## Nyone 16.7 21.86333
## Orbe 15.3 21.42348
## Oron 21.0 20.44748
## Payerne 23.8 21.86165
## Paysd'enhaut 18.0 21.19866
## Rolle 16.3 21.90650
## Vevey 20.9 21.75657
## Yverdon 22.5 21.66002
## Conthey 15.1 21.15715
## Entremont 19.8 21.87789
## Herens 18.3 21.20681
## Martigwy 19.4 21.83250
## Monthey 20.2 21.28703
## St Maurice 17.8 22.03646
## Sierre 16.3 21.58771
## Sion 18.1 22.29554
## Boudry 20.3 21.81887
## La Chauxdfnd 20.5 20.50721
## Le Locle 18.9 21.30228
## Neuchatel 23.0 21.89673
## Val de Ruz 20.0 21.48998
## ValdeTravers 19.5 20.86845
## V. De Geneve 18.0 19.75361
## Rive Droite 18.2 22.24578
## Rive Gauche 19.3 21.85948#Number 9
kotadiatasratarata <- subset(k, IPM > mean(k$IPM))
kotadibawahratarata <- subset(k, IPM <= mean(k$IPM))
summary(kotadiatasratarata)## Fertility Agriculture Examination Education
## Min. :42.80 Min. :17.60 Min. : 6.00 Min. : 6.00
## 1st Qu.:64.17 1st Qu.:45.12 1st Qu.:14.00 1st Qu.: 8.00
## Median :67.90 Median :58.95 Median :16.00 Median :10.00
## Mean :69.08 Mean :54.25 Mean :17.23 Mean :12.92
## 3rd Qu.:78.70 3rd Qu.:66.75 3rd Qu.:21.75 3rd Qu.:13.00
## Max. :92.40 Max. :84.90 Max. :35.00 Max. :32.00
## Catholic Infant.Mortality IPM
## Min. : 2.27 Min. :16.30 Min. :21.66
## 1st Qu.: 5.74 1st Qu.:18.43 1st Qu.:21.84
## Median :17.69 Median :20.25 Median :21.89
## Mean :45.44 Mean :20.53 Mean :21.94
## 3rd Qu.:95.83 3rd Qu.:22.65 3rd Qu.:22.07
## Max. :99.68 Max. :24.90 Max. :22.30summary(kotadibawahratarata)## Fertility Agriculture Examination Education
## Min. :35.00 Min. : 1.20 Min. : 3.00 Min. : 1.000
## 1st Qu.:65.70 1st Qu.:18.70 1st Qu.: 7.00 1st Qu.: 3.000
## Median :72.50 Median :39.70 Median :15.00 Median : 6.000
## Mean :71.45 Mean :46.21 Mean :15.57 Mean : 8.571
## 3rd Qu.:77.60 3rd Qu.:69.30 3rd Qu.:22.00 3rd Qu.: 8.000
## Max. :92.50 Max. :89.70 Max. :37.00 Max. :53.000
## Catholic Infant.Mortality IPM
## Min. : 2.15 Min. :10.80 Min. :19.75
## 1st Qu.: 4.20 1st Qu.:18.00 1st Qu.:21.02
## Median : 11.22 Median :20.00 Median :21.30
## Mean : 35.82 Mean :19.21 Mean :21.15
## 3rd Qu.: 90.57 3rd Qu.:20.50 3rd Qu.:21.43
## Max. :100.00 Max. :26.60 Max. :21.59