STATISTIKA

data<-read.csv("C:/Users/Rizqa Aprillia P/Downloads/iris.csv",header=TRUE)
head(data)
##   Id SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm     Species
## 1  1           5.1          3.5           1.4          0.2 Iris-setosa
## 2  2           4.9          3.0           1.4          0.2 Iris-setosa
## 3  3           4.7          3.2           1.3          0.2 Iris-setosa
## 4  4           4.6          3.1           1.5          0.2 Iris-setosa
## 5  5           5.0          3.6           1.4          0.2 Iris-setosa
## 6  6           5.4          3.9           1.7          0.4 Iris-setosa

Statistika Deskriptif

summary(data)
##        Id         SepalLengthCm    SepalWidthCm   PetalLengthCm  
##  Min.   :  1.00   Min.   :4.300   Min.   :2.000   Min.   :1.000  
##  1st Qu.: 38.25   1st Qu.:5.100   1st Qu.:2.800   1st Qu.:1.600  
##  Median : 75.50   Median :5.800   Median :3.000   Median :4.350  
##  Mean   : 75.50   Mean   :5.843   Mean   :3.054   Mean   :3.759  
##  3rd Qu.:112.75   3rd Qu.:6.400   3rd Qu.:3.300   3rd Qu.:5.100  
##  Max.   :150.00   Max.   :7.900   Max.   :4.400   Max.   :6.900  
##   PetalWidthCm     Species         
##  Min.   :0.100   Length:150        
##  1st Qu.:0.300   Class :character  
##  Median :1.300   Mode  :character  
##  Mean   :1.199                     
##  3rd Qu.:1.800                     
##  Max.   :2.500

Modus

modus <- function(x){
  uqx <- unique(x)
  uqx[which.max(tabulate(match(x, uqx)))]
}
modus(data$SepalLengthCm)
## [1] 5
modus(data$SepalWidthCm)
## [1] 3
modus(data$PetalLengthCm)
## [1] 1.5
modus(data$PetalWidthCm)
## [1] 0.2
modus(data$Species)
## [1] "Iris-setosa"

Rata-Rata Geometri

mean.geom <- function(x){
  exp(mean(log(x)))
}
mean.geom(data$SepalLengthCm)
## [1] 5.78572
mean.geom(data$SepalWidthCm)
## [1] 3.023582
mean.geom(data$PetalLengthCm)
## [1] 3.239757
mean.geom(data$PetalWidthCm)
## [1] 0.837827

Rata-Rata Harmoni

mean.harm <- function(x){
  1/(mean(1/x))
}
mean.harm(data$SepalLengthCm)
## [1] 5.728905
mean.harm(data$SepalWidthCm)
## [1] 2.993137
mean.harm(data$PetalLengthCm)
## [1] 2.696472
mean.harm(data$PetalWidthCm)
## [1] 0.4866465

Nilai Skewness dan Kurtosis

library(e1071)
skewness(data$SepalLengthCm)
## [1] 0.3086407
skewness(data$SepalWidthCm)
## [1] 0.3274013
skewness(data$PetalLengthCm)
## [1] -0.2689994
skewness(data$PetalWidthCm)
## [1] -0.102906
library(e1071)
kurtosis(data$SepalLengthCm)
## [1] -0.6058125
kurtosis(data$SepalWidthCm)
## [1] 0.1983681
kurtosis(data$PetalLengthCm)
## [1] -1.416683
kurtosis(data$PetalWidthCm)
## [1] -1.357368

Menampilkan Grafik

Boxplot

boxplot(data$SepalLengthCm, xlab="Sepal Length", ylab="cm", col="skyblue")

boxplot(data$SepalWidthCm, xlab="Sepal Width", ylab="cm", col="mediumpurple1")

boxplot(data$PetalLengthCm, xlab="Petal Length", ylab="cm", col="Dark Sea Green")

boxplot(data$PetalWidthCm, xlab="Petal Width", ylab="cm", col="lightpink1")

Histogram

par(mfrow = c(2, 2))
hist(data$SepalLengthCm,xlab = "Sepal Length (Cm)",col = "turquoise",border = "black", main="Sepal Length")
hist(data$SepalWidthCm,xlab = "Sepal Width (Cm)",col = "salmon3",border = "black", main="Sepal Width")
hist(data$PetalLengthCm,xlab = "Petal Length (Cm)",col = "thistle",border = "black", main="Petal Length")
hist(data$PetalWidthCm,xlab = "Petal Width (Cm)",col = "violetred1",border = "black", main="Petal Width")

Scatter Plot

par(mfrow = c(2, 2))
plot(data$SepalLengthCm,data$SepalWidthCm,pch = 19, cex = 0.8, frame = FALSE, ylab="Sepal Width" ,xlab = "Sepal Length",col = "tomato1", main="Korelasi")
plot(data$SepalLengthCm,data$PetalLengthCm,pch = 19, cex = 0.8, frame = FALSE, ylab="Petal Length", xlab = "Sepal Length",col = "lightblue3", main="Korelasi")
plot(data$SepalLengthCm,data$PetalWidthCm, pch = 19, cex = 0.8, frame = FALSE, ylab= "Petal Width", xlab = "Sepal Length",col = "salmon1", main="Korelasi")
plot(data$SepalWidthCm,data$PetalLengthCm, pch = 19, cex = 0.8, frame = FALSE, ylab="Sepal Length (Cm)", xlab = "Sepal Width",col = "lightpink1", main="Korelasi") 

par(mfrow = c(2, 2))
plot(data$SepalWidthCm,data$PetalWidthCm, pch = 19, cex = 0.8, frame = FALSE, ylab="Petal Width", xlab = "Sepal Width",col = "seagreen", main="Korelasi") 
plot(data$PetalLengthCm,data$PetalWidthCm, pch = 19, cex = 0.8, frame = FALSE, ylab="Petal Width", xlab = "Sepal Width",col = "royalblue1", main="Korelasi")

Bar Plot

barplot(data$SepalLengthCm, xlab="Sepal Length (Cm)", ylab="Nilai", col = "pink", main="BAR CHART")

barplot(data$SepalWidthCm, xlab="Sepal Length (Cm)", ylab="Nilai", col = "skyblue", main="BAR CHART")

barplot(data$PetalLengthCm, xlab="Sepal Length (Cm)", ylab="Nilai", col = "wheat4", main="BAR CHART")

barplot(data$PetalWidthCm, xlab="Sepal Length (Cm)", ylab="Nilai", col = "violetred1", main="BAR CHART")

PIE CHART

library(plotrix)
lbl<-c("Iris-sentosa","Iris-versicolor","Iris-virginica")

pie(table(data$Species),
    col=c("darkolivegreen1","cadetblue1","indianred1"))

pie3D(table(data$Species),
      labels=lbl,
      explode=0.1,
      main="Pie Chart Of Species",
      col=c("darkolivegreen1","cadetblue1","indianred1"))