TUGAS DATA IRIS

MEMANGGIL DATA

df <- read.csv("C:/Users/Saintek10/Documents/R NINDY/iris.csv", header = TRUE)
head (df)

##   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

MENGHITUNG STATISTIK

summary (df)

##        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

MELIHAT BERAPA KOLOM DAN BARIS

dim(df)

## [1] 150   6

MEMANGGIL VARIABEL SETIAP KOLOM

names(df)

## [1] "Id"            "SepalLengthCm" "SepalWidthCm"  "PetalLengthCm"
## [5] "PetalWidthCm"  "Species"

MODUS

mode <- function(x){
  uqx <- unique(x)
  tab <- table(x)
  sort(uqx)[tab == max(tab)]
}
mode(df$SepalLengthCm)

## [1] 5

mode(df$SepalWidthCm)

## [1] 3

mode(df$PetalLengthCm)

## [1] 1.5

mode(df$PetalWidthCm)

## [1] 0.2

mode(df$Species)

## [1] "Iris-setosa"     "Iris-versicolor" "Iris-virginica"

RATA - RATA GEOMETRI

mean.geom <- function(x){
  exp(mean(log(x)))
}
mean.geom(df$SepalLengthCm)

## [1] 5.78572

mean.geom(df$SepalWidthCm)

## [1] 3.023582

mean.geom(df$PetalLengthCm)

## [1] 3.239757

mean.geom(df$PetalWidthCm)

## [1] 0.837827

RATA - RATA HARMONI

mean.harm <- function(x){
  1/(mean(1/x))
}
mean.harm(df$SepalLengthCm)

## [1] 5.728905

mean.harm(df$SepalWidthCm)

## [1] 2.993137

mean.harm(df$PetalLengthCm)

## [1] 2.696472

mean.harm(df$PetalWidthCm)

## [1] 0.4866465

NILAI SKEWNESS

library(e1071)
skewness(df$SepalLengthCm)

## [1] 0.3086407

skewness(df$SepalWidthCm)

## [1] 0.3274013

skewness(df$PetalLengthCm)

## [1] -0.2689994

skewness(df$PetalWidthCm)

## [1] -0.102906

NILAI KURTOSIS

library(e1071)
kurtosis(df$SepalLengthCm)

## [1] -0.6058125

kurtosis(df$SepalWidthCm)

## [1] 0.1983681

kurtosis(df$PetalLengthCm)

## [1] -1.416683

kurtosis(df$PetalWidthCm)

## [1] -1.357368

BOXPLOT

par(mfrow=c(2,2))
boxplot(df$SepalLengthCm,
        xlab="Sepal Length Cm",
        ylab="Nilai",
        col =c("lightblue"),
        main="BOXPLOT")

boxplot(df$SepalWidthCm,
        xlab="Sepal Width Cm",
        ylab="Nilai",
        col =c("lightpink"),
        main="BOXPLOT")

boxplot(df$PetalLengthCm,
        xlab="Petal Length Cm",
        ylab="Nilai",
        col =c("lightgreen"),
        main="BOXPLOT")

boxplot(df$PetalWidthCm,
        xlab="Petal Width Cm",
        ylab="Nilai",
        col =c("mediumpurple3"),
        main="BOXPLOT")

BOXPLOT OF SEPAL LENGTH VS SPECIES

boxplot(Sepal.Length~Species,
        data=iris,
        main="Sepal Length by Species",
        xlab="Species",
        ylab="Sepal Length",
        col="lightblue")

BOXPLOT OF SEPAL WIDTH VS SPECIES

boxplot(Sepal.Width~Species,
        data=iris,
        main="Sepal Width by Species",
        xlab="Species",
        ylab="Sepal Width",
        col="lightpink")

BOXPLOT OF PETAL LENGTH VS SPECIES

boxplot(Petal.Length~Species,
        data=iris,
        main="Petal Length by Species",
        xlab="Species",
        ylab="Petal Length",
        col="lightgreen")

BOXPLOT OF PETAL WIDTH VS SPECIES

boxplot(Petal.Width~Species,
        data=iris,
        main="Petal Width by Species",
        xlab="Species",
        ylab="Petal Width",
        col="mediumpurple3")

HISTOGRAM

par(mfrow=c(2,2))
hist(df$SepalLengthCm,
     xlab="Sepal Length Cm",
     ylab="Nilai",
     col =c("lightblue"),
     main="HISTOGRAM")

hist(df$SepalWidthCm,
     xlab="Sepal Width Cm",
     ylab="Nilai",
     col =c("lightpink"),
     main="HISTOGRAM")

hist(df$PetalLengthCm,
     xlab="Petal Length Cm",
     ylab="Nilai",
     col =c("lightgreen"),
     main="HISTOGRAM")

hist(df$PetalWidthCm,
     xlab="Petal Width Cm",
     ylab="Nilai",
     col =c("mediumpurple3"),
     main="HISTOGRAM")

SCATTERPLOT

par(mfrow=c(2,2))
plot(df$SepalLengthCm,
     xlab="Sepal Length Cm",
     ylab="Nilai",
     col =c("lightblue"),
     main="SCATTERPLOT")

plot(df$SepalWidthCm,
     xlab="Sepal Width Cm",
     ylab="Nilai",
     col =c("lightpink"),
     main="SCATTERPLOT")

plot(df$PetalLengthCm,
     xlab="Petal Length Cm",
     ylab="Nilai",
     col =c("lightgreen"),
     main="SCATTERPLOT")

plot(df$PetalWidthCm,
     xlab="Petal Width Cm",
     ylab="Nilai",
     col =c("mediumpurple3"),
     main="SCATTERPLOT")

BAR CHART

barplot(df$SepalLengthCm,
     xlab="Sepal Length Cm",
     ylab="Nilai",
     col =c("lightblue"),
     main="BAR CHART")

barplot(df$SepalWidthCm,
     xlab="Sepal Width Cm",
     ylab="Nilai",
     col =c("lightpink"),
     main="BAR CHART")

barplot(df$PetalLengthCm,
     xlab="Petal Length Cm",
     ylab="Nilai",
     col =c("lightgreen"),
     main="BAR CHART")

barplot(df$PetalWidthCm,
     xlab="Petal Width Cm",
     ylab="Nilai",
     col =c("mediumpurple3"),
     main="BAR CHART")

PIE CHART

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

pie(table(df$Species),
    col=c("lightpink","lightblue","mediumpurple3"))

pie3D(table(df$Species),
      labels = lbl,
      explode = 0.1,
      main = "Pie Chart of Species",
      col=c("lightpink","lightblue","mediumpurple3"))