library(ggplot2)
ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
geom_boxplot() +
theme_minimal() +
labs(title = "Perbandingan Distribusi Sepal Length")
korelasi <- cor(mtcars$mpg, mtcars$cyl)
print(korelasi)
## [1] -0.852162
chick1 <- subset(ChickWeight, Chick == 1)
# Membuat Line Chart
plot(chick1$Time, chick1$weight, type = "b",
xlab = "Waktu (Hari)",
ylab = "Berat Anak Ayam",
main = "Tren Berat Anak Ayam dari Waktu ke Waktu",
col = "red", pch = 16)
nilai_korelasi <- cor(faithful$eruptions, faithful$waiting)
print(nilai_korelasi)
## [1] 0.9008112
plot(faithful$eruptions, faithful$waiting,
main = "Korelasi Erupsi vs Waktu Tunggu",
xlab = "Durasi Erupsi (menit)",
ylab = "Waktu Tunggu (menit)",
pch = 19, col = "darkred")
library(ggplot2)
ggplot(diamonds, aes(x = cut, y = price, fill = cut)) +
geom_boxplot() +
theme_minimal() +
labs(title = "Distribusi Harga Berlian berdasarkan Kualitas Potongan (Cut)",
x = "Kualitas Potongan",
y = "Harga (USD)")
library(ggplot2)
str(diamonds$cut)
## Ord.factor w/ 5 levels "Fair"<"Good"<..: 5 4 2 4 2 3 3 3 1 3 ...
class(diamonds$cut)
## [1] "ordered" "factor"
is.ordered(diamonds$cut)
## [1] TRUE
str(mtcars)
## 'data.frame': 32 obs. of 11 variables:
## $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
## $ cyl : num 6 6 4 6 8 6 8 4 4 6 ...
## $ disp: num 160 160 108 258 360 ...
## $ hp : num 110 110 93 110 175 105 245 62 95 123 ...
## $ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
## $ wt : num 2.62 2.88 2.32 3.21 3.44 ...
## $ qsec: num 16.5 17 18.6 19.4 17 ...
## $ vs : num 0 0 1 1 0 1 0 1 1 1 ...
## $ am : num 1 1 1 0 0 0 0 0 0 0 ...
## $ gear: num 4 4 4 3 3 3 3 4 4 4 ...
## $ carb: num 4 4 1 1 2 1 4 2 2 4 ...
summary(mtcars)
## mpg cyl disp hp
## Min. :10.40 Min. :4.000 Min. : 71.1 Min. : 52.0
## 1st Qu.:15.43 1st Qu.:4.000 1st Qu.:120.8 1st Qu.: 96.5
## Median :19.20 Median :6.000 Median :196.3 Median :123.0
## Mean :20.09 Mean :6.188 Mean :230.7 Mean :146.7
## 3rd Qu.:22.80 3rd Qu.:8.000 3rd Qu.:326.0 3rd Qu.:180.0
## Max. :33.90 Max. :8.000 Max. :472.0 Max. :335.0
## drat wt qsec vs
## Min. :2.760 Min. :1.513 Min. :14.50 Min. :0.0000
## 1st Qu.:3.080 1st Qu.:2.581 1st Qu.:16.89 1st Qu.:0.0000
## Median :3.695 Median :3.325 Median :17.71 Median :0.0000
## Mean :3.597 Mean :3.217 Mean :17.85 Mean :0.4375
## 3rd Qu.:3.920 3rd Qu.:3.610 3rd Qu.:18.90 3rd Qu.:1.0000
## Max. :4.930 Max. :5.424 Max. :22.90 Max. :1.0000
## am gear carb
## Min. :0.0000 Min. :3.000 Min. :1.000
## 1st Qu.:0.0000 1st Qu.:3.000 1st Qu.:2.000
## Median :0.0000 Median :4.000 Median :2.000
## Mean :0.4062 Mean :3.688 Mean :2.812
## 3rd Qu.:1.0000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :1.0000 Max. :5.000 Max. :8.000
head(mtcars)
## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
library(ggplot2)
ggplot(mtcars, aes(x = wt, y = mpg)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, color = "red") + # Menambah garis tren
labs(title = "Scatter Plot: wt vs mpg")
## `geom_smooth()` using formula = 'y ~ x'
sum(is.na(airquality$Ozone))
## [1] 37
nilai_median <- median(airquality$Ozone, na.rm = TRUE)
airquality$Ozone[is.na(airquality$Ozone)] <- nilai_median
sum(is.na(airquality$Ozone))
## [1] 0
titanic_df <- as.data.frame(Titanic)
titanic_full <- titanic_df[rep(1:nrow(titanic_df), titanic_df$Freq), ]
sum(titanic_full$Survived == "Yes")
## [1] 711