summary(cars)
## speed dist
## Min. : 4.0 Min. : 2.00
## 1st Qu.:12.0 1st Qu.: 26.00
## Median :15.0 Median : 36.00
## Mean :15.4 Mean : 42.98
## 3rd Qu.:19.0 3rd Qu.: 56.00
## Max. :25.0 Max. :120.00
data(mtcars)
mean(mtcars$mpg)
## [1] 20.09062
median(mtcars$mpg)
## [1] 19.2
sd(mtcars$mpg)
## [1] 6.026948
# Memuat library ggplot2
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 4.3.3
# Membuat boxplot mpg berdasarkan cyl
ggplot(mtcars, aes(x = factor(cyl), y = mpg)) +
geom_boxplot(fill = "lightblue", color = "black") +
labs(
title = "Boxplot MPG berdasarkan Cylinders",
x = "Jumlah Silinder (cyl)",
y = "Miles Per Gallon (mpg)"
) +
theme_minimal()
# Membuat histogram dengan garis densitas
library(ggplot2)
ggplot(mtcars, aes(x = hp)) +
geom_histogram(aes(y = ..density..), bins = 10, fill = "lightblue", color = "black") +
geom_density(color = "red", size = 1) +
labs(
title = "Histogram dan Densitas Horsepower (hp)",
x = "Horsepower (hp)",
y = "Density"
) +
theme_minimal()
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: The dot-dot notation (`..density..`) was deprecated in ggplot2 3.4.0.
## ℹ Please use `after_stat(density)` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
# Memuat dataset bawaan iris
data(iris)
# Uji ANOVA
anova_result <- aov(Sepal.Length ~ Species, data = iris)
summary(anova_result)
## Df Sum Sq Mean Sq F value Pr(>F)
## Species 2 63.21 31.606 119.3 <2e-16 ***
## Residuals 147 38.96 0.265
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Filter data untuk spesies setosa dan versicolor
setosa <- iris$Petal.Length[iris$Species == "setosa"]
versicolor <- iris$Petal.Length[iris$Species == "versicolor"]
# Uji t-test
t_test_result <- t.test(setosa, versicolor, var.equal = TRUE)
t_test_result
##
## Two Sample t-test
##
## data: setosa and versicolor
## t = -39.493, df = 98, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -2.938597 -2.657403
## sample estimates:
## mean of x mean of y
## 1.462 4.260
# Membuat model regresi linear
model <- lm(mpg ~ wt, data = mtcars)
summary(model)
##
## Call:
## lm(formula = mpg ~ wt, data = mtcars)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.5432 -2.3647 -0.1252 1.4096 6.8727
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 37.2851 1.8776 19.858 < 2e-16 ***
## wt -5.3445 0.5591 -9.559 1.29e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.046 on 30 degrees of freedom
## Multiple R-squared: 0.7528, Adjusted R-squared: 0.7446
## F-statistic: 91.38 on 1 and 30 DF, p-value: 1.294e-10
# Scatter plot dengan garis regresi
ggplot(mtcars, aes(x = wt, y = mpg)) +
geom_point(color = "blue") +
geom_smooth(method = "lm", color = "red", se = FALSE) +
labs(
title = "Scatter Plot MPG vs WT dengan Garis Regresi",
x = "Berat Mobil (wt)",
y = "Miles Per Gallon (mpg)"
) +
theme_minimal()
## `geom_smooth()` using formula = 'y ~ x'
