options(repos = c(CRAN = "https://cran.rstudio.com"))
install.packages("dplyr")
## package 'dplyr' successfully unpacked and MD5 sums checked
##
## The downloaded binary packages are in
## C:\Users\User\AppData\Local\Temp\RtmpsfCzxd\downloaded_packages
install.packages("ggplot2")
## package 'ggplot2' successfully unpacked and MD5 sums checked
##
## The downloaded binary packages are in
## C:\Users\User\AppData\Local\Temp\RtmpsfCzxd\downloaded_packages
library(dplyr)
## Warning: package 'dplyr' was built under R version 4.4.2
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 4.4.2
# Memuat data mtcars
data("mtcars")
# 1. Cek data awal
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
# 2. Meringkas data (Rata-rata mpg dan hp)
ringkasan <- mtcars %>%
summarise(
Avg_MPG = mean(mpg), # Rata-rata miles per gallon
Avg_HP = mean(hp) # Rata-rata horsepower
)
print(ringkasan)
## Avg_MPG Avg_HP
## 1 20.09062 146.6875
# 3. Mengurutkan data berdasarkan mpg
data_urut <- mtcars %>%
arrange(desc(mpg))
head(data_urut)
## mpg cyl disp hp drat wt qsec vs am gear carb
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
# 4. Menyaring data dengan mpg lebih dari 20
data_filter <- mtcars %>%
filter(mpg > 20)
head(data_filter)
## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
# 5.konversi horsepower ke kilowatt
mtcars_with_kw <- mtcars %>%
mutate(hp_kw = hp * 0.7457)
head(mtcars_with_kw)
## mpg cyl disp hp drat wt qsec vs am gear carb hp_kw
## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 82.0270
## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 82.0270
## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 69.3501
## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 82.0270
## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 130.4975
## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1 78.2985
# 6. Memilih kolom mpg, hp, dan wt
data_terpilih <- mtcars %>%
select(mpg, hp, wt)
head(data_terpilih)
## mpg hp wt
## Mazda RX4 21.0 110 2.620
## Mazda RX4 Wag 21.0 110 2.875
## Datsun 710 22.8 93 2.320
## Hornet 4 Drive 21.4 110 3.215
## Hornet Sportabout 18.7 175 3.440
## Valiant 18.1 105 3.460
# 7. mengurutkan berdasarkan mpg
data_combined <- mtcars %>%
select(mpg, hp, wt) %>%
filter(mpg > 20) %>%
arrange(desc(mpg))
head(data_combined)
## mpg hp wt
## Toyota Corolla 33.9 65 1.835
## Fiat 128 32.4 66 2.200
## Honda Civic 30.4 52 1.615
## Lotus Europa 30.4 113 1.513
## Fiat X1-9 27.3 66 1.935
## Porsche 914-2 26.0 91 2.140
# 8. Membuat scatterplot antara mpg dan hp
plot(mtcars$mpg, mtcars$hp,
xlab = "Miles per Gallon (mpg)",
ylab = "Horsepower (hp)",
main = "Scatterplot MPG vs Horsepower",
pch = 19, col = "blue")
