Libraries
library(tidyverse)## Warning: package 'tidyverse' was built under R version 4.4.2## Warning: package 'ggplot2' was built under R version 4.4.2## Warning: package 'tibble' was built under R version 4.4.2## Warning: package 'tidyr' was built under R version 4.4.2## Warning: package 'readr' was built under R version 4.4.2## Warning: package 'purrr' was built under R version 4.4.2## Warning: package 'dplyr' was built under R version 4.4.2## Warning: package 'stringr' was built under R version 4.4.2## Warning: package 'forcats' was built under R version 4.4.2## Warning: package 'lubridate' was built under R version 4.4.2## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(dplyr)
library(datasets)Dataset Exploration
Dataset yang digunakan merupakan bawaan dari RStudio, yaitu “mtcars”
mtcars## 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
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## 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
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## 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
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 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
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2colnames(mtcars)## [1] "mpg" "cyl" "disp" "hp" "drat" "wt" "qsec" "vs" "am" "gear"
## [11] "carb"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 ...Tipe data pada seluruh kolom adalah number
any(is.na(mtcars))## [1] FALSEDataset ini tidak memiliki value yang kosong
mtcars_new <- mtcars %>% rownames_to_column(var = "nama_mobil")
mtcars_new## nama_mobil mpg cyl disp hp drat wt qsec vs am gear carb
## 1 Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## 2 Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## 3 Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## 4 Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## 5 Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## 6 Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## 7 Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## 8 Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## 9 Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## 10 Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## 11 Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## 12 Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## 13 Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## 14 Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## 15 Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## 16 Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## 17 Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## 18 Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## 19 Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## 20 Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## 21 Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## 22 Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## 23 AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## 24 Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## 25 Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## 26 Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## 27 Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## 28 Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## 29 Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## 30 Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## 31 Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## 32 Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2Karena sebelumnya nama mobil terdapat pada baris yang belum terdapat nama kolom, saya menambahnya agar menjadi sebuah kolom baru bernama nama_mobil
Dataset Analysis
Menggunakan function Summarise
avg_bensin <- mtcars_new %>% summarise(avg_mpg = mean(mpg, na.rm = TRUE))
avg_bensin## avg_mpg
## 1 20.09062Hasil rata-rata dari konsumsi bensin per mile adalah 20.09 dengan mengabaikan value yang kosong (Jika ada)
Menggunakan function Arrange dan Select
urutan_hp <- mtcars_new %>% arrange(desc(hp)) %>%select(nama_mobil, hp)
urutan_hp## nama_mobil hp
## 1 Maserati Bora 335
## 2 Ford Pantera L 264
## 3 Duster 360 245
## 4 Camaro Z28 245
## 5 Chrysler Imperial 230
## 6 Lincoln Continental 215
## 7 Cadillac Fleetwood 205
## 8 Merc 450SE 180
## 9 Merc 450SL 180
## 10 Merc 450SLC 180
## 11 Hornet Sportabout 175
## 12 Pontiac Firebird 175
## 13 Ferrari Dino 175
## 14 Dodge Challenger 150
## 15 AMC Javelin 150
## 16 Merc 280 123
## 17 Merc 280C 123
## 18 Lotus Europa 113
## 19 Mazda RX4 110
## 20 Mazda RX4 Wag 110
## 21 Hornet 4 Drive 110
## 22 Volvo 142E 109
## 23 Valiant 105
## 24 Toyota Corona 97
## 25 Merc 230 95
## 26 Datsun 710 93
## 27 Porsche 914-2 91
## 28 Fiat 128 66
## 29 Fiat X1-9 66
## 30 Toyota Corolla 65
## 31 Merc 240D 62
## 32 Honda Civic 52Mengurutkan nama mobil berdasarkan horsepower dari yang terbesar hingga terkecil
Menggunakan function Filter dan Select
mobil_4cylynder <- mtcars_new %>% filter(cyl == 4) %>% select(nama_mobil, cyl)
mobil_4cylynder## nama_mobil cyl
## 1 Datsun 710 4
## 2 Merc 240D 4
## 3 Merc 230 4
## 4 Fiat 128 4
## 5 Honda Civic 4
## 6 Toyota Corolla 4
## 7 Toyota Corona 4
## 8 Fiat X1-9 4
## 9 Porsche 914-2 4
## 10 Lotus Europa 4
## 11 Volvo 142E 4Memfilter agar mobil dengan 4 cylynder saja yang muncul
Menggunakan function Select
select_mtcars <- mtcars_new %>% select(nama_mobil, mpg, hp, wt)
select_mtcars## nama_mobil mpg hp wt
## 1 Mazda RX4 21.0 110 2.620
## 2 Mazda RX4 Wag 21.0 110 2.875
## 3 Datsun 710 22.8 93 2.320
## 4 Hornet 4 Drive 21.4 110 3.215
## 5 Hornet Sportabout 18.7 175 3.440
## 6 Valiant 18.1 105 3.460
## 7 Duster 360 14.3 245 3.570
## 8 Merc 240D 24.4 62 3.190
## 9 Merc 230 22.8 95 3.150
## 10 Merc 280 19.2 123 3.440
## 11 Merc 280C 17.8 123 3.440
## 12 Merc 450SE 16.4 180 4.070
## 13 Merc 450SL 17.3 180 3.730
## 14 Merc 450SLC 15.2 180 3.780
## 15 Cadillac Fleetwood 10.4 205 5.250
## 16 Lincoln Continental 10.4 215 5.424
## 17 Chrysler Imperial 14.7 230 5.345
## 18 Fiat 128 32.4 66 2.200
## 19 Honda Civic 30.4 52 1.615
## 20 Toyota Corolla 33.9 65 1.835
## 21 Toyota Corona 21.5 97 2.465
## 22 Dodge Challenger 15.5 150 3.520
## 23 AMC Javelin 15.2 150 3.435
## 24 Camaro Z28 13.3 245 3.840
## 25 Pontiac Firebird 19.2 175 3.845
## 26 Fiat X1-9 27.3 66 1.935
## 27 Porsche 914-2 26.0 91 2.140
## 28 Lotus Europa 30.4 113 1.513
## 29 Ford Pantera L 15.8 264 3.170
## 30 Ferrari Dino 19.7 175 2.770
## 31 Maserati Bora 15.0 335 3.570
## 32 Volvo 142E 21.4 109 2.780Melihat nama mobil dengan setiap mpg, hp, dan wt nya
##Menggunakan function Mutate
efisiensi_bensin <- mtcars_new %>% mutate(efisiensi = ifelse(mpg > 20, "Efisien", "Tidak Efisien")) %>% select(nama_mobil, mpg, efisiensi)
efisiensi_bensin## nama_mobil mpg efisiensi
## 1 Mazda RX4 21.0 Efisien
## 2 Mazda RX4 Wag 21.0 Efisien
## 3 Datsun 710 22.8 Efisien
## 4 Hornet 4 Drive 21.4 Efisien
## 5 Hornet Sportabout 18.7 Tidak Efisien
## 6 Valiant 18.1 Tidak Efisien
## 7 Duster 360 14.3 Tidak Efisien
## 8 Merc 240D 24.4 Efisien
## 9 Merc 230 22.8 Efisien
## 10 Merc 280 19.2 Tidak Efisien
## 11 Merc 280C 17.8 Tidak Efisien
## 12 Merc 450SE 16.4 Tidak Efisien
## 13 Merc 450SL 17.3 Tidak Efisien
## 14 Merc 450SLC 15.2 Tidak Efisien
## 15 Cadillac Fleetwood 10.4 Tidak Efisien
## 16 Lincoln Continental 10.4 Tidak Efisien
## 17 Chrysler Imperial 14.7 Tidak Efisien
## 18 Fiat 128 32.4 Efisien
## 19 Honda Civic 30.4 Efisien
## 20 Toyota Corolla 33.9 Efisien
## 21 Toyota Corona 21.5 Efisien
## 22 Dodge Challenger 15.5 Tidak Efisien
## 23 AMC Javelin 15.2 Tidak Efisien
## 24 Camaro Z28 13.3 Tidak Efisien
## 25 Pontiac Firebird 19.2 Tidak Efisien
## 26 Fiat X1-9 27.3 Efisien
## 27 Porsche 914-2 26.0 Efisien
## 28 Lotus Europa 30.4 Efisien
## 29 Ford Pantera L 15.8 Tidak Efisien
## 30 Ferrari Dino 19.7 Tidak Efisien
## 31 Maserati Bora 15.0 Tidak Efisien
## 32 Volvo 142E 21.4 Efisien