library(tidyverse)

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library(knitr)

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knitr::opts_chunk$set(echo = TRUE)

Menampilkan mtcars dengan rownames sebagai kolom

mtcars %>%
  rownames_to_column(var = "Car") %>%
  kable(caption = "Tabel mtcars dengan rownames sebagai kolom")

Tabel mtcars dengan rownames sebagai kolom
Car	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	2

Rangkuman Statistik

mtcars %>%
  summarise(
    Avg_MPG = mean(mpg),
    Avg_HP = mean(hp),
    Avg_WT = mean(wt)
  ) %>%
  kable(caption = "Rangkuman Statistik mtcars")

Rangkuman Statistik mtcars
Avg_MPG	Avg_HP	Avg_WT
20.09062	146.6875	3.21725

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

glimpse(mtcars)

## Rows: 32
## Columns: 11
## $ mpg  <dbl> 21.0, 21.0, 22.8, 21.4, 18.7, 18.1, 14.3, 24.4, 22.8, 19.2, 17.8,…
## $ cyl  <dbl> 6, 6, 4, 6, 8, 6, 8, 4, 4, 6, 6, 8, 8, 8, 8, 8, 8, 4, 4, 4, 4, 8,…
## $ disp <dbl> 160.0, 160.0, 108.0, 258.0, 360.0, 225.0, 360.0, 146.7, 140.8, 16…
## $ hp   <dbl> 110, 110, 93, 110, 175, 105, 245, 62, 95, 123, 123, 180, 180, 180…
## $ drat <dbl> 3.90, 3.90, 3.85, 3.08, 3.15, 2.76, 3.21, 3.69, 3.92, 3.92, 3.92,…
## $ wt   <dbl> 2.620, 2.875, 2.320, 3.215, 3.440, 3.460, 3.570, 3.190, 3.150, 3.…
## $ qsec <dbl> 16.46, 17.02, 18.61, 19.44, 17.02, 20.22, 15.84, 20.00, 22.90, 18…
## $ vs   <dbl> 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0,…
## $ am   <dbl> 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0,…
## $ gear <dbl> 4, 4, 4, 3, 3, 3, 3, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 4, 4, 4, 3, 3,…
## $ carb <dbl> 4, 4, 1, 1, 2, 1, 4, 2, 2, 4, 4, 3, 3, 3, 4, 4, 4, 1, 2, 1, 1, 2,…

SUMMARISE

Menghitung jumlah kemunculan tiap kategori jumlah silinder

cyl_count <- mtcars %>%
  group_by(cyl) %>%            
  summarise(count = n()) %>%   
  arrange(desc(count))   
cyl_count

## # A tibble: 3 × 2
##     cyl count
##   <dbl> <int>
## 1     8    14
## 2     4    11
## 3     6     7

ARRANGE

Mengurutkan berdasarkan jumlah kemunculan (count) secara menurun

count_arrange <- cyl_count %>%
  arrange(desc(count))
count_arrange

## # A tibble: 3 × 2
##     cyl count
##   <dbl> <int>
## 1     8    14
## 2     4    11
## 3     6     7

Mengurutkan data berdasarkan horsepower (hp) secara menurun

arranged_mtcars <- mtcars %>% arrange(desc(hp))
arranged_mtcars

##                      mpg cyl  disp  hp drat    wt  qsec vs am gear carb
## Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
## Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
## Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
## Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
## Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
## Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
## Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    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
## Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
## Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
## Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
## 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
## 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
## Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
## 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
## Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
## Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2
## Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
## Toyota Corona       21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
## Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
## Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
## Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
## Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
## Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
## Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
## Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
## Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2

FILTER

Memilih mobil dengan mpg lebih dari 25

filtered_mtcars <- mtcars %>% filter(mpg > 25)
filtered_mtcars

##                 mpg cyl  disp  hp drat    wt  qsec vs am gear carb
## 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
## 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

MUTATE

Membuat kolom baru yang merupakan rasio hp terhadap berat mobil (hp/wt)

mutated_mtcars <- mtcars %>% mutate(hp_per_wt = hp / wt)
mutated_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    2
##                     hp_per_wt
## Mazda RX4            41.98473
## Mazda RX4 Wag        38.26087
## Datsun 710           40.08621
## Hornet 4 Drive       34.21462
## Hornet Sportabout    50.87209
## Valiant              30.34682
## Duster 360           68.62745
## Merc 240D            19.43574
## Merc 230             30.15873
## Merc 280             35.75581
## Merc 280C            35.75581
## Merc 450SE           44.22604
## Merc 450SL           48.25737
## Merc 450SLC          47.61905
## Cadillac Fleetwood   39.04762
## Lincoln Continental  39.63864
## Chrysler Imperial    43.03087
## Fiat 128             30.00000
## Honda Civic          32.19814
## Toyota Corolla       35.42234
## Toyota Corona        39.35091
## Dodge Challenger     42.61364
## AMC Javelin          43.66812
## Camaro Z28           63.80208
## Pontiac Firebird     45.51365
## Fiat X1-9            34.10853
## Porsche 914-2        42.52336
## Lotus Europa         74.68605
## Ford Pantera L       83.28076
## Ferrari Dino         63.17690
## Maserati Bora        93.83754
## Volvo 142E           39.20863

SELECT

Memilih kolom mpg, hp, dan wt saja

selected_mtcars <- mtcars %>% select(mpg, hp, wt)
selected_mtcars

##                      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
## Duster 360          14.3 245 3.570
## Merc 240D           24.4  62 3.190
## Merc 230            22.8  95 3.150
## Merc 280            19.2 123 3.440
## Merc 280C           17.8 123 3.440
## Merc 450SE          16.4 180 4.070
## Merc 450SL          17.3 180 3.730
## Merc 450SLC         15.2 180 3.780
## Cadillac Fleetwood  10.4 205 5.250
## Lincoln Continental 10.4 215 5.424
## Chrysler Imperial   14.7 230 5.345
## Fiat 128            32.4  66 2.200
## Honda Civic         30.4  52 1.615
## Toyota Corolla      33.9  65 1.835
## Toyota Corona       21.5  97 2.465
## Dodge Challenger    15.5 150 3.520
## AMC Javelin         15.2 150 3.435
## Camaro Z28          13.3 245 3.840
## Pontiac Firebird    19.2 175 3.845
## Fiat X1-9           27.3  66 1.935
## Porsche 914-2       26.0  91 2.140
## Lotus Europa        30.4 113 1.513
## Ford Pantera L      15.8 264 3.170
## Ferrari Dino        19.7 175 2.770
## Maserati Bora       15.0 335 3.570
## Volvo 142E          21.4 109 2.780

2 FUNGSI

Memilih mobil dengan mpg > 20 dan mengurutkan berdasarkan hp secara menurun

result <- mtcars %>%
  filter(mpg > 20) %>% arrange(desc(hp))
result

##                 mpg cyl  disp  hp drat    wt  qsec vs am gear carb
## Lotus Europa   30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
## 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
## Hornet 4 Drive 21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
## Volvo 142E     21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2
## Toyota Corona  21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
## Merc 230       22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
## Datsun 710     22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
## Porsche 914-2  26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
## Fiat 128       32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
## Fiat X1-9      27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
## Toyota Corolla 33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
## Merc 240D      24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
## Honda Civic    30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2

Membuat kolom hp/wt, memilih mobil dengan hp/wt > 60, dan mengurutkan berdasarkan mpg secara menaik

result <- mtcars %>%
  mutate(hp_per_wt = hp / wt) %>%
  filter(hp_per_wt > 60) %>%
  arrange(mpg)
result

##                 mpg cyl  disp  hp drat    wt  qsec vs am gear carb hp_per_wt
## Camaro Z28     13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4  63.80208
## Duster 360     14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4  68.62745
## Maserati Bora  15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8  93.83754
## Ford Pantera L 15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4  83.28076
## Ferrari Dino   19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6  63.17690
## Lotus Europa   30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2  74.68605

Tugas Mandarel SESI UAS

Nika Restu Widya Mujianasta G1401231095

2024-12-06

Menampilkan mtcars dengan rownames sebagai kolom

Rangkuman Statistik

SUMMARISE

Menghitung jumlah kemunculan tiap kategori jumlah silinder

ARRANGE

Mengurutkan berdasarkan jumlah kemunculan (count) secara menurun

Mengurutkan data berdasarkan horsepower (hp) secara menurun

FILTER

Memilih mobil dengan mpg lebih dari 25

MUTATE

Membuat kolom baru yang merupakan rasio hp terhadap berat mobil (hp/wt)

SELECT

Memilih kolom mpg, hp, dan wt saja

2 FUNGSI

Memilih mobil dengan mpg > 20 dan mengurutkan berdasarkan hp secara menurun

Membuat kolom hp/wt, memilih mobil dengan hp/wt > 60, dan mengurutkan berdasarkan mpg secara menaik