The “mtcars” dataset in R is a built-in dataset that contains information about various car models. This dataset is often used for learning and practicing data analysis and statistics in R.
#spotify dataset
data=read.csv("C://Users//AISHWARYA//OneDrive//ドキュメント//SPOTIFYdv.csv")
data## track_name
## 1 Seven (feat. Latto) (Explicit Ver.)
## 2 LALA
## 3 vampire
## 4 Cruel Summer
## 5 WHERE SHE GOES
## 6 Sprinter
## 7 Ella Baila Sola
## 8 Columbia
## 9 fukumean
## 10 La Bebe - Remix
## 11 un x100to
## 12 Super Shy
## 13 Flowers
## 14 Daylight
## 15 As It Was
## 16 Kill Bill
## 17 Cupid - Twin Ver.
## 18 What Was I Made For? [From The Motion Picture "Barbie"]
## 19 Classy 101
## 20 Like Crazy
## 21 LADY GAGA
## 22 I Can See You (Taylor���s Version) (From The
## 23 I Wanna Be Yours
## 24 Peso Pluma: Bzrp Music Sessions, Vol. 55
## 25 Popular (with Playboi Carti & Madonna) - The Idol Vol. 1 (Music from the HBO Original Series)
## 26 SABOR FRESA
## 27 Calm Down (with Selena Gomez)
## 28 MOJABI GHOST
## 29 Last Night
## 30 Dance The Night (From Barbie The Album)
## 31 Rush
## 32 TULUM
## 33 Creepin'
## 34 Anti-Hero
## 35 TQG
## 36 Los del Espacio
## 37 Fr��gil (feat. Grupo Front
## 38 Blank Space
## 39 Style
## 40 TQM
## 41 El Azul
## 42 Sunflower - Spider-Man: Into the Spider-Verse
## 43 I'm Good (Blue)
## 44 See You Again
## 45 Barbie World (with Aqua) [From Barbie The Album]
## 46 Angels Like You
## 47 I Ain't Worried
## 48 Die For You
## 49 Starboy
## 50 Die For You - Remix
## 51 El Cielo
## 52 Baby Don't Hurt Me
## 53 AMARGURA
## 54 (It Goes Like) Nanana - Edit
## 55 Another Love
## 56 Blinding Lights
## 57 Moonlight
## 58 La Bachata
## 59 S91
## 60 cardigan
## 61 T�ï¿
## 62 Boy's a liar Pt. 2
## 63 Left and Right (Feat. Jung Kook of BTS)
## 64 BESO
## 65 Hey Mor
## 66 Yellow
## 67 Karma
## 68 People
## 69 Overdrive
## 70 Enchanted (Taylor's Version)
## 71 BABY HELLO
## 72 Heat Waves
## 73 golden hour
## 74 Sweater Weather
## 75 Quevedo: Bzrp Music Sessions, Vol. 52
## 76 Viva La Vida
## 77 Here With Me
## 78 Unholy (feat. Kim Petras)
## 79 Yandel 150
## 80 CORAZ��N VA
## 81 Riptide
## 82 Until I Found You (with Em Beihold) - Em Beihold Version
## 83 Novidade na �ï¿
## 84 Back To December (Taylor's Version)
## 85 STAY (with Justin Bieber)
## 86 El Merengue
## 87 Someone You Loved
## 88 Me Porto Bonito
## 89 Makeba
## 90 MONTAGEM - FR PUNK
## 91 Fast Car
## 92 What It Is (Solo Version)
## 93 Coco Chanel
## 94 Don���t Bl
## 95 Still With You
## 96 All My Life (feat. J. Cole)
## 97 Say Yes To Heaven
## 98 Snooze
## 99 Summertime Sadness
## artist.s._name
## 1 Latto, Jung Kook
## 2 Myke Towers
## 3 Olivia Rodrigo
## 4 Taylor Swift
## 5 Bad Bunny
## 6 Dave, Central Cee
## 7 Eslabon Armado, Peso Pluma
## 8 Quevedo
## 9 Gunna
## 10 Peso Pluma, Yng Lvcas
## 11 Bad Bunny, Grupo Frontera
## 12 NewJeans
## 13 Miley Cyrus
## 14 David Kushner
## 15 Harry Styles
## 16 SZA
## 17 Fifty Fifty
## 18 Billie Eilish
## 19 Feid, Young Miko
## 20 Jimin
## 21 Gabito Ballesteros, Junior H, Peso Pluma
## 22 Taylor Swift
## 23 Arctic Monkeys
## 24 Bizarrap, Peso Pluma
## 25 The Weeknd, Madonna, Playboi Carti
## 26 Fuerza Regida
## 27 R��ma, Selena G
## 28 Tainy, Bad Bunny
## 29 Morgan Wallen
## 30 Dua Lipa
## 31 Troye Sivan
## 32 Peso Pluma, Grupo Frontera
## 33 The Weeknd, 21 Savage, Metro Boomin
## 34 Taylor Swift
## 35 Karol G, Shakira
## 36 Big One, Duki, Lit Killah, Maria Becerra, FMK, Rusherking, Emilia, Tiago pzk
## 37 Yahritza Y Su Esencia, Grupo Frontera
## 38 Taylor Swift
## 39 Taylor Swift
## 40 Fuerza Regida
## 41 Junior H, Peso Pluma
## 42 Post Malone, Swae Lee
## 43 Bebe Rexha, David Guetta
## 44 Tyler, The Creator, Kali Uchis
## 45 Nicki Minaj, Aqua, Ice Spice
## 46 Miley Cyrus
## 47 OneRepublic
## 48 The Weeknd
## 49 The Weeknd, Daft Punk
## 50 Ariana Grande, The Weeknd
## 51 Feid, Myke Towers, Sky Rompiendo
## 52 David Guetta, Anne-Marie, Coi Leray
## 53 Karol G
## 54 Peggy Gou
## 55 Tom Odell
## 56 The Weeknd
## 57 Kali Uchis
## 58 Manuel Turizo
## 59 Karol G
## 60 Taylor Swift
## 61 dennis, MC Kevin o Chris
## 62 PinkPantheress, Ice Spice
## 63 Charlie Puth, BTS, Jung Kook
## 64 Rauw Alejandro, ROSAL�
## 65 Ozuna, Feid
## 66 Chris Molitor
## 67 Taylor Swift
## 68 Libianca
## 69 Post Malone
## 70 Taylor Swift
## 71 Rauw Alejandro, Bizarrap
## 72 Glass Animals
## 73 JVKE
## 74 The Neighbourhood
## 75 Bizarrap, Quevedo
## 76 Coldplay
## 77 d4vd
## 78 Sam Smith, Kim Petras
## 79 Yandel, Feid
## 80 Maria Becerra
## 81 Vance Joy
## 82 Em Beihold, Stephen Sanchez
## 83 Mc Livinho, DJ Matt D
## 84 Taylor Swift
## 85 Justin Bieber, The Kid Laroi
## 86 Marshmello, Manuel Turizo
## 87 Lewis Capaldi
## 88 Chencho Corleone, Bad Bunny
## 89 Jain
## 90 Ayparia, unxbected
## 91 Luke Combs
## 92 Doechii
## 93 Bad Bunny, Eladio Carrion
## 94 Taylor Swift
## 95 Jung Kook
## 96 J. Cole, Lil Durk
## 97 Lana Del Rey
## 98 SZA
## 99 Lana Del Rey
## artist_count released_year released_month released_day in_spotify_playlists
## 1 2 2023 7 14 553
## 2 1 2023 3 23 1474
## 3 1 2023 6 30 1397
## 4 1 2019 8 23 7858
## 5 1 2023 5 18 3133
## 6 2 2023 6 1 2186
## 7 2 2023 3 16 3090
## 8 1 2023 7 7 714
## 9 1 2023 5 15 1096
## 10 2 2023 3 17 2953
## 11 2 2023 4 17 2876
## 12 1 2023 7 7 422
## 13 1 2023 1 12 12211
## 14 1 2023 4 14 3528
## 15 1 2022 3 31 23575
## 16 1 2022 12 8 8109
## 17 1 2023 2 24 2942
## 18 1 2023 7 13 873
## 19 2 2023 3 31 2610
## 20 1 2023 3 24 596
## 21 3 2023 6 22 332
## 22 1 2023 7 7 516
## 23 1 2013 1 1 12859
## 24 2 2023 5 31 1313
## 25 3 2023 6 2 1945
## 26 1 2023 6 22 250
## 27 2 2022 3 25 7112
## 28 2 2023 6 29 859
## 29 1 2023 1 31 2420
## 30 1 2023 5 25 2988
## 31 1 2023 7 13 864
## 32 2 2023 6 28 266
## 33 3 2022 12 2 6036
## 34 1 2022 10 21 9082
## 35 2 2023 2 23 4284
## 36 8 2023 6 1 1150
## 37 2 2023 4 7 672
## 38 1 2014 1 1 11434
## 39 1 2014 1 1 7830
## 40 1 2023 5 19 584
## 41 2 2023 2 10 692
## 42 2 2018 10 9 24094
## 43 2 2022 8 26 12482
## 44 3 2017 7 21 13387
## 45 3 2023 6 23 1117
## 46 1 2020 11 27 3372
## 47 1 2022 5 13 8431
## 48 1 2016 11 24 2483
## 49 2 2016 9 21 29536
## 50 2 2023 2 24 3408
## 51 3 2023 6 2 1298
## 52 3 2023 4 6 4277
## 53 1 2023 2 24 1133
## 54 1 2023 6 15 2259
## 55 1 2012 10 15 18371
## 56 1 2019 11 29 43899
## 57 1 2023 2 24 2649
## 58 1 2022 5 26 6804
## 59 1 2023 7 14 525
## 60 1 2020 7 24 7923
## 61 2 2023 5 4 731
## 62 2 2023 2 3 5184
## 63 3 2022 6 24 3107
## 64 2 2023 3 24 4053
## 65 2 2022 10 6 4637
## 66 1 1999 1 1 31358
## 67 1 2022 10 21 3818
## 68 1 2022 12 2 3506
## 69 1 2023 7 14 410
## 70 1 2023 7 7 148
## 71 2 2023 6 23 1004
## 72 1 2020 6 28 22543
## 73 1 2022 7 15 4511
## 74 1 2012 5 14 16413
## 75 2 2022 7 6 8506
## 76 1 2008 1 1 33898
## 77 1 2022 7 17 3246
## 78 2 2022 9 22 8576
## 79 2 2022 12 20 3618
## 80 1 2023 6 22 370
## 81 1 1975 1 1 31123
## 82 2 2022 4 22 2790
## 83 2 2023 6 23 267
## 84 1 2023 7 7 139
## 85 2 2021 7 9 17050
## 86 2 2023 3 3 2114
## 87 1 2018 11 8 17836
## 88 2 2022 5 6 8870
## 89 1 2015 6 22 6060
## 90 2 2012 6 20 641
## 91 1 2023 3 24 1446
## 92 1 2023 3 17 804
## 93 2 2023 3 17 1962
## 94 1 2017 11 8 4875
## 95 1 2020 6 5 31
## 96 2 2023 5 12 2175
## 97 1 2023 3 17 2000
## 98 1 2022 12 9 2839
## 99 1 2011 1 1 20333
## in_spotify_charts streams in_apple_playlists in_apple_charts
## 1 147 141381703 43 263
## 2 48 133716286 48 126
## 3 113 140003974 94 207
## 4 100 800840817 116 207
## 5 50 303236322 84 133
## 6 91 183706234 67 213
## 7 50 725980112 34 222
## 8 43 58149378 25 89
## 9 83 95217315 60 210
## 10 44 553634067 49 110
## 11 40 505671438 41 205
## 12 55 58255150 37 202
## 13 115 1316855716 300 215
## 14 98 387570742 80 156
## 15 130 2513188493 403 198
## 16 77 1163093654 183 162
## 17 77 496795686 91 212
## 18 104 30546883 80 227
## 19 40 335222234 43 100
## 20 68 363369738 8 104
## 21 26 86444842 11 163
## 22 38 52135248 73 119
## 23 110 1297026226 24 98
## 24 40 200647221 17 152
## 25 87 115364561 74 182
## 26 26 78300654 16 149
## 27 77 899183384 202 119
## 28 40 61245289 35 109
## 29 19 429829812 52 107
## 30 101 127408954 0 0
## 31 78 22581161 71 135
## 32 34 52294266 20 185
## 33 88 843957510 113 149
## 34 56 999748277 242 142
## 35 49 618990393 115 123
## 36 31 123122413 22 33
## 37 34 188933502 19 108
## 38 53 1355959075 154 123
## 39 42 786181836 94 111
## 40 28 176553476 16 159
## 41 25 354495408 10 107
## 42 78 2808096550 372 117
## 43 80 1109433169 291 184
## 44 64 1047101291 77 58
## 45 80 65156199 82 145
## 46 19 570515054 65 48
## 47 76 1085685420 241 127
## 48 59 1647990401 68 21
## 49 79 2565529693 281 137
## 50 47 518745108 87 86
## 51 38 107753850 44 64
## 52 66 177740666 145 111
## 53 39 153372011 14 71
## 54 59 57876440 0 0
## 55 83 1813673666 250 122
## 56 69 3703895074 672 199
## 57 42 256483385 67 79
## 58 45 1214083358 139 111
## 59 41 16011326 34 115
## 60 29 812019557 106 112
## 61 15 111947664 27 17
## 62 41 156338624 154 84
## 63 39 720434240 38 0
## 64 50 357925728 82 121
## 65 38 674072710 63 79
## 66 43 1755214421 196 2
## 67 23 404562836 37 55
## 68 56 373199958 105 64
## 69 36 14780425 36 32
## 70 24 39578178 32 93
## 71 35 54266102 42 80
## 72 63 2557975762 386 144
## 73 36 751134527 70 58
## 74 61 2282771485 166 87
## 75 45 1356565093 94 65
## 76 62 1592909789 233 0
## 77 23 635412045 94 85
## 78 42 1230675890 216 108
## 79 38 585695368 47 74
## 80 20 43857627 12 16
## 81 55 2009094673 300 65
## 82 30 600976848 60 96
## 83 9 39709092 9 6
## 84 17 39228929 16 72
## 85 36 2665343922 492 99
## 86 44 223633238 80 75
## 87 53 2887241814 440 125
## 88 43 1440757818 104 120
## 89 53 165484133 150 148
## 90 50 58054811 1 52
## 91 12 157058870 57 97
## 92 25 95131998 29 76
## 93 38 250305248 28 89
## 94 23 685032533 19 45
## 95 39 38411956 2 107
## 96 23 144565150 69 145
## 97 46 127567540 49 105
## 98 25 399686758 58 156
## 99 52 983637508 89 143
## in_deezer_playlists in_deezer_charts in_shazam_charts bpm key mode
## 1 45 10 826 125 B Major
## 2 58 14 382 92 C# Major
## 3 91 14 949 138 F Major
## 4 125 12 548 170 A Major
## 5 87 15 425 144 A Minor
## 6 88 17 946 141 C# Major
## 7 43 13 418 148 F Minor
## 8 30 13 194 100 F Major
## 9 48 11 953 130 C# Minor
## 10 66 13 339 170 D Minor
## 11 54 12 251 83 F# Minor
## 12 21 5 168 150 F Minor
## 13 745 58 1,021 118 Major
## 14 182 24 1,281 130 D Minor
## 15 863 46 174 F# Minor
## 16 161 12 187 89 G# Major
## 17 78 6 0 120 B Minor
## 18 95 24 1,173 78 Major
## 19 54 14 187 100 B Major
## 20 23 2 29 120 G Major
## 21 10 4 0 140 F Minor
## 22 42 1 150 123 F# Major
## 23 582 2 73 135 Minor
## 24 32 11 139 133 F Minor
## 25 87 14 1,093 99 C# Major
## 26 10 5 168 130 G Minor
## 27 318 38 96 107 B Major
## 28 41 14 211 122 F# Minor
## 29 15 1 325 204 F# Major
## 30 143 38 0 110 B Minor
## 31 50 1 294 126 F Minor
## 32 13 8 197 168 F# Major
## 33 245 23 27 98 C# Minor
## 34 165 9 310 97 E Major
## 35 184 18 354 180 E Minor
## 36 34 7 184 120 Major
## 37 24 9 212 150 F# Major
## 38 410 2 81 96 F Major
## 39 151 4 82 95 D Major
## 40 15 6 100 125 F Minor
## 41 6 3 62 144 A Minor
## 42 843 4 69 90 D Major
## 43 537 45 727 128 G Minor
## 44 247 1 311 79 F# Major
## 45 65 16 1,133 144 Major
## 46 138 1 102 122 F Major
## 47 458 37 332 140 Major
## 48 24 0 259 134 C# Minor
## 49 2,445 1 140 186 G Major
## 50 74 1 16 67 C# Minor
## 51 57 10 110 106 A# Minor
## 52 213 11 810 128 G Major
## 53 23 10 176 107 F# Minor
## 54 109 17 0 130 G Minor
## 55 3,394 19 123 E Minor
## 56 3,421 20 171 C# Major
## 57 57 1 615 137 G Minor
## 58 161 15 210 125 G Minor
## 59 39 6 216 128 Minor
## 60 142 4 215 130 Minor
## 61 73 4 167 130 B Major
## 62 102 14 37 133 F Major
## 63 4 0 0 101 D Major
## 64 182 12 171 95 F Minor
## 65 89 11 16 98 C# Minor
## 66 4,053 5 0 173 B Major
## 67 32 0 272 90 G# Major
## 68 169 8 529 198 A# Minor
## 69 31 1 26 140 C# Major
## 70 8 2 5 82 G# Major
## 71 58 3 169 130 C# Minor
## 72 707 28 81 B Major
## 73 109 18 230 94 C# Minor
## 74 1,056 1 124 A# Major
## 75 164 14 176 128 D Major
## 76 4,095 9 0 138 F Minor
## 77 68 1 84 132 E Major
## 78 331 26 154 131 D Major
## 79 80 14 194 168 F# Minor
## 80 18 4 93 98 C# Major
## 81 1,003 1 0 102 C# Major
## 82 71 0 115 101 A# Major
## 83 25 2 72 130 F Major
## 84 5 0 8 142 D Major
## 85 798 31 0 170 C# Major
## 86 110 11 323 124 G# Minor
## 87 1,800 0 110 C# Major
## 88 141 26 49 92 C# Minor
## 89 2,703 22 1,451 116 D Major
## 90 8 0 1,170 129 A Major
## 91 35 0 429 98 G# Major
## 92 24 0 162 172 C# Minor
## 93 29 5 82 150 D Major
## 94 0 0 10 136 A Minor
## 95 8 0 0 88 C# Minor
## 96 69 2 478 143 D# Major
## 97 63 1 0 100 F# Minor
## 98 42 1 236 143 F Major
## 99 1,632 3 200 112 C# Minor‘spotify’ is a sample data.
#a. and b. structure and summary of the data
str(data)#structure## 'data.frame': 99 obs. of 17 variables:
## $ track_name : chr "Seven (feat. Latto) (Explicit Ver.)" "LALA" "vampire" "Cruel Summer" ...
## $ artist.s._name : chr "Latto, Jung Kook" "Myke Towers" "Olivia Rodrigo" "Taylor Swift" ...
## $ artist_count : int 2 1 1 1 1 2 2 1 1 2 ...
## $ released_year : int 2023 2023 2023 2019 2023 2023 2023 2023 2023 2023 ...
## $ released_month : int 7 3 6 8 5 6 3 7 5 3 ...
## $ released_day : int 14 23 30 23 18 1 16 7 15 17 ...
## $ in_spotify_playlists: int 553 1474 1397 7858 3133 2186 3090 714 1096 2953 ...
## $ in_spotify_charts : int 147 48 113 100 50 91 50 43 83 44 ...
## $ streams : num 1.41e+08 1.34e+08 1.40e+08 8.01e+08 3.03e+08 ...
## $ in_apple_playlists : int 43 48 94 116 84 67 34 25 60 49 ...
## $ in_apple_charts : int 263 126 207 207 133 213 222 89 210 110 ...
## $ in_deezer_playlists : chr "45" "58" "91" "125" ...
## $ in_deezer_charts : int 10 14 14 12 15 17 13 13 11 13 ...
## $ in_shazam_charts : chr "826" "382" "949" "548" ...
## $ bpm : int 125 92 138 170 144 141 148 100 130 170 ...
## $ key : chr "B" "C#" "F" "A" ...
## $ mode : chr "Major" "Major" "Major" "Major" ...summary(data)#summary## track_name artist.s._name artist_count released_year
## Length:99 Length:99 Min. :1.000 Min. :1975
## Class :character Class :character 1st Qu.:1.000 1st Qu.:2022
## Mode :character Mode :character Median :1.000 Median :2023
## Mean :1.566 Mean :2021
## 3rd Qu.:2.000 3rd Qu.:2023
## Max. :8.000 Max. :2023
## released_month released_day in_spotify_playlists in_spotify_charts
## Min. : 1.000 Min. : 1.00 Min. : 31 Min. : 9.00
## 1st Qu.: 3.000 1st Qu.: 7.00 1st Qu.: 1050 1st Qu.: 36.00
## Median : 6.000 Median :17.00 Median : 2953 Median : 45.00
## Mean : 5.677 Mean :15.54 Mean : 6324 Mean : 52.64
## 3rd Qu.: 7.000 3rd Qu.:23.00 3rd Qu.: 7890 3rd Qu.: 67.00
## Max. :12.000 Max. :31.00 Max. :43899 Max. :147.00
## streams in_apple_playlists in_apple_charts in_deezer_playlists
## Min. :1.478e+07 Min. : 0.0 Min. : 0.0 Length:99
## 1st Qu.:1.192e+08 1st Qu.: 34.0 1st Qu.: 75.5 Class :character
## Median :3.732e+08 Median : 68.0 Median :110.0 Mode :character
## Mean :6.830e+08 Mean :105.9 Mean :112.4
## 3rd Qu.:9.917e+08 3rd Qu.:115.5 3rd Qu.:146.5
## Max. :3.704e+09 Max. :672.0 Max. :263.0
## in_deezer_charts in_shazam_charts bpm key
## Min. : 0.00 Length:99 Min. : 67.0 Length:99
## 1st Qu.: 1.50 Class :character 1st Qu.:100.5 Class :character
## Median : 8.00 Mode :character Median :128.0 Mode :character
## Mean :10.63 Mean :125.4
## 3rd Qu.:14.00 3rd Qu.:140.0
## Max. :58.00 Max. :204.0
## mode
## Length:99
## Class :character
## Mode :character
##
##
## #conversion functions
library(dplyr)##
## 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# Example 1: Convert a column to a different data type (e.g., from character to numeric)
my_data <- as.numeric(data$artist.s._name)## Warning: NAs introduced by coercionmy_data## [1] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [26] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [51] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [76] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA# Example 2: Convert a column to a factor
my_data <- as.factor(data$artist_count)
my_data## [1] 2 1 1 1 1 2 2 1 1 2 2 1 1 1 1 1 1 1 2 1 3 1 1 2 3 1 2 2 1 1 1 2 3 1 2 8 2 1
## [39] 1 1 2 2 2 3 3 1 1 1 2 2 3 3 1 1 1 1 1 1 1 1 2 2 3 2 2 1 1 1 1 1 2 1 1 1 2 1
## [77] 1 2 2 1 1 2 2 1 2 2 1 2 1 2 1 1 2 1 1 2 1 1 1
## Levels: 1 2 3 8# After performing the desired conversion and transformation operations, you can view the modified data frame:
str(my_data)## Factor w/ 4 levels "1","2","3","8": 2 1 1 1 1 2 2 1 1 2 ...dplyr is an R package for efficient data manipulation.
It offers easy-to-use functions like filtering, sorting, and summarizing
data, following tidy data principles for analysis.
#c.head of the data
head(data)## track_name artist.s._name artist_count
## 1 Seven (feat. Latto) (Explicit Ver.) Latto, Jung Kook 2
## 2 LALA Myke Towers 1
## 3 vampire Olivia Rodrigo 1
## 4 Cruel Summer Taylor Swift 1
## 5 WHERE SHE GOES Bad Bunny 1
## 6 Sprinter Dave, Central Cee 2
## released_year released_month released_day in_spotify_playlists
## 1 2023 7 14 553
## 2 2023 3 23 1474
## 3 2023 6 30 1397
## 4 2019 8 23 7858
## 5 2023 5 18 3133
## 6 2023 6 1 2186
## in_spotify_charts streams in_apple_playlists in_apple_charts
## 1 147 141381703 43 263
## 2 48 133716286 48 126
## 3 113 140003974 94 207
## 4 100 800840817 116 207
## 5 50 303236322 84 133
## 6 91 183706234 67 213
## in_deezer_playlists in_deezer_charts in_shazam_charts bpm key mode
## 1 45 10 826 125 B Major
## 2 58 14 382 92 C# Major
## 3 91 14 949 138 F Major
## 4 125 12 548 170 A Major
## 5 87 15 425 144 A Minor
## 6 88 17 946 141 C# Major# Load the stringr package to use str_detect
library(stringr)
# Print pattern matched data
print("Pattern Matched Data:")## [1] "Pattern Matched Data:"The stringr package is an R package that provides a set
of functions for working with strings, making string manipulation and
pattern matching more convenient and intuitive.
# g) Write data to a CSV file
write.csv(data, file = "data.csv", row.names = FALSE)# h) View the structure and summary of the given data
str(data)## 'data.frame': 99 obs. of 17 variables:
## $ track_name : chr "Seven (feat. Latto) (Explicit Ver.)" "LALA" "vampire" "Cruel Summer" ...
## $ artist.s._name : chr "Latto, Jung Kook" "Myke Towers" "Olivia Rodrigo" "Taylor Swift" ...
## $ artist_count : int 2 1 1 1 1 2 2 1 1 2 ...
## $ released_year : int 2023 2023 2023 2019 2023 2023 2023 2023 2023 2023 ...
## $ released_month : int 7 3 6 8 5 6 3 7 5 3 ...
## $ released_day : int 14 23 30 23 18 1 16 7 15 17 ...
## $ in_spotify_playlists: int 553 1474 1397 7858 3133 2186 3090 714 1096 2953 ...
## $ in_spotify_charts : int 147 48 113 100 50 91 50 43 83 44 ...
## $ streams : num 1.41e+08 1.34e+08 1.40e+08 8.01e+08 3.03e+08 ...
## $ in_apple_playlists : int 43 48 94 116 84 67 34 25 60 49 ...
## $ in_apple_charts : int 263 126 207 207 133 213 222 89 210 110 ...
## $ in_deezer_playlists : chr "45" "58" "91" "125" ...
## $ in_deezer_charts : int 10 14 14 12 15 17 13 13 11 13 ...
## $ in_shazam_charts : chr "826" "382" "949" "548" ...
## $ bpm : int 125 92 138 170 144 141 148 100 130 170 ...
## $ key : chr "B" "C#" "F" "A" ...
## $ mode : chr "Major" "Major" "Major" "Major" ...summary(data)## track_name artist.s._name artist_count released_year
## Length:99 Length:99 Min. :1.000 Min. :1975
## Class :character Class :character 1st Qu.:1.000 1st Qu.:2022
## Mode :character Mode :character Median :1.000 Median :2023
## Mean :1.566 Mean :2021
## 3rd Qu.:2.000 3rd Qu.:2023
## Max. :8.000 Max. :2023
## released_month released_day in_spotify_playlists in_spotify_charts
## Min. : 1.000 Min. : 1.00 Min. : 31 Min. : 9.00
## 1st Qu.: 3.000 1st Qu.: 7.00 1st Qu.: 1050 1st Qu.: 36.00
## Median : 6.000 Median :17.00 Median : 2953 Median : 45.00
## Mean : 5.677 Mean :15.54 Mean : 6324 Mean : 52.64
## 3rd Qu.: 7.000 3rd Qu.:23.00 3rd Qu.: 7890 3rd Qu.: 67.00
## Max. :12.000 Max. :31.00 Max. :43899 Max. :147.00
## streams in_apple_playlists in_apple_charts in_deezer_playlists
## Min. :1.478e+07 Min. : 0.0 Min. : 0.0 Length:99
## 1st Qu.:1.192e+08 1st Qu.: 34.0 1st Qu.: 75.5 Class :character
## Median :3.732e+08 Median : 68.0 Median :110.0 Mode :character
## Mean :6.830e+08 Mean :105.9 Mean :112.4
## 3rd Qu.:9.917e+08 3rd Qu.:115.5 3rd Qu.:146.5
## Max. :3.704e+09 Max. :672.0 Max. :263.0
## in_deezer_charts in_shazam_charts bpm key
## Min. : 0.00 Length:99 Min. : 67.0 Length:99
## 1st Qu.: 1.50 Class :character 1st Qu.:100.5 Class :character
## Median : 8.00 Mode :character Median :128.0 Mode :character
## Mean :10.63 Mean :125.4
## 3rd Qu.:14.00 3rd Qu.:140.0
## Max. :58.00 Max. :204.0
## mode
## Length:99
## Class :character
## Mode :character
##
##
## # i) Convert the 'Total' column to a numeric variable
data$released_month <- as.numeric(data$released_month)
data$released_month## [1] 7 3 6 8 5 6 3 7 5 3 4 7 1 4 3 12 2 7 3 3 6 7 1 5 6
## [26] 6 3 6 1 5 7 6 12 10 2 6 4 1 1 5 2 10 8 7 6 11 5 11 9 2
## [51] 6 4 2 6 10 11 2 5 7 7 5 2 6 3 10 1 10 12 7 7 6 6 7 5 7
## [76] 1 7 9 12 6 1 4 6 7 7 3 11 5 6 6 3 3 3 11 6 5 3 12 1#mtcars dataset
data("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 2summary(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. :8.000str(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 ...is.null(mtcars)## [1] FALSEmtcars is an in-built dataset of rstudio. no null values are there in this dataset.
selecting specific columns using select function:
# Select the mpg and cyl columns
mtcars_select <- select(mtcars, mpg, cyl)
mtcars_select## mpg cyl
## Mazda RX4 21.0 6
## Mazda RX4 Wag 21.0 6
## Datsun 710 22.8 4
## Hornet 4 Drive 21.4 6
## Hornet Sportabout 18.7 8
## Valiant 18.1 6
## Duster 360 14.3 8
## Merc 240D 24.4 4
## Merc 230 22.8 4
## Merc 280 19.2 6
## Merc 280C 17.8 6
## Merc 450SE 16.4 8
## Merc 450SL 17.3 8
## Merc 450SLC 15.2 8
## Cadillac Fleetwood 10.4 8
## Lincoln Continental 10.4 8
## Chrysler Imperial 14.7 8
## Fiat 128 32.4 4
## Honda Civic 30.4 4
## Toyota Corolla 33.9 4
## Toyota Corona 21.5 4
## Dodge Challenger 15.5 8
## AMC Javelin 15.2 8
## Camaro Z28 13.3 8
## Pontiac Firebird 19.2 8
## Fiat X1-9 27.3 4
## Porsche 914-2 26.0 4
## Lotus Europa 30.4 4
## Ford Pantera L 15.8 8
## Ferrari Dino 19.7 6
## Maserati Bora 15.0 8
## Volvo 142E 21.4 4filtering the data through filter function using a condition:
# Filter for cars with more than 6 cylinders
mtcars_filter <- filter(mtcars, cyl > 6)
mtcars_filter## mpg cyl disp hp drat wt qsec vs am gear carb
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 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
## 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
## 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
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8sorting using arrange function:
# Arrange the dataset by descending mpg
mtcars_arrange <- arrange(mtcars, desc(mpg))
mtcars_arrange## 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
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## 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
## 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
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## 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
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 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
## 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 4desc function sorts data in descending order.
renaming a column using rename function:
# Rename the mpg column to miles_per_gallon
mtcars_rename <- rename(mtcars, miles_per_gallon = mpg)
mtcars_rename## miles_per_gallon cyl disp hp drat wt qsec vs am gear
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4
## carb
## Mazda RX4 4
## Mazda RX4 Wag 4
## Datsun 710 1
## Hornet 4 Drive 1
## Hornet Sportabout 2
## Valiant 1
## Duster 360 4
## Merc 240D 2
## Merc 230 2
## Merc 280 4
## Merc 280C 4
## Merc 450SE 3
## Merc 450SL 3
## Merc 450SLC 3
## Cadillac Fleetwood 4
## Lincoln Continental 4
## Chrysler Imperial 4
## Fiat 128 1
## Honda Civic 2
## Toyota Corolla 1
## Toyota Corona 1
## Dodge Challenger 2
## AMC Javelin 2
## Camaro Z28 4
## Pontiac Firebird 2
## Fiat X1-9 1
## Porsche 914-2 2
## Lotus Europa 2
## Ford Pantera L 4
## Ferrari Dino 6
## Maserati Bora 8
## Volvo 142E 2add a column using mutate function:
# Add a new column for horsepower per weight
mtcars_mutate <- mutate(mtcars, hp_per_wt = hp / wt)
mtcars_mutate## 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# Combine two datasets with the same columns
mtcars_combined <- bind_rows(mtcars, mtcars)
mtcars_combined## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4...1 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag...2 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710...3 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive...4 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout...5 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Valiant...6 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Duster 360...7 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Merc 240D...8 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Merc 230...9 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Merc 280...10 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## Merc 280C...11 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## Merc 450SE...12 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## Merc 450SL...13 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## Merc 450SLC...14 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## Cadillac Fleetwood...15 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## Lincoln Continental...16 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## Chrysler Imperial...17 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## Fiat 128...18 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Honda Civic...19 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Toyota Corolla...20 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## Toyota Corona...21 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## Dodge Challenger...22 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## AMC Javelin...23 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## Camaro Z28...24 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## Pontiac Firebird...25 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## Fiat X1-9...26 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## Porsche 914-2...27 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## Lotus Europa...28 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## Ford Pantera L...29 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## Ferrari Dino...30 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## Maserati Bora...31 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## Volvo 142E...32 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
## Mazda RX4...33 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag...34 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710...35 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive...36 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout...37 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Valiant...38 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Duster 360...39 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Merc 240D...40 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Merc 230...41 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Merc 280...42 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## Merc 280C...43 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## Merc 450SE...44 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## Merc 450SL...45 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## Merc 450SLC...46 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## Cadillac Fleetwood...47 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## Lincoln Continental...48 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## Chrysler Imperial...49 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## Fiat 128...50 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Honda Civic...51 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Toyota Corolla...52 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## Toyota Corona...53 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## Dodge Challenger...54 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## AMC Javelin...55 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## Camaro Z28...56 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## Pontiac Firebird...57 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## Fiat X1-9...58 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## Porsche 914-2...59 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## Lotus Europa...60 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## Ford Pantera L...61 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## Ferrari Dino...62 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## Maserati Bora...63 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## Volvo 142E...64 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2# Group the dataset by number of cylinders and summarize the mean mpg
mtcars_grouped <- group_by(mtcars, cyl)
mtcars_summarized <- summarize(mtcars_grouped, mean_mpg = mean(mpg))
mtcars_grouped## # A tibble: 32 × 11
## # Groups: cyl [3]
## mpg cyl disp hp drat wt qsec vs am gear carb
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4
## 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4
## 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
## 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1
## 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2
## 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1
## 7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4
## 8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2
## 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2
## 10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4
## # ℹ 22 more rowsmtcars_summarized## # A tibble: 3 × 2
## cyl mean_mpg
## <dbl> <dbl>
## 1 4 26.7
## 2 6 19.7
## 3 8 15.1the above functions are called data manipulation functions. these functions are available in dplyr package.