Project2
2025-04-24
The Report:
What is the dataset?
The database that I ended up choosing for this project was a database that housed the top 1000 most played spotify songs of all time. The reason for why I chose this database is because, when I saw it, I thought it could be good data to use for a project and I enjoy music.
This dataset has, of course, 1000 rows of data. The 8 variables are:
- track_name: The title of the song or track
- artist: Name(s) of the track’s performing artist(s)
- album: The album in which the track was initially released
- release_date: The official release date of the track or album
- popularity: A score (0-100) based on how frequently the track is streamed, shared, and added to playlists. Higher means more popular
- spotify_url: Direct URL to the track on Spotify, useful for previewing or sharing.
- id: ID given to song by Spotify
- duration_min: Duration of the track in minutes (converted from milliseconds)
Interesting Finding
What I wanted to look at with this data was if there was any connect between the release date of the song and its popularity. The first thing I decided to look at was how many of the 1000 songs were released in each year.
spotify_data <- read.csv(file="spotify_top_1000_tracks.csv")
spotify_dates <- as.Date(spotify_data$release_date, format = "%Y-%m-%d")
spotify_dates <- format(spotify_dates, "%Y")
years_table <-as.data.frame(table(spotify_dates))
print(ggplot(years_table, aes(x=Freq, y=spotify_dates)) +
geom_bar(stat="identity") +
labs(title = "How many Top 1000 Songs were release in each year",
x = "Number of songs in Top 1000", y = "Year of Release"))
As shown from the graph, either we are coming out of one of the greatest
years of music ever, or there is a bit of recency bias for songs
released last year. I decided to take 2025 and 2024 out of the equation
because of the recency bias that they hold.
spotify_data <- read.csv(file="spotify_top_1000_tracks.csv")
spotify_dates <- as.Date(spotify_data$release_date, format = "%Y-%m-%d")
spotify_dates <- format(spotify_dates, "%Y")
spotify_dates <- subset(spotify_dates, spotify_dates < 2024)
years_table <-as.data.frame(table(spotify_dates))
years_table <-arrange(years_table, spotify_dates)
print(ggplot(years_table, aes(x=Freq, y=spotify_dates)) +
geom_bar(stat="identity") +
labs(title = "How many Top 1000 Songs were release in each year",
x = "Number of songs in Top 1000", y = "Year of Release"))
In the graph, we can see a clear gradual increase from 2008 to 2018 followed by a gradual decrease following after that.
spotify_data <- read.csv(file="spotify_top_1000_tracks.csv")
spotify_dates <- as.Date(spotify_data$release_date, format = "%Y-%m-%d")
spotify_dates <- format(spotify_dates, "%m")
spotify_dates <- subset(spotify_dates, spotify_dates < 2024)
years_table <-as.data.frame(table(spotify_dates))
years_table <-arrange(years_table, years_table$spotify_dates)
print(ggplot(years_table, aes(x=spotify_dates, y=Freq)) +
geom_bar(stat="identity") +
labs(title = "How many Top 1000 Songs were release in each year",
x = "Month of Release", y = "Number of songs in Top 1000"))
This graph, similarly to the last, is looking at the month that these
Top 1000 songs were released in. The months are in their numerical form
so 01 is January and so on. February is going to have less song releases
in that month because it is a shorter month in general. What surprised
me the most when looking at this graph is December being the second
lowest month. You would think with the release of Christmas music
December would be at least average. After looking it up, I learned that
a lot of Christmas songs are actually released in late November in
preparation for December. Next I wanted to see what the average
popularity of each month was. All of the songs are in the top 1000,
which means that even being on this list is a sign of major popularity.
But, if all of a single month is of much lower quality popularity
compared to others, that could change some things.
spotify_data <- read.csv(file="spotify_top_1000_tracks.csv")
spotify_data$release_date <- as.Date(spotify_data$release_date, format = "%Y-%m-%d")
spotify_data$release_date <- format(spotify_data$release_date, "%m")
testing <- spotify_data %>%
group_by(release_date) %>%
summarize(
count = n(),
mean_popularity = mean(popularity, na.rm = TRUE)
)
print(ggplot(testing, aes(x=release_date, y=mean_popularity, group = 1)) +
geom_line() +
labs(title = "How many Top 1000 Songs were release in each month",
x = "Month of Release", y = "Average Popularity of Songs"))
NA is representing songs that didn’t have a release date attached to
them. It being average in popularity compared to everything else is
interesting and definitely builds a case between release date and
popularity being connected. Once again, February and July are at the
bottom of the list.
spotify_data <- read.csv(file="spotify_top_1000_tracks.csv")
spotify_data$release_date <- as.Date(spotify_data$release_date, format = "%Y-%m-%d")
spotify_data$release_date <- format(spotify_data$release_date, "%m")
testing <- spotify_data %>%
group_by(release_date) %>%
summarize(
count = n(),
mean_popularity = mean(popularity, na.rm = TRUE)
)
library(ggplot2)
ggp <- ggplot(testing) +
geom_bar(aes(x=release_date, y=count, group = 1), stat="identity") +
geom_line(aes(x=release_date, y=mean_popularity, group = 1), stat="identity", color="red", size=1) +
labs(title = "How many Top 1000 Songs were release in each month",
x = "Month of Release", y = "Number of Songs")+
scale_y_continuous(sec.axis=sec_axis(~.*.01,name = "Mean Popularity"))## 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.print(ggp)
This graph combines both the number of songs in that month and the average popularity of those songs. As we can see, the average popularity and the number of songs released in that month are somewhat following the same trajectory. February and July are both consistently at the bottom. The pattern of the data is relatively the same, but it doesn’t follow 100% of the time. April takes a big dive in average popularity compared to the loss in number of songs released in April. This leads me to believe that there is probably a small connection to popularity of a song and when it is released. Songs released in early spring and late fall are more likely to get popular than songs released in the middle of summer.
Skills Demonstration
1.Read in data from a .csv file.
library(tidyverse)
spotify_data <- read.csv(file="spotify_top_1000_tracks.csv")2. Illustrate the use of summary on a data frame.
summary(spotify_data)## track_name artist album release_date
## Length:1000 Length:1000 Length:1000 Length:1000
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## popularity spotify_url id duration_min
## Min. : 0.00 Length:1000 Length:1000 Min. :0.9691
## 1st Qu.:37.00 Class :character Class :character 1st Qu.:2.7540
## Median :68.00 Mode :character Mode :character Median :3.2938
## Mean :56.67 Mean :3.3185
## 3rd Qu.:79.00 3rd Qu.:3.7457
## Max. :97.00 Max. :9.49793. Illustrate the use of table on an attribute of a dataframe.
artist_table <- as.data.frame(table(spotify_data$artist))
artist_table <- arrange(artist_table, desc(Freq))
print(artist_table)## Var1 Freq
## 1 The Weeknd 26
## 2 Taylor Swift 25
## 3 Avril Lavigne 21
## 4 Alan Walker 14
## 5 Ariana Grande 12
## 6 Selena Gomez 12
## 7 Ed Sheeran 11
## 8 Justin Bieber 11
## 9 Badscandal 9
## 10 Demi Lovato 9
## 11 Imagine Dragons 9
## 12 Katy Perry 9
## 13 Rihanna 9
## 14 Billie Eilish 8
## 15 Camila Cabello 8
## 16 David Guetta 8
## 17 Lana Del Rey 8
## 18 Maroon 5 8
## 19 PACANI 8
## 20 Drake 7
## 21 The Chainsmokers 7
## 22 Arc North 6
## 23 Brent Faiyaz 6
## 24 Doja Cat 6
## 25 Meghan Trainor 6
## 26 Shawn Mendes 6
## 27 Sia 6
## 28 Adele 5
## 29 Anne-Marie 5
## 30 Ava Max 5
## 31 Bebe Rexha 5
## 32 Beyoncé 5
## 33 Calvin Harris 5
## 34 Dua Lipa 5
## 35 Ellie Goulding 5
## 36 Eminem 5
## 37 Goodscandal 5
## 38 Harddope 5
## 39 Marshmello 5
## 40 Miley Cyrus 5
## 41 Nito-Onna 5
## 42 One Direction 5
## 43 Post Malone 5
## 44 Sabrina Carpenter 5
## 45 The Neighbourhood 5
## 46 54GODART 4
## 47 Aqua 4
## 48 Blvck Cobrv 4
## 49 Bruno Mars 4
## 50 Cigarettes After Sex 4
## 51 Justin Timberlake 4
## 52 Kavinsky 4
## 53 Lil Nas X 4
## 54 Miguel 4
## 55 Nightcore Reality 4
## 56 P!nk 4
## 57 Shakira 4
## 58 TEZIS 4
## 59 The Wanted 4
## 60 5lowers 3
## 61 A$AP Rocky 3
## 62 Clean Bandit 3
## 63 DJ Snake 3
## 64 EQRIC 3
## 65 Frank Ocean 3
## 66 Halsey 3
## 67 Into The Nightcore 3
## 68 Jennifer Lopez 3
## 69 Joji 3
## 70 Kendrick Lamar 3
## 71 LexMorris 3
## 72 Little Mix 3
## 73 Majid Jordan 3
## 74 Olivia Rodrigo 3
## 75 The xx 3
## 76 Twin 3
## 77 Yellow Pvnk 3
## 78 ZAYN 3
## 79 Zedd 3
## 80 347aidan 2
## 81 ABBA 2
## 82 Alessia Cara 2
## 83 Ali Gatie 2
## 84 Arash 2
## 85 B Martin 2
## 86 B.o.B 2
## 87 Backstreet Boys 2
## 88 Bella Poarch 2
## 89 Black Eyed Peas 2
## 90 blackbear 2
## 91 Bryson Tiller 2
## 92 Cascada 2
## 93 Charlie Puth 2
## 94 Childish Gambino 2
## 95 Christina Aguilera 2
## 96 Christina Perri 2
## 97 Coldplay 2
## 98 Daddy Yankee 2
## 99 Doechii 2
## 100 Empire Of The Sun 2
## 101 Ericovich 2
## 102 Evanescence 2
## 103 Flo Rida 2
## 104 GIVĒON 2
## 105 Glass Animals 2
## 106 Gym Class Heroes 2
## 107 Indila 2
## 108 Isabel LaRosa 2
## 109 J Balvin 2
## 110 Jay Sean 2
## 111 Jessie J 2
## 112 Just Lowkey 2
## 113 Kali Uchis 2
## 114 Kesha 2
## 115 Labrinth 2
## 116 Lady Gaga 2
## 117 Lil Peep 2
## 118 Luis Fonsi 2
## 119 Lukas Graham 2
## 120 Mac Miller 2
## 121 MANSA 2
## 122 Maria Beyer 2
## 123 Mark Ronson 2
## 124 Max Martis 2
## 125 Medusa 2
## 126 MetaBoy 2
## 127 Ne-Yo 2
## 128 Nick Giardino 2
## 129 NUUD 2
## 130 OneRepublic 2
## 131 Phantogram 2
## 132 PHURS 2
## 133 RAYE 2
## 134 Route 94 2
## 135 Sam Smith 2
## 136 SAMMY & LESEN 2
## 137 Sasha Alex Sloan 2
## 138 Selena Gomez & The Scene 2
## 139 Skylar Grey 2
## 140 Swedish House Mafia 2
## 141 The Kid LAROI 2
## 142 The Pussycat Dolls 2
## 143 The Vamps 2
## 144 Timbaland 2
## 145 Tones And I 2
## 146 Tory Lanez 2
## 147 Travis Scott 2
## 148 Two Feet 2
## 149 vaultboy 2
## 150 XXXTENTACION 2
## 151 Yohan Gerber 2
## 152 ✝✝✝ (Crosses) 1
## 153 24kGoldn 1
## 154 2WEI 1
## 155 5 Seconds of Summer 1
## 156 5UNDER 1
## 157 6LACK 1
## 158 7 Hills Worship 1
## 159 Aanysa 1
## 160 AFROUZEN 1
## 161 Ainae 1
## 162 Akcent 1
## 163 Akon 1
## 164 Alan Jackson 1
## 165 Alban Chela 1
## 166 Alec Benjamin 1
## 167 Alex & Sierra 1
## 168 Alex C. 1
## 169 Alexander Rybak 1
## 170 Alexandra Stan 1
## 171 Alfons 1
## 172 Alicia Keys 1
## 173 Alosa 1
## 174 ANN 1
## 175 ANRY 1
## 176 Anson Seabra 1
## 177 Arctic Monkeys 1
## 178 Arizona Zervas 1
## 179 Armin van Buuren 1
## 180 Artemas 1
## 181 Ashe 1
## 182 Astrid S 1
## 183 Asura Ghai 1
## 184 AURORA 1
## 185 AVAION 1
## 186 Avicii 1
## 187 Axel Johansson 1
## 188 Ayo & Teo 1
## 189 Baby Tate 1
## 190 Bad Meets Evil 1
## 191 Baltra 1
## 192 Bazzi 1
## 193 BEATSMASH 1
## 194 Becky G 1
## 195 Ben Delay 1
## 196 Ben Leuman 1
## 197 benny blanco 1
## 198 Benson Boone 1
## 199 Besomage 1
## 200 Besomorph 1
## 201 BICEP 1
## 202 BIMONTE 1
## 203 Bishop Briggs 1
## 204 Blackjack 1
## 205 Blue 1
## 206 Blue Violets 1
## 207 Bo Burnham 1
## 208 Bon Jovi 1
## 209 BoyWithUke 1
## 210 Bridgit Mendler 1
## 211 Britney Spears 1
## 212 Bronski Beat 1
## 213 Bruklin 1
## 214 Capri Everitt 1
## 215 Cardi B 1
## 216 Carly Rae Jepsen 1
## 217 Cartoon 1
## 218 CARYS 1
## 219 Catiso 1
## 220 Celina Sharma 1
## 221 Céline Dion 1
## 222 Cesqeaux 1
## 223 Charly Black 1
## 224 Cher Lloyd 1
## 225 chicago city 1
## 226 Chord Overstreet 1
## 227 citrulinq 1
## 228 CKay 1
## 229 Clara Mae 1
## 230 Conan Gray 1
## 231 Corm!! 1
## 232 Corona 1
## 233 Cour 1
## 234 CRÜPO 1
## 235 CryJaxx 1
## 236 Crystal Castles 1
## 237 Culture Code 1
## 238 CYRIL 1
## 239 Dan + Shay 1
## 240 Daniel Bellomo 1
## 241 Daryl Hall & John Oates 1
## 242 Davy Fresh 1
## 243 Daya 1
## 244 Dean Lewis 1
## 245 Depeche Mode 1
## 246 Destiny Rogers 1
## 247 Devinity 1
## 248 Dillin Hoox 1
## 249 Dimitri Vegas & Like Mike 1
## 250 Divine Deluxe Hitz 1
## 251 DJ Fronteo 1
## 252 DJ Goja 1
## 253 DJ Shadow 1
## 254 Djo 1
## 255 Dmitrii G 1
## 256 dnvn 1
## 257 Don Omar 1
## 258 Don Toliver 1
## 259 Dove Cameron 1
## 260 Dream Chaos 1
## 261 Duncan Laurence 1
## 262 E S 1
## 263 Edward Maya 1
## 264 Egzod 1
## 265 Elley Duhé 1
## 266 ElyOtto 1
## 267 Em Beihold 1
## 268 EMELINE 1
## 269 Eniru 1
## 270 Enrique Iglesias 1
## 271 Faul & Wad 1
## 272 Fifth Harmony 1
## 273 Flame Runner 1
## 274 Foster The People 1
## 275 Future 1
## 276 G-Eazy 1
## 277 Gabry Ponte 1
## 278 Gang of Youths 1
## 279 Gaullin 1
## 280 GAYLE 1
## 281 Gesaffelstein 1
## 282 gnash 1
## 283 GonSu 1
## 284 Gorillaz 1
## 285 Gotye 1
## 286 Gracie Abrams 1
## 287 Green Day 1
## 288 GYMBRO 1
## 289 Haddaway 1
## 290 Harmless 1
## 291 Harry Styles 1
## 292 HAWK. 1
## 293 Home 1
## 294 HUGEL 1
## 295 HXDES 1
## 296 HXPETRAIN 1
## 297 Hyper VIPER 1
## 298 iamnotshane 1
## 299 IDER 1
## 300 Idina Menzel 1
## 301 Iggy Azalea 1
## 302 Infinity Ink 1
## 303 Iniko 1
## 304 ISAEV 1
## 305 Isaiah Falls 1
## 306 J.Tajor 1
## 307 Jaden 1
## 308 Jai Waetford 1
## 309 James Blunt 1
## 310 Jax 1
## 311 JAYEM 1
## 312 Jaymes Young 1
## 313 JAZMYNE 1
## 314 Jean Dawson 1
## 315 Jessica Mauboy 1
## 316 Joel Adams 1
## 317 Johnny Orlando 1
## 318 Joinnus 1
## 319 Jon Lajoie 1
## 320 Jonas Brothers 1
## 321 Jordan Clarke 1
## 322 Josh Caballes 1
## 323 JubyPhonic 1
## 324 Juice WRLD 1
## 325 JVKE 1
## 326 K'NAAN 1
## 327 K-391 1
## 328 K3YN0T3 1
## 329 Kacey Musgraves 1
## 330 Kaia Jette 1
## 331 KALUMA 1
## 332 KAROL G 1
## 333 Kate Bush 1
## 334 KAYTRANADA 1
## 335 Kelly Clarkson 1
## 336 Ken 1
## 337 Kenya Grace 1
## 338 Kid Cudi 1
## 339 Kid Travis 1
## 340 KiDi 1
## 341 KiLLTEQ 1
## 342 Kina 1
## 343 Kurt Hugo Schneider 1
## 344 kwn 1
## 345 Kygo 1
## 346 Kyle Hume 1
## 347 Lauv 1
## 348 League of Legends 1
## 349 LEDUC 1
## 350 Leona Lewis 1
## 351 Lew Heart 1
## 352 Lewis Capaldi 1
## 353 Leyla Blue 1
## 354 Liam Dakota 1
## 355 Libianca 1
## 356 Lil Skies 1
## 357 Lil Tecca 1
## 358 Lil Wayne 1
## 359 Lilly Wood and The Prick 1
## 360 Lindsey Stirling 1
## 361 Lintrepy 1
## 362 Lorde 1
## 363 Loren Gray 1
## 364 LØST SIGNAL 1
## 365 Lost Sky 1
## 366 Louis Theroux 1
## 367 Luke Christopher 1
## 368 Lykke Li 1
## 369 M83 1
## 370 Madilyn Bailey 1
## 371 Madison Beer 1
## 372 Madonna 1
## 373 MAGIC! 1
## 374 Magnus Gunn 1
## 375 Mahalia 1
## 376 Mandrazo 1
## 377 Marco Nobel 1
## 378 Mario 1
## 379 Mark Ambor 1
## 380 Martin Garrix 1
## 381 Masked Wolf 1
## 382 Melanie Martinez 1
## 383 Melodream 1
## 384 Mentol 1
## 385 Milky Chance 1
## 386 MNA55 1
## 387 Modern Talking 1
## 388 MOHA 1
## 389 Mohombi 1
## 390 Montell Fish 1
## 391 Mr Saxobeat 1
## 392 Mr.Kitty 1
## 393 Mura Masa 1
## 394 Muse 1
## 395 MXZI 1
## 396 n$vd 1
## 397 nashi 1
## 398 Naughty Boy 1
## 399 NEFFEX 1
## 400 Neoni 1
## 401 Nessa Barrett 1
## 402 New West 1
## 403 NEWER 1
## 404 Nicky Youre 1
## 405 Night Inn 1
## 406 Nightcore Collectives 1
## 407 Nightcore Dreams 1
## 408 Nightcore Red 1
## 409 Niklas Dee 1
## 410 Nitecore 1
## 411 NO FEELS 1
## 412 Noah Cyrus 1
## 413 Noizy 1
## 414 NoMBe 1
## 415 NxWorries 1
## 416 Ocean Roses 1
## 417 Oleria 1
## 418 PARTYNEXTDOOR 1
## 419 Passenger 1
## 420 PHARØ 1
## 421 Phil Good 1
## 422 Pink Sweat$ 1
## 423 Pixia 1
## 424 Pokaraet 1
## 425 Pop Mage 1
## 426 Poylow 1
## 427 Princess Nokia 1
## 428 PRIYANX 1
## 429 ptasinski 1
## 430 PUBLIC 1
## 431 Raaban 1
## 432 Rachel Platten 1
## 433 Ramin Djawadi 1
## 434 Rawanne 1
## 435 Reed Deming 1
## 436 Rema 1
## 437 Retronaut 1
## 438 RezaDead 1
## 439 Ricky Montgomery 1
## 440 Ridi 1
## 441 Rixton 1
## 442 Rosa Linn 1
## 443 ROSÉ 1
## 444 ROY KNOX 1
## 445 Royal & the Serpent 1
## 446 Rui Da Silva 1
## 447 Ruth B. 1
## 448 SABRINA G 1
## 449 Saige 1
## 450 salem ilese 1
## 451 Sam Tinnesz 1
## 452 Sandëro 1
## 453 SANDICE 1
## 454 Santana 1
## 455 Sarah Jeffery 1
## 456 Saweetie 1
## 457 Scity 1
## 458 Scythermane 1
## 459 Sean Kingston 1
## 460 Sean Paul 1
## 461 Sevdaliza 1
## 462 Shontelle 1
## 463 Sidewalks and Skeletons 1
## 464 SiR 1
## 465 Skan 1
## 466 Ski Mask The Slump God 1
## 467 SKYXLINER 1
## 468 SLANDER 1
## 469 Snakehips 1
## 470 Snoh Aalegra 1
## 471 Soap&Skin 1
## 472 SouthmadeVelly 1
## 473 sped up nightcore 1
## 474 Spice Girls 1
## 475 Stela Cole 1
## 476 Stephen Sanchez 1
## 477 STICKY KEY 1
## 478 Sub Urban 1
## 479 Subspace Super Highway 1
## 480 Sugar Jesus 1
## 481 SZA 1
## 482 t.A.T.u. 1
## 483 Tasha Cobbs Leonard 1
## 484 Tate McRae 1
## 485 Tech N9ne 1
## 486 Telenova 1
## 487 Tesher 1
## 488 THE ANXIETY 1
## 489 The Cab 1
## 490 The Chemical Brothers 1
## 491 The Cramps 1
## 492 The Score 1
## 493 The Script 1
## 494 The Tech Thieves 1
## 495 The Temper Trap 1
## 496 TheFatRat 1
## 497 Tiësto 1
## 498 Tommee Profitt 1
## 499 Tove Lo 1
## 500 Train 1
## 501 Trevor Daniel 1
## 502 TRFN 1
## 503 Trinidad Cardona 1
## 504 Twenty One Pilots 1
## 505 UNKLFNKL 1
## 506 Used 1
## 507 Van Snyder 1
## 508 Vanillaz 1
## 509 VIC MENSA 1
## 510 We Architects 1
## 511 WEEDMANE 1
## 512 WILLOW 1
## 513 Witt Lowry 1
## 514 Wiz Khalifa 1
## 515 WizTheMc 1
## 516 Xanemusic 1
## 517 Xizt 1
## 518 Yagih Mael 1
## 519 YG Marley 1
## 520 Yooniq 1
## 521 yung kai 1
## 522 Yung Lean 1
## 523 Zac Efron 1
## 524 Zara Larsson 1
## 525 ZODIVK 1
## 526 Zum 14. Output all the column names in a data frame.
colnames(spotify_data)## [1] "track_name" "artist" "album" "release_date" "popularity"
## [6] "spotify_url" "id" "duration_min"5. Output the min, max, average, and standard deviation of a variable from a data frame.
"Shortest song in Top 1000: "## [1] "Shortest song in Top 1000: "min(spotify_data$duration_min)## [1] 0.96915"Longest song in Top 1000: "## [1] "Longest song in Top 1000: "max(spotify_data$duration_min)## [1] 9.497883"Average song length in Top 1000: "## [1] "Average song length in Top 1000: "mean(spotify_data$duration_min)## [1] 3.318516"Standard Deviation of song length in Top 1000: "## [1] "Standard Deviation of song length in Top 1000: "sd(spotify_data$duration_min)## [1] 0.84959056. Illustrate how you can select columns of a data frame into a new data frame. Show your result by executing a summary or glimpse of the new data frame.
new_dataframe <- select(spotify_data, "artist", "album",
"release_date")
summary(new_dataframe)## artist album release_date
## Length:1000 Length:1000 Length:1000
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character7. Illustrate renaming a column.
spotify_rename <- rename(spotify_data, length_min = duration_min)
glimpse(spotify_rename)## Rows: 1,000
## Columns: 8
## $ track_name <chr> "All The Stars (with SZA)", "Starboy", "Señorita", "Heat …
## $ artist <chr> "Kendrick Lamar", "The Weeknd", "Shawn Mendes", "Glass An…
## $ album <chr> "Black Panther The Album Music From And Inspired By", "St…
## $ release_date <chr> "2018-02-09", "2016-11-25", "2019-06-21", "2020-08-07", "…
## $ popularity <int> 95, 90, 80, 87, 87, 77, 73, 80, 84, 88, 88, 81, 85, 86, 6…
## $ spotify_url <chr> "https://open.spotify.com/track/3GCdLUSnKSMJhs4Tj6CV3s", …
## $ id <chr> "3GCdLUSnKSMJhs4Tj6CV3s", "7MXVkk9YMctZqd1Srtv4MB", "0TK2…
## $ length_min <dbl> 3.869767, 3.840883, 3.182667, 3.980083, 3.432433, 3.67965…8. Illustrate the use of filter (from tidyverse) on a data frame.
filter_spotify <- filter(spotify_data, duration_min > 4)
glimpse(filter_spotify)## Rows: 155
## Columns: 8
## $ track_name <chr> "Apocalypse", "Love Me Like You Do", "Summertime Sadness"…
## $ artist <chr> "Cigarettes After Sex", "Ellie Goulding", "Lana Del Rey",…
## $ album <chr> "Cigarettes After Sex", "Fifty Shades Freed (Original Mot…
## $ release_date <chr> "2017-06-09", "2018-02-09", "2012-01-01", "2016-11-25", "…
## $ popularity <int> 73, 81, 85, 81, 87, 83, 88, 89, 83, 41, 75, 87, 81, 71, 7…
## $ spotify_url <chr> "https://open.spotify.com/track/0yc6Gst2xkRu0eMLeRMGCX", …
## $ id <chr> "0yc6Gst2xkRu0eMLeRMGCX", "0Cy7wt6IlRfBPHXXjmZbcP", "3BJe…
## $ duration_min <dbl> 4.843600, 4.225333, 4.423783, 4.337533, 4.179333, 4.74776…9. Illustrate the use of arrange on a dataframe. Use any column of your choosing, but sort from greatest to least.
arrange_spotify <- arrange(spotify_data, desc(popularity))
glimpse(arrange_spotify)## Rows: 1,000
## Columns: 8
## $ track_name <chr> "That’s So True", "APT.", "All The Stars (with SZA)", "I …
## $ artist <chr> "Gracie Abrams", "ROSÉ", "Kendrick Lamar", "Arctic Monkey…
## $ album <chr> "The Secret of Us (Deluxe)", "APT.", "Black Panther The A…
## $ release_date <chr> "2024-10-18", "2024-10-18", "2018-02-09", "2013-09-09", "…
## $ popularity <int> 97, 96, 95, 93, 93, 92, 92, 92, 92, 90, 90, 90, 90, 90, 9…
## $ spotify_url <chr> "https://open.spotify.com/track/7ne4VBA60CxGM75vw0EYad", …
## $ id <chr> "7ne4VBA60CxGM75vw0EYad", "5vNRhkKd0yEAg8suGBpjeY", "3GCd…
## $ duration_min <dbl> 2.771667, 2.831950, 3.869767, 3.065933, 4.078067, 4.44621…10. Use slice_max to output the top 4 rows of a data frame. Use any column of your choosing.
slice_max(spotify_data, n=4, popularity)## track_name artist
## 1 That’s So True Gracie Abrams
## 2 APT. ROSÉ
## 3 All The Stars (with SZA) Kendrick Lamar
## 4 I Wanna Be Yours Arctic Monkeys
## 5 One Of The Girls (with JENNIE, Lily Rose Depp) The Weeknd
## album release_date
## 1 The Secret of Us (Deluxe) 2024-10-18
## 2 APT. 2024-10-18
## 3 Black Panther The Album Music From And Inspired By 2018-02-09
## 4 AM 2013-09-09
## 5 The Idol Episode 4 (Music from the HBO Original Series) 2023-06-23
## popularity spotify_url
## 1 97 https://open.spotify.com/track/7ne4VBA60CxGM75vw0EYad
## 2 96 https://open.spotify.com/track/5vNRhkKd0yEAg8suGBpjeY
## 3 95 https://open.spotify.com/track/3GCdLUSnKSMJhs4Tj6CV3s
## 4 93 https://open.spotify.com/track/5XeFesFbtLpXzIVDNQP22n
## 5 93 https://open.spotify.com/track/7CyPwkp0oE8Ro9Dd5CUDjW
## id duration_min
## 1 7ne4VBA60CxGM75vw0EYad 2.771667
## 2 5vNRhkKd0yEAg8suGBpjeY 2.831950
## 3 3GCdLUSnKSMJhs4Tj6CV3s 3.869767
## 4 5XeFesFbtLpXzIVDNQP22n 3.065933
## 5 7CyPwkp0oE8Ro9Dd5CUDjW 4.07806711. Illustrate the use of a pipe operation (%>%)
spotify_data %>%
arrange(desc(duration_min)) %>%
slice_head(n = 5) %>%
select(duration_min, track_name, album, artist)## duration_min track_name
## 1 9.497883 Lost Boy
## 2 8.436667 I'm Getting Ready
## 3 8.069100 Mirrors
## 4 7.476217 What Goes Around.../...Comes Around (Interlude)
## 5 7.035767 Achilles Come Down
## album artist
## 1 SYRE Jaden
## 2 Heart. Passion. Pursuit. Tasha Cobbs Leonard
## 3 The 20/20 Experience - The Complete Experience Justin Timberlake
## 4 FutureSex/LoveSounds Justin Timberlake
## 5 Go Farther In Lightness Gang of Youths12. Use ggplot to create 2 different visualizations of your data
ggplot(spotify_data, aes(x=popularity, y=duration_min)) +
geom_point() +
labs(title = "Popularity vs Duration of Song",
x = "Popularity of Song", y = "Duration of Song")
glimpse(spotify_data)## Rows: 1,000
## Columns: 8
## $ track_name <chr> "All The Stars (with SZA)", "Starboy", "Señorita", "Heat …
## $ artist <chr> "Kendrick Lamar", "The Weeknd", "Shawn Mendes", "Glass An…
## $ album <chr> "Black Panther The Album Music From And Inspired By", "St…
## $ release_date <chr> "2018-02-09", "2016-11-25", "2019-06-21", "2020-08-07", "…
## $ popularity <int> 95, 90, 80, 87, 87, 77, 73, 80, 84, 88, 88, 81, 85, 86, 6…
## $ spotify_url <chr> "https://open.spotify.com/track/3GCdLUSnKSMJhs4Tj6CV3s", …
## $ id <chr> "3GCdLUSnKSMJhs4Tj6CV3s", "7MXVkk9YMctZqd1Srtv4MB", "0TK2…
## $ duration_min <dbl> 3.869767, 3.840883, 3.182667, 3.980083, 3.432433, 3.67965…artist_table <- as.data.frame(table(spotify_data$artist))
artist_table <- arrange(artist_table, desc(Freq))
artist_table <- filter(artist_table, Freq > 6)
ggplot(artist_table, aes(x=Freq, y=Var1)) +
geom_bar(stat="identity") +
labs(title = "Artists* and the amount of top 1000 songs they have",
caption = "*Only artists with more than 6 songs are included",
x = "# of Songs",
y = "Artist Name")