Data-Informed Thinking + Doing
Data Visualization With Grammar of Graphics
Visualizing TikTok data—using ggplot2 for R, plotnine for Python, and Gadfly for Julia.
Data visualization allows us to communicate complex information and patterns in a visual and intuitive way. It enhances understanding, reveals insights, and helps in identifying trends, outliers, and relationships. Visualizations aid in decision-making, storytelling, and conveying information effectively to a wider audience.
In advancing this art and science, Leland Wilkinson pioneered the Grammar of Graphics, which laid the foundation for a systematic and principled approach to data visualization. It introduced the concept of mapping data to visual aesthetics and provided a grammar-based framework for constructing visualizations. Hadley Wickham’s implementation of Grammar of Graphics (ggplot2 for R), built upon Wilkinson’s ideas and revolutionized data visualization by providing a powerful, flexible, and user-friendly toolset for creating sophisticated and customizable plots. Together, Wilkinson’s work and Wickham’s implementation have significantly advanced the field of data visualization, enabling data analysts/scientists to create insightful and impactful visual representations of their data.
Let’s see ggplot2 for R—as well as the ggplot2 “forks” for Python (plotnine) and Julia (Gadfly)—in action.
Getting Started
If you are interested in reproducing this work, here are the versions of R, Python, and Julia used (as well as the respective packages for each). Additionally, my coding style here is verbose, in order to trace back where functions/methods and variables are originating from, and make this a learning experience for everyone—including me.
cat(R.version$version.string, R.version$nickname)R version 4.2.3 (2023-03-15) Shortstop Beaglerequire(devtools)
devtools::install_version("readxl", version="1.4.3", repos="http://cran.us.r-project.org")
devtools::install_version("tibble", version="3.2.1", repos="http://cran.us.r-project.org")
devtools::install_version("dplyr", version="1.1.2", repos="http://cran.us.r-project.org")
devtools::install_version("scales", version="1.2.1", repos="http://cran.us.r-project.org")
devtools::install_version("ggplot2", version="3.4.2", repos="http://cran.us.r-project.org")
devtools::install_github("wilkelab/ggridges", dependencies=FALSE)
devtools::install_github("ricardo-bion/ggradar", dependencies=FALSE)library(readxl)
library(tibble)
library(dplyr)
library(scales)
library(ggplot2)
library(ggridges)
library(ggradar)import sys
print(sys.version)3.11.4 (v3.11.4:d2340ef257, Jun 6 2023, 19:15:51) [Clang 13.0.0 (clang-1300.0.29.30)]!pip install pandas==2.0.3
!pip install plotnine==0.12.1import pandas
import plotnineusing InteractiveUtils
InteractiveUtils.versioninfo()Julia Version 1.9.2
Commit e4ee485e909 (2023-07-05 09:39 UTC)
Platform Info:
OS: macOS (x86_64-apple-darwin22.4.0)
CPU: 8 × Intel(R) Core(TM) i5-8259U CPU @ 2.30GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-14.0.6 (ORCJIT, skylake)
Threads: 1 on 8 virtual cores
Environment:
DYLD_FALLBACK_LIBRARY_PATH = /Library/Frameworks/R.framework/Resources/lib:/Library/Java/JavaVirtualMachines/jdk1.8.0_241.jdk/Contents/Home/jre/lib/serverusing Pkg
Pkg.add(name="CSV", version="0.10.11")
Pkg.add(name="DataFrames", version="1.5.0")
Pkg.add(name="CategoricalArrays", version="0.10.8")
Pkg.add(name="Colors", version="0.12.10")
Pkg.add(name="Cairo", version="1.0.5")
Pkg.add(name="Gadfly", version="1.3.4")using Dates
using CSV
using DataFrames
using CategoricalArrays
using Colors
using Cairo
using GadflyImporting and Examining Dataset
Upon importing and examining the dataset, we can see that the songs data frame dimension is 223 rows and 18 columns, while the hashtags data frame dimension is 1200 rows and 52 columns.
# https://www.kaggle.com/datasets/sveta151/tiktok-popular-songs-2019
tiktok_songs_r <- read.csv("../../dataset/tiktok-songs-2019.csv", stringsAsFactors=TRUE)
utils::str(object=tiktok_songs_r)'data.frame': 223 obs. of 18 variables:
$ track_name : Factor w/ 221 levels "223's (feat. 9lokknine)",..: 161 39 14 181 158 59 163 60 141 72 ...
$ artist_name : Factor w/ 209 levels "24kGoldn","88rising",..: 128 153 32 29 129 139 19 167 14 61 ...
$ artist_pop : int 53 61 57 60 58 57 53 65 43 70 ...
$ album : Factor w/ 220 levels "#3 Deluxe Version",..: 117 146 26 177 9 52 19 53 129 174 ...
$ track_pop : int 68 53 69 2 0 1 0 0 58 64 ...
$ danceability : num 0.618 0.744 0.829 0.847 0.738 0.516 0.649 0.877 0.736 0.474 ...
$ energy : num 0.955 0.845 0.792 0.678 0.983 0.358 0.716 0.503 0.81 0.948 ...
$ loudness : num -3.84 -7.42 -3.75 -8.63 -4.37 ...
$ mode : int 1 0 0 1 0 0 1 0 1 1 ...
$ key : int 4 4 2 9 5 8 11 7 5 3 ...
$ speechiness : num 0.0798 0.253 0.0668 0.109 0.0855 0.424 0.0445 0.22 0.0906 0.122 ...
$ acousticness : num 0.00221 0.759 0.726 0.0669 0.038 0.743 0.0879 0.164 0.077 0.0223 ...
$ instrumentalness: num 0.00000309 0.232 0.00000583 0 0.00000593 0.0000013 0.03 0.0132 0 0 ...
$ liveness : num 0.486 0.1 0.122 0.274 0.183 0.0813 0.0824 0.102 0.0523 0.43 ...
$ valence : num 0.79 0.749 0.758 0.811 0.957 0.397 0.503 0.529 0.387 0.611 ...
$ tempo : num 150 75.2 118 98 93 ...
$ time_signature : int 4 4 4 4 4 4 4 4 4 4 ...
$ duration_ms : int 179947 160000 215507 200594 235760 124000 235813 144000 154448 195373 ...utils::head(x=tiktok_songs_r, n=8) track_name artist_name artist_pop album track_pop danceability energy loudness mode key speechiness acousticness instrumentalness liveness valence tempo time_signature duration_ms
1 Shake It Metro Station 53 Metro Station 68 0.62 0.95 -3.8 1 4 0.080 0.0022 0.0000031 0.486 0.79 150 4 179947
2 Chinese New Year SALES 61 SALES - EP 53 0.74 0.84 -7.4 0 4 0.253 0.7590 0.2320000 0.100 0.75 75 4 160000
3 Baby I'm Yours Breakbot 57 By Your Side 69 0.83 0.79 -3.8 0 2 0.067 0.7260 0.0000058 0.122 0.76 118 4 215507
4 The Git Up Blanco Brown 60 The Git Up 2 0.85 0.68 -8.6 1 9 0.109 0.0669 0.0000000 0.274 0.81 98 4 200594
5 Say Hey (I Love You) Michael Franti & Spearhead 58 All Rebel Rockers 0 0.74 0.98 -4.4 0 5 0.086 0.0380 0.0000059 0.183 0.96 93 4 235760
6 Falling for U Peachy! 57 Falling for U 1 0.52 0.36 -12.0 0 8 0.424 0.7430 0.0000013 0.081 0.40 80 4 124000
7 Shooting Stars Bag Raiders 53 Bag Raiders (Deluxe) 0 0.65 0.72 -6.2 1 11 0.044 0.0879 0.0300000 0.082 0.50 125 4 235813
8 fast Sueco 65 fast 0 0.88 0.50 -10.3 0 7 0.220 0.1640 0.0132000 0.102 0.53 100 4 144000which(is.na(tiktok_songs_r))integer(0)# https://www.kaggle.com/datasets/anasmahmood000/tiktok-dataset
tiktok_hashtags_r <- readxl::read_excel("../../dataset/tiktok-hashtags.xlsx")
utils::str(object=tiktok_hashtags_r)tibble [1,200 × 52] (S3: tbl_df/tbl/data.frame)
$ authorMeta/avatar : chr [1:1200] "https://p16-sign-sg.tiktokcdn.com/aweme/720x720/tos-alisg-avt-0068/3d7f8d376e2ae1e5bf2d5d34ce4227cf.jpeg?x-expi"| __truncated__ "https://p16-sign-va.tiktokcdn.com/tos-maliva-avt-0068/481098764045c3ce40803bbb7a4ce8f5~c5_720x720.jpeg?x-expire"| __truncated__ "https://p16-sign-va.tiktokcdn.com/tos-maliva-avt-0068/25e7644ef6de2fda3ceb6b3ded7ec1e6~c5_720x720.jpeg?x-expire"| __truncated__ "https://p16-sign-va.tiktokcdn.com/tos-maliva-avt-0068/1fb842873d85dbffa71c9787e577cb52~c5_720x720.jpeg?x-expire"| __truncated__ ...
$ authorMeta/digg : num [1:1200] 420 10800 251 218 47600 ...
$ authorMeta/fans : num [1:1200] 13200000 1300000 696800 510200 3300000 ...
$ authorMeta/following : num [1:1200] 28 182 42 56 690 ...
$ authorMeta/heart : num [1:1200] 150400000 35100000 15900000 32100000 137300000 ...
$ authorMeta/id : chr [1:1200] "6713126981665686530" "6929583089811522566" "7083448802635596842" "7087287470497645573" ...
$ authorMeta/name : chr [1:1200] "miso_ara" "crinka11" "iampets_com" "dailydosevideos_" ...
$ authorMeta/nickName : chr [1:1200] "미소아라 Miso_Ara" "Chris Rinker" "IamPéts" "dailydosevideos" ...
$ authorMeta/signature : chr [1:1200] "soonent@soon-ent.co.kr\r\n.\r\n Miso Ara IG" "insta: chrisrinker73" "Pet supplies, toys online store. All products in our video can buy in below link" "Daily dose of videos/memes \r\nsupport the page if you want ❤" ...
$ authorMeta/verified : chr [1:1200] "true" "false" "false" "false" ...
$ authorMeta/video : num [1:1200] 303 518 90 84 247 ...
$ commentCount : num [1:1200] 66000 154100 55300 70600 35500 ...
$ createTime : num [1:1200] 1582632666 1620176163 1649340306 1652274907 1586829887 ...
$ createTimeISO : chr [1:1200] "2020-02-25T12:11:06.000Z" "2021-05-05T00:56:03.000Z" "2022-04-07T14:05:06.000Z" "2022-05-11T13:15:07.000Z" ...
$ diggCount : num [1:1200] 19300000 15400000 13900000 13100000 13100000 12800000 12500000 12100000 11600000 11300000 ...
$ downloaded : chr [1:1200] "false" "false" "false" "false" ...
$ effectStickers/0/ID : chr [1:1200] NA NA NA NA ...
$ effectStickers/0/name : chr [1:1200] NA NA NA NA ...
$ effectStickers/0/stickerStats/useCount: num [1:1200] NA NA NA NA NA NA NA NA NA NA ...
$ effectStickers/1/ID : chr [1:1200] NA NA NA NA ...
$ effectStickers/1/name : chr [1:1200] NA NA NA NA ...
$ effectStickers/1/stickerStats/useCount: num [1:1200] NA NA NA NA NA NA NA NA NA NA ...
$ hashtags/0/cover : chr [1:1200] "https://p16-amd-va.tiktokcdn.com/obj/musically-maliva-obj/1e6b1debcaef7ccdb2ad6107feb180b8" NA NA "https://p16-amd-va.tiktokcdn.com/obj/musically-maliva-obj/c9361f827e1dcb19d2e54e58654c9f55.jpeg" ...
$ hashtags/0/id : chr [1:1200] "20408" "229207" "5424" "23864" ...
$ hashtags/0/name : chr [1:1200] "woah" "fyp" "funny" "meme" ...
$ hashtags/0/title : chr [1:1200] "Wait... one second... okay, WOAH!" NA "What's so #Funny?" NA ...
$ hashtags/1/cover : chr [1:1200] "https://p16-sg.tiktokcdn.com/obj/tiktok-obj/1641017550056449.PNG" "https://p16-amd-va.tiktokcdn.com/obj/musically-maliva-obj/c9361f827e1dcb19d2e54e58654c9f55.jpeg" NA NA ...
$ hashtags/1/id : chr [1:1200] "3780738" "23864" "107738" "7032147077300682757" ...
$ hashtags/1/name : chr [1:1200] "woahchallenge" "meme" "funnyvideos" "trynottolaughtiktoktv" ...
$ hashtags/1/title : chr [1:1200] "Woah woah woah, stop right there! Have you done the latest dance challange yet! Dab, floss, or just show off "| __truncated__ NA NA "TikTok's Funniest Home Videos!" ...
$ id : chr [1:1200] "6797294685082619137" "6958603581675031814" "7083862667613490474" "7096466682046188805" ...
$ mediaUrls/0 : chr [1:1200] "https://v16-webapp.tiktok.com/c5e1c65e3846e6fe306f3bee52c825e4/62e2021c/video/n/v0102/e99ec24738854b7985d2926c1"| __truncated__ "https://v16-webapp.tiktok.com/fcc73bd189606c1de0970ba0a9307e3d/62e20215/video/tos/useast2a/tos-useast2a-ve-0068"| __truncated__ "https://v16-webapp.tiktok.com/6fdb8074e5ec9438b97d77db00ed7296/62e20223/video/tos/maliva/tos-maliva-ve-0068c799"| __truncated__ "https://v16-webapp.tiktok.com/7cd241572bcadf38463d6c07c543d181/62e20248/video/tos/useast2a/tos-useast2a-ve-0068"| __truncated__ ...
$ mentions/0 : chr [1:1200] NA NA NA NA ...
$ mentions/1 : chr [1:1200] NA NA NA NA ...
$ mentions/2 : chr [1:1200] NA NA NA NA ...
$ mentions/3 : chr [1:1200] NA NA NA NA ...
$ mentions/4 : chr [1:1200] NA NA NA NA ...
$ mentions/5 : chr [1:1200] NA NA NA NA ...
$ musicMeta/musicAlbum : chr [1:1200] NA NA NA NA ...
$ musicMeta/musicAuthor : chr [1:1200] "미소아라 Miso_Ara" "Chris Rinker" "IamPéts" "dailydosevideos" ...
$ musicMeta/musicName : chr [1:1200] "오리지널 사운드 - Miso_Ara" "original sound" "nhạc nền" "original sound" ...
$ musicMeta/musicOriginal : chr [1:1200] "true" "true" "true" "true" ...
$ musicMeta/playUrl : chr [1:1200] "https://sf16-ies-music-sg.tiktokcdn.com/obj/tiktok-obj/1659493776138258.mp3" "https://sf16-ies-music-va.tiktokcdn.com/obj/musically-maliva-obj/6958603596321655557.mp3" "https://sf16-ies-music-va.tiktokcdn.com/obj/ies-music-ttp-dup-us/7083862650291211054.mp3" "https://sf16-ies-music-va.tiktokcdn.com/obj/musically-maliva-obj/7096789979888372485.mp3" ...
$ playCount : num [1:1200] 200600000 79600000 106100000 72500000 62700000 ...
$ searchHashtag/name : chr [1:1200] "meme" "meme" "meme" "meme" ...
$ searchHashtag/views : chr [1:1200] "556B" "556B" "556B" "556B" ...
$ shareCount : num [1:1200] 377700 205400 323000 133400 252900 ...
$ text : chr [1:1200] "Ara Woah #woah #woahchallenge #foryou #fyp #meme #이아라 #틱톡 #tiktok" "#fyp #meme #funny #meme #vine" "The end #funny #funnyvideos #animals #haha #meme #dog #cat #fypage #viral #pets" "Try not to laugh hard #meme #trynottolaughtiktoktv" ...
$ videoMeta/duration : num [1:1200] 17 11 25 62 36 10 18 16 13 19 ...
$ videoMeta/height : num [1:1200] 1280 1024 1024 1024 1024 ...
$ videoMeta/width : num [1:1200] 720 576 576 576 576 540 720 576 540 576 ...
$ webVideoUrl : chr [1:1200] "https://www.tiktok.com/@miso_ara/video/6797294685082619137" "https://www.tiktok.com/@crinka11/video/6958603581675031814" "https://www.tiktok.com/@iampets_com/video/7083862667613490474" "https://www.tiktok.com/@dailydosevideos_/video/7096466682046188805" ...utils::head(x=tiktok_hashtags_r, n=8)# A tibble: 8 × 52
`authorMeta/avatar` `authorMeta/digg` `authorMeta/fans` `authorMeta/following` `authorMeta/heart` `authorMeta/id` `authorMeta/name` `authorMeta/nickName` `authorMeta/signature` `authorMeta/verified` `authorMeta/video` commentCount createTime createTimeISO diggCount downloaded `effectStickers/0/ID` `effectStickers/0/name` `effectStickers/0/stickerStats/useCount` `effectStickers/1/ID` `effectStickers/1/name` `effectStickers/1/stickerStats/useCount` `hashtags/0/cover` `hashtags/0/id` `hashtags/0/name` `hashtags/0/title` `hashtags/1/cover` `hashtags/1/id` `hashtags/1/name` `hashtags/1/title` id `mediaUrls/0` `mentions/0` `mentions/1` `mentions/2` `mentions/3` `mentions/4` `mentions/5` `musicMeta/musicAlbum` `musicMeta/musicAuthor` `musicMeta/musicName` `musicMeta/musicOriginal` `musicMeta/playUrl` playCount `searchHashtag/name` `searchHashtag/views` shareCount text `videoMeta/duration` `videoMeta/height` `videoMeta/width` webVideoUrl
<chr> <dbl> <dbl> <dbl> <dbl> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <chr> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <chr> <dbl> <chr> <dbl> <dbl> <dbl> <chr>
1 https://p16-sign-sg.tiktokcdn.com/aweme/720x720/tos-alisg-avt-0068/3d7f8d376e2ae1e5bf2d5d34ce4227cf.jpeg?x-expires=1659128400&x-signature=fP%2BSSNIqUeMNNLO5Az7FN27SJ1k%3D 420 13200000 28 150400000 6713126981665686530 miso_ara 미소아라 Miso_Ara "soonent@soon-ent.co.kr\r\n.\r\n Miso Ara IG" true 303 66000 1582632666 2020-02-25T12:11:06.000Z 19300000 false <NA> <NA> NA <NA> <NA> NA https://p16-amd-va.tiktokcdn.com/obj/musically-maliva-obj/1e6b1debcaef7ccdb2ad6107feb180b8 20408 woah "Wait... one second... okay, WOAH!" https://p16-sg.tiktokcdn.com/obj/tiktok-obj/1641017550056449.PNG 3780738 woahchallenge Woah woah woah, stop right there! Have you done the latest dance challange yet! Dab, floss, or just show off your moves with the #WoahChallenge! 6797294685082619137 https://v16-webapp.tiktok.com/c5e1c65e3846e6fe306f3bee52c825e4/62e2021c/video/n/v0102/e99ec24738854b7985d2926c1410767d/?a=1988&ch=0&cr=0&dr=0&lr=tiktok&cd=0%7C0%7C1%7C0&cv=1&br=1492&bt=746&btag=80000&cs=0&ds=3&ft=gKSYZ8beo0PD1.hipsg9w.FE75LiaQ2D~j8&mime_type=video_mp4&qs=0&rc=NDplM2VlNTY4NDNoPGRkN0BpM286bGxvazZqczMzPDgzM0BgLjIxLy9iXjAxY2EzXmAvYSNvNTFkZm1rcF5fLS00LzRzcw%3D%3D&l=202207272127060102230831572562751B <NA> <NA> <NA> <NA> <NA> <NA> <NA> 미소아라 Miso_Ara 오리지널 사운드 - Miso_Ara true https://sf16-ies-music-sg.tiktokcdn.com/obj/tiktok-obj/1659493776138258.mp3 200600000 meme 556B 377700 Ara Woah #woah #woahchallenge #foryou #fyp #meme #이아라 #틱톡 #tiktok 17 1280 720 https://www.tiktok.com/@miso_ara/video/6797294685082619137
2 https://p16-sign-va.tiktokcdn.com/tos-maliva-avt-0068/481098764045c3ce40803bbb7a4ce8f5~c5_720x720.jpeg?x-expires=1659128400&x-signature=SgmuV2iwipES%2BPUxZ0e%2Bl8xaCRA%3D 10800 1300000 182 35100000 6929583089811522566 crinka11 Chris Rinker "insta: chrisrinker73" false 518 154100 1620176163 2021-05-05T00:56:03.000Z 15400000 false <NA> <NA> NA <NA> <NA> NA <NA> 229207 fyp <NA> https://p16-amd-va.tiktokcdn.com/obj/musically-maliva-obj/c9361f827e1dcb19d2e54e58654c9f55.jpeg 23864 meme <NA> 6958603581675031814 https://v16-webapp.tiktok.com/fcc73bd189606c1de0970ba0a9307e3d/62e20215/video/tos/useast2a/tos-useast2a-ve-0068c002/3e9a31d1340143e1ad520396bf4f585a/?a=1988&ch=0&cr=0&dr=0&lr=tiktok_m&cd=0%7C0%7C1%7C0&cv=1&br=1380&bt=690&btag=80000&cs=0&ds=3&ft=gKSYZ8beo0PD1khipsg9w.FE75LiaQ2D~j8&mime_type=video_mp4&qs=0&rc=OTM0PDxmNDw0OGc7ZDU5M0BpanVpc3U7dTh4NTMzNzczM0A2MGMtMTQtXzIxXi0tMC4zYSNmYzJsM2xebjVgLS1kMTZzcw%3D%3D&l=202207272127060102230831572562751B <NA> <NA> <NA> <NA> <NA> <NA> <NA> Chris Rinker original sound true https://sf16-ies-music-va.tiktokcdn.com/obj/musically-maliva-obj/6958603596321655557.mp3 79600000 meme 556B 205400 #fyp #meme #funny #meme #vine 11 1024 576 https://www.tiktok.com/@crinka11/video/6958603581675031814
3 https://p16-sign-va.tiktokcdn.com/tos-maliva-avt-0068/25e7644ef6de2fda3ceb6b3ded7ec1e6~c5_720x720.jpeg?x-expires=1659128400&x-signature=e%2BwijcH%2B%2BQDtDjI%2B1Xi98ORpxkc%3D 251 696800 42 15900000 7083448802635596842 iampets_com IamPéts "Pet supplies, toys online store. All products in our video can buy in below link" false 90 55300 1649340306 2022-04-07T14:05:06.000Z 13900000 false <NA> <NA> NA <NA> <NA> NA <NA> 5424 funny "What's so #Funny?" <NA> 107738 funnyvideos <NA> 7083862667613490474 https://v16-webapp.tiktok.com/6fdb8074e5ec9438b97d77db00ed7296/62e20223/video/tos/maliva/tos-maliva-ve-0068c799-us/805206f34a7b4d8aaa52488af92e6cf7/?a=1988&ch=0&cr=0&dr=0&lr=tiktok_m&cd=0%7C0%7C1%7C0&cv=1&br=2798&bt=1399&btag=80000&cs=0&ds=3&ft=gKSYZ8beo0PD1khipsg9w.FE75LiaQ2D~j8&mime_type=video_mp4&qs=0&rc=ZTxnMzY2PGdkOjQ8ODQzaEBpanUzaGg6ZmloPDMzZzczNEBhXy81MTIzXzYxX2BeMF42YSMyamY1cjRncjRgLS1kMS9zcw%3D%3D&l=202207272127060102230831572562751B <NA> <NA> <NA> <NA> <NA> <NA> <NA> IamPéts nhạc nền true https://sf16-ies-music-va.tiktokcdn.com/obj/ies-music-ttp-dup-us/7083862650291211054.mp3 106100000 meme 556B 323000 The end #funny #funnyvideos #animals #haha #meme #dog #cat #fypage #viral #pets 25 1024 576 https://www.tiktok.com/@iampets_com/video/7083862667613490474
4 https://p16-sign-va.tiktokcdn.com/tos-maliva-avt-0068/1fb842873d85dbffa71c9787e577cb52~c5_720x720.jpeg?x-expires=1659128400&x-signature=zdwbd9tw%2BTDzHA75lExyguBBioA%3D 218 510200 56 32100000 7087287470497645573 dailydosevideos_ dailydosevideos "Daily dose of videos/memes \r\nsupport the page if you want ❤" false 84 70600 1652274907 2022-05-11T13:15:07.000Z 13100000 false <NA> <NA> NA <NA> <NA> NA https://p16-amd-va.tiktokcdn.com/obj/musically-maliva-obj/c9361f827e1dcb19d2e54e58654c9f55.jpeg 23864 meme <NA> <NA> 7032147077300682757 trynottolaughtiktoktv TikTok's Funniest Home Videos! 7096466682046188805 https://v16-webapp.tiktok.com/7cd241572bcadf38463d6c07c543d181/62e20248/video/tos/useast2a/tos-useast2a-ve-0068c001/2bed6611071f43eba329d53b9045862f/?a=1988&ch=0&cr=0&dr=0&lr=tiktok_m&cd=0%7C0%7C1%7C0&cv=1&br=1224&bt=612&btag=80000&cs=0&ds=3&ft=gKSYZ8beo0PD1khipsg9w.FE75LiaQ2D~j8&mime_type=video_mp4&qs=0&rc=Ozc8NmU2ZjU1ZDtlNTxmNUBpM3JkZTk6ZjR1PDMzNzczM0AtMzNhY18zNTMxLmFgLzMxYSM0cHFtcjRnYHFgLS1kMTZzcw%3D%3D&l=202207272127060102230831572562751B <NA> <NA> <NA> <NA> <NA> <NA> <NA> dailydosevideos original sound true https://sf16-ies-music-va.tiktokcdn.com/obj/musically-maliva-obj/7096789979888372485.mp3 72500000 meme 556B 133400 Try not to laugh hard #meme #trynottolaughtiktoktv 62 1024 576 https://www.tiktok.com/@dailydosevideos_/video/7096466682046188805
5 https://p19-sign.tiktokcdn-us.com/tos-useast5-avt-0068-tx/384066bbbd657c12639f9af2112962c7~c5_720x720.jpeg?x-expires=1659128400&x-signature=2fjbTOlibdNwKwKHp7%2B8raRkWxs%3D 47600 3300000 690 137300000 6621521206107717638 jakeypoov Jake Sherman "-.-- --- ..- .-. . .-.. --- ...- . -.. ---··· -·--·-\r\nIm the vent guy" true 247 35500 1586829887 2020-04-14T02:04:47.000Z 13100000 false <NA> <NA> NA <NA> <NA> NA <NA> 229207 fyp <NA> <NA> 1447628 vent <NA> 6815382456242326789 https://v16-webapp.tiktok.com/89f3c196e4ebdafc5fa78e32cca5bec3/62e2022e/video/tos/useast2a/tos-useast2a-pve-0068/44685dd2b5464044a46418ba8fc8fb34/?a=1988&ch=0&cr=0&dr=0&lr=tiktok_m&cd=0%7C0%7C1%7C0&cv=1&br=1414&bt=707&btag=80000&cs=0&ds=3&ft=gKSYZ8beo0PD1khipsg9w.FE75LiaQ2D~j8&mime_type=video_mp4&qs=0&rc=NDozMzc6ZThoNGY6NzxpZUBpMzg5OTk2aTZrdDMzZDczM0AyYWAuXjBfNjMxMC1eXjIwYSNmZWducmFkaF5fLS0yMTZzcw%3D%3D&l=202207272127060102230831572562751B @abbysherm <NA> <NA> <NA> <NA> <NA> <NA> Jake Sherman original sound true https://sf16-ies-music-va.tiktokcdn.com/obj/musically-maliva-obj/1663909986295846.mp3 62700000 meme 556B 252900 HE DIDN’T HAVE HIS MASK ON @abbysherm (Follow my insta: JakeyPooV) #fyp #vent #meme #joke 36 1024 576 https://www.tiktok.com/@jakeypoov/video/6815382456242326789
6 https://p16-sign-va.tiktokcdn.com/tos-maliva-avt-0068/8a65c3315cb6050b22237ae56c2ae83d~c5_720x720.jpeg?x-expires=1659128400&x-signature=87gqAyFHZkFsBUbmTQWoJN7tz%2Fw%3D 19000 646700 86 13000000 6725538523384693765 jiraer23 Jiraer Koullokian "2nd account : 4k_cars_clips" false 127 205200 1613856627 2021-02-20T21:30:27.000Z 12800000 false 1017488 Sugar Crash NA <NA> <NA> NA <NA> 229207 fyp <NA> <NA> 42164 foryou <NA> 6931461393459809542 https://v16-webapp.tiktok.com/5777c8f766ae0285c0b04f28d05ebfe9/62e20214/video/tos/useast2a/tos-useast2a-ve-0068c001/14b585d830124655b50381890beb25a1/?a=1988&ch=0&cr=0&dr=0&lr=tiktok_m&cd=0%7C0%7C1%7C0&cv=1&br=1228&bt=614&btag=80000&cs=0&ds=3&ft=gKSYZ8beo0PD1khipsg9w.FE75LiaQ2D~j8&mime_type=video_mp4&qs=0&rc=ZjU3aTZoNGc1NjZoNjs8O0BpanB5b215bnVxMzMzaTczM0AtXzAyMWE0Xi8xYS81MTRgYSNlcWgzZjVxcmxgLS1jMTZzcw%3D%3D&l=202207272127060102230831572562751B <NA> <NA> <NA> <NA> <NA> <NA> SugarCrash! ElyOtto SugarCrash! false https://sf16-ies-music-va.tiktokcdn.com/obj/tos-useast2a-ve-2774/1a6bb53e79284426b463cdc7d10a80d5 100800000 meme 556B 795700 DOGS ARE THE BEST . #fyp #foryou #veryfunny #dog #meme #clip 10 960 540 https://www.tiktok.com/@jiraer23/video/6931461393459809542
7 https://p16-sign-va.tiktokcdn.com/musically-maliva-obj/2490ca8e0e269775f31be844a87c062e~c5_720x720.jpeg?x-expires=1659128400&x-signature=TAW5%2B4BZSJJx5vM3vnBcJ%2B%2FYMsk%3D 230 8000000 69 313100000 6734403205457642502 samuelgrubbs Samuel Grubbs "Emmy Nominee\r\n✞ Romans 10:9-13 ✞\r\nSamuelGrubbsTeam@unitedtalent.com" true 147 59900 1570229474 2019-10-04T22:51:14.000Z 12500000 false <NA> <NA> NA <NA> <NA> NA https://p16-amd-va.tiktokcdn.com/obj/musically-maliva-obj/a3237c664af342f99d4f97515e4834a5 1745 lol "Can you laugh any louder?\r\nHow to shoot: \r\n\r\n1. Click the shoot icon. \r\n2. Click on &quot;add sound&quot;\r\n3. Add your meme text. \r\n4. Have fun!" <NA> 5424 funny What's so #Funny? 6744084223445044485 https://v16-webapp.tiktok.com/49d3b2088a432b4bf2b2a65a90fcec73/62e2021c/video/tos/useast2a/tos-useast2a-ve-0068/ed617165988b4b5d995eefb45fd0ab23/?a=1988&ch=0&cr=0&dr=0&lr=tiktok_m&cd=0%7C0%7C1%7C0&cv=1&br=3970&bt=1985&btag=80000&cs=0&ds=3&ft=gKSYZ8beo0PD1khipsg9w.FE75LiaQ2D~j8&mime_type=video_mp4&qs=0&rc=NWZpZzg8ZmQ4aTtoZDQ6OUBpMzhsb3c7NHh2cDMzNTczM0A1YDA2NmIvNmExYTEzX2I2YSNiXjBtZC1oZ19fLS0xMTZzcw%3D%3D&l=202207272127060102230831572562751B <NA> <NA> <NA> <NA> <NA> <NA> Woah (feat. D3Mstreet) KRYPTO9095 Woah (feat. D3Mstreet) false https://sf16-ies-music-va.tiktokcdn.com/obj/tos-useast2a-ve-2774/8b7574ae7d864d259bc0ee18880505fe 133200000 meme 556B 707400 The last guy tho #lol #funny #foryou #foryoupage #fail #meme #relatable #crazy #wow #haha 18 1280 720 https://www.tiktok.com/@samuelgrubbs/video/6744084223445044485
8 https://p16-sign-va.tiktokcdn.com/tos-maliva-avt-0068/525b411cc1b4cf6afe58305bbfed7d74~c5_720x720.jpeg?x-expires=1659128400&x-signature=vB6WejAuBsQBfAWj9NDXQkL5maU%3D 9929 227000 208 38100000 6901676902784730118 movienerd74 Movie-Nerd74 "18 love Marvel/DC/StarWars/etc! I post news! \r\nLet’s get that 300k!!!, TY❤️" false 3173 113200 1640379934 2021-12-24T21:05:34.000Z 12100000 false <NA> <NA> NA <NA> <NA> NA <NA> 12413 spiderman <NA> <NA> 18657157 tobeymaguire <NA> 7045378167200075055 https://v16-webapp.tiktok.com/1287c00b5d2a357cc93c504850e26e85/62e2021a/video/tos/maliva/tos-maliva-ve-0068c799-us/30a8f0ff02424c1f83e811565051aff5/?a=1988&ch=0&cr=0&dr=0&lr=tiktok_m&cd=0%7C0%7C1%7C0&cv=1&br=1460&bt=730&btag=80000&cs=0&ds=3&ft=gKSYZ8beo0PD1khipsg9w.FE75LiaQ2D~j8&mime_type=video_mp4&qs=0&rc=OjU1aGdpaTVpODplOTgzN0BpajVnNTU6Zjo2OjMzZzczNEBeYDMwXy8wXzUxXmIvYV5hYSMzZmQ2cjRvNjBgLS1kMS9zcw%3D%3D&l=202207272127060102230831572562751B <NA> <NA> <NA> <NA> <NA> <NA> <NA> Movie-Nerd74 original sound true https://sf16-ies-music-va.tiktokcdn.com/obj/ies-music-ttp-dup-us/7045378150121032495.mp3 69400000 meme 556B 337900 It’s funny how each of them are so different #spiderman #tobeymaguire #andrewgarfield #tomholland #meme #mcu #marveltok #fyp #viral #nowayhome 16 1024 576 https://www.tiktok.com/@movienerd74/video/7045378167200075055 utils::tail(x=tiktok_hashtags_r, n=8)# A tibble: 8 × 52
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1 https://p16-sign-sg.tiktokcdn.com/aweme/720x720/tos-alisg-avt-0068/4eada0876c5ae48c8ca290ca0ff51c93.jpeg?x-expires=1659128400&x-signature=F2YUnqkFVhd5AdMOM2k57JJNa2U%3D 12900 21400000 39 641900000 6558418321485414402 cutybeautykhan Beauty khan "Hi" true 2054 17500 1586604332 2020-04-11T11:25:32.000Z 2500000 false <NA> <NA> NA <NA> <NA> NA <NA> 57511693 tiktokindia Be the change.musical.ly is now TikTok@indiatiktok #tiktokindia <NA> 15787069 cutebeuty <NA> 6814413705694940418 https://v16-webapp.tiktok.com/6c07d18af6028f1593df3bda793bc86f/62e205e7/video/tos/useast2a/tos-useast2a-ve-0068c003/48a5335b6b764a35a8b998046205e4af/?a=1988&ch=0&cr=0&dr=0&lr=tiktok_m&cd=0%7C0%7C1%7C0&cv=1&br=4634&bt=2317&btag=80000&cs=0&ds=3&ft=gKSYZ8fuo0PD1lY.psg9wIF2O5LiaQ2D~6a&mime_type=video_mp4&qs=0&rc=OzQ7ZDw7OGU6NTwzOzNmM0BpM2o6bGRtdWp0dDMzaDczM0A2NjJjMmE1XjYxYy1gYGBgYSNuaWs2NS4wZTVfLS1gMTZzcw%3D%3D&l=202207272143190102170192260264CEB8 <NA> <NA> <NA> <NA> <NA> <NA> <NA> azamansari516 Instag azam true https://sf16-ies-music-sg.tiktokcdn.com/obj/tiktok-obj/1658121603166241.mp3 25100000 tiktokdance 17.2B 23600 #tiktokindia #cutebeuty #tiktokdance 15 1024 576 https://www.tiktok.com/@cutybeautykhan/video/6814413705694940418
2 https://p16-sign-sg.tiktokcdn.com/aweme/720x720/tos-alisg-avt-0068/4eada0876c5ae48c8ca290ca0ff51c93.jpeg?x-expires=1659128400&x-signature=F2YUnqkFVhd5AdMOM2k57JJNa2U%3D 12900 21400000 39 641900000 6558418321485414402 cutybeautykhan Beauty khan "Hi" true 2054 14900 1587461917 2020-04-21T09:38:37.000Z 2500000 false <NA> <NA> NA <NA> <NA> NA <NA> 1629042268403717 jiya_khan <NA> <NA> 15787069 cutebeuty <NA> 6818097008646032641 https://v16-webapp.tiktok.com/9f206361bfc35f5556198fe3f1b52f2d/62e205e7/video/tos/useast2a/tos-useast2a-ve-0068c001/a0c00b2c05734df98957125ae8ef6ddf/?a=1988&ch=0&cr=0&dr=0&lr=tiktok_m&cd=0%7C0%7C1%7C0&cv=1&br=5804&bt=2902&btag=80000&cs=0&ds=3&ft=gKSYZ8fuo0PD1lY.psg9wIF2O5LiaQ2D~6a&mime_type=video_mp4&qs=0&rc=N2QzNjpkOTlnNTU8Z2g7NEBpamg5cXc1OjRldDMzNjczM0BhMy80XjViXi8xNDI1L19hYSNpNm9mLjFrcmNfLS0vMTZzcw%3D%3D&l=202207272143190102170192260264CEB8 @cuteipiejiya <NA> <NA> <NA> <NA> <NA> Laxed - Siren Beat Jawsh 685 Laxed false https://sf16-ies-music-va.tiktokcdn.com/obj/tos-useast2a-ve-2774/98f717da273b4c52ae7ec1d520194f2d 32500000 tiktokdance 17.2B 10400 #jiya_khan #cutebeuty #tiktok #tiktokcomedy #tiktokdance @cuteipiejiya 15 1024 576 https://www.tiktok.com/@cutybeautykhan/video/6818097008646032641
3 https://p16-sign-va.tiktokcdn.com/musically-maliva-obj/64d0354d8b58c532b0a09169964c0bc3~c5_720x720.jpeg?x-expires=1659128400&x-signature=CBQ9cLy5x1ntilSR1SzpWxO%2BLow%3D 27900 6100000 422 51500000 6806465339817673733 thejohnnydshow John Di Domenico "John Di Domenico \r\nEmmy Nominated, Award Winning\r\nComedian, Actor, Impersonator" true 373 19500 1588643612 2020-05-05T01:53:32.000Z 2400000 false <NA> <NA> NA <NA> <NA> NA <NA> 38970 trump <NA> <NA> 1606334392378370 comedy <NA> 6823172346300484869 https://v16-webapp.tiktok.com/9ac3f9ed25a7ddda5d01e397e534ef7a/62e205e7/video/tos/useast2a/tos-useast2a-ve-0068c004/f9914dec2cd8421580100b491ae146cb/?a=1988&ch=0&cr=0&dr=0&lr=tiktok_m&cd=0%7C0%7C1%7C0&cv=1&br=2942&bt=1471&btag=80000&cs=0&ds=3&ft=gKSYZ8fuo0PD1lY.psg9wIF2O5LiaQ2D~6a&mime_type=video_mp4&qs=0&rc=OzpkNjo0ZjwzNGdnaGg6aEBpM2xuNTk8dDdmdDMzZzczM0A2NmFfYy41XmIxXjAuX2AvYSNtXmdiZW5wYmxfLS1fMTZzcw%3D%3D&l=202207272143190102170192260264CEB8 @johndidomenico <NA> <NA> <NA> <NA> <NA> Laxed - Siren Beat Jawsh 685 Laxed false https://sf16-ies-music-va.tiktokcdn.com/obj/tos-useast2a-ve-2774/491ebe57a5f7445c8258f3d39a65bf0a 38700000 tiktokdance 17.2B 77500 #trump #comedy #tiktokdance #fyp @johndidomenico 15 1024 576 https://www.tiktok.com/@thejohnnydshow/video/6823172346300484869
4 https://p16-sign-va.tiktokcdn.com/musically-maliva-obj/1651531957900293~c5_720x720.jpeg?x-expires=1659128400&x-signature=FaRAg%2BaUfvcIp0rKHcGppfQmCNg%3D 38 251100 0 2600000 6764660438212592646 tiktok.dance.tuttorial dance tutorials ✌️ "MMTHANKYOU\r\nscorDN" false 2 5400 1575093425 2019-11-30T05:57:05.000Z 2400000 false <NA> <NA> NA <NA> <NA> NA <NA> 88764338 foryoupage <NA> <NA> 42164 foryou <NA> 6764974729607597318 https://v16-webapp.tiktok.com/8e605da6abb83a28b9dcd22fe1279bf2/62e205fa/video/tos/useast2a/tos-useast2a-ve-0068/ed3c60755e1c4830ad420f6e9785e239/?a=1988&ch=0&cr=0&dr=0&lr=tiktok_m&cd=0%7C0%7C1%7C0&cv=1&br=1228&bt=614&btag=80000&cs=0&ds=3&ft=eXd.6HQ9Myq8Z..x5we2NLH6yl7Gb&mime_type=video_mp4&qs=0&rc=NDw4aTQ8NTk0OjlpZ2dnN0BpM206amd2ZzgzcTMzZTczM0AzYjBeMDAvXmIxNV8tMS0zYSMvM2NsYmQ0NmVfLS1jMTZzcw%3D%3D&l=202207272143270102170191440C61812C <NA> <NA> <NA> <NA> <NA> <NA> <NA> dance tutorials ✌️ original sound true https://sf16-ies-music-va.tiktokcdn.com/obj/musically-maliva-obj/1651604113076277.mp3 16700000 tiktokdance 17.2B 198000 tutorial on how to do “american boy” #foryoupage #foryou #fyp #dancetutorial #tiktok #tiktokdance #itsblackfriday #happythanksgiving #vsco #dance#fy 27 960 540 https://www.tiktok.com/@tiktok.dance.tuttorial/video/6764974729607597318
5 https://p16-sign-sg.tiktokcdn.com/aweme/720x720/tos-alisg-avt-0068/4eada0876c5ae48c8ca290ca0ff51c93.jpeg?x-expires=1659128400&x-signature=F2YUnqkFVhd5AdMOM2k57JJNa2U%3D 12900 21400000 39 641900000 6558418321485414402 cutybeautykhan Beauty khan "Hi" true 2054 12200 1591270930 2020-06-04T11:42:10.000Z 2400000 false <NA> <NA> NA <NA> <NA> NA <NA> 18693583 tiktokcomedy <NA> <NA> 15787069 cutebeuty <NA> 6834456591979367682 https://v16-webapp.tiktok.com/a749b70fba1ca94f224432f45783fa41/62e205eb/video/tos/useast2a/tos-useast2a-pve-0068/b67230c2c6a6403f8b6e6be83c9fffe7/?a=1988&ch=0&cr=0&dr=0&lr=tiktok_m&cd=0%7C0%7C1%7C0&cv=1&br=3404&bt=1702&btag=80000&cs=0&ds=3&ft=eXd.6HQ9Myq8Z..x5we2NLH6yl7Gb&mime_type=video_mp4&qs=0&rc=ZGZnZmc8PDg7NDM1O2Y8NkBpM2k1NHVrN3dndTMzZjczM0BfYy1gNl8uNi4xYDUuYy4xYSNmYWhtMHJlb2BfLS1eMTZzcw%3D%3D&l=202207272143270102170191440C61812C @__angel_sneha__ @arbaz_mallick_official @cuteipiejiya <NA> <NA> <NA> <NA> Zia_sulthan1 original sound true https://sf16-ies-music-va.tiktokcdn.com/obj/musically-maliva-obj/1638348581178406.mp3 35600000 tiktokdance 17.2B 17200 #tiktokcomedy #cutebeuty #tiktokdance @__angel_sneha__ @arbaz_mallick_official @cuteipiejiya 12 1024 576 https://www.tiktok.com/@cutybeautykhan/video/6834456591979367682
6 https://p16-sign-va.tiktokcdn.com/tos-maliva-avt-0068/b79df7b3a90aecfcca1455a564dd0b3d~c5_720x720.jpeg?x-expires=1659128400&x-signature=b6CubmaoeMTCwKidPywfNZXL6a4%3D 195200 44000 1336 2900000 6687361564112733189 julespods jules "hello there" false 5 7317 1583475241 2020-03-06T06:14:01.000Z 2300000 false <NA> <NA> NA <NA> <NA> NA <NA> 229207 fyp <NA> <NA> 42164 foryou <NA> 6800974337714261253 https://v16-webapp.tiktok.com/b2290f1e35fb5747dcb2a2b0765f736b/62e2060f/video/tos/useast2a/tos-useast2a-pve-0068/50f001a9320642d7a3dbc10dee5e0c7c/?a=1988&ch=0&cr=0&dr=0&lr=tiktok_m&cd=0%7C0%7C1%7C0&cv=1&br=3858&bt=1929&btag=80000&cs=0&ds=3&ft=eXd.6HQ9Myq8Z..x5we2NLH6yl7Gb&mime_type=video_mp4&qs=0&rc=NWU4OGg5aDU4NmgzaDozOEBpM20zdG9uMzd4czMzPDczM0BiXmA2YWE2X14xMi4xLzYvYSNfaGRjX18zZ2RfLS0uMTZzcw%3D%3D&l=202207272143270102170191440C61812C <NA> <NA> <NA> <NA> <NA> <NA> <NA> Steven & Archie original sound true https://sf16-ies-music-va.tiktokcdn.com/obj/musically-maliva-obj/1656476600046613.mp3 8600000 tiktokdance 17.2B 53000 what our pep assemblies are like #fyp #foryou #pepassembly #tiktokdance #dance 48 1280 720 https://www.tiktok.com/@julespods/video/6800974337714261253
7 https://p16-sign-sg.tiktokcdn.com/aweme/720x720/tos-alisg-avt-0068/4eada0876c5ae48c8ca290ca0ff51c93.jpeg?x-expires=1659128400&x-signature=F2YUnqkFVhd5AdMOM2k57JJNa2U%3D 12900 21400000 39 641900000 6558418321485414402 cutybeautykhan Beauty khan "Hi" true 2054 17100 1590213794 2020-05-23T06:03:14.000Z 2300000 false <NA> <NA> NA <NA> <NA> NA <NA> 57511693 tiktokindia Be the change.musical.ly is now TikTok@indiatiktok #tiktokindia <NA> 15787069 cutebeuty <NA> 6829916227352284417 https://v16-webapp.tiktok.com/146ecf998365aa4d753d2cd4be4d8767/62e205ed/video/tos/useast2a/tos-useast2a-ve-0068c002/ef539f034d434343b42b56172ad0fb11/?a=1988&ch=0&cr=0&dr=0&lr=tiktok_m&cd=0%7C0%7C1%7C0&cv=1&br=3616&bt=1808&btag=80000&cs=0&ds=3&ft=eXd.6HQ9Myq8Z..x5we2NLH6yl7Gb&mime_type=video_mp4&qs=0&rc=PDw2NTU2PGVnM2k4aGg1NEBpajQ3bTl4NzNldTMzOzczM0BhLzQyNGAwNjUxLmJgMmJeYSNvbGtpLTIxbTFfLS1iMTZzcw%3D%3D&l=202207272143270102170191440C61812C @__angel_sneha__ @arbaz_mallick_official @cuteipiejiya <NA> <NA> <NA> Pogaru Chandan Shetty Karabuu false <NA> 27400000 tiktokdance 17.2B 10400 #tiktokindia #cutebeuty #tiktokdance @__angel_sneha__ @arbaz_mallick_official @cuteipiejiya 14 1024 576 https://www.tiktok.com/@cutybeautykhan/video/6829916227352284417
8 https://p16-sign-va.tiktokcdn.com/musically-maliva-obj/64d0354d8b58c532b0a09169964c0bc3~c5_720x720.jpeg?x-expires=1659128400&x-signature=CBQ9cLy5x1ntilSR1SzpWxO%2BLow%3D 27900 6100000 422 51500000 6806465339817673733 thejohnnydshow John Di Domenico "John Di Domenico \r\nEmmy Nominated, Award Winning\r\nComedian, Actor, Impersonator" true 373 32600 1595019999 2020-07-17T21:06:39.000Z 2300000 false <NA> <NA> NA <NA> <NA> NA <NA> 11251 go Ever feel like telling somebody to just GO? We do too! Make a #GoChallenge musical.ly to @VBozeman's new song and she'll give a social shout out and a prize to her faves! <NA> 95636 gogogo 魔性音樂接力挑戰，gogogo 誰會是下一個接力達人？ 6850558720049515781 https://v16-webapp.tiktok.com/cf96a20cfdc7a0e28f85f38b49fbd472/62e205e9/video/tos/useast2a/tos-useast2a-ve-0068c002/f8e78dc86aa2454bb673b8ec4974bc87/?a=1988&ch=0&cr=0&dr=0&lr=tiktok_m&cd=0%7C0%7C1%7C0&cv=1&br=2980&bt=1490&btag=80000&cs=0&ds=3&ft=eXd.6HQ9Myq8Z..x5we2NLH6yl7Gb&mime_type=video_mp4&qs=0&rc=NmQ5NGg4OjxoZjY6NGY5aEBpanQ0bjgzM3A0djMzODczM0AtNS01L14vNWExXzBjNS0zYSNsMC00LmYyMjZfLS1hMTZzcw%3D%3D&l=202207272143270102170191440C61812C @johndidomenico <NA> <NA> <NA> <NA> <NA> Go Go Go Who's Next? Hip Hop Harry Go Go Go Who's Next? false https://sf16-ies-music-va.tiktokcdn.com/obj/tos-useast2a-ve-2774/d7367ee036094bbfa7844052976dad25 28500000 tiktokdance 17.2B 48500 #go #gogogo #whosnext #trump #trump2020 #tiktokdance #thejohnald @johndidomenico 10 1024 576 https://www.tiktok.com/@thejohnnydshow/video/6850558720049515781 # https://www.kaggle.com/datasets/sveta151/tiktok-popular-songs-2019
tiktok_songs_py = pandas.read_csv("../../dataset/tiktok-songs-2019.csv")
tiktok_songs_py.info()<class 'pandas.core.frame.DataFrame'>
RangeIndex: 223 entries, 0 to 222
Data columns (total 18 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 track_name 223 non-null object
1 artist_name 223 non-null object
2 artist_pop 223 non-null int64
3 album 223 non-null object
4 track_pop 223 non-null int64
5 danceability 223 non-null float64
6 energy 223 non-null float64
7 loudness 223 non-null float64
8 mode 223 non-null int64
9 key 223 non-null int64
10 speechiness 223 non-null float64
11 acousticness 223 non-null float64
12 instrumentalness 223 non-null float64
13 liveness 223 non-null float64
14 valence 223 non-null float64
15 tempo 223 non-null float64
16 time_signature 223 non-null int64
17 duration_ms 223 non-null int64
dtypes: float64(9), int64(6), object(3)
memory usage: 31.5+ KBtiktok_songs_py.head(n=8) track_name artist_name artist_pop album track_pop danceability energy loudness mode key speechiness acousticness instrumentalness liveness valence tempo time_signature duration_ms
0 Shake It Metro Station 53 Metro Station 68 0.618 0.955 -3.836 1 4 0.0798 0.00221 0.000003 0.4860 0.790 150.034 4 179947
1 Chinese New Year SALES 61 SALES - EP 53 0.744 0.845 -7.422 0 4 0.2530 0.75900 0.232000 0.1000 0.749 75.221 4 160000
2 Baby I'm Yours Breakbot 57 By Your Side 69 0.829 0.792 -3.755 0 2 0.0668 0.72600 0.000006 0.1220 0.758 118.050 4 215507
3 The Git Up Blanco Brown 60 The Git Up 2 0.847 0.678 -8.635 1 9 0.1090 0.06690 0.000000 0.2740 0.811 97.984 4 200594
4 Say Hey (I Love You) Michael Franti & Spearhead 58 All Rebel Rockers 0 0.738 0.983 -4.374 0 5 0.0855 0.03800 0.000006 0.1830 0.957 92.998 4 235760
5 Falling for U Peachy! 57 Falling for U 1 0.516 0.358 -12.018 0 8 0.4240 0.74300 0.000001 0.0813 0.397 79.509 4 124000
6 Shooting Stars Bag Raiders 53 Bag Raiders (Deluxe) 0 0.649 0.716 -6.232 1 11 0.0445 0.08790 0.030000 0.0824 0.503 124.968 4 235813
7 fast Sueco 65 fast 0 0.877 0.503 -10.269 0 7 0.2200 0.16400 0.013200 0.1020 0.529 100.069 4 144000tiktok_songs_py.tail(n=8) track_name artist_name artist_pop album track_pop danceability energy loudness mode key speechiness acousticness instrumentalness liveness valence tempo time_signature duration_ms
215 Stereo Love Edward Maya 64 The Stereo Love Show 18 0.771 0.728 -6.809 1 9 0.0359 0.01740 0.029900 0.0779 0.297 127.019 4 320637
216 Never Ever Getting Rid of Me Christopher Fitzgerald 48 Waitress (Original Broadway Cast Recording) 63 0.622 0.542 -6.215 0 7 0.0510 0.38700 0.000000 0.1140 0.939 174.494 4 136200
217 Wanna Be Like You Jacob 4 Wanna Be Like You 17 0.816 0.363 -10.362 1 5 0.1010 0.48700 0.000003 0.2050 0.595 104.274 4 229415
218 Skinny Legend Anthem Ava Louise 32 Skinny Legend Anthem 50 0.845 0.518 -7.568 1 11 0.2990 0.07030 0.000000 0.1220 0.443 129.951 4 135993
219 Material Girl Madonna 77 Celebration (double disc version) 78 0.742 0.883 -3.419 1 0 0.0329 0.33300 0.000008 0.0964 0.978 136.506 4 240280
220 I Wish - Radio Edit Skee-Lo 51 I Wish 63 0.715 0.738 -10.139 1 7 0.1570 0.00826 0.000000 0.2600 0.573 97.877 4 252307
221 Oh No I Got a Disease Buss Crew 11 Oh No I Got a Disease 26 0.649 0.735 -4.365 0 10 0.7300 0.14600 0.000000 0.1240 0.845 86.704 5 134769
222 Woahh Jufu 31 Woahh 22 0.749 0.283 -12.368 0 0 0.3280 0.61800 0.000004 0.0825 0.269 142.046 4 172888# https://www.kaggle.com/datasets/sveta151/tiktok-popular-songs-2019
# Semicolon prevents the variable from being printed
tiktok_songs_jl = CSV.File("../../dataset/tiktok-songs-2019.csv") |> DataFrames.DataFrame223×18 DataFrame
Row │ track_name artist_name artist_pop album track_pop danceability energy loudness mode key speechiness acousticness instrumentalness liveness valence tempo time_signature duration_ms
│ String String31 Int64 String Int64 Float64 Float64 Float64 Int64 Int64 Float64 Float64 Float64 Float64 Float64 Float64 Int64 Int64
─────┼──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
1 │ Shake It Metro Station 53 Metro Station 68 0.618 0.955 -3.836 1 4 0.0798 0.00221 3.09e-6 0.486 0.79 150.034 4 179947
2 │ Chinese New Year SALES 61 SALES - EP 53 0.744 0.845 -7.422 0 4 0.253 0.759 0.232 0.1 0.749 75.221 4 160000
3 │ Baby I'm Yours Breakbot 57 By Your Side 69 0.829 0.792 -3.755 0 2 0.0668 0.726 5.83e-6 0.122 0.758 118.05 4 215507
4 │ The Git Up Blanco Brown 60 The Git Up 2 0.847 0.678 -8.635 1 9 0.109 0.0669 0.0 0.274 0.811 97.984 4 200594
5 │ Say Hey (I Love You) Michael Franti & Spearhead 58 All Rebel Rockers 0 0.738 0.983 -4.374 0 5 0.0855 0.038 5.93e-6 0.183 0.957 92.998 4 235760
6 │ Falling for U Peachy! 57 Falling for U 1 0.516 0.358 -12.018 0 8 0.424 0.743 1.3e-6 0.0813 0.397 79.509 4 124000
7 │ Shooting Stars Bag Raiders 53 Bag Raiders (Deluxe) 0 0.649 0.716 -6.232 1 11 0.0445 0.0879 0.03 0.0824 0.503 124.968 4 235813
8 │ fast Sueco 65 fast 0 0.877 0.503 -10.269 0 7 0.22 0.164 0.0132 0.102 0.529 100.069 4 144000
9 │ On A Roll Ashley O 43 On A Roll 58 0.736 0.81 -6.354 1 5 0.0906 0.077 0.0 0.0523 0.387 125.011 4 154448
10 │ Good Girls Bad Guys Falling In Reverse 70 The Drug In Me Is You 64 0.474 0.948 -2.592 1 3 0.122 0.0223 0.0 0.43 0.611 146.46 4 195373
11 │ Just Did a Bad Thing Bill Wurtz 43 Just Did a Bad Thing 43 0.754 0.526 -12.96 1 1 0.0435 0.00261 0.466 0.113 0.716 117.051 4 167180
12 │ Wait a Minute! WILLOW 76 ARDIPITHECUS 86 0.764 0.705 -5.279 0 3 0.0278 0.0371 1.94e-5 0.0943 0.672 101.003 4 196520
13 │ Spooky, Scary Skeletons Andrew Gold 49 Halloween Howls: Fun & Scary Mus… 50 0.806 0.447 -10.181 1 7 0.0461 0.27 3.7e-6 0.335 0.914 77.033 5 127627
14 │ Livin' in the Sunlight, Lovin' i… Tiny Tim 41 God Bless Tiny Tim 51 0.603 0.276 -19.238 1 4 0.0871 0.911 0.00237 0.484 0.86 99.561 4 125933
15 │ Tell Me Something I Don't Know -… Selena Gomez 80 Another Cinderella Story (Origin… 56 0.611 0.936 -3.697 0 7 0.11 0.0309 0.0 0.12 0.697 144.901 4 201427
16 │ STUPID (feat. Yung Baby Tate) Ashnikko 69 Hi, It's Me 70 0.772 0.637 -6.881 1 2 0.114 0.00459 0.0 0.0778 0.54 149.906 4 167317
17 │ Beef FloMix Flo Milli 67 Beef FloMix 53 0.857 0.575 -8.36 1 7 0.221 0.239 0.0 0.293 0.901 116.993 4 147692
18 │ Prom Queen Beach Bunny 70 Prom Queen 80 0.528 0.797 -7.487 1 4 0.0485 0.000391 0.011 0.14 0.751 143.785 4 136562
19 │ Don't Lose Ur Head SIX 61 Six: The Musical (Studio Cast Re… 66 0.859 0.605 -7.635 0 7 0.357 0.251 0.0 0.132 0.722 82.91 4 245303
20 │ Legs (From "RuPaul's Drag Race 8… Lucian Piane 32 Legs (From "RuPaul's Drag Race 8… 44 0.9 0.72 -5.486 0 5 0.232 0.00502 6.26e-6 0.543 0.749 122.994 4 106364
21 │ Woah (feat. D3Mstreet) KRYPTO9095 39 Woah (feat. D3Mstreet) 56 0.888 0.522 -8.936 1 1 0.43 0.00739 0.0 0.0452 0.623 144.011 4 123373
22 │ Steppin' Supa Dupa Humble 38 Steppin' 55 0.85 0.58 -4.502 1 1 0.104 0.000404 0.0821 0.0714 0.12 160.055 4 145253
23 │ Hungry Hippo Tierra Whack 58 Whack World 57 0.893 0.61 -5.691 0 4 0.0488 0.0227 0.00744 0.102 0.778 136.64 4 60000
24 │ I'm Sorry Swell 47 Theres Still Us 0 0.944 0.541 -9.61 0 6 0.166 0.0879 0.000119 0.033 0.724 134.942 4 157333
25 │ Vibin' Kesh Kesh 27 Vibin' 41 0.686 0.805 -6.816 0 11 0.154 0.00478 1.09e-6 0.0887 0.289 130.981 4 154567
26 │ Fly Me To The Moon (In Other Wor… Frank Sinatra 74 Sinatra/Basie: The Complete Repr… 71 0.668 0.26 -14.256 1 7 0.0523 0.453 0.0 0.0621 0.368 119.416 4 147000
27 │ Yellow Hearts Ant Saunders 52 Yellow Hearts 0 0.866 0.399 -10.938 1 7 0.0745 0.117 0.0 0.0689 0.713 129.047 4 223256
28 │ Old Town Road Lil Nas X 81 7 EP 76 0.907 0.53 -6.112 1 1 0.127 0.0578 2.23e-6 0.101 0.507 135.998 4 113000
29 │ I Don't Mind (feat. Juicy J) Usher 78 I Don't Mind (feat. Juicy J) 67 0.87 0.464 -8.337 1 4 0.178 0.205 0.0 0.0902 0.457 112.974 4 251989
30 │ ROXANNE Arizona Zervas 66 ROXANNE 78 0.621 0.601 -5.616 0 6 0.148 0.0522 0.0 0.46 0.457 116.735 5 163636
31 │ American Boy Estelle 65 Shine 78 0.727 0.729 -2.99 1 0 0.326 0.171 0.0 0.07 0.512 117.932 4 284733
32 │ Beggin (original version) Madcon 61 So Dark The Con Of Man 70 0.715 0.8 -5.144 0 4 0.057 0.0271 0.0 0.0648 0.445 129.023 4 216147
33 │ Put Your Head on My Shoulder Paul Anka 59 Sings His Favorites 63 0.549 0.433 -8.526 1 7 0.0327 0.906 0.0108 0.0774 0.486 116.068 3 155080
34 │ prom dress mxmtoon 67 the masquerade 73 0.56 0.674 -6.048 1 10 0.0446 0.698 0.0 0.123 0.421 119.942 4 197933
35 │ Criminal Britney Spears 78 Femme Fatale (Deluxe Version) 71 0.696 0.734 -5.294 1 7 0.0298 0.0485 0.0 0.183 0.711 107.987 4 225080
36 │ Runway Stunna Girl 45 YKWTFGO 47 0.638 0.912 -3.3 1 1 0.474 0.0155 0.0 0.234 0.725 190.178 4 152464
37 │ It's The Hard-Knock Life Quvenzhané Wallis 45 Annie (Original Motion Picture S… 53 0.738 0.841 -5.352 1 9 0.0924 0.0452 0.0105 0.03 0.836 163.913 4 130467
38 │ Faucet Failure Ski Mask The Slump God 75 STOKELEY 75 0.935 0.552 -9.373 0 10 0.335 0.111 0.0 0.0952 0.615 99.993 4 145627
39 │ Buttercup Jack Stauber 62 Pop Food 72 0.705 0.373 -9.066 1 7 0.0384 0.723 0.81 0.289 0.551 120.046 4 208026
40 │ Hey Julie! (feat. Lil Yachty) KYLE 65 Light of Mine (Deluxe) 55 0.803 0.499 -5.932 1 11 0.306 0.0363 3.46e-6 0.129 0.749 161.042 4 156637
41 │ Uno Ambjaay 41 Uno 56 0.978 0.477 -8.159 1 8 0.155 0.000143 0.0 0.133 0.196 110.002 4 109091
42 │ Tunnel of Love ilyTOMMY 61 Tunnel of Love 58 0.683 0.636 -12.096 0 7 0.0978 0.302 0.498 0.0972 0.615 160.012 4 142365
43 │ Walk Man Tiny Meat Gang 52 Walk Man 59 0.928 0.696 -4.972 0 4 0.0866 0.331 0.000119 0.121 0.396 94.994 4 165938
44 │ CRACKHEAD iLOVEFRiDAY 42 CRACKHEAD 31 0.746 0.419 -9.083 1 5 0.478 0.0828 0.00457 0.561 0.421 145.059 4 43427
45 │ Fake ID Riton 68 Foreign Ororo (Special Edition) 63 0.746 0.862 -6.11 1 8 0.0776 0.21 0.0363 0.225 0.604 122.011 4 246970
46 │ Wasabi Little Mix 77 LM5 (Deluxe) 66 0.847 0.656 -4.976 1 2 0.115 0.00451 0.000854 0.143 0.234 114.001 4 154787
⋮ │ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮
179 │ A Thousand Years Christina Perri 70 A Thousand Years 80 0.421 0.407 -7.445 1 10 0.0267 0.309 0.000961 0.11 0.161 139.028 3 285120
180 │ Mr. Saxobeat - Radio Edit Alexandra Stan 61 Sundown (Original Motion Picture… 0 0.72 0.925 -4.165 0 4 0.0511 0.0276 0.000238 0.14 0.781 127.004 4 195105
181 │ We R Who We R Kesha 76 Cannibal (Expanded Edition) 72 0.736 0.817 -4.9 1 8 0.0407 0.00987 0.00167 0.117 0.653 119.95 4 204760
182 │ Crank That (Soulja Boy) Soulja Boy 69 souljaboytellem.com 74 0.736 0.74 -2.18 1 0 0.0786 0.515 0.0 0.0468 0.803 140.141 4 221933
183 │ No More ?'s Eazy-E 64 Eazy-Duz-It 69 0.935 0.805 -4.782 1 7 0.225 0.0143 1.53e-6 0.298 0.624 105.641 4 234360
184 │ Y U Gotta B Like That Audrey Mika 56 Y U Gotta B Like That 58 0.732 0.413 -6.353 1 9 0.0702 0.287 0.0 0.0565 0.761 92.066 4 154456
185 │ Kill the Director The Wombats 65 Proudly Present... A Guide to Lo… 64 0.41 0.858 -5.295 0 1 0.0485 0.00347 0.0 0.0897 0.962 161.487 4 161560
186 │ Ligma Billy Marchiafava 53 Ligma 49 0.937 0.397 -9.414 1 8 0.418 0.0915 0.0 0.0919 0.603 128.561 4 75717
187 │ Airplane Mode Limbo 50 Holo 0 0.665 0.373 -10.631 1 7 0.158 0.852 0.118 0.108 0.07 119.905 4 164113
188 │ It's You Ali Gatie 70 It's You 5 0.732 0.463 -6.972 0 11 0.0287 0.374 0.0 0.194 0.397 95.971 4 212607
189 │ Baby Yoda Song a Day 25 Baby Yoda 38 0.822 0.408 -16.616 1 2 0.206 0.373 0.0685 0.118 0.848 160.074 4 87000
190 │ Monsters, Inc. Randy Newman 60 Monsters, Inc. (Original Motion … 55 0.68 0.465 -8.353 1 8 0.029 0.743 0.676 0.136 0.847 109.972 4 126387
191 │ hot girl bummer blackbear 79 hot girl bummer 5 0.778 0.559 -7.109 0 6 0.0776 0.128 0.0 0.399 0.678 129.989 1 185093
192 │ Get Down SIX 61 Six: The Musical (Studio Cast Re… 61 0.898 0.406 -9.905 0 4 0.102 0.00986 0.0 0.0509 0.39 102.985 4 253993
193 │ Falling Trevor Daniel 65 Nicotine 3 0.784 0.43 -8.756 0 10 0.0364 0.123 0.0 0.0887 0.236 127.087 4 159382
194 │ successful Ariana Grande 87 Sweetener 65 0.847 0.603 -4.607 0 0 0.0397 0.107 7.4e-6 0.106 0.735 114.045 4 227387
195 │ Chucky Cheese MadeinTYO 62 Sincerely, Tokyo 55 0.89 0.542 -4.826 0 10 0.314 0.523 0.0 0.123 0.565 78.458 4 165293
196 │ Sin City Chrishan 42 Sin City 58 0.886 0.225 -11.437 1 4 0.457 0.676 0.0 0.0938 0.387 120.1 4 212000
197 │ MICKEY Lil Yachty 75 Lil Boat 2 54 0.971 0.465 -5.655 1 1 0.23 0.0295 0.0 0.097 0.131 119.994 4 160518
198 │ Another Day Of Sun La La Land Cast 46 La La Land (Original Motion Pict… 64 0.588 0.742 -6.757 1 8 0.0528 0.0162 3.98e-6 0.653 0.824 125.819 4 228173
199 │ Forget Me Thots Yung Gravy 75 Yung Gravity - EP 58 0.887 0.737 -6.489 0 6 0.16 0.243 4.11e-5 0.109 0.685 104.924 4 184000
200 │ Act Up City Girls 67 Girl Code 67 0.938 0.638 -4.713 1 8 0.189 0.0167 0.0 0.111 0.313 97.075 4 158332
201 │ This Is for Rachel Gxrrixon 0 This Is for Rachel 0 0.9 0.461 -14.453 1 8 0.515 0.161 1.78e-6 0.089 0.35 96.985 4 165698
202 │ Hokus Pokus Insane Clown Posse 63 The Great Milenko 62 0.87 0.841 -5.084 0 11 0.0948 0.0193 7.71e-5 0.333 0.857 92.176 4 261173
203 │ 恋愛サーキュレーション 物語シリーズ 53 Utamonogatari Special Edition (O… 65 0.814 0.785 -5.679 0 1 0.0396 0.0524 0.00111 0.111 0.941 120.009 4 255040
204 │ WTF Young Spool 3 WTF 0 0.819 0.791 -5.736 1 8 0.324 0.372 6.22e-5 0.471 0.902 172.094 4 89293
205 │ 223's (feat. 9lokknine) YNW Melly 73 Melly vs. Melvin 66 0.932 0.547 -7.787 0 0 0.333 0.0388 0.0 0.0924 0.695 94.998 4 176640
206 │ cheatercheaterbestfriendeater Never Shout Never 49 Harmony 49 0.768 0.683 -7.173 1 7 0.0604 0.388 0.0 0.286 0.669 99.955 4 178144
207 │ Myself Bazzi 72 COSMIC 67 0.745 0.561 -5.513 0 9 0.072 0.465 1.12e-6 0.0338 0.902 195.918 4 167553
208 │ Obsessed Mariah Carey 76 Memoirs of an imperfect Angel (I… 68 0.742 0.468 -5.557 0 10 0.0625 0.0465 0.0 0.826 0.369 86.443 4 242200
209 │ GOODMORNINGTOKYO! TOKYO’S REVENGE 59 GOODMORNINGTOKYO! 70 0.907 0.539 -7.782 1 4 0.36 0.0408 0.0 0.253 0.664 124.918 4 150115
210 │ Walked In Ultradiox 29 Orichalcos 42 0.891 0.538 -6.724 1 2 0.394 0.123 0.0 0.0848 0.643 80.032 4 107404
211 │ Psycho! MASN 60 Psycho! 0 0.868 0.365 -9.51 1 7 0.0383 0.433 0.0 0.207 0.471 114.979 4 197217
212 │ You Need To Calm Down Taylor Swift 93 Lover 76 0.771 0.671 -5.617 1 2 0.0553 0.00929 0.0 0.0637 0.714 85.026 4 171360
213 │ Moskau Dschinghis Khan 49 7 Leben 59 0.676 0.724 -5.941 0 5 0.039 0.0681 9.29e-5 0.139 0.676 132.056 4 221373
214 │ Big Fun Jon Eidson 44 Heathers: The Musical (World Pre… 54 0.864 0.681 -6.032 1 8 0.232 0.538 0.0 0.555 0.734 118.061 4 254667
215 │ Eenie Meenie Sean Kingston 69 Eenie Meenie 74 0.72 0.607 -4.168 1 1 0.0322 0.0543 0.0 0.113 0.828 121.223 4 201947
216 │ Stereo Love Edward Maya 64 The Stereo Love Show 18 0.771 0.728 -6.809 1 9 0.0359 0.0174 0.0299 0.0779 0.297 127.019 4 320637
217 │ Never Ever Getting Rid of Me Christopher Fitzgerald 48 Waitress (Original Broadway Cast… 63 0.622 0.542 -6.215 0 7 0.051 0.387 0.0 0.114 0.939 174.494 4 136200
218 │ Wanna Be Like You Jacob 4 Wanna Be Like You 17 0.816 0.363 -10.362 1 5 0.101 0.487 3.41e-6 0.205 0.595 104.274 4 229415
219 │ Skinny Legend Anthem Ava Louise 32 Skinny Legend Anthem 50 0.845 0.518 -7.568 1 11 0.299 0.0703 0.0 0.122 0.443 129.951 4 135993
220 │ Material Girl Madonna 77 Celebration (double disc version) 78 0.742 0.883 -3.419 1 0 0.0329 0.333 7.51e-6 0.0964 0.978 136.506 4 240280
221 │ I Wish - Radio Edit Skee-Lo 51 I Wish 63 0.715 0.738 -10.139 1 7 0.157 0.00826 0.0 0.26 0.573 97.877 4 252307
222 │ Oh No I Got a Disease Buss Crew 11 Oh No I Got a Disease 26 0.649 0.735 -4.365 0 10 0.73 0.146 0.0 0.124 0.845 86.704 5 134769
223 │ Woahh Jufu 31 Woahh 22 0.749 0.283 -12.368 0 0 0.328 0.618 3.73e-6 0.0825 0.269 142.046 4 172888
132 rows omittedWrangling Data
tiktok_songs_clean_r <- tiktok_songs_r
tiktok_songs_clean_r$mode <- factor(tiktok_songs_clean_r$mode)
tiktok_songs_clean_r$mode <- recode(tiktok_songs_clean_r$mode, "0" = "Minor", "1" = "Major")
tiktok_songs_clean_r$key <- ordered(factor(tiktok_songs_clean_r$key), levels=c("0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11"))
tiktok_songs_clean_r$key <- recode(tiktok_songs_clean_r$key, "0" = "C", "1" = "C#/Db", "2" = "D", "3" = "D#/Eb", "4" = "E", "5" = "F", "6" = "F#/Gb", "7" = "G", "8" = "G#/Ab", "9" = "A", "10" = "A#/Bb", "11" = "B")
tiktok_songs_clean_r$major_key <- paste(as.character(tiktok_songs_clean_r$key), as.character(tiktok_songs_clean_r$mode), sep=" ")
tiktok_songs_clean_r$major_key[tiktok_songs_clean_r$major_key == "A Minor"] <- "C Major"; tiktok_songs_clean_r$major_key[tiktok_songs_clean_r$major_key == "A#/Bb Minor"] <- "C#/Db Major"; tiktok_songs_clean_r$major_key[tiktok_songs_clean_r$major_key == "B Minor"] <- "D Major"; tiktok_songs_clean_r$major_key[tiktok_songs_clean_r$major_key == "C Minor"] <- "D#/Eb Major"; tiktok_songs_clean_r$major_key[tiktok_songs_clean_r$major_key == "C#/Db Minor"] <- "E Major"; tiktok_songs_clean_r$major_key[tiktok_songs_clean_r$major_key == "D Minor"] <- "F Major"; tiktok_songs_clean_r$major_key[tiktok_songs_clean_r$major_key == "D#/Eb Minor"] <- "F#/Gb Major"; tiktok_songs_clean_r$major_key[tiktok_songs_clean_r$major_key == "E Minor"] <- "G Major"; tiktok_songs_clean_r$major_key[tiktok_songs_clean_r$major_key == "F Minor"] <- "G#/Ab Major"; tiktok_songs_clean_r$major_key[tiktok_songs_clean_r$major_key == "F#/Gb Minor"] <- "A Major"; tiktok_songs_clean_r$major_key[tiktok_songs_clean_r$major_key == "G Minor"] <- "A#/Bb Major"; tiktok_songs_clean_r$major_key[tiktok_songs_clean_r$major_key == "G#/Ab Minor"] <- "B Major"
tiktok_songs_clean_r$major_key <- ordered(factor(tiktok_songs_clean_r$major_key), levels=c("C Major", "C#/Db Major", "D Major", "D#/Eb Major", "E Major", "F Major", "F#/Gb Major", "G Major", "G#/Ab Major", "A Major", "A#/Bb Major", "B Major"))
tiktok_songs_clean_r$major_key <- recode(tiktok_songs_clean_r$major_key, "C Major" = "C Major (A Minor)", "C#/Db Major" = "C#/Db Major (A#/Bb Minor)", "D Major" = "D Major (B Minor)", "D#/Eb Major" = "D#/Eb Major (C Minor)", "E Major" = "E Major (C#/Db Minor)", "F Major" = "F Major (D Minor)", "F#/Gb Major" = "F#/Gb Major (D#/Eb Minor)", "G Major" = "G Major (E Minor)", "G#/Ab Major" = "G#/Ab Major (F Minor)", "A Major" = "A Major (F#/Gb Minor)", "A#/Bb Major" = "A#/Bb Major (G Minor)", "B Major" = "B Major (G#/Ab Minor)")
tiktok_songs_clean_r <- tiktok_songs_clean_r[, c("major_key", "key", "mode", "artist_name", "artist_pop", "track_name", "track_pop", "album", "duration_ms", "time_signature", "tempo", "valence","energy", "loudness", "instrumentalness", "acousticness", "liveness", "speechiness", "danceability")]
str(object=tiktok_songs_clean_r)'data.frame': 223 obs. of 19 variables:
$ major_key : Ord.factor w/ 12 levels "C Major (A Minor)"<..: 5 8 6 10 9 12 12 11 6 4 ...
$ key : Ord.factor w/ 12 levels "C"<"C#/Db"<"D"<..: 5 5 3 10 6 9 12 8 6 4 ...
$ mode : Factor w/ 2 levels "Minor","Major": 2 1 1 2 1 1 2 1 2 2 ...
$ artist_name : Factor w/ 209 levels "24kGoldn","88rising",..: 128 153 32 29 129 139 19 167 14 61 ...
$ artist_pop : int 53 61 57 60 58 57 53 65 43 70 ...
$ track_name : Factor w/ 221 levels "223's (feat. 9lokknine)",..: 161 39 14 181 158 59 163 60 141 72 ...
$ track_pop : int 68 53 69 2 0 1 0 0 58 64 ...
$ album : Factor w/ 220 levels "#3 Deluxe Version",..: 117 146 26 177 9 52 19 53 129 174 ...
$ duration_ms : int 179947 160000 215507 200594 235760 124000 235813 144000 154448 195373 ...
$ time_signature : int 4 4 4 4 4 4 4 4 4 4 ...
$ tempo : num 150 75.2 118 98 93 ...
$ valence : num 0.79 0.749 0.758 0.811 0.957 0.397 0.503 0.529 0.387 0.611 ...
$ energy : num 0.955 0.845 0.792 0.678 0.983 0.358 0.716 0.503 0.81 0.948 ...
$ loudness : num -3.84 -7.42 -3.75 -8.63 -4.37 ...
$ instrumentalness: num 0.00000309 0.232 0.00000583 0 0.00000593 0.0000013 0.03 0.0132 0 0 ...
$ acousticness : num 0.00221 0.759 0.726 0.0669 0.038 0.743 0.0879 0.164 0.077 0.0223 ...
$ liveness : num 0.486 0.1 0.122 0.274 0.183 0.0813 0.0824 0.102 0.0523 0.43 ...
$ speechiness : num 0.0798 0.253 0.0668 0.109 0.0855 0.424 0.0445 0.22 0.0906 0.122 ...
$ danceability : num 0.618 0.744 0.829 0.847 0.738 0.516 0.649 0.877 0.736 0.474 ...head(x=tiktok_songs_clean_r, n=7) major_key key mode artist_name artist_pop track_name track_pop album duration_ms time_signature tempo valence energy loudness instrumentalness acousticness liveness speechiness danceability
1 E Major (C#/Db Minor) E Major Metro Station 53 Shake It 68 Metro Station 179947 4 150 0.79 0.95 -3.8 0.0000031 0.0022 0.486 0.080 0.62
2 G Major (E Minor) E Minor SALES 61 Chinese New Year 53 SALES - EP 160000 4 75 0.75 0.84 -7.4 0.2320000 0.7590 0.100 0.253 0.74
3 F Major (D Minor) D Minor Breakbot 57 Baby I'm Yours 69 By Your Side 215507 4 118 0.76 0.79 -3.8 0.0000058 0.7260 0.122 0.067 0.83
4 A Major (F#/Gb Minor) A Major Blanco Brown 60 The Git Up 2 The Git Up 200594 4 98 0.81 0.68 -8.6 0.0000000 0.0669 0.274 0.109 0.85
5 G#/Ab Major (F Minor) F Minor Michael Franti & Spearhead 58 Say Hey (I Love You) 0 All Rebel Rockers 235760 4 93 0.96 0.98 -4.4 0.0000059 0.0380 0.183 0.086 0.74
6 B Major (G#/Ab Minor) G#/Ab Minor Peachy! 57 Falling for U 1 Falling for U 124000 4 80 0.40 0.36 -12.0 0.0000013 0.7430 0.081 0.424 0.52
7 B Major (G#/Ab Minor) B Major Bag Raiders 53 Shooting Stars 0 Bag Raiders (Deluxe) 235813 4 125 0.50 0.72 -6.2 0.0300000 0.0879 0.082 0.044 0.65which(is.na(tiktok_songs_clean_r))integer(0)Let’s see how we implement the major components of Grammar of Graphics to tell various stories.
Theme
The theme I’ll use is a custom theme that I’ve created (but not without influence from elsewhere). As the famous quote from Oscar Wilde goes, “Imitation is the sincerest form of flattery.” The honor of who (or what) I imitated goes to the London-based publisher that made me a job offer very early in my career—the The Economist.
I chose a color palette around Pantone 2028 C (orange) and its complement, Pantone 3551 C (blue)—which aren’t necessarily my favorite colors (but do accurately represent the vibrancy and excitement I have about the art & science of almost everything I’ve done in my career).
The product designer in me is happy with style guide choices. The MBA in me is thoroughly satisfied with the personal brand in-the-making (I’ve stuck with it, surprisingly). The data journalist in me is pleasantly surprised that everything came together for an effective data visualization. It has been a win-win-win.
palette_michaelmallari_r <- c(
"#ffffff",
"#eb3300", # Primary - Pantone 2028 C
"#00a6c8", # Complement - Pantone 3551 C
"#4cae04", "#9063cd", # Triadic - Pantone 3501 C, 265 C
"#0084d4", "#00af66", # Split Complement - Pantone 2184 C, 3405 C
"#ee3831", "#d76b00", # Analogous - Pantone 3556 C, 2019 C
"#ca3604", "#ff7f41", # Monochromatic - Pantone 2349 C, 164 C
"#1d252d", "#5b6770", "#c1c6c8", # Pantone 433 C; Monochromatic - Pantone 431 C, 428 C
"#d7d2cb", "#d9d9d6", # Pantone Warm Gray 1 C, Cool Gray 1 C
"#f1f0eb", "#d7e7f0", # The Economist warm gray, blue gray
"#00587c", "#00a6c8", "#3ccbda" # Pantone 3551 C (middle); Monochromatic - Pantone 308 C (left), 2226 C (right)
)
theme_michaelmallari_r <- function(){
font = "Open Sans"
theme_minimal() %+replace%
theme(
plot.background = element_rect(fill=palette_michaelmallari_r[18], linewidth=0),
plot.margin = margin(16, 16, 16, 16, "pt"),
plot.title = element_text(
family="Open Sans",
face="bold",
colour=palette_michaelmallari_r[12],
size=14,
hjust=0,
margin=margin(0, 0, 4, 0, "pt")
),
plot.subtitle = element_text(
family="Open Sans",
colour=palette_michaelmallari_r[12],
size=12,
hjust=0,
margin=margin(0, 0, 24, 0, "pt")
),
plot.caption = element_text(
family="Open Sans",
colour=palette_michaelmallari_r[13],
size=8,
hjust=0,
margin=margin(16, 0, 0, 0, "pt")
),
panel.grid.major.x = element_blank(),
panel.grid.major.y = element_line(color=palette_michaelmallari_r[14], size=0.25, linetype="solid"),
panel.grid.minor = element_blank(),
axis.line.x.bottom = element_line(color=palette_michaelmallari_r[12], linewidth=0.75, linetype="solid"),
legend.position = "top"
)
}Distribution
Histogram
distribution_histogram_r <- ggplot2::ggplot(tiktok_songs_clean_r, aes(y=major_key)) + # Data, aesthetics
geom_histogram(stat="count", colour=palette_michaelmallari_r[19], fill=palette_michaelmallari_r[19]) + # Geometric object, statistics
geom_text(
aes(label=..count..),
stat="count",
hjust=1.5,
colour=palette_michaelmallari_r[1]
) +
scale_y_discrete(limits=rev(levels(tiktok_songs_clean_r$major_key)), expand=c(0, 0), position="right") + # Scale
labs(
title="Dependable Major Key of G and D (and Drop-Tuned C#/Db)",
alt="Dependable Major Key of G and D (and Drop-Tuned C#/Db)",
subtitle="Count of keys used on tracks (on TikTok videos), n = 223",
x=NULL,
y=NULL,
caption="Source: https://www.kaggle.com/datasets/sveta151/tiktok-popular-songs-2019"
) +
theme_michaelmallari_r()
distribution_histogram_r
Pyramid Chart
Box-and-Whisker Plot (or Boxplot or Schematic Plot)
distribution_box_and_whisker_plot_r <- ggplot2::ggplot(
tiktok_songs_clean_r, # Data
aes(x=key, y=track_pop) # Aesthetics
) +
geom_boxplot(color=palette_michaelmallari_r[19], fill=palette_michaelmallari_r[20]) + # Geometric object
scale_y_continuous(expand=c(0, 0), position="right") + # Scale
labs(
title="Key to Pop Songs on TikTok",
alt="Key to Pop Songs on TikTok",
subtitle="Keys that tend to make-up more pop songs than other genre, n = 223",
x=NULL,
y="Track's Pop Sensibilities",
caption="Source: https://www.kaggle.com/datasets/sveta151/tiktok-popular-songs-2019"
) +
theme_michaelmallari_r()
distribution_box_and_whisker_plot_r
Violin Chart
distribution_violin_chart_r <- ggplot2::ggplot(
tiktok_songs_clean_r,
aes(x=key, y=tempo)
) +
geom_violin() +
scale_y_continuous(expand=c(0, 0), position="right") + # Scale
labs(
title="Pick-Up the Tempo on TikTok",
alt="Pick-Up the Tempo on TikTok",
subtitle="Keys that tend to be more up-tempo songs, n = 223",
x=NULL,
y="Tempo",
caption="Source: https://www.kaggle.com/datasets/sveta151/tiktok-popular-songs-2019"
) +
theme_michaelmallari_r()
distribution_violin_chart_r
Ridgeline Plot
distribution_ridgeline_plot_r <- ggplot2::ggplot(tiktok_songs_clean_r, aes(x=artist_pop, y=key, color=mode, fill=mode)) + # Data, aesthetics
ggridges::geom_density_ridges2(alpha=0.3, scale=1) + # Geometric object
scale_y_discrete(limits=rev(levels(tiktok_songs_clean_r$key)), expand=c(0, 0), position="right") + # Scale
scale_color_manual(values=c(palette_michaelmallari_r[2], palette_michaelmallari_r[19])) +
scale_fill_manual(values=c(palette_michaelmallari_r[2], palette_michaelmallari_r[19])) +
guides(color=guide_legend(reverse=TRUE), fill=guide_legend(reverse=TRUE)) +
labs(
title="Songs on TikTok From Pop Artists Using Major Key",
alt="Songs on TikTok From Pop Artists Using Major Key",
subtitle="Artists dependent on proven major keys for pop sensibilities, n = 223",
x="Artist's Pop Sensibilities",
y=NULL,
color=NULL,
fill=NULL,
caption="Source: https://www.kaggle.com/datasets/sveta151/tiktok-popular-songs-2019"
) +
theme_michaelmallari_r() +
theme(
panel.grid.major.x = element_line(color=palette_michaelmallari_r[14], size=0.25, linetype="solid"),
panel.grid.major.y = element_blank()
)
distribution_ridgeline_plot_r
Comparing Categories
Stacked Bar
comparing_categories_stacked_bar_r <- ggplot2::ggplot(tiktok_songs_clean_r, aes(x=..count.., y=major_key, fill=mode)) + # Data, aesthetics
geom_bar(position="stack", stat="count") + # Geometric object, statistics
geom_text(
aes(label=..count..),
stat="count",
position=position_stack(vjust=0.5),
colour=palette_michaelmallari_r[1]
) +
scale_y_discrete(limits=rev(levels(tiktok_songs_clean_r$major_key)), expand=c(0, 0), position="right") + # Scale
scale_fill_manual(values=c(palette_michaelmallari_r[20], palette_michaelmallari_r[19])) +
guides(fill=guide_legend(reverse=TRUE)) +
labs(
title="Majority Rules!",
alt="Majority Rules!",
subtitle="Frequency of major vs. minor keys on tracks (on TikTok videos), n = 223",
x=NULL,
y=NULL,
fill="Mode on Track",
caption="Source: https://www.kaggle.com/datasets/sveta151/tiktok-popular-songs-2019"
) +
theme_michaelmallari_r()
comparing_categories_stacked_bar_r
Relationship
Scatterplot
relationship_scatterplot_r <- ggplot2::ggplot(tiktok_songs_clean_r, aes(x=artist_pop, y=speechiness)) + # Data, aesthetics
geom_point(color=palette_michaelmallari_r[19], alpha=0.3) + # Geometric object
geom_smooth(method=lm, colour=palette_michaelmallari_r[2]) + # Geometric object
scale_y_continuous(expand=c(0, 0), position="right") + # Scale
labs(
title="TikTok Not a One-Trick Pony",
alt="TikTok Not a One-Trick Pony",
subtitle="Spreading its net wide by not focusing on killing two birds with one stone, n = 223",
x="Danceability",
y="Speechiness",
caption="Source: https://www.kaggle.com/datasets/sveta151/tiktok-popular-songs-2019"
) +
theme_michaelmallari_r()
relationship_scatterplot_r
Bubble Plot (or Bubble Scatterplot)
relationship_bubble_plot_r <- ggplot2::ggplot(tiktok_songs_clean_r, aes(x=loudness, y=energy)) + # Data, aesthetics
geom_point(aes(size=acousticness), color=palette_michaelmallari_r[19], alpha=0.3) + # Geometric object, aesthetic
geom_smooth(method=lm, colour=palette_michaelmallari_r[2]) + # Geometric object
scale_y_continuous(expand=c(0, 0), position="right") + # Scale
labs(
title="Pump Up the Volume, Dance Dance on TikTok",
alt="Pump Up the Volume, Dance Dance on TikTok",
subtitle="Dance energy from song energy, and song energy from loudness, n = 223",
x="Loudness",
y="Energy",
size="Acousticness",
caption="Source: https://www.kaggle.com/datasets/sveta151/tiktok-popular-songs-2019"
) +
theme_michaelmallari_r()
relationship_bubble_plot_r
Parallel Coordinates Plot
Radar Chart (or Spider Chart or Star Chart)
Chord Diagram
Arc Diagram
Correlation Matrix
correlation_pearson_r <- cor(
subset(tiktok_songs_clean_r, select=-c(major_key, key, mode, artist_name, track_name, album)),
method="pearson"
) %>%
as.data.frame()
correlation_pearson_r artist_pop track_pop duration_ms time_signature tempo valence energy loudness instrumentalness acousticness liveness speechiness danceability
artist_pop 1.000 0.372 0.311 -0.106 -0.047 -0.080 0.149 0.306 -0.063 -0.051 -0.154 -0.359 -0.130
track_pop 0.372 1.000 0.188 0.022 0.033 -0.025 0.112 0.228 -0.090 -0.141 -0.035 -0.204 -0.128
duration_ms 0.311 0.188 1.000 -0.015 -0.096 -0.115 0.249 0.223 -0.193 -0.121 -0.129 -0.233 -0.071
time_signature -0.106 0.022 -0.015 1.000 -0.092 0.095 0.108 0.088 -0.084 -0.054 -0.038 0.140 0.127
tempo -0.047 0.033 -0.096 -0.092 1.000 0.059 0.131 0.092 0.088 -0.154 -0.112 0.036 -0.095
valence -0.080 -0.025 -0.115 0.095 0.059 1.000 0.312 0.133 0.144 0.033 0.123 -0.090 0.102
energy 0.149 0.112 0.249 0.108 0.131 0.312 1.000 0.681 -0.173 -0.409 0.068 -0.117 -0.074
loudness 0.306 0.228 0.223 0.088 0.092 0.133 0.681 1.000 -0.350 -0.366 -0.032 -0.080 0.028
instrumentalness -0.063 -0.090 -0.193 -0.084 0.088 0.144 -0.173 -0.350 1.000 0.268 -0.070 -0.139 -0.074
acousticness -0.051 -0.141 -0.121 -0.054 -0.154 0.033 -0.409 -0.366 0.268 1.000 -0.023 -0.168 -0.255
liveness -0.154 -0.035 -0.129 -0.038 -0.112 0.123 0.068 -0.032 -0.070 -0.023 1.000 0.004 -0.137
speechiness -0.359 -0.204 -0.233 0.140 0.036 -0.090 -0.117 -0.080 -0.139 -0.168 0.004 1.000 0.368
danceability -0.130 -0.128 -0.071 0.127 -0.095 0.102 -0.074 0.028 -0.074 -0.255 -0.137 0.368 1.000correlation_pearson_r$var_1 <- rownames(correlation_pearson_r)
relationship_correlation_matrix_r <- correlation_pearson_r %>%
tidyr::gather(key=var_2, value=r, 1:13) %>%
ggplot(aes(x=var_1, y=var_2, fill=r)) +
geom_tile() +
geom_text(aes(label=round(r, 2)), size=2.2) +
scale_y_discrete(position="right", limits=rev) +
scale_fill_gradient2(limits=c(-1, 1), low=palette_michaelmallari_r[3], mid=palette_michaelmallari_r[1], high=palette_michaelmallari_r[2]) +
labs(
title="Starting a Dance Challenge on TikTok?",
subtitle="Pearson correlation (r) to determine track's suitability for dancing, n = 223",
x="",
y="",
fill="r",
caption="Source: https://www.kaggle.com/datasets/sveta151/tiktok-popular-songs-2019"
) +
theme_michaelmallari_r() +
theme(
axis.text.x=element_text(angle=90, hjust=1),
plot.caption = element_text(margin=margin(8, 0, 0, 0, "pt")),
legend.position = "right"
)
relationship_correlation_matrix_r
Network Diagrams
Tree Diagrams
References
- Schwabish, J. (2021). Better Data Visualizations: A Guide for Scholars, Researchers, and Wonks. Columbia University Press.
- Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis (2nd ed.). Springer. https://doi.org/10.1007/978-3-319-24277-4
- Wickham, H. (2010). A Layered Grammar of Graphics. Journal of Computational and Graphical Statistics, 19(1), 3–28. https://www.jstor.org/stable/25651297
- Wilkinson, L. (2005). The Grammar of Graphics (2nd ed.). Springer.
- Michael Mallari (michaelmallari) - Profile | Pinterest. (n.d.). Pinterest. https://www.pinterest.com/michaelmallari/
Applied Advanced Analytics & AI in Sports