1 Penjelasan

Data USvideos adalah data yang berisi tentang video trending di Youtube pada tahun 2017. Kolom-kolom yang ada di dalam data ini adalah:

trending_date: tanggal trending
title: judul video
channel_title: nama channel Youtube
category_id: kategori video
publish_time: tanggal upload video
views: jumlah views
likes: jumlah likes
dislikes: jumlah dislikes
comment_count: jumlah komentar
comment_disabled: apakah kolom komentar tidak diaktifkan
rating_disabled: apakah rating video tidak diaktifkan
video_error_or_removed: apakah video dihapus

2 Eksplorasi Data

Install Package

options(scipen = 999)
library(lubridate)
library(ggplot2)
library(tidyr)

2.1 Membuka data

vids <- read.csv("USvideos.csv")
head(vids)

##   trending_date                                                          title
## 1      17.14.11                             WE WANT TO TALK ABOUT OUR MARRIAGE
## 2      17.14.11 The Trump Presidency: Last Week Tonight with John Oliver (HBO)
## 3      17.14.11          Racist Superman | Rudy Mancuso, King Bach & Lele Pons
## 4      17.14.11                               Nickelback Lyrics: Real or Fake?
## 5      17.14.11                                       I Dare You: GOING BALD!?
## 6      17.14.11                                          2 Weeks with iPhone X
##           channel_title category_id             publish_time   views  likes
## 1          CaseyNeistat          22 2017-11-13T17:13:01.000Z  748374  57527
## 2       LastWeekTonight          24 2017-11-13T07:30:00.000Z 2418783  97185
## 3          Rudy Mancuso          23 2017-11-12T19:05:24.000Z 3191434 146033
## 4 Good Mythical Morning          24 2017-11-13T11:00:04.000Z  343168  10172
## 5              nigahiga          24 2017-11-12T18:01:41.000Z 2095731 132235
## 6              iJustine          28 2017-11-13T19:07:23.000Z  119180   9763
##   dislikes comment_count comments_disabled ratings_disabled
## 1     2966         15954             FALSE            FALSE
## 2     6146         12703             FALSE            FALSE
## 3     5339          8181             FALSE            FALSE
## 4      666          2146             FALSE            FALSE
## 5     1989         17518             FALSE            FALSE
## 6      511          1434             FALSE            FALSE
##   video_error_or_removed
## 1                  FALSE
## 2                  FALSE
## 3                  FALSE
## 4                  FALSE
## 5                  FALSE
## 6                  FALSE

2.2 Melihat struktur data

str(vids)

## 'data.frame':    13400 obs. of  12 variables:
##  $ trending_date         : chr  "17.14.11" "17.14.11" "17.14.11" "17.14.11" ...
##  $ title                 : chr  "WE WANT TO TALK ABOUT OUR MARRIAGE" "The Trump Presidency: Last Week Tonight with John Oliver (HBO)" "Racist Superman | Rudy Mancuso, King Bach & Lele Pons" "Nickelback Lyrics: Real or Fake?" ...
##  $ channel_title         : chr  "CaseyNeistat" "LastWeekTonight" "Rudy Mancuso" "Good Mythical Morning" ...
##  $ category_id           : int  22 24 23 24 24 28 24 28 1 25 ...
##  $ publish_time          : chr  "2017-11-13T17:13:01.000Z" "2017-11-13T07:30:00.000Z" "2017-11-12T19:05:24.000Z" "2017-11-13T11:00:04.000Z" ...
##  $ views                 : int  748374 2418783 3191434 343168 2095731 119180 2103417 817732 826059 256426 ...
##  $ likes                 : int  57527 97185 146033 10172 132235 9763 15993 23663 3543 12654 ...
##  $ dislikes              : int  2966 6146 5339 666 1989 511 2445 778 119 1363 ...
##  $ comment_count         : int  15954 12703 8181 2146 17518 1434 1970 3432 340 2368 ...
##  $ comments_disabled     : logi  FALSE FALSE FALSE FALSE FALSE FALSE ...
##  $ ratings_disabled      : logi  FALSE FALSE FALSE FALSE FALSE FALSE ...
##  $ video_error_or_removed: logi  FALSE FALSE FALSE FALSE FALSE FALSE ...

2.3 Ubah tipe data

Mengubah kolom trending_date dan publish_time menjadi data datetime.

vids$trending_date <- ydm(vids$trending_date)
vids$publish_time <-ymd_hms(vids$publish_time, tz = "Asia/Jakarta")

Mengubah kolom category_id untuk tiap row dengan switch() dengan bantuan sapply() dan mengubah hasilnya menjadi data factor

vids$category_id <- sapply(X =  as.character(vids$category_id), 
                           FUN = switch, 
                           "1" = "Film and Animation",
                           "2" = "Autos and Vehicles", 
                           "10" = "Music", 
                           "15" = "Pets and Animals", 
                           "17" = "Sports",
                           "19" = "Travel and Events", 
                           "20" = "Gaming", 
                           "22" = "People and Blogs", 
                           "23" = "Comedy",
                           "24" = "Entertainment", 
                           "25" = "News and Politics",
                           "26" = "Howto and Style", 
                           "27" = "Education",
                           "28" = "Science and Technology", 
                           "29" = "Nonprofit and Activism",
                           "43" = "Shows")

vids$category_id <- as.factor(vids$category_id)

2.4 Membuat kolom baru

Membuat kolom publish_day, publish_date dan publish_hour masing-masing berisi hari, tanggal, dan jam publish video

vids$publish_day <- wday(vids$publish_time, label = T, abbr = F)
vids$publish_date <- date(vids$publish_time)
vids$publish_hour <- hour(vids$publish_time)

Membuat kolom ‘time_to_trend’ berisi waktu yang dibutuhkan untuk trending

vids$time_to_trend <- as.numeric(vids$trending_date - date(vids$publish_time))

2.5 Membuat data baru dengan mengeliminasi judul video yang berulang

Membuat data baru yang hanya berisi judul-judul video yang trending untuk mempermudah proses analisis data

vids_title <- vids[match(x = unique(vids$title), table = vids$title),]

3 Analisis Data

Ada 3 hal yang akan kita analisis dari data USvideos, yaitu:

10 Channel yang paling sering trending
Kategori video paling populer
Hubungan komen/view dan likes/view dari kategori video paling populer

3.1 10 Channel paling sering trending

vids_trend <- aggregate(title ~ channel_title, vids_title, length)
vids_trend10 <- head(vids_trend[order (-vids_trend$title),],10)
vids_trend10

##                               channel_title title
## 1012                             Refinery29    31
## 1218 The Tonight Show Starring Jimmy Fallon    30
## 1346                                    Vox    29
## 1233                           TheEllenShow    28
## 876                                 Netflix    27
## 884                                     NFL    25
## 390                                    ESPN    24
## 606                       Jimmy Kimmel Live    24
## 1192     The Late Show with Stephen Colbert    22
## 681             Late Night with Seth Meyers    21

vids_trend10 %>%
  ggplot(aes(x=reorder(channel_title,title), y=title, fill = channel_title) ) +
    geom_bar(stat="identity",show.legend = FALSE) +
    coord_flip() +
  labs( x="Nama Channel",
        y="Jumlah Video Trending",
    title = "US Trending Videos",
    subtitle = "10 Channels dengan Video Trending Terbanyak"
  ) +
    theme(
    plot.title = element_text(color = "#696969", size = 16),
    plot.subtitle = element_text(face = "italic")
  )

Dari hasil di atas, channel Refinery29 memiliki jumlah video trending terbanyak yaitu 31 video.

3.2 Kategori video paling populer

vids_popular <- aggregate(views ~ category_id, vids_title, sum)
category_popular <- head(vids_popular[order(-vids_popular$views),], 5)
category_popular

##          category_id     views
## 4      Entertainment 445350694
## 8              Music 386197869
## 2             Comedy 179900099
## 7    Howto and Style 110259469
## 5 Film and Animation  77476128

category_popular %>%
  ggplot(aes(x=reorder(category_id,-views), y=views, fill=category_id ) ) +
  geom_col(stat="identity",show.legend = FALSE) +
  geom_text(aes(label = format(views, big.mark = ",",
                               scientific = FALSE) ),
            vjust = -0.5,
            size = 3,
            colour = "Red")+
  scale_y_continuous(labels = scales::comma)+
  labs( x="Kategori",
        y="Frekuensi ditonton",
    title = "US Trending Videos",
    subtitle = "5 Kategori Video Paling Sering Ditonton"
  ) +
    theme(
    plot.title = element_text(color = "#696969", size = 16),
    plot.subtitle = element_text(face = "italic")
  )

Dari hasil di atas, lategori Refinery29Entertainment` menjadi kategori paling sering ditonton.

3.3 Hubungan komen/view dan likes/view dari kategori video paling populer

vids_subset <- vids_title[vids_title$category_id %in% c("Entertainment","Music","Comedy","Howto and Style","Film and Animation"),]
vids_subset$like_per_view <- round(vids_subset$likes/vids_subset$views,3)
vids_subset$comment_per_view <- round(vids_subset$comment_count/vids_subset$views,3)

ggplot(vids_subset, aes(x=comment_per_view, y=like_per_view, color=category_id)) + 
    geom_point(size=2) +
    theme_minimal()+
  labs(x="comment per view",
       y="like per view",
       title = "US Trending Videos",
    subtitle = "Comment per View and Like per View from Most Viewed Video Categories") +
  theme(
    plot.title = element_text(color = "#696969", size = 16),
    plot.subtitle = element_text(face = "italic", size =10)
  )

Dari hasil di atas, kategori Music cenderung memiliki rasio like/view paling tinggi, sementara How to and Style cenderung memiliki rasio comment/view paling tinggi

LBB DV - US VIDEOS

Ali Ramadhani

2023-01-03