Dalam pelajaran kali ini, kita mencoba menganalisis data perjalanan historis Cylistic untuk menganalisis dan mengidentifikasi tren yang sedang terjadi. Ini merupakan data publik yang disediakan oleh Motivate International Inc, yang tentunya dataset tersebut dapat kita gunakan untuk media belajar Link Dataset Di sini saya menggunakan dataset : Divvy_Trips_2020_Q1,Divvy_Trips_2019_Q2,Divvy_Trips_2019_Q3 dan Divvy_Trips_2019_Q4

==================================

LANGKAH PERTAMA : MENYIAPKAN DATA

==================================

Jalankan Library Yang Sudah Diinstall

library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.2     ✔ readr     2.1.4
## ✔ forcats   1.0.0     ✔ stringr   1.5.0
## ✔ ggplot2   3.4.2     ✔ tibble    3.2.1
## ✔ lubridate 1.9.2     ✔ tidyr     1.3.0
## ✔ purrr     1.0.1     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(ggplot2)
library(readr)
library(lubridate)

Upload Dataset Tersebut Sesuai Nama Yang Kamu Inginkan

Disini saya menggunakan penamaan q1 untuk Dataset Divvy_Trips_2020_Q1.csv dan seterusnya.

q1 <- read.csv("Divvy_Trips_2020_Q1.csv")
q2 <- read.csv("Divvy_Trips_2019_Q2.csv")
q3 <- read.csv("Divvy_Trips_2019_Q3.csv")
q4 <- read.csv("Divvy_Trips_2019_Q4.csv")

=======================================================

LANGKAH KEDUA : MENGGABUNGKAN DATASET MENJADI SATU FILE

=======================================================

Sebelum menggabungkan dataset menjadi satu file dataset, alangkah lebih baiknya kita terlebih dahulu memeriksa apakah semua data telah sesuai atau tidak, khususnya untuk penamaan kolom, jika nama kolom sudah sesuai maka kita bisa lanjutkan untuk menggabungkannya. Jika tidak, maka kita harus sesuaikan terlebih dahulu.

Melihat Penamaan Kolom Dataset

colnames(q1)
##  [1] "ride_id"            "rideable_type"      "started_at"        
##  [4] "ended_at"           "start_station_name" "start_station_id"  
##  [7] "end_station_name"   "end_station_id"     "start_lat"         
## [10] "start_lng"          "end_lat"            "end_lng"           
## [13] "member_casual"
colnames(q2)
##  [1] "X01...Rental.Details.Rental.ID"                   
##  [2] "X01...Rental.Details.Local.Start.Time"            
##  [3] "X01...Rental.Details.Local.End.Time"              
##  [4] "X01...Rental.Details.Bike.ID"                     
##  [5] "X01...Rental.Details.Duration.In.Seconds.Uncapped"
##  [6] "X03...Rental.Start.Station.ID"                    
##  [7] "X03...Rental.Start.Station.Name"                  
##  [8] "X02...Rental.End.Station.ID"                      
##  [9] "X02...Rental.End.Station.Name"                    
## [10] "User.Type"                                        
## [11] "Member.Gender"                                    
## [12] "X05...Member.Details.Member.Birthday.Year"
colnames(q3)
##  [1] "trip_id"           "start_time"        "end_time"         
##  [4] "bikeid"            "tripduration"      "from_station_id"  
##  [7] "from_station_name" "to_station_id"     "to_station_name"  
## [10] "usertype"          "gender"            "birthyear"
colnames(q4)
##  [1] "trip_id"           "start_time"        "end_time"         
##  [4] "bikeid"            "tripduration"      "from_station_id"  
##  [7] "from_station_name" "to_station_id"     "to_station_name"  
## [10] "usertype"          "gender"            "birthyear"
str(q2)
## 'data.frame':    1108163 obs. of  12 variables:
##  $ X01...Rental.Details.Rental.ID                   : int  22178529 22178530 22178531 22178532 22178533 22178534 22178535 22178536 22178537 22178538 ...
##  $ X01...Rental.Details.Local.Start.Time            : chr  "2019-04-01 00:02:22" "2019-04-01 00:03:02" "2019-04-01 00:11:07" "2019-04-01 00:13:01" ...
##  $ X01...Rental.Details.Local.End.Time              : chr  "2019-04-01 00:09:48" "2019-04-01 00:20:30" "2019-04-01 00:15:19" "2019-04-01 00:18:58" ...
##  $ X01...Rental.Details.Bike.ID                     : int  6251 6226 5649 4151 3270 3123 6418 4513 3280 5534 ...
##  $ X01...Rental.Details.Duration.In.Seconds.Uncapped: chr  "446.0" "1,048.0" "252.0" "357.0" ...
##  $ X03...Rental.Start.Station.ID                    : int  81 317 283 26 202 420 503 260 211 211 ...
##  $ X03...Rental.Start.Station.Name                  : chr  "Daley Center Plaza" "Wood St & Taylor St" "LaSalle St & Jackson Blvd" "McClurg Ct & Illinois St" ...
##  $ X02...Rental.End.Station.ID                      : int  56 59 174 133 129 426 500 499 211 211 ...
##  $ X02...Rental.End.Station.Name                    : chr  "Desplaines St & Kinzie St" "Wabash Ave & Roosevelt Rd" "Canal St & Madison St" "Kingsbury St & Kinzie St" ...
##  $ User.Type                                        : chr  "Subscriber" "Subscriber" "Subscriber" "Subscriber" ...
##  $ Member.Gender                                    : chr  "Male" "Female" "Male" "Male" ...
##  $ X05...Member.Details.Member.Birthday.Year        : int  1975 1984 1990 1993 1992 1999 1969 1991 NA NA ...

Merubah Nama Kolom

Agar semua dataset memiliki nama kolom yang sesuai maka kita harus merubah namanya. Disini saya menggunakan pernamaan seperi pada dataset Q1 2020 karena memang itu merupakan dataset terbaru yang dimiliki perusahaan. Maka kita harus merubah beberapa nama kolom di dataset q2,q3 dan q4.

(q4 <- rename(q4,ride_id = trip_id
                ,rideable_type = bikeid 
                ,started_at = start_time  
                ,ended_at = end_time  
                ,start_station_name = from_station_name 
                ,start_station_id = from_station_id 
                ,end_station_name = to_station_name 
                ,end_station_id = to_station_id 
                ,member_casual = usertype))
(q3 <- rename(q3
                   ,ride_id = trip_id
                   ,rideable_type = bikeid 
                   ,started_at = start_time  
                   ,ended_at = end_time  
                   ,start_station_name = from_station_name 
                   ,start_station_id = from_station_id 
                   ,end_station_name = to_station_name 
                   ,end_station_id = to_station_id 
                   ,member_casual = usertype))
(q2 <- rename(q2
                   ,ride_id = "X01...Rental.Details.Rental.ID"
                   ,rideable_type = "X01...Rental.Details.Bike.ID" 
                   ,started_at = "X01...Rental.Details.Local.Start.Time"  
                   ,ended_at = "X01...Rental.Details.Local.End.Time"  
                   ,start_station_name = "X03...Rental.Start.Station.Name" 
                   ,start_station_id = "X03...Rental.Start.Station.ID"
                   ,end_station_name = "X02...Rental.End.Station.Name" 
                   ,end_station_id = "X02...Rental.End.Station.ID"
                   ,member_casual = "User.Type"))

Melihat Kembali Struktur Dataset

str(q1)
## 'data.frame':    426887 obs. of  13 variables:
##  $ ride_id           : chr  "EACB19130B0CDA4A" "8FED874C809DC021" "789F3C21E472CA96" "C9A388DAC6ABF313" ...
##  $ rideable_type     : chr  "docked_bike" "docked_bike" "docked_bike" "docked_bike" ...
##  $ started_at        : chr  "2020-01-21 20:06:59" "2020-01-30 14:22:39" "2020-01-09 19:29:26" "2020-01-06 16:17:07" ...
##  $ ended_at          : chr  "2020-01-21 20:14:30" "2020-01-30 14:26:22" "2020-01-09 19:32:17" "2020-01-06 16:25:56" ...
##  $ start_station_name: chr  "Western Ave & Leland Ave" "Clark St & Montrose Ave" "Broadway & Belmont Ave" "Clark St & Randolph St" ...
##  $ start_station_id  : int  239 234 296 51 66 212 96 96 212 38 ...
##  $ end_station_name  : chr  "Clark St & Leland Ave" "Southport Ave & Irving Park Rd" "Wilton Ave & Belmont Ave" "Fairbanks Ct & Grand Ave" ...
##  $ end_station_id    : int  326 318 117 24 212 96 212 212 96 100 ...
##  $ start_lat         : num  42 42 41.9 41.9 41.9 ...
##  $ start_lng         : num  -87.7 -87.7 -87.6 -87.6 -87.6 ...
##  $ end_lat           : num  42 42 41.9 41.9 41.9 ...
##  $ end_lng           : num  -87.7 -87.7 -87.7 -87.6 -87.6 ...
##  $ member_casual     : chr  "member" "member" "member" "member" ...
str(q2)
## 'data.frame':    1108163 obs. of  12 variables:
##  $ ride_id                                          : int  22178529 22178530 22178531 22178532 22178533 22178534 22178535 22178536 22178537 22178538 ...
##  $ started_at                                       : chr  "2019-04-01 00:02:22" "2019-04-01 00:03:02" "2019-04-01 00:11:07" "2019-04-01 00:13:01" ...
##  $ ended_at                                         : chr  "2019-04-01 00:09:48" "2019-04-01 00:20:30" "2019-04-01 00:15:19" "2019-04-01 00:18:58" ...
##  $ rideable_type                                    : int  6251 6226 5649 4151 3270 3123 6418 4513 3280 5534 ...
##  $ X01...Rental.Details.Duration.In.Seconds.Uncapped: chr  "446.0" "1,048.0" "252.0" "357.0" ...
##  $ start_station_id                                 : int  81 317 283 26 202 420 503 260 211 211 ...
##  $ start_station_name                               : chr  "Daley Center Plaza" "Wood St & Taylor St" "LaSalle St & Jackson Blvd" "McClurg Ct & Illinois St" ...
##  $ end_station_id                                   : int  56 59 174 133 129 426 500 499 211 211 ...
##  $ end_station_name                                 : chr  "Desplaines St & Kinzie St" "Wabash Ave & Roosevelt Rd" "Canal St & Madison St" "Kingsbury St & Kinzie St" ...
##  $ member_casual                                    : chr  "Subscriber" "Subscriber" "Subscriber" "Subscriber" ...
##  $ Member.Gender                                    : chr  "Male" "Female" "Male" "Male" ...
##  $ X05...Member.Details.Member.Birthday.Year        : int  1975 1984 1990 1993 1992 1999 1969 1991 NA NA ...
str(q3)
## 'data.frame':    1640718 obs. of  12 variables:
##  $ ride_id           : int  23479388 23479389 23479390 23479391 23479392 23479393 23479394 23479395 23479396 23479397 ...
##  $ started_at        : chr  "2019-07-01 00:00:27" "2019-07-01 00:01:16" "2019-07-01 00:01:48" "2019-07-01 00:02:07" ...
##  $ ended_at          : chr  "2019-07-01 00:20:41" "2019-07-01 00:18:44" "2019-07-01 00:27:42" "2019-07-01 00:27:10" ...
##  $ rideable_type     : int  3591 5353 6180 5540 6014 4941 3770 5442 2957 6091 ...
##  $ tripduration      : chr  "1,214.0" "1,048.0" "1,554.0" "1,503.0" ...
##  $ start_station_id  : int  117 381 313 313 168 300 168 313 43 43 ...
##  $ start_station_name: chr  "Wilton Ave & Belmont Ave" "Western Ave & Monroe St" "Lakeview Ave & Fullerton Pkwy" "Lakeview Ave & Fullerton Pkwy" ...
##  $ end_station_id    : int  497 203 144 144 62 232 62 144 195 195 ...
##  $ end_station_name  : chr  "Kimball Ave & Belmont Ave" "Western Ave & 21st St" "Larrabee St & Webster Ave" "Larrabee St & Webster Ave" ...
##  $ member_casual     : chr  "Subscriber" "Customer" "Customer" "Customer" ...
##  $ gender            : chr  "Male" "" "" "" ...
##  $ birthyear         : int  1992 NA NA NA NA 1990 NA NA NA NA ...
str(q4)
## 'data.frame':    704054 obs. of  12 variables:
##  $ ride_id           : int  25223640 25223641 25223642 25223643 25223644 25223645 25223646 25223647 25223648 25223649 ...
##  $ started_at        : chr  "2019-10-01 00:01:39" "2019-10-01 00:02:16" "2019-10-01 00:04:32" "2019-10-01 00:04:32" ...
##  $ ended_at          : chr  "2019-10-01 00:17:20" "2019-10-01 00:06:34" "2019-10-01 00:18:43" "2019-10-01 00:43:43" ...
##  $ rideable_type     : int  2215 6328 3003 3275 5294 1891 1061 1274 6011 2957 ...
##  $ tripduration      : chr  "940.0" "258.0" "850.0" "2,350.0" ...
##  $ start_station_id  : int  20 19 84 313 210 156 84 156 156 336 ...
##  $ start_station_name: chr  "Sheffield Ave & Kingsbury St" "Throop (Loomis) St & Taylor St" "Milwaukee Ave & Grand Ave" "Lakeview Ave & Fullerton Pkwy" ...
##  $ end_station_id    : int  309 241 199 290 382 226 142 463 463 336 ...
##  $ end_station_name  : chr  "Leavitt St & Armitage Ave" "Morgan St & Polk St" "Wabash Ave & Grand Ave" "Kedzie Ave & Palmer Ct" ...
##  $ member_casual     : chr  "Subscriber" "Subscriber" "Subscriber" "Subscriber" ...
##  $ gender            : chr  "Male" "Male" "Female" "Male" ...
##  $ birthyear         : int  1987 1998 1991 1990 1987 1994 1991 1995 1993 NA ...

Merubah Type Data

Untuk menyesuaikan dengan format data di dataset Q1 2020, maka kita perlu merubah type data pada kolom ride_id dan rideable_type pada q4,q3,q2 yang awalnya integer menjadi character.

q4 <-  mutate(q4, ride_id = as.character(ride_id)
                   ,rideable_type = as.character(rideable_type)) 
q3 <-  mutate(q3, ride_id = as.character(ride_id)
                   ,rideable_type = as.character(rideable_type)) 
q2 <-  mutate(q2, ride_id = as.character(ride_id)
                   ,rideable_type = as.character(rideable_type))

Menggabungkan dataset menjadi satu file

all_trips <- bind_rows(q1,q2,q3,q4)

Menghapus kolom yang tidak diperlukan

Kita akan menghapus beberapa kolom yang tidak dibutuhkan seperti lat, long, birthyear, dan gender karena pada data tahun 2020, kolom tersebut tidak dibutuhkan lagi.

all_trips <- all_trips %>%  
  select(-c(start_lat, start_lng, end_lat, end_lng, birthyear, gender, "X01...Rental.Details.Duration.In.Seconds.Uncapped", "X05...Member.Details.Member.Birthday.Year", "Member.Gender", "tripduration"))

=========================================================================

LANGKAH KETIGA: MEMBERSIHKAN DATA DAN MENAMBAH DATA UNTUK PROSES ANALISIS

=========================================================================

Lihat kembali keseluruhan struktur dataset yang sudah kita buat

colnames(all_trips)
## [1] "ride_id"            "rideable_type"      "started_at"        
## [4] "ended_at"           "start_station_name" "start_station_id"  
## [7] "end_station_name"   "end_station_id"     "member_casual"
dim(all_trips)
## [1] 3879822       9
head(all_trips)
nrow(all_trips)
## [1] 3879822
summary(all_trips)
##    ride_id          rideable_type       started_at          ended_at        
##  Length:3879822     Length:3879822     Length:3879822     Length:3879822    
##  Class :character   Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character   Mode  :character  
##                                                                             
##                                                                             
##                                                                             
##                                                                             
##  start_station_name start_station_id end_station_name   end_station_id 
##  Length:3879822     Min.   :  1.0    Length:3879822     Min.   :  1.0  
##  Class :character   1st Qu.: 77.0    Class :character   1st Qu.: 77.0  
##  Mode  :character   Median :174.0    Mode  :character   Median :174.0  
##                     Mean   :202.9                       Mean   :203.8  
##                     3rd Qu.:291.0                       3rd Qu.:291.0  
##                     Max.   :675.0                       Max.   :675.0  
##                                                         NA's   :1      
##  member_casual     
##  Length:3879822    
##  Class :character  
##  Mode  :character  
##                    
##                    
##                    
## 
table(all_trips$member_casual)
## 
##     casual   Customer     member Subscriber 
##      48480     857474     378407    2595461

Disini kita dapat melihat bahwa di dalam kolom member casual terdapat dua data untuk member,yaitu : member dan subscriber. Dan data untuk casual rider,yaitu : casual dan customer. Selanjutnya kita akan menggabungkan data tersebut menjadi dua data saja.Yaitu: member dan casual saja.

all_trips <-  all_trips %>% 
  mutate(member_casual = recode(member_casual
                           ,"Subscriber" = "member"
                           ,"Customer" = "casual"))
table(all_trips$member_casual)
## 
##  casual  member 
##  905954 2973868
head(all_trips)

Menambahkan Kolom Tanggal,Bulan dan Tahun pada setiap perjalanan

Dengan ini kita dapat mengumpulkan data perjalanan untuk setiap bulan, hari ataupun tahun

all_trips$date <- as.Date(all_trips$started_at)
all_trips$month <- format(as.Date(all_trips$date), "%m")
all_trips$day <- format(as.Date(all_trips$date), "%d")
all_trips$year <- format(as.Date(all_trips$date), "%Y")
all_trips$day_of_week <- format(as.Date(all_trips$date), "%A")

Menambahkan Perhitungan “ride_length” ke semua data perjalanan dengan ukuran detik.

all_trips$ride_length <- difftime(all_trips$ended_at,all_trips$started_at)

Periksa Kembali Struktur Dataset

str(all_trips)
## 'data.frame':    3879822 obs. of  15 variables:
##  $ ride_id           : chr  "EACB19130B0CDA4A" "8FED874C809DC021" "789F3C21E472CA96" "C9A388DAC6ABF313" ...
##  $ rideable_type     : chr  "docked_bike" "docked_bike" "docked_bike" "docked_bike" ...
##  $ started_at        : chr  "2020-01-21 20:06:59" "2020-01-30 14:22:39" "2020-01-09 19:29:26" "2020-01-06 16:17:07" ...
##  $ ended_at          : chr  "2020-01-21 20:14:30" "2020-01-30 14:26:22" "2020-01-09 19:32:17" "2020-01-06 16:25:56" ...
##  $ start_station_name: chr  "Western Ave & Leland Ave" "Clark St & Montrose Ave" "Broadway & Belmont Ave" "Clark St & Randolph St" ...
##  $ start_station_id  : int  239 234 296 51 66 212 96 96 212 38 ...
##  $ end_station_name  : chr  "Clark St & Leland Ave" "Southport Ave & Irving Park Rd" "Wilton Ave & Belmont Ave" "Fairbanks Ct & Grand Ave" ...
##  $ end_station_id    : int  326 318 117 24 212 96 212 212 96 100 ...
##  $ member_casual     : chr  "member" "member" "member" "member" ...
##  $ date              : Date, format: "2020-01-21" "2020-01-30" ...
##  $ month             : chr  "01" "01" "01" "01" ...
##  $ day               : chr  "21" "30" "09" "06" ...
##  $ year              : chr  "2020" "2020" "2020" "2020" ...
##  $ day_of_week       : chr  "Tuesday" "Thursday" "Thursday" "Monday" ...
##  $ ride_length       : 'difftime' num  451 223 171 529 ...
##   ..- attr(*, "units")= chr "secs"
head(all_trips)

Mengkonversi “ride_length” dari factor ke numerik agar kita dapat melakukan perhitungan pada data

is.factor(all_trips$ride_length)
## [1] FALSE
all_trips$ride_length <- as.numeric(as.character(all_trips$ride_length))
is.numeric(all_trips$ride_length)
## [1] TRUE

Membuang Data yang tidak dibutuhkan

Untuk memudahkan proses analisis, selanjutnya kita akan membuang data yang tidak dibutuhkan, misalkan yang mengandung angka negatif atau kurang dari nol. Dan kita akan membuat data baru dengan nama all_trips_v2

all_trips_v2 <- all_trips[!(all_trips$start_station_name == "HQ QR" | all_trips$ride_length<0),]

=================================================

LANGKAH KEEMPAT : MELAKUKAN ANALISIS DESCRIPTIVE

=================================================

Analisis Descriptive tentang panjang perjalanan(ride_length) dalam detik

mean(all_trips_v2$ride_length) 
## [1] 1479.139
median(all_trips_v2$ride_length)
## [1] 712
max(all_trips_v2$ride_length)
## [1] 9387024
min(all_trips_v2$ride_length)
## [1] 1

Ataupun kita dapat memadatkannya dari 4 baris menjadi satu baris agar mempermudah.

summary(all_trips_v2$ride_length)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##       1     412     712    1479    1289 9387024

Membandingkan member dan casual user

aggregate(all_trips_v2$ride_length ~ all_trips_v2$member_casual, FUN = mean)
aggregate(all_trips_v2$ride_length ~ all_trips_v2$member_casual, FUN = median)
aggregate(all_trips_v2$ride_length ~ all_trips_v2$member_casual, FUN = max)
aggregate(all_trips_v2$ride_length ~ all_trips_v2$member_casual, FUN = min)

Melihat rata - rata waktu perjalanan setiap hari untuk members vs casual users

aggregate(all_trips_v2$ride_length ~ all_trips_v2$member_casual + all_trips_v2$day_of_week, FUN = mean)

Mengurutkan Hari

Kita lihat sebelumnya bahwa hari tidak teratur. Selanjutnya kita akan memperbaikinya.

all_trips_v2$day_of_week <- ordered(all_trips_v2$day_of_week, levels=c("Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday"))

Melihat waktu rata-rata untuk setiap hari dari members vs casual users

aggregate(all_trips_v2$ride_length ~ all_trips_v2$member_casual + all_trips_v2$day_of_week, FUN = mean)

Menganalisis berdasarkan type dan weekdays

all_trips_v2 %>% 
  mutate(weekday = wday(started_at, label = TRUE)) %>%  #membuat weekday field menggunakan wday()
  group_by(member_casual, weekday) %>%  #mengelompokkan berdasarkan usertype dan weekday
  summarise(number_of_rides = n()                           
  ,average_duration = mean(ride_length)) %>%        # menghitung rata-rata
  arrange(member_casual, weekday)   
## `summarise()` has grouped output by 'member_casual'. You can override using the
## `.groups` argument.

Membuat Visualisasi Data jumlah perjalanan (ride_length) berdasarkan rider type

all_trips_v2 %>% 
  mutate(weekday = wday(started_at, label = TRUE)) %>% 
  group_by(member_casual, weekday) %>% 
  summarise(number_of_rides = n()
            ,average_duration = mean(ride_length)) %>% 
  arrange(member_casual, weekday)  %>% 
  ggplot(aes(x = weekday, y = number_of_rides, fill = member_casual)) +
  geom_col(position = "dodge")
## `summarise()` has grouped output by 'member_casual'. You can override using the
## `.groups` argument.

Membuat Visualisasi Data rata-rata durasi perjalanan.

all_trips_v2 %>% 
  mutate(weekday = wday(started_at, label = TRUE)) %>% 
  group_by(member_casual, weekday) %>% 
  summarise(number_of_rides = n()
            ,average_duration = mean(ride_length)) %>% 
  arrange(member_casual, weekday)  %>% 
  ggplot(aes(x = weekday, y = average_duration, fill = member_casual)) +
  geom_col(position = "dodge")
## `summarise()` has grouped output by 'member_casual'. You can override using the
## `.groups` argument.

=========================================================

LANGKAH KELIMA : EXPORT FILE UNTUK ANALISIS LEBIH LANJUT

=========================================================

Selanjutnya untuk menganalisis lebih lanjut tentang data yang sudah kita analisis tadi kita dapat mengeksport file csv dataset tersebut dan menganalisisnya menggunakan Excel,tableau, looker studio ataupun yang lainnya. Sekaligus membuat presentasi yang bagus untuk hasil analisis yang sudah dilakukan.

Terimakasih, selamat mencoba.