Praktikum Sains (Data Database dan Data Spasial)

Email         : moch.ridwan@apps.ipb.ac.id
RPubs       : https://rpubs.com/Mochamamad_Ridwan/
Department : Department of Statistics
Address : Kampus IPB Darmaga
   Jl. Meranti Wing 22 Level 4,Bogor 16680, Jawa Barat, Indonesia.

Package yang digunakan.

library(knitr)

## Warning: package 'knitr' was built under R version 4.0.4

library(tidyverse)

## Warning: package 'tidyverse' was built under R version 4.0.4

## -- Attaching packages --------------------------------------- tidyverse 1.3.0 --

## v ggplot2 3.3.3     v purrr   0.3.4
## v tibble  3.0.5     v dplyr   1.0.4
## v tidyr   1.1.3     v stringr 1.4.0
## v readr   1.4.0     v forcats 0.5.1

## Warning: package 'ggplot2' was built under R version 4.0.4

## Warning: package 'forcats' was built under R version 4.0.4

## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

library(RSQLite)
library(DBI)
library(sf)

## Linking to GEOS 3.8.0, GDAL 3.0.4, PROJ 6.3.1

library(ggplot2)
library(tigris)

## Warning: package 'tigris' was built under R version 4.0.4

## To enable 
## caching of data, set `options(tigris_use_cache = TRUE)` in your R script or .Rprofile.

library(sp)

## Warning: package 'sp' was built under R version 4.0.4

library(dplyr)
library(DT)
library(kableExtra)

## 
## Attaching package: 'kableExtra'

## The following object is masked from 'package:dplyr':
## 
##     group_rows

Koneksi Database

Database yang digunakan adalah chinook.db

chinook <-DBI::dbConnect(RSQLite::SQLite(), "C:/sqlite/db/chinook.db")

Daftar Tabel yang terdapat pada database chinook adalah:

RSQLite::dbListTables(chinook)

##  [1] "albums"          "artists"         "customers"       "employees"      
##  [5] "genres"          "invoice_items"   "invoices"        "media_types"    
##  [9] "playlist_track"  "playlists"       "sqlite_sequence" "sqlite_stat1"   
## [13] "tracks"

Tables

Skema Database chinook.db

Tabel-tabel yang akan digunakan pada praktikum terdiri dari:

albums, artists , customers , invoice_items , invoices , playlists , playlist_track , tracks

albums<-dplyr::tbl(chinook,"albums")
artists<-dplyr::tbl(chinook,"artists")
customers<-dplyr::tbl(chinook,"customers")
invoice_items<-dplyr::tbl(chinook,"invoice_items")
invoices<-dplyr::tbl(chinook,"invoices")
playlists<-dplyr::tbl(chinook,"playlists")
playlist_track<-dplyr::tbl(chinook,"playlist_track")
tracks<-dplyr::tbl(chinook,"tracks")

kbl(head(albums), caption = "Head(Albums)") %>%  kable_styling(font_size=14)

Head(Albums)
AlbumId	Title	ArtistId
1	For Those About To Rock We Salute You	1
2	Balls to the Wall	2
3	Restless and Wild	2
4	Let There Be Rock	1
5	Big Ones	3
6	Jagged Little Pill	4

kbl(head(artists), caption = "Head(artists)") %>%  kable_styling(font_size=14)

Head(artists)
ArtistId	Name
1	AC/DC
2	Accept
3	Aerosmith
4	Alanis Morissette
5	Alice In Chains
6	Antônio Carlos Jobim

kbl(head(customers), caption = "Head(customers)") %>%  kable_styling(font_size=14)

Head(customers)
CustomerId	FirstName	LastName	Company	Address	City	State	Country	PostalCode	Phone	Fax	Email	SupportRepId
1	Luís	Gonçalves	Embraer - Empresa Brasileira de Aeronáutica S.A.	Av. Brigadeiro Faria Lima, 2170	São José dos Campos	SP	Brazil	12227-000	+55 (12) 3923-5555	+55 (12) 3923-5566	luisg@embraer.com.br	3
2	Leonie	Köhler	NA	Theodor-Heuss-Straße 34	Stuttgart	NA	Germany	70174	+49 0711 2842222	NA	leonekohler@surfeu.de	5
3	François	Tremblay	NA	1498 rue Bélanger	Montréal	QC	Canada	H2G 1A7	+1 (514) 721-4711	NA	ftremblay@gmail.com	3
4	Bjørn	Hansen	NA	Ullevålsveien 14	Oslo	NA	Norway	0171	+47 22 44 22 22	NA	bjorn.hansen@yahoo.no	4
5	František	Wichterlová	JetBrains s.r.o.	Klanova 9/506	Prague	NA	Czech Republic	14700	+420 2 4172 5555	+420 2 4172 5555	frantisekw@jetbrains.com	4
6	Helena	Holý	NA	Rilská 3174/6	Prague	NA	Czech Republic	14300	+420 2 4177 0449	NA	hholy@gmail.com	5

kbl(head(invoice_items), caption = "Head(invoice_items)") %>%  kable_styling(font_size=14)

Head(invoice_items)
InvoiceLineId	InvoiceId	TrackId	UnitPrice	Quantity
1	1	2	0.99	1
2	1	4	0.99	1
3	2	6	0.99	1
4	2	8	0.99	1
5	2	10	0.99	1
6	2	12	0.99	1

kbl(head(invoices), caption = "Head(invoices)") %>%  kable_styling(font_size=14)

Head(invoices)
InvoiceId	CustomerId	InvoiceDate	BillingAddress	BillingCity	BillingState	BillingCountry	BillingPostalCode	Total
1	2	2009-01-01 00:00:00	Theodor-Heuss-Straße 34	Stuttgart	NA	Germany	70174	1.98
2	4	2009-01-02 00:00:00	Ullevålsveien 14	Oslo	NA	Norway	0171	3.96
3	8	2009-01-03 00:00:00	Grétrystraat 63	Brussels	NA	Belgium	1000	5.94
4	14	2009-01-06 00:00:00	8210 111 ST NW	Edmonton	AB	Canada	T6G 2C7	8.91
5	23	2009-01-11 00:00:00	69 Salem Street	Boston	MA	USA	2113	13.86
6	37	2009-01-19 00:00:00	Berger Straße 10	Frankfurt	NA	Germany	60316	0.99

kbl(head(playlists), caption = "Head(playlists)") %>%  kable_styling(font_size=14)

Head(playlists)
PlaylistId	Name
1	Music
2	Movies
3	TV Shows
4	Audiobooks
5	90’s Music
6	Audiobooks

kbl(head(playlist_track), caption = "Head(playlist_track)") %>%  kable_styling(font_size=14)

Head(playlist_track)
PlaylistId	TrackId
1	3402
1	3389
1	3390
1	3391
1	3392
1	3393

kbl(head(tracks), caption = "Head(tracks)") %>%  kable_styling(font_size=14)

Head(tracks)
TrackId	Name	AlbumId	MediaTypeId	GenreId	Composer	Milliseconds	Bytes	UnitPrice
1	For Those About To Rock (We Salute You)	1	1	1	Angus Young, Malcolm Young, Brian Johnson	343719	11170334	0.99
2	Balls to the Wall	2	2	1	NA	342562	5510424	0.99
3	Fast As a Shark	3	2	1	F. Baltes, S. Kaufman, U. Dirkscneider & W. Hoffman	230619	3990994	0.99
4	Restless and Wild	3	2	1	F. Baltes, R.A. Smith-Diesel, S. Kaufman, U. Dirkscneider & W. Hoffman	252051	4331779	0.99
5	Princess of the Dawn	3	2	1	Deaffy & R.A. Smith-Diesel	375418	6290521	0.99
6	Put The Finger On You	1	1	1	Angus Young, Malcolm Young, Brian Johnson	205662	6713451	0.99

Fungsi Join

inner_join

inner_join() menghasilkan semua baris pada table x yang memiliki kesamaan nilai dengan table y, dan semua kolom dari x dan y.

inner_join() yang dicoba adalah antara tabel invoices dan invoice_items

inner_join_t = inner_join(invoices,invoice_items)

## Joining, by = "InvoiceId"

inner_join_t

## # Source:   lazy query [?? x 13]
## # Database: sqlite 3.34.1 [C:\sqlite\db\chinook.db]
##    InvoiceId CustomerId InvoiceDate    BillingAddress   BillingCity BillingState
##        <int>      <int> <chr>          <chr>            <chr>       <chr>       
##  1         1          2 2009-01-01 00~ Theodor-Heuss-S~ Stuttgart   <NA>        
##  2         1          2 2009-01-01 00~ Theodor-Heuss-S~ Stuttgart   <NA>        
##  3         2          4 2009-01-02 00~ Ullevålsveien 14 Oslo        <NA>        
##  4         2          4 2009-01-02 00~ Ullevålsveien 14 Oslo        <NA>        
##  5         2          4 2009-01-02 00~ Ullevålsveien 14 Oslo        <NA>        
##  6         2          4 2009-01-02 00~ Ullevålsveien 14 Oslo        <NA>        
##  7         3          8 2009-01-03 00~ Grétrystraat 63  Brussels    <NA>        
##  8         3          8 2009-01-03 00~ Grétrystraat 63  Brussels    <NA>        
##  9         3          8 2009-01-03 00~ Grétrystraat 63  Brussels    <NA>        
## 10         3          8 2009-01-03 00~ Grétrystraat 63  Brussels    <NA>        
## # ... with more rows, and 7 more variables: BillingCountry <chr>,
## #   BillingPostalCode <chr>, Total <dbl>, InvoiceLineId <int>, TrackId <int>,
## #   UnitPrice <dbl>, Quantity <int>

left_join

left_join() menghasilkan semua baris pada table x, dan semua kolom pada x dan y, dimana untuk baris pada x yang tidak memiliki kesamaan dengan y diisi dengan nilai NA pada kolom yang baru.

left_join() yang dicoba adalah antara tabel playlist_track dan playlists

left_join_t = left_join(playlist_track,playlists)

## Joining, by = "PlaylistId"

left_join_t

## # Source:   lazy query [?? x 3]
## # Database: sqlite 3.34.1 [C:\sqlite\db\chinook.db]
##    PlaylistId TrackId Name 
##         <int>   <int> <chr>
##  1          1    3402 Music
##  2          1    3389 Music
##  3          1    3390 Music
##  4          1    3391 Music
##  5          1    3392 Music
##  6          1    3393 Music
##  7          1    3394 Music
##  8          1    3395 Music
##  9          1    3396 Music
## 10          1    3397 Music
## # ... with more rows

right_join

right_join() menghasilkan semua baris pada table y, dan semua kolom pada x dan y, dimana untuk baris pada y yang tidak memiliki kesamaan dengan x akan diisi dengan nilai NA pada kolom yang baru.

right_join() yang dicoba adalah antara tabel tracks dan playlist_track

right_join_t = right_join(tracks,playlist_track)

## Joining, by = "TrackId"

right_join_t

## # Source:   lazy query [?? x 10]
## # Database: sqlite 3.34.1 [C:\sqlite\db\chinook.db]
##    TrackId Name         AlbumId MediaTypeId GenreId Composer Milliseconds  Bytes
##      <int> <chr>          <int>       <int>   <int> <chr>           <int>  <int>
##  1    3402 "Band Membe~     271           3      23 <NA>           294294 6.11e7
##  2    3389 "Revelation~     271           2      23 <NA>           252376 4.11e6
##  3    3390 "One and th~     271           2      23 <NA>           217732 3.56e6
##  4    3391 "Sound of a~     271           2      23 <NA>           260154 4.23e6
##  5    3392 "Until We F~     271           2      23 <NA>           230758 3.77e6
##  6    3393 "Original F~     271           2      23 <NA>           218916 3.58e6
##  7    3394 "Broken Cit~     271           2      23 <NA>           228366 3.73e6
##  8    3395 "Somedays"       271           2      23 <NA>           213831 3.50e6
##  9    3396 "Shape of T~     271           2      23 <NA>           274597 4.47e6
## 10    3397 "Jewel of t~     271           2      23 <NA>           233242 3.81e6
## # ... with more rows, and 2 more variables: UnitPrice <dbl>, PlaylistId <int>

full_join

full_join() menghasilkan semua baris dan kolom dari x dan y. Jika ada nilai yang tidak sama (match) antara x dan y, maka akan bernilai NA.

full_join() yang dicoba adalah antara tabel artists dan albums

full_join_t = full_join(artists,albums)

## Joining, by = "ArtistId"

full_join_t

## # Source:   lazy query [?? x 4]
## # Database: sqlite 3.34.1 [C:\sqlite\db\chinook.db]
##    ArtistId Name                 AlbumId Title                                
##       <int> <chr>                  <int> <chr>                                
##  1        1 AC/DC                      1 For Those About To Rock We Salute You
##  2        1 AC/DC                      4 Let There Be Rock                    
##  3        2 Accept                     2 Balls to the Wall                    
##  4        2 Accept                     3 Restless and Wild                    
##  5        3 Aerosmith                  5 Big Ones                             
##  6        4 Alanis Morissette          6 Jagged Little Pill                   
##  7        5 Alice In Chains            7 Facelift                             
##  8        6 Antônio Carlos Jobim       8 Warner 25 Anos                       
##  9        6 Antônio Carlos Jobim      34 Chill: Brazil (Disc 2)               
## 10        7 Apocalyptica               9 Plays Metallica By Four Cellos       
## # ... with more rows

semi_join

semi_join() menghasilkan semua baris pada table x yang memiliki kesamaan nilai dengan table y, dan semua kolom dari x. Ini mirip dengan inner_join(), bedanya inner_join() mengembalikan semua kolom dari x dan y.

semi_join() yang dicoba adalah antara tabel invoices dan customers

semi_join_t = semi_join(invoices,customers)

## Joining, by = "CustomerId"

semi_join_t

## # Source:   lazy query [?? x 9]
## # Database: sqlite 3.34.1 [C:\sqlite\db\chinook.db]
##    InvoiceId CustomerId InvoiceDate    BillingAddress   BillingCity BillingState
##        <int>      <int> <chr>          <chr>            <chr>       <chr>       
##  1         1          2 2009-01-01 00~ Theodor-Heuss-S~ Stuttgart   <NA>        
##  2         2          4 2009-01-02 00~ Ullevålsveien 14 Oslo        <NA>        
##  3         3          8 2009-01-03 00~ Grétrystraat 63  Brussels    <NA>        
##  4         4         14 2009-01-06 00~ 8210 111 ST NW   Edmonton    AB          
##  5         5         23 2009-01-11 00~ 69 Salem Street  Boston      MA          
##  6         6         37 2009-01-19 00~ Berger Straße 10 Frankfurt   <NA>        
##  7         7         38 2009-02-01 00~ Barbarossastraß~ Berlin      <NA>        
##  8         8         40 2009-02-01 00~ 8, Rue Hanovre   Paris       <NA>        
##  9         9         42 2009-02-02 00~ 9, Place Louis ~ Bordeaux    <NA>        
## 10        10         46 2009-02-03 00~ 3 Chatham Street Dublin      Dublin      
## # ... with more rows, and 3 more variables: BillingCountry <chr>,
## #   BillingPostalCode <chr>, Total <dbl>

anti_join

anti_join() menghasilkan semua baris dari x yang TIDAK memiliki kesamaan dengan y, dan semua kolom yang berasal dari x.

anti_join() yang dicoba adalah antara tabel artists dan albums

anti_join_t = anti_join(artists,albums)

## Joining, by = "ArtistId"

anti_join_t

## # Source:   lazy query [?? x 2]
## # Database: sqlite 3.34.1 [C:\sqlite\db\chinook.db]
##    ArtistId Name                      
##       <int> <chr>                     
##  1       25 Milton Nascimento & Bebeto
##  2       26 Azymuth                   
##  3       28 João Gilberto             
##  4       29 Bebel Gilberto            
##  5       30 Jorge Vercilo             
##  6       31 Baby Consuelo             
##  7       32 Ney Matogrosso            
##  8       33 Luiz Melodia              
##  9       34 Nando Reis                
## 10       35 Pedro Luís & A Parede     
## # ... with more rows

Data Spasial

Yang ditampilkan di pratikum ini, kepadatan Penduduk per Kecamatan di Kabupaten Bekasi

Tahun 2020

-Peta

AdminKecamatan<-"D:/BAHAN KULIAH/SM 1/SAINS DATA/SHP FILE/idn_admbnda_adm3_bps_20200401.shp"

glimpse(AdminKecamatan)

##  chr "D:/BAHAN KULIAH/SM 1/SAINS DATA/SHP FILE/idn_admbnda_adm3_bps_20200401.shp"

AdminKecamatan<-st_read(AdminKecamatan)

## Reading layer `idn_admbnda_adm3_bps_20200401' from data source `D:\BAHAN KULIAH\SM 1\SAINS DATA\SHP FILE\idn_admbnda_adm3_bps_20200401.shp' using driver `ESRI Shapefile'
## Simple feature collection with 7069 features and 16 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: 95.01079 ymin: -11.00762 xmax: 141.0194 ymax: 6.07693
## geographic CRS: WGS 84

glimpse(AdminKecamatan)

## Rows: 7,069
## Columns: 17
## $ Shape_Leng <dbl> 0.2798656, 0.7514001, 0.6900061, 0.6483629, 0.2437073, 1.35~
## $ Shape_Area <dbl> 0.003107633, 0.016925540, 0.024636382, 0.010761277, 0.00116~
## $ ADM3_EN    <chr> "2 X 11 Enam Lingkung", "2 X 11 Kayu Tanam", "Abab", "Abang~
## $ ADM3_PCODE <chr> "ID1306050", "ID1306052", "ID1612030", "ID5107050", "ID7471~
## $ ADM3_REF   <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,~
## $ ADM3ALT1EN <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,~
## $ ADM3ALT2EN <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,~
## $ ADM2_EN    <chr> "Padang Pariaman", "Padang Pariaman", "Penukal Abab Lematan~
## $ ADM2_PCODE <chr> "ID1306", "ID1306", "ID1612", "ID5107", "ID7471", "ID9432",~
## $ ADM1_EN    <chr> "Sumatera Barat", "Sumatera Barat", "Sumatera Selatan", "Ba~
## $ ADM1_PCODE <chr> "ID13", "ID13", "ID16", "ID51", "ID74", "ID94", "ID94", "ID~
## $ ADM0_EN    <chr> "Indonesia", "Indonesia", "Indonesia", "Indonesia", "Indone~
## $ ADM0_PCODE <chr> "ID", "ID", "ID", "ID", "ID", "ID", "ID", "ID", "ID", "ID",~
## $ date       <date> 2019-12-20, 2019-12-20, 2019-12-20, 2019-12-20, 2019-12-20~
## $ validOn    <date> 2020-04-01, 2020-04-01, 2020-04-01, 2020-04-01, 2020-04-01~
## $ validTo    <date> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA~
## $ geometry   <MULTIPOLYGON [°]> MULTIPOLYGON (((100.2811 -0..., MULTIPOLYGON (~

-Data

PendudukKabBekasi<-read.csv("D:/BAHAN KULIAH/SM 1/SAINS DATA/SHP FILE/Data Penduduk Kab. Bekasi.csv")
PendudukKabBekasi

##    Kode.Kecamatan   Nama.Kecamatan Jumlah.Penduduk Kepadatan
## 1       ID3216010             Setu          144624      2327
## 2       ID3216021      Serang Baru          173585      2721
## 3       ID3216022   Cikarang Pusat          100714      2116
## 4       ID3216023 Cikarang Selatan          278476      5382
## 5       ID3216030        Cibarusah           92168      1829
## 6       ID3216031      Bojongmangu           27363       456
## 7       ID3216041   Cikarang Timur          107854      2102
## 8       ID3216050   Kedungwaringin           63660      2019
## 9       ID3216061   Cikarang Utara          278421      6430
## 10      ID3216062    Karangbahagia           97369      2112
## 11      ID3216070         Cibitung          281824      6221
## 12      ID3216071   Cikarang Barat          278237      5182
## 13      ID3216081   Tambun Selatan          505012     11717
## 14      ID3216082     Tambun Utara          215879      6272
## 15      ID3216090          Babelan          297645      4680
## 16      ID3216100       Tarumajaya          158437      2900
## 17      ID3216110        Tambelang           38461      1015
## 18      ID3216111        Sukawangi           48297       719
## 19      ID3216120         Sukatani           74944      1997
## 20      ID3216121        Sukakarya           46817      1104
## 21      ID3216130        Pebayuran          102400      1063
## 22      ID3216140     Cabangbungin           49018       986
## 23      ID3216150    Muara Gembong          378818       277

kbl(head(PendudukKabBekasi), caption = "Head(PendudukKabBekasi)") %>%  kable_styling(font_size = 14)

Head(PendudukKabBekasi)
Kode.Kecamatan	Nama.Kecamatan	Jumlah.Penduduk	Kepadatan
ID3216010	Setu	144624	2327
ID3216021	Serang Baru	173585	2721
ID3216022	Cikarang Pusat	100714	2116
ID3216023	Cikarang Selatan	278476	5382
ID3216030	Cibarusah	92168	1829
ID3216031	Bojongmangu	27363	456

-Merger Data

Merge shapefiles dan dataset

merged_KabBekasi <- geo_join(spatial_data=AdminKecamatan, 
                             data_frame=PendudukKabBekasi, by_sp="ADM3_PCODE", 
                             by_df="Kode.Kecamatan", how = "inner")
merged_KabBekasi

## Simple feature collection with 23 features and 19 fields
## geometry type:  MULTIPOLYGON
## dimension:      XY
## bbox:           xmin: 106.9686 ymin: -6.482019 xmax: 107.2966 ymax: -5.918148
## geographic CRS: WGS 84
## First 10 features:
##    Shape_Leng  Shape_Area          ADM3_EN ADM3_PCODE ADM3_REF ADM3ALT1EN
## 1   0.5871964 0.005388774          Babelan  ID3216090     <NA>       <NA>
## 2   0.5015490 0.004320796      Bojongmangu  ID3216031     <NA>       <NA>
## 3   0.4066188 0.004046014     Cabangbungin  ID3216140     <NA>       <NA>
## 4   0.3513376 0.003286197        Cibarusah  ID3216030     <NA>       <NA>
## 5   0.4748041 0.003603693         Cibitung  ID3216070     <NA>       <NA>
## 6   0.4915341 0.004413896   Cikarang Barat  ID3216071     <NA>       <NA>
## 7   0.5120708 0.004369193   Cikarang Pusat  ID3216022     <NA>       <NA>
## 8   0.4528430 0.004397012 Cikarang Selatan  ID3216023     <NA>       <NA>
## 9   0.4833011 0.004313361   Cikarang Timur  ID3216041     <NA>       <NA>
## 10  0.3364092 0.003265586   Cikarang Utara  ID3216061     <NA>       <NA>
##    ADM3ALT2EN ADM2_EN ADM2_PCODE    ADM1_EN ADM1_PCODE   ADM0_EN ADM0_PCODE
## 1        <NA>  Bekasi     ID3216 Jawa Barat       ID32 Indonesia         ID
## 2        <NA>  Bekasi     ID3216 Jawa Barat       ID32 Indonesia         ID
## 3        <NA>  Bekasi     ID3216 Jawa Barat       ID32 Indonesia         ID
## 4        <NA>  Bekasi     ID3216 Jawa Barat       ID32 Indonesia         ID
## 5        <NA>  Bekasi     ID3216 Jawa Barat       ID32 Indonesia         ID
## 6        <NA>  Bekasi     ID3216 Jawa Barat       ID32 Indonesia         ID
## 7        <NA>  Bekasi     ID3216 Jawa Barat       ID32 Indonesia         ID
## 8        <NA>  Bekasi     ID3216 Jawa Barat       ID32 Indonesia         ID
## 9        <NA>  Bekasi     ID3216 Jawa Barat       ID32 Indonesia         ID
## 10       <NA>  Bekasi     ID3216 Jawa Barat       ID32 Indonesia         ID
##          date    validOn validTo   Nama.Kecamatan Jumlah.Penduduk Kepadatan
## 1  2019-12-20 2020-04-01    <NA>          Babelan          297645      4680
## 2  2019-12-20 2020-04-01    <NA>      Bojongmangu           27363       456
## 3  2019-12-20 2020-04-01    <NA>     Cabangbungin           49018       986
## 4  2019-12-20 2020-04-01    <NA>        Cibarusah           92168      1829
## 5  2019-12-20 2020-04-01    <NA>         Cibitung          281824      6221
## 6  2019-12-20 2020-04-01    <NA>   Cikarang Barat          278237      5182
## 7  2019-12-20 2020-04-01    <NA>   Cikarang Pusat          100714      2116
## 8  2019-12-20 2020-04-01    <NA> Cikarang Selatan          278476      5382
## 9  2019-12-20 2020-04-01    <NA>   Cikarang Timur          107854      2102
## 10 2019-12-20 2020-04-01    <NA>   Cikarang Utara          278421      6430
##                          geometry
## 1  MULTIPOLYGON (((107.0586 -6...
## 2  MULTIPOLYGON (((107.1884 -6...
## 3  MULTIPOLYGON (((107.1142 -6...
## 4  MULTIPOLYGON (((107.0972 -6...
## 5  MULTIPOLYGON (((107.092 -6....
## 6  MULTIPOLYGON (((107.1401 -6...
## 7  MULTIPOLYGON (((107.169 -6....
## 8  MULTIPOLYGON (((107.1477 -6...
## 9  MULTIPOLYGON (((107.2017 -6...
## 10 MULTIPOLYGON (((107.1982 -6...

-Data Dalam Peta

gradient <- c("#ffb3d9","#ff4da6","#e60073","#800040")
pDATA1<-ggplot()+geom_sf(data=merged_KabBekasi,aes(fill=Kepadatan))+ scale_color_gradientn(colours = gradient)+labs(title="Kepadatan Penduduk Kabupaten Bekasi Per Kecamatan 2020")
pDATA1