Bagian 6 Visualisasi Data

Salah satu keunggulan R dibandingkan dengan software statistika lainnya adalah kemampuan menghasilkan grafik yang sangat mumpuni, baik untuk membuat plot untuk eksplorasi data awal, validasi model, atau untuk keperluan publikasi. Setidaknya ada tiga sistem utama untuk menghasilkan grafik dalam R yaitu grafik R dasar (base R graphics), lattice dan ggplot2. Masing-masing sistem ini memiliki kekuatan dan kelemahannya masing-masing. Pada kesempatan ini, kita akan lebih fokus pada grafik R dasar dan ggplot2.

Untuk keperluan praktik pembuatan grafik, kita akan menggunakan salah satu dari data “NYS Salary Information for the Public Sector” yang dapat diperoleh di laman kaggle, di samping data-data lainnya. Untuk memudahkan proses latihan, data telah saya olah terlebih dahulu dengan perintah berikut

library(dplyr)

read.csv("data/salary-information-for-local-authorities.csv") %>%
  filter(Has.Employees != 'No') %>%
  mutate(Employee.Group = substr(Employee.Group.End.Date, 1, 4),
         Other.Allowances = Overtime.Paid + 
           Performance.Bonus + 
           Extra.Pay + 
           Other.Compensation ) %>%
  mutate_all(na_if, "") %>%
  select(Authority.Name, Employee.Group, Title, Group, Department, Pay.Type, 
         Exempt.Indicator, Base.Annualized.Salary, Actual.Salary.Paid,
         Other.Allowances, Total.Compensation, Paid.By.Another.Entity,
         Paid.by.State.or.Local.Government ) %>%
  saveRDS("data/salary.rds")
Salary <- readRDS("data/salary.rds")
glimpse(Salary)
## Rows: 444,894
## Columns: 13
## $ Authority.Name                    <chr> "Albany County Airport Authority", "~
## $ Fiscal.Year                       <chr> "2019", "2019", "2019", "2019", "201~
## $ Title                             <chr> "Senior Account Technician", "Chief ~
## $ Employee.Group                    <chr> "Administrative", "Executive", "Mana~
## $ Department                        <chr> "Finance", "Administration", "Financ~
## $ Pay.Type                          <chr> "FT", "FT", "FT", "FT", "PT", "FT", ~
## $ Exempt.Indicator                  <chr> "Y", "Y", "Y", "Y", "Y", "Y", "Y", "~
## $ Base.Annualized.Salary            <dbl> 57331.92, 185000.00, 86828.74, 84504~
## $ Actual.Salary.Paid                <dbl> 57331.92, 15416.66, 86828.74, 84504.~
## $ Other.Allowances                  <dbl> 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, ~
## $ Total.Compensation                <dbl> 57331.92, 15416.66, 86828.74, 84504.~
## $ Paid.By.Another.Entity            <chr> "N", "N", "N", "N", "N", "N", "N", "~
## $ Paid.by.State.or.Local.Government <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, ~

6.1 Grafik R dasar

6.1.1 Histogram

Membuat histogram dapat dilakukan dengan fungsi hist()

hist(Salary$Actual.Salary.Paid)

hist(Salary$Actual.Salary.Paid, freq = FALSE)

hist(Salary$Actual.Salary.Paid, freq = FALSE, breaks = 50)

plot(density(Salary$Actual.Salary.Paid, bw = 0.5))

6.1.2 Bar plot

counts <- table(Salary$Employee.Group)
counts 
## 
## Administrative      Executive     Managerial    Operational   Professional 
##          65857           1864          37986         115146         132906 
##      Technical 
##          91135
barplot(counts)

barplot(counts, cex.names=.8)

counts <- table(Salary$Pay.Type, Salary$Employee.Group)
barplot(counts, cex.names=.8)

barplot(counts,
        col = c("#ffd166","#06d6a0"), #custom colors
        legend = rownames(counts),
        cex.names=.8)

barplot(counts,
        col = c("#ffd166","#06d6a0"),
        legend = rownames(counts),
        beside = TRUE,
        cex.names=.8)

6.1.3 Scatter plot

x = seq(from = -2*pi, to = 2*pi, length.out = 100)
y = 2*sin(x)
plot(x,y)

plot(x,y, type = "l")

plot(x,y, type = "h")

Salary100 <- Salary[1:1000,]
plot(Salary100$Base.Annualized.Salary, Salary100$Total.Compensation)

plot(Salary100$Base.Annualized.Salary, Salary100$Total.Compensation, pch = 19)

Salary100$Employee.Group <- as.factor(Salary100$Employee.Group)
plot(Salary100$Base.Annualized.Salary, Salary100$Total.Compensation, 
     col = Salary100$Employee.Group, pch = 16)

Salary100$Employee.Group <- as.factor(Salary100$Employee.Group)
plot(Salary100$Base.Annualized.Salary, Salary100$Total.Compensation, 
     col = Salary100$Employee.Group, pch = 16)
legend(x = 0, y = 200000, legend = levels(Salary100$Employee.Group), col = 1:6, pch = 19)

mean.Total.Compensation <- mean(Salary100$Total.Compensation)
mean.Base.Annualized.Salary <- mean(Salary100$Base.Annualized.Salary)
plot(Salary100$Base.Annualized.Salary, Salary100$Total.Compensation, pch = 19)
points(mean.Base.Annualized.Salary, mean.Total.Compensation , pch = 15, col = 'red', cex = 1.5)

pairs(~ Total.Compensation + Base.Annualized.Salary + Actual.Salary.Paid + Other.Allowances, data = Salary100)

6.1.4 Line plot

plot(x,y)
lines(x, sin(x))

x <- 1:40
y <- rnorm(40,5,1)
plot(x,y,type="o")

plot(Salary100$Base.Annualized.Salary, Salary100$Total.Compensation, pch = 19)
# regression line (y~x)
abline(lm(Total.Compensation~Base.Annualized.Salary, data = Salary100), col = "red") 
# lowess line (x,y)
lines(lowess(Salary100$Base.Annualized.Salary,Salary100$Total.Compensation), col = "blue")

6.1.5 Pie chart

tbl.Employee <- table(Salary$Employee.Group)
pie(tbl.Employee)

mytable <- table(iris$Species)
lbls <- paste(names(mytable), "\n", mytable, sep="")
pie(mytable, labels = lbls,
   main="Pie Chart of Species\n (with sample sizes)")

6.1.6 Boxplot

boxplot(Salary$Actual.Salary.Paid)

boxplot(Actual.Salary.Paid ~ Employee.Group, data = Salary, cex.axis=.8)

boxplot(Actual.Salary.Paid ~ Employee.Group, data = Salary,
        notch = TRUE, 
        col = RColorBrewer::brewer.pal(length(unique(Salary$Employee.Group)),'Set2'),
        cex.axis=.8)

6.1.7 Layout

par(mfrow = c(2,2), cex.main = 0.75)

## plot 1
plot(Salary$Base.Annualized.Salary, Salary$Total.Compensation, 
     main = "Scatterplot of Base Annualized Salary vs. Total Compensation")

## plot 2
hist(Salary$Total.Compensation, 
     main = "Histogram of Total Compensation")

## plot 3
boxplot(Salary$Actual.Salary.Paid, 
        main = "Actual Salary Paid")

## plot 4
barplot(table(Salary$Employee.Group), 
        las = 2, 
        cex.names = 0.65,
        main = "Barplot of Employee Group")

par(mfrow = c(2,1), cex.main = 0.75)

## plot 1
plot(Salary$Base.Annualized.Salary, Salary$Total.Compensation, 
     main = "Scatterplot of Base Annualized Salary vs. Total Compensation")

## plot 2
barplot(table(Salary$Employee.Group), 
        las = 2, 
        cex.names = 0.65,
        main = "Barplot of Employee Group")

layout(matrix(c(1,1,2,3), 2, 2, byrow = TRUE))
hist(Salary$Actual.Salary.Paid, breaks = 50)
hist(Salary$Base.Annualized.Salary)
hist(Salary$Total.Compensation)

par(cex.main = 0.7)
layout(matrix(c(1,1,2,3), 2, 2, byrow = TRUE),
       widths = c(3,1)) # column 2 is 1/3 the width of the column 1

hist(Salary$Actual.Salary.Paid, breaks = 50)
hist(Salary$Base.Annualized.Salary)
hist(Salary$Total.Compensation)

6.1.8 Latihan/Responsi

6.1.8.1 Latihan 1

Buatlah scatter plot seperti gambar berikut

# data awal
x <- 1:40
y <- rnorm(40,5,1)

# data baru
x1 <- 41:50
y1 <- rnorm(10,5,1)

# scatter plot dg data awal
plot(x, y, type="p", 
     xlab="Sumbu x",
     ylab="Sumbu y",
     main="Bilangan Acak Normal",
     col=topo.colors(40),
     pch=16, cex=2, xlim=c(0,50), ylim=c(2.5,7.5))

# plot data baru
points(x1, y1, cex=2)

#menambahkan garis
x2 <- rep(40.5,20)
y2 <- seq(min(c(y,y1)), max(c(y,y1)), length=20)

abline(v = 40.2, col="blue")
abline(h=mean(y), col="red", lwd=2.5)
abline(a=2, b=1/10, col="purple", lwd=2, lty=2)

#menambahkan tanda panah
arrows(x0=30, y0=3.5, x1=40, y1=mean(y)-.1, lwd=2)

#menambahkan tulisan
text(x=29,y=3.3, labels="Titik potong", cex=0.7)
text(x=3,y=7.3, labels="Data awal", cex=0.7)
text(x=46,y=7.3, labels="Data baru", cex=0.7)

6.1.8.2 Latihan 2

Buatlah density histogram dan kurva dari contoh acak yang menyebar Chi-Squared dengan derajat bebas 4.

x <- rchisq(1000,df=4)
hist(x, freq=FALSE, ylim=c(0,0.2),
     main = "Histogram of Random Variable ~ Chi-Squared(4)")
curve(dchisq(x,df=4), col=2, lty=2, lwd=2, add=TRUE)

6.1.8.3 Latihan 3

Buatlah grafik seperti berikut

a1 <- 1:25
a2 <- rnorm(25,4,2)

x <- seq(0,10,0.1)
sin <- sin(x)

par(mfrow=c(2,2))

plot(1:40,y,type="p",xlab="Sumbu x",ylab="Sumbu y",
     main="Bilangan Acak Normal",col=2,pch=16)

y=plot(x, sin, type="l")

plot(table(rpois(100,5)),type="h",col="red",
     lwd=1,main="rpois(100,lambda=5)")

plot(a1,a2,type="n",main="W")
text(a1,a2,labels=paste("w",1:25,sep=""),
     col=rainbow(25),cex=0.8)

6.1.8.4 Latihan 4

Buat empat grafil dalam satu windows, dengan format berikut:

yb <- rnorm(100,5,1)
xb <- 1:100

par(mfrow=c(2,2))

plot(xb, yb, pch=16, col=rainbow(100))
title("Scatter Plot Bilangan Acak Normal",cex.main=0.7)

x <- yb
hist(yb, freq=FALSE, main=NULL, ylim=c(0,0.5))
curve(dnorm(x,5,1), col="red", lty=2, lwd=2, add=TRUE)
title("Histogram Bilangan Acak Normal", cex.main=0.7)

boxplot(yb)
title("Boxplot Bilangan Acak Normal",cex.main=0.7)

plot(xb, yb, type="l",lwd=2,col="blue")
title("Line Plot Bilangan Acak Normal",cex.main=0.7)

6.2 ggplot2

library(ggplot2)

6.2.1 Histogram

ggplot(Salary, aes(x=Total.Compensation)) +
    geom_histogram()

ggplot(Salary, aes(x=Total.Compensation)) +
    geom_histogram(binwidth=10000, colour="black", fill="white")

ggplot(Salary, aes(x=Total.Compensation)) + geom_density()

ggplot(Salary, aes(x=Total.Compensation)) + 
    geom_histogram(aes(y=..density..),
                   binwidth=10000,
                   colour="black", fill="white") +
    geom_density(alpha=.2, fill="#FF6666")

ggplot(Salary, aes(x=Total.Compensation, fill=Pay.Type)) +
    geom_histogram(binwidth=10000, alpha=.5, position="identity")

ggplot(Salary, aes(x=Total.Compensation)) + 
  geom_histogram(binwidth=10000, colour="black", fill="white") + 
  facet_grid(Pay.Type ~ .)

6.2.2 Scatterplot

ggplot(Salary100, aes(x=Base.Annualized.Salary, y=Total.Compensation)) +
    geom_point(shape=1) # hollow circles

ggplot(Salary100, aes(x=Base.Annualized.Salary, y=Total.Compensation, color = Employee.Group)) +
    geom_point(shape=1) # hollow circles

6.2.3 Barplot

ggplot(Salary, aes(x=Employee.Group)) +
    geom_bar(stat="count")

ggplot(Salary, aes(x=Employee.Group, fill=Employee.Group)) +
    geom_bar(stat="count")

ggplot(Salary, aes(x=Employee.Group, fill=Pay.Type)) +
    geom_bar(stat="count")

ggplot(Salary, aes(x=Employee.Group, fill=Pay.Type)) +
    geom_bar(stat="count", position="fill")

ggplot(Salary, aes(x=Employee.Group, fill=Pay.Type)) +
    geom_bar(stat="count", position=position_dodge())

6.2.4 Pie chart

Total.Employee <- Salary %>%
  group_by(Employee.Group) %>%
  summarise(Total.Employee = n()) %>%
  ungroup() %>%
  arrange(desc(Employee.Group)) %>%
  mutate(lab.ypos = cumsum(Total.Employee) - 0.5*Total.Employee)

ggplot(Total.Employee, aes(x="", y=Total.Employee, fill=Employee.Group)) + 
  geom_bar(stat="identity") +
  coord_polar("y", start = 0) +
  theme_void()

ggplot(Total.Employee, aes(x = 2, y = Total.Employee, fill = Employee.Group)) +
  geom_bar(stat = "identity", color = "white") +
  coord_polar(theta = "y", start = 0)+
  geom_text(aes(y = lab.ypos, label = Employee.Group), color = "white")+
  theme_void()+
  xlim(0.5, 2.5)

6.2.5 Line plot

Salary.by.Year <- Salary %>% 
  group_by(Fiscal.Year) %>%
  summarise(Actual.Salary = mean(Actual.Salary.Paid))
  
ggplot(Salary.by.Year, aes(x=factor(Fiscal.Year), group=1, y=Actual.Salary)) +
    geom_line()

Salary.by.Year <- Salary %>% 
  group_by(Fiscal.Year, Employee.Group) %>%
  summarise(Actual.Salary = mean(Actual.Salary.Paid))
  
ggplot(Salary.by.Year, 
       aes(x=factor(Fiscal.Year), y=Actual.Salary, group=Employee.Group, col=Employee.Group)) +
    geom_line()

ggplot(Salary.by.Year, 
       aes(x=factor(Fiscal.Year), y=Actual.Salary, group=Employee.Group, col=Employee.Group)) + 
  geom_line() +
  geom_point()

6.2.6 Boxplot

ggplot(Salary, aes(x=Total.Compensation)) + geom_boxplot()

ggplot(Salary, aes(y=Total.Compensation, fill = Employee.Group)) + 
  geom_boxplot()

6.2.7 Layout

Menggunakan fungsi plot_grid() dari library cowplot.

library(cowplot)
p1 <- ggplot(Salary, aes(y=Total.Compensation)) + geom_boxplot()
p2 <- ggplot(Salary100, aes(x=Base.Annualized.Salary, y=Total.Compensation)) +
  geom_point(shape=1)
p3 <- ggplot(Salary, aes(x=Total.Compensation)) +
  geom_histogram()
p4 <- ggplot(Salary, aes(x=Total.Compensation)) + geom_density()
plot_grid(p1, p2, labels = NULL)

plot_grid(p1, p2, p3, p4, labels = NULL)

6.3 Visualisasi data spasial

6.3.1 Membaca data

library(sf)
mapIndonesia <- st_read("map/shp/idn_admbnda_adm3_bps_20200401.shp", 
                        quiet = TRUE)

Shape file di peroleh dari: data.humdata.org

dplyr::glimpse(mapIndonesia)
## 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 (~
dataBogor <- read.csv("data/Demografi-Bogor.csv")
dataBogor
##         Kota     Kecamatan   KodeBPS KodeKemendagri JumlahPenduduk LuasWilayah
## 1 Kota Bogor   Bogor Barat ID3271050       32.71.04         244708       23.08
## 2 Kota Bogor Bogor Selatan ID3271010       32.71.01         208185       31.16
## 3 Kota Bogor  Bogor Tengah ID3271040       32.71.03         106359        8.11
## 4 Kota Bogor   Bogor Timur ID3271020       32.71.02         105233       10.75
## 5 Kota Bogor   Bogor Utara ID3271030       32.71.05         192837       18.88
## 6 Kota Bogor  Tanah Sareal ID3271060       32.71.06         218135       21.25
##   KepadatanPenduduk
## 1             10603
## 2              6681
## 3             13115
## 4              9789
## 5             10214
## 6             10265
mapBogor<- mapIndonesia %>%
  dplyr::inner_join(dataBogor, by = c("ADM3_PCODE" = "KodeBPS"))
mapBogor
## Simple feature collection with 6 features and 22 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: 106.735 ymin: -6.679585 xmax: 106.8488 ymax: -6.510802
## Geodetic CRS:  WGS 84
##   Shape_Leng   Shape_Area       ADM3_EN ADM3_PCODE ADM3_REF ADM3ALT1EN
## 1  0.3644271 0.0018624008   Bogor Barat  ID3271050     <NA>       <NA>
## 2  0.3413273 0.0025144096 Bogor Selatan  ID3271010     <NA>       <NA>
## 3  0.1849809 0.0006540943  Bogor Tengah  ID3271040     <NA>       <NA>
## 4  0.1943379 0.0008671386   Bogor Timur  ID3271020     <NA>       <NA>
## 5  0.2430896 0.0015239343   Bogor Utara  ID3271030     <NA>       <NA>
## 6  0.2357695 0.0017147180  Tanah Sereal  ID3271060     <NA>       <NA>
##   ADM3ALT2EN    ADM2_EN ADM2_PCODE    ADM1_EN ADM1_PCODE   ADM0_EN ADM0_PCODE
## 1       <NA> Kota Bogor     ID3271 Jawa Barat       ID32 Indonesia         ID
## 2       <NA> Kota Bogor     ID3271 Jawa Barat       ID32 Indonesia         ID
## 3       <NA> Kota Bogor     ID3271 Jawa Barat       ID32 Indonesia         ID
## 4       <NA> Kota Bogor     ID3271 Jawa Barat       ID32 Indonesia         ID
## 5       <NA> Kota Bogor     ID3271 Jawa Barat       ID32 Indonesia         ID
## 6       <NA> Kota Bogor     ID3271 Jawa Barat       ID32 Indonesia         ID
##         date    validOn validTo       Kota     Kecamatan KodeKemendagri
## 1 2019-12-20 2020-04-01    <NA> Kota Bogor   Bogor Barat       32.71.04
## 2 2019-12-20 2020-04-01    <NA> Kota Bogor Bogor Selatan       32.71.01
## 3 2019-12-20 2020-04-01    <NA> Kota Bogor  Bogor Tengah       32.71.03
## 4 2019-12-20 2020-04-01    <NA> Kota Bogor   Bogor Timur       32.71.02
## 5 2019-12-20 2020-04-01    <NA> Kota Bogor   Bogor Utara       32.71.05
## 6 2019-12-20 2020-04-01    <NA> Kota Bogor  Tanah Sareal       32.71.06
##   JumlahPenduduk LuasWilayah KepadatanPenduduk                       geometry
## 1         244708       23.08             10603 MULTIPOLYGON (((106.7649 -6...
## 2         208185       31.16              6681 MULTIPOLYGON (((106.7961 -6...
## 3         106359        8.11             13115 MULTIPOLYGON (((106.7885 -6...
## 4         105233       10.75              9789 MULTIPOLYGON (((106.8315 -6...
## 5         192837       18.88             10214 MULTIPOLYGON (((106.8183 -6...
## 6         218135       21.25             10265 MULTIPOLYGON (((106.7822 -6...

6.3.2 Peta Statis

Peta statis dapat dibuat menggunakan ggplot2

p <- ggplot() +  
  geom_sf(data=mapBogor, aes(fill=JumlahPenduduk))
p

colorPalette = RColorBrewer::brewer.pal(5,"YlGnBu")
legendBreak = c(120,170,230)*1000
yBreak = seq(106.72, 106.86, by=0.04)

p + scale_fill_gradientn(colors = colorPalette, 
                       breaks = legendBreak, 
                       name = "Jumlah Penduduk") +
  labs(title = "Jumlah Penduduk Kota Bogor")  +
  theme(legend.text = element_text(size=7),
        legend.title = element_text(size=7),
        axis.text.x = element_text(size = 7),
        axis.text.y = element_text(size = 7),
        title = element_text(size=12, face='bold')) +
  scale_x_continuous(breaks = yBreak)

6.3.3 Peta Interaktif

Peta statis dapat dibuat menggunakan leaflet

# opsional, jika belum terinstal
install.packages("leaflet")
library(leaflet)

Berikut perintah-perintah untuk menampilkan jumlah penduduk Kota Bogor dengan peta leaflet.

  1. leaflet(): inisiasi peta dengan memanggil fungsi leaflet()
  2. addProviderTiles(): menambahkan peta dasar (base map) dengan perintah
  3. addPolygons(): menabahkan poligon dengan gradasi warna berdasarkan jumlah penduduk. Pengaturan warna gradasi menggunakan colorNumeric(). Ditambahkan pula opsi label untu menampilkan popup, yang akan muncul ketika pengguna menyorot area tertentu.
  4. addLegend(): menambahkan legenda
  5. addLayersControl(): menampilkan tombol untuk memilih layer yang akan ditampilkan
  6. setView(): mengatur posisi dan zooming default
# membuat custom palette warna
populationPalette <- colorNumeric(
  palette = "YlGnBu",
  domain = mapBogor$JumlahPenduduk
)

# membuat custom popup
popupLabel <- paste0(
    "<b>Kecamatan ", mapBogor$Kecamatan,"</b><br/>", 
    "Jumlah Penduduk (jiwa): ", mapBogor$JumlahPenduduk, "<br/>", 
    "Luas Wilayah (km2): ", mapBogor$LuasWilayah, "<br/>", 
    "Kepadatan Penduduk (jiwa/km2): ", mapBogor$KepadatanPenduduk) %>%
  lapply(htmltools::HTML)
# membuat peta leaflet
leaflet(mapBogor) %>% 
  addProviderTiles(providers$CartoDB.PositronNoLabels, group = "Light Mode") %>%
  addProviderTiles(providers$CartoDB.DarkMatterNoLabels, group = "Dark Mode") %>%
  
  addPolygons(weight = 1,
              opacity = 1, 
              fillOpacity = 0.9,
              label = popupLabel,
              color = ~populationPalette(JumlahPenduduk),
              highlightOptions = highlightOptions(color = "white", 
                                                  weight = 2, 
                                                  bringToFront = TRUE) ) %>%
  addLegend(position = "bottomright", 
            pal = populationPalette, 
            values = ~JumlahPenduduk,
            title = "Jumlah\nPenduduk",
            opacity = 1) %>%
  
  addLayersControl(position = 'topright',
                   baseGroups = c("Light Mode", "Dark Mode"),
                   options = layersControlOptions(collapsed = FALSE)) %>%
  
  setView(lat = - 6.595, lng = 106.87, zoom = 12)

Jika peta tidak tampil atau terpotong, bisa dilihat di RPubs berikut: https://rpubs.com/nurandi/simple-choropleth-r-leaflet .