Teori Kependudukan

1 Source Data

2 Library

2.1 Install Libraries

Tanda pagar # bisa dihapus apabila belum mendonwload pustaka, ketika sudah selesai mendonwload bisa di kasih pagar lagi agar tidak terdonwload ulang kembali.

# remotes::install_github('JohnCoene/echarts4r.maps')
# install.packages(tidyverse)
# install.packages(echarts4r)
# install.packages("remotes")
# install.packages(echarts4r.maps)
# install.packages(readxl)
# install.packages(htmlwidgets)
# install.packages(janitor)

2.2 Import Libraries

# Buat baca file excel
library(readxl)

# Pengolahan data
library(tidyverse)
library(janitor)

# Untuk dapatin map dan visualisasi
library(echarts4r)
library(echarts4r.maps)
library(htmlwidgets)
library(highcharter)
library(mapdata)

3 Map Chart

3.1 Global

3.1.1 Data Preparation

Read Data

df <- read.csv("csvData.csv")

# Melihat data
df

Add New Column

Membuat kolom baru yang berisi perubahan skala nilai agar memudahkan pembaca.

Dari 131000 -> 131K

df$`Population 2020` <- df$pop2020/1000

3.1.2 Visualization

plot_map <- 
        df %>%
        e_charts(name) %>% 
        e_map(`Population 2020`, map = "world") %>% 
        e_visual_map(`Population 2020`) %>% 
        e_tooltip(trigger = "item",        
                  formatter = JS("
                                 function(params){return(
                                 '<b>' + params.name
                                 + ':' + '</b>' +
                                 (params.value).toLocaleString('en-US',
                                 {maximumFractionDigits: 2, minimumFractionDigits: 2 })
                                 )}")) %>%
  e_title(text = "Komposisi Populasi Dunia Pada Tahun 2022 (dalam juta)") %>%
  e_theme("vintage")

plot_map

3.2 Country (Indonesia)

3.2.1 Data Preparation

# Membaca file Excel
indo <- read_excel("Jumlah Penduduk Hasil SP2020 menurut Wilayah dan Jenis Kelamin.xlsx")

# Menghapus kolom 35
indo <- indo[-c(35),]

# Menormalisasi skala data
indo$Jumlah <- as.numeric(indo$Jumlah)
indo$Jumlah_nor <- indo$Jumlah/1000000

# Melihat Data
indo

3.2.2 Visualization

indo_map <- 
indo %>%
        e_charts(Province) %>%
        em_map("Indonesia") %>%
        e_map(Jumlah_nor, map = "Indonesia") %>% 
        e_visual_map(Jumlah_nor) %>% 
  e_tooltip(trigger = "item",        
                  formatter = JS("
                                 function(params){return(
                                 '<b>' + params.name
                                 + ':' + '</b>' +
                                 (params.value).toLocaleString('en-US',
                                 {maximumFractionDigits: 2, minimumFractionDigits: 2 })
                                 )}")) %>%
  e_title(text = "Komposisi Populasi Indonesia Pada Tahun 2020 (dalam juta)") %>%
  e_theme("vintage")

indo_map

4 Template Map

Isi garis ___

plot_map <- 
        # ------------- Membuat map ---------------------
        df %>%
        e_charts(______) %>%  
        e_map(______, map = "____") %>% 
        e_visual_map(_____) %>% 
        
        
       # -------------- Membuat Interactive -------------- (G usah diganti)
        e_tooltip(trigger = "item",        
                  formatter = JS("
                                 function(params){return(
                                 '<b>' + params.name
                                 + ':' + '</b>' +
                                 (params.value).toLocaleString('en-US',
                                 {maximumFractionDigits: 2, minimumFractionDigits: 2 })
                                 )}")) %>%
      # -------------- Ganti Judul --------------------
  e_title(text = "World Population Composition (In Milions)") %>%
  
  
      # ------------- Ganti tema ------------- (Refrensi opsi tema dibawah)
  e_theme("vintage")

plot_map

5 Line Chart

5.1 Data Preparation

df_line <- 
df %>% arrange(-pop2022) %>% 
  head(5)

df_line$cca2 <- NULL

df_line <-as.data.frame(t(df_line)) %>% row_to_names(row_number = 1)
df_line_ok <- readxl::read_excel("data_ok.xlsx")

df_line_ok <- 
df_line_ok %>% mutate(
 China = as.numeric(China),
 India = as.numeric(India),
`United States` = as.numeric(`United States`),
 Indonesia = as.numeric(Indonesia),
 Pakistan = as.numeric(Pakistan)
) %>% arrange(Period)

5.2 Visualization

hc <- highchart() %>%
      hc_xAxis(categories = df_line_ok$Period) %>% 
hc_chart(type = "line") %>%
    hc_title(text = "Tren Pertumbuhan Penduduk Top 5 Negara",
             align = "center",
             style = list(color = "black", useHTML = TRUE)) %>%
    hc_subtitle(text = "Period 1970 - 2050",
                align = "center",
                style = list(color = "black", useHTML = TRUE)) %>%
    hc_add_series(data = df_line_ok$China, name = "China") %>%
    hc_add_series(data = df_line_ok$India, name = "India") %>%
    hc_add_series(data = df_line_ok$`United States`, name = "United States") %>% 
    hc_add_series(data = df_line_ok$Indonesia, name = "Indonesia") %>% 
    hc_add_series(data = df_line_ok$Pakistan, name = "Pakistan") %>% 
    hc_yAxis(labels = list(format = "{value:,.0f}")) %>% 
    hc_colors(c("red","blue","green","purple","brown")) %>% 
    hc_tooltip(enabled = TRUE, pointFormat = "{point.y:,.2f}") %>% 
    hc_add_theme(hc_theme_ffx())

hc

Refrensi Tema

https://echarts4r.john-coene.com/reference/theme.html