1 Geological Resources

The term geological resource refers to all the solid, gas, and liquid elements originating from the earth’s crust both on the surface and below the surface with optimal concentration to be extracted.

Copper Mine is an example of Geological Resources

1.1 Brief Explanation

Geologically, Indonesia is a potential region for the formation of various energy and mineral resources. Exploration activities that have been carried out since 1800’s up to the present time, have successfully uncovered these resources in numerous areas. Among these various resources, oil, gas, coal, coal, tin, nickel, copper, and gold have so far given important contribution to the Indonesian economy. The majority of Indonesian territory has not been explored thoroughly, and therefore the chance to discover new economical energy and mineral deposit is likely to be substantial.

1.2 Analysis Objective

Data includes all mineral resources from all over Indonesia. Resources including metal, geothermal, non-metal, and coal resources. Data sets is combined for easier visualization. Visualization of data is used to gain insights on how geological resources in Indonesia varied.

2 Data Preparation

#Load all required library
library(tidyverse)
library(ggplot2)
library(readxl)

#Import all data
coal <- read_excel("BB.xlsx")
non_metal <- read_excel("BL.xlsx")
metal <- read_excel("ML.xlsx")
geothermal <- read_excel("PB.xlsx")

#Bind all data
resources <- rbind(coal, non_metal, metal, geothermal)
resources

2.1 Data Inspection

#Inspect data types
glimpse(resources)

#> Rows: 8,288
#> Columns: 9
#> $ potensi        <chr> "Batubara", "Batubara", "Batubara", "Batubara", "Batuba~
#> $ nama_provinsi  <chr> "Aceh", "Aceh", "Aceh", "Aceh", "Aceh", "Aceh", "Aceh",~
#> $ nama_kabupaten <chr> "Aceh Barat", "Aceh Barat", "Aceh Barat", "Aceh Barat",~
#> $ Komoditas      <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,~
#> $ tahun          <dbl> 2015, 2016, 2017, 2018, 2015, 2016, 2017, 2018, 2016, 2~
#> $ total_sd       <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,~
#> $ total_cad      <chr> "450.15", "1195.919382", "766.699382", "954.367382", "0~
#> $ total_sd_2     <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,~
#> $ total_cad_2    <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,~

#Change data types
resources <- resources %>% 
  mutate(potensi = as.factor(potensi),
         total_sd = as.integer(total_sd),
         total_cad = as.integer(total_cad),
         total_sd_2 = as.integer(total_sd_2),
         total_cad_2 = as.integer(total_cad_2))

#Inspect data type after mutate
glimpse(resources)

#> Rows: 8,288
#> Columns: 9
#> $ potensi        <fct> Batubara, Batubara, Batubara, Batubara, Batubara, Batub~
#> $ nama_provinsi  <chr> "Aceh", "Aceh", "Aceh", "Aceh", "Aceh", "Aceh", "Aceh",~
#> $ nama_kabupaten <chr> "Aceh Barat", "Aceh Barat", "Aceh Barat", "Aceh Barat",~
#> $ Komoditas      <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,~
#> $ tahun          <dbl> 2015, 2016, 2017, 2018, 2015, 2016, 2017, 2018, 2016, 2~
#> $ total_sd       <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,~
#> $ total_cad      <int> 450, 1195, 766, 954, 0, 0, 0, 0, 53, 53, 319, 44061, 98~
#> $ total_sd_2     <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,~
#> $ total_cad_2    <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,~

2.2 Data Cleaning

#Check missing value
colSums(is.na(resources))

#>        potensi  nama_provinsi nama_kabupaten      Komoditas          tahun 
#>              0              0              0           1255              0 
#>       total_sd      total_cad     total_sd_2    total_cad_2 
#>           6263            411           6254           6254

#Fill missing total reserve with 0
resources$total_cad <- resources $total_cad %>% 
  replace_na(0)

#Fill missing total resources with 0
resources$total_sd <- resources $total_sd %>% 
  replace_na(0)

#Removing sd_2 and cad_2 and changing column name
resources <- resources %>% 
  select(-c(total_sd_2, total_cad_2))

#Check missing value
colSums(is.na(resources))

#>        potensi  nama_provinsi nama_kabupaten      Komoditas          tahun 
#>              0              0              0           1255              0 
#>       total_sd      total_cad 
#>              0              0

2.3 Data Explanation

resources

potensi : geological resources potential
nama_provinsi : province name in Indonesia
nama_kabupaten : district name in Indonesia
Komoditas : resources commodity
tahun : year
total_sd : total resources
total_cad : total energy reserve

3 Visualization

3.1 Potential Comparison

#Potential Count
potential_count <- data.frame(count = table(resources$potensi))

ggplot(data = potential_count,
       mapping = aes(y = reorder(count.Var1,count.Freq), x = count.Freq)) +
  geom_col(aes(fill = count.Freq), show.legend = F) +
  scale_fill_gradient(low = "orange", high = "red") +
  geom_label(data = potential_count, mapping = aes(label = count.Freq)) +
  labs(title = "Indonesia Potential Resources Count",
       x = "Frequency",
       y = NULL)

Insights :

Mineral & Bukan Logam & Batuan or a non-metal resources is the highest with 4999 resources all accross Indonesia
Batubara or a coal resources is the fewest with 504 resources all accross Indonesia

3.2 Java Island Resources

#Filter data to be only in java
resources_jawa <- resources %>% 
  filter(nama_provinsi == "Jawa Barat" | nama_provinsi == "Jawa Tengah" |
         nama_provinsi == "Jawa Timur" | nama_provinsi == "Banten" |
         nama_provinsi == "D.I. Yogyakarta")

resources_jawa_agg <- aggregate(formula = total_cad~nama_provinsi+tahun,
                                data = resources_jawa,
                                FUN = sum)

resources_jawa_agg <- resources_jawa_agg %>% 
  mutate(total_cad = round(total_cad/1000000, 2))

ggplot(data = resources_jawa_agg,
       mapping = aes(x = tahun,
                     y = total_cad)) +
  geom_line(aes(group = nama_provinsi, col = nama_provinsi), lwd = 0.9) +
  geom_point(aes(col = nama_provinsi)) +
  facet_wrap(~nama_provinsi, nrow = 3) +
  labs(title = "Java Province Reserve Growth Comparison",
       x = "Year",
       y = "Total Reserves") +
  theme(legend.position = "none")