Provincial income, convergence clubs, and structural change in Indonesia 2001-2017:

An interactive and automated exploration in R

Suggested citation:

Gunawan, A. and Mendez, C. (2020). Provincial income, convergence clubs and structural change in Indonesia 2001-2017: An interactive and automated exploration in R. R Studio/RPubs. Available at https://rpubs.com/quarcs-lab/data-indonesia-34-provinces-clubs-2001-2017

This work is licensed under the Creative Commons Attribution-Share Alike 4.0 International License.

1 Original data source

The source data are taken from the Indonesia Central Bureau of Statistics (Badan Pusat Statistik Republik of Indonesia), INDO-DAPOER World Bank Database.

dat <- read_csv("Dat.csv")

2 Data transformations

2.1 Select wide panels for each variable

• GDP per capita

dat_GDPpc <- dat %>% 
  filter(Series == "GDPPC")  %>% 
  select(-ID_Series, -Series)

• (Gross) Physical Capital formation

dat_inv <- dat %>% 
  filter(Series == "GFCF")  %>% 
  select(-ID_Series, -Series)

• Labor

dat_labor <- dat %>% 
  filter(Series == "Labor")  %>% 
  select(-ID_Series, -Series)

• Human Capital

dat_hc <- dat %>% 
  filter(Series == "Human")  %>% 
  select(-ID_Series, -Series)

• Population

dat_pop <- dat %>% 
  filter(Series == "Population")  %>% 
  select(-ID_Series, -Series)

• Population growth

dat_Gpop <- dat %>% 
  filter(Series == "Population Growth")  %>% 
  select(-ID_Series, -Series)

• Agricultural share

dat_agri <- dat %>% 
  filter(Series == "Agri")  %>% 
  select(-ID_Series, -Series)

• Construction share

dat_cons <- dat %>% 
  filter(Series == "Construct")  %>% 
  select(-ID_Series, -Series)

• Manufacturing share

dat_man <- dat %>% 
  filter(Series == "Manufacture")  %>% 
  select(-ID_Series, -Series)

• Mining

dat_min <- dat %>% 
  filter(Series == "Mining")  %>% 
  select(-ID_Series, -Series)

• Services

dat_ser <- dat %>% 
  filter(Series == "Services") %>% 
  select(-ID_Series, -Series)

2.2 Pivot from wide to long

long_dat_GDPpc <- dat_GDPpc %>% 
  pivot_longer(
    cols = starts_with("yr"),
    names_to = "year",
    names_prefix = "yr",
    values_to = "GDPpc",
  ) %>% 
  mutate(
    year = as.numeric(year)
  )

long_dat_inv <- dat_inv %>% 
  pivot_longer(
    cols = starts_with("yr"),
    names_to = "year",
    names_prefix = "yr",
    values_to = "inv",
  ) %>% 
  mutate(
    year = as.numeric(year)
  )

long_dat_labor <- dat_labor %>% 
  pivot_longer(
    cols = starts_with("yr"),
    names_to = "year",
    names_prefix = "yr",
    values_to = "labor",
  ) %>% 
  mutate(
    year = as.numeric(year)
  )

long_dat_hc <- dat_hc %>% 
  pivot_longer(
    cols = starts_with("yr"),
    names_to = "year",
    names_prefix = "yr",
    values_to = "hc",
  ) %>% 
  mutate(
    year = as.numeric(year)
  )

long_dat_pop <- dat_pop %>% 
  pivot_longer(
    cols = starts_with("yr"),
    names_to = "year",
    names_prefix = "yr",
    values_to = "pop",
  ) %>% 
  mutate(
    year = as.numeric(year)
  )

long_dat_Gpop <- dat_Gpop %>% 
  pivot_longer(
    cols = starts_with("yr"),
    names_to = "year",
    names_prefix = "yr",
    values_to = "Gpop",
  ) %>% 
  mutate(
    year = as.numeric(year)
  )

long_dat_agri <- dat_agri %>% 
  pivot_longer(
    cols = starts_with("yr"),
    names_to = "year",
    names_prefix = "yr",
    values_to = "agri",
  ) %>% 
  mutate(
    year = as.numeric(year)
  )

long_dat_cons <- dat_cons %>% 
  pivot_longer(
    cols = starts_with("yr"),
    names_to = "year",
    names_prefix = "yr",
    values_to = "cons",
  ) %>% 
  mutate(
    year = as.numeric(year)
  )

long_dat_min <- dat_min %>% 
  pivot_longer(
    cols = starts_with("yr"),
    names_to = "year",
    names_prefix = "yr",
    values_to = "min",
  ) %>% 
  mutate(
    year = as.numeric(year)
  )

long_dat_man <- dat_man %>% 
  pivot_longer(
    cols = starts_with("yr"),
    names_to = "year",
    names_prefix = "yr",
    values_to = "man",
  ) %>% 
  mutate(
    year = as.numeric(year)
  )

long_dat_ser <- dat_ser %>% 
  pivot_longer(
    cols = starts_with("yr"),
    names_to = "year",
    names_prefix = "yr",
    values_to = "ser",
  ) %>% 
  mutate(
    year = as.numeric(year)
  )

2.3 Merge all long panels

mergeCols <- c("ID_Prov", "year", "Status", "Club", "Province")

dat_PANEL <- long_dat_GDPpc %>% 
  inner_join(long_dat_inv, by = mergeCols) %>% 
  inner_join(long_dat_pop, by = mergeCols) %>% 
  inner_join(long_dat_Gpop,   by = mergeCols) %>% 
  inner_join(long_dat_labor,  by = mergeCols) %>% 
  inner_join(long_dat_hc, by = mergeCols) %>% 
  inner_join(long_dat_agri, by = mergeCols) %>% 
  inner_join(long_dat_cons, by = mergeCols) %>% 
  inner_join(long_dat_min, by = mergeCols) %>% 
  inner_join(long_dat_man, by = mergeCols) %>% 
  inner_join(long_dat_ser, by = mergeCols) 

• Add variable: \(GDPpc^2\)

dat_PANEL <- dat_PANEL %>% 
  mutate(
    GDPpc2 = (GDPpc)^2
  )

Sample data from the provinces of Aceh

dat_PANEL %>% 
  head(17)

3 Definitions of variables

3.1 Select and re-order variables

dat_PANEL <- dat_PANEL %>% 
  select(
    Province,
    year,
    GDPpc,
    GDPpc2,
    inv,
    pop,
    Gpop,
    labor,
    hc,
    agri,
    cons,
    min,
    man,
    ser,
    Status,
    Club,
    ID_Prov
  )

3.2 Show definitions of variables

df_definitions <- read_csv("df_def.csv") 
df_definitions

4 Interactive Table

# Define function for  interactive table
create_dt <- function(x){
  DT::datatable(x,
                extensions = 'Buttons',
                options = list(dom = 'Blfrtip',
                               buttons = c('copy', 'csv', 'excel', 'pdf', 'print'),
                               lengthMenu = list(c(10,25,50,-1),
                                                 c(10,25,50,"All"))))
}

dat_PANEL %>% 
  create_dt() %>% 
  formatRound(columns =c(
"GDPpc",
"GDPpc2",
"inv",
"pop",
"Gpop",
"labor",
"hc",
"agri",
"cons",
"min",
"man",
"ser"
  ),
  digits = 4)

5 Interactive and Automated exploration

• Initial exploration

# Run it in the console

ExPanD(
  df = dat_PANEL,
  df_def = df_definitions,
  export_nb_option = TRUE,
  title = "Provincial income, convergence clubs, and structural change in  Indonesia 2001-2017",
  abstract = paste("Suggested citation:  Gunawan, A. and Mendez, C. (2020). Provincial income, convergence clubs and structural change in Indonesia 2001-2017: An interactive and automated exploration in R. R Studio/RPubs. Available at https://rpubs.com/quarcs-lab/data-indonesia-34-provinces-clubs-2001-2017")
    )

• Automated exploration: First you customize the exploration, export configuration file, and include it in the new set up of the exploration.

config_list_fromRDS <- readRDS("ExPanD.RDS")

# Run it in the console

ExPanD(
  df = dat_PANEL,
  df_def = df_definitions,
  export_nb_option = TRUE,
  config_list = config_list_fromRDS,
  title = "Provincial income, convergence clubs, and structural change in  Indonesia 2001-2017",
  abstract = paste("Suggested citation:  Gunawan, A. and Mendez, C. (2020). Provincial income, convergence clubs and structural change in Indonesia 2001-2017: An interactive and automated exploration in R. R Studio/RPubs. Available at https://rpubs.com/quarcs-lab/data-indonesia-34-provinces-clubs-2001-2017")
    )

• Interactive Dashborad (Shiny Web App)

https://carlosmendez777.shinyapps.io/data-indonesia-34-provinces-clubs-2001-2017

6 References

END