econio provides a set of functions for input-output analysis.
You can install the development version of econio from GitHub with:
# install.packages("pak")
# pak::pak("UchidaMizuki/econio")
install.packages("econio", repos = c('https://uchidamizuki.r-universe.dev', 'https://cloud.r-project.org'))
library(econio)
library(econiodatajp)
library(ggplot2)
econiodatajp provides
Japan’s input-output tables as ready-to-use econ_io_table objects, so
you don’t need to build one from a tidy data frame yourself. Use
io_table_get() to fetch a specific table:
iotable <- io_table_get(
region_type = "regional",
region_class = "nation",
year = 2020,
sector_class = "large",
language = "en"
)
iotable
#> # Input-output table: regional
#> # Dimensions: input [43], output [47]
#> # Input: 43 sectors
#> # Output: 47 sectors
#> # Import type: competitive
#> input$sector output$sector .
#> <sector> <sector> <dbl>
#> 1 <industry> 01_Agriculture, forestry and fishery <industry> 01_Agriculture, forestry and fishery 1602100000
#> 2 <industry> 01_Agriculture, forestry and fishery <industry> 06_Mining 0
#> 3 <industry> 01_Agriculture, forestry and fishery <industry> 11_Beverages and Foods 7253300000
#> 4 <industry> 01_Agriculture, forestry and fishery <industry> 15_Textile products 16500000
#> 5 <industry> 01_Agriculture, forestry and fishery <industry> 16_Pulp, paper and wooden products 289700000
#> 6 <industry> 01_Agriculture, forestry and fishery <industry> 39_Miscellaneous manufacturing products 39900000
#> 7 <industry> 01_Agriculture, forestry and fishery <industry> 20_Chemical products 41700000
#> 8 <industry> 01_Agriculture, forestry and fishery <industry> 21_Petroleum and coal products 0
#> 9 <industry> 01_Agriculture, forestry and fishery <industry> 22_Plastic products and rubber products 103500000
#> 10 <industry> 01_Agriculture, forestry and fishery <industry> 25_Ceramic, stone and clay products 800000
#> # ℹ 2,011 more rows
io_input_coef(iotable)
#> Assuming `open_economy = FALSE`.
#> # Input-output table: regional
#> # Dimensions: input [37], output [37]
#> # Input: 37 sectors
#> # Output: 37 sectors
#> # Import type: competitive
#> input$sector output$sector .
#> <sector> <sector> <dbl>
#> 1 <industry> 01_Agriculture, forestry and fishery <industry> 01_Agriculture, forestry and fishery 0.130
#> 2 <industry> 01_Agriculture, forestry and fishery <industry> 06_Mining 0
#> 3 <industry> 01_Agriculture, forestry and fishery <industry> 11_Beverages and Foods 0.191
#> 4 <industry> 01_Agriculture, forestry and fishery <industry> 15_Textile products 0.00568
#> 5 <industry> 01_Agriculture, forestry and fishery <industry> 16_Pulp, paper and wooden products 0.0253
#> 6 <industry> 01_Agriculture, forestry and fishery <industry> 39_Miscellaneous manufacturing products 0.00455
#> 7 <industry> 01_Agriculture, forestry and fishery <industry> 20_Chemical products 0.00147
#> 8 <industry> 01_Agriculture, forestry and fishery <industry> 21_Petroleum and coal products 0
#> 9 <industry> 01_Agriculture, forestry and fishery <industry> 22_Plastic products and rubber products 0.00756
#> 10 <industry> 01_Agriculture, forestry and fishery <industry> 25_Ceramic, stone and clay products 0.000122
#> # ℹ 1,359 more rows
io_leontief_inverse(iotable)
#> Assuming `open_economy = FALSE`.
#> # Input-output table: regional
#> # Dimensions: output [37], input [37]
#> # Input: 37 sectors
#> # Output: 37 sectors
#> # Import type: competitive
#> output$sector input$sector .
#> <sector> <sector> <dbl>
#> 1 <industry> 01_Agriculture, forestry and fishery <industry> 01_Agriculture, forestry and fishery 1.19
#> 2 <industry> 01_Agriculture, forestry and fishery <industry> 06_Mining 0.000704
#> 3 <industry> 01_Agriculture, forestry and fishery <industry> 11_Beverages and Foods 0.286
#> 4 <industry> 01_Agriculture, forestry and fishery <industry> 15_Textile products 0.0116
#> 5 <industry> 01_Agriculture, forestry and fishery <industry> 16_Pulp, paper and wooden products 0.0436
#> 6 <industry> 01_Agriculture, forestry and fishery <industry> 39_Miscellaneous manufacturing products 0.0108
#> 7 <industry> 01_Agriculture, forestry and fishery <industry> 20_Chemical products 0.00860
#> 8 <industry> 01_Agriculture, forestry and fishery <industry> 21_Petroleum and coal products 0.000467
#> 9 <industry> 01_Agriculture, forestry and fishery <industry> 22_Plastic products and rubber products 0.0136
#> 10 <industry> 01_Agriculture, forestry and fishery <industry> 25_Ceramic, stone and clay products 0.00202
#> # ℹ 1,359 more rows
autoplot(iotable, type = "skyline")