not_included/Croatia_example.R

In rOpenGov/iotables: Reproducible Input-Output Economics Analysis, Economic and Environmental Impact Assessment with Empirical Data

##Croatia example
library(iotables) ; library (dplyr) ; library (tidyr)
source = "croatia_2010_1700" 
geo = "HR"; geo_input = "HR"
year = 2010
unit = "T_NAC"; unit_input <- unit
labelling = "iotables" 


hr_io_1800 <- iotable_get ( source = "croatia_2010_1800", geo = "HR",
                         year = 2010, unit = "T_NAC", labelling  = "iotables")
hr_io_1800 <- iotable_get ( source = "croatia_2010_1800", geo = "HR",
                            year = 2010, unit = "T_NAC", labelling  = "short")

hr_io_1900 <- iotable_get ( source = "croatia_2010_1900", geo = "HR",
                             year = 2010, unit = "T_NAC")

hr_use_1900 <- use_table_get ( source = "croatia_2010_1900", geo = "HR",
                            year = 2010, unit = "T_NAC")
hr_use_1900 <- use_table_get ( source = "croatia_2010_1900", geo = "HR",
                               year = 2010, unit = "T_NAC", 
                               labelling = "short")

#gives warning
output_vector_hr <- output_get(source = "croatia_2010_1900", geo = "HR",
                               year = 2010, unit = "T_NAC", labelling = "iotables")
output_vector_hr <- output_get(source = "croatia_2010_1800", geo = "HR",
                               year = 2010, unit = "T_NAC", labelling = "iotables")

View ( employment_hr)

data ("croatia_employment_2013")
data ("croatia_employment_aggregation")

employment_vector <- employment_aggregate (
  employment_df = croatia_employment_2013,
  matching = croatia_employment_aggregation ) 

employment_indicator_hr <- iotables::input_indicator_create (
  employment_vector, output_vector_hr, digits = 4)

input_coefficients <- iotables::input_coefficient_matrix_create(
  input_flow_hr, output_vector_hr, digits = 4)

L_hr <- iotables::leontief_matrix_create( technology_coefficients_matrix =
                                          input_coefficients)

I_hr <- iotables::leontief_inverse_create(L_hr)

input_vector = hr_emp
Im = I_hr

employment_multipliers_hr <- multiplier_create ( 
  input_vector  =   employment_indicator_hr, Im = I_hr, digits = 4 ) %>%
  tidyr::gather ( t_cols2, values, !! 2:ncol(.)) %>%
  mutate ( values = values * 1000 )

value_added_hr <- input_value_added_get( labelled_io_data = croatia_2010_1700, 
                           technology = tech_hr, geo = "HR", 
                           year = 2010, unit = "T_NAC", named = TRUE) 

va_indicator_hr <- input_indicator_create (
  value_added_hr, output_vector_hr, digits = 4) 

va_multiplier_hr <- multiplier_create ( 
  input_vector  =   va_indicator_hr, Im = I_hr, digits = 4 ) %>%
  tidyr::gather ( t_cols2, values, !! 2:ncol(.))  %>%
  mutate ( group = 'services') %>%
  mutate ( group = ifelse(grepl("CPA_A", t_cols2), "agriculture", group )) %>%
  mutate ( group = ifelse(grepl("CPA_B", t_cols2), "mining", group )) %>%
  mutate ( group = ifelse(grepl("CPA_C", t_cols2), "manufacturing", group )) %>%
  mutate ( group = ifelse(grepl("CPA_F", t_cols2), "construction", group )) %>%
  mutate ( group = ifelse(grepl("CPA_R90.R92", t_cols2), "live music & arts", group )) %>%
  mutate ( group = ifelse(grepl("CPA_J59_J60", t_cols2), "music & audiovisual", group )) %>%
  filter ( t_cols2 != "CPA_L68A")

#there is a naming problem 90.92

 data (croatia_2010_1700)
 labelled_io_data = croatia_2010_1700
 primary_input_get <- primary_input_get(input = "compensation_employees", 
                      source = "croatia_2010_1700", geo = "HR",unit = "T_NAC", 
                       year = 2010, households = TRUE)