ComDef: fix ag_production queries

R/vehicleStockAndSalesUsingUCD.R

@@ -0,0 +1,146 @@
+#
+# library(Rcpp)
+# library(magrittr)
+# library(devtools)
+# library(dplyr)
+# library(tidyr)
+# library(rgcam)
+# library(readr)
+# # library(gcammaptools)
+# library(ggplot2)
+# library(gtable)
+# library(grid)
+# # We also need to make sure that the fonts we want to use are properly loaded into R
+# library(extrafont)
+# library(RColorBrewer)
+# library(gridExtra)
+# library(stringr)
+# library(tibble)
+# library(readxl)
+# library(writexl)
+#
+# # Create a simple function to format all maps into a long table
+# gather_map <- function(df){
+#   untouched_cols <- names(df) %>% .[!grepl("var", names(df))]
+#   df %>%
+#     pivot_longer(cols = -all_of(untouched_cols), names_to = "identifier", values_to = "var") %>%
+#     select(-identifier) %>%
+#     filter(!is.na(var), var != "") %>%
+#     return()
+# }
+#
+# long_columns <- c("scenario", "region", "var", "year", "value")
+#
+#
+#
+#
+# # Energy Service ----------------------------------------------------------
+# ## Transportation ------------------
+#
+# trans_serv <- getQuery(prj, "transport service output by tech and vintage") |>
+#   separate(technology, into = c("technology", "vintage"), sep = ",") |>
+#   mutate(vintage = as.integer(sub("year=", "", vintage))) |>
+#   filter(vintage <= year) |>    ##Only vintages from the model year or before will be in existence
+#   rename("mode" = "subsector")
+#
+#
+#
+# #### Vehicle stock ----------------------------------
+#
+# gcam_regions <- unique(trans_serv$region)
+#
+# # Use the UCD dataset which has load factors and vehicle miles traveled values
+# ucd_core_values <- ucd_core |>
+#   pivot_longer(names_to = "year", values_to = "value", cols = starts_with("2")) |>
+#   mutate(year=as.integer(year)) |>
+#   left_join(ucd_size_class) |>
+#   # Both types of rail simply called 'Rail' causing errors later
+#   mutate(rev.mode = ifelse(rev.mode == "Rail",
+#                            ifelse(UCD_sector == "Passenger",
+#                                   "Passenger Rail", ifelse(UCD_sector == "Freight",
+#                                                            "Freight Rail", rev.mode)), rev.mode)) |>
+#   select(-UCD_fuel, -UCD_sector, -mode, -size.class)
+#
+# ucd_techs <- unique(ucd_core_values$UCD_technology)
+# ucd_techs <- ucd_techs[!(ucd_techs == "All")]
+#
+# # Annual vehicle, is the same across all fuels of cars, so explicity show that:
+# ucd_core_loads <- ucd_core_values |>
+#   filter(variable == "annual travel per vehicle") |>
+#   mutate(UCD_technology = ifelse(UCD_technology == "All", ucd_techs[1], UCD_technology)) |>
+#   complete(nesting(UCD_region, rev_size.class, rev.mode, variable, unit, year, value), UCD_technology = ucd_techs) |>
+#   bind_rows(ucd_core_values |>
+#               filter(variable == "load factor"))
+#
+#
+# # For regions in the UCD dataset that are GCAM regions, keep their values,
+# # and for regions that aren't represented in there, use the mean of the other regions
+#
+# ucd_core_A <- ucd_core_loads |>
+#   filter(UCD_region %in% gcam_regions) |>
+#   group_by(UCD_region, rev_size.class, rev.mode, variable, UCD_technology, unit, year) |>
+#   summarise(value=mean(value, na.rm = T)) |>
+#   ungroup()
+#
+# ucd_core_B <- ucd_core_loads |>
+#   filter(!(UCD_region %in% gcam_regions)) |>
+#   ##filter(rev_size.class %in% c("Car", "Light truck")) |>
+#   group_by(rev_size.class, rev.mode, variable, UCD_technology, unit, year) |>
+#   summarise(value=mean(value, na.rm = T)) |>
+#   ungroup() |>
+#   mutate(UCD_region = "South America_Northern") |>
+#   complete(nesting(rev_size.class, rev.mode, variable, UCD_technology, unit, year, value), UCD_region = gcam_regions[!(gcam_regions %in% unique(ucd_core_A$UCD_region))])
+#
+# ## View the following to see that annual travel per vehicle is in vkt/(vehicle*yr)
+# ## and load factors are in pass/vehicle and tonnes/vehicle
+# # unique(ucd_core_loads |>
+# #          select(-UCD_region, -year, -value, -UCD_technology))
+#
+# ucd_core_gcamRegions <- bind_rows(ucd_core_A, ucd_core_B) |>
+#   select(-unit) |>
+#   pivot_wider(names_from = "variable", values_from = "value")
+#
+# trans_stock_clean <- trans_serv |>
+#   left_join(ucd_core_gcamRegions, by = c("region"="UCD_region","mode"="rev_size.class", "year", "technology"="UCD_technology")) |>
+#   filter(!(is.na(`annual travel per vehicle`)),
+#          !(is.na(`load factor`))) |>
+#   mutate(value=(value / `load factor` / `annual travel per vehicle`),
+#          Units = "million vehicles") |>
+#   left_join(transport_stock_map, by = c("sector", "mode", "technology"), relationship = "many-to-many") |>
+#   filter(!is.na(var)) |>
+#   mutate(value = value * unit_conv) |>
+#   group_by(scenario, region, year, var, Units) |>
+#   summarise(value = sum(value, na.rm = T)) |>
+#   ungroup() |>
+#   select(all_of(long_columns), Units)
+#
+#
+# ### Vehicle sales -------------------------
+#
+# vintaged_modes = trans_serv |>
+#   filter(year != vintage,
+#          value != 0) |>
+#   select(sector, mode, Units) |>
+#   unique()
+#
+# trans_serv_new <- trans_serv %>%
+#   filter(year == vintage)    ## only look at new sales in each year
+#
+# trans_sales_clean <- trans_serv_new |>
+#   left_join(ucd_core_gcamRegions, by = c("region"="UCD_region","mode"="rev_size.class", "year", "technology"="UCD_technology")) |>
+#   filter(!(is.na(`annual travel per vehicle`)),
+#          !(is.na(`load factor`)),
+#          mode %in% unique(vintaged_modes$mode)) |>
+#   # Assume constant sales during the 5 year period
+#   mutate(value=(value / `load factor` / `annual travel per vehicle` / 5),
+#          Units = "million vehicles") |>
+#   left_join(transport_sales_map, by = c("sector", "mode", "technology"), relationship = "many-to-many") |>
+#   filter(!is.na(var)) |>
+#   mutate(value = value * unit_conv) |>
+#   group_by(scenario, region, year, var, Units) |>
+#   summarise(value = sum(value, na.rm = T)) |>
+#   ungroup() |>
+#   select(all_of(long_columns), Units)
+#
+# rm(ucd_core_A, ucd_core_B)
+#

inst/extdata/mappings/GCAM7.0/variables_functions_mapping.csv

@@ -21,7 +21,7 @@ kyoto_gases_clean;get_kyoto_gases;LUC_emiss;nonCO2 emissions by sector (excludin
 co2_sequestration_clean;get_co2_sequestration;LUC_emiss;CO2 sequestration by tech
 co2_sequestration_raw;get_co2_sequestration;LUC_emiss;CO2 sequestration by tech
 ag_demand_clean;get_ag_demand;;demand balances by crop commodity,demand balances by meat and dairy commodity,regional biomass consumption
-ag_production_clean;get_ag_production;;ag production by crop type
+ag_production_clean;get_ag_production;;ag production by crop type,meat and dairy production by type
 land_clean;get_land;;land allocation by crop and water source
 primary_energy_clean;get_primary_energy;;primary energy consumption with CCS by region (direct equivalent)
 pe_trade_prod;get_pe_trade_prod;;resource production

inst/extdata/mappings/GCAM7.1/variables_functions_mapping.csv

@@ -21,7 +21,7 @@ kyoto_gases_clean;get_kyoto_gases;LUC_emiss;nonCO2 emissions by sector (excludin
 co2_sequestration_clean;get_co2_sequestration;LUC_emiss;CO2 sequestration by tech
 co2_sequestration_raw;get_co2_sequestration;LUC_emiss;CO2 sequestration by tech
 ag_demand_clean;get_ag_demand;;demand balances by crop commodity,demand balances by meat and dairy commodity,regional biomass consumption
-ag_production_clean;get_ag_production;;ag production by crop type
+ag_production_clean;get_ag_production;;ag production by crop type,meat and dairy production by type
 land_clean;get_land;;land allocation by crop and water source
 primary_energy_clean;get_primary_energy;;primary energy consumption with CCS by region (direct equivalent)
 pe_trade_prod;get_pe_trade_prod;;resource production

inst/extdata/mappings/GCAM7.2/variables_functions_mapping.csv

@@ -21,7 +21,7 @@ kyoto_gases_clean;get_kyoto_gases;LUC_emiss;nonCO2 emissions by sector (excludin
 co2_sequestration_clean;get_co2_sequestration;LUC_emiss;CO2 sequestration by tech
 co2_sequestration_raw;get_co2_sequestration;LUC_emiss;CO2 sequestration by tech
 ag_demand_clean;get_ag_demand;;demand balances by crop commodity,demand balances by meat and dairy commodity,regional biomass consumption
-ag_production_clean;get_ag_production;;ag production by crop type
+ag_production_clean;get_ag_production;;ag production by crop type,meat and dairy production by type
 land_clean;get_land;;land allocation by crop and water source
 primary_energy_clean;get_primary_energy;;primary energy consumption with CCS by region (direct equivalent)
 pe_trade_prod;get_pe_trade_prod;;resource production