Merge pull request #408 from 0UmfHxcvx5J7JoaOhFSs5mncnisTJJ6q/dev/Ind…

…ustry_Paper_A

update Industry scripts with publication figures

.buildlibrary

@@ -1,4 +1,4 @@
-ValidationKey: '32076470'
+ValidationKey: '32259150'
 AcceptedWarnings:
 - 'Warning: package ''.*'' was built under R version'
 - 'Warning: namespace ''.*'' is not available and has been replaced'

CITATION.cff

@@ -2,8 +2,8 @@ cff-version: 1.2.0
 message: If you use this software, please cite it using the metadata from this file.
 type: software
 title: 'mrremind: MadRat REMIND Input Data Package'
-version: 0.164.2
-date-released: '2023-06-27'
+version: 0.165.0
+date-released: '2023-07-13'
 abstract: The mrremind packages contains data preprocessing for the REMIND model.
 authors:
 - family-names: Baumstark

DESCRIPTION

@@ -1,8 +1,8 @@
 Type: Package
 Package: mrremind
 Title: MadRat REMIND Input Data Package
-Version: 0.164.2
-Date: 2023-06-27
+Version: 0.165.0
+Date: 2023-07-13
 Authors@R: c(
     person("Lavinia", "Baumstark", , "[email protected]", role = c("aut", "cre")),
     person("Renato", "Rodrigues", role = "aut"),

NAMESPACE

@@ -133,6 +133,7 @@ importFrom(dplyr,tribble)
 importFrom(dplyr,ungroup)
 importFrom(dplyr,vars)
 importFrom(ggplot2,aes)
+importFrom(ggplot2,coord_cartesian)
 importFrom(ggplot2,expand_limits)
 importFrom(ggplot2,facet_wrap)
 importFrom(ggplot2,geom_area)
@@ -145,6 +146,7 @@ importFrom(ggplot2,guide_legend)
 importFrom(ggplot2,labs)
 importFrom(ggplot2,scale_colour_manual)
 importFrom(ggplot2,scale_fill_discrete)
+importFrom(ggplot2,scale_fill_manual)
 importFrom(ggplot2,scale_linetype_manual)
 importFrom(ggplot2,scale_shape_manual)
 importFrom(ggplot2,theme)

R/EDGE-Industry.R

@@ -32,23 +32,24 @@
 #'
 #' @seealso [`calcOutput()`]
 #'
-#' @importFrom assertr assert verify within_bounds
+#' @importFrom assertr assert not_na verify within_bounds
 #' @importFrom broom tidy
 #' @importFrom car logit
-#' @importFrom dplyr %>% case_when between distinct first last n right_join
-#'   semi_join vars
+#' @importFrom dplyr %>% case_when bind_rows between distinct first last n
+#'   mutate pull right_join select semi_join vars
 #' @importFrom Hmisc wtd.quantile
-#' @importFrom ggplot2 aes expand_limits facet_wrap geom_area geom_line
-#'   geom_path geom_point ggplot ggsave guide_legend labs scale_colour_manual
-#'   scale_fill_discrete scale_linetype_manual scale_shape_manual theme
-#'   theme_minimal
+#' @importFrom ggplot2 aes coord_cartesian expand_limits facet_wrap geom_area
+#'   geom_line geom_path geom_point ggplot ggsave guide_legend labs
+#'   scale_colour_manual scale_fill_discrete scale_fill_manual
+#'   scale_linetype_manual scale_shape_manual theme theme_minimal
 #' @importFrom madrat calcOutput readSource toolGetMapping
-#' @importFrom quitte calc_mode df_populate_range duplicate duplicate_
-#'   list_to_data_frame madrat_mule magclass_to_tibble order.levels seq_range
-#'   sum_total_
+#' @importFrom quitte calc_mode character.data.frame df_populate_range duplicate
+#'   duplicate_ list_to_data_frame madrat_mule magclass_to_tibble order.levels
+#'   seq_range sum_total_
 #' @importFrom readr write_rds
 #' @importFrom stats nls SSlogis sd
-#' @importFrom tidyr pivot_longer pivot_wider
+#' @importFrom tibble as_tibble tibble tribble
+#' @importFrom tidyr expand_grid pivot_longer pivot_wider
 #' @importFrom zoo na.approx rollmean
 
 #' @rdname EDGE-Industry
@@ -1242,6 +1243,48 @@ calcSteel_Projections <- function(subtype = 'production',
     )
   }
 
+  if (!is.null(save.plots)) {
+
+    p <- ggplot() +
+      geom_area(
+        data = x %>%
+          as_tibble() %>%
+          filter('gdp_SSP2EU' == .data$scenario) %>%
+          left_join(region_mapping, 'iso3c') %>%
+          full_join(
+            tibble(
+              pf = c('ue_steel_primary', 'ue_steel_secondary'),
+              production = factor(c('Primary Production',
+                                    'Secondary Production'),
+                                  rev(c('Primary Production',
+                                        'Secondary Production')))),
+
+            'pf'
+          ) %>%
+          group_by(.data$region, .data$year, .data$production) %>%
+          summarise(value = sum(.data$value), .groups = 'drop') %>%
+          sum_total_('region', name = 'World'),
+        mapping = aes(x = !!sym('year'), y = !!sym('value') * 1e-3,
+                      fill = !!sym('production'))) +
+      facet_wrap(~ region, scales = 'free_y') +
+      labs(x = NULL, y = 'Mt Steel/year') +
+      scale_fill_manual(values = c('Primary Production' = 'orange',
+                                   'Secondary Production' = 'yellow'),
+                        name = NULL) +
+      coord_cartesian(xlim = c(NA, 2100), expand = FALSE) +
+      theme_minimal() +
+      theme(legend.position = c(1, 0),
+            legend.justification = c(1, 0))
+
+    ggsave(plot = p, filename = '6_Steel_production.png',
+           device = 'png', path = save.plots, bg = 'white',
+           width = 18, height = 14, units = 'cm', scale = 1.73)
+
+    write_rds(x = p,
+              file = file.path(save.plots,
+                               '6_Steel_production.rds'))
+  }
+
   # return statement ----
   return(list(x = x,
               weight = NULL,
@@ -1417,8 +1460,9 @@ calcIndustry_Value_Added <- function(subtype = 'physical',
     filter(!.data$censor) %>%
     duplicate(region = 'World') %>%
     pivot_longer(c('population', 'GDP', 'manufacturing')) %>%
-    group_by(.data$region, .data$year, .data$name) %>%
+    group_by(.data$region, .data$iso3c, .data$year, .data$name) %>%
     summarise(value = sum(.data$value), .groups = 'drop') %>%
+    sum_total_('iso3c') %>%
     pivot_wider() %>%
     mutate(
       # mfg.share = .data$manufacturing / .data$GDP,  FIXME
@@ -1431,7 +1475,8 @@ calcIndustry_Value_Added <- function(subtype = 'physical',
 
       nls(formula = manufacturing / population ~ a * exp(b / GDPpC),
           data = regression_data %>%
-            filter(.data$region == r),
+            filter(.data$region == r,
+                   'Total' != .data$iso3c),
           start = list(a = 1000, b = -2000),
           trace = FALSE) %>%
         tidy() %>%
@@ -1753,8 +1798,8 @@ calcIndustry_Value_Added <- function(subtype = 'physical',
       theme(legend.justification = c(1, 0),
             legend.position = c(1, 0))
 
-    ggsave(plot = p, filename = '04_Steel_VA_regressions_projections.svg',
-           device = 'svg', path = save.plots, bg = 'white',
+    ggsave(plot = p, filename = '04_Steel_VA_regressions_projections.png',
+           device = 'png', path = save.plots, bg = 'white',
            width = 18, height = 14, units = 'cm', scale = 1.73)
 
     write_rds(x = p,
@@ -2156,8 +2201,8 @@ calcIndustry_Value_Added <- function(subtype = 'physical',
       theme_minimal()
 
 
-    ggsave(plot = p, filename = '01_Cement_regression_projection.svg',
-           device = 'svg', path = save.plots, bg = 'white',
+    ggsave(plot = p, filename = '01_Cement_regression_projection.png',
+           device = 'png', path = save.plots, bg = 'white',
            width = 18, height = 14, units = 'cm', scale = 1.73)
 
     write_rds(x = p,
@@ -2234,8 +2279,8 @@ calcIndustry_Value_Added <- function(subtype = 'physical',
             legend.position = c(1, 0))
 
 
-    ggsave(plot = p, filename = '05a_Cement_VA_regressions_projections.svg',
-           device = 'svg', path = save.plots, bg = 'white',
+    ggsave(plot = p, filename = '05a_Cement_VA_regressions_projections.png',
+           device = 'png', path = save.plots, bg = 'white',
            width = 18, height = 14, units = 'cm', scale = 1.73)
 
     write_rds(x = p,
@@ -2467,8 +2512,8 @@ calcIndustry_Value_Added <- function(subtype = 'physical',
            y = 'per-capita Chemicals Value Added [$/year]') +
       theme_minimal()
 
-    ggsave(plot = p, filename = '02_Chemicals_regression_projection.svg',
-           device = 'svg', path = save.plots, bg = 'white',
+    ggsave(plot = p, filename = '02_Chemicals_regression_projection.png',
+           device = 'png', path = save.plots, bg = 'white',
            width = 18, height = 14, units = 'cm', scale = 1.73)
 
     write_rds(x = p,
@@ -2478,6 +2523,93 @@ calcIndustry_Value_Added <- function(subtype = 'physical',
 
   # ======================================================================== ===
 
+  if (!is.null(save.plots)) {
+    d_plot_region_totals <- regression_data %>%
+      filter('Total' == .data$iso3c)
+
+    d_plot_countries <- regression_data %>%
+      semi_join(
+        regression_data %>%
+          filter('World' != .data$region,
+                 'Total' != .data$iso3c) %>%
+          distinct(.data$region, .data$iso3c) %>%
+          group_by(.data$region) %>%
+          filter(1 != n()) %>%
+          ungroup(),
+
+        c('region', 'iso3c')
+      ) %>%
+      mutate(GDPpC = .data$GDP / .data$population)
+
+    d_plot_regression <- full_join(
+      regression_parameters,
+
+      regression_data %>%
+        select('region', 'GDPpC') %>%
+        df_populate_range('GDPpC'),
+
+      'region'
+    ) %>%
+      mutate(value = .data$a * exp(.data$b / .data$GDPpC))
+
+    d_plot_projections <- projected_data %>%
+      pivot_longer(c('population', 'GDP', 'manufacturing')) %>%
+      sum_total_('iso3c') %>%
+      pivot_wider() %>%
+      filter(.data$scenario %in% names(linetype_scenarios),
+             'Total' == .data$iso3c,
+             between(.data$year, max(d_plot_region_totals$year), 2100)) %>%
+      mutate(GDPpC = .data$GDP / .data$population) %>%
+      select('scenario', 'region', 'year', 'GDPpC', 'manufacturing',
+             'population') %>%
+      group_by(.data$region) %>%
+      filter(.data$GDPpC <= .data$GDPpC[ 'SSP2' == .data$scenario
+                                         & 2100 == .data$year]) %>%
+      ungroup()
+
+    p <- ggplot(
+      mapping = aes(x = !!sym('GDPpC') / 1000,
+                    y = !!sym('manufacturing') / !!sym('population') / 1000)) +
+      # plot region totals
+      geom_point(
+        data = d_plot_region_totals,
+        mapping = aes(shape = 'region totals')) +
+      # # plot regression line
+      geom_path(
+        data = d_plot_regression,
+        mapping = aes(y = !!sym('value') / 1000, colour = 'regression')) +
+      # # plot projections
+      geom_path(
+        data = d_plot_projections,
+        mapping = aes(colour = 'projection')) +
+      geom_point(
+        data = d_plot_projections %>%
+          filter(.data$year %in% projection_points),
+        mapping = aes(shape = as.character(!!sym('year'))),
+        size = 3) +
+      scale_shape_manual(
+        values = c('region totals' = 'o',
+                   setNames(rep('x', length(projection_points)),
+                            projection_points)),
+        name = NULL) +
+      scale_colour_manual(values = c('regression' = 'red',
+                                     'projection' = 'black'),
+                          name = NULL) +
+      facet_wrap(vars(!!sym('region')), scales = 'free') +
+      expand_limits(y = c(0, ceiling(y_max * 2) / 2)) +
+      labs(x = 'per-capita GDP [1000 $/year]',
+           y = 'per-capita Industry Value Added [1000 $/year]') +
+      theme_minimal()
+
+    ggsave(plot = p, filename = '03_Industry_regression_projection.png',
+           device = 'png', path = save.plots, bg = 'white',
+           width = 18, height = 14, units = 'cm', scale = 1.73)
+
+    write_rds(x = p,
+              file = file.path(save.plots,
+                               '03_Industry_regression_projection.rds'))
+  }
+
   # calculate other Industries Value Added projections ----
   projections <- bind_rows(
     projected_data %>%
@@ -2610,8 +2742,8 @@ calcIndustry_Value_Added <- function(subtype = 'physical',
       theme_minimal() +
       theme(legend.justification = c(1, 0), legend.position = c(1, 0))
 
-    ggsave(plot = p, filename = '05b_Value_Added_projection.svg',
-           device = 'svg', path = save.plots, bg = 'white',
+    ggsave(plot = p, filename = '05b_Value_Added_projection.png',
+           device = 'png', path = save.plots, bg = 'white',
            width = 18, height = 14, units = 'cm', scale = 1.73)
 
     write_rds(x = p,

README.md

@@ -1,6 +1,6 @@
 # MadRat REMIND Input Data Package
 
-R package **mrremind**, version **0.164.2**
+R package **mrremind**, version **0.165.0**
 
 [![CRAN status](https://www.r-pkg.org/badges/version/mrremind)](https://cran.r-project.org/package=mrremind)  [![R build status](https://github.com/pik-piam/mrremind/workflows/check/badge.svg)](https://github.com/pik-piam/mrremind/actions) [![codecov](https://codecov.io/gh/pik-piam/mrremind/branch/master/graph/badge.svg)](https://app.codecov.io/gh/pik-piam/mrremind) [![r-universe](https://pik-piam.r-universe.dev/badges/mrremind)](https://pik-piam.r-universe.dev/builds)
 
@@ -39,7 +39,7 @@ In case of questions / problems please contact Lavinia Baumstark <lavinia@pik-po
 
 To cite package **mrremind** in publications use:
 
-Baumstark L, Rodrigues R, Levesque A, Oeser J, Bertram C, Mouratiadou I, Malik A, Schreyer F, Soergel B, Rottoli M, Mishra A, Dirnaichner A, Pehl M, Giannousakis A, Klein D, Strefler J, Feldhaus L, Brecha R, Rauner S, Dietrich J, Bi S, Benke F, Weigmann P, Richters O, Hasse R, Fuchs S, Mandaroux R (2023). _mrremind: MadRat REMIND Input Data Package_. R package version 0.164.2, <URL: https://github.com/pik-piam/mrremind>.
+Baumstark L, Rodrigues R, Levesque A, Oeser J, Bertram C, Mouratiadou I, Malik A, Schreyer F, Soergel B, Rottoli M, Mishra A, Dirnaichner A, Pehl M, Giannousakis A, Klein D, Strefler J, Feldhaus L, Brecha R, Rauner S, Dietrich J, Bi S, Benke F, Weigmann P, Richters O, Hasse R, Fuchs S, Mandaroux R (2023). _mrremind: MadRat REMIND Input Data Package_. R package version 0.165.0, <https://github.com/pik-piam/mrremind>.
 
 A BibTeX entry for LaTeX users is
 
@@ -48,7 +48,7 @@ A BibTeX entry for LaTeX users is
   title = {mrremind: MadRat REMIND Input Data Package},
   author = {Lavinia Baumstark and Renato Rodrigues and Antoine Levesque and Julian Oeser and Christoph Bertram and Ioanna Mouratiadou and Aman Malik and Felix Schreyer and Bjoern Soergel and Marianna Rottoli and Abhijeet Mishra and Alois Dirnaichner and Michaja Pehl and Anastasis Giannousakis and David Klein and Jessica Strefler and Lukas Feldhaus and Regina Brecha and Sebastian Rauner and Jan Philipp Dietrich and Stephen Bi and Falk Benke and Pascal Weigmann and Oliver Richters and Robin Hasse and Sophie Fuchs and Rahel Mandaroux},
   year = {2023},
-  note = {R package version 0.164.2},
+  note = {R package version 0.165.0},
   url = {https://github.com/pik-piam/mrremind},
 }
 ```