コミット: c198afa4ce92a530891334fddc93b6506436d579 - myprojects-hg-reborn (hg)

コミットメタ情報

• リビジョン: c198afa4ce92a530891334fddc93b6506436d579 (tree)
• 日時: 2021-03-18 05:21:13
• 作者: Lorenzo Isella <lorenzo.isella@gmai...>
• コミッター: Lorenzo Isella

ログメッセージ

Improvements to the bar plot and to the report.

変更サマリ

• modified: markdown/fiches.Rmd

差分

diff -r 7f9aa6cadca4 -r c198afa4ce92 markdown/fiches.Rmd
--- a/markdown/fiches.Rmd	Wed Mar 17 21:18:41 2021 +0100
+++ b/markdown/fiches.Rmd	Wed Mar 17 21:21:13 2021 +0100
@@ -17,6 +17,17 @@
 library(scales)
 library(treemap)
 
+iso_map <- tibble(iso3=c("AUT", "BEL", "BGR", "CYP", "CZE", "DEU", "DNK", "ESP", "EST", "FIN",
+"FRA", 
+"GRC", "HRV", "HUN", "IRL", "ITA", "LTU", "LUX", "LVA", "MLT",
+"NLD", "POL", "PRT",
+"ROM", "SVK", "SVN", "SWE"),
+country=c("Austria", "Belgium", "Bulgaria", "Cyprus", "Czech Republic", "Germany",
+  "Denmark", "Spain", "Estonia", "Finland", "France", "Greece",
+  "Croatia", "Hungary", "Ireland","Italy", "Lituania", "Luxembourg",
+  "Latvia", "Malta", "Netherlands", "Poland", "Portugal",
+  "Romania", "Slovakia", "Slovenia", "Sweden")
+)
 
 
 # Suppress summarise info
@@ -97,8 +108,16 @@
 
 
 
-MS <- "AUT"
-    
+MS <- "FRA"
+
+ms3 <- iso_map %>%
+    filter(iso3==MS) %>%
+    pull(country) %>%
+    as.character
+
+
+
+
 scoreboard <- readRDS("../scoreboard.RDS")
 
 scoreboard_ms <- scoreboard %>%
@@ -243,7 +262,9 @@
     filter(year==max(year),
            ## duration_end>=max(year),
            amount_spent_aid_element_in_eur_million>0
-           )%>% 
+           )%>%
+    mutate(scoreboard_objective=recode(scoreboard_objective,
+             "Other"="Other policy objectives"       )) %>% 
     group_by(scoreboard_objective) %>%
     summarise(expenditure_objective=sum(amount_spent_aid_element_in_eur_million)) %>%
     ungroup %>%
@@ -294,7 +315,7 @@
 
 
 ---
-title: "Country Focus on `r MS` in the Year `r year_focus`."
+title: "Country Focus on `r ms3` in the Year `r year_focus`."
 ---
 
 
@@ -304,13 +325,13 @@
 `r year_focus` of which `r stat_cases$n[2]` GBER (X), `r stat_cases$n[3]`
 notified (N) and `r stat_cases$n[1]` BER.
 
-In `r year_focus`, the share of GBER measures in `r MS` reached 
+In `r year_focus`, the share of GBER measures in `r ms3` reached 
 `r stat_cases$percent[2]`% of the total, with 
 `r stat_cases_new$percent[1]`% of all newly implemented measured falling
 under GBER.
 
 # State Aid Spending - Overview
-Between `r ini_focus` and  `r year_focus` `r MS` spent 
+Between `r ini_focus` and  `r year_focus` `r ms3` spent 
 `r total_expenditure` billion EUR for non-agricultural State aid, of
 which around `r stat_cases3$expenditure[2]` billion EUR under notified
 measures and around `r stat_cases3$expenditure[1]` billion EUR under
@@ -328,7 +349,7 @@
 
 ggplot(data = stat_cases2, aes(x  = year, y=expenditure,
                                       fill=procedure_name)) +
-    geom_bar(position="dodge", stat="identity", alpha=1, color="black")+
+    geom_bar(position=position_dodge2(preserve="single"), stat="identity", alpha=1, color="black")+
     ## scale_fill_viridis("Vehicle Brand\nOrigin",breaks=mybreaks, labels= mylabels, discrete=T)+
     ##     scale_colour_viridis("Vehicle Brand\nOrigin",breaks=mybreaks, labels= mylabels, discrete=T)+
 
@@ -355,10 +376,10 @@
 
 
 In `r year_focus`, State aid spending for the 5 biggest State aid
- measures in `r MS` absorbed `r top5_share`% of the total spending
+ measures in `r ms3` absorbed `r top5_share`% of the total spending
  (around `r total_expenditure_last` billion EUR).
  
- Finally, the amount of co-financed in `r MS` corresponded to
+ Finally, the amount of co-financed in `r ms3` corresponded to
  `r cofinancing_expenditure` million EUR (around
  `r share_cofinancing`% of the total non-agricultural spending)and
  was mostly concentrated in
@@ -394,13 +415,13 @@
 
 
  Around `r sum(expenditure_by_objective$share2[1:2])`% of State aid
- spending in `r MS` was concentrated in two main policy objectives. Around
+ spending in `r ms3` was concentrated in two main policy objectives. Around
 `r expenditure_by_objective$share2[1]`% was directed towards
 "`r expenditure_by_objective$objectives_reduced[1]`" while
 `r expenditure_by_objective$share2[2]`%  to
 "`r expenditure_by_objective$objectives_reduced[2]`".
 
-`r MS` devoted around
+`r ms3` devoted around
 `r expenditure_by_objective$share2[3]`% towards
 "`r expenditure_by_objective$objectives_reduced[3]`" and
 `r expenditure_by_objective$share2[4]`% to
@@ -428,13 +449,13 @@
 "`r expenditure_gber$all_objective_names_gber_only[4]`", 
 (`r expenditure_gber$share2[4]`%).
 
-In terms of State aid instruments, `r MS` privileged the use of 
+In terms of State aid instruments, `r ms3` privileged the use of 
 "`r expenditure_aid$aid_instrument_name[1]`" (around 
 `r expenditure_aid$expenditure2[1]` million EUR, 
 `r expenditure_aid$share2[1]`% of total State aid spending), 
 followed by
-"`r expenditure_aid$aid_instrument_name[2]`" ( 
-`r expenditure_aid$expenditure2[2]` million EUR, 
+"`r expenditure_aid$aid_instrument_name[2]`" 
+(`r expenditure_aid$expenditure2[2]` million EUR, 
 `r expenditure_aid$share2[2]`% of total State aid spending), 
 and 
 "`r expenditure_aid$aid_instrument_name[3]`" (around