Files needed to replicate benchmarking from Lozano Gracia, Rossitti, …

…Sanchez (2021)
worldbank · May 18, 2021 · 12cd715 · 12cd715
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diff --git a/Benchmarking/Benchmarking Variables_V1205.xlsx b/Benchmarking/Benchmarking Variables_V1205.xlsx
diff --git a/Benchmarking/Benchmarking_db_V1.Rmd b/Benchmarking/Benchmarking_db_V1.Rmd
diff --git a/Benchmarking/Benchmarking_db_V2.Rmd b/Benchmarking/Benchmarking_db_V2.Rmd
diff --git a/Benchmarking/Benchmarking_graphs_V1.Rmd b/Benchmarking/Benchmarking_graphs_V1.Rmd
diff --git a/Benchmarking/Benchmarking_graphs_V2_EGYnolgbt.Rmd b/Benchmarking/Benchmarking_graphs_V2_EGYnolgbt.Rmd
diff --git a/Benchmarking/Benchmarking_graphs_V3.Rmd b/Benchmarking/Benchmarking_graphs_V3.Rmd
@@ -1,13 +1,13 @@
 ---
-title: "Benchmarking_Graphs"
+title: "RISE Benchmarking Graphs"
 author: "Giuseppe Rossitti & Evelyn Sanchez"
 date: "12/05/2020"
 output: html_document
 ---
 
 ```{r setup, include=FALSE}
 #knitr::opts_knit$set(root.dir = "C:/Users/####/OneDrive/")
-knitr::opts_knit$set(root.dir = "/Users/eves/Dropbox/MENA/Data/Benchmarking2/")
+#knitr::opts_knit$set(root.dir = "/Users/eves/Dropbox/MENA/Data/Benchmarking2/")
 ```
 
 ```{r}
@@ -29,7 +29,7 @@ library(Hmisc)
 
 # Set Directories
 ```{r}
-dir_int<-"./Integrated data/" #Folder with data generated in code Benchmarking_Data_V2
+dir_int<-"./Integrated_data/" #Folder with data generated in code Benchmarking_Data_V2
 #dir_out<- "./Data/Benchmarking/SD_Profiles/Output/"              
 dir_out<-"./Output/"
 #In your coumputer, this directory create  separate folders for income group - ex: ./My Files/Output/lmic -./My Files/Output/umic, etc, in oder to save countries info per income group

diff --git a/Benchmarking/Integrated_data/DB_SD_MENA_score.csv b/Benchmarking/Integrated_data/DB_SD_MENA_score.csv
diff --git a/Benchmarking/Integrated_data/DB_SD_all_score.csv b/Benchmarking/Integrated_data/DB_SD_all_score.csv
diff --git a/Benchmarking/Integrated_data/DB_SD_hics_score.csv b/Benchmarking/Integrated_data/DB_SD_hics_score.csv
diff --git a/Benchmarking/Integrated_data/DB_SD_lics_score.csv b/Benchmarking/Integrated_data/DB_SD_lics_score.csv
diff --git a/Benchmarking/Integrated_data/DB_SD_lmics_score.csv b/Benchmarking/Integrated_data/DB_SD_lmics_score.csv
diff --git a/Benchmarking/Integrated_data/DB_SD_umics_score.csv b/Benchmarking/Integrated_data/DB_SD_umics_score.csv
diff --git a/Benchmarking/Integrated_data/Integrated_v5.csv b/Benchmarking/Integrated_data/Integrated_v5.csv
diff --git a/Benchmarking/Integrated_data/World_SD_values_V5.csv b/Benchmarking/Integrated_data/World_SD_values_V5.csv
diff --git a/Benchmarking/Integrated_data/World_SD_values_V5_index.csv b/Benchmarking/Integrated_data/World_SD_values_V5_index.csv
diff --git a/Benchmarking/Integrated_data/World_SD_values_V5_index_v2.csv b/Benchmarking/Integrated_data/World_SD_values_V5_index_v2.csv
diff --git a/Benchmarking/Benchmarking_db_V3.Rmd → Benchmarking/RISE_Benchmarking_Data.Rmd b/Benchmarking/Benchmarking_db_V3.Rmd → Benchmarking/RISE_Benchmarking_Data.Rmd
@@ -1,14 +1,14 @@
 ---
-title: "Benchmarking db new"
-author: "Giuseppe Rossitti"
+title: "Database for international benchmarking"
+author: "Giuseppe Rossitti & Evelyn Sanchez"
 date: "02/15/2020"
 output: html_document
 
 ---
 
 ```{r setup, include=FALSE}
 #knitr::opts_knit$set(root.dir = "C:/Users/####/OneDrive/")
-knitr::opts_knit$set(root.dir = "/Users/eves/Dropbox/MENA/Data/Benchmarking2/")
+#knitr::opts_knit$set(root.dir = "/Users/eves/Dropbox/MENA/Data/Benchmarking2/")
 ```
 
 # Required libraries
@@ -29,8 +29,8 @@ Create the following set of folders
  dir_int = This folder is the outcome folder for the global results, name it as "Integrated data" to be consistent with the mapping script "Benchmarking_Graphs"
 
 ```{r}
-dir_raw<-"./Raw Indicators/"
-dir_int<- "./Integrated data/"
+dir_raw<-"./Raw_Indicators/"
+dir_int<- "./Integrated_data/"
 getwd()
 ```
 
@@ -864,112 +864,7 @@ integrated3 <- integrated2
 
 ```
 
-<!-- # Score Formulas For Income  Groups -->
-<!-- Formulas to calculate the score of each country against its income group -->
-<!-- ```{r} -->
-<!-- #detach plyr or the formulas won't work  -->
-<!-- detach(package:plyr)  -->
-<!-- #Indicators - Higher value = better performance -->
-<!-- #score = worst country value (worldwide) - countries value/ worst country value(worldwide) - worst country value from its income group -->
-<!-- scores_nor <- function(data, y){ -->
-<!--   data <- data[colnames(data) %in% two_side] -->
-<!--   data %>% -->
-<!--     dplyr::mutate(across(where(is.numeric), function(x){ -->
-<!--     (min(x, na.rm=TRUE) - x)/ -->
-<!--       (min(x, na.rm=TRUE) - max(x[which(.$income == y)], na.rm=TRUE))})) -->
-<!-- } -->
-
-<!-- #Indicators- Higher value= worst performance -->
-<!-- scores_inv <- function(data, y){ -->
-<!--   data <- data[colnames(data) %in% inv_side] -->
-<!--   data %>% -->
-<!--     mutate(across(where(is.numeric), function(x){ -->
-<!--     (max(x, na.rm=TRUE) - x)/ -->
-<!--       (max(x, na.rm=TRUE) - min(x[which(.$income == y)], na.rm=TRUE))})) -->
-<!-- } -->
-<!-- ``` -->
-
-<!-- #Score Formulas for Regional Comparison -->
-<!-- Formulas to calculate the score of each country against its regional group (we don't currently use this output, but it's possible to calculate these scores if desired) -->
-<!-- # ```{r} -->
-<!-- # #Indicators - Higher value = better performance -->
-<!-- # scores_reg_nor <- function(data, y){ -->
-<!-- #   data <- data[colnames(data) %in% two_side] -->
-<!-- #   data %>% -->
-<!-- #     mutate(across(where(is.numeric), function(x){ -->
-<!-- #     (min(x, na.rm=TRUE) - x)/ -->
-<!-- #       (min(x, na.rm=TRUE) - max(x[which(.$region == y)], na.rm=TRUE))})) -->
-<!-- # } -->
-<!-- # -->
-<!-- # -->
-<!-- # -->
-<!-- # #Indicators- Higher value= worst performance -->
-<!-- # scores_reg_inv <- function(data, y){ -->
-<!-- #   data <- data[colnames(data) %in% inv_side] -->
-<!-- #   data %>% -->
-<!-- #     mutate(across(where(is.numeric), function(x){ -->
-<!-- #     (max(x, na.rm=TRUE) - x)/ -->
-<!-- #       (max(x, na.rm=TRUE) - min(x[which(.$region == y)], na.rm=TRUE))})) -->
-<!-- # } -->
-<!-- # -->
-<!-- # -->
-<!-- # -->
-<!-- # -->
-<!-- # ##LMICS## -->
-<!-- # integrated_lmics_score<- scores_nor(integrated2, "Lower middle income") -->
-<!-- # integrated_lmics_score_neg<-scores_inv(integrated2, "Lower middle income") -->
-<!-- # #Join scores -->
-<!-- # integrated_lmics_score<-left_join(integrated_lmics_score,integrated_lmics_score_neg, by=c('country', "iso3", "region", "income")) -->
-<!-- # #Filter by income group -->
-<!-- # integrated_lmics_score <- integrated_lmics_score %>% filter(income=='Lower middle income') -->
-<!-- # # Save -->
-<!-- # write.csv(integrated_lmics_score, paste0(dir_int, "DB_SD_lmics_score.csv"), row.names = FALSE) -->
-<!-- # -->
-<!-- # ##UMICS## -->
-<!-- # integrated_umics_score<- scores_nor(integrated2, "Upper middle income") -->
-<!-- # integrated_umics_score_neg<-scores_inv(integrated2, "Upper middle income") -->
-<!-- # #Join scores -->
-<!-- # integrated_umics_score<-left_join(integrated_umics_score,integrated_umics_score_neg, by=c('country', "iso3", "region", "income")) -->
-<!-- # #Filter by income group -->
-<!-- # integrated_umics_score <- integrated_umics_score %>% filter(income=='Upper middle income') -->
-<!-- # -->
-<!-- # # Save -->
-<!-- # write.csv(integrated_umics_score, paste0(dir_int, "DB_SD_umics_score.csv"), row.names = FALSE) -->
-<!-- # -->
-<!-- # #LICS -->
-<!-- # integrated_lics_score<- scores_nor(integrated2, "Low income") -->
-<!-- # integrated_lics_score_neg<-scores_inv(integrated2, "Low income") -->
-<!-- # #Join scores -->
-<!-- # integrated_lics_score<-left_join(integrated_lics_score,integrated_lics_score_neg, by=c('country', "iso3", "region", "income")) -->
-<!-- # #Filter by income group -->
-<!-- # integrated_lics_score <- integrated_lics_score %>% filter(income=='Low income') -->
-<!-- # -->
-<!-- # # Save -->
-<!-- # write.csv(integrated_lics_score, paste0(dir_int, "DB_SD_lics_score.csv"), row.names = FALSE) -->
-<!-- # -->
-<!-- # #HICS -->
-<!-- # integrated_hics_score<- scores_nor(integrated2, "High income") -->
-<!-- # integrated_hics_score_neg<-scores_inv(integrated2, "High income") -->
-<!-- # #Join scores -->
-<!-- # integrated_hics_score<-left_join(integrated_hics_score,integrated_hics_score_neg, by=c('country', "iso3", "region", "income")) -->
-<!-- # #Filter by income group -->
-<!-- # integrated_hics_score <- integrated_hics_score %>% filter(income=="High income") -->
-<!-- # -->
-<!-- # # Save -->
-<!-- # write.csv(integrated_hics_score, paste0(dir_int, "DB_SD_hics_score.csv"), row.names = FALSE) -->
-<!-- # -->
-<!-- # -->
-<!-- # ##MENA## (no used, only calculated for internal use) -->
-<!-- # integrated_MENA_score<- scores_reg_nor(integrated2, "Middle East & North Africa") -->
-<!-- # integrated_MENA_score_neg<-scores_reg_inv(integrated2, "Middle East & North Africa") -->
-<!-- # #Join scores -->
-<!-- # integrated_MENA_score<-left_join(integrated_MENA_score,integrated_MENA_score_neg, by=c('country', "iso3", "region", "income")) -->
-<!-- # #Filter by income group -->
-<!-- # integrated_MENA_score <- integrated_MENA_score %>% filter(region=='Middle East & North Africa') -->
-<!-- # # Save -->
-<!-- # write.csv(integrated_MENA_score, paste0(dir_int, "DB_SD_MENA_score.csv"), row.names = FALSE) -->
-<!-- # -->
-<!-- # ``` -->
+
 
 #Calculate Percentile Rank
 Calculates country percentile value comparing all countries in the dataset