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Ctitical Minerals Supply Chain Phase I.Rmd

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+---
+title: "Phase 1 + 2"
+output: html_document
+date: "2025-06-12"
+---
+
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(echo = TRUE)
+```
+
+## Phase I
+
+```{r cars}
+# Load necessary package
+library(readr)
+library(tidyverse)
+library(readxl)
+library(ggplot2)
+library(dplyr)
+```
+
+```{r pressure, echo=FALSE}
+df <- read_csv("TradeData_6_17_2025_14_10_57.csv", locale = locale(encoding = "ISO-8859-1"))
+```
+
+```{r}
+# Define classification mapping
+mineral_type <- c(
+  "Copper ores and concentrates" = "Raw + processed",
+  "Carbonates; lithium carbonate" = "Processed",
+  "Cobalt; mattes and other intermediate products of cobalt metallurgy, cobalt and articles thereof, including waste and scrap" = "Processed",
+  "Copper; refined and copper alloys, unwrought" = "Processed + raw",
+  "Graphite; natural" = "Raw",
+  "Cobalt ores and concentrates" = "Raw + processed",
+  "Cobalt oxides and hydroxides; commercial cobalt oxides" = "Processed",
+  "Lithium oxide and hydroxide" = "Processed",
+  "Nickel ores and concentrates" = "Raw + processed",
+  "Nickel; unwrought" = "raw",
+  "Manganese ores and concentrates, including ferruginous manganese ores and concentrates with a manganese content of 20% or more, calculated on the dry weight" = "raw + processed"
+)
+
+# Only contain the mineral's ores and concentrates. 
+ore_con = c("Cobalt ores and concentrates (Raw + processed)",
+            "Copper ores and concentrates (Raw + processed)",
+            "Graphite; natural (Raw)",
+            "Nickel ores and concentrates (Raw + processed)",
+            "Manganese ores and concentrates, including ferruginous manganese ores and concentrates with a manganese content of 20% or more, calculated on the dry weight (raw + processed)",
+            "Lithium oxide and hydroxide (Processed)")
+
+df2 <- df %>%
+  mutate(cmdDesc = paste0(cmdDesc, " (", mineral_type[cmdDesc], ")"))
+
+
+summary = df2 %>%
+  group_by(reporterDesc, cmdDesc) %>%
+  summarise(NetWeight_kg = sum(netWgt, na.rm = TRUE), .groups = "drop")
+#View(summary)
+```
+
+```{r}
+summary_wide <- summary %>%
+  pivot_wider(names_from = cmdDesc, values_from = NetWeight_kg, values_fill = 0)
+
+summary_wide <- summary_wide %>%
+  mutate(Participation_Count = rowSums(select(., -reporterDesc) > 0))
+
+# View result
+#View(summary_wide)
+
+```
+
+### Without China
+
+```{r}
+fmf_countries <- c("Algeria", "Angola", "Benin", "Botswana", "Burkina Faso", "Burundi",
+            "Cabo Verde", "Cameroon", "Central African Republic", "Chad",
+            "Comoros", "Democratic Republic of the Congo", "Republic of the Congo",
+            "Côte d’Ivoire", "Djibouti", "Egypt", "Equatorial Guinea", "Eritrea",
+            "Eswatini", "Ethiopia", "Gabon", "Gambia", "Ghana", "Guinea",
+            "Guinea-Bissau", "Kenya", "Lesotho", "Liberia", "Libya", "Madagascar",
+            "Malawi", "Mali", "Mauritania", "Mauritius", "Morocco", "Mozambique",
+            "Namibia", "Niger", "Nigeria", "Rwanda", "São Tomé and Príncipe",
+            "Senegal", "Seychelles", "Sierra Leone", "Somalia", "South Africa",
+            "South Sudan", "Sudan", "Tanzania", "Togo", "Tunisia", "Uganda",
+            "Zambia", "Zimbabwe", "Kazakhstan", "Kyrgyzstan", "Tajikistan", "Turkmenistan", "Uzbekistan", "Bahrain", "Cyprus", "Egypt", "Iran", "Iraq", "Israel", "Jordan",
+                  "Kuwait", "Lebanon", "Oman", "Palestine", "Qatar", "Saudi Arabia",
+                  "Syria", "Turkey", "United Arab Emirates", "Yemen","Congo, Dem. Rep.")
+
+
+df_fmf <- df2 %>%
+  filter(reporterDesc %in% fmf_countries)
+
+
+
+country_totals <- df_fmf %>%
+  group_by(reporterDesc, cmdDesc) %>%
+  summarise(NetWeight_kg = sum(netWgt, na.rm = TRUE), .groups = "drop")
+
+
+# Total export per mineral
+mineral_totals <- country_totals %>%
+  group_by(cmdDesc) %>%
+  summarise(TotalMineralWeight = sum(NetWeight_kg, na.rm = TRUE))
+
+# Join and calculate share
+participation_share <- country_totals %>%
+  left_join(mineral_totals, by = "cmdDesc") %>%
+  mutate(Participation_Share = round(100 * NetWeight_kg / TotalMineralWeight, 4)) %>%
+  mutate(
+         Scale_1_to_6 = case_when(
+           Participation_Share >= 90 ~ 6,
+           Participation_Share >= 70 ~ 5,
+           Participation_Share >= 50 ~ 4,
+           Participation_Share >= 30 ~ 3,
+           Participation_Share >= 10 ~ 2,
+           Participation_Share < 10 ~ 1
+         ))
+
+# Only Contain ore and concentrates:
+participation_share_ore = participation_share %>% filter(cmdDesc %in% ore_con)
+
+
+
+
+```
+
+### With China
+
+```{r}
+fmf_CN <- c("Algeria", "Angola", "Benin", "Botswana", "Burkina Faso", "Burundi",
+            "Cabo Verde", "Cameroon", "Central African Republic", "Chad",
+            "Comoros", "Democratic Republic of the Congo", "Republic of the Congo",
+            "Côte d’Ivoire", "Djibouti", "Egypt", "Equatorial Guinea", "Eritrea",
+            "Eswatini", "Ethiopia", "Gabon", "Gambia", "Ghana", "Guinea",
+            "Guinea-Bissau", "Kenya", "Lesotho", "Liberia", "Libya", "Madagascar",
+            "Malawi", "Mali", "Mauritania", "Mauritius", "Morocco", "Mozambique",
+            "Namibia", "Niger", "Nigeria", "Rwanda", "São Tomé and Príncipe",
+            "Senegal", "Seychelles", "Sierra Leone", "Somalia", "South Africa",
+            "South Sudan", "Sudan", "Tanzania", "Togo", "Tunisia", "Uganda",
+            "Zambia", "Zimbabwe", "Kazakhstan", "Kyrgyzstan", "Tajikistan", "Turkmenistan", "Uzbekistan", "Bahrain", "Cyprus", "Egypt", "Iran", "Iraq", "Israel", "Jordan",
+                  "Kuwait", "Lebanon", "Oman", "Palestine", "Qatar", "Saudi Arabia",
+                  "Syria", "Turkey", "United Arab Emirates", "Yemen", "China", "China, Hong Kong SAR")
+
+df_china = df2 %>%
+  filter(reporterDesc %in% fmf_CN)
+
+country_totals_china <- df_china %>%
+  group_by(reporterDesc, cmdDesc) %>%
+  summarise(NetWeight_kg = sum(netWgt, na.rm = TRUE), .groups = "drop")
+
+mineral_totals_china <- country_totals_china %>%
+  group_by(cmdDesc) %>%
+  summarise(TotalMineralWeight = sum(NetWeight_kg, na.rm = TRUE))
+
+participation_share_china <- country_totals_china %>%
+  left_join(mineral_totals_china, by = "cmdDesc") %>%
+  mutate(Participation_Share = round(100 * NetWeight_kg / TotalMineralWeight, 4)) %>%
+  mutate(
+         Scale_1_to_6 = case_when(
+           Participation_Share >= 90 ~ 6,
+           Participation_Share >= 70 ~ 5,
+           Participation_Share >= 50 ~ 4,
+           Participation_Share >= 30 ~ 3,
+           Participation_Share >= 10 ~ 2,
+           Participation_Share < 10 ~ 1
+         ))
+
+
+participation_share_china_ore = participation_share_china %>% filter(cmdDesc %in%ore_con)
+
+```
+
+```{r}
+# Without China
+participation_wide_percentage <- participation_share %>%
+  select(reporterDesc, cmdDesc, Participation_Share) %>%
+  pivot_wider(names_from = cmdDesc, values_from = Participation_Share, values_fill = 0)
+
+participation_wide_scale <- participation_share %>%
+  select(reporterDesc, cmdDesc, Scale_1_to_6) %>%
+  pivot_wider(names_from = cmdDesc, values_from = Scale_1_to_6, values_fill = 0)
+
+#With China
+participation_wide_percentage_china <- participation_share_china %>%
+  select(reporterDesc, cmdDesc, Participation_Share) %>%
+  pivot_wider(names_from = cmdDesc, values_from = Participation_Share, values_fill = 0)
+
+participation_wide_scale_china <- participation_share_china %>%
+  select(reporterDesc, cmdDesc, Scale_1_to_6) %>%
+  pivot_wider(names_from = cmdDesc, values_from = Scale_1_to_6, values_fill = 0)
+
+# View result
+#print(participation_wide)
+```
+
+```{r}
+participation_wide_percentage_ore <- participation_share_ore %>%
+  select(reporterDesc, cmdDesc, Participation_Share) %>%
+  pivot_wider(names_from = cmdDesc, values_from = Participation_Share, values_fill = 0)
+
+participation_wide_scale_ore <- participation_share_ore %>%
+  select(reporterDesc, cmdDesc, Scale_1_to_6) %>%
+  pivot_wider(names_from = cmdDesc, values_from = Scale_1_to_6, values_fill = 0)
+
+#With China
+participation_wide_percentage_china_ore <- participation_share_china_ore %>%
+  select(reporterDesc, cmdDesc, Participation_Share) %>%
+  pivot_wider(names_from = cmdDesc, values_from = Participation_Share, values_fill = 0)
+
+participation_wide_scale_china_ore <- participation_share_china_ore %>%
+  select(reporterDesc, cmdDesc, Scale_1_to_6) %>%
+  pivot_wider(names_from = cmdDesc, values_from = Scale_1_to_6, values_fill = 0)
+
+# Combined percentage and scale table for all minerals and without china
+share <- participation_wide_percentage
+for (col in names(participation_wide_percentage)[-1]) {
+  share[[col]] <- paste0(
+    round(participation_wide_percentage[[col]], 4), "% (", participation_wide_scale[[col]], ")"
+  )
+}
+# Combined percentage and scale table for all minerals and with china
+share_china <- participation_wide_percentage_china
+for (col in names(participation_wide_percentage_china)[-1]) {
+  share_china[[col]] <- paste0(
+    round(participation_wide_percentage_china[[col]], 4), "% (", participation_wide_scale_china[[col]], ")"
+  )
+}
+
+# Combined percentage and scale table for ore and concentrates minerals and without china
+share_ore <- participation_wide_percentage_ore
+for (col in names(participation_wide_percentage_ore)[-1]) {
+  share_ore[[col]] <- paste0(
+    round(participation_wide_percentage_ore[[col]], 4), "% (", participation_wide_scale_ore[[col]], ")"
+  )
+}
+
+
+# Combined percentage and scale table for ore and concentrates minerals and with china
+share_china_ore <- participation_wide_percentage_china_ore
+for (col in names(participation_wide_percentage_china_ore)[-1]) {
+  share_china_ore[[col]] <- paste0(
+    round(participation_wide_percentage_china_ore[[col]], 4), "% (", participation_wide_scale_china_ore[[col]], ")"
+  )
+}
+View(share)  
+View(share_china) 
+View(share_ore)
+View(share_china_ore) 
+
+
+```
+
+```{r fig.height=10, fig.width=18, echo=FALSE, message=FALSE, warning=FALSE}
+
+
+ggplot(participation_share_china, aes(x = reorder(cmdDesc, Scale_1_to_6), y = Scale_1_to_6)) +
+  geom_col(fill = "skyblue") +
+  facet_wrap(~ reporterDesc) +
+  coord_flip() +
+  labs(x = "Mineral", y = "Participation Share (%)",
+        title = "Participation Share by Mineral and Country (China and FMF)") +
+  theme_minimal() +
+  theme(
+    axis.text.y = element_text(face = "bold")
+  )
+
+```
+
+```{r fig.height=10, fig.width=18, echo=FALSE, message=FALSE, warning=FALSE}
+ggplot(participation_share, aes(x = reorder(cmdDesc, Scale_1_to_6), y = Scale_1_to_6)) +
+  geom_col(fill = "skyblue") +
+  facet_wrap(~ reporterDesc) +
+  coord_flip() +
+  labs(x = "Mineral", 
+       y = "Participation Share (%)", 
+       title = "Participation Share by Mineral and Country (FMF)") +
+  theme_minimal() +
+  theme(
+    axis.text.y = element_text(face = "bold")
+  )
+
+```
+
+```{r fig.height=10, fig.width=15, echo=FALSE, message=FALSE, warning=FALSE}
+
+
+ggplot(participation_share_china_ore, aes(x = reorder(cmdDesc, Scale_1_to_6), y = Scale_1_to_6)) +
+  geom_col(fill = "skyblue") +
+  facet_wrap(~ reporterDesc) +
+  coord_flip() +
+  labs(x = "Mineral", y = "Participation Share (%)",
+        title = "Participation Share by Mineral and Country (China and FMF)") +
+  theme_minimal() +
+  theme(
+    axis.text.y = element_text(face = "bold")
+  )
+```
+
+```{r fig.height=10, fig.width=15, echo=FALSE, message=FALSE, warning=FALSE}
+
+
+ggplot(participation_share_ore, aes(x = reorder(cmdDesc, Scale_1_to_6), y = Scale_1_to_6)) +
+  geom_col(fill = "skyblue") +
+  facet_wrap(~ reporterDesc) +
+  coord_flip() +
+  labs(x = "Mineral", y = "Participation Share (%)",
+        title = "Participation Share by Mineral and FMF") +
+  theme_minimal() +
+  theme(
+    axis.text.y = element_text(face = "bold")
+  )
+```
+
+## Phase II
+
+### Ease of Doing Business
+
+```{r}
+edb_ranking = read_excel("Rankings.xlsx", sheet = "Sheet1")
+head(edb_ranking)
+```
+
+Old ranking: 1 - 190
+
+New Scale bring it down to the new range(1 to 6):
+
+6: 1-31
+
+5: 32--63
+
+4: 64--95
+
+3: 96--127
+
+2: 128--159
+
+1: 160--190
+
+```{r}
+ranking_cols <- names(edb_ranking)[-1]  
+
+
+edb_ranking_scaled <- edb_ranking %>%
+  mutate(across(
+    all_of(ranking_cols),
+    ~ ntile(-., 6),  
+    .names = "{.col}_scale"
+  ))
+
+
+head(edb_ranking_scaled)
+
+fit <- edb_ranking_scaled %>%
+  filter(Economy %in% fmf_countries) %>% 
+  arrange(desc(globalRank_scale))
+```
+
+### Resource of government index
+
+```{r}
+rgi_mining =  read_csv("nrgi_data.csv")
+
+rgi_country <- read_excel("2021_Resource_Governance_Index_scores_workbook_English.xlsx", sheet = "2021_RGI_scores ")
+
+
+View(rgi_country)
+
+```
+
+```{r}
+
+rgi_country_numeric <- rgi_country
+colnames(rgi_country_numeric) <- as.character(unlist(rgi_country_numeric[1, ]))
+
+rgi_country_numeric <- rgi_country_numeric[-1, ]
+
+
+rgi_country_numeric[, 4:ncol(rgi_country_numeric)] <- lapply(rgi_country_numeric[, 4:ncol(rgi_country_numeric)], function(x) as.numeric(as.character(x)))
+
+
+
+```
+
+```{r}
+
+rescale_bins <- function(x) {
+  case_when(
+    x >= 90 ~ 6,
+    x >= 70 ~ 5,
+    x >= 50 ~ 4,
+    x >= 30 ~ 3,
+    x >= 10 ~ 2,
+    x < 10 ~ 1,
+    TRUE ~ NA_real_
+  )
+}
+
+rgi_scaled <- rgi_country_numeric
+rgi_scaled[, 4:ncol(rgi_scaled)] <- lapply(
+  rgi_scaled[, 4:ncol(rgi_scaled)],
+  rescale_bins
+)
+
+=
+rgi_scaled[, 4:ncol(rgi_scaled)] <- lapply(
+  rgi_scaled[, 4:ncol(rgi_scaled)],
+  function(x) ifelse(is.na(x), 0, x)
+)
+
+
+View(rgi_scaled)
+```
+
+### CPIA
+
+```{r}
+
+# Read the main data file
+df_data <- read_csv("d8a9bd65-917a-4bf1-9500-5a67bbfe672a_Data.csv")
+
+# Read the metadata file
+df_metadata <- read_csv("d8a9bd65-917a-4bf1-9500-5a67bbfe672a_Series - Metadata.csv")
+
+# View first few rows of each
+print(head(df_data))
+
+
+```

nrgi_data.csv

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+name,sector,region,Composite/component,Subcomponent,Indicator,Score2017,Score2021
+Chile ,mining,LAC,Composite,,,81,
+Australia (Western) ,mining,ASIAP,Composite,,,71,
+Colombia ,mining,LAC,Composite,,,69,75
+Indonesia ,mining,ASIAP,Composite,,,68,
+Mongolia ,mining,EURA,Composite,,,64,70
+Peru ,mining,LAC,Composite,,,62,75
+Botswana ,mining,SSA-AF,Composite,,,61,
+Mexico ,mining,LAC,Composite,,,60,59
+Burkina Faso ,mining,SSA-AF,Composite,,,59,
+Philippines ,mining,ASIAP,Composite,,,58,
+South Africa ,mining,SSA-AF,Composite,,,57,
+Ghana ,mining,SSA-AF,Composite,,,56,69
+Niger ,mining,SSA-AF,Composite,,,54,
+Mali ,mining,SSA-AF,Composite,,,53,
+Morocco ,mining,MENA-AF,Composite,,,52,49
+Kyrgyz Republic ,mining,EURA,Composite,,,51,
+Zambia ,mining,SSA-AF,Composite,,,50,
+Tanzania ,mining,SSA-AF,Composite,,,49,58
+Papua New Guinea ,mining,ASIAP,Composite,,,47,
+Sierra Leone ,mining,SSA-AF,Composite,,,46,
+Tunisia ,mining,MENA-AF,Composite,,,46,50
+Liberia ,mining,SSA-AF,Composite,,,44,
+Guatemala ,mining,LAC,Composite,,,41,
+Ethiopia ,mining,SSA-AF,Composite,,,40,
+Guinea ,mining,SSA-AF,Composite,,,38,62
+Lao PDR ,mining,ASIAP,Composite,,,38,
+Madagascar ,mining,SSA-AF,Composite,,,36,
+Afghanistan ,mining,EURA,Composite,,,34,
+Democratic Republic of Congo ,mining,SSA-AF,Composite,,,33,36
+Cambodia ,mining,ASIAP,Composite,,,30,
+Mauritania ,mining,MENA-AF,Composite,,,29,
+Zimbabwe ,mining,SSA-AF,Composite,,,29,
+Myanmar ,mining,ASIAP,Composite,,,27,
+Eritrea ,mining,SSA-AF,Composite,,,10,
+Senegal ,mining,SSA,Composite,,,,75
+Uganda ,mining,SSA-AF,Composite,,,,55