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--- |
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title: "CPIA+RGI" |
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output: html_document |
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date: "2025-06-26" |
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--- |
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```{r setup, include=FALSE} |
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knitr::opts_chunk$set(echo = TRUE) |
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library(readr) |
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library(tidyverse) |
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library(readxl) |
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library(ggplot2) |
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library(dplyr) |
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``` |
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# CPIA |
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```{r} |
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df <- read_csv("d8a9bd65-917a-4bf1-9500-5a67bbfe672a_Series - Metadata.csv") |
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cpia_24 = read_csv("2024.csv") |
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``` |
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```{r} |
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highlighted_vars <- c( |
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"CPIA business regulatory environment rating (1=low to 6=high)", |
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"CPIA financial sector rating (1=low to 6=high)", |
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"CPIA fiscal policy rating (1=low to 6=high)", |
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"CPIA macroeconomic management rating (1=low to 6=high)", |
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"CPIA policy and institutions for environmental sustainability rating (1=low to 6=high)", |
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"CPIA property rights and rule-based governance rating (1=low to 6=high)", |
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"CPIA quality of budgetary and financial management rating (1=low to 6=high)", |
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"CPIA quality of public administration rating (1=low to 6=high)", |
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"CPIA transparency, accountability, and corruption in the public sector rating (1=low to 6=high)", |
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"IDA resource allocation index (1=low to 6=high)" |
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) |
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cpia_cols <- c( |
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"CPIA business regulatory environment rating (1=low to 6=high)", |
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"CPIA financial sector rating (1=low to 6=high)", |
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"CPIA fiscal policy rating (1=low to 6=high)", |
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"CPIA macroeconomic management rating (1=low to 6=high)", |
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"CPIA policy and institutions for environmental sustainability rating (1=low to 6=high)", |
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"CPIA property rights and rule-based governance rating (1=low to 6=high)", |
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"CPIA quality of budgetary and financial management rating (1=low to 6=high)", |
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"CPIA quality of public administration rating (1=low to 6=high)", |
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"CPIA transparency, accountability, and corruption in the public sector rating (1=low to 6=high)" |
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) |
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``` |
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```{r} |
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df_highlighted <- subset(df, `Series Name` %in% highlighted_vars) |
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cpia_highlighted <- subset(cpia_24, `Series Name` %in% highlighted_vars) |
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``` |
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```{r} |
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tb1 = df_highlighted %>% |
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select(`Country Name`, `Country Code`, `Series Name`, `2023 [YR2023]`) %>% |
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pivot_wider( |
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names_from = `Series Name`, |
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values_from = `2023 [YR2023]` |
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) |
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View(tb1) |
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df1 = cpia_highlighted %>% |
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select(`Country Name`, `Country Code`, `Series Name`, `2024 [YR2024]`) %>% |
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pivot_wider( |
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names_from = `Series Name`, |
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values_from = `2024 [YR2024]` |
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) |
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``` |
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```{r} |
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tb2 <- tb1 %>% |
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mutate(across(all_of(highlighted_vars), as.numeric)) %>% |
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rowwise() %>% |
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mutate(CPIA_Average_9 = mean(c_across(all_of(cpia_cols)), na.rm = TRUE)) %>% |
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ungroup() %>% |
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filter(`Country Name` %in% fmf$Country) |
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View(tb2) |
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df2 <- df1 %>% |
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mutate(across(all_of(highlighted_vars), as.numeric)) %>% |
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rowwise() %>% |
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mutate(CPIA_Average_9 = mean(c_across(all_of(cpia_cols)), na.rm = TRUE)) %>% |
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ungroup() %>% |
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filter(`Country Name` %in% fmf$Country) |
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``` |
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## Visualization |
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```{r} |
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library(ggplot2) |
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ggplot(tb2, aes(x = CPIA_Average_9, y = `IDA resource allocation index (1=low to 6=high)`)) + |
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geom_point() + |
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geom_smooth(method = "lm", se = FALSE, color = "blue") + |
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labs(x = "Average of 9 CPIA Indicators", y = "IDA Resource Allocation Index", |
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title = "CPIA Average vs IDA Index") |
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``` |
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```{r} |
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cor(tb2$CPIA_Average_9, tb2$`IDA resource allocation index (1=low to 6=high)`, use = "complete.obs") |
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``` |
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# RGI |
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```{r} |
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rgi <- read_csv("nrgi_data.csv") |
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``` |
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```{r} |
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rgi_scaled = rgi %>% |
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select(name, sector, region, `Composite/component`, Score2017, Score2021) %>% |
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mutate( |
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scaled_2017 = case_when( |
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Score2017 >= 90 ~ 6, |
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Score2017 >= 70 ~ 5, |
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Score2017 >= 50 ~ 4, |
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Score2017 >= 30 ~ 3, |
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Score2017 >= 10 ~ 2, |
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Score2017 < 10 ~ 1 |
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), |
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scaled_2017 = replace_na(scaled_2017, 0), |
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scaled_2021 = case_when( |
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Score2021 >= 90 ~ 6, |
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Score2021 >= 70 ~ 5, |
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Score2021 >= 50 ~ 4, |
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Score2021 >= 30 ~ 3, |
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Score2021 >= 10 ~ 2, |
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Score2021 < 10 ~ 1), |
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scaled_2021 = replace_na(scaled_2021, 0)) |
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``` |
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## Filter super region country |
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```{r} |
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fmf <- read_excel("Countries of the Super Region.xlsx", skip = 2) |
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colnames(fmf) <- c("No", "Country", "Official_Name") |
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fmf <- fmf %>% select(Country, Official_Name) |
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head(fmf) |
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``` |
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## Join data, Correlation test |
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```{r} |
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merged_df <- dplyr::inner_join(rgi_scaled, tb2, by = c("name" = "Country Name")) %>% |
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filter(name %in% fmf$Country) |
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cor_test_result <- cor.test(merged_df$scaled_2021, merged_df$CPIA_Average_9, method = "pearson") |
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print(cor_test_result) |
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``` |
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```{r} |
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plot(merged_df$scaled_2021, merged_df$CPIA_Average_9, |
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xlab = "RGI Scaled Score (2021)", ylab = "CPIA Average Score", |
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main = "Correlation between RGI and CPIA") |
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abline(lm(CPIA_Average_9 ~ scaled_2021, data = merged_df), col = "blue") |
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``` |
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```{r} |
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cor_test_result_2 <- cor.test(merged_df$Score2017, merged_df$`IDA resource allocation index (1=low to 6=high)`, method = "pearson") |
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print(cor_test_result_2) |
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``` |
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```{r} |
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plot(merged_df$Score2017, merged_df$`IDA resource allocation index (1=low to 6=high)`, |
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xlab = "RGI Scaled Score (2017)", ylab = "ida Score", |
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main = "Correlation between RGI and CPIA") |
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abline(lm(CPIA_Average_9 ~ scaled_2021, data = merged_df), col = "blue") |
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``` |
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```{r} |
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mi <- read_xlsx("SuperRegion_Mineral_Shares.xlsx") |
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``` |
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```{r} |
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rgi_scaled = rgi_scaled %>% filter(name %in% fmf$Country) |
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tb3 = tb2 %>% |
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select(`Country Name` ,`IDA resource allocation index (1=low to 6=high)`) %>% |
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rename( |
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`IDA resource allocation index 2023` = `IDA resource allocation index (1=low to 6=high)` |
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) %>% |
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dplyr::left_join(df2, by = c( "Country Name" = "Country Name")) %>% |
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# rename( |
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# `IDA resource allocation index 2024` = `IDA resource allocation index (1=low to 6=high)` |
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# ) %>% |
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select(`Country Name` ,`IDA resource allocation index 2023`, `IDA resource allocation index 2024`) %>% |
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dplyr::left_join(rgi_scaled, by = c( "Country Name" = "name")) %>% |
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select(`Country Name`, `IDA resource allocation index 2023`, `IDA resource allocation index 2024`, Score2017 , Score2021) %>% |
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mutate(Increment = (Score2021-Score2017)/Score2017) %>% |
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full_join(mi, by = c( "Country Name" = "reporterDesc")) |
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View(tb3) |
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#write.csv(fmf, "fmf.csv", row.names = FALSE) |
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tb3$non_na_count <- rowSums(!is.na(tb3 %>% select(-`Country Name`))) |
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# 找出非 NA 值最多的行 |
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tb3[which.max(tb3$non_na_count), ] |
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tb4 = tb3 %>% |
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select(`Country Name`, non_na_count) %>% |
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arrange(desc(non_na_count)) |
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``` |
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```{r} |
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tb5 = tb3 %>% |
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select(`Country Name`, `IDA resource allocation index 2024`, percentage_Lithium, percentage_Cobalt, percentage_Graphite, percentage_Nickel) |
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write.csv(tb5, "Super Region Country minerals.csv", row.names = FALSE) |
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``` |
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```{r} |
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cpia_22 = read_csv("2022.csv") |
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``` |
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```{r} |
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cpia_22_high <- subset(cpia_22, `Series Name` %in% highlighted_vars) |
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df_22 = cpia_22_high %>% |
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select(`Country Name`, `Country Code`, `Series Name`, `2022 [YR2022]`) %>% |
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pivot_wider( |
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names_from = `Series Name`, |
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values_from = `2022 [YR2022]` |
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) |
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df2_22 <- df_22 %>% |
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mutate(across(all_of(highlighted_vars), as.numeric)) %>% |
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rowwise() %>% |
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mutate(CPIA_Average_9 = mean(c_across(all_of(cpia_cols)), na.rm = TRUE)) %>% |
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ungroup() %>% |
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filter(`Country Name` %in% fmf$Country) %>% |
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select(`Country Name`,`IDA resource allocation index (1=low to 6=high)` ) %>% |
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rename( |
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`IDA resource allocation index 2022` = `IDA resource allocation index (1=low to 6=high)` |
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) %>% |
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dplyr::left_join(df2, by = c( "Country Name" = "Country Name")) |
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df2 = df2 %>% |
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select(`Country Name` , `IDA resource allocation index (1=low to 6=high)`) %>% |
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rename( |
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`IDA resource allocation index 2024` = `IDA resource allocation index (1=low to 6=high)` |
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) |
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``` |
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## GRAPHITE (NATURAL) |
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```{r} |
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library(tibble) |
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df_graphite <- tribble( |
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~Country, ~Graphite_Production_2022, ~Graphite_Production_2023, ~Graphite_Reserves, |
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"United States", NA, NA, NA, |
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"Austria", 500, 500, NA, |
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"Brazil", 72000, 73000, 74000000, |
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"Canada", 13000, 3500, 5700000, |
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"China", 1210000, 1230000, 78000000, |
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"Germany", 170, 150, NA, |
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"India", 11000, 11500, 8600000, |
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"Korea, North", 8100, 8100, 2000000, |
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"Korea, Republic of", 23800, 27000, 1800000, |
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"Madagascar", 130000, 100000, 24000000, |
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"Mexico", 2000, 2000, 3100000, |
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"Mozambique", 166000, 96000, 25000000, |
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"Norway", 10380, 7200, 600000, |
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"Russia", 16000, 16000, 14000000, |
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"Sri Lanka", 2600, 2200, 1500000, |
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"Tanzania", 6120, 6000, 18000000, |
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"Turkey", 2800, 2000, 6900000, |
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"Ukraine", 1000, 2000, NA, |
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"Vietnam", 500, 500, NA, |
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"World total", 1680000, 1600000, 280000000 |
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) |
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``` |
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## NICKEL |
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```{r} |
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df_nickel <- tribble( |
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~Country, ~Nickel_Production_2022, ~Nickel_Production_2023, ~Nickel_Reserves, |
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"United States", 17500, 17000, 340000, |
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"Australia", 155000, 160000, 24000000, |
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"Brazil", 88500, 89000, 16000000, |
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"Canada", 143000, 180000, 2200000, |
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"China", 114000, 110000, 4200000, |
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"Indonesia", 1580000, 1800000, 55000000, |
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"New Caledonia", 200000, 230000, 7100000, |
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"Philippines", 345000, 400000, 4800000, |
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"Russia", 222000, 200000, 8300000, |
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"Other countries", 404000, 380000, 9100000, |
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"World total", 3270000, 3600000, 130000000 |
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) |
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``` |
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## COBALT |
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```{r} |
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df_cobalt <- tribble( |
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~Country, ~Cobalt_Production_2022, ~Cobalt_Production_2023, ~Cobalt_Reserves, |
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"United States", 500, 500, 69000, |
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"Australia", 5790, 4600, 1700000, |
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"Canada", 3060, 2100, 230000, |
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"Congo (Kinshasa)", 144000, 170000, 6000000, |
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"Cuba", 3700, 3200, 500000, |
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"Indonesia", 9600, 17000, 500000, |
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"Madagascar", 3500, 4000, 100000, |
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"New Caledonia", 2000, 3000, NA, |
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"Papua New Guinea", 2990, 2900, 49000, |
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"Philippines", 3900, 3800, 260000, |
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"Russia", 9200, 8800, 250000, |
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"Turkey", 2100, 2800, 91000, |
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"Other countries", 6600, 6600, 780000, |
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"World total", 197000, 230000, 11000000 |
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) |
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``` |
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## LITHIUM |
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```{r} |
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df_lithium <- tribble( |
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~Country, ~Lithium_Production_2023, ~Lithium_Production_2024, ~Lithium_Reserves, |
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"United States", NA, NA, 1800000, |
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"Argentina", 8630, 18000, 4000000, |
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"Australia", 91700, 88000, 57000000, |
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"Brazil", 5260, 10000, 390000, |
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"Canada", 3240, 4300, 1200000, |
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"Chile", 41400, 49000, 9300000, |
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"China", 35700, 41000, 3000000, |
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"Namibia", 2700, 2700, 14000, |
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"Portugal", 380, 380, 60000, |
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"Zimbabwe", 14900, 22000, 480000, |
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"Other countries", NA, NA, 2800000, |
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"World total", 204000, 240000, 30000000 |
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) |
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``` |
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## MANGANESE |
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```{r} |
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df_manganese <- tribble( |
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~Country, ~Manganese_Production_2022, ~Manganese_Production_2023, ~Manganese_Reserves, |
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"United States", NA, NA, NA, |
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"Australia", 3040, 3000, 500000, |
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"Brazil", 624, 620, 270000, |
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"Burma", 207, 210, NA, |
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"China", 743, 740, 280000, |
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"Côte d’Ivoire", 394, 390, NA, |
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"Gabon", 4670, 4600, 61000, |
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"Georgia", 166, 160, NA, |
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"Ghana", 844, 840, 13000, |
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"India", 721, 720, 34000, |
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"Kazakhstan, concentrate", 129, 130, 5000, |
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"Malaysia", 247, 250, NA, |
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"Mexico", 221, 220, 5000, |
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"South Africa", 7300, 7200, 600000, |
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"Ukraine, concentrate", 323, 320, 140000, |
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"Vietnam", 155, 160, NA, |
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"Other countries", 325, 330, NA, # "Small" mapped to NA |
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"World total", 19800, 20000, 1900000 |
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) |
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``` |
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## COPPER |
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```{r} |
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df_copper <- tribble( |
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~Country, ~Copper_Production_2022, ~Copper_Production_2023, ~Copper_Refinery_Production_2022, ~Copper_Refinery_Production_2023, ~Copper_Reserves, |
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"United States", 1230, 1100, 952, 890, 50000, |
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"Australia", 819, 810, 401, 450, 100000, |
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"Canada", 520, 480, 278, 310, 7600, |
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"Chile", 5330, 5000, 2150, 2000, 190000, |
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"China", 1940, 1700, 11100, 12000, 41000, |
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"Congo (Kinshasa)", 2350, 2500, 1770, 1900, 80000, |
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"Germany", NA, NA, 609, 610, NA, |
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"Indonesia", 941, 840, 310, 200, 24000, |
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"Japan", NA, NA, 1550, 1500, NA, |
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"Kazakhstan", 593, 600, 494, 440, 20000, |
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"Korea, Republic of", NA, NA, 638, 620, NA, |
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"Mexico", 754, 750, 486, 480, 53000, |
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"Peru", 2450, 2600, 391, 400, 120000, |
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"Poland", 393, 400, 586, 590, 34000, |
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"Russia", 936, 910, 1010, 1000, 80000, |
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"Zambia", 797, 760, 349, 380, 21000, |
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"Other countries", 2850, 3100, 2830, 2900, 180000, |
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"World total", 21900, 22000, 25900, 27000, 1000000 |
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) |
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``` |
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```{r} |
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tb5$`Country Name`[tb5$`Country Name` == "Congo (DRC)"] <- "Congo (Kinshasa)" |
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tb6 = tb5 %>% |
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left_join(df_graphite, by = c("Country Name" = "Country")) %>% |
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left_join(df_cobalt, by = c("Country Name" = "Country")) %>% |
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left_join(df_copper,by = c("Country Name" = "Country")) %>% |
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left_join(df_lithium,by = c("Country Name" = "Country")) %>% |
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left_join(df_manganese,by = c("Country Name" = "Country")) %>% |
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left_join(df_nickel,by = c("Country Name" = "Country")) %>% |
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arrange(`Country Name`) |
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|
|
|
write.csv(tb6, "Super Region Country minerals.csv", row.names = FALSE) |
|
|
``` |
|
|
|
|
|
# Correlation test |
|
|
|
|
|
```{r} |
|
|
congo = read_csv("cor.csv") |
|
|
|
|
|
|
|
|
corr = read_csv("cor_matrix.csv") |
|
|
fmf = read_csv("Commodity_super_region_ranked.csv") |
|
|
``` |
|
|
|
|
|
```{r} |
|
|
#congo = corr %>% |
|
|
# filter(`Series Name` != "Human capital index (HCI), upper bound (scale 0-1)" ) |
|
|
corr$`Country Name`[corr$`Country Name` == "Congo, Dem. Rep."] <- "Democratic Republic of the Congo" |
|
|
corr$`Country Name`[corr$`Country Name` == "Congo, Rep."] <- "Congo (Republic of the)" |
|
|
corr$`Country Name`[corr$`Country Name` == "Yemen, Rep."] = "Yemen" |
|
|
corr$`Country Name`[corr$`Country Name` == "Gambia, The"] = "Gambia" |
|
|
#corr$`Country Name`[corr$`Country Name` == "Sao Tome and Principe"] = "Sao Tome & Principe" |
|
|
corr$`Country Name`[corr$`Country Name` == "Egypt, Arab Rep."] = "Egypt" |
|
|
#corr$`Country Name`[corr$`Country Name` == " |
|
|
#Central African Republic"] = "Central African Republic" |
|
|
corr$`Country Name`[corr$`Country Name` == "Syrian Arab Republic"] = "Syria" |
|
|
corr$`Country Name`[corr$`Country Name` == "Kyrgyz Republic"] = "Kyrgyzstan" |
|
|
#corr$`Country Name`[corr$`Country Name` == "Morocco"] = "Morroco" |
|
|
#corr$`Country Name`[corr$`Country Name` == "South Africa"] = "South Africa (RSA)" |
|
|
``` |
|
|
|
|
|
```{r} |
|
|
# 指定保留的 series 列表 |
|
|
series_keep <- c( |
|
|
"Average time to clear exports through customs (days)", |
|
|
|
|
|
"Liner shipping connectivity index (maximum value in 2004 = 100)", |
|
|
|
|
|
"Rail lines (total route-km)", |
|
|
"Container port traffic (TEU: 20 foot equivalent units)" |
|
|
) |
|
|
|
|
|
df_clean <- corr %>% |
|
|
select(`Country Name`,`Series Name`, `2020 [YR2020]`) %>% |
|
|
#filter(`2020 [YR2020]` != "..") %>% |
|
|
mutate(`2020 [YR2020]` = as.numeric(`2020 [YR2020]`)) %>% |
|
|
filter(`Country Name` %in% fmf$Country.Name)%>% |
|
|
filter(`Series Name` %in% series_keep) |
|
|
#filter(`Country Name` != "Afghanistan") |
|
|
|
|
|
df_clean_2 <- corr %>% |
|
|
select(`Country Name`,`Series Name`, `2020 [YR2020]`) %>% |
|
|
mutate(`2020 [YR2020]` = as.numeric(`2020 [YR2020]`)) %>% |
|
|
filter(`Country Name` %in% fmf$Country) |
|
|
#df_focus <- df_clean %>% |
|
|
# filter(`Series Name` %in% series_keep) |
|
|
|
|
|
|
|
|
df_wide_focus <- df_clean %>% |
|
|
select(`Country Name`, `Series Name`, `2020 [YR2020]`) %>% |
|
|
pivot_wider(names_from = `Series Name`, values_from = `2020 [YR2020]`) |
|
|
|
|
|
|
|
|
cor_focus <- cor(df_wide_focus %>% select(-`Country Name`), use = "pairwise.complete.obs") |
|
|
|
|
|
|
|
|
View(round(cor_focus, 5)) |
|
|
|
|
|
|
|
|
``` |
|
|
|
|
|
```{r fig.height=10, fig.width=15, echo=FALSE, message=FALSE, warning=FALSE} |
|
|
#library(ggplot2) |
|
|
#library(reshape2) |
|
|
|
|
|
# reshape for ggplot |
|
|
cor_melted <- melt(cor_focus, na.rm = TRUE) |
|
|
|
|
|
ggplot(data = cor_melted, aes(x = Var1, y = Var2, fill = value)) + |
|
|
geom_tile(color = "white") + |
|
|
scale_fill_gradient2(low = "blue", high = "red", mid = "white", |
|
|
midpoint = 0, limit = c(-1,1), space = "Lab", |
|
|
name="Pearson\nCorrelation") + |
|
|
theme_minimal() + |
|
|
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) + |
|
|
theme( |
|
|
axis.text.y = element_text(face = "bold") |
|
|
)+ |
|
|
geom_text(aes(label = round(value, 2)), size = 3) |
|
|
|
|
|
``` |
|
|
|
|
|
```{r fig.height=8, fig.width=15, echo=FALSE, message=FALSE, warning=FALSE} |
|
|
library(ggplot2) |
|
|
library(reshape2) |
|
|
|
|
|
# melt correlation matrix |
|
|
cor_melted <- melt(cor_focus, na.rm = TRUE) |
|
|
|
|
|
# 添加标记字段：是否显著 |
|
|
cor_melted$label_color <- ifelse(abs(cor_melted$value) > 0.5, "red", "black") |
|
|
cor_melted$label_fontface <- ifelse(abs(cor_melted$value) > 0.5, "bold", "plain") |
|
|
|
|
|
# 绘图 |
|
|
ggplot(data = cor_melted, aes(x = Var1, y = Var2, fill = value)) + |
|
|
geom_tile(color = "white") + |
|
|
geom_text(aes(label = round(value, 4), color = label_color, fontface = label_fontface), size = 3) + |
|
|
scale_fill_gradient2( |
|
|
low = "green", high = "yellow", mid = "white", |
|
|
midpoint = 0, limit = c(-1,1), space = "Lab", |
|
|
name = "Pearson\nCorrelation" |
|
|
) + |
|
|
scale_color_identity() + # 使用原始颜色变量 |
|
|
theme_minimal() + |
|
|
theme( |
|
|
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1), |
|
|
axis.text.y = element_text(face = "bold") |
|
|
) |
|
|
|
|
|
``` |
|
|
|
|
|
## 5indicators for all FMF countries in 2020, sub GDP with newest 2024 or 2023 |
|
|
|
|
|
```{r} |
|
|
|
|
|
|
|
|
library(dplyr) |
|
|
|
|
|
# Step 1: 先把 ".." 替换成 NA，并转为 numeric |
|
|
xx <- corr %>% |
|
|
mutate( |
|
|
`2024 [YR2024]` = na_if(`2024 [YR2024]`, ".."), |
|
|
`2023 [YR2023]` = na_if(`2023 [YR2023]`, ".."), |
|
|
`2024 [YR2024]` = as.numeric(`2024 [YR2024]`), |
|
|
`2023 [YR2023]` = as.numeric(`2023 [YR2023]`) |
|
|
) |
|
|
|
|
|
# Step 2: 替换 GDP per capita 的 2020 列为 2024，若没有则用 2023 |
|
|
xx <- xx %>% |
|
|
mutate( |
|
|
`2020 [YR2020]` = ifelse( |
|
|
`Series Name` == "GDP per capita (current US$)", |
|
|
coalesce(`2024 [YR2024]`, `2023 [YR2023]`), |
|
|
`2020 [YR2020]` |
|
|
), |
|
|
`2020 [YR2020]` = ifelse( |
|
|
`Series Name` == "GDP growth (annual %)", |
|
|
coalesce(`2024 [YR2024]`, `2023 [YR2023]`), |
|
|
`2020 [YR2020]` |
|
|
) |
|
|
) |
|
|
corr = xx |
|
|
|
|
|
df_wide_2 <- df_clean %>% |
|
|
pivot_wider( |
|
|
names_from = `Series Name`, |
|
|
values_from = `2020 [YR2020]` |
|
|
) |
|
|
|
|
|
|
|
|
series_keep_2 <- c( |
|
|
"GDP growth (annual %)", |
|
|
"GDP per capita (current US$)" |
|
|
) |
|
|
|
|
|
df_clean_2 <- corr %>% |
|
|
select(`Country Name`,`Series Name`, `2020 [YR2020]`) %>% |
|
|
#filter(`2020 [YR2020]` != "..") %>% |
|
|
mutate(`2020 [YR2020]` = as.numeric(`2020 [YR2020]`)) %>% |
|
|
#filter(`Country Name` %in% fmf$Country.Name)%>% |
|
|
filter(`Series Name` %in% series_keep_2) |
|
|
|
|
|
|
|
|
df_wide <- df_clean_2 %>% |
|
|
pivot_wider( |
|
|
names_from = `Series Name`, |
|
|
values_from = `2020 [YR2020]` |
|
|
) |
|
|
|
|
|
|
|
|
result = df_wide %>% |
|
|
left_join(df_wide_2, by = c("Country Name" = "Country Name")) |
|
|
|
|
|
|
|
|
readr::write_csv(result, "all_countries_5_indicators.csv") |
|
|
|
|
|
``` |
|
|
|
