Data Tidying

library(tidyverse) 

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✔ ggplot2   3.4.4     ✔ tibble    3.2.1
✔ lubridate 1.9.3     ✔ tidyr     1.3.0
✔ purrr     1.0.2     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(janitor)

Attaching package: 'janitor'

The following objects are masked from 'package:stats':

    chisq.test, fisher.test

imf_climate_dashboards_green_debt_url <- "https://opendata.arcgis.com/datasets/8e2772e0b65f4e33a80183ce9583d062_0.csv"

green_debt <- imf_climate_dashboards_green_debt_url |> 
  read_csv() 

Rows: 355 Columns: 42
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (12): Country, ISO2, ISO3, Indicator, Unit, Source, CTS_Code, CTS_Name, ...
dbl (30): ObjectId, F1985, F1986, F1987, F1990, F1991, F1992, F1993, F1994, ...

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

green_debt

# A tibble: 355 × 42
   ObjectId Country         ISO2  ISO3  Indicator Unit  Source CTS_Code CTS_Name
      <dbl> <chr>           <chr> <chr> <chr>     <chr> <chr>  <chr>    <chr>   
 1        1 Argentina       AR    ARG   Green Bo… Bill… Refin… ECFFI    Green B…
 2        2 Australia       AU    AUS   Green Bo… Bill… Refin… ECFFI    Green B…
 3        3 Austria         AT    AUT   Green Bo… Bill… Refin… ECFFI    Green B…
 4        4 Austria         AT    AUT   Sovereig… Bill… Refin… ECFF     Green B…
 5        5 Bangladesh      BD    BGD   Green Bo… Bill… Refin… ECFFI    Green B…
 6        6 Belarus, Rep. … BY    BLR   Green Bo… Bill… Refin… ECFFI    Green B…
 7        7 Belarus, Rep. … BY    BLR   Sovereig… Bill… Refin… ECFF     Green B…
 8        8 Belgium         BE    BEL   Green Bo… Bill… Refin… ECFFI    Green B…
 9        9 Belgium         BE    BEL   Sovereig… Bill… Refin… ECFF     Green B…
10       10 Bermuda         BM    BMU   Green Bo… Bill… Refin… ECFFI    Green B…
# ℹ 345 more rows
# ℹ 33 more variables: CTS_Full_Descriptor <chr>, Type_of_Issuer <chr>,
#   Use_of_Proceed <chr>, Principal_Currency <chr>, F1985 <dbl>, F1986 <dbl>,
#   F1987 <dbl>, F1990 <dbl>, F1991 <dbl>, F1992 <dbl>, F1993 <dbl>,
#   F1994 <dbl>, F1999 <dbl>, F2000 <dbl>, F2002 <dbl>, F2003 <dbl>,
#   F2004 <dbl>, F2007 <dbl>, F2008 <dbl>, F2009 <dbl>, F2010 <dbl>,
#   F2011 <dbl>, F2012 <dbl>, F2013 <dbl>, F2014 <dbl>, F2015 <dbl>, …

3.5.0.1 Homework problem 1:

Use the countrycode package to add the region as a variable to green_debt_subset.

# we want to compare these two indicators
indicators_we_want <- c("Green Bond Issuances by Country", "Sovereign Green Bond Issuances")

green_debt_subset <- green_debt |> 
  # from the janitor package -- makes variables snake_case so they are easier to work with
  clean_names() |> 
  # filter for the vector of indicators we defined above
  filter(indicator %in% indicators_we_want) |> 
  # "f\\d{4}" is a regular expression (regex) that searches for all columns that are f + four digits.
  # Ask ChatGPT to explain this to you.
  select(country, iso3, indicator, matches("f\\d{4}")) 
 
green_debt_subset 

# A tibble: 107 × 32
   country iso3  indicator f1985 f1986 f1987 f1990 f1991 f1992 f1993 f1994 f1999
   <chr>   <chr> <chr>     <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
 1 Argent… ARG   Green Bo…    NA    NA    NA    NA    NA    NA    NA    NA    NA
 2 Austra… AUS   Green Bo…    NA    NA    NA    NA    NA    NA    NA    NA    NA
 3 Austria AUT   Green Bo…    NA    NA    NA    NA    NA    NA    NA    NA    NA
 4 Austria AUT   Sovereig…    NA    NA    NA    NA    NA    NA    NA    NA    NA
 5 Bangla… BGD   Green Bo…    NA    NA    NA    NA    NA    NA    NA    NA    NA
 6 Belaru… BLR   Green Bo…    NA    NA    NA    NA    NA    NA    NA    NA    NA
 7 Belaru… BLR   Sovereig…    NA    NA    NA    NA    NA    NA    NA    NA    NA
 8 Belgium BEL   Green Bo…    NA    NA    NA    NA    NA    NA    NA    NA    NA
 9 Belgium BEL   Sovereig…    NA    NA    NA    NA    NA    NA    NA    NA    NA
10 Bermuda BMU   Green Bo…    NA    NA    NA    NA    NA    NA    NA    NA    NA
# ℹ 97 more rows
# ℹ 20 more variables: f2000 <dbl>, f2002 <dbl>, f2003 <dbl>, f2004 <dbl>,
#   f2007 <dbl>, f2008 <dbl>, f2009 <dbl>, f2010 <dbl>, f2011 <dbl>,
#   f2012 <dbl>, f2013 <dbl>, f2014 <dbl>, f2015 <dbl>, f2016 <dbl>,
#   f2017 <dbl>, f2018 <dbl>, f2019 <dbl>, f2020 <dbl>, f2021 <dbl>,
#   f2022 <dbl>

library(dplyr)
library(countrycode)
green_debt_subset <- green_debt_subset |> 
  mutate(region = countrycode(iso3, "iso3c", "region")) |> 
  select(region, everything())
green_debt_subset

# A tibble: 107 × 33
   region      country iso3  indicator f1985 f1986 f1987 f1990 f1991 f1992 f1993
   <chr>       <chr>   <chr> <chr>     <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
 1 Latin Amer… Argent… ARG   Green Bo…    NA    NA    NA    NA    NA    NA    NA
 2 East Asia … Austra… AUS   Green Bo…    NA    NA    NA    NA    NA    NA    NA
 3 Europe & C… Austria AUT   Green Bo…    NA    NA    NA    NA    NA    NA    NA
 4 Europe & C… Austria AUT   Sovereig…    NA    NA    NA    NA    NA    NA    NA
 5 South Asia  Bangla… BGD   Green Bo…    NA    NA    NA    NA    NA    NA    NA
 6 Europe & C… Belaru… BLR   Green Bo…    NA    NA    NA    NA    NA    NA    NA
 7 Europe & C… Belaru… BLR   Sovereig…    NA    NA    NA    NA    NA    NA    NA
 8 Europe & C… Belgium BEL   Green Bo…    NA    NA    NA    NA    NA    NA    NA
 9 Europe & C… Belgium BEL   Sovereig…    NA    NA    NA    NA    NA    NA    NA
10 North Amer… Bermuda BMU   Green Bo…    NA    NA    NA    NA    NA    NA    NA
# ℹ 97 more rows
# ℹ 22 more variables: f1994 <dbl>, f1999 <dbl>, f2000 <dbl>, f2002 <dbl>,
#   f2003 <dbl>, f2004 <dbl>, f2007 <dbl>, f2008 <dbl>, f2009 <dbl>,
#   f2010 <dbl>, f2011 <dbl>, f2012 <dbl>, f2013 <dbl>, f2014 <dbl>,
#   f2015 <dbl>, f2016 <dbl>, f2017 <dbl>, f2018 <dbl>, f2019 <dbl>,
#   f2020 <dbl>, f2021 <dbl>, f2022 <dbl>

Calculate the cumulative issuance of green bonds by region.

cumulative_issuance_by_region <- green_debt_subset |>
  group_by(region) |>
  mutate(total_issuance = rowSums(across(starts_with("f")), na.rm = TRUE))|>
  summarize(cumulative_issuance = sum(total_issuance, na.rm = TRUE))

cumulative_issuance_by_region

# A tibble: 7 × 2
  region                     cumulative_issuance
  <chr>                                    <dbl>
1 East Asia & Pacific                      586. 
2 Europe & Central Asia                   1395. 
3 Latin America & Caribbean                 99.8
4 Middle East & North Africa                10.3
5 North America                            239. 
6 South Asia                                15.0
7 Sub-Saharan Africa                        16.2

Use ggplot2 to make a data visualization comparing regional cumulative issuance.

library(ggplot2)

ggplot(cumulative_issuance_by_region, aes(x = reorder(region, cumulative_issuance), y = cumulative_issuance, fill = region)) +
  geom_bar(stat = "identity") +
  theme_minimal() +
  scale_y_continuous(labels = scales::label_dollar(suffix = " bn"),
                     expand = c(0,0)) +
  labs(x = "Region", y = "Cumulative Issuance of Green Bonds", title = "Comparative Cumulative Issuance of Green Bonds by Region") +
  coord_flip() 

3.5.0.2 Homework problem 2:

Use the full green_debt dataset

Use clean_names() to make the variable names snake_case

Filter out observations where type_of_issuer is “Not Applicable”

Use the tools taught in this chapter to provide a compelling data visualization and some repeatable factoids that provide actionable insights about green bond issuers.

library(tidyverse)
library(janitor)
library(countrycode)

green_debt_problem2 <- green_debt |> 
  clean_names() |>
  filter(type_of_issuer != "Not Applicable")
green_debt_problem2 

# A tibble: 7 × 42
  object_id country iso2  iso3  indicator         unit  source cts_code cts_name
      <dbl> <chr>   <chr> <chr> <chr>             <chr> <chr>  <chr>    <chr>   
1       347 World   <NA>  WLD   Green Bond Issua… Bill… Refin… ECFFI    Green B…
2       348 World   <NA>  WLD   Green Bond Issua… Bill… Refin… ECFFI    Green B…
3       349 World   <NA>  WLD   Green Bond Issua… Bill… Refin… ECFFI    Green B…
4       350 World   <NA>  WLD   Green Bond Issua… Bill… Refin… ECFFI    Green B…
5       351 World   <NA>  WLD   Green Bond Issua… Bill… Refin… ECFFI    Green B…
6       352 World   <NA>  WLD   Green Bond Issua… Bill… Refin… ECFFI    Green B…
7       353 World   <NA>  WLD   Green Bond Issua… Bill… Refin… ECFFI    Green B…
# ℹ 33 more variables: cts_full_descriptor <chr>, type_of_issuer <chr>,
#   use_of_proceed <chr>, principal_currency <chr>, f1985 <dbl>, f1986 <dbl>,
#   f1987 <dbl>, f1990 <dbl>, f1991 <dbl>, f1992 <dbl>, f1993 <dbl>,
#   f1994 <dbl>, f1999 <dbl>, f2000 <dbl>, f2002 <dbl>, f2003 <dbl>,
#   f2004 <dbl>, f2007 <dbl>, f2008 <dbl>, f2009 <dbl>, f2010 <dbl>,
#   f2011 <dbl>, f2012 <dbl>, f2013 <dbl>, f2014 <dbl>, f2015 <dbl>,
#   f2016 <dbl>, f2017 <dbl>, f2018 <dbl>, f2019 <dbl>, f2020 <dbl>, …

 total_issuance_by_issuer <- green_debt_problem2 |>
  group_by(type_of_issuer) |>
  mutate(total_issuance = rowSums(across(starts_with("f")), na.rm = TRUE))|>
  arrange(desc(total_issuance))
total_issuance_by_issuer

# A tibble: 7 × 43
# Groups:   type_of_issuer [7]
  object_id country iso2  iso3  indicator         unit  source cts_code cts_name
      <dbl> <chr>   <chr> <chr> <chr>             <chr> <chr>  <chr>    <chr>   
1       350 World   <NA>  WLD   Green Bond Issua… Bill… Refin… ECFFI    Green B…
2       347 World   <NA>  WLD   Green Bond Issua… Bill… Refin… ECFFI    Green B…
3       351 World   <NA>  WLD   Green Bond Issua… Bill… Refin… ECFFI    Green B…
4       353 World   <NA>  WLD   Green Bond Issua… Bill… Refin… ECFFI    Green B…
5       352 World   <NA>  WLD   Green Bond Issua… Bill… Refin… ECFFI    Green B…
6       348 World   <NA>  WLD   Green Bond Issua… Bill… Refin… ECFFI    Green B…
7       349 World   <NA>  WLD   Green Bond Issua… Bill… Refin… ECFFI    Green B…
# ℹ 34 more variables: cts_full_descriptor <chr>, type_of_issuer <chr>,
#   use_of_proceed <chr>, principal_currency <chr>, f1985 <dbl>, f1986 <dbl>,
#   f1987 <dbl>, f1990 <dbl>, f1991 <dbl>, f1992 <dbl>, f1993 <dbl>,
#   f1994 <dbl>, f1999 <dbl>, f2000 <dbl>, f2002 <dbl>, f2003 <dbl>,
#   f2004 <dbl>, f2007 <dbl>, f2008 <dbl>, f2009 <dbl>, f2010 <dbl>,
#   f2011 <dbl>, f2012 <dbl>, f2013 <dbl>, f2014 <dbl>, f2015 <dbl>,
#   f2016 <dbl>, f2017 <dbl>, f2018 <dbl>, f2019 <dbl>, f2020 <dbl>, …

ggplot(total_issuance_by_issuer, aes(x = reorder(type_of_issuer, total_issuance), y = total_issuance, fill = type_of_issuer)) +
  geom_bar(stat = "identity") +
  theme_minimal() +
  labs(x = "Type of Issuer", y = "Total Issuance", title = "Total Green Bond Issuance by Issuer Type") +
  coord_flip() 

Nonfinancial corporations are the leading issuers of green bonds. This indicates a significant engagement from the corporate sector in raising capital for green projects, which suggests a strong corporate commitment to sustainability initiatives. The presence of various issuer types, such as sovereign entities, state-owned entities, and local and state governments, illustrates the multi-faceted approach to funding green initiatives. It suggests that green finance is a priority across different levels of governance and institutional structures.

3.5.0.3 Homework problem 3:

Repeat the process from problem 2 for use_of_proceed and for principal_currency

green_debt_problem3 <- green_debt |> 
  clean_names() |>
  filter(use_of_proceed != "Not Applicable")
green_debt_problem3 

# A tibble: 138 × 42
   object_id country iso2  iso3  indicator        unit  source cts_code cts_name
       <dbl> <chr>   <chr> <chr> <chr>            <chr> <chr>  <chr>    <chr>   
 1       209 World   <NA>  WLD   Cumulative Gree… Share Refin… ECFF     Green B…
 2       210 World   <NA>  WLD   Cumulative Gree… Bill… Refin… ECFF     Green B…
 3       211 World   <NA>  WLD   Cumulative Gree… Share Refin… ECFF     Green B…
 4       212 World   <NA>  WLD   Cumulative Gree… Bill… Refin… ECFF     Green B…
 5       213 World   <NA>  WLD   Cumulative Gree… Share Refin… ECFF     Green B…
 6       214 World   <NA>  WLD   Cumulative Gree… Bill… Refin… ECFF     Green B…
 7       215 World   <NA>  WLD   Cumulative Gree… Share Refin… ECFF     Green B…
 8       216 World   <NA>  WLD   Cumulative Gree… Bill… Refin… ECFF     Green B…
 9       217 World   <NA>  WLD   Cumulative Gree… Share Refin… ECFF     Green B…
10       218 World   <NA>  WLD   Cumulative Gree… Bill… Refin… ECFF     Green B…
# ℹ 128 more rows
# ℹ 33 more variables: cts_full_descriptor <chr>, type_of_issuer <chr>,
#   use_of_proceed <chr>, principal_currency <chr>, f1985 <dbl>, f1986 <dbl>,
#   f1987 <dbl>, f1990 <dbl>, f1991 <dbl>, f1992 <dbl>, f1993 <dbl>,
#   f1994 <dbl>, f1999 <dbl>, f2000 <dbl>, f2002 <dbl>, f2003 <dbl>,
#   f2004 <dbl>, f2007 <dbl>, f2008 <dbl>, f2009 <dbl>, f2010 <dbl>,
#   f2011 <dbl>, f2012 <dbl>, f2013 <dbl>, f2014 <dbl>, f2015 <dbl>, …

# Calculate total issuance by use_of_proceed
total_issuance_by_use_top10 <- green_debt_problem3 |>
  group_by(use_of_proceed) |>
  mutate(total_issuance = rowSums(across(starts_with("f")), na.rm = TRUE))|>
  arrange(desc(total_issuance))|>
  ungroup()|>
  top_n(10, total_issuance)
total_issuance_by_use_top10

# A tibble: 10 × 43
   object_id country iso2  iso3  indicator        unit  source cts_code cts_name
       <dbl> <chr>   <chr> <chr> <chr>            <chr> <chr>  <chr>    <chr>   
 1       234 World   <NA>  WLD   Cumulative Gree… Bill… Refin… ECFF     Green B…
 2       248 World   <NA>  WLD   Cumulative Gree… Bill… Refin… ECFF     Green B…
 3       236 World   <NA>  WLD   Cumulative Gree… Bill… Refin… ECFF     Green B…
 4       244 World   <NA>  WLD   Cumulative Gree… Bill… Refin… ECFF     Green B…
 5       270 World   <NA>  WLD   Cumulative Gree… Bill… Refin… ECFF     Green B…
 6       308 World   <NA>  WLD   Cumulative Gree… Bill… Refin… ECFF     Green B…
 7       222 World   <NA>  WLD   Cumulative Gree… Bill… Refin… ECFF     Green B…
 8       232 World   <NA>  WLD   Cumulative Gree… Bill… Refin… ECFF     Green B…
 9       220 World   <NA>  WLD   Cumulative Gree… Bill… Refin… ECFF     Green B…
10       334 World   <NA>  WLD   Cumulative Gree… Bill… Refin… ECFF     Green B…
# ℹ 34 more variables: cts_full_descriptor <chr>, type_of_issuer <chr>,
#   use_of_proceed <chr>, principal_currency <chr>, f1985 <dbl>, f1986 <dbl>,
#   f1987 <dbl>, f1990 <dbl>, f1991 <dbl>, f1992 <dbl>, f1993 <dbl>,
#   f1994 <dbl>, f1999 <dbl>, f2000 <dbl>, f2002 <dbl>, f2003 <dbl>,
#   f2004 <dbl>, f2007 <dbl>, f2008 <dbl>, f2009 <dbl>, f2010 <dbl>,
#   f2011 <dbl>, f2012 <dbl>, f2013 <dbl>, f2014 <dbl>, f2015 <dbl>,
#   f2016 <dbl>, f2017 <dbl>, f2018 <dbl>, f2019 <dbl>, f2020 <dbl>, …

What are green bond proceeds used for?

# Plot total issuance by use_of_proceed
library(ggplot2)
ggplot(total_issuance_by_use_top10, aes(x = reorder(use_of_proceed, total_issuance), y = total_issuance, fill = use_of_proceed)) +
  geom_bar(stat = "identity") +
  theme_minimal() +
  scale_y_continuous(labels = scales::label_dollar(suffix = " bn"),
                     expand = c(0,0)) +
  labs(x = "Use of Proceed", y = "Total Issuance", title = "Top 10 Uses of Green Bond Proceeds by Total Issuance") +
  coord_flip() +
  theme(
    legend.position = "none",
    axis.text.x = element_text(angle = 45, hjust = 1),
    plot.title = element_text(hjust = 0.5),
    axis.text.y = element_text(size = 7)  # Adjust the text size if the labels are overlapping
  )

The top 1 green bond proceeds is used for clean transport energy.

What do we know about the currency of issuance? Is that changing over time?

green_debt_problem3.1 <- green_debt |> 
  clean_names() |>
  filter(principal_currency!= "Not Applicable")
green_debt_problem3.1

# A tibble: 100 × 42
   object_id country iso2  iso3  indicator        unit  source cts_code cts_name
       <dbl> <chr>   <chr> <chr> <chr>            <chr> <chr>  <chr>    <chr>   
 1       109 World   <NA>  WLD   Cumulative Gree… Share Refin… ECFF     Green B…
 2       110 World   <NA>  WLD   Cumulative Gree… Bill… Refin… ECFF     Green B…
 3       111 World   <NA>  WLD   Cumulative Gree… Share Refin… ECFF     Green B…
 4       112 World   <NA>  WLD   Cumulative Gree… Bill… Refin… ECFF     Green B…
 5       113 World   <NA>  WLD   Cumulative Gree… Share Refin… ECFF     Green B…
 6       114 World   <NA>  WLD   Cumulative Gree… Bill… Refin… ECFF     Green B…
 7       115 World   <NA>  WLD   Cumulative Gree… Share Refin… ECFF     Green B…
 8       116 World   <NA>  WLD   Cumulative Gree… Bill… Refin… ECFF     Green B…
 9       117 World   <NA>  WLD   Cumulative Gree… Share Refin… ECFF     Green B…
10       118 World   <NA>  WLD   Cumulative Gree… Bill… Refin… ECFF     Green B…
# ℹ 90 more rows
# ℹ 33 more variables: cts_full_descriptor <chr>, type_of_issuer <chr>,
#   use_of_proceed <chr>, principal_currency <chr>, f1985 <dbl>, f1986 <dbl>,
#   f1987 <dbl>, f1990 <dbl>, f1991 <dbl>, f1992 <dbl>, f1993 <dbl>,
#   f1994 <dbl>, f1999 <dbl>, f2000 <dbl>, f2002 <dbl>, f2003 <dbl>,
#   f2004 <dbl>, f2007 <dbl>, f2008 <dbl>, f2009 <dbl>, f2010 <dbl>,
#   f2011 <dbl>, f2012 <dbl>, f2013 <dbl>, f2014 <dbl>, f2015 <dbl>, …

total_currency_top10 <- green_debt_problem3.1 |>
  group_by(principal_currency) |>
  mutate(total_issuance = rowSums(across(starts_with("f")), na.rm = TRUE))|>
  arrange(desc(total_issuance))|>
  ungroup()|>
  top_n(10, total_issuance)
total_currency_top10

# A tibble: 10 × 43
   object_id country iso2  iso3  indicator        unit  source cts_code cts_name
       <dbl> <chr>   <chr> <chr> <chr>            <chr> <chr>  <chr>    <chr>   
 1       138 World   <NA>  WLD   Cumulative Gree… Bill… Refin… ECFF     Green B…
 2       206 World   <NA>  WLD   Cumulative Gree… Bill… Refin… ECFF     Green B…
 3       128 World   <NA>  WLD   Cumulative Gree… Bill… Refin… ECFF     Green B…
 4       194 World   <NA>  WLD   Cumulative Gree… Bill… Refin… ECFF     Green B…
 5       120 World   <NA>  WLD   Cumulative Gree… Bill… Refin… ECFF     Green B…
 6       122 World   <NA>  WLD   Cumulative Gree… Bill… Refin… ECFF     Green B…
 7       154 World   <NA>  WLD   Cumulative Gree… Bill… Refin… ECFF     Green B…
 8       114 World   <NA>  WLD   Cumulative Gree… Bill… Refin… ECFF     Green B…
 9       196 World   <NA>  WLD   Cumulative Gree… Bill… Refin… ECFF     Green B…
10       134 World   <NA>  WLD   Cumulative Gree… Bill… Refin… ECFF     Green B…
# ℹ 34 more variables: cts_full_descriptor <chr>, type_of_issuer <chr>,
#   use_of_proceed <chr>, principal_currency <chr>, f1985 <dbl>, f1986 <dbl>,
#   f1987 <dbl>, f1990 <dbl>, f1991 <dbl>, f1992 <dbl>, f1993 <dbl>,
#   f1994 <dbl>, f1999 <dbl>, f2000 <dbl>, f2002 <dbl>, f2003 <dbl>,
#   f2004 <dbl>, f2007 <dbl>, f2008 <dbl>, f2009 <dbl>, f2010 <dbl>,
#   f2011 <dbl>, f2012 <dbl>, f2013 <dbl>, f2014 <dbl>, f2015 <dbl>,
#   f2016 <dbl>, f2017 <dbl>, f2018 <dbl>, f2019 <dbl>, f2020 <dbl>, …

# Plot total issuance by principal currency
ggplot(total_currency_top10, aes(x = principal_currency, y = total_issuance, fill = principal_currency)) +
  geom_bar(stat = "identity") +
  theme_minimal() +
  labs(x = "Principal Currency", y = "Total Issuance", title = "Total Green Bond Issuance by Principal Currency") +
  coord_flip()