R Notebook

library(readr)
read_csv("/Users/xingyuning/Desktop/susfin/final project/01_data_cleaning/comparison.csv")

## Rows: 664 Columns: 27
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (14): link, handle, ISIN, CUSIP, FIGI, ticker, bond_name, issuer_name, ...
## dbl  (11): issuance_year, volume_usd_billion, volume_usd, volume_bond_curren...
## lgl   (1): fund
## date  (1): issuance_date.y
## 
## 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.

read_csv("/Users/xingyuning/Desktop/susfin/final project/01_data_cleaning/bonds_new.csv")

## Rows: 160 Columns: 20
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (15): link, handle, ISIN, CUSIP, FIGI, ticker, bond_name, issuer_name, i...
## dbl  (4): issuance_year, volume_usd_billion, volume_usd, volume_bond_currency
## lgl  (1): fund
## 
## 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.

read_csv("/Users/xingyuning/Desktop/susfin/final project/01_data_cleaning/allocations_new.csv")

## Rows: 2278 Columns: 17
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (11): bond_handle, bond_name, nomenclature, category, subcategory, proje...
## dbl  (5): allocated_allocation_currency, disbursed_allocation_currency, disb...
## lgl  (1): refinancing
## 
## 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.

Question1: What is the trend of the issuance of green bonds in LAC?

library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ purrr     1.0.2
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.4.4     ✔ tibble    3.2.1
## ✔ lubridate 1.9.3     ✔ tidyr     1.3.1
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(readxl)
library(plotly)

## 
## Attaching package: 'plotly'
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library(ggplot2)
library(scales)

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## Attaching package: 'scales'
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comparison <- read_csv("/Users/xingyuning/Desktop/susfin/final project/01_data_cleaning/comparison.csv")

## Rows: 664 Columns: 27
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (14): link, handle, ISIN, CUSIP, FIGI, ticker, bond_name, issuer_name, ...
## dbl  (11): issuance_year, volume_usd_billion, volume_usd, volume_bond_curren...
## lgl   (1): fund
## date  (1): issuance_date.y
## 
## 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.

data_long_comparison <- comparison |> 
  pivot_longer(cols = c("LAC_volume_usd", "global_volume_usd"), names_to = "volume_type", values_to = "issuance_volume")

# Plot the data
p <- ggplot(data_long_comparison, aes(x = issuance_year, y = issuance_volume, color = volume_type)) +
  geom_line() +   
  geom_point() +  
  scale_color_manual(values = c("LAC_volume_usd" = "#377eb8", "global_volume_usd" = "#4daf4a")) + 
  theme_minimal() +
  labs(title = "The trend of Issuance of Green Bonds", x = "Year", y = "Issuance Volume (bn)", color = "Volume Type")
ggplotly(p)

Question2: What is the Yearly Green Bond Issuance Growth: Latin America vs. Global

library(dplyr)

data_growth <- comparison %>%
  arrange(issuance_year) %>% 
  mutate(
    LAC_growth = (LAC_volume_usd - lag(LAC_volume_usd)) / lag(LAC_volume_usd) * 100,
    Global_growth = (global_volume_usd - lag(global_volume_usd)) / lag(global_volume_usd) * 100
  )

print(data_growth)

## # A tibble: 664 × 29
##    link        handle ISIN  CUSIP FIGI  ticker bond_name issuer_name issuer_type
##    <chr>       <chr>  <chr> <chr> <chr> <chr>  <chr>     <chr>       <chr>      
##  1 https://ww… fe8a8… MX90… AM35… BBG0… 90_GC… GCDMXCB … Gobierno d… Local gove…
##  2 https://ww… fe8a8… MX90… AM35… BBG0… 90_GC… GCDMXCB … Gobierno d… Local gove…
##  3 https://ww… fe8a8… MX90… AM35… BBG0… 90_GC… GCDMXCB … Gobierno d… Local gove…
##  4 https://ww… fe8a8… MX90… AM35… BBG0… 90_GC… GCDMXCB … Gobierno d… Local gove…
##  5 https://ww… ee7a6… US05… 0567… <NA>  <NA>   Suzano P… Suzano Pap… Non-Financ…
##  6 https://ww… ee7a6… US05… 0567… <NA>  <NA>   Suzano P… Suzano Pap… Non-Financ…
##  7 https://ww… ee7a6… US05… 0567… <NA>  <NA>   Suzano P… Suzano Pap… Non-Financ…
##  8 https://ww… ee7a6… US05… 0567… <NA>  <NA>   Suzano P… Suzano Pap… Non-Financ…
##  9 https://ww… 0f91b… USP1… JK87… <NA>  <NA>   Banco Na… Banco Naci… Developmen…
## 10 https://ww… 0f91b… USP1… JK87… <NA>  <NA>   Banco Na… Banco Naci… Developmen…
## # ℹ 654 more rows
## # ℹ 20 more variables: issuer_jurisdiction <chr>, fund <lgl>,
## #   framework_link <chr>, issuance_year <dbl>, issuance_date.x <chr>,
## #   maturity_date <chr>, volume_usd_billion <dbl>, volume_usd <dbl>,
## #   volume_bond_currency <dbl>, bond_currency <chr>, LAC_volume_usd <dbl>,
## #   issuance_date.y <date>, `Mature markets` <dbl>, `Emerging markets` <dbl>,
## #   `Offshore centers` <dbl>, Supranationals <dbl>, Global <dbl>, …

library(tidyr)
library(ggplot2)

data_growth_long <- data_growth %>%
  pivot_longer(cols = c("LAC_growth", "Global_growth"), names_to = "growth_type", values_to = "growth_rate")


p_growth <- ggplot(data_growth_long, aes(x = issuance_year, y = growth_rate, color = growth_type)) +
  geom_line() +
  geom_point() +
  scale_color_viridis_d(begin = 0.3, end = 0.9, option = "D") +  
  theme_minimal() +
  labs(title = "Annual Growth Rate of Green Bond Issuance", x = "Year", y = "Growth Rate (%)", color = "Growth Type")

ggplotly(p_growth)

library(leaflet)
library(countrycode)
library(rnaturalearth)
bonds_new <- read_csv("/Users/xingyuning/Desktop/susfin/final project/01_data_cleaning/bonds_new.csv")

## Rows: 160 Columns: 20
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (15): link, handle, ISIN, CUSIP, FIGI, ticker, bond_name, issuer_name, i...
## dbl  (4): issuance_year, volume_usd_billion, volume_usd, volume_bond_currency
## lgl  (1): fund
## 
## 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.

bonds_new$issuer_jurisdiction <- ifelse(bonds_new$issuer_jurisdiction == "Supranational", NA, bonds_new$issuer_jurisdiction)

bonds_new$ISO_code <- countrycode(bonds_new$issuer_jurisdiction, origin = "country.name", destination = "iso3c", warn = FALSE)

bonds_new <- bonds_new[!is.na(bonds_new$ISO_code), ]

# Calculate the total volume issued by ISO code
bonds_summary <- bonds_new %>%
  group_by(ISO_code) %>%
  summarise(volume_usd_total = sum(volume_usd, na.rm = TRUE))

# Get the world map and filter for Latin America and the Caribbean
world_map <- ne_countries(scale = "medium", returnclass = "sf")
latin_america_caribbean_map <- world_map[world_map$region_un == "Americas" & 
                                         world_map$subregion %in% c("Central America", "South America", "Caribbean"), ]

# Merge the map data with the summary data
latin_america_bonds <- merge(latin_america_caribbean_map, bonds_summary, by.x = "iso_a3", by.y = "ISO_code")

# Create an interactive map using leaflet
leaflet(latin_america_bonds) %>%
  addProviderTiles(providers$CartoDB.Positron) %>%
  addPolygons(fillColor = ~colorQuantile("Blues", volume_usd_total)(volume_usd_total),
              color = "#BDBDC3",
              weight = 1,
              opacity = 1,
              fillOpacity = 0.7,
              highlight = highlightOptions(weight = 2,
                                           color = "#666",
                                           fillOpacity = 0.7),
              label = ~paste(iso_a3, ":", formatC(volume_usd_total, format = "f", big.mark = ","))) %>%
  addLegend("bottomright", pal = colorQuantile("Blues", latin_america_bonds$volume_usd_total), 
            values = ~volume_usd_total,
            title = "Issuance Volume (USD)",
            labFormat = labelFormat(big.mark = ","))

Question3: How is the allocation of green bonds in Latin America?

library(dplyr)
library(ggplot2)
allocations_new <- read_csv("/Users/xingyuning/Desktop/susfin/final project/01_data_cleaning/allocations_new.csv")

## Rows: 2278 Columns: 17
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (11): bond_handle, bond_name, nomenclature, category, subcategory, proje...
## dbl  (5): allocated_allocation_currency, disbursed_allocation_currency, disb...
## lgl  (1): refinancing
## 
## 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.

distinct_allocations <- allocations_new %>%
  distinct(bond_name, .keep_all = TRUE)

allocation_counts <- distinct_allocations %>%
  group_by(category) %>%
  summarise(count = n())

allocation_counts <- allocation_counts %>%
  mutate(percentage = count / sum(count) * 100)
library(plotly)
fig <- plot_ly(allocation_counts, labels = ~category, values = ~percentage, type = 'pie', textinfo = 'label+percent',
               insidetextorientation = 'radial') %>%
  layout(title = 'Proportion of Green Bond Allocation By issuance Number')
fig

library(dplyr)
library(plotly)

allocation_sums <- distinct_allocations %>%
  group_by(category) %>%
  summarise(total_amount = sum(disbursed_usd))


allocation_sums <- allocation_sums %>%
  mutate(percentage_amount = total_amount / sum(total_amount) * 100)


allocation_counts <- distinct_allocations %>%
  group_by(category) %>%
  summarise(count = n())


allocation_counts <- allocation_counts %>%
  mutate(percentage_count = count / sum(count) * 100)


fig <- subplot(
  plot_ly(allocation_counts, labels = ~category, values = ~percentage_count, name = 'Count', type = 'pie', textinfo = 'label+percent', insidetextorientation = 'radial') %>%
    layout(title = 'Proportion of Green Bond Allocation by Count'),
  plot_ly(allocation_sums, labels = ~category, values = ~percentage_amount, name = 'Amount', type = 'pie', textinfo = 'label+percent', insidetextorientation = 'radial') %>%
    layout(title = 'Proportion of Green Bond Allocation by Amount')
)


fig

## Warning: 'layout' objects don't have these attributes: 'NA'
## Valid attributes include:
## '_deprecated', 'activeshape', 'annotations', 'autosize', 'autotypenumbers', 'calendar', 'clickmode', 'coloraxis', 'colorscale', 'colorway', 'computed', 'datarevision', 'dragmode', 'editrevision', 'editType', 'font', 'geo', 'grid', 'height', 'hidesources', 'hoverdistance', 'hoverlabel', 'hovermode', 'images', 'legend', 'mapbox', 'margin', 'meta', 'metasrc', 'modebar', 'newshape', 'paper_bgcolor', 'plot_bgcolor', 'polar', 'scene', 'selectdirection', 'selectionrevision', 'separators', 'shapes', 'showlegend', 'sliders', 'smith', 'spikedistance', 'template', 'ternary', 'title', 'transition', 'uirevision', 'uniformtext', 'updatemenus', 'width', 'xaxis', 'yaxis', 'barmode', 'bargap', 'mapType'

library(dplyr)
library(plotly)

allocation_sums <- distinct_allocations %>%
  group_by(category) %>%
  summarise(total_amount = sum(disbursed_usd))


allocation_sums <- allocation_sums %>%
  mutate(percentage_amount = total_amount / sum(total_amount) * 100)


allocation_counts <- distinct_allocations %>%
  group_by(category) %>%
  summarise(count = n())


allocation_counts <- allocation_counts %>%
  mutate(percentage_count = count / sum(count) * 100)


fig <- subplot(
  plot_ly(allocation_counts, labels = ~category, values = ~percentage_count, name = 'Count', type = 'pie', textinfo = 'label+percent', insidetextorientation = 'radial') %>%
    layout(title = 'Proportion of Green Bond Allocation by Count'),
  plot_ly(allocation_sums, labels = ~category, values = ~percentage_amount, name = 'Amount', type = 'pie', textinfo = 'label+percent', insidetextorientation = 'radial') %>%
    layout(title = 'Proportion of Green Bond Allocation by Amount')
)


fig

## Warning: 'layout' objects don't have these attributes: 'NA'
## Valid attributes include:
## '_deprecated', 'activeshape', 'annotations', 'autosize', 'autotypenumbers', 'calendar', 'clickmode', 'coloraxis', 'colorscale', 'colorway', 'computed', 'datarevision', 'dragmode', 'editrevision', 'editType', 'font', 'geo', 'grid', 'height', 'hidesources', 'hoverdistance', 'hoverlabel', 'hovermode', 'images', 'legend', 'mapbox', 'margin', 'meta', 'metasrc', 'modebar', 'newshape', 'paper_bgcolor', 'plot_bgcolor', 'polar', 'scene', 'selectdirection', 'selectionrevision', 'separators', 'shapes', 'showlegend', 'sliders', 'smith', 'spikedistance', 'template', 'ternary', 'title', 'transition', 'uirevision', 'uniformtext', 'updatemenus', 'width', 'xaxis', 'yaxis', 'barmode', 'bargap', 'mapType'

Question4: Issuer type

library(dplyr)
library(plotly)
bonds_new_issuer <- bonds_new %>%
  distinct(bond_name, .keep_all = TRUE)

issuer_counts <- bonds_new_issuer %>%
  group_by(issuer_type) %>%
  summarise(count = n())

# Calculate the percentage on the correct data frame
issuer_counts <- issuer_counts %>%
  mutate(percentage = count / sum(count) * 100)

# Create the pie chart with plotly
fig <- plot_ly(issuer_counts, labels = ~issuer_type, values = ~percentage, type = 'pie', textinfo = 'label+percent',
               insidetextorientation = 'radial') %>%
  layout(title = 'Proportion of Issuer Type of Green Bonds in LAC')

# Display the figure
fig