bonds_and_loans <- process_iif_gb_issuance(range = "B4:G41",
issuance_type = "Sustainable debt (bonds and loans)")
bonds <- process_iif_gb_issuance(range = "I4:N41",
issuance_type = "Sustainable bonds")
iif_issuance_data <- bonds_and_loans %>%
bind_rows(bonds)
iif_issuance_data
## # A tibble: 370 × 4
## date geography issuance_bn_usd issuance_type
## <date> <chr> <dbl> <chr>
## 1 2013-03-31 Mature markets 6.19 Sustainable debt (bonds and loan…
## 2 2013-03-31 Emerging markets 0.451 Sustainable debt (bonds and loan…
## 3 2013-03-31 Offshore centers 0 Sustainable debt (bonds and loan…
## 4 2013-03-31 Supranationals 1.16 Sustainable debt (bonds and loan…
## 5 2013-03-31 Global 7.80 Sustainable debt (bonds and loan…
## 6 2013-06-30 Mature markets 4.08 Sustainable debt (bonds and loan…
## 7 2013-06-30 Emerging markets 0.595 Sustainable debt (bonds and loan…
## 8 2013-06-30 Offshore centers 0 Sustainable debt (bonds and loan…
## 9 2013-06-30 Supranationals 0.106 Sustainable debt (bonds and loan…
## 10 2013-06-30 Global 4.78 Sustainable debt (bonds and loan…
## # … with 360 more rows
loans <- process_iif_gb_issuance(range = "P4:U41",
issuance_type = "Sustainable loans")
iif_issuance_data <- iif_issuance_data %>%
bind_rows(loans)
green_bonds <- process_iif_gb_issuance(range = "W4:AB41",
issuance_type = "Green bonds")
iif_issuance_data <- iif_issuance_data %>%
bind_rows(green_bonds)
green_abs <- process_iif_gb_issuance(range = "AD4:AI41",
issuance_type = "Green ABS")
iif_issuance_data <- iif_issuance_data %>%
bind_rows(green_abs)
sustainability_bonds <- process_iif_gb_issuance(range = "AK4:AP41",
issuance_type = "Sustainability bonds")
iif_issuance_data <- iif_issuance_data %>%
bind_rows(sustainability_bonds)
social_bonds <- process_iif_gb_issuance(range = "AR4:AW41",
issuance_type = "Social bonds")
iif_issuance_data <- iif_issuance_data %>%
bind_rows(social_bonds)
green_municipal_bonds <- process_iif_gb_issuance(range = "AY4:BD41",
issuance_type = "Green municipal bonds")
iif_issuance_data <- iif_issuance_data %>%
bind_rows(green_municipal_bonds)
sustainability_linked_bonds <- process_iif_gb_issuance(range = "BF4:BK41",
issuance_type = "Sustainability-linked bonds")
iif_issuance_data <- iif_issuance_data %>%
bind_rows(sustainability_linked_bonds)
green_loans <- process_iif_gb_issuance(range = "BM4:BR41",
issuance_type = "Green loans")
iif_issuance_data <- iif_issuance_data %>%
bind_rows(green_loans)
sustainability_linked_loans <- process_iif_gb_issuance(range = "BT4:BY41",
issuance_type = "Sustainability-linked loans")
iif_issuance_data <- iif_issuance_data %>%
bind_rows(sustainability_linked_loans)
iif_issuance_data
## # A tibble: 2,035 × 4
## date geography issuance_bn_usd issuance_type
## <date> <chr> <dbl> <chr>
## 1 2013-03-31 Mature markets 6.19 Sustainable debt (bonds and loan…
## 2 2013-03-31 Emerging markets 0.451 Sustainable debt (bonds and loan…
## 3 2013-03-31 Offshore centers 0 Sustainable debt (bonds and loan…
## 4 2013-03-31 Supranationals 1.16 Sustainable debt (bonds and loan…
## 5 2013-03-31 Global 7.80 Sustainable debt (bonds and loan…
## 6 2013-06-30 Mature markets 4.08 Sustainable debt (bonds and loan…
## 7 2013-06-30 Emerging markets 0.595 Sustainable debt (bonds and loan…
## 8 2013-06-30 Offshore centers 0 Sustainable debt (bonds and loan…
## 9 2013-06-30 Supranationals 0.106 Sustainable debt (bonds and loan…
## 10 2013-06-30 Global 4.78 Sustainable debt (bonds and loan…
## # … with 2,025 more rows
iif_issuance_data %>%
mutate(year = lubridate::year(date))%>%
filter(geography == c("Mature markets","Emerging markets")) %>%
group_by(year, geography, issuance_type) %>%
summarize(total = sum(issuance_bn_usd)) %>%
ggplot(aes(x = year, y = total)) +
geom_line(aes(color = issuance_type)) +
labs(title = 'Trend',
subtitle = "in Emerging and Mature Markets",
x = "Time",
y = "Issuance in USD [Bn]") +
theme_minimal()+
facet_grid(geography ~.)
## Warning in geography == c("Mature markets", "Emerging markets"): longer object
## length is not a multiple of shorter object length
## `summarise()` has grouped output by 'year', 'geography'. You can override using
## the `.groups` argument.
iif_issuance_data %>%
filter(issuance_type == "Sustainability-linked loans" | issuance_type == "Green loans" | issuance_type == "Sustainable loans" & date >= ymd("2020-03-31")) %>%
group_by(geography) %>%
ggplot(aes(x = geography, y = issuance_bn_usd, fill = issuance_type)) +
geom_col(position = "dodge") +
labs(title = 'Loan Issuance by Geography, recent 3 years',
x = "Geography",
y = "Total [Bn]",
caption = "Source: IIF") +
theme(axis.text.x = element_text(angle = 90, vjust = 0 , hjust = 1))
issuance_pre_pandemic <- iif_issuance_data %>%
filter(geography == c("Emerging markets","Mature markets")
& date <= ymd("2020-03-01"))
## Warning in geography == c("Emerging markets", "Mature markets"): longer object
## length is not a multiple of shorter object length
treemap(issuance_pre_pandemic,
index = c("geography","issuance_type"),
vSize = "issuance_bn_usd",
fontsize.labels=c(12,8),
fontcolor.labels=c("blue","black"),
fontface.labels=c(2,1),
align.labels=list(c("left", "top"), c("center", "center")),
overlap.labels=0.5,
inflate.labels=F,
palette = "Blues",
title = "Issuance type: Comparison between emerging and mature markets"
)
