options(repos = c(CRAN = "https://cloud.r-project.org/"))
install.packages("countrycode")
The downloaded binary packages are in
/var/folders/pz/gf8r5yfj7r93ln6kz_gbkjtc0000gn/T//RtmpOm6y7M/downloaded_packagesHW4_NadiaXING_dataTidying
Problem1
library(tidyverse)── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.1.4 ✔ readr 2.1.5
✔ forcats 1.0.0 ✔ stringr 1.5.1
✔ ggplot2 3.4.4 ✔ tibble 3.2.1
✔ lubridate 1.9.3 ✔ tidyr 1.3.1
✔ 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 errorslibrary(janitor)
Attaching package: 'janitor'
The following objects are masked from 'package:stats':
chisq.test, fisher.testimf_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.# 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>green_debt_subset <- green_debt_subset |>
mutate(total_issuance = rowSums(across(starts_with("f")), na.rm = TRUE))
cumulative_issuance_by_region <- green_debt_subset |>
group_by(region) |>
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 16.2library(ggplot2)
plot1 <- 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(
title = "Cumulative Issuance of Green Bonds by Region",
x = "Region",
y = "Cumulative Issuance (USD)",
fill = "Region"
) +
coord_flip()
print(plot1)
Problem2
library(tidyverse)
library(janitor)
library(scales)
Attaching package: 'scales'The following object is masked from 'package:purrr':
discardThe following object is masked from 'package:readr':
col_factorgreen_debt_plot2 <- green_debt |>
clean_names() |>
filter(type_of_issuer != "Not Applicable") |>
pivot_longer(
cols = matches("f\\d{4}"),
names_to = "year",
values_to = "issuance_bn_usd",
names_transform = list(year = readr::parse_number),
values_drop_na = TRUE
) %>%
group_by(type_of_issuer, year) %>%
summarise(total_issuance_bn_usd = sum(issuance_bn_usd, na.rm = TRUE))`summarise()` has grouped output by 'type_of_issuer'. You can override using
the `.groups` argument.green_debt_plot2 %>%
ggplot(aes(x = year, y = total_issuance_bn_usd, fill = type_of_issuer)) +
geom_col() +
coord_flip()+
scale_x_reverse(limits = c(2020,2010)) +
scale_y_continuous(labels = label_dollar(suffix = " bn"), expand = c(0, 0)) +
labs(
title = "Total Green Bond Issuance by Type of Issuer",
x = "Year",
y = "Total Issuance (USD)"
) +
theme_minimal() +
theme(legend.position = "bottom")Warning: Removed 37 rows containing missing values (`position_stack()`).Warning: Removed 9 rows containing missing values (`geom_col()`).
Factoids:
From 2010 to 2020, the types of issuer are becoming more and more diversified.
Total Issuance in 2020 is more than five times bigger than it in 2015.
Problem3 Q1
library(tidyverse)
library(janitor)
green_debt_plot3 <- green_debt |>
clean_names() |>
filter(use_of_proceed != "Not Applicable") |>
pivot_longer(
cols = matches("f\\d{4}"),
names_to = "year",
values_to = "issuance_bn_usd",
names_transform = list(year = readr::parse_number),
values_drop_na = TRUE
)
print(green_debt_plot3)# A tibble: 138 × 15
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 6 more variables: cts_full_descriptor <chr>, type_of_issuer <chr>,
# use_of_proceed <chr>, principal_currency <chr>, year <dbl>,
# issuance_bn_usd <dbl>green_debt_plot4 <- green_debt_plot3 |>
filter(year == 2022 & unit == "Billion US Dollars")
issuance_sum_2022 <- green_debt_plot4 |>
group_by(use_of_proceed) |>
summarize(total_issuance = sum(issuance_bn_usd, na.rm = TRUE)) |>
ungroup()
top_issuance_2022 <-issuance_sum_2022 |>
top_n(10, total_issuance)
# Plotting the top 10 uses of proceeds for 2022
ggplot(top_issuance_2022, aes(x = reorder(use_of_proceed, total_issuance),
y = total_issuance, fill = use_of_proceed)) +
geom_bar(stat = "identity") +
coord_flip() +
scale_fill_viridis_d() +
scale_y_continuous(labels = label_dollar(suffix = " bn"), expand = c(0, 0)) +
labs(title = "Top 10 Green Bond Proceeds in 2022", x = "Use of Proceeds", y = "Total Issuance (USD)") +
theme_minimal() +
theme(legend.title = element_blank(), legend.position = "none")
industry_comparision_factoids <- top_issuance_2022 |>
mutate(percentage = (total_issuance / sum(total_issuance))*100)
print(industry_comparision_factoids)# A tibble: 10 × 3
use_of_proceed total_issuance percentage
<chr> <dbl> <dbl>
1 Alternative Energy 25.7 1.18
2 Aquatic Biodiversity Conservation 75.7 3.47
3 Circular Economy Adapted/Eco-efficient Products, P… 33.6 1.54
4 Clean Transport 833. 38.2
5 Climate Change Adaptation 277. 12.7
6 Eligible Green Projects 204. 9.35
7 Energy Efficiency 480. 22.0
8 Green Construction/Buildings 127. 5.83
9 Renewable Energy Projects 108. 4.97
10 Sustainable Water or Wastewater management 17.7 0.809Factoids:
In 2022, the top 1 issuance of green bond in proceed is in clean transport energy, which is nearly two times bigger than the second bond in energy efficiency.
Problem3 Q2
library(tidyverse)
library(janitor)
green_debt_plot5 <- green_debt |>
clean_names() |>
filter(principal_currency != "Not Applicable") |>
pivot_longer(
cols = matches("f\\d{4}"),
names_to = "year",
values_to = "issuance_bn_usd",
names_transform = list(year = readr::parse_number),
values_drop_na = TRUE
)
print(green_debt_plot5)# A tibble: 100 × 15
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 6 more variables: cts_full_descriptor <chr>, type_of_issuer <chr>,
# use_of_proceed <chr>, principal_currency <chr>, year <dbl>,
# issuance_bn_usd <dbl>library(tidyverse)
# Assuming green_debt_plot5 is your dataset and it has been prepared similarly to green_debt_plot2
currency_trends <- green_debt_plot5 %>%
group_by(year, principal_currency) %>%
summarise(total_issuance = sum(issuance_bn_usd, na.rm = TRUE), .groups = 'drop') %>%
arrange(year, principal_currency)
# Assuming you want the top currencies across the entire dataset, not per year
top_currencies <- currency_trends %>%
group_by(principal_currency) %>%
summarise(total_issuance_overall = sum(total_issuance, na.rm = TRUE), .groups = 'drop') %>%
top_n(10, total_issuance_overall) %>%
pull(principal_currency)
# Filter the main dataset to include only top currencies
currency_trends_filtered <- currency_trends %>%
filter(principal_currency %in% top_currencies)
# Visualization
currency_trends_filtered %>%
ggplot(aes(x = year, y = total_issuance, color = principal_currency, group = principal_currency)) +
geom_line() + # Assuming you want a line plot; use geom_col() for bars
theme_minimal() +
labs(title = "Green Bond Issuance by Currency Over Time",
x = "Year",
y = "Total Issuance Amount",
color = "Currency")`geom_line()`: Each group consists of only one observation.
ℹ Do you need to adjust the group aesthetic?