Installing package into 'C:/Users/ashwi/AppData/Local/R/win-library/4.3'
(as 'lib' is unspecified)
package 'janitor' successfully unpacked and MD5 sums checked
The downloaded binary packages are in
C:\Users\ashwi\AppData\Local\Temp\Rtmp2ThF7O\downloaded_packages
── 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) # for clean_names() - makes variable names snake_case
Attaching package: 'janitor'
The following objects are masked from 'package:stats':
chisq.test, fisher.test
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>, …
# we want to compare these two indicatorsindicators_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 withclean_names() |># filter for the vector of indicators we defined abovefilter(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>
# A tibble: 3,103 × 5
country iso3 indicator name value
<chr> <chr> <chr> <chr> <dbl>
1 Argentina ARG Green Bond Issuances by Country f1985 NA
2 Argentina ARG Green Bond Issuances by Country f1986 NA
3 Argentina ARG Green Bond Issuances by Country f1987 NA
4 Argentina ARG Green Bond Issuances by Country f1990 NA
5 Argentina ARG Green Bond Issuances by Country f1991 NA
6 Argentina ARG Green Bond Issuances by Country f1992 NA
7 Argentina ARG Green Bond Issuances by Country f1993 NA
8 Argentina ARG Green Bond Issuances by Country f1994 NA
9 Argentina ARG Green Bond Issuances by Country f1999 NA
10 Argentina ARG Green Bond Issuances by Country f2000 NA
# ℹ 3,093 more rows
green_bonds_tidy <- green_debt_subset |>pivot_longer(# select all coluns with f + 4 numbers cols =matches("f\\d{4}"),# change from default ("names")names_to ="year",# same with the valuesvalues_to ="issuance_bn_usd",# readr::parse_number is a handy function that changes the character string # "f2222" into the number 2222. Very useful! names_transform = readr::parse_number,# green bonds are new-ish, we can drop all those NA values in the 80s and 90s for now.values_drop_na =TRUE )green_bonds_tidy
# A tibble: 465 × 5
country iso3 indicator year issuance_bn_usd
<chr> <chr> <chr> <dbl> <dbl>
1 Argentina ARG Green Bond Issuances by Country 2017 0.974
2 Argentina ARG Green Bond Issuances by Country 2020 0.0500
3 Argentina ARG Green Bond Issuances by Country 2021 0.916
4 Argentina ARG Green Bond Issuances by Country 2022 0.207
5 Australia AUS Green Bond Issuances by Country 2014 0.526
6 Australia AUS Green Bond Issuances by Country 2015 0.413
7 Australia AUS Green Bond Issuances by Country 2016 0.531
8 Australia AUS Green Bond Issuances by Country 2017 2.53
9 Australia AUS Green Bond Issuances by Country 2018 2.22
10 Australia AUS Green Bond Issuances by Country 2019 1.98
# ℹ 455 more rows
green_bonds_tidy_cumulative <- green_bonds_tidy |># we don't need that here. get rid of clutter.select(-iso3) |># when calculating cumulative totals, make sure the years are in order firstarrange(country, year) |>group_by(country, indicator) |>mutate(cumulative_bn_usd =cumsum(issuance_bn_usd)) |># when in doubt, always ungroup after group_by() functions. Will stop weird behavior.ungroup()green_bonds_tidy_cumulative
# A tibble: 465 × 5
country indicator year issuance_bn_usd cumulative_bn_usd
<chr> <chr> <dbl> <dbl> <dbl>
1 Argentina Green Bond Issuances by Co… 2017 0.974 0.974
2 Argentina Green Bond Issuances by Co… 2020 0.0500 1.02
3 Argentina Green Bond Issuances by Co… 2021 0.916 1.94
4 Argentina Green Bond Issuances by Co… 2022 0.207 2.15
5 Australia Green Bond Issuances by Co… 2014 0.526 0.526
6 Australia Green Bond Issuances by Co… 2015 0.413 0.938
7 Australia Green Bond Issuances by Co… 2016 0.531 1.47
8 Australia Green Bond Issuances by Co… 2017 2.53 4.00
9 Australia Green Bond Issuances by Co… 2018 2.22 6.22
10 Australia Green Bond Issuances by Co… 2019 1.98 8.21
# ℹ 455 more rows
biggest_green_bond_issuers <- green_bonds_tidy_cumulative |>filter(indicator =="Green Bond Issuances by Country") |>group_by(country) |>slice_max(order_by = year) |>arrange(cumulative_bn_usd|>desc()) |>select(country, cumulative_bn_usd) |>ungroup()biggest_green_bond_issuers
# A tibble: 79 × 2
country cumulative_bn_usd
<chr> <dbl>
1 China, P.R.: Mainland 325.
2 Germany 253.
3 France 213.
4 United States 172.
5 Netherlands, The 155.
6 United Kingdom 84.0
7 Sweden 76.6
8 Spain 65.7
9 Japan 61.8
10 Italy 59.9
# ℹ 69 more rows
top_10_chart <- biggest_green_bond_issuers |># take the top 10, ordered by cumulative issuanceslice_max(order_by = cumulative_bn_usd, n =10) |>ggplot(aes(x = cumulative_bn_usd, # order countries by cumulative issuancey =fct_reorder(.f = country, .x = cumulative_bn_usd) )) +geom_col(fill ="forestgreen") top_10_chart
top_10_chart +theme_minimal() +scale_x_continuous(labels = scales::label_dollar(suffix =" bn"),expand =c(0,0)) +labs(title ="Top 10 Green Bond Issuers",subtitle ="The Dutch sure do issue a whole lot of green bonds per capita",x ="Cumulative Issuance (USD)",y ="",caption ="Data: IMF Climate Change Dashboard | Insight: Me!")
# A tibble: 420 × 5
country iso3 year Green Bond Issuances by Countr…¹ Sovereign Green Bond…²
<chr> <chr> <dbl> <dbl> <dbl>
1 Argentina ARG 2017 0.974 NA
2 Argentina ARG 2020 0.0500 NA
3 Argentina ARG 2021 0.916 NA
4 Argentina ARG 2022 0.207 NA
5 Australia AUS 2014 0.526 NA
6 Australia AUS 2015 0.413 NA
7 Australia AUS 2016 0.531 NA
8 Australia AUS 2017 2.53 NA
9 Australia AUS 2018 2.22 NA
10 Australia AUS 2019 1.98 NA
# ℹ 410 more rows
# ℹ abbreviated names: ¹`Green Bond Issuances by Country`,
# ²`Sovereign Green Bond Issuances`
# A tibble: 1,260 × 4
country year name value
<chr> <dbl> <chr> <dbl>
1 Argentina 2017 green_bond_issuances_by_country 0.974
2 Argentina 2017 sovereign_green_bond_issuances 0
3 Argentina 2017 corporate_green_bond_issuances 0.974
4 Argentina 2020 green_bond_issuances_by_country 0.0500
5 Argentina 2020 sovereign_green_bond_issuances 0
6 Argentina 2020 corporate_green_bond_issuances 0.0500
7 Argentina 2021 green_bond_issuances_by_country 0.916
8 Argentina 2021 sovereign_green_bond_issuances 0
9 Argentina 2021 corporate_green_bond_issuances 0.916
10 Argentina 2022 green_bond_issuances_by_country 0.207
# ℹ 1,250 more rows
snake_case_to_title_case <-function(input_string) { input_string |># Replace underscores with spacesstr_replace_all(pattern ="_", replacement =" ") |># change capitalization to Title Casestr_to_title()}corporate_vs_sovereign_green_bonds_long <- corporate_vs_sovereign_green_bonds |>pivot_longer(cols =contains("green_bond"),names_to ="indicator",values_to ="issuance_bn_usd",names_transform = snake_case_to_title_case) corporate_vs_sovereign_green_bonds_long
# A tibble: 1,260 × 4
country year indicator issuance_bn_usd
<chr> <dbl> <chr> <dbl>
1 Argentina 2017 Green Bond Issuances By Country 0.974
2 Argentina 2017 Sovereign Green Bond Issuances 0
3 Argentina 2017 Corporate Green Bond Issuances 0.974
4 Argentina 2020 Green Bond Issuances By Country 0.0500
5 Argentina 2020 Sovereign Green Bond Issuances 0
6 Argentina 2020 Corporate Green Bond Issuances 0.0500
7 Argentina 2021 Green Bond Issuances By Country 0.916
8 Argentina 2021 Sovereign Green Bond Issuances 0
9 Argentina 2021 Corporate Green Bond Issuances 0.916
10 Argentina 2022 Green Bond Issuances By Country 0.207
# ℹ 1,250 more rows
# A tibble: 48 × 4
year indicator germany china_p_r_mainland
<dbl> <chr> <dbl> <dbl>
1 1991 Green Bond Issuances By Country 0.0292 0
2 1991 Sovereign Green Bond Issuances 0 0
3 1991 Corporate Green Bond Issuances 0.0292 0
4 1992 Green Bond Issuances By Country 0.0350 0
5 1992 Sovereign Green Bond Issuances 0 0
6 1992 Corporate Green Bond Issuances 0.0350 0
7 1993 Green Bond Issuances By Country 0.0175 0
8 1993 Sovereign Green Bond Issuances 0 0
9 1993 Corporate Green Bond Issuances 0.0175 0
10 2000 Green Bond Issuances By Country 0.0272 0
# ℹ 38 more rows
china_vs_germany_green_bonds_repeatable_factoids <- china_vs_germany_green_bonds |>mutate(# multiples:# "Germany issued x times more than China"germany_x_than_china = germany/china_p_r_mainland,# "China issued x times more than Germany"china_x_than_germany = china_p_r_mainland/germany,# percent more:# "Germany issued x% more than China"germany_pct_more_than_china = (germany/china_p_r_mainland-1) *100,# "China issued x% more than Germany"china_pct_more_than_germany = (china_p_r_mainland/germany-1) *100# could do absolute difference, etc.... anything that makes intuitive sense. )china_vs_germany_green_bonds_repeatable_factoids
# A tibble: 48 × 8
year indicator germany china_p_r_mainland germany_x_than_china
<dbl> <chr> <dbl> <dbl> <dbl>
1 1991 Green Bond Issuances B… 0.0292 0 Inf
2 1991 Sovereign Green Bond I… 0 0 NaN
3 1991 Corporate Green Bond I… 0.0292 0 Inf
4 1992 Green Bond Issuances B… 0.0350 0 Inf
5 1992 Sovereign Green Bond I… 0 0 NaN
6 1992 Corporate Green Bond I… 0.0350 0 Inf
7 1993 Green Bond Issuances B… 0.0175 0 Inf
8 1993 Sovereign Green Bond I… 0 0 NaN
9 1993 Corporate Green Bond I… 0.0175 0 Inf
10 2000 Green Bond Issuances B… 0.0272 0 Inf
# ℹ 38 more rows
# ℹ 3 more variables: china_x_than_germany <dbl>,
# germany_pct_more_than_china <dbl>, china_pct_more_than_germany <dbl>
factoid_2014 <- china_vs_germany_green_bonds_repeatable_factoids |>filter(year ==2014) |>filter(indicator =="Green Bond Issuances By Country") |>pull(germany_x_than_china) |>round()factoid_2014
[1] 25
factoid_2022 <- china_vs_germany_green_bonds_repeatable_factoids |>filter(year ==2022) |>filter(indicator =="Green Bond Issuances By Country") |>pull(china_pct_more_than_germany) |>round()factoid_2022
[1] 19
Homework Problem 1
#installing packages and creating region columninstall.packages("countrycode")
Installing package into 'C:/Users/ashwi/AppData/Local/R/win-library/4.3'
(as 'lib' is unspecified)
package 'countrycode' successfully unpacked and MD5 sums checked
The downloaded binary packages are in
C:\Users\ashwi\AppData\Local\Temp\Rtmp2ThF7O\downloaded_packages
library(tidyr)green_bonds_tidy_region <- green_debt_subset |>pivot_longer(# select all coluns with f + 4 numbers cols =matches("f\\d{4}"),# change from default ("names")names_to ="year",# same with the valuesvalues_to ="issuance_bn_usd",# readr::parse_number is a handy function that changes the character string # "f2222" into the number 2222. Very useful! names_transform = readr::parse_number,# green bonds are new-ish, we can drop all those NA values in the 80s and 90s for now.values_drop_na =TRUE )green_bonds_tidy_region
# A tibble: 465 × 6
country iso3 indicator region year issuance_bn_usd
<chr> <chr> <chr> <chr> <dbl> <dbl>
1 Argentina ARG Green Bond Issuances by Country Latin … 2017 0.974
2 Argentina ARG Green Bond Issuances by Country Latin … 2020 0.0500
3 Argentina ARG Green Bond Issuances by Country Latin … 2021 0.916
4 Argentina ARG Green Bond Issuances by Country Latin … 2022 0.207
5 Australia AUS Green Bond Issuances by Country East A… 2014 0.526
6 Australia AUS Green Bond Issuances by Country East A… 2015 0.413
7 Australia AUS Green Bond Issuances by Country East A… 2016 0.531
8 Australia AUS Green Bond Issuances by Country East A… 2017 2.53
9 Australia AUS Green Bond Issuances by Country East A… 2018 2.22
10 Australia AUS Green Bond Issuances by Country East A… 2019 1.98
# ℹ 455 more rows
# 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
library(ggplot2)#Making the plotcumulative_issuance_by_region <- cumulative_issuance_by_region %>%arrange(cumulative_issuance)# Create a bar plot with dark blue fill and reordered regionsggplot(cumulative_issuance_by_region, aes(x = cumulative_issuance, y =reorder(region, cumulative_issuance))) +geom_bar(stat ="identity", fill ="darkblue") +labs(title ="Cumulative Issuance of Green Bonds by Region",x ="Cumulative Issuance (in USD bn)",y ="Region",caption ="Data: IMF Climate Change Dashboard") +theme_minimal() +scale_x_continuous(labels = scales::label_dollar(suffix =" bn"),expand =c(0, 0))
Homework Problem 2
install.packages("janitor")
Warning: package 'janitor' is in use and will not be installed
library(janitor)problem2 <- green_debt |>clean_names() |>filter(type_of_issuer !="Not Applicable") |>pivot_longer(# select all coluns with f + 4 numbers cols =matches("f\\d{4}"),# change from default ("names")names_to ="year",# same with the valuesvalues_to ="issuance_bn_usd",# readr::parse_number is a handy function that changes the character string # "f2222" into the number 2222. Very useful! names_transform = readr::parse_number,# green bonds are new-ish, we can drop all those NA values in the 80s and 90s for now.values_drop_na =TRUE )
##Plot for homework 2library(ggplot2)custom_colors <-c("#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#FFFF33", "#A65628", "#F781BF")ggplot(problem2, aes(x = year, y = issuance_bn_usd, color = type_of_issuer)) +geom_line() +scale_color_manual(values = custom_colors) +labs(title ="Green Bond Issuance by Type of Issuer",x ="Year",y ="Issuance (in USD bn)",color ="Type of Issuer") +theme_minimal()
[1] "Access to Essential Services"
[2] "Acquiring and distribution of vaccine"
[3] "Acquisition"
[4] "Affordable Basic Infrastructure"
[5] "Agriculture"
[6] "Alternative Energy"
[7] "Aquatic Biodiversity Conservation"
[8] "Capital expenditure"
[9] "Capital expenditure/Financing expenses"
[10] "Carbon reduction through reforestation and avoided deforestation"
[11] "China Urban Construction"
[12] "Circular Economy Adapted/Eco-efficient Products, Production Technologies/Processes"
[13] "Clean Transport"
[14] "Climate Change Adaptation"
[15] "E-education programs - Education Projects"
[16] "Economic Development"
[17] "Electric & Public Power"
[18] "Eligible Green Projects"
[19] "Employee stock ownership plan"
[20] "Energy Efficiency"
[21] "Environmental Protection Projects"
[22] "Environmentally Sustainable Products"
[23] "Equipment Upgrade/Construction"
[24] "Financing of Subordinated Loan"
[25] "Funding new technologies to reduce GHS emissions"
[26] "Gas"
[27] "General Purpose"
[28] "General Purpose/Acquisition"
[29] "General Purpose/Refinance"
[30] "General Purpose/Working Capital"
[31] "Green Construction/Buildings"
[32] "Higher Education"
[33] "Industrial Development"
[34] "Infrastructure"
[35] "Land Preservation"
[36] "Merger or Acquisition"
[37] "Other"
[38] "Other Education"
[39] "Other Housing"
[40] "Other Public Service"
[41] "Other Transportation"
[42] "Pandemic"
[43] "Pollution Control"
[44] "Pollution Prevention & Control"
[45] "Production/Supply of Cannabis"
[46] "Project Finance"
[47] "Property Expendit (acquisit/development)"
[48] "Redeem Existing Bonds or Securities"
[49] "Refinance/Financing expenses"
[50] "Renewable Energy Projects"
[51] "Repay Bank Loan or Bridge Financing"
[52] "Repay Intercompany Debt"
[53] "Ship finance"
[54] "Social Housing/Affordable Housing"
[55] "Socioeconomic Advancement And Empowerment"
[56] "Solar projects"
[57] "Sustainable Development Projects"
[58] "Sustainable Economic Growth"
[59] "Sustainable Forestry"
[60] "Sustainable Management of Land Use"
[61] "Sustainable Management of Living Natural Resources"
[62] "Sustainable Transport"
[63] "Sustainable Water or Wastewater management"
[64] "Terrestrial Biodiversity Conservation"
[65] "The Belt and Road Initiative"
[66] "Waste Management"
[67] "Water & Sewer"
[68] "Wind projects"
[69] "Working capital"
Homework Problem 3 Part 2
library(dplyr)library(tidyr)problem3b <- green_debt |>clean_names() |>filter(principal_currency !="Not Applicable") |>pivot_longer(# select all coluns with f + 4 numbers cols =matches("f\\d{4}"),# change from default ("names")names_to ="year",# same with the valuesvalues_to ="issuance_bn_usd",names_transform = readr::parse_number,values_drop_na =TRUE )
library(ggplot2)ggplot(problem3b, aes(x = year, y = principal_currency)) +geom_point() +labs(x ="Year", y ="Principal Currency") +ggtitle("Scatter Plot of Year vs. Principal Currency")
We only have data on cumulative issuance as of 2022. We cannot say anything about the time trend with rest to primary currency.
