Finding and Cleaning Data

Author

Okung Obang

Finding and Cleaning Data

PCAF Methodology - Sovereign Debt

Find data points needed to calculate metrics of sovereign debt.

Sovereign debt includes sovereign bonds and sovereign loans of all maturities issued in domestic or foreign currencies. (PCAF Methodology 134)
PCAF has built a scope definition for sovereign debt based on three levels.
- Scope 1: Domestic GHG emissions in the country
- Scope 2: GHG emissions as a result of domestic usage of electricity, heat, or cooling. that is imported from another country.
- Scope 3: Emissions that are attributed to non-energy imports from activities within the country.
Consumption Emissions = Production Emissions - Exported Emissions + Imported emissions

Consumption Emissions = Scope 1 + 2 +3 - Exported Emissions

Code

# Install necessary packages
install.packages('rnaturalearth', repos = "http://cran.us.r-project.org")

Installing package into 'C:/Users/okung/AppData/Local/R/win-library/4.2'
(as 'lib' is unspecified)

package 'rnaturalearth' successfully unpacked and MD5 sums checked

The downloaded binary packages are in
    C:\Users\okung\AppData\Local\Temp\RtmpclAXw5\downloaded_packages

Code

install.packages('wbstats', repos = "http://cran.us.r-project.org")

Installing package into 'C:/Users/okung/AppData/Local/R/win-library/4.2'
(as 'lib' is unspecified)

package 'wbstats' successfully unpacked and MD5 sums checked

The downloaded binary packages are in
    C:\Users\okung\AppData\Local\Temp\RtmpclAXw5\downloaded_packages

Code

install.packages('here', repos = "http://cran.us.r-project.org")

Installing package into 'C:/Users/okung/AppData/Local/R/win-library/4.2'
(as 'lib' is unspecified)

package 'here' successfully unpacked and MD5 sums checked

The downloaded binary packages are in
    C:\Users\okung\AppData\Local\Temp\RtmpclAXw5\downloaded_packages

Code

install.packages('readxl1', repos = "http://cran.us.r-project.org")

Installing package into 'C:/Users/okung/AppData/Local/R/win-library/4.2'
(as 'lib' is unspecified)

Warning: package 'readxl1' is not available for this version of R

A version of this package for your version of R might be available elsewhere,
see the ideas at
https://cran.r-project.org/doc/manuals/r-patched/R-admin.html#Installing-packages

Code

# Load necessary library
library(tidyverse)

Warning: package 'tidyverse' was built under R version 4.2.2

── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
✔ ggplot2 3.4.0     ✔ purrr   1.0.1
✔ tibble  3.1.8     ✔ dplyr   1.1.0
✔ tidyr   1.3.0     ✔ stringr 1.5.0
✔ readr   2.1.2     ✔ forcats 0.5.2

Warning: package 'ggplot2' was built under R version 4.2.2

Warning: package 'tidyr' was built under R version 4.2.2

Warning: package 'purrr' was built under R version 4.2.2

Warning: package 'dplyr' was built under R version 4.2.2

Warning: package 'stringr' was built under R version 4.2.2

── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()

Code

library(janitor)

Warning: package 'janitor' was built under R version 4.2.2


Attaching package: 'janitor'

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

    chisq.test, fisher.test

Code

library(here)

Warning: package 'here' was built under R version 4.2.3

here() starts at C:/Users/okung/OneDrive/Johns Hopkins - SAIS/2022-2023/SP23/Sustainable Finance/Week 7

Code

library(readxl)

Warning: package 'readxl' was built under R version 4.2.2

Code

library(rnaturalearth)

Warning: package 'rnaturalearth' was built under R version 4.2.3

Code

library(countrycode)

Warning: package 'countrycode' was built under R version 4.2.2

Code

library(wbstats)

Warning: package 'wbstats' was built under R version 4.2.3

Code

# Make function to standardize country code for each data set

# Country Name to Iso3c
country_name_regex_to_iso3c <- function(country_name){
  country_name |>
    countrycode(origin = "country.name",
                destination = "iso3c",
                origin_regex = TRUE)
}

# Iso3c to Country Name
iso3c_to_country_name <- function(iso3c){
  iso3c |>
    countrycode(origin = "iso3c",
                destination = "country.name")
}

"USA" |>
  iso3c_to_country_name()

[1] "United States"

Code

# Create function for numeric values

char_num_to_numeric <- function(char_num) {
   char_num |>
    stringr::str_remove_all(pattern = ",") |>
    stringr::str_trim() |>
    as.numeric()
}

Code

# For SCOPE 1 Absolute Emissions -- need UNFCC, Climate Watch, EDGAR, and OECD imported data

# Import UNFCC Annex EIT and non-EIT CO2 emisisons

excel_sheets("C:/Users/okung/OneDrive/Johns Hopkins - SAIS/2022-2023/SP23/Sustainable Finance/Week 7/UNFCC - Annex 1 - EIT and non-EIT countries (Scope 1).xlsx")

[1] "Data_by_sector" "Data_by_gas"    "Summary Data"   "ChartData"

Code

read_excel("C:/Users/okung/OneDrive/Johns Hopkins - SAIS/2022-2023/SP23/Sustainable Finance/Week 7/UNFCC - Annex 1 - EIT and non-EIT countries (Scope 1).xlsx")

# A tibble: 109 × 34
   GHG emissio…¹ Base …² `1990` `1991` `1992` `1993` `1994` `1995` `1996` `1997`
   <chr>         <chr>   <chr>  <chr>  <chr>  <chr>  <chr>  <chr>  <chr>  <chr> 
 1 Summary Total <NA>    <NA>   <NA>   <NA>   <NA>   <NA>   <NA>   <NA>   <NA>  
 2 CO2 emission… 152614… 15101… 14795… 14305… 14135… 13951… 14042… 14324… 14223…
 3 CO2 net emis… -13032… -1320… -1442… -1466… -1458… -1582… -1652… -1772… -1829…
 4 CO2 net emis… 139582… 13781… 13352… 12838… 12676… 12369… 12389… 12552… 12393…
 5 GHG emission… 192254… 19032… 18630… 18061… 17843… 17594… 17686… 17972… 17834…
 6 GHG net emis… -11798… -1197… -1318… -1344… -1338… -1457… -1531… -1646… -1709…
 7 GHG net emis… 180455… 17834… 17312… 16716… 16505… 16136… 16154… 16326… 16124…
 8 Indirect CO2  11104.… 11104… 10627… 10359… 10162… 9861.… 9776.… 9659.… 9238.…
 9 <NA>          <NA>    <NA>   <NA>   <NA>   <NA>   <NA>   <NA>   <NA>   <NA>  
10 Breakdown by… <NA>    <NA>   <NA>   <NA>   <NA>   <NA>   <NA>   <NA>   <NA>  
# … with 99 more rows, 24 more variables: `1998` <chr>, `1999` <chr>,
#   `2000` <chr>, `2001` <chr>, `2002` <chr>, `2003` <chr>, `2004` <chr>,
#   `2005` <chr>, `2006` <chr>, `2007` <chr>, `2008` <chr>, `2009` <chr>,
#   `2010` <chr>, `2011` <chr>, `2012` <chr>, `2013` <chr>, `2014` <chr>,
#   `2015` <chr>, `2016` <chr>, `2017` <chr>, `2018` <chr>, `2019` <chr>,
#   `2020` <chr>, `Change from BY (Convention) to 2020` <chr>, and abbreviated
#   variable names ¹`GHG emissions, kt CO2 equivalent`, ²`Base year`

Code

  # Import specific sheets (sector and gas) in R

    sector_unfcc <- read_excel("C:/Users/okung/OneDrive/Johns Hopkins - SAIS/2022-2023/SP23/Sustainable Finance/Week 7/UNFCC - Annex 1 - EIT and non-EIT countries (Scope 1).xlsx",
                           sheet = "Data_by_sector")

    gas_unfcc <- read_excel("C:/Users/okung/OneDrive/Johns Hopkins - SAIS/2022-2023/SP23/Sustainable Finance/Week 7/UNFCC - Annex 1 - EIT and non-EIT countries (Scope 1).xlsx",
                        sheet = "Data_by_gas")

New names:
• `` -> `...34`

Code

  # Take a look at the individual sheets
head(sector_unfcc)

# A tibble: 6 × 34
  GHG e…¹ Base …² `1990` `1991` `1992` `1993` `1994` `1995` `1996` `1997` `1998`
  <chr>   <chr>   <chr>  <chr>  <chr>  <chr>  <chr>  <chr>  <chr>  <chr>  <chr> 
1 Summar… <NA>    <NA>   <NA>   <NA>   <NA>   <NA>   <NA>   <NA>   <NA>   <NA>  
2 CO2 em… 152614… 15101… 14795… 14305… 14135… 13951… 14042… 14324… 14223… 14217…
3 CO2 ne… -13032… -1320… -1442… -1466… -1458… -1582… -1652… -1772… -1829… -1944…
4 CO2 ne… 139582… 13781… 13352… 12838… 12676… 12369… 12389… 12552… 12393… 12272…
5 GHG em… 192254… 19032… 18630… 18061… 17843… 17594… 17686… 17972… 17834… 17766…
6 GHG ne… -11798… -1197… -1318… -1344… -1338… -1457… -1531… -1646… -1709… -1816…
# … with 23 more variables: `1999` <chr>, `2000` <chr>, `2001` <chr>,
#   `2002` <chr>, `2003` <chr>, `2004` <chr>, `2005` <chr>, `2006` <chr>,
#   `2007` <chr>, `2008` <chr>, `2009` <chr>, `2010` <chr>, `2011` <chr>,
#   `2012` <chr>, `2013` <chr>, `2014` <chr>, `2015` <chr>, `2016` <chr>,
#   `2017` <chr>, `2018` <chr>, `2019` <chr>, `2020` <chr>,
#   `Change from BY (Convention) to 2020` <chr>, and abbreviated variable names
#   ¹`GHG emissions, kt CO2 equivalent`, ²`Base year`

Code

head(gas_unfcc)

# A tibble: 6 × 34
  GHG e…¹ Base …² `1990` `1991` `1992` `1993` `1994` `1995` `1996` `1997` `1998`
  <chr>     <dbl> <chr>  <chr>  <chr>  <chr>  <chr>  <chr>  <chr>  <chr>  <chr> 
1 GHG em… NA      <NA>   <NA>   <NA>   <NA>   <NA>   <NA>   <NA>   <NA>   <NA>  
2 CO₂      1.53e7 15101… 14795… 14305… 14135… 13951… 14042… 14324… 14223… 14217…
3 CH₄      2.50e6 24812… 24382… 23964… 23493… 23051… 22711… 22485… 22155… 21706…
4 N₂O      1.17e6 11633… 11192… 10892… 10954… 10650… 10674… 10820… 10631… 10301…
5 Aggreg…  2.88e5 28584… 27811… 27053… 26357… 27197… 30510… 31808… 33219… 34803…
6 HFCs     1.30e5 12969… 12486… 12722… 12034… 13091… 15838… 17194… 19429… 21930…
# … with 23 more variables: `1999` <chr>, `2000` <chr>, `2001` <chr>,
#   `2002` <chr>, `2003` <chr>, `2004` <chr>, `2005` <chr>, `2006` <chr>,
#   `2007` <chr>, `2008` <chr>, `2009` <chr>, `2010` <chr>, `2011` <chr>,
#   `2012` <chr>, `2013` <chr>, `2014` <chr>, `2015` <chr>, `2016` <chr>,
#   `2017` <chr>, `2018` <chr>, `2019` <chr>, `2020` <chr>, ...34 <chr>, and
#   abbreviated variable names ¹`GHG emissions, kt CO2 equivalent`,
#   ²`Base year`

Code

# Glimpse at sector data

glimpse(sector_unfcc)

Rows: 109
Columns: 34
$ `GHG emissions, kt CO2 equivalent`    <chr> "Summary Total", "CO2 emissions …
$ `Base year`                           <chr> NA, "15261482.375342378", "-1303…
$ `1990`                                <chr> NA, "15101954.987090057", "-1320…
$ `1991`                                <chr> NA, "14795294.073265605", "-1442…
$ `1992`                                <chr> NA, "14305035.591432285", "-1466…
$ `1993`                                <chr> NA, "14135343.415722789", "-1458…
$ `1994`                                <chr> NA, "13951854.921257397", "-1582…
$ `1995`                                <chr> NA, "14042451.287054965", "-1652…
$ `1996`                                <chr> NA, "14324189.748468436", "-1772…
$ `1997`                                <chr> NA, "14223103.958734708", "-1829…
$ `1998`                                <chr> NA, "14217470.517219175", "-1944…
$ `1999`                                <chr> NA, "14283660.287627032", "-1962…
$ `2000`                                <chr> NA, "14551670.277607076", "-1918…
$ `2001`                                <chr> NA, "14541760.660387155", "-2008…
$ `2002`                                <chr> NA, "14593764.702863885", "-1959…
$ `2003`                                <chr> NA, "14847045.615214812", "-1986…
$ `2004`                                <chr> NA, "14991259.602795839", "-1910…
$ `2005`                                <chr> NA, "15036928.469028927", "-1922…
$ `2006`                                <chr> NA, "15039808.343995195", "-1985…
$ `2007`                                <chr> NA, "15176046.80006142", "-19086…
$ `2008`                                <chr> NA, "14797166.164090879", "-1989…
$ `2009`                                <chr> NA, "13758957.0119556", "-205739…
$ `2010`                                <chr> NA, "14248114.387404246", "-2116…
$ `2011`                                <chr> NA, "14116700.97951787", "-20985…
$ `2012`                                <chr> NA, "13933928.087081773", "-2155…
$ `2013`                                <chr> NA, "13906818.337857056", "-2050…
$ `2014`                                <chr> NA, "13694012.611343743", "-2094…
$ `2015`                                <chr> NA, "13529996.932762189", "-1936…
$ `2016`                                <chr> NA, "13389312.254193606", "-2089…
$ `2017`                                <chr> NA, "13403278.812732266", "-2022…
$ `2018`                                <chr> NA, "13506467.408464242", "-1975…
$ `2019`                                <chr> NA, "13155326.330164419", "-1874…
$ `2020`                                <chr> NA, "12075214.491071226", "-1922…
$ `Change from BY (Convention) to 2020` <chr> NA, "-0.20877840080719362", "0.4…

Code

# Clean the sector data by dropping 'Change from BY'
 sector <- subset(sector_unfcc, select = -c(`Change from BY (Convention) to 2020`,
                                            `Base year`))

# Pivot data 
sector <- sector |>
   pivot_longer(cols = matches("\\d{4}"),
                names_to = "year",
                values_to = "GHG emissions",
               values_drop_na = TRUE)
sector

# A tibble: 3,193 × 3
   `GHG emissions, kt CO2 equivalent` year  `GHG emissions`   
   <chr>                              <chr> <chr>             
 1 CO2 emissions without LULUCF       1990  15101954.987090057
 2 CO2 emissions without LULUCF       1991  14795294.073265605
 3 CO2 emissions without LULUCF       1992  14305035.591432285
 4 CO2 emissions without LULUCF       1993  14135343.415722789
 5 CO2 emissions without LULUCF       1994  13951854.921257397
 6 CO2 emissions without LULUCF       1995  14042451.287054965
 7 CO2 emissions without LULUCF       1996  14324189.748468436
 8 CO2 emissions without LULUCF       1997  14223103.958734708
 9 CO2 emissions without LULUCF       1998  14217470.517219175
10 CO2 emissions without LULUCF       1999  14283660.287627032
# … with 3,183 more rows

Code

# Glimpse at the gas data
glimpse(gas_unfcc)

Rows: 51
Columns: 34
$ `GHG emissions, kt CO2 equivalent` <chr> "GHG emissions without LULUCF", "CO…
$ `Base year`                        <dbl> NA, 1.526148e+07, 2.501937e+06, 1.1…
$ `1990`                             <chr> NA, "15101954.987090057", "2481252.…
$ `1991`                             <chr> NA, "14795294.073265605", "2438238.…
$ `1992`                             <chr> NA, "14305035.591432285", "2396469.…
$ `1993`                             <chr> NA, "14135343.415722789", "2349342.…
$ `1994`                             <chr> NA, "13951854.921257397", "2305157.…
$ `1995`                             <chr> NA, "14042451.287054965", "2271193.…
$ `1996`                             <chr> NA, "14324189.748468436", "2248576.…
$ `1997`                             <chr> NA, "14223103.958734708", "2215594.…
$ `1998`                             <chr> NA, "14217470.517219175", "2170641.…
$ `1999`                             <chr> NA, "14283660.287627032", "2144313.…
$ `2000`                             <chr> NA, "14551670.277607076", "2117401.…
$ `2001`                             <chr> NA, "14541760.660387155", "2096380.…
$ `2002`                             <chr> NA, "14593764.702863885", "2062071.…
$ `2003`                             <chr> NA, "14847045.615214812", "2057715.…
$ `2004`                             <chr> NA, "14991259.602795839", "2036966.…
$ `2005`                             <chr> NA, "15036928.469028927", "2025517.…
$ `2006`                             <chr> NA, "15039808.343995195", "2021161.…
$ `2007`                             <chr> NA, "15176046.80006142", "2013123.3…
$ `2008`                             <chr> NA, "14797166.164090879", "1995635.…
$ `2009`                             <chr> NA, "13758957.0119556", "1934594.67…
$ `2010`                             <chr> NA, "14248114.387404246", "1932542.…
$ `2011`                             <chr> NA, "14116700.97951787", "1903789.7…
$ `2012`                             <chr> NA, "13933928.087081773", "1895475.…
$ `2013`                             <chr> NA, "13906818.337857056", "1870819.…
$ `2014`                             <chr> NA, "13694012.611343743", "1849320.…
$ `2015`                             <chr> NA, "13529996.932762189", "1830443.…
$ `2016`                             <chr> NA, "13389312.254193606", "1815920.…
$ `2017`                             <chr> NA, "13403278.812732266", "1827281.…
$ `2018`                             <chr> NA, "13506467.408464242", "1845773.…
$ `2019`                             <chr> NA, "13155326.330164419", "1833584.…
$ `2020`                             <chr> NA, "12075214.491071226", "1783329.…
$ ...34                              <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA,…

Code

# Clean the gas data by dropping '...34' and the base year column
 gas <- subset(gas_unfcc, select = -c(`...34`, `Base year`))

# Pivot data 
gas <- gas |>
   pivot_longer(cols = matches("\\d{4}"),
                names_to = "year",
                values_to = "GHG emissions, kt CO2",
                values_drop_na = TRUE)
gas

# A tibble: 1,395 × 3
   `GHG emissions, kt CO2 equivalent` year  `GHG emissions, kt CO2`
   <chr>                              <chr> <chr>                  
 1 CO₂                                1990  15101954.987090057     
 2 CO₂                                1991  14795294.073265605     
 3 CO₂                                1992  14305035.591432285     
 4 CO₂                                1993  14135343.415722789     
 5 CO₂                                1994  13951854.921257397     
 6 CO₂                                1995  14042451.287054965     
 7 CO₂                                1996  14324189.748468436     
 8 CO₂                                1997  14223103.958734708     
 9 CO₂                                1998  14217470.517219175     
10 CO₂                                1999  14283660.287627032     
# … with 1,385 more rows

Code

# Look at Excel Sheets available
excel_sheets("C:/Users/okung/OneDrive/Johns Hopkins - SAIS/2022-2023/SP23/Sustainable Finance/Week 7/IEA_EDGAR_CO2_1970-2021.xlsx")

[1] "IPCC 2006"                     "IPCC 1996"                    
[3] "TOTALS BY COUNTRY"             "conditions of use how to cite"
[5] "References and data sources"

Code

# Import EDGAR data excel sheets - 2006, 1996, and total

ipcc06 <- read_excel("C:/Users/okung/OneDrive/Johns Hopkins - SAIS/2022-2023/SP23/Sustainable Finance/Week 7/IEA_EDGAR_CO2_1970-2021.xlsx",
                     sheet = "IPCC 2006",
                     skip = 9)
ipcc96 <- read_excel("C:/Users/okung/OneDrive/Johns Hopkins - SAIS/2022-2023/SP23/Sustainable Finance/Week 7/IEA_EDGAR_CO2_1970-2021.xlsx",
                     sheet = "IPCC 1996",
                     skip = 9)
ipcc_total <- read_excel("C:/Users/okung/OneDrive/Johns Hopkins - SAIS/2022-2023/SP23/Sustainable Finance/Week 7/IEA_EDGAR_CO2_1970-2021.xlsx",
                     sheet = "TOTALS BY COUNTRY",
                     skip = 9)

Code

# Glimpse at EDGAR data

# Clean EDGAR data, 2006 as it is the most recent
glimpse(ipcc06)

Rows: 4,773
Columns: 60
$ IPCC_annex                              <chr> "Non-Annex_I", "Non-Annex_I", …
$ C_group_IM24_sh                         <chr> "Rest Central America", "Rest …
$ Countrcode_A3                           <chr> "ABW", "ABW", "ABW", "ABW", "A…
$ Name                                    <chr> "Aruba", "Aruba", "Aruba", "Ar…
$ ipcc_code_2006_for_standard_report      <chr> "1.A.1.a", "1.A.2", "1.A.4", "…
$ ipcc_code_2006_for_standard_report_name <chr> "Main Activity Electricity and…
$ Substance                               <chr> "CO2", "CO2", "CO2", "CO2", "C…
$ fossil_bio                              <chr> "bio", "bio", "bio", "bio", "b…
$ `1970`                                  <dbl> 2.239684e-02, 1.222867e+01, 8.…
$ `1971`                                  <dbl> 2.141198e-02, 1.169094e+01, 8.…
$ `1972`                                  <dbl> 2.145059e-02, 9.504931e+00, 8.…
$ `1973`                                  <dbl> 2.140661e-02, 9.306670e+00, 8.…
$ `1974`                                  <dbl> 2.121367e-02, 9.962400e+00, 8.…
$ `1975`                                  <dbl> 7.538256e-02, 8.591961e+00, 7.…
$ `1976`                                  <dbl> 6.984433e-02, 8.193199e+00, 7.…
$ `1977`                                  <dbl> 8.622520e-02, 7.324682e+00, 7.…
$ `1978`                                  <dbl> 8.435763e-02, 8.102211e+00, 7.…
$ `1979`                                  <dbl> 9.632511e-02, 7.708302e+00, 6.…
$ `1980`                                  <dbl> 1.678404e-02, 7.307864e+00, 7.…
$ `1981`                                  <dbl> 1.767219e-02, 7.513545e+00, 7.…
$ `1982`                                  <dbl> 1.777171e-02, 7.622621e+00, 7.…
$ `1983`                                  <dbl> 1.551308e-02, 6.042972e+00, 7.…
$ `1984`                                  <dbl> 1.493371e-02, 5.600142e+00, 7.…
$ `1985`                                  <dbl> 1.561534e-02, 5.868836e+00, 7.…
$ `1986`                                  <dbl> 1.594304e-02, 6.440987e+00, 8.…
$ `1987`                                  <dbl> NA, 5.668128e+00, 7.379820e-01…
$ `1988`                                  <dbl> NA, 4.863276e+00, 7.108548e-01…
$ `1989`                                  <dbl> NA, 4.616512e+00, 7.428863e-01…
$ `1990`                                  <dbl> 5.387378e-01, 3.314972e+00, 2.…
$ `1991`                                  <dbl> 5.622445e-01, 3.724820e+00, 1.…
$ `1992`                                  <dbl> 5.207171e-01, 4.239844e+00, 1.…
$ `1993`                                  <dbl> 5.173337e-01, 4.171985e+00, 1.…
$ `1994`                                  <dbl> 4.605809e-01, 3.965637e+00, 1.…
$ `1995`                                  <dbl> 4.814207e-01, 4.014347e+00, 1.…
$ `1996`                                  <dbl> 5.284975e-01, 4.272527e+00, 1.…
$ `1997`                                  <dbl> 5.370325e-01, 4.239913e+00, 1.…
$ `1998`                                  <dbl> 7.308508e-01, 3.924123e+00, 1.…
$ `1999`                                  <dbl> 1.507130e+00, 3.741790e+00, 1.…
$ `2000`                                  <dbl> 1.0736144, 4.5010232, 1.394168…
$ `2001`                                  <dbl> 1.096967e+00, 4.598926e+00, 1.…
$ `2002`                                  <dbl> 1.173199e+00, 5.183306e+00, 1.…
$ `2003`                                  <dbl> 1.096257e+00, 4.961172e+00, 1.…
$ `2004`                                  <dbl> 1.309499e+00, 5.466171e+00, 1.…
$ `2005`                                  <dbl> 1.022938e+00, 4.800241e+00, 1.…
$ `2006`                                  <dbl> 1.027601e+00, 4.871320e+00, 1.…
$ `2007`                                  <dbl> 1.055025e+00, 4.677820e+00, 1.…
$ `2008`                                  <dbl> 9.780647e-01, 4.369749e+00, 1.…
$ `2009`                                  <dbl> 0.9727575, 4.3234223, 2.191615…
$ `2010`                                  <dbl> 1.160650e+00, 8.075207e+00, 2.…
$ `2011`                                  <dbl> 1.118420e+00, 1.098245e+01, 5.…
$ `2012`                                  <dbl> 1.337603e+00, 9.856529e+00, 6.…
$ `2013`                                  <dbl> 2.371433e+00, 6.886571e+00, 6.…
$ `2014`                                  <dbl> 2.497066e+00, 6.890052e+00, 6.…
$ `2015`                                  <dbl> 2.970795e+00, 7.378661e+00, 5.…
$ `2016`                                  <dbl> 2.438748e+00, 7.853166e+00, 6.…
$ `2017`                                  <dbl> 2.509098e+00, 9.340506e+00, 6.…
$ `2018`                                  <dbl> 3.096665e+00, 7.365402e+00, 6.…
$ `2019`                                  <dbl> 3.255611e+00, 7.718069e+00, 4.…
$ `2020`                                  <dbl> 3.378899e+00, 8.047569e+00, 7.…
$ `2021`                                  <dbl> 3.378899e+00, 8.047569e+00, 7.…

Code

# Rename columns for tidying
ipcc06 <- ipcc06 |>
  rename("standard_report" = "ipcc_code_2006_for_standard_report",
         "standard_report_name" = "ipcc_code_2006_for_standard_report_name")
ipcc06

# A tibble: 4,773 × 60
   IPCC_…¹ C_gro…² Count…³ Name  stand…⁴ stand…⁵ Subst…⁶ fossi…⁷  `1970`  `1971`
   <chr>   <chr>   <chr>   <chr> <chr>   <chr>   <chr>   <chr>     <dbl>   <dbl>
 1 Non-An… Rest C… ABW     Aruba 1.A.1.a Main A… CO2     bio      0.0224  0.0214
 2 Non-An… Rest C… ABW     Aruba 1.A.2   Manufa… CO2     bio     12.2    11.7   
 3 Non-An… Rest C… ABW     Aruba 1.A.4   Reside… CO2     bio      0.880   0.845 
 4 Non-An… Rest C… ABW     Aruba 1.A.5   Non-Sp… CO2     bio      0.0584  0.0568
 5 Non-An… Rest C… ABW     Aruba 1.B.1   Solid … CO2     bio     NA      NA     
 6 Non-An… Rest C… ABW     Aruba 1.A.1.a Main A… CO2     fossil  34.7    34.7   
 7 Non-An… Rest C… ABW     Aruba 1.A.2   Manufa… CO2     fossil   0.476   0.476 
 8 Non-An… Rest C… ABW     Aruba 1.A.3.a Civil … CO2     fossil   1.57    1.57  
 9 Non-An… Rest C… ABW     Aruba 1.A.3.… Road T… CO2     fossil   2.57    2.57  
10 Non-An… Rest C… ABW     Aruba 1.A.3.d Water-… CO2     fossil  NA      NA     
# … with 4,763 more rows, 50 more variables: `1972` <dbl>, `1973` <dbl>,
#   `1974` <dbl>, `1975` <dbl>, `1976` <dbl>, `1977` <dbl>, `1978` <dbl>,
#   `1979` <dbl>, `1980` <dbl>, `1981` <dbl>, `1982` <dbl>, `1983` <dbl>,
#   `1984` <dbl>, `1985` <dbl>, `1986` <dbl>, `1987` <dbl>, `1988` <dbl>,
#   `1989` <dbl>, `1990` <dbl>, `1991` <dbl>, `1992` <dbl>, `1993` <dbl>,
#   `1994` <dbl>, `1995` <dbl>, `1996` <dbl>, `1997` <dbl>, `1998` <dbl>,
#   `1999` <dbl>, `2000` <dbl>, `2001` <dbl>, `2002` <dbl>, `2003` <dbl>, …

Code

# Pivot data
ipcc06 |>
  pivot_longer(cols = matches("\\d{4}"),
               names_to = "year",
               values_to = "CO2 Emissions") |>
  mutate(year = char_num_to_numeric(year))

# A tibble: 248,196 × 10
   IPCC_an…¹ C_gro…² Count…³ Name  stand…⁴ stand…⁵ Subst…⁶ fossi…⁷  year CO2 E…⁸
   <chr>     <chr>   <chr>   <chr> <chr>   <chr>   <chr>   <chr>   <dbl>   <dbl>
 1 Non-Anne… Rest C… ABW     Aruba 1.A.1.a Main A… CO2     bio      1970  0.0224
 2 Non-Anne… Rest C… ABW     Aruba 1.A.1.a Main A… CO2     bio      1971  0.0214
 3 Non-Anne… Rest C… ABW     Aruba 1.A.1.a Main A… CO2     bio      1972  0.0215
 4 Non-Anne… Rest C… ABW     Aruba 1.A.1.a Main A… CO2     bio      1973  0.0214
 5 Non-Anne… Rest C… ABW     Aruba 1.A.1.a Main A… CO2     bio      1974  0.0212
 6 Non-Anne… Rest C… ABW     Aruba 1.A.1.a Main A… CO2     bio      1975  0.0754
 7 Non-Anne… Rest C… ABW     Aruba 1.A.1.a Main A… CO2     bio      1976  0.0698
 8 Non-Anne… Rest C… ABW     Aruba 1.A.1.a Main A… CO2     bio      1977  0.0862
 9 Non-Anne… Rest C… ABW     Aruba 1.A.1.a Main A… CO2     bio      1978  0.0844
10 Non-Anne… Rest C… ABW     Aruba 1.A.1.a Main A… CO2     bio      1979  0.0963
# … with 248,186 more rows, and abbreviated variable names ¹IPCC_annex,
#   ²C_group_IM24_sh, ³Countrcode_A3, ⁴standard_report, ⁵standard_report_name,
#   ⁶Substance, ⁷fossil_bio, ⁸`CO2 Emissions`

Code

ipcc06

# A tibble: 4,773 × 60
   IPCC_…¹ C_gro…² Count…³ Name  stand…⁴ stand…⁵ Subst…⁶ fossi…⁷  `1970`  `1971`
   <chr>   <chr>   <chr>   <chr> <chr>   <chr>   <chr>   <chr>     <dbl>   <dbl>
 1 Non-An… Rest C… ABW     Aruba 1.A.1.a Main A… CO2     bio      0.0224  0.0214
 2 Non-An… Rest C… ABW     Aruba 1.A.2   Manufa… CO2     bio     12.2    11.7   
 3 Non-An… Rest C… ABW     Aruba 1.A.4   Reside… CO2     bio      0.880   0.845 
 4 Non-An… Rest C… ABW     Aruba 1.A.5   Non-Sp… CO2     bio      0.0584  0.0568
 5 Non-An… Rest C… ABW     Aruba 1.B.1   Solid … CO2     bio     NA      NA     
 6 Non-An… Rest C… ABW     Aruba 1.A.1.a Main A… CO2     fossil  34.7    34.7   
 7 Non-An… Rest C… ABW     Aruba 1.A.2   Manufa… CO2     fossil   0.476   0.476 
 8 Non-An… Rest C… ABW     Aruba 1.A.3.a Civil … CO2     fossil   1.57    1.57  
 9 Non-An… Rest C… ABW     Aruba 1.A.3.… Road T… CO2     fossil   2.57    2.57  
10 Non-An… Rest C… ABW     Aruba 1.A.3.d Water-… CO2     fossil  NA      NA     
# … with 4,763 more rows, 50 more variables: `1972` <dbl>, `1973` <dbl>,
#   `1974` <dbl>, `1975` <dbl>, `1976` <dbl>, `1977` <dbl>, `1978` <dbl>,
#   `1979` <dbl>, `1980` <dbl>, `1981` <dbl>, `1982` <dbl>, `1983` <dbl>,
#   `1984` <dbl>, `1985` <dbl>, `1986` <dbl>, `1987` <dbl>, `1988` <dbl>,
#   `1989` <dbl>, `1990` <dbl>, `1991` <dbl>, `1992` <dbl>, `1993` <dbl>,
#   `1994` <dbl>, `1995` <dbl>, `1996` <dbl>, `1997` <dbl>, `1998` <dbl>,
#   `1999` <dbl>, `2000` <dbl>, `2001` <dbl>, `2002` <dbl>, `2003` <dbl>, …

Code

# Clean EDGAR data total

glimpse(ipcc_total)

Rows: 210
Columns: 57
$ IPCC_annex      <chr> "Non-Annex_I", "Non-Annex_I", "Non-Annex_I", "Non-Anne…
$ C_group_IM24_sh <chr> "Rest Central America", "India +", "Southern_Africa", …
$ Countrcode_A3   <chr> "ABW", "AFG", "AGO", "AIA", "AIR", "ALB", "ANT", "ARE"…
$ Name            <chr> "Aruba", "Afghanistan", "Angola", "Anguilla", "Int. Av…
$ Substance       <chr> "CO2", "CO2", "CO2", "CO2", "CO2", "CO2", "CO2", "CO2"…
$ `1970`          <dbl> 9.506542e+01, 3.343055e+03, 2.039327e+04, 2.168292e+00…
$ `1971`          <dbl> 9.450371e+01, 3.318411e+03, 1.995993e+04, 2.168325e+00…
$ `1972`          <dbl> 1.058478e+02, 3.545865e+03, 2.186085e+04, 2.263753e+00…
$ `1973`          <dbl> 1.098639e+02, 3.592564e+03, 2.296088e+04, 2.108963e+00…
$ `1974`          <dbl> 9.997718e+01, 4.075639e+03, 2.343633e+04, 2.350261e+00…
$ `1975`          <dbl> 114.65054, 3968.13598, 22666.19627, 2.58403, 173866.01…
$ `1976`          <dbl> 1.050986e+02, 3.876509e+03, 1.920130e+04, 2.434477e+00…
$ `1977`          <dbl> 1.134857e+02, 4.152041e+03, 2.402070e+04, 2.537065e+00…
$ `1978`          <dbl> 1.156101e+02, 3.871999e+03, 2.641071e+04, 2.901614e+00…
$ `1979`          <dbl> 1.093610e+02, 3.934219e+03, 2.648703e+04, 3.213011e+00…
$ `1980`          <dbl> 1.102027e+02, 3.832229e+03, 2.686131e+04, 4.412635e+00…
$ `1981`          <dbl> 1.119356e+02, 4.021254e+03, 2.588863e+04, 6.639773e+00…
$ `1982`          <dbl> 1.028311e+02, 4.005966e+03, 2.562081e+04, 7.018853e+00…
$ `1983`          <dbl> 1.049941e+02, 4.637964e+03, 2.629385e+04, 6.088891e+00…
$ `1984`          <dbl> 1.211857e+02, 4.786285e+03, 2.664365e+04, 5.801136e+00…
$ `1985`          <dbl> 1.188711e+02, 5.632903e+03, 2.761624e+04, 3.670826e+00…
$ `1986`          <dbl> 1.175976e+02, 5.392432e+03, 2.809909e+04, 3.154646e+00…
$ `1987`          <dbl> 1.209983e+02, 4.841959e+03, 2.863224e+04, 6.125876e+00…
$ `1988`          <dbl> 4.283110e+02, 4.544738e+03, 2.931078e+04, 5.797213e+00…
$ `1989`          <dbl> 5.225964e+02, 4.318739e+03, 2.970218e+04, 6.148498e+00…
$ `1990`          <dbl> 5.316736e+02, 4.315742e+03, 2.602538e+04, 6.114424e+00…
$ `1991`          <dbl> 626.16991, 4218.64615, 27096.48165, 7.57944, 251900.90…
$ `1992`          <dbl> 7.263464e+02, 3.105437e+03, 2.780115e+04, 7.902936e+00…
$ `1993`          <dbl> 6.976715e+02, 3.246796e+03, 2.812761e+04, 9.952196e+00…
$ `1994`          <dbl> 763.67244, 3269.47069, 27787.67687, 13.13127, 278583.5…
$ `1995`          <dbl> 820.73047, 3483.69143, 29453.90060, 16.39567, 289103.4…
$ `1996`          <dbl> 550.82343, 3716.70615, 32430.32598, 16.21334, 300847.6…
$ `1997`          <dbl> 838.03622, 3910.14035, 33295.92083, 13.56375, 315607.7…
$ `1998`          <dbl> 839.62348, 4036.37567, 34310.55649, 12.44876, 326243.0…
$ `1999`          <dbl> 886.53698, 3884.13027, 35354.81008, 11.75083, 340142.0…
$ `2000`          <dbl> 950.98809, 3504.44967, 34104.66959, 15.53153, 352628.8…
$ `2001`          <dbl> 977.16641, 3456.67087, 34184.39714, 15.91076, 346128.0…
$ `2002`          <dbl> 996.51179, 3967.05003, 34324.97698, 10.85892, 366703.2…
$ `2003`          <dbl> 1288.47109, 4382.53077, 36045.57175, 14.10782, 364670.…
$ `2004`          <dbl> 1338.63889, 4027.98958, 36788.07882, 13.47189, 393904.…
$ `2005`          <dbl> 1377.27413, 5001.56961, 35325.28345, 13.66162, 418838.…
$ `2006`          <dbl> 1339.13200, 4949.22584, 36353.19347, 12.81244, 434148.…
$ `2007`          <dbl> 1369.65669, 5880.81707, 37491.02093, 12.35473, 448063.…
$ `2008`          <dbl> 1288.13964, 7366.30549, 39679.77403, 15.26848, 455899.…
$ `2009`          <dbl> 1387.89906, 10791.18753, 42655.56410, 20.16096, 434205…
$ `2010`          <dbl> 1345.9690, 12061.6376, 44770.4994, 24.0045, 458205.041…
$ `2011`          <dbl> 1286.36928, 15573.22410, 46455.16848, 26.00805, 472112…
$ `2012`          <dbl> 1637.82283, 14462.28881, 45747.27698, 26.02323, 472721…
$ `2013`          <dbl> 1605.68973, 12796.26848, 51282.17427, 27.94051, 480909…
$ `2014`          <dbl> 1633.58442, 12478.10878, 53677.19199, 27.89606, 496627…
$ `2015`          <dbl> 1626.23405, 12551.08767, 56211.79289, 28.00543, 524654…
$ `2016`          <dbl> 1597.39826, 11760.79938, 55507.49308, 28.33988, 548207…
$ `2017`          <dbl> 1335.79288, 12117.69374, 51285.30007, 29.06245, 582230…
$ `2018`          <dbl> 1366.61457, 12736.81183, 49702.30585, 28.22299, 609630…
$ `2019`          <dbl> 1404.70512, 12215.71709, 51343.73554, 27.57999, 617763…
$ `2020`          <dbl> 1166.01841, 12525.01133, 49153.12691, 22.77949, 338144…
$ `2021`          <dbl> 1295.81880, 12553.57637, 50275.12347, 25.40872, 390166…

Code

ipcc_total <- ipcc_total|>
  pivot_longer(cols = matches("\\d{4}"),
               names_to = "year",
               values_to = "CO2 Emissions") |>
  mutate(year = char_num_to_numeric(year)) |>
  rename("Country Name" = "Name")
ipcc_total

# A tibble: 10,920 × 7
   IPCC_annex  C_group_IM24_sh      Countrcode_A3 Countr…¹ Subst…²  year CO2 E…³
   <chr>       <chr>                <chr>         <chr>    <chr>   <dbl>   <dbl>
 1 Non-Annex_I Rest Central America ABW           Aruba    CO2      1970    95.1
 2 Non-Annex_I Rest Central America ABW           Aruba    CO2      1971    94.5
 3 Non-Annex_I Rest Central America ABW           Aruba    CO2      1972   106. 
 4 Non-Annex_I Rest Central America ABW           Aruba    CO2      1973   110. 
 5 Non-Annex_I Rest Central America ABW           Aruba    CO2      1974   100. 
 6 Non-Annex_I Rest Central America ABW           Aruba    CO2      1975   115. 
 7 Non-Annex_I Rest Central America ABW           Aruba    CO2      1976   105. 
 8 Non-Annex_I Rest Central America ABW           Aruba    CO2      1977   113. 
 9 Non-Annex_I Rest Central America ABW           Aruba    CO2      1978   116. 
10 Non-Annex_I Rest Central America ABW           Aruba    CO2      1979   109. 
# … with 10,910 more rows, and abbreviated variable names ¹`Country Name`,
#   ²Substance, ³`CO2 Emissions`

Code

glimpse(ipcc_total)

Rows: 10,920
Columns: 7
$ IPCC_annex      <chr> "Non-Annex_I", "Non-Annex_I", "Non-Annex_I", "Non-Anne…
$ C_group_IM24_sh <chr> "Rest Central America", "Rest Central America", "Rest …
$ Countrcode_A3   <chr> "ABW", "ABW", "ABW", "ABW", "ABW", "ABW", "ABW", "ABW"…
$ `Country Name`  <chr> "Aruba", "Aruba", "Aruba", "Aruba", "Aruba", "Aruba", …
$ Substance       <chr> "CO2", "CO2", "CO2", "CO2", "CO2", "CO2", "CO2", "CO2"…
$ year            <dbl> 1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, …
$ `CO2 Emissions` <dbl> 95.06542, 94.50371, 105.84781, 109.86386, 99.97718, 11…

Code

ipcc_total |>
  group_by(IPCC_annex, year) |>
  mutate(yearly_emissions = sum(`CO2 Emissions`),
         avg_yr_emissions = mean(yearly_emissions))

# A tibble: 10,920 × 9
# Groups:   IPCC_annex, year [208]
   IPCC_annex  C_group_I…¹ Count…² Count…³ Subst…⁴  year CO2 E…⁵ yearl…⁶ avg_y…⁷
   <chr>       <chr>       <chr>   <chr>   <chr>   <dbl>   <dbl>   <dbl>   <dbl>
 1 Non-Annex_I Rest Centr… ABW     Aruba   CO2      1970    95.1  5.93e6  5.93e6
 2 Non-Annex_I Rest Centr… ABW     Aruba   CO2      1971    94.5  5.98e6  5.98e6
 3 Non-Annex_I Rest Centr… ABW     Aruba   CO2      1972   106.   6.22e6  6.22e6
 4 Non-Annex_I Rest Centr… ABW     Aruba   CO2      1973   110.   6.54e6  6.54e6
 5 Non-Annex_I Rest Centr… ABW     Aruba   CO2      1974   100.   6.73e6  6.73e6
 6 Non-Annex_I Rest Centr… ABW     Aruba   CO2      1975   115.   7.02e6  7.02e6
 7 Non-Annex_I Rest Centr… ABW     Aruba   CO2      1976   105.   7.32e6  7.32e6
 8 Non-Annex_I Rest Centr… ABW     Aruba   CO2      1977   113.   7.65e6  7.65e6
 9 Non-Annex_I Rest Centr… ABW     Aruba   CO2      1978   116.   7.99e6  7.99e6
10 Non-Annex_I Rest Centr… ABW     Aruba   CO2      1979   109.   8.25e6  8.25e6
# … with 10,910 more rows, and abbreviated variable names ¹C_group_IM24_sh,
#   ²Countrcode_A3, ³`Country Name`, ⁴Substance, ⁵`CO2 Emissions`,
#   ⁶yearly_emissions, ⁷avg_yr_emissions

Code

ipcc_total

# A tibble: 10,920 × 7
   IPCC_annex  C_group_IM24_sh      Countrcode_A3 Countr…¹ Subst…²  year CO2 E…³
   <chr>       <chr>                <chr>         <chr>    <chr>   <dbl>   <dbl>
 1 Non-Annex_I Rest Central America ABW           Aruba    CO2      1970    95.1
 2 Non-Annex_I Rest Central America ABW           Aruba    CO2      1971    94.5
 3 Non-Annex_I Rest Central America ABW           Aruba    CO2      1972   106. 
 4 Non-Annex_I Rest Central America ABW           Aruba    CO2      1973   110. 
 5 Non-Annex_I Rest Central America ABW           Aruba    CO2      1974   100. 
 6 Non-Annex_I Rest Central America ABW           Aruba    CO2      1975   115. 
 7 Non-Annex_I Rest Central America ABW           Aruba    CO2      1976   105. 
 8 Non-Annex_I Rest Central America ABW           Aruba    CO2      1977   113. 
 9 Non-Annex_I Rest Central America ABW           Aruba    CO2      1978   116. 
10 Non-Annex_I Rest Central America ABW           Aruba    CO2      1979   109. 
# … with 10,910 more rows, and abbreviated variable names ¹`Country Name`,
#   ²Substance, ³`CO2 Emissions`

Code

# Import GHG Emissions Excel Sheet

ghg_emission <- read_excel("C:/Users/okung/OneDrive/Johns Hopkins - SAIS/2022-2023/SP23/Sustainable Finance/Week 7/GHG Emissions - Climate Watch.xlsx")

Warning: Coercing boolean to numeric in C122 / R122C3

Code

ghg_emission

# A tibble: 195 × 31
   Countr…¹ unit  `1990` `1991` `1992` `1993` `1994` `1995` `1996` `1997` `1998`
   <chr>    <chr>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>
 1 China    MtCO…  2892.  3039.  3168.  3398.  3557.  3961.  3982.  3978.  4096.
 2 United … MtCO…  5417.  5372.  5456.  5568.  5662.  5730.  5901   6161.  6209.
 3 India    MtCO…  1003.  1056.  1081.  1114.  1158.  1224.  1273.  1332.  1362.
 4 Indones… MtCO…  1227.  1246.  1267.  1282.  1303.  1339.  1164.  2135.  1367.
 5 Russia   MtCO…  2648.  2585.  2428.  2234.  1996.  1918.  1875.  1723.  1725.
 6 Brazil   MtCO…  1639.  1660.  1670.  1681.  1698.  1725.  1730.  1761.  1797.
 7 Japan    MtCO…  1106.  1122.  1135.  1128.  1186.  1201.  1216.  1204.  1158.
 8 Iran     MtCO…   304.   333.   354.   362.   395.   405.   420.   439.   442.
 9 Canada   MtCO…   607.   603.   617.   613.   631.   646.   668.   680.   696.
10 Saudi A… MtCO…   241.   274.   292.   290.   289.   289.   304.   311.   327.
# … with 185 more rows, 20 more variables: `1999` <dbl>, `2000` <dbl>,
#   `2001` <dbl>, `2002` <dbl>, `2003` <dbl>, `2004` <dbl>, `2005` <dbl>,
#   `2006` <dbl>, `2007` <dbl>, `2008` <dbl>, `2009` <dbl>, `2010` <dbl>,
#   `2011` <dbl>, `2012` <dbl>, `2013` <dbl>, `2014` <dbl>, `2015` <dbl>,
#   `2016` <dbl>, `2017` <dbl>, `2018` <dbl>, and abbreviated variable name
#   ¹`Country/Region`

Code

glimpse(ghg_emission)

Rows: 195
Columns: 31
$ `Country/Region` <chr> "China", "United States", "India", "Indonesia", "Russ…
$ unit             <chr> "MtCO2e", "MtCO2e", "MtCO2e", "MtCO2e", "MtCO2e", "Mt…
$ `1990`           <dbl> 2891.73, 5417.32, 1002.56, 1226.82, 2648.36, 1638.68,…
$ `1991`           <dbl> 3039.14, 5372.07, 1056.25, 1246.27, 2585.28, 1659.83,…
$ `1992`           <dbl> 3168.05, 5456.12, 1081.28, 1266.98, 2428.18, 1669.79,…
$ `1993`           <dbl> 3397.80, 5567.55, 1114.22, 1282.35, 2233.86, 1681.12,…
$ `1994`           <dbl> 3557.37, 5661.57, 1158.48, 1302.70, 1995.87, 1697.94,…
$ `1995`           <dbl> 3960.70, 5729.69, 1223.65, 1339.10, 1918.33, 1724.55,…
$ `1996`           <dbl> 3982.11, 5901.00, 1272.74, 1164.23, 1874.95, 1729.84,…
$ `1997`           <dbl> 3977.65, 6160.86, 1331.88, 2134.80, 1723.24, 1761.29,…
$ `1998`           <dbl> 4095.97, 6208.83, 1362.33, 1366.90, 1725.07, 1796.63,…
$ `1999`           <dbl> 4028.58, 6210.12, 1440.38, 1258.63, 1759.66, 1807.27,…
$ `2000`           <dbl> 4221.08, 6372.54, 1477.87, 1190.41, 1812.87, 1809.18,…
$ `2001`           <dbl> 4430.04, 6335.10, 1725.86, 1018.47, 1423.46, 1897.80,…
$ `2002`           <dbl> 4736.95, 6182.64, 1744.38, 1500.73, 1422.39, 1932.79,…
$ `2003`           <dbl> 5387.28, 6245.34, 1787.88, 1168.00, 1498.74, 1946.72,…
$ `2004`           <dbl> 6172.83, 6331.91, 1876.85, 1517.63, 1491.42, 1993.39,…
$ `2005`           <dbl> 6934.85, 6352.14, 1948.11, 1245.04, 1513.85, 2006.42,…
$ `2006`           <dbl> 7614.35, 6260.20, 2045.40, 1664.11, 1553.64, 1998.22,…
$ `2007`           <dbl> 8224.19, 6367.28, 2191.17, 1107.10, 1559.85, 2032.89,…
$ `2008`           <dbl> 8480.50, 6184.08, 2289.14, 1093.88, 1570.09, 2036.16,…
$ `2009`           <dbl> 9055.11, 5757.60, 2438.92, 1487.25, 1427.44, 2009.23,…
$ `2010`           <dbl> 9887.06, 6026.14, 2546.79, 1131.73, 1521.96, 2109.66,…
$ `2011`           <dbl> 10388.48, 5811.96, 2584.75, 1683.13, 1693.95, 1276.46…
$ `2012`           <dbl> 10675.66, 5593.24, 2740.40, 1702.30, 1674.58, 1319.48…
$ `2013`           <dbl> 11168.26, 5734.28, 2804.34, 1638.39, 1633.10, 1344.89…
$ `2014`           <dbl> 11228.48, 5779.53, 2984.52, 2015.50, 1621.85, 1384.99…
$ `2015`           <dbl> 11108.86, 5665.20, 3003.07, 2067.75, 1602.81, 1366.89…
$ `2016`           <dbl> 11151.31, 5743.85, 3076.48, 1434.45, 1733.91, 1455.86…
$ `2017`           <dbl> 11385.48, 5689.61, 3215.07, 1447.22, 1769.68, 1475.82…
$ `2018`           <dbl> 11821.66, 5892.37, 3360.56, 1692.36, 1868.15, 1434.51…

Code

# Clean data
ghg_emission <- ghg_emission |>
  pivot_longer(cols = matches("\\d{4}"),
               names_to = "year",
               values_to = "MtCO2e") |>
  mutate(year = char_num_to_numeric(year)) |>
  rename("Country Name" = "Country/Region")
ghg_emission

# A tibble: 5,655 × 4
   `Country Name` unit    year MtCO2e
   <chr>          <chr>  <dbl>  <dbl>
 1 China          MtCO2e  1990  2892.
 2 China          MtCO2e  1991  3039.
 3 China          MtCO2e  1992  3168.
 4 China          MtCO2e  1993  3398.
 5 China          MtCO2e  1994  3557.
 6 China          MtCO2e  1995  3961.
 7 China          MtCO2e  1996  3982.
 8 China          MtCO2e  1997  3978.
 9 China          MtCO2e  1998  4096.
10 China          MtCO2e  1999  4029.
# … with 5,645 more rows

Code

# Import OECD CO2 Emissions Excel Sheet

oecd_co2 <- read_xlsx("C:/Users/okung/OneDrive/Johns Hopkins - SAIS/2022-2023/SP23/Sustainable Finance/Week 7/OECD CO2 Emissions.xlsx", skip = 6)

New names:
• `` -> `...2`

Code

oecd_co2

# A tibble: 86 × 26
   Time    ...2   `1995`  `1996`  `1997`  `1998`  `1999`  `2000`  `2001`  `2002`
   <chr>   <lgl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
 1 Country NA    NA      NA      NA      NA      NA      NA      NA      NA     
 2 WLD: W… NA     2.14e4  2.18e4  2.22e4  2.24e4  2.25e4  2.32e4  2.36e4  2.39e4
 3 OECD: … NA     1.30e4  1.33e4  1.35e4  1.37e4  1.40e4  1.46e4  1.44e4  1.44e4
 4 AUS: A… NA     2.78e2  2.84e2  2.98e2  3.22e2  3.20e2  3.24e2  3.22e2  3.40e2
 5 AUT: A… NA     8.51e1  8.94e1  8.36e1  8.57e1  8.37e1  8.39e1  8.57e1  8.44e1
 6 BEL: B… NA     1.30e2  1.32e2  1.27e2  1.31e2  1.27e2  1.27e2  1.25e2  1.21e2
 7 CAN: C… NA     4.09e2  4.17e2  4.36e2  4.39e2  4.46e2  4.57e2  4.50e2  4.71e2
 8 CHL: C… NA     3.99e1  4.62e1  5.31e1  5.66e1  5.59e1  5.39e1  5.10e1  5.37e1
 9 COL: C… NA     6.55e1  6.56e1  7.22e1  7.23e1  5.97e1  5.99e1  6.11e1  6.09e1
10 CRI: C… NA     8.05e0  7.77e0  8.17e0  9.12e0  9.01e0  8.71e0  8.84e0  9.31e0
# … with 76 more rows, and 16 more variables: `2003` <dbl>, `2004` <dbl>,
#   `2005` <dbl>, `2006` <dbl>, `2007` <dbl>, `2008` <dbl>, `2009` <dbl>,
#   `2010` <dbl>, `2011` <dbl>, `2012` <dbl>, `2013` <dbl>, `2014` <dbl>,
#   `2015` <dbl>, `2016` <dbl>, `2017` <dbl>, `2018` <dbl>

Code

# Clean the sector data by dropping 'Change from BY'
oecd_co2 <- subset(oecd_co2, select = -c(`...2`))

# Pivot data
oecd_co2 <- oecd_co2|>
   pivot_longer(cols = matches("\\d{4}"),
                names_to = "year",
                values_to = "CO2 Emissions ",
                values_drop_na = TRUE) |>
  rename("Country/Region" = "Time") |>
 # mutate(Country = country_name_regex_to_iso3c(Country)) |>
  #Change year into a numeric
  mutate(year = char_num_to_numeric(year))
oecd_co2

# A tibble: 2,016 × 3
   `Country/Region`  year `CO2 Emissions `
   <chr>            <dbl>            <dbl>
 1 WLD: World        1995           21367.
 2 WLD: World        1996           21815.
 3 WLD: World        1997           22214.
 4 WLD: World        1998           22391.
 5 WLD: World        1999           22527.
 6 WLD: World        2000           23240.
 7 WLD: World        2001           23587.
 8 WLD: World        2002           23924.
 9 WLD: World        2003           24956.
10 WLD: World        2004           26130.
# … with 2,006 more rows

Code

# Import World Bank GPD PPP Excel Sheet
wb_gdp_ppp <- read_xls("C:/Users/okung/OneDrive/Johns Hopkins - SAIS/2022-2023/SP23/Sustainable Finance/Week 7/The World Bank - GDP, PPP.xls", skip = 2)

wb_gdp_ppp

# A tibble: 266 × 66
   Country N…¹ Count…² Indic…³ Indic…⁴ `1960` `1961` `1962` `1963` `1964` `1965`
   <chr>       <chr>   <chr>   <chr>   <lgl>  <lgl>  <lgl>  <lgl>  <lgl>  <lgl> 
 1 Aruba       ABW     GDP, P… NY.GDP… NA     NA     NA     NA     NA     NA    
 2 Africa Eas… AFE     GDP, P… NY.GDP… NA     NA     NA     NA     NA     NA    
 3 Afghanistan AFG     GDP, P… NY.GDP… NA     NA     NA     NA     NA     NA    
 4 Africa Wes… AFW     GDP, P… NY.GDP… NA     NA     NA     NA     NA     NA    
 5 Angola      AGO     GDP, P… NY.GDP… NA     NA     NA     NA     NA     NA    
 6 Albania     ALB     GDP, P… NY.GDP… NA     NA     NA     NA     NA     NA    
 7 Andorra     AND     GDP, P… NY.GDP… NA     NA     NA     NA     NA     NA    
 8 Arab World  ARB     GDP, P… NY.GDP… NA     NA     NA     NA     NA     NA    
 9 United Ara… ARE     GDP, P… NY.GDP… NA     NA     NA     NA     NA     NA    
10 Argentina   ARG     GDP, P… NY.GDP… NA     NA     NA     NA     NA     NA    
# … with 256 more rows, 56 more variables: `1966` <lgl>, `1967` <lgl>,
#   `1968` <lgl>, `1969` <lgl>, `1970` <lgl>, `1971` <lgl>, `1972` <lgl>,
#   `1973` <lgl>, `1974` <lgl>, `1975` <lgl>, `1976` <lgl>, `1977` <lgl>,
#   `1978` <lgl>, `1979` <lgl>, `1980` <lgl>, `1981` <lgl>, `1982` <lgl>,
#   `1983` <lgl>, `1984` <lgl>, `1985` <lgl>, `1986` <lgl>, `1987` <lgl>,
#   `1988` <lgl>, `1989` <lgl>, `1990` <dbl>, `1991` <dbl>, `1992` <dbl>,
#   `1993` <dbl>, `1994` <dbl>, `1995` <dbl>, `1996` <dbl>, `1997` <dbl>, …

Code

glimpse(wb_gdp_ppp)

Rows: 266
Columns: 66
$ `Country Name`   <chr> "Aruba", "Africa Eastern and Southern", "Afghanistan"…
$ `Country Code`   <chr> "ABW", "AFE", "AFG", "AFW", "AGO", "ALB", "AND", "ARB…
$ `Indicator Name` <chr> "GDP, PPP (current international $)", "GDP, PPP (curr…
$ `Indicator Code` <chr> "NY.GDP.MKTP.PP.CD", "NY.GDP.MKTP.PP.CD", "NY.GDP.MKT…
$ `1960`           <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1961`           <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1962`           <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1963`           <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1964`           <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1965`           <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1966`           <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1967`           <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1968`           <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1969`           <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1970`           <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1971`           <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1972`           <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1973`           <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1974`           <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1975`           <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1976`           <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1977`           <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1978`           <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1979`           <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1980`           <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1981`           <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1982`           <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1983`           <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1984`           <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1985`           <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1986`           <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1987`           <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1988`           <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1989`           <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1990`           <dbl> 1.363756e+09, 5.624269e+11, NA, 3.614384e+11, 3.86652…
$ `1991`           <dbl> 1.522141e+09, 5.821837e+11, NA, 3.784312e+11, 4.03690…
$ `1992`           <dbl> 1.648408e+09, 5.843592e+11, NA, 3.969616e+11, 3.88784…
$ `1993`           <dbl> 1.810797e+09, 5.973217e+11, NA, 4.022159e+11, 3.02546…
$ `1994`           <dbl> 2.001193e+09, 6.217997e+11, NA, 4.105030e+11, 3.13145…
$ `1995`           <dbl> 2.095198e+09, 6.632115e+11, NA, 4.281540e+11, 3.67668…
$ `1996`           <dbl> 2.158860e+09, 7.125338e+11, NA, 4.564509e+11, 4.25110…
$ `1997`           <dbl> 2.350844e+09, 7.562446e+11, NA, 4.845204e+11, 4.63898…
$ `1998`           <dbl> 2.424659e+09, 7.789363e+11, NA, 5.076107e+11, 4.91127…
$ `1999`           <dbl> 2.489269e+09, 8.123454e+11, NA, 5.230025e+11, 5.08913…
$ `2000`           <dbl> 2.739558e+09, 8.596596e+11, NA, 5.541251e+11, 5.36340…
$ `2001`           <dbl> 2.918819e+09, 9.113566e+11, NA, 5.960935e+11, 5.71490…
$ `2002`           <dbl> 2.935918e+09, 9.619397e+11, 1.980568e+10, 6.620087e+1…
$ `2003`           <dbl> 3.027419e+09, 1.013126e+12, 2.198038e+10, 7.122699e+1…
$ `2004`           <dbl> 3.335032e+09, 1.098990e+12, 2.288960e+10, 7.890903e+1…
$ `2005`           <dbl> 3.426373e+09, 1.204046e+12, 2.625842e+10, 8.611850e+1…
$ `2006`           <dbl> 3.572323e+09, 1.323943e+12, 2.851882e+10, 9.352603e+1…
$ `2007`           <dbl> 3.782481e+09, 1.450842e+12, 3.333922e+10, 1.012247e+1…
$ `2008`           <dbl> 3.925706e+09, 1.545880e+12, 3.531230e+10, 1.096373e+1…
$ `2009`           <dbl> 3.489265e+09, 1.567515e+12, 4.314053e+10, 1.170222e+1…
$ `2010`           <dbl> 3.434705e+09, 1.668456e+12, 4.992949e+10, 1.265388e+1…
$ `2011`           <dbl> 3.624110e+09, 1.770820e+12, 5.118418e+10, 1.355467e+1…
$ `2012`           <dbl> 3.552129e+09, 1.772528e+12, 5.966700e+10, 1.432229e+1…
$ `2013`           <dbl> 3.799583e+09, 1.893079e+12, 6.503984e+10, 1.529825e+1…
$ `2014`           <dbl> 3.817112e+09, 2.025590e+12, 6.905834e+10, 1.648386e+1…
$ `2015`           <dbl> 3.893364e+09, 2.098904e+12, 7.183170e+10, 1.665820e+1…
$ `2016`           <dbl> 3.941568e+09, 2.213473e+12, 7.009796e+10, 1.678674e+1…
$ `2017`           <dbl> 4.097907e+09, 2.300390e+12, 7.471192e+10, 1.744087e+1…
$ `2018`           <dbl> 4.416415e+09, 2.420259e+12, 7.740652e+10, 1.841650e+1…
$ `2019`           <dbl> 4.523960e+09, 2.520825e+12, 8.187310e+10, 1.937493e+1…
$ `2020`           <dbl> 3.727385e+09, 2.480833e+12, 8.091174e+10, 1.946109e+1…
$ `2021`           <dbl> 4.548955e+09, 2.699058e+12, 6.679792e+10, 2.108537e+1…

Code

# Clean data
# Pivot from wide to longer, change year to a numeric
wb_gdp_ppp <- wb_gdp_ppp |>
  pivot_longer(col = matches("\\d{4}"),
               names_to = 'year',
               values_to = "GDP PPP",
               values_drop_na = TRUE)|>
  mutate(year = char_num_to_numeric(year))
wb_gdp_ppp

# A tibble: 7,484 × 6
   `Country Name` `Country Code` `Indicator Name`          Indic…¹  year GDP P…²
   <chr>          <chr>          <chr>                     <chr>   <dbl>   <dbl>
 1 Aruba          ABW            GDP, PPP (current intern… NY.GDP…  1990  1.36e9
 2 Aruba          ABW            GDP, PPP (current intern… NY.GDP…  1991  1.52e9
 3 Aruba          ABW            GDP, PPP (current intern… NY.GDP…  1992  1.65e9
 4 Aruba          ABW            GDP, PPP (current intern… NY.GDP…  1993  1.81e9
 5 Aruba          ABW            GDP, PPP (current intern… NY.GDP…  1994  2.00e9
 6 Aruba          ABW            GDP, PPP (current intern… NY.GDP…  1995  2.10e9
 7 Aruba          ABW            GDP, PPP (current intern… NY.GDP…  1996  2.16e9
 8 Aruba          ABW            GDP, PPP (current intern… NY.GDP…  1997  2.35e9
 9 Aruba          ABW            GDP, PPP (current intern… NY.GDP…  1998  2.42e9
10 Aruba          ABW            GDP, PPP (current intern… NY.GDP…  1999  2.49e9
# … with 7,474 more rows, and abbreviated variable names ¹`Indicator Code`,
#   ²`GDP PPP`

Code

# Import IMF Export GDP Excel Sheet
imf_export_gdp <- read_xls("C:/Users/okung/OneDrive/Johns Hopkins - SAIS/2022-2023/SP23/Sustainable Finance/Week 7/IMF DF Export GDP.xls")

imf_export_gdp

# A tibble: 231 × 49
   GDP, …¹ `1980` `1981` `1982` `1983` `1984` `1985` `1986` `1987` `1988` `1989`
   <chr>   <chr>  <chr>  <chr>  <chr>  <chr>  <chr>  <chr>  <chr>  <chr>  <chr> 
 1 <NA>    <NA>   <NA>   <NA>   <NA>   <NA>   <NA>   <NA>   <NA>   <NA>   <NA>  
 2 Afghan… no da… no da… no da… no da… no da… no da… no da… no da… no da… no da…
 3 Albania 1.946  2.229… 2.295… 2.319  2.29   2.339  2.587… 2.565… 2.529… 2.778…
 4 Algeria 42.34… 44.372 44.78… 47.52… 51.51… 61.13… 61.53… 63.29… 51.66… 52.558
 5 Andorra no da… no da… no da… no da… no da… no da… no da… no da… no da… no da…
 6 Angola  6.639… 6.214… 6.214… 6.476  6.863… 8.457… 7.918… 9.050… 9.817… 11.42…
 7 Antigu… 0.131… 0.147… 0.164… 0.182  0.207… 0.240… 0.289… 0.337… 0.399… 0.439 
 8 Argent… 233.6… 189.8… 94.25  116.2… 130.5… 98.59… 118.5… 121.5… 142.3… 91.35…
 9 Armenia no da… no da… no da… no da… no da… no da… no da… no da… no da… no da…
10 Aruba   no da… no da… no da… no da… no da… no da… no da… no da… no da… no da…
# … with 221 more rows, 38 more variables: `1990` <chr>, `1991` <chr>,
#   `1992` <chr>, `1993` <chr>, `1994` <chr>, `1995` <chr>, `1996` <chr>,
#   `1997` <chr>, `1998` <chr>, `1999` <chr>, `2000` <chr>, `2001` <chr>,
#   `2002` <chr>, `2003` <chr>, `2004` <chr>, `2005` <chr>, `2006` <chr>,
#   `2007` <chr>, `2008` <chr>, `2009` <chr>, `2010` <chr>, `2011` <chr>,
#   `2012` <chr>, `2013` <chr>, `2014` <chr>, `2015` <chr>, `2016` <chr>,
#   `2017` <chr>, `2018` <chr>, `2019` <chr>, `2020` <chr>, `2021` <chr>, …

Code

# Clean data
imf_export_gdp <- imf_export_gdp |>
  pivot_longer(col = matches("\\d{4}"),
               names_to = 'year',
               values_to = "GDP, bn of USD",
               values_drop_na = TRUE) |>
  # Rename column for country name
  rename("Country Name" = "GDP, current prices (Billions of U.S. dollars)") |>
  # Change year and GDP to a numeric
  mutate(year = char_num_to_numeric(year),
         `GDP, bn of USD`= char_num_to_numeric(`GDP, bn of USD`))

Warning: There was 1 warning in `mutate()`.
ℹ In argument: `GDP, bn of USD = char_num_to_numeric(`GDP, bn of USD`)`.
Caused by warning in `char_num_to_numeric()`:
! NAs introduced by coercion

Code

imf_export_gdp

# A tibble: 10,944 × 3
   `Country Name`  year `GDP, bn of USD`
   <chr>          <dbl>            <dbl>
 1 Afghanistan     1980               NA
 2 Afghanistan     1981               NA
 3 Afghanistan     1982               NA
 4 Afghanistan     1983               NA
 5 Afghanistan     1984               NA
 6 Afghanistan     1985               NA
 7 Afghanistan     1986               NA
 8 Afghanistan     1987               NA
 9 Afghanistan     1988               NA
10 Afghanistan     1989               NA
# … with 10,934 more rows

Code

# Join IPCC Total Data and WB GDP PPP

merged_data <-  full_join(ipcc_total, wb_gdp_ppp, by = c("Country Name", "year"))

merged_data

# A tibble: 13,503 × 11
   IPCC_…¹ C_gro…² Count…³ Count…⁴ Subst…⁵  year CO2 E…⁶ Count…⁷ Indic…⁸ Indic…⁹
   <chr>   <chr>   <chr>   <chr>   <chr>   <dbl>   <dbl> <chr>   <chr>   <chr>  
 1 Non-An… Rest C… ABW     Aruba   CO2      1970    95.1 <NA>    <NA>    <NA>   
 2 Non-An… Rest C… ABW     Aruba   CO2      1971    94.5 <NA>    <NA>    <NA>   
 3 Non-An… Rest C… ABW     Aruba   CO2      1972   106.  <NA>    <NA>    <NA>   
 4 Non-An… Rest C… ABW     Aruba   CO2      1973   110.  <NA>    <NA>    <NA>   
 5 Non-An… Rest C… ABW     Aruba   CO2      1974   100.  <NA>    <NA>    <NA>   
 6 Non-An… Rest C… ABW     Aruba   CO2      1975   115.  <NA>    <NA>    <NA>   
 7 Non-An… Rest C… ABW     Aruba   CO2      1976   105.  <NA>    <NA>    <NA>   
 8 Non-An… Rest C… ABW     Aruba   CO2      1977   113.  <NA>    <NA>    <NA>   
 9 Non-An… Rest C… ABW     Aruba   CO2      1978   116.  <NA>    <NA>    <NA>   
10 Non-An… Rest C… ABW     Aruba   CO2      1979   109.  <NA>    <NA>    <NA>   
# … with 13,493 more rows, 1 more variable: `GDP PPP` <dbl>, and abbreviated
#   variable names ¹IPCC_annex, ²C_group_IM24_sh, ³Countrcode_A3,
#   ⁴`Country Name`, ⁵Substance, ⁶`CO2 Emissions`, ⁷`Country Code`,
#   ⁸`Indicator Name`, ⁹`Indicator Code`

Code

glimpse(merged_data)

Rows: 13,503
Columns: 11
$ IPCC_annex       <chr> "Non-Annex_I", "Non-Annex_I", "Non-Annex_I", "Non-Ann…
$ C_group_IM24_sh  <chr> "Rest Central America", "Rest Central America", "Rest…
$ Countrcode_A3    <chr> "ABW", "ABW", "ABW", "ABW", "ABW", "ABW", "ABW", "ABW…
$ `Country Name`   <chr> "Aruba", "Aruba", "Aruba", "Aruba", "Aruba", "Aruba",…
$ Substance        <chr> "CO2", "CO2", "CO2", "CO2", "CO2", "CO2", "CO2", "CO2…
$ year             <dbl> 1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978,…
$ `CO2 Emissions`  <dbl> 95.06542, 94.50371, 105.84781, 109.86386, 99.97718, 1…
$ `Country Code`   <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `Indicator Name` <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `Indicator Code` <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `GDP PPP`        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…

Code

# Join the merged IPCC and WB data with IMF export data
merged_data_2 <- full_join(merged_data, imf_export_gdp, by = c("Country Name", "year"))
  
merged_data_2

# A tibble: 16,918 × 12
   IPCC_…¹ C_gro…² Count…³ Count…⁴ Subst…⁵  year CO2 E…⁶ Count…⁷ Indic…⁸ Indic…⁹
   <chr>   <chr>   <chr>   <chr>   <chr>   <dbl>   <dbl> <chr>   <chr>   <chr>  
 1 Non-An… Rest C… ABW     Aruba   CO2      1970    95.1 <NA>    <NA>    <NA>   
 2 Non-An… Rest C… ABW     Aruba   CO2      1971    94.5 <NA>    <NA>    <NA>   
 3 Non-An… Rest C… ABW     Aruba   CO2      1972   106.  <NA>    <NA>    <NA>   
 4 Non-An… Rest C… ABW     Aruba   CO2      1973   110.  <NA>    <NA>    <NA>   
 5 Non-An… Rest C… ABW     Aruba   CO2      1974   100.  <NA>    <NA>    <NA>   
 6 Non-An… Rest C… ABW     Aruba   CO2      1975   115.  <NA>    <NA>    <NA>   
 7 Non-An… Rest C… ABW     Aruba   CO2      1976   105.  <NA>    <NA>    <NA>   
 8 Non-An… Rest C… ABW     Aruba   CO2      1977   113.  <NA>    <NA>    <NA>   
 9 Non-An… Rest C… ABW     Aruba   CO2      1978   116.  <NA>    <NA>    <NA>   
10 Non-An… Rest C… ABW     Aruba   CO2      1979   109.  <NA>    <NA>    <NA>   
# … with 16,908 more rows, 2 more variables: `GDP PPP` <dbl>,
#   `GDP, bn of USD` <dbl>, and abbreviated variable names ¹IPCC_annex,
#   ²C_group_IM24_sh, ³Countrcode_A3, ⁴`Country Name`, ⁵Substance,
#   ⁶`CO2 Emissions`, ⁷`Country Code`, ⁸`Indicator Name`, ⁹`Indicator Code`

Code

# Make a csv of the data
write_csv(merged_data_2, here("ipcc_wb_imf_processed.csv"))