library(tidyverse)── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
✔ ggplot2 3.4.1 ✔ purrr 1.0.1
✔ tibble 3.1.8 ✔ dplyr 1.1.0
✔ tidyr 1.3.0 ✔ stringr 1.5.0
✔ readr 2.1.4 ✔ forcats 1.0.0
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()library(readr)
library(readxl)
library(countrycode)
library(ggplot2)temperature <-
read_csv("01_data_cleaning/Annual_Surface_Temperature_Change.csv")Rows: 225 Columns: 72
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (9): Country, ISO2, ISO3, Indicator, Unit, Source, CTS_Code, CTS_Name, ...
dbl (63): ObjectId, F1961, F1962, F1963, F1964, F1965, F1966, F1967, F1968, ...
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.temperature_mean <- temperature |>
select(Country, F1961:F2022) |>
pivot_longer(F1961:F2022, names_to = "year", values_to = "temp", values_drop_na = TRUE) |>
group_by(year) |>
summarize(avg_temperature = mean(temp))
year <- temperature_mean$year
year <- as.numeric(gsub("F","", year))
temperature_mean$year <- year
temperature_mean# A tibble: 62 × 2
year avg_temperature
<dbl> <dbl>
1 1961 0.163
2 1962 -0.0135
3 1963 -0.00604
4 1964 -0.0701
5 1965 -0.247
6 1966 0.106
7 1967 -0.111
8 1968 -0.199
9 1969 0.158
10 1970 0.0925
# … with 52 more rows#view(temperature)temperature_mean |>
ggplot(aes(x=year,y=avg_temperature))+
geom_line() +
scale_x_continuous(breaks = c(1960,1980,2000,2020)) +
xlab("Year") +
ylab("Mean Surface Temperature Change") +
ggtitle("Annual Surface Mean Temperature Change Over Time")
disasters_frequency <- read_csv("01_data_cleaning/Climate-related_Disasters_Frequency.csv") |>
select(Country, Indicator, F1980:F2022) |>
filter(Indicator!="Climate related disasters frequency, Number of Disasters: TOTAL")Rows: 970 Columns: 53
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (9): Country, ISO2, ISO3, Indicator, Unit, Source, CTS_Code, CTS_Name, ...
dbl (44): ObjectId, F1980, F1981, F1982, F1983, F1984, F1985, F1986, F1987, ...
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.disasters_frequency$Indicator <- gsub("Climate related disasters frequency, Number of Disasters:", "", disasters_frequency$Indicator)
disasters_frequency# A tibble: 755 × 45
Country Indic…¹ F1980 F1981 F1982 F1983 F1984 F1985 F1986 F1987 F1988 F1989
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Afghanis… " Drou… NA NA NA NA NA NA NA NA NA NA
2 Afghanis… " Extr… NA NA NA NA NA NA NA NA NA NA
3 Afghanis… " Floo… 1 NA NA NA NA NA NA NA 1 NA
4 Afghanis… " Land… NA NA NA NA NA NA NA 1 NA NA
5 Afghanis… " Stor… NA NA NA NA NA NA NA NA NA NA
6 Afghanis… " Wild… NA NA NA NA NA NA NA NA NA NA
7 Albania " Drou… NA NA NA NA NA NA NA NA NA 1
8 Albania " Extr… NA NA NA NA NA 1 NA NA NA NA
9 Albania " Floo… NA NA NA NA NA NA NA NA NA NA
10 Albania " Land… NA NA NA NA NA 1 NA NA NA NA
# … with 745 more rows, 33 more variables: F1990 <dbl>, F1991 <dbl>,
# F1992 <dbl>, F1993 <dbl>, F1994 <dbl>, F1995 <dbl>, F1996 <dbl>,
# F1997 <dbl>, F1998 <dbl>, F1999 <dbl>, F2000 <dbl>, F2001 <dbl>,
# F2002 <dbl>, F2003 <dbl>, F2004 <dbl>, F2005 <dbl>, F2006 <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>, …disasters_frequency <- disasters_frequency |>
pivot_longer(F1980:F2022, names_to = "year", values_to = "count", values_drop_na = TRUE)
disasters_frequency$year <-
as.numeric(gsub("F","", disasters_frequency$year))
disasters_frequency# A tibble: 6,360 × 4
Country Indicator year count
<chr> <chr> <dbl> <dbl>
1 Afghanistan, Islamic Rep. of " Drought" 2000 1
2 Afghanistan, Islamic Rep. of " Drought" 2006 1
3 Afghanistan, Islamic Rep. of " Drought" 2008 1
4 Afghanistan, Islamic Rep. of " Drought" 2011 1
5 Afghanistan, Islamic Rep. of " Drought" 2018 1
6 Afghanistan, Islamic Rep. of " Drought" 2021 1
7 Afghanistan, Islamic Rep. of " Extreme temperature" 1990 1
8 Afghanistan, Islamic Rep. of " Extreme temperature" 1991 1
9 Afghanistan, Islamic Rep. of " Extreme temperature" 2001 2
10 Afghanistan, Islamic Rep. of " Extreme temperature" 2005 1
# … with 6,350 more rowsyearly_disasters <- disasters_frequency |>
group_by(year, Indicator) |>
summarize(totalDisaster = sum(count))`summarise()` has grouped output by 'year'. You can override using the
`.groups` argument.yearly_disasters# A tibble: 257 × 3
# Groups: year [43]
year Indicator totalDisaster
<dbl> <chr> <dbl>
1 1980 " Drought" 24
2 1980 " Extreme temperature" 3
3 1980 " Flood" 39
4 1980 " Landslide" 3
5 1980 " Storm" 34
6 1980 " Wildfire" 1
7 1981 " Drought" 18
8 1981 " Extreme temperature" 1
9 1981 " Flood" 38
10 1981 " Landslide" 7
# … with 247 more rowsyearly_disasters |>
group_by(year) |>
ggplot(aes(x=year,y=totalDisaster,fill=Indicator)) +
geom_bar(stat="identity", position = "stack") +
labs(fill="Types of Disasters") +
xlab("Year") +
ylab("Number of Natural Disasters") +
ggtitle("Frequency of Natural Disasters")
climate_risk_2022 <- read_csv("01_data_cleaning/Climate-driven_INFORM_Risk.csv") |>
select(Country, ISO3, Indicator, F2022) |>
filter(Indicator=="Climate-driven INFORM Risk Indicator")Rows: 764 Columns: 20
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (9): Country, ISO2, ISO3, Indicator, Unit, Source, CTS_Code, CTS_Name, ...
dbl (11): ObjectId, F2013, F2014, F2015, F2016, F2017, F2018, F2019, F2020, ...
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.climate_risk_2022# A tibble: 188 × 4
Country ISO3 Indicator F2022
<chr> <chr> <chr> <dbl>
1 Afghanistan, Islamic Rep. of AFG Climate-driven INFORM Risk Indicator 7.3
2 Albania ALB Climate-driven INFORM Risk Indicator 3.2
3 Algeria DZA Climate-driven INFORM Risk Indicator 3.3
4 Angola AGO Climate-driven INFORM Risk Indicator 4.8
5 Antigua and Barbuda ATG Climate-driven INFORM Risk Indicator 2.9
6 Argentina ARG Climate-driven INFORM Risk Indicator 3.5
7 Armenia, Rep. of ARM Climate-driven INFORM Risk Indicator 3.4
8 Australia AUS Climate-driven INFORM Risk Indicator 2.9
9 Austria AUT Climate-driven INFORM Risk Indicator 2.3
10 Azerbaijan, Rep. of AZE Climate-driven INFORM Risk Indicator 4.4
# … with 178 more rowscountry_name_regex_to_iso3c <- function(country_name) {
country_name |>
countrycode(origin = "country.name",
destination = "iso3c",
origin_regex = TRUE)
}
world <- map_data("world") |>
mutate(ISO3 = country_name_regex_to_iso3c(region))Warning: There was 1 warning in `mutate()`.
ℹ In argument: `ISO3 = country_name_regex_to_iso3c(region)`.
Caused by warning in `countrycode_convert()`:
! Some values were not matched unambiguously: Ascension Island, Azores, Barbuda, Bonaire, Canary Islands, Chagos Archipelago, Grenadines, Heard Island, Kosovo, Madeira Islands, Micronesia, Saba, Saint Martin, Siachen Glacier, Sint Eustatius, Virgin Islandsrisk_map <- left_join(world, climate_risk_2022, by="ISO3") |>
ggplot(aes(x=long, y=lat, group=group)) +
geom_polygon(aes(fill=F2022), color="#7f7f7f") +
scale_fill_gradient(low="yellow", high="red", na.value="beige") +
labs(fill="Risk Index") +
xlab("") +
ylab("") +
ggtitle("Climate-driven Risk")
risk_map
world <- map_data(“world”) |> rename(Country = region)
risk_map <- left_join(world, climate_risk_2022, by=“Country”) #filter(!is.na(F2022)) risk_map
ggplot(aes(x=long, y=lat, map_id=Country, fill=F2022)) + geom_map(map=world, color=“#7f7f7f”) + scale_fill_gradient(low=“white”, high=“red”, guide=“legend”) + theme_void()
transition_preparedness <- read_csv("01_data_cleaning/Preparedness_of_Countries_for_a_Low-Carbon_Transition.csv") |>
select(Country, ISO3, Indicator, F2019) |>
pivot_wider(names_from = Indicator, values_from = F2019)Rows: 202 Columns: 11
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (9): Country, ISO2, ISO3, Indicator, Unit, Source, CTS_Code, CTS_Name, C...
dbl (2): ObjectId, F2019
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.transition_preparedness# A tibble: 101 × 4
Country ISO3 Exposure Resilience
<chr> <chr> <dbl> <dbl>
1 Albania ALB 0.265 0.717
2 Algeria DZA 0.605 0.800
3 Angola AGO 0.417 0.970
4 Argentina ARG 0.321 0.713
5 Australia AUS 0.353 0.330
6 Austria AUT 0.259 0.309
7 Azerbaijan, Rep. of AZE 0.683 0.650
8 Bangladesh BGD 0.349 0.823
9 Belgium BEL 0.206 0.357
10 Bolivia BOL 0.423 0.795
# … with 91 more rowscountry_region <- read_csv("01_data_cleaning/Country_region.csv") |>
select(`Country Code`, Region, IncomeGroup) |>
rename(ISO3=`Country Code`)New names:
Rows: 265 Columns: 6
── Column specification
──────────────────────────────────────────────────────── Delimiter: "," chr
(5): Country Code, Region, IncomeGroup, SpecialNotes, TableName lgl (1): ...6
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.
• `` -> `...6`country_region# A tibble: 265 × 3
ISO3 Region IncomeGroup
<chr> <chr> <chr>
1 ABW Latin America & Caribbean High income
2 AFE <NA> <NA>
3 AFG South Asia Low income
4 AFW <NA> <NA>
5 AGO Sub-Saharan Africa Lower middle income
6 ALB Europe & Central Asia Upper middle income
7 AND Europe & Central Asia High income
8 ARB <NA> <NA>
9 ARE Middle East & North Africa High income
10 ARG Latin America & Caribbean Upper middle income
# … with 255 more rowsexposure_resilience <-
left_join(transition_preparedness, country_region, by="ISO3")
exposure_resilience |>
ggplot(aes(x=Resilience, y=Exposure, color=IncomeGroup)) +
geom_point(alpha=0.8,size=2) +
geom_text(aes(label=ISO3),size=2,color="grey25") +
labs(fill="Income Group") +
ggtitle("Exposure and Resilience to Low-Carbon Economy Transition") +
xlab("Resilience from 0 (high resilience) to 1 (low resilience)") +
ylab("Exposure from 0 (low exposure) to 1 (high exposure)") +
theme(axis.title.x = element_text(size = 8),
axis.title.y = element_text(size = 8),
title = element_text(size = 10))
GSCIscore_2022 <- read_excel("01_data_cleaning/GSCI_Scores_2022.xlsx",
sheet = "Sustainable Score") |>
mutate(ISO3 = country_name_regex_to_iso3c(Country)) |>
left_join(country_region, by="ISO3") Warning: There was 1 warning in `mutate()`.
ℹ In argument: `ISO3 = country_name_regex_to_iso3c(Country)`.
Caused by warning in `countrycode_convert()`:
! Some values were not matched unambiguously: Kyrgistan, MicronesiaGSCIscore_2022# A tibble: 180 × 5
Country `Sustainable Competitiveness Score` ISO3 Region Incom…¹
<chr> <dbl> <chr> <chr> <chr>
1 Sweden 60.7 SWE Europe & Ce… High i…
2 Finland 59.3 FIN Europe & Ce… High i…
3 Switzerland 58.3 CHE Europe & Ce… High i…
4 Denmark 58.1 DNK Europe & Ce… High i…
5 Norway 57.6 NOR Europe & Ce… High i…
6 Iceland 57.1 ISL Europe & Ce… High i…
7 United Kingdom 56.4 GBR Europe & Ce… High i…
8 France 56.3 FRA Europe & Ce… High i…
9 Slovenia 56.3 SVN Europe & Ce… High i…
10 Japan 56.2 JPN East Asia &… High i…
# … with 170 more rows, and abbreviated variable name ¹IncomeGrouptop10
top10_sustainability <- GSCIscore_2022 |>
slice_max(order_by = `Sustainable Competitiveness Score`, n = 10)
top10_sustainability# A tibble: 10 × 5
Country `Sustainable Competitiveness Score` ISO3 Region Incom…¹
<chr> <dbl> <chr> <chr> <chr>
1 Sweden 60.7 SWE Europe & Ce… High i…
2 Finland 59.3 FIN Europe & Ce… High i…
3 Switzerland 58.3 CHE Europe & Ce… High i…
4 Denmark 58.1 DNK Europe & Ce… High i…
5 Norway 57.6 NOR Europe & Ce… High i…
6 Iceland 57.1 ISL Europe & Ce… High i…
7 United Kingdom 56.4 GBR Europe & Ce… High i…
8 France 56.3 FRA Europe & Ce… High i…
9 Slovenia 56.3 SVN Europe & Ce… High i…
10 Japan 56.2 JPN East Asia &… High i…
# … with abbreviated variable name ¹IncomeGrouptop10_sustainability |>
ggplot(aes(x = `Sustainable Competitiveness Score`, y = fct_reorder(.f = Country, .x = `Sustainable Competitiveness Score`), fill = IncomeGroup)) +
geom_bar(stat = "identity") +
scale_fill_manual(values = "steelblue") +
ylab("") +
ggtitle("Top 10 Countries by Sustainability Index")
FDI_net_inflows <-
read_csv("01_data_cleaning/FDI_net inflows.csv", skip = 3) |>
select(`Country Name`, `Country Code`, "2021") |>
rename(Country = `Country Name`, ISO3 = `Country Code`, FDI = "2021") |>
mutate(FDI_billions = FDI/1000000000) New names:
Rows: 266 Columns: 67
── Column specification
──────────────────────────────────────────────────────── Delimiter: "," chr
(4): Country Name, Country Code, Indicator Name, Indicator Code dbl (52): 1970,
1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, ... lgl (11): 1960,
1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, ...67
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.
• `` -> `...67`FDI_net_inflows# A tibble: 266 × 4
Country ISO3 FDI FDI_billions
<chr> <chr> <dbl> <dbl>
1 Aruba ABW 133545121. 0.134
2 Africa Eastern and Southern AFE 52974609490. 53.0
3 Afghanistan AFG 20600976. 0.0206
4 Africa Western and Central AFW 20677155529. 20.7
5 Angola AGO -4355116553. -4.36
6 Albania ALB 1218586452. 1.22
7 Andorra AND NA NA
8 Arab World ARB 54104486022. 54.1
9 United Arab Emirates ARE 20667120490. 20.7
10 Argentina ARG 6782348887. 6.78
# … with 256 more rowsGDP_per_capita <-
read_csv("01_data_cleaning/GDP_per_capita.csv", skip = 3) |>
select(`Country Name`, "2021") |>
rename(Country = `Country Name`, GDP_per_capita = "2021") New names:
Rows: 266 Columns: 67
── Column specification
──────────────────────────────────────────────────────── Delimiter: "," chr
(4): Country Name, Country Code, Indicator Name, Indicator Code dbl (62): 1960,
1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, ... lgl (1): ...67
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.
• `` -> `...67`GDP_per_capita# A tibble: 266 × 2
Country GDP_per_capita
<chr> <dbl>
1 Aruba 29342.
2 Africa Eastern and Southern 1550.
3 Afghanistan 369.
4 Africa Western and Central 1757.
5 Angola 1954.
6 Albania 6493.
7 Andorra 42137.
8 Arab World 6271.
9 United Arab Emirates 44316.
10 Argentina 10636.
# … with 256 more rowsFDI_GDP <- left_join(FDI_net_inflows, GDP_per_capita, by = "Country")
FDI_GDP# A tibble: 266 × 5
Country ISO3 FDI FDI_billions GDP_per_capita
<chr> <chr> <dbl> <dbl> <dbl>
1 Aruba ABW 133545121. 0.134 29342.
2 Africa Eastern and Southern AFE 52974609490. 53.0 1550.
3 Afghanistan AFG 20600976. 0.0206 369.
4 Africa Western and Central AFW 20677155529. 20.7 1757.
5 Angola AGO -4355116553. -4.36 1954.
6 Albania ALB 1218586452. 1.22 6493.
7 Andorra AND NA NA 42137.
8 Arab World ARB 54104486022. 54.1 6271.
9 United Arab Emirates ARE 20667120490. 20.7 44316.
10 Argentina ARG 6782348887. 6.78 10636.
# … with 256 more rowssus_score <- GSCIscore_2022 |>
select(!Country)
GSCIscore_FDI_GDP <- left_join(sus_score, FDI_GDP, by="ISO3") |>
select(Country, everything())
GSCIscore_FDI_GDP# A tibble: 180 × 8
Country Sustainable Com…¹ ISO3 Region Incom…² FDI FDI_b…³ GDP_p…⁴
<chr> <dbl> <chr> <chr> <chr> <dbl> <dbl> <dbl>
1 Sweden 60.7 SWE Europ… High i… 5.27e10 52.7 61029.
2 Finland 59.3 FIN Europ… High i… 2.39e10 23.9 53655.
3 Switzerland 58.3 CHE Europ… High i… 9.31e 9 9.31 91992.
4 Denmark 58.1 DNK Europ… High i… 1.47e10 14.7 68008.
5 Norway 57.6 NOR Europ… High i… 1.42e10 14.2 89154.
6 Iceland 57.1 ISL Europ… High i… 9.31e 7 0.0931 68728.
7 United Kingdom 56.4 GBR Europ… High i… 5.92e 9 5.92 46510.
8 France 56.3 FRA Europ… High i… 8.84e10 88.4 43659.
9 Slovenia 56.3 SVN Europ… High i… 2.15e 9 2.15 29291.
10 Japan 56.2 JPN East … High i… 3.31e10 33.1 39313.
# … with 170 more rows, and abbreviated variable names
# ¹`Sustainable Competitiveness Score`, ²IncomeGroup, ³FDI_billions,
# ⁴GDP_per_capitaGSCIscore_FDI_GDP |>
na.omit() |>
ggplot(aes(x=`Sustainable Competitiveness Score`, y=FDI_billions,
color = Region, shape = IncomeGroup)) +
geom_point(alpha=0.8,size=2) +
#geom_text(aes(label=ISO3, alpha =FDI_billions),size=2,color="grey25") +
scale_y_continuous(labels = scales::label_dollar(suffix = "b")) +
ylab("FDI in billion of dollars") +
theme(legend.text = element_text(size = 7),
legend.title = element_text(size = 8),
legend.key.size = unit(3,"mm"),
title = element_text(size = 9)) +
ggtitle("Relation between Sustainable Competitiveness Score and FDI")
GSCIscore_FDI_GDP |>
na.omit() |>
ggplot(aes(x=`Sustainable Competitiveness Score`, y=GDP_per_capita,
color = Region, shape = IncomeGroup)) +
geom_point(alpha=0.8,size=2) +
#geom_text(aes(label=ISO3, alpha =FDI_billions),size=2,color="grey25") +
scale_y_continuous(labels = scales::label_dollar()) +
ylab("GDP per capita in dollars") +
theme(legend.text = element_text(size = 7),
legend.title = element_text(size = 8),
legend.key.size = unit(3,"mm"),
title = element_text(size = 9)) +
ggtitle("Relation between Sustainable Competitiveness Score and GDP") 