Climate Change and Impacts in Africa (R)

Climate Change and Impacts in Africa

According to the United Nations, Climate change refers to long-term shifts in temperatures and weather patterns. Such shifts can be natural, due to changes in the sun’s activity or large volcanic eruptions. But since the 1800s, human activities have been the main driver of climate change, primarily due to the burning of fossil fuels like coal, oil, and gas.

The consequences of climate change now include, among others, intense droughts, water scarcity, severe fires, rising sea levels, flooding, melting polar ice, catastrophic storms, and declining biodiversity.

You work for a Non-governmental organization tasked with reporting the state of climate change in Africa at the upcoming African Union Summit. The head of analytics has provided you with IEA-EDGAR CO2 dataset which you will clean, combine and analyze to create a report on the state of climate change in Africa. You will also provide insights on the impact of climate change on African regions (with four countries, one from each African region, as case studies).

Dataset

The dataset, IEA-EDGAR CO2, is a component of the EDGAR (Emissions Database for Global Atmospheric Research) Community GHG database version 7.0 (2022) including or based on data from IEA (2021) Greenhouse Gas Emissions from Energy, www.iea.org/statistics, as modified by the Joint Research Centre. The data source was the EDGARv7.0_GHG website provided by Crippa et. al. (2022) and with DOI.

The dataset contains three sheets - IPCC 2006, 1PCC 1996, and TOTALS BY COUNTRY on the amount of CO2 (a greenhouse gas) generated by countries between 1970 and 2021. You can download the dataset from your workspace or inspect the dataset directly here.

TOTALS BY COUNTRY SHEET

This sheet contains the annual CO2 (kt) produced between 1970 - 2021 in each country. The relevant columns in this sheet are:

Columns	Description
`C_group_IM24_sh`	The region of the world
`Country_code_A3`	The country code
`Name`	The name of the country
`Y_1970 - Y_2021`	The amount of CO2 (kt) from 1970 - 2021

IPCC 2006

These sheets contain the amount of CO2 by country and the industry responsible.

Columns	Description
`C_group_IM24_sh`	The region of the world
`Country_code_A3`	The country code
`Name`	The name of the country
`Y_1970 - Y_2021`	The amount of CO2 (kt) from 1970 - 2021
`ipcc_code_2006_for_standard_report_name`	The industry responsible for generating CO2

Instructions

The head of analytics in your organization has specifically asked you to do the following:

Clean and tidy the datasets.
Create a line plot to show the trend of CO2 levels across the African regions.
Determine the relationship between time (Year) and CO2 levels across the African regions.
Determine if there is a significant difference in the CO2 levels among the African Regions.
Determine the most common (top 5) industries in each African region.
Determine the industry responsible for the most amount of CO2 (on average) in each African Region.
Predict the CO2 levels (at each African region) in the year 2025 using linear regression.
Determine if CO2 levels affect annual temperature in the selected African countries using linear regression.

# Setup
library(dplyr)
library(readxl)
library(readr)
library(tidyr)
library(ggplot2)
library(assertthat)
library(broom)

# we need only the African regions
african_regions <- c('Eastern_Africa', 'Western_Africa', 'Southern_Africa', 'Northern_Africa')

ipcc_2006_africa <- read_xlsx("IEA_EDGAR_CO2_1970-2021.xlsx", sheet = 'IPCC 2006', skip = 10) %>% 
  filter(C_group_IM24_sh %in% african_regions)

totals_by_country_africa <- read_xlsx("IEA_EDGAR_CO2_1970-2021.xlsx", sheet = 'TOTALS BY COUNTRY', skip = 10) %>% 
  filter(C_group_IM24_sh %in% african_regions)

# Read the temperatures datasets containing four African countries
# One from each African Region:
# Nigeria:    West Africa
# Ethiopa :   East Africa
# Tunisia:    North Africa
# Mozambique: South Africa
temperatures <- read_csv('temperatures.csv')

Instruction 1: Clean and tidy the datasets

Tasks

Rename C_group_IM24_sh to Region, Country_code_A3 to Code, and ipcc_code_2006_for_standard_report_name to Industry in the corresponding African datasets.
Drop IPCC_annex, ipcc_code_2006_for_standard_report, and Substance from the corresponding datasets.
Gather Y_1970 to Y_2021 into a two columns Year and CO2. Drop rows where CO2 is missing.
Convert Year to int type.

# Your code here
ipcc_2006_africa <- ipcc_2006_africa %>%
	rename('Region' = 'C_group_IM24_sh',
		   'Code' = 'Country_code_A3',
		   'Industry' = 'ipcc_code_2006_for_standard_report_name')

totals_by_country_africa <- totals_by_country_africa %>% 
        rename('Region' = 'C_group_IM24_sh',
		   'Code' = 'Country_code_A3')

ipcc_2006_africa = select(ipcc_2006_africa, -IPCC_annex, -ipcc_code_2006_for_standard_report, -Substance)
totals_by_country_africa = select(totals_by_country_africa, -IPCC_annex, -Substance)

ipcc_2006_africa <- ipcc_2006_africa %>%
	pivot_longer(
	cols = starts_with("Y_"),
    names_to = "Year",
	names_prefix = "Y_",
    values_to = "CO2",
    values_drop_na = TRUE)

totals_by_country_africa <- totals_by_country_africa %>%
	pivot_longer(
	cols = starts_with("Y_"),
    names_to = "Year",
	names_prefix = "Y_",
    values_to = "CO2",
    values_drop_na = TRUE)

ipcc_2006_africa$Year <- as.integer(ipcc_2006_africa$Year)
totals_by_country_africa$Year <- as.integer(totals_by_country_africa$Year) 

colnames(ipcc_2006_africa)
colnames(totals_by_country_africa)

head(ipcc_2006_africa)
head(totals_by_country_africa)

Instruction 2: Show the trend of CO2 levels across the African regions

Tasks

Group the totals_by_country_africa dataset by Region and Year and summarise the CO2 column using the mean() function.
Save the summarised column as co2_level and the resulting data frame as co2_level_by_region_per_year.
Create a line plot using ggplot() to show the trend of CO2 levels by Year across the African Regions. For testing purposes, save the line plot as the trend_of_CO2_emission_plot variable.

# Your code here
co2_level_by_region_per_year <- totals_by_country_africa %>%
group_by(Region, Year) %>%
summarize(co2_level = mean(CO2))

trend_of_CO2_emission_plot <- ggplot(co2_level_by_region_per_year, aes(Year, co2_level, color = Region)) +
geom_line() +
ggtitle("CO2 levels across the African Regions between 1970 and 2021")

Instruction 3: Determine the relationship between time and CO2 levels in each African region

Tasks

Using the totals_by_country_africa dataset, conduct a Spearman's correlation to determine the relationship between time (Year) and CO2 within each African Region.
Save the results in a variable called relationship_btw_time_CO2.

# Your code here

relationship_btw_time_CO2 <- totals_by_country_africa %>%
group_by(Region) %>%
summarise(r = cor(Year, CO2, method = 'spearman'))

relationship_btw_time_CO2

Instruction 4: Determine if there is a significant difference in the `CO2` levels among the African Regions

Tasks

Using totals_by_country_africa, conduct an ANOVA using aov() function on the CO2 by Region. Save the results as aov_results.
Conduct a posthoc test (with Bonferroni correction) to find the source of the significant difference. Save the results as pw_ttest_result.
Is it true that the CO2 levels of the Southern_Africa and Northern_Africa regions do not differ significantly? The previous task should provide you with the answer.

# Your code here
aov_results <- aov(CO2 ~ Region, data = totals_by_country_africa)

pw_ttest_result <- pairwise.t.test(
  totals_by_country_africa$CO2, 
  totals_by_country_africa$Region, 
  p.adjust.method = "bonferroni"
)

Instruction 5: Determine the most common (top 5) industries in each African region.

Tasks

Group ipcc_2006_africa by Region and then count Industry to derive the counts of the industries in each region.
Select the top 5 industries using slice_max() and save the result as top_5_industries. Do not set the with_ties parameter of slice_max() to FALSE.

# Your code here
top_5_industries <- ipcc_2006_africa %>%
group_by(Region)%>%
count(Industry)%>%
slice_max(n=5, order_by = n)

Instruction 6: Determine the industry responsible for the most amount of CO2 (on average) in each African Region

Tasks

Group ipcc_2006_africa by Region and Industry and summarise CO2 using mean() to get the average CO2 by industry in each region.
Next, select the top industry from each region using slice_max(). Save the results as top_industry_by_co2_emission. The results should contain four (4) rows and three (3) columns.

# Your code here
top_industry_by_co2_emission <- ipcc_2006_africa %>%
group_by(Region, Industry)%>%
summarize(co2_level = mean(CO2))%>%
slice_max(n = 1, order_by = co2_level)

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Climate Change and Impacts in Africa (R)

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Dataset

TOTALS BY COUNTRY SHEET

IPCC 2006

Instructions

Instruction 1: Clean and tidy the datasets

Tasks

Instruction 2: Show the trend of CO2 levels across the African regions

Tasks

Instruction 3: Determine the relationship between time and CO2 levels in each African region

Tasks

Instruction 4: Determine if there is a significant difference in the CO2 levels among the African Regions

Tasks

Instruction 5: Determine the most common (top 5) industries in each African region.

Tasks

Instruction 6: Determine the industry responsible for the most amount of CO2 (on average) in each African Region

Tasks

Climate Change and Impacts in Africa

Instruction 4: Determine if there is a significant difference in the `CO2` levels among the African Regions