Import Data

The data is imported from an excel format using the “readxl” library.

## # A tibble: 44 × 18
##     Time `Time Code` `Country Name` ac_electricity agri_land atm_per_lac
##    <dbl> <chr>       <chr>                   <dbl>     <dbl>       <dbl>
##  1  2011 YR2011      Bangladesh               59.6      70.1        3.76
##  2  2012 YR2012      Bangladesh               65.5      70.1        4.09
##  3  2013 YR2013      Bangladesh               61.5      70.0        5.00
##  4  2014 YR2014      Bangladesh               62.4      69.9        5.81
##  5  2015 YR2015      Bangladesh               74        70.4        7.12
##  6  2016 YR2016      Bangladesh               75.9      70.6        8.02
##  7  2017 YR2017      Bangladesh               88        70.6        8.32
##  8  2018 YR2018      Bangladesh               86.9      70.7        8.85
##  9  2019 YR2019      Bangladesh               92.2      72.2        9.32
## 10  2020 YR2020      Bangladesh               96.2      72.4       10.4 
## # ℹ 34 more rows
## # ℹ 12 more variables: co2_emission_total <dbl>,
## #   electricity_prod_renewable <dbl>, employment_female <dbl>,
## #   employment_male <dbl>, gdp <dbl>, gdp_growth <dbl>, gdp_per_capita <dbl>,
## #   unemploy_male <dbl>, unemploy_female <dbl>, urban_pop <dbl>,
## #   urban_pop_growth <dbl>, women_hiv <dbl>

Plot 1

In this first plot, a scatterplot of GDP and CO2 Emission. On the horizontal axis (x-axis), GDP in Billion dollar is plotted against CO2 Emission in Megatonnes (Mt) in vertical axis (y-axis). The graph shows a positive correlation between GDP and CO2 emission in case of Bangladesh.

Plot 2

The Bar graphs of 4 countries shows that over the 11 year period (from 2011 to 2021), Malaysia remained the highest CO2 emitter amongst the four countries, whereas Singapore has remained the lowest and steady. Pakistan’s CO2 emission has risen dramatically over the period. We can see a similar increasing pattern in the case of Bangladesh.

Plot 3

The third plot is between two neighboring country of south-Asia, Bangladesh and Pakistan.The non-linear line plot and scatterplot is shown in two different graphs for each country. We see a decreasing correlation (linear, downward sloping), of GDP per capita and Urban Population Growth in case of Bangladesh over the time-period of 2011-2021. On the other hand, Pakistan’s pattern is following a quadratic trend, where it is seen that after 2015, there is a steep upward trend in Urban Population Growth Rate with the increase of GDP Per Capita.

Plot 4

This plot shows boxplot of average ATM avability per 100k adults in the 4 countries. We can see that Singapore has the highest number of ATMs available per 100k adults, where Bangladesh is at the bottom.

Plot 5

This bar chart shows the agricultural land available of the total land area of the four countries. As we can see that Bangladesh has the major share of Agricultural land of the total land area (72.35%), where Singapore has the lowest only around 1% of the total land.

Plot 6

This boxplot visualizes the comparison between Male and Female average unemployment rate over time accross the four countries. It is evident that, Bangladesh has the most disparity between the Male and Female workforce where for the female’s unemployment rate is the highest amongst the countries and twice as much as the males.

Plot 7

The bar charts compare the GDP Growth Rate between Bangladesh and Pakistan between 2011 and 2021. Pakistan’s GDP Growth Rate was significantly lower than the Bangladesh’s before 2016. Eventually, Pakistan’s GDP growth rate come near to Bangladesh’s until 2021.

Plot 8

Finally, this bar plot shows the women percentage share of total HIV population in the four countries. We can clearly see that Singapore is the only country which has a decreasing pattern of HIV affected women. Bangladesh shares the most number of women share of the total HIV affected population which is around 33% upto 2021. Singapore has the lowest percentage (8.8%) of women share of HIV of the total HIV population.