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This post is following of the above post.
In the previous post, I find data start from 2000 to 2020. So, let's see how each countries changed living in slums.
First, I make data frame which contains only 2000 data.
I also make a data frame which only contains 2020 data.
I use inner_join() function to merge the both data frames.
Let's see a histogram of "change".
Some countries have increased living in slums, but almost countries has decreased.
Let's see which country has increased\decreased living in slums?
Angola, Mynmar, Iraq, Cuba, Suriname, Guinea and Ireland has inclrased living in slums and Burkina Faso has the most decreased.
Let's make a scatter plot living in slums vs. sulums_2000.
I see there is negative correlation between living in slums and change.
Then, let's see summary statistics for "change" by Region.
East Asia and Pacific has the lowest avarage change, Latin America and Carribbean has the high est one.
In South Asia, there is no country which ahs increased living in slums.
Let's see summary "change" statistics by IncomeGroup.
High income group has the highest change average while Low income group has the lowest average change. It is no surprise.
That's it. Thank you!
Next post is
To read from the 1st pot,
