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This repository has been archived by the owner on Feb 13, 2020. It is now read-only.
I am a bit confused regarding the instructions for this question:
Filter gapminder to all entries that have experienced a drop in life expectancy. Be sure to include a new variable that’s the increase in life expectancy in your tibble. Hint: you might find the lag() or diff() functions useful.
This seems a bit contradictory.
Does this mean filter out countries that have a drop in life expectancies over all the years or only keep those have experienced a drop?
Is it the whole gapminder dataset and not the filtered one from 1.1? Also are we looking at the general trend over all the years , or just the 1970s?
Thank you in advance.
The text was updated successfully, but these errors were encountered:
Did you mean to close the question? I was wondering something similar. I know we're supposed to use the gapminder dataset, but I'm specifically wondering whether we're supposed to analyze:
Any lines in the data that have a reduced life expectancy from the previous line
Any countries that have experienced a drop in life expectancy overall
Any rows where the country has experienced a drop in life expectancy from year to year (AKA doesn't count lines where we're changing countries)
I am a bit confused regarding the instructions for this question:
Filter gapminder to all entries that have experienced a drop in life expectancy. Be sure to include a new variable that’s the increase in life expectancy in your tibble. Hint: you might find the lag() or diff() functions useful.
This seems a bit contradictory.
Does this mean filter out countries that have a drop in life expectancies over all the years or only keep those have experienced a drop?
Is it the whole gapminder dataset and not the filtered one from 1.1? Also are we looking at the general trend over all the years , or just the 1970s?
Thank you in advance.
The text was updated successfully, but these errors were encountered: