Assignment 2 Q 1.3

[almas2019]

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.

[armetcal]

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)

[almas2019]

@vincenzocoia

[wvictor14]

@almas2019 ,
I agree it sounds contradictory. But the instructions does not say that the new variable "an increase in life expectancy" can't have negative values. I think you should also try using the whole gapminder dataset (not the filtered one from 1.1).

@armetcal My interpretation is the second or third one (leaning towards the third one), maybe @vincenzocoia can comment.

Hopefully that helps..

[vincenzocoia]

Yep, you can just filter "increase in lifeExp" to be less than 0 to get those rows with a drop in lifeExp.

[armetcal]

Great, thanks!