Most maps here are presented such that colour = f(x, y), with x the principle measure and y the normalising factor. Both of these statements are true for mine....er.... similarities to infectious diseases, and it's about people.
Related to allergies? Although that'd be weird.
So are the values in a way that the log transform actually transforms how we'd perceive them?
Or more clear: Is equatorial guinea a leader by far in this, and would we notice?
No.So is your normalizing factor population?
Equatorial Guinea is only just outside the IQR of both both x and y. I still stand by it being a big outlier, and my f(x, y) as a more meaningful measure than each individually, but I do think my imperfect f(x, y) is causing some artifacts.Or more clear: Is equatorial guinea a leader by far in this, and would we notice?
Haiti is right in the middle, 89th of 171. It is very similar to Equatorial Guinea on one axis, and on the opposite extreme on the other axis. It is a very bad thing.A bad thing that happens way more in Haiti?
No, nothing like that.Connected to literacy?
But I am not sure what some of these countries are doing with 2 mobile phone subscriptions per person
It is.Equatorial Guinea is highest, Nigeria and Gabon are I think second.
And it is a very bad thing to do with people.
But not that.Well Nigeria is in the news regarding kidnapping school girls.
I have plotted x against y. I probably should have swapped round the letters I use, the primary measure here is the horizontal axis, x and the normalising factor is the vertical axis, y.Most maps here are presented such that colour = f(x, y), with x the principle measure and y the normalising factor. Both of these statements are true for mine.
In most cases y = population, and f(x, y) = x/y. Neither of these statements are true for mine.
My y is very conventional. My f(x, y) is not, but it is the best I could come up with given the data and I have not worked out a better. I think I could if I did some numerical maths, but I shall not.
Both x and y you will really notice, and totally assume that they would be related. You see that the values on the scale are the actual values (though after going through f(x, y) they have some very odd unit). It is log transformed because it is a very long tailed distribution.
Not allergies.
Yes. Y is GDP per capita. This is the map of that (the reciprocal actually to get the colour right):Is one of the values GDP per capita (or a similar measure of wealth?)
> head(world_stats[order(world_stats$function_result),c("name","function_result","first_value","second_value","rank_x")], n = 10)
name function_result first_value second_value rank_x
214 Tajikistan 0.7372186 0.00035 2106.339 29
28 Belarus 0.8117586 0.00005 16235.171 110
84 Greece 0.8617824 0.00003 28726.079 134
174 Poland 0.8708258 0.00004 21770.644 125
141 Macedonia 0.9084294 0.00008 11355.367 91
145 Montenegro 1.1227668 0.00008 14034.585 101
69 Finland 1.1954440 0.00003 39848.134 150
26 Bosnia and Herz. 1.2636445 0.00013 9720.342 82
136 Moldova 1.3296926 0.00034 3910.861 45
56 Czech Rep. 1.4176475 0.00005 28352.949 131
> tail(world_stats[order(world_stats$function_result),c("name","function_result","first_value","second_value","rank_x")], n = 10)
name function_result first_value second_value rank_x
36 Botswana 22.53480 0.00169 13334.199 99
149 Mauritania 23.98173 0.00723 3316.975 42
202 Suriname 24.01807 0.00169 14211.878 102
25 Bahamas 24.83869 0.00085 29221.991 135
45 Congo 26.39849 0.00509 5186.345 57
154 Namibia 26.99010 0.00319 8460.847 77
2 Angola 33.08600 0.00561 5897.683 63
158 Nigeria 44.65188 0.00867 5150.159 56
75 Gabon 49.44563 0.00322 15355.785 105
83 Eq. Guinea 127.81156 0.00379 33723.366 142
It is death by some means, but not hunger.Death by some means? By hunger?