Guess the map 13: Mercator maps are cool, actually

[Birdjaguar]

Could i assume that China and NK would be more red than not?

 

[TheCleanerDragon]

Is it affected by a country's distance from the equator?

 

[Samson]

Could i assume that China and NK would be more red than not?

No. I would guess NK would be pretty red, China I am not really sure, but I would guess much bluer.

Is it affected by a country's distance from the equator?

No, nothing like than.

 

[The_J]

No. I would guess NK would be pretty red, China I am not really sure, but I would guess much bluer.

So it's not something, which correlates somewhat with general development?

Something disease related?

 

[Samson]

So it's not something, which correlates somewhat with general development?

Something disease related?

Not disease related, but your question is a good one. This is the relationship to GDP per capita:

> cor.test(world_stats$value, world_stats$gdp_head, method = "pearson")
    Pearson's product-moment correlation
data:  world_stats$value and world_stats$gdp_head
t = 9.0487, df = 74, p-value = 1.346e-13
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.5968217 0.8167540
sample estimates:
      cor
0.7247564

 

[EnglishEdward]

Visits to the doctor each year?

 

[Birdjaguar]

No. I would guess NK would be pretty red, China I am not really sure, but I would guess much bluer.

Visits to the doctor each year?

I like the guess, but NK would probably not be red.

 

[Samson]

Visits to the doctor each year?

No. Sort of a little closer than other guess.

 

[EnglishEdward]

Gymnastic work outs per year?

 

[Samson]

Gymnastic work outs per year?

No. It is a really important metric.

 

[GinandTonic]

Wealth disparity?

 

[Birdjaguar]

No. It is a really important metric.

Important how? Health wise? Politically? Economically? Culturally?

 

[Samson]

Important how? Health wise? Politically? Economically? Culturally?

3 out of 4.

 

[Birdjaguar]

I'm not sure that is helpful....

 

[Samson]

> head(world_stats[,c("name","rank")], n = 10)
           name rank
60      Denmark   76
205      Sweden   75
72       France   74
19      Belgium   73
69      Finland   72
16      Austria   71
106       Italy   70
162      Norway   69
39  Switzerland   68
161 Netherlands   67
> tail(world_stats[,c("name","rank")], n = 10)
               name rank
87        Guatemala   10
156           Niger    9
61   Dominican Rep.    8
139          Mexico    7
184          Rwanda    6
43         Cameroon    5
206       Swaziland    4
96        Indonesia    3
223          Uganda    2
44  Dem. Rep. Congo    1
> world_stats[grepl("Unit", world_stats$name),c("name","rank")]
              name rank
226  United States   57
76  United Kingdom   52

Does this make sense? I have to admit, I have done some mixing of data sources here, I may have made some mistakes. I was most surprised how close the UK and US are.

 

[Samson]

Wealth disparity?

I missed this. No.

 

[Samson]

I'm not sure that is helpful....

Culturally is the one it is not. And both answers should really help.

 

[Birdjaguar]

Life expectancy?

 

[amadeus]

Paved road or railroad track mileage per capita?

 

[Samson]

Life expectancy?

Paved road or railroad track mileage per capita?

No and no. Neither very close.