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ISO3 code to 7 continents names

6 messages · Miluji Sb, David Winsemius, Jeff Newmiller

Miluji Sb
# 
Dear all.

Is it possible to convert.identify iso3 country names to the seven
continent names?

# Asia, Africa, Antarctica, Australia, Europe, South America, and North
America,

I have tried the following:

###
region <- merge(countryExData,df,by.x='ISO3V10',by.y='iso3')

where df is the name of my dataset with iso3 the identification variable
but there seems to be a a lot of missing values.

Thank you!

Sincerely,

Milu
David Winsemius
# 
Please provide a sufficient amount of the dataframe named `df` to allow a properly tested response.
And do read the Posting Guide. This is a plain text mailing list.
David Winsemius
Alameda, CA, USA

'Any technology distinguishable from magic is insufficiently advanced.'   -Gehm's Corollary to Clarke's Third Law
Miluji Sb
# 
df is a data frame consisting of one variable (iso3 codes) such as

USA
RUS
ARG
BGD
ITA
FRA


Some of these iso3 codes are repeated and I would like the corresponding
continent name, the countrycode package does not seem to distinguish
between North and South America. Thanks.

Sincerely,

Milu

On Thu, Sep 7, 2017 at 9:00 PM, David Winsemius <dwinsemius at comcast.net>
wrote:

  
  


  


      

    

    

      
      

        


  

        

      David Winsemius
    

    

      
      #
    

  


  

          

      
      
      
    

        
Well it does actually: There are two different region codes:

First load the package that has countryExData. I'm presuming this is:

library(rworldmap)

            

      
      
      
    

        
Central and Eastern Europ    East Asia and the Pacific                       Europe 
                          19                           18                           24 
   Latin America and Caribbe Middle East and North Africa                North America 
                          24                           19                            2 
                  South Asia           Sub-Saharan Africa 
                           5                           38

            

      
      
      
    

        
Arabian Peninsula Australia + New Zealand               Caribbean 
                      5                       2                       5 
         Central Africa            Central Asia          Central Europe 
                      6                       5                      16 
         Eastern Africa          Eastern Europe                 Mashriq 
                      7                       7                       4 
           Meso America           North America          Northeast Asia 
                      8                       2                       5 
        Northern Africa           South America              South Asia 
                      5                      11                       6 
        South East Asia           South Pacific         Southern Africa 
                      8                       3                      10 
         Western Africa          Western Europe    Western Indian Ocean 
                     13                      19                       2 

Then create the described dataframe:

df<- data.frame(iso3=scan(what="") )
1: USA
2: RUS
3: ARG
4: BGD
5: ITA
6: FRA
7: 
Read 6 items

            

      
      
      
    

        
[1] "Latin America and Caribbe" "South Asia"                "Europe"                   
[4] "Europe"                    "Central and Eastern Europ" "North America"

  
    

      
      
    

  


  


      

    

    

      
      

        


  

        

      Jeff Newmiller
    

    

      
      #
    

  


  

      
The unequivocal answer is that it is possible, and most likely you have bad data or are referring to an incomplete lookup table. 

For us to see what your problem is would rewquire a reproducible example, but what you have provided is not reproducible [1][2][3].

[1] http://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example

[2] http://adv-r.had.co.nz/Reproducibility.html

[3] https://cran.r-project.org/web/packages/reprex/index.html (read the vignette)

  
    

      
      
    

  


  


      

    

    
1 day later

    

      
      

        


  

        

      David Winsemius
    

    

      
      #
    

  


  

          

      
      
      
    

        
Looking at that one might wonder about the degree of success. The ordering of the matched results was not determined by the order in the second df-object:

 merge(countryExData,df,by.x='ISO3V10',by.y='iso3')
  ISO3V10       Country               EPI_regions  GEO_subregion Population2005
1     ARG     Argentina Latin America and Caribbe  South America        38747.2
2     BGD    Bangladesh                South Asia     South Asia       141822.3
3     FRA        France                    Europe Western Europe        60495.5
4     ITA         Italy                    Europe Western Europe        58092.7
5     RUS        Russia Central and Eastern Europ Eastern Europe       143201.6
6     USA United States             North America  North America       298212.9
  GDP_capita.MRYA landlock   landarea density  EPI ENVHEALTH ECOSYSTEM ENVHEALTH.1 AIR_E
1         13652.4        0  2736296.0     1.3 81.8      91.1      72.5        91.1  87.3
2          1916.2        0   136248.1    95.0 58.0      53.6      62.4        53.6  95.7
3         28876.5        0   547106.7    17.9 87.8      99.4      76.2        99.4  95.9
snipped