R: Carbon emissions and demographic covariables by country for...

WorldBankCO2 {fields} | R Documentation

## Carbon emissions and demographic covariables by country for 1999.

### Description

These data are a small subset of the demographic data compiled by the World Bank. The data has been restricted to 1999 and to countries with a population larger than 1 million. Also, only countries reporting all the covariables are included.

### Usage

     data(WorldBankCO2)

### Format

This a 75X5 matrix with the row names identifying countries and columns the covariables: `"GDP.cap" "Pop.mid" "Pop.urb" "CO2.cap" "Pop"`

- GDP.cap: Gross domestic product (in US dollars) per capita.

- Pop.mid: percentage of the population within the ages of 15 through 65.

- Pop.urb: Precentage of the population living in an urban environment

- CO2.cap: Equivalent CO2 emmissions per capita

- Pop: Population

### Reference

Romero-Lankao, P., J. L. Tribbia and D. Nychka (2008) Development and greenhouse gas emissions deviate from the modernization theory and convergence hypothesis. Cli- mate Research 38, 17-29.

### Creating dataset

Listed below are scripts to create this data set from spread sheet on the World Bank CDs:

    ## read in comma delimited spread sheet
      read.csv("climatedemo.csv", stringsAsFactors=FALSE)->hold
    ## convert numbers to matrix of data
      Ddata<- as.matrix(  hold[,5:51] )
      Ddata[Ddata==".."] <- NA
    ## still in character form parse as numeric
      Ddata<- matrix( as.numeric( Ddata), nrow=1248, ncol=ncol( Ddata),
      dimnames=list( NULL, format( 1960:2006) ))
    ## these are the factors indicating the different variables
    ### unique( Fac) gives the names of factors
      Fac<- as.character( hold[,1])
      years<- 1960:2006
    # create separate tables of data for each factor
     temp<- unique( Fac)
    ## also subset Country id and name
      Country.id<- as.character( hold[Fac== temp[1],3])
      Country<- as.character( hold[Fac== temp[1],4])
      Pop<- Ddata[ Fac== temp[2],]
      CO2<- Ddata[ Fac== temp[1],]
      Pop.mid<- Ddata[ Fac== temp[3],]
      GDP.cap<- Ddata[ Fac== temp[4],]
      Pop.urb<- Ddata[ Fac== temp[5],]
      CO2.cap<- CO2/Pop
      dimnames( Pop)<- list( Country.id,format(years))
      dimnames( CO2)<- list( Country.id,format(years))
      dimnames( Pop.mid)<- list( Country.id,format(years))
      dimnames( Pop.urb)<- list( Country.id,format(years))
      dimnames( CO2.cap)<- list( Country.id,format(years))
    # delete temp data sets
      rm( temp)
      rm( hold)
      rm( Fac)
    # define year to do clustering.
      yr<- "1999"
    # variables for clustering combined as columns in a matrix
      temp<-cbind( GDP.cap[,yr], Pop.mid[,yr], Pop.urb[,yr],CO2[,yr],Pop[,yr])
    # add column names and figure how many good data rows there are.
            dimnames( temp)<-list( Country, c("GDP.cap","Pop.mid","Pop.urb",
                                              "CO2.cap", "Pop"))
            good<-complete.cases(temp)
            good<- good & Pop[,yr] > 10e6
    # subset with only the complete data rows
            WorldBankCO2<- temp[good,]
      save(WorldBankCO2, file="WorldBankCO2.rda")

### Examples

    data(WorldBankCO2)
    plot( WorldBankCO2[,"GDP.cap"], WorldBankCO2[,"CO2.cap"], log="xy")

[Package fields version 9.6.1 Index]