I am trying to create new variables based on some specific variables ending with "dac2a". Dataex contains a number of donors, which are listed below. My objective is to demonstrate the similarities and groupings between donor countries, based on their aid flows to recipients.
I prepare these data for hierarchical clustering using Euclidian or Cosine metrics (y ax represents recipients and x represents donors). The amount of aid volume varies greatly across different donors, so I need to standardize all variables. Standardizing all variables will allow me to make a more accurate comparison between donors. Some other way may be getting a percentage of aid amount received by a specific country, as a share of a specific donors' total aid flows. For instance, Ethiopia gets 20 percent of Germany's total ODA flows, but it also gets 10 percent of Switzerland's total ODA flows.
At this time, instead of creating percentages, I want to get new standardized variables for all variables ending with "dac2a" (*dac2a). However, I couldn't create standardized variables for each donor at once. As an example, I provide a code that I used to remove negative values from each variable. A similar code structure should be used to create new standardized variables.
Code:
* Example generated by -dataex-. For more info, type help dataex clear input float(year usdac2a) str3 ccode float(switdac2a slovakdac2a slovendac2a pordac2a polanddac2a norwaydac2a newzedac2a) 2000 . "ABW" . . . . . . . 2001 . "ABW" . . . . . . . 2002 . "ABW" . . . . . . . 2003 . "ABW" . . . . . . . 2004 . "ABW" . . . . . . . 2005 . "ABW" . . . . . . . 2006 . "ABW" . . . . . . . 2007 . "ABW" . . . . . . . 2008 . "ABW" . . . . . . . 2009 . "ABW" . . . . . . . 2010 . "ABW" . . . . . . . 2011 . "ABW" . . . . . . . 2012 . "ABW" . . . . . . . 2013 . "ABW" . . . . . . . 2014 . "ABW" . . . . . . . 2015 . "ABW" . . . . . . . 2016 . "ABW" . . . . . . . 2017 . "ABW" . . . . . . . 2018 . "ABW" . . . . . . . 2019 . "ABW" . . . . . . . 2020 . "ABW" . . . . . . . 2021 . "ABW" . . . . . . . 2000 3.52 "AFG" 9.86 . . . .04 19.95 .25 2001 10.97 "AFG" 14.26 . . . .16 63.36 1.39 2002 515.61 "AFG" 23.5 . . 1.66 .47 87.66 4.27 2003 668.18 "AFG" 22.7 .32 . .32 .16 85.37 2.31 2004 1042.52 "AFG" 26.15 .56 . 2.35 .28 75.62 13.96 2005 1712.17 "AFG" 24.39 6.12 . 6.37 .38 58.83 11.81 2006 1768.53 "AFG" 23.89 1.98 . 7.25 .59 62.61 4.51 2007 1857.63 "AFG" 25.03 1.41 . 8.82 1.68 75.25 4.29 2008 2541.62 "AFG" 18.08 .24 .14 12.97 1.49 90.75 9.16 2009 3563.97 "AFG" 22.57 .48 .13 10.91 6.14 94.62 5.36 2010 3473.09 "AFG" 19.08 .49 .5 13.72 6.27 89.12 3.79 2011 3536.82 "AFG" 21.85 1.13 .57 1.96 8.29 89.35 5.72 2012 3084.5 "AFG" 27.56 .78 .43 2.39 8.55 81.52 14.96 2013 1892.14 "AFG" 26.69 2.21 .29 .22 3.13 80.74 6.7 2014 2113.98 "AFG" 30.61 .52 .15 .05 1.17 80.46 3.24 2015 1771.05 "AFG" 35.55 .29 .14 .76 .59 76.05 3.7 2016 1478.64 "AFG" 33.99 .35 .09 .9 .58 78.41 3.47 2017 1281.66 "AFG" 29.76 .85 .08 2.08 .66 67.52 5.15 2018 941.94 "AFG" 34.91 .35 .06 .97 .61 81.01 .09 2019 1445.42 "AFG" 33 .36 .07 1.71 .82 71.62 2 2020 1009.47 "AFG" 36.04 .42 .05 1.05 .69 79.24 2.15 2021 1423.89 "AFG" 59.35 .64 .46 1.04 .98 63.61 3.76 2000 54.34 "AGO" 6.73 . . 24.43 .05 26.33 . 2001 48.36 "AGO" 8.25 . . 21.56 .1 27.87 . 2002 148.13 "AGO" 10.44 . . 23.92 . 31.98 . 2003 210.32 "AGO" 10.41 .04 . 26.03 .06 30.08 .63 2004 162.47 "AGO" 9.87 .03 . 848.78 .08 27.64 . 2005 87.33 "AGO" 7.05 .03 . 24.02 .21 20.52 . 2006 41.39 "AGO" 4.77 . . 22.11 99.9 21.02 .11 2007 48.62 "AGO" 2.06 . . 18.76 .46 17.06 .13 2008 51.37 "AGO" 1.58 . . 17.48 5.43 12.51 . 2009 49.63 "AGO" .76 . . -9.23 7.1 14.56 . 2010 63.26 "AGO" .43 . . -12.53 1.27 9.8 . 2011 75.18 "AGO" .54 . . -7 -.09 8.41 . 2012 87.84 "AGO" .41 . . 1.57 -.23 7.85 . 2013 68.29 "AGO" 1.18 . . -9.31 36.95 6.37 . 2014 64.37 "AGO" .14 . . -21.99 13.11 8.31 . 2015 66.12 "AGO" .04 . . -21.73 14.87 5.92 . 2016 56.23 "AGO" .78 .01 . -25.1 4.85 7.39 .04 2017 59.6 "AGO" .59 .01 . -24.78 -2.07 6.91 . 2018 52.09 "AGO" .48 0 . -23.02 -1.88 5.35 . 2019 45.31 "AGO" .06 . . -22.89 -2.17 4.83 . 2020 40.82 "AGO" .67 . . -23.83 -2.25 5.89 . 2021 40.11 "AGO" .11 . .95 5.27 -2.31 4.91 .02 2000 . "AIA" . . . . . . . 2001 . "AIA" . . . . . . . 2002 . "AIA" . . . . . . . 2003 . "AIA" . . . . . . . 2004 . "AIA" . . . . . . . 2005 . "AIA" . . . . . . . 2006 . "AIA" . . . . . . . 2007 . "AIA" . . . . . . . 2008 . "AIA" . . . . . . . 2009 . "AIA" . . . . . . . 2010 . "AIA" . . . . . . . 2011 . "AIA" . . . . . . . 2012 . "AIA" . . . . . . . 2013 .02 "AIA" . . . . . . . 2014 . "AIA" . . . . . . . 2015 . "AIA" . . . . . . . 2016 . "AIA" . . . . . . . 2017 . "AIA" . . . . . . . 2018 . "AIA" . . . . . . . 2019 . "AIA" . . . . . . . 2020 . "AIA" . . . . . . . 2021 . "AIA" . . . . . . . 2000 65.14 "ALB" 13.51 . . . .74 5.63 . 2001 60.2 "ALB" 11.07 . . . .29 4.8 . 2002 86.67 "ALB" 16.63 .05 . . .05 8.41 . 2003 55 "ALB" 15.96 .32 . . .28 7.44 . 2004 54 "ALB" 10.2 .31 . . .27 8.93 . 2005 46.79 "ALB" 13.21 1.76 . . .22 6.39 . 2006 51.13 "ALB" 12.64 . . . . 5.1 . 2007 39.4 "ALB" 10.05 .01 . . .38 4.69 . 2008 43.2 "ALB" 16.85 . .15 . .36 2.87 . 2009 39.47 "ALB" 14.04 . .58 . .2 .85 . 2010 33.56 "ALB" 15.35 . .22 . .4 1.86 . 2011 28.33 "ALB" 11.64 . .21 . .05 1.78 . end
Listed 100 out of 3894 observations
Use the count() option to list more
Code:
foreach var of varlist var1 var2 var3 {
replace `var' = 0 if `var' < 0
}
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