this id small part of my data set. in my dataset, there are 18 villages. I want to average cotton_yield_qtl over the village, but lets what I want is that when I average in village dhamangaon then for hhid 11101, the average should not include cotton_yield_qtl data for hhid 11101. can you please help me how to do it.
Thanks in advance
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Code:
* Example generated by -dataex-. To install: ssc install dataex clear input long hhid float year str8 district str10 tehsil str19 village float(tur_yield_qtl tur_price soya_yield_qtl soya_price cotton_yield_qtl cotton_price) 11101 2016 "wardha" "wardha" "dhamangaon" 11.627907 4160 . . 6.589148 4700 11101 2017 "wardha" "wardha" "dhamangaon" 4 4166.6665 . . 10.222222 5050 11101 2015 "wardha" "wardha" "dhamangaon" 8.064516 7550 . . 8.870968 3700 11102 2015 "wardha" "wardha" "dhamangaon" 6.896552 8100 3.2 3400 6.976744 4400 11102 2016 "wardha" "wardha" "dhamangaon" 1.3157895 4000 .75 2000 2.6785715 4200 11102 2017 "wardha" "wardha" "dhamangaon" 7.384615 430 . . 2.871795 5000 11103 2017 "wardha" "wardha" "dhamangaon" 2.8 3771.4285 0 . 5.2 5000 11103 2016 "wardha" "wardha" "dhamangaon" 17.741936 4939.394 . . 7.623319 4711.7646 11103 2015 "wardha" "wardha" "dhamangaon" 4 7125 3.333333 3200 7.5 4000 11104 2015 "wardha" "wardha" "dhamangaon" 0 . 1.0152284 2700 . . 11104 2016 "wardha" "wardha" "dhamangaon" .5 4000 . . 4.2372885 5000 11104 2017 "wardha" "wardha" "dhamangaon" 1.6 0 4.8 3600 . . 11104 2019 "wardha" "" "dhamangaon" 1.6 4900 10.4 4100 . . 11104 2020 "wardha" "" "dhamangaon" 3 5000 . . 4.928571 5550 11105 2017 "wardha" "wardha" "dhamangaon" 2.5 3440 . . 7.333333 5000 11105 2015 "wardha" "wardha" "dhamangaon" 2.5 7250 . . 2.34375 3000 11105 2016 "wardha" "wardha" "dhamangaon" 6.956522 4000 . . 5.102041 5000 11106 2016 "wardha" "wardha" "dhamangaon" 0 . . . 1.9417475 5000 11106 2017 "wardha" "wardha" "dhamangaon" 2 2866.667 . . 3.5555556 5100 11106 2015 "wardha" "wardha" "dhamangaon" 1.3333334 7550 . . 1.3333334 3700 11107 2017 "wardha" "wardha" "dhamangaon" 3 0 . . 11.333333 5050 11107 2020 "wardha" "" "dhamangaon" 1.4 5000 . . 4.2619047 5550 11107 2015 "wardha" "wardha" "dhamangaon" 5.172414 8686.667 . . 6.976744 4000 11107 2019 "wardha" "" "dhamangaon" 6 5000 . . 6.666667 6000 11107 2016 "wardha" "wardha" "dhamangaon" 1.3793104 4000 . . 4.0697675 4500 11108 2015 "wardha" "wardha" "dhamangaon" 2.5 8000 . . 8.333333 4600 11108 2017 "wardha" "wardha" "dhamangaon" 2.666667 0 . . 6.222222 5000 11108 2016 "wardha" "wardha" "dhamangaon" 3 4000 . . 7.666667 5700 11109 2015 "wardha" "wardha" "dhamangaon" 5.434783 7550 4 4000 13.623978 4000 11109 2016 "wardha" "wardha" "dhamangaon" 8.62069 4000 .75 2500 8.746355 4750 11109 2017 "wardha" "wardha" "dhamangaon" 2.1333334 4375 . . 9.6 5100 11110 2017 "wardha" "wardha" "dhamangaon" 2 3360 2.3333333 3400 5.066667 4950 11110 2016 "wardha" "wardha" "dhamangaon" .6944444 4000 4 2900 2.797203 5000 11110 2015 "wardha" "wardha" "dhamangaon" 7 9428.571 1.3333334 3600 5.25 5000 11111 2016 "wardha" "wardha" "dhamangaon" 9.722221 3700 . . 1.3986014 4600 11111 2017 "wardha" "wardha" "dhamangaon" 1.2 0 . . 4.5333333 5100 11111 2015 "wardha" "wardha" "dhamangaon" 7 7000 . . 7.5 4500 11112 2020 "wardha" "" "dhamangaon" . . . . 1.5 5700 11112 2016 "wardha" "wardha" "dhamangaon" 27.58621 4000 . . 5.813953 5100 11112 2019 "wardha" "" "dhamangaon" . . . . 5 5100 11112 2017 "wardha" "wardha" "dhamangaon" .6 0 5 3200 . . 11112 2015 "wardha" "wardha" "dhamangaon" 3.448276 8000 1.1627907 3900 . . 11113 2015 "wardha" "wardha" "dhamangaon" 1.1627907 8000 3 3200 3.883495 3900 11113 2016 "wardha" "wardha" "dhamangaon" 4.1666665 3900 3 4200 2.797203 5000 11113 2017 "wardha" "wardha" "dhamangaon" 4 3514.286 . . 6.476191 4900 11114 2015 "wardha" "wardha" "dhamangaon" 4 8000 2.25 3500 . . 11115 2015 "wardha" "wardha" "dhamangaon" 6.779661 6687.5 3 3000 5.136986 4000 11115 2016 "wardha" "wardha" "dhamangaon" 16 4000 5 2100 10 4275 11115 2017 "wardha" "wardha" "dhamangaon" 4 2733.333 . . 6.222222 5050 11116 2020 "wardha" "" "dhamangaon" 4 6000 . . 4 5500 11116 2019 "wardha" "" "dhamangaon" 2.1818182 5000 . . 2.909091 5250 11116 2016 "wardha" "wardha" "dhamangaon" 5.555555 3700 1.6666666 3000 3.030303 5000 11116 2015 "wardha" "wardha" "dhamangaon" 5.952381 7280 1 3100 3.597122 4000 11116 2017 "wardha" "wardha" "dhamangaon" 4 3416.667 . . 6.888889 4950 11117 2015 "wardha" "wardha" "dhamangaon" 3 8000 . . 4 4000 11118 2017 "wardha" "wardha" "dhamangaon" 4 3280 . . 8 4950 11118 2015 "wardha" "wardha" "dhamangaon" 2.0833333 8000 . . 1.3986014 4000 11118 2016 "wardha" "wardha" "dhamangaon" 6.944444 4100 . . 4.6620045 5100 11119 2016 "wardha" "wardha" "dhamangaon" 15.822784 4000 3.2857144 2600 4.984093 5400 11119 2015 "wardha" "wardha" "dhamangaon" 6.912442 8466.667 2.8 3200 4.1512914 4000 11119 2020 "wardha" "" "dhamangaon" 4.6666665 6500 . . 4.428571 5550 11119 2017 "wardha" "wardha" "dhamangaon" 4.095238 0 . . 10.4 5050 11119 2019 "wardha" "" "dhamangaon" 3.7 5000 5 5000 5.6 6000 11120 2015 "wardha" "wardha" "dhamangaon" 3.333333 8000 0 . 3.75 3700 11120 2016 "wardha" "wardha" "dhamangaon" 3.448276 4000 . . 2.915452 4800 11120 2017 "wardha" "wardha" "dhamangaon" 2.857143 3440 . . 6.222222 5000 11121 2015 "wardha" "wardha" "dhamangaon" 10 5916.667 . . 6.25 4500 11121 2016 "wardha" "wardha" "dhamangaon" 20.930233 5050 . . 7.751938 5200 11122 2015 "wardha" "wardha" "dhamangaon" 1.724138 8000 .8746356 3400 . . 11122 2016 "wardha" "wardha" "dhamangaon" 8.62069 4000 . . 2.3255813 5200 11122 2019 "wardha" "" "dhamangaon" 1 6000 6.5 5000 . . 11122 2020 "wardha" "" "dhamangaon" 1.5 6000 0 . . . 11123 2015 "wardha" "wardha" "dhamangaon" 4.5454545 8000 . . 6.060606 4000 11123 2017 "wardha" "wardha" "dhamangaon" . . . . 4 5040 11123 2019 "wardha" "" "dhamangaon" 2 0 . . 5.333333 5400 11123 2020 "wardha" "" "dhamangaon" 4 6500 . . 6 5700 11123 2016 "wardha" "wardha" "dhamangaon" 10.344828 4066.667 . . 6.395349 5300 11124 2016 "wardha" "wardha" "dhamangaon" 9.722221 4042.857 . . 6.993007 5200 11124 2019 "wardha" "" "dhamangaon" 1.3333334 5000 . . 3.5555556 6000 11124 2015 "wardha" "wardha" "dhamangaon" 1.875 8400 4 3000 5.3125 4200 11124 2020 "wardha" "" "dhamangaon" 0 . 2.666667 6000 .7619048 5300 11125 2015 "wardha" "wardha" "dhamangaon" 6.428572 8000 1.5384616 2800 4.642857 4000 11125 2017 "wardha" "wardha" "dhamangaon" 3.6 4844.4443 3.142857 3350 8.4 5000 11125 2016 "wardha" "wardha" "dhamangaon" 10.434783 4000 6.4 2600 4.3731775 5500 11126 2015 "wardha" "wardha" "dhamangaon" 0 . . . 2.3255813 4800 11126 2017 "wardha" "wardha" "dhamangaon" 0 . . . 3.333333 5000 11126 2016 "wardha" "wardha" "dhamangaon" 3.125 4000 . . 2.1164021 5550 11127 2017 "wardha" "wardha" "dhamangaon" 5 0 . . 6 5000 11127 2016 "wardha" "wardha" "dhamangaon" 13.793104 4000 . . 4.0697675 5200 11127 2015 "wardha" "wardha" "dhamangaon" 7.462687 7500 7.462687 3000 . . 11128 2017 "wardha" "wardha" "dhamangaon" 4 3300 4.5 3600 7.666667 5100 11128 2015 "wardha" "wardha" "dhamangaon" 16.949154 8800 1.5 3250 6.849315 4000 11128 2016 "wardha" "wardha" "dhamangaon" 15 3611.111 3 2700 11.111112 5500 11129 2015 "wardha" "wardha" "dhamangaon" 7.894737 6400 4 3000 8.849558 4700 11129 2016 "wardha" "wardha" "dhamangaon" 12.068966 3857.143 3 2200 6.997085 5200 11129 2017 "wardha" "wardha" "dhamangaon" 5 4360 3 3200 11.666667 5050 11130 2015 "wardha" "wardha" "dhamangaon" 5.952381 6080 5 2800 11.976048 4900 11130 2017 "wardha" "wardha" "dhamangaon" 0 . 3.333333 3300 12.444445 5100 11130 2016 "wardha" "wardha" "dhamangaon" 12.307693 4000 . . 7.772021 5500 11131 2015 "wardha" "wardha" "dhamangaon" 4 8628.571 .5 3600 8.571428 4700 end
0 Response to conditional averaging
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