unable to reshape .i need help

[Uwalaka ikechukwu]

i am trying to reshape ave_exp by schoolid. i want each school to just one ave_exp as dummy . like i want dummy430 to have just 21.2

Reshape the data so that observations are uniquely identified by school.

teacherid schoolid avg_exp
1 430 21.2
2 430 21.2
3 430 21.2
4 430 21.2
5 430 21.2
6 430 21.2
7 430 21.2
1 432 18.8
2 432 18.8
3 432 18.8
4 432 18.8
5 432 18.8
1 436 19
2 436 19
3 436 19
4 436 19
5 436 19
1 443 12.83333
2 443 12.83333
3 443 12.83333
1 451 19.6
2 451 19.6
3 451 19.6
4 451 19.6
5 451 19.6
6 451 19.6
1 452 16.66667
2 452 16.66667
3 452 16.66667
1 456 11.2
2 456 11.2
3 456 11.2
4 456 11.2
5 456 11.2
1 480 7.944445
2 480 7.944445
3 480 7.944445
1 483 17.9375
2 483 17.9375
3 483 17.9375
4 483 17.9375
5 483 17.9375
6 483 17.9375
7 483 17.9375
8 483 17.9375
1 484 10.375
2 484 10.375
3 484 10.375
4 484 10.375
5 484 10.375
6 484 10.375
7 484 10.375
8 484 10.375
1 485 8.75
2 485 8.75
3 485 8.75
4 485 8.75
5 485 8.75
6 485 8.75
1 488 10.75
2 488 10.75
3 488 10.75
4 488 10.75
1 490 21
2 490 21
3 490 21
4 490 21
5 490 21
6 490 21
7 490 21
8 490 21
1 492 12.8
2 492 12.8
3 492 12.8
4 492 12.8
5 492 12.8
1 494 16.125
2 494 16.125
3 494 16.125
4 494 16.125
5 494 16.125
6 494 16.125
1 495 13.42857
2 495 13.42857
3 495 13.42857
4 495 13.42857
5 495 13.42857
6 495 13.42857
7 495 13.42857
8 495 13.42857
1 496 15.57143
2 496 15.57143
3 496 15.57143
4 496 15.57143
5 496 15.57143
6 496 15.57143
7 496 15.57143
1 498 13.5
2 498 13.5
3 498 13.5
4 498 13.5
5 498 13.5
1 500 2.333333
2 500 2.333333
3 500 2.333333
1 666 9.9375
2 666 9.9375
3 666 9.9375
4 666 9.9375
5 666 9.9375
6 666 9.9375
7 666 9.9375
8 666 9.9375
1 668 4.875
2 668 4.875
3 668 4.875
4 668 4.875
1 670 7.25
2 670 7.25
3 670 7.25
4 670 7.25
5 670 7.25
6 670 7.25
1 675 1.333333
2 675 1.333333
3 675 1.333333
4 675 1.333333
5 675 1.333333
1 676 12.9375
2 676 12.9375
3 676 12.9375
4 676 12.9375
5 676 12.9375
6 676 12.9375
7 676 12.9375
8 676 12.9375
1 678 6.3
2 678 6.3
3 678 6.3
4 678 6.3
5 678 6.3
1 680 13
2 680 13
3 680 13
4 680 13
1 685 20.5
2 685 20.5
3 685 20.5
4 685 20.5
5 685 20.5
6 685 20.5
7 685 20.5
8 685 20.5
1 687 6.583333
2 687 6.583333
3 687 6.583333
4 687 6.583333
5 687 6.583333
6 687 6.583333
7 687 6.583333
1 691 11.22222
2 691 11.22222
3 691 11.22222
4 691 11.22222
5 691 11.22222
6 691 11.22222
7 691 11.22222
1 692 20.91667
2 692 20.91667
3 692 20.91667
4 692 20.91667
5 692 20.91667
6 692 20.91667
7 692 20.91667
1 693 18.72222
2 693 18.72222
3 693 18.72222
4 693 18.72222
5 693 18.72222
6 693 18.72222
1 695 17.375
2 695 17.375
3 695 17.375
4 695 17.375
5 695 17.375
6 695 17.375
1 699 13.125
2 699 13.125
3 699 13.125
4 699 13.125
5 699 13.125
1 708 12.07143
2 708 12.07143
3 708 12.07143
4 708 12.07143
5 708 12.07143
6 708 12.07143
7 708 12.07143
1 709 14.03571
2 709 14.03571
3 709 14.03571
4 709 14.03571
5 709 14.03571
6 709 14.03571
7 709 14.03571
1 710 9
2 710 9
3 710 9
4 710 9
5 710 9
6 710 9
1 711 13.5
2 711 13.5
3 711 13.5
4 711 13.5
5 711 13.5
1 712 14.9
2 712 14.9
3 712 14.9
4 712 14.9
5 712 14.9
1 715 15.61111
2 715 15.61111
3 715 15.61111
4 715 15.61111
5 715 15.61111
6 715 15.61111
7 715 15.61111
8 715 15.61111
9 715 15.61111
1 716 5
2 716 5
3 716 5
1 717 9.444445
2 717 9.444445
3 717 9.444445
4 717 9.444445
5 717 9.444445
6 717 9.444445
7 717 9.444445
8 717 9.444445
9 717 9.444445
1 719 11.4
2 719 11.4
3 719 11.4
4 719 11.4
5 719 11.4
1 720 7.6
2 720 7.6
3 720 7.6
4 720 7.6
5 720 7.6
1 722 13.375
2 722 13.375
3 722 13.375
4 722 13.375
1 723 12.5
2 723 12.5
3 723 12.5
4 723 12.5
1 725 6
2 725 6
3 725 6
4 725 6
5 725 6
6 725 6
1 732 12.9
2 732 12.9
3 732 12.9
4 732 12.9
5 732 12.9
1 734 7.555555
2 734 7.555555
3 734 7.555555
4 734 7.555555
5 734 7.555555
6 734 7.555555
1 735 6.666667
2 735 6.666667
3 735 6.666667
4 735 6.666667
5 735 6.666667
6 735 6.666667
1 739 9.645833
2 739 9.645833
3 739 9.645833
4 739 9.645833
1 740 13.08333
2 740 13.08333
3 740 13.08333
4 740 13.08333
5 740 13.08333
6 740 13.08333
1 767 18.9
2 767 18.9
3 767 18.9
4 767 18.9
5 767 18.9
1 769 10.5
2 769 10.5
3 769 10.5
4 769 10.5
5 769 10.5
6 769 10.5
7 769 10.5
1 770 16.83333
2 770 16.83333
3 770 16.83333
4 770 16.83333
5 770 16.83333
1 771 13
2 771 13
3 771 13
4 771 13
5 771 13
6 771 13
1 777 9.4
2 777 9.4
3 777 9.4
4 777 9.4
5 777 9.4
6 777 9.4
1 778 21.41667
2 778 21.41667
3 778 21.41667
4 778 21.41667
5 778 21.41667
6 778 21.41667
1 786 15.2
2 786 15.2
3 786 15.2
4 786 15.2
5 786 15.2
1 789 12.71429
2 789 12.71429
3 789 12.71429
4 789 12.71429
5 789 12.71429
6 789 12.71429
7 789 12.71429
1 790 8
2 790 8
3 790 8
4 790 8
1 793 8.928572
2 793 8.928572
3 793 8.928572
4 793 8.928572
5 793 8.928572
6 793 8.928572
7 793 8.928572
1 795 20.8
2 795 20.8
3 795 20.8
4 795 20.8
5 795 20.8
6 795 20.8
1 856 1.666667
2 856 1.666667
3 856 1.666667
4 856 1.666667
5 856 1.666667
1 857 7.283333
2 857 7.283333
3 857 7.283333
4 857 7.283333
5 857 7.283333
6 857 7.283333
1 858 10.20238
2 858 10.20238
3 858 10.20238
4 858 10.20238
5 858 10.20238
6 858 10.20238
7 858 10.20238
1 859 9.666667
2 859 9.666667
3 859 9.666667
4 859 9.666667
5 859 9.666667
6 859 9.666667
7 859 9.666667
8 859 9.666667
9 859 9.666667
1 860 14.5
2 860 14.5
3 860 14.5
4 860 14.5
1 861 6.166667
2 861 6.166667
3 861 6.166667
4 861 6.166667
1 862 11.125
2 862 11.125
3 862 11.125
4 862 11.125
5 862 11.125
6 862 11.125
7 862 11.125
8 862 11.125
1 863 14.2
2 863 14.2
3 863 14.2
4 863 14.2
5 863 14.2
6 863 14.2
1 864 1.5
2 864 1.5
1 866 14.8
2 866 14.8
3 866 14.8
4 866 14.8
5 866 14.8
1 867 12.8125
2 867 12.8125
3 867 12.8125
4 867 12.8125
5 867 12.8125
6 867 12.8125
7 867 12.8125
8 867 12.8125
1 871 4.571429
2 871 4.571429
3 871 4.571429
4 871 4.571429
5 871 4.571429
6 871 4.571429
7 871 4.571429
1 872 9.416667
2 872 9.416667
3 872 9.416667
4 872 9.416667
5 872 9.416667
6 872 9.416667
7 872 9.416667
1 873 18.66667
2 873 18.66667
3 873 18.66667
1 878 6.2
2 878 6.2
3 878 6.2
4 878 6.2
5 878 6.2
1 881 9.736111
2 881 9.736111
3 881 9.736111
4 881 9.736111
5 881 9.736111
6 881 9.736111
7 881 9.736111
8 881 9.736111
1 883 13.41667
2 883 13.41667
3 883 13.41667
4 883 13.41667
5 883 13.41667
6 883 13.41667
1 886 14.5
2 886 14.5
3 886 14.5
4 886 14.5
1 887 11.5
2 887 11.5
3 887 11.5
4 887 11.5
5 887 11.5
1 889 13.2
2 889 13.2
3 889 13.2
4 889 13.2
5 889 13.2
6 889 13.2
1 890 16.25
2 890 16.25
3 890 16.25
4 890 16.25
5 890 16.25
1 892 12.8
2 892 12.8
3 892 12.8
4 892 12.8
5 892 12.8
1 893 13.66667
2 893 13.66667
3 893 13.66667
4 893 13.66667
5 893 13.66667
6 893 13.66667
1 895 9.8
2 895 9.8
3 895 9.8
4 895 9.8
5 895 9.8
1 896 2.097222
2 896 2.097222
3 896 2.097222
4 896 2.097222
5 896 2.097222
6 896 2.097222
1 897 13.01042
2 897 13.01042
3 897 13.01042
4 897 13.01042
1 898 10.33333
2 898 10.33333
3 898 10.33333
4 898 10.33333
5 898 10.33333
6 898 10.33333
1 899 19.5
2 899 19.5
3 899 19.5
4 899 19.5
5 899 19.5
6 899 19.5
7 899 19.5
1 938 20.57143
2 938 20.57143
3 938 20.57143
4 938 20.57143
5 938 20.57143
6 938 20.57143
7 938 20.57143
1 939 11.65278
2 939 11.65278
3 939 11.65278
4 939 11.65278
5 939 11.65278
6 939 11.65278
1 942 10.75
2 942 10.75
3 942 10.75
4 942 10.75
5 942 10.75
1 943 13.52778
2 943 13.52778
3 943 13.52778
4 943 13.52778
5 943 13.52778
6 943 13.52778
1 945 6.25
2 945 6.25
3 945 6.25
4 945 6.25
1 946 16.83333
2 946 16.83333
3 946 16.83333
4 946 16.83333
5 946 16.83333
6 946 16.83333
1 950 18.5
2 950 18.5
3 950 18.5
4 950 18.5
5 950 18.5
1 953 13.4
2 953 13.4
3 953 13.4
4 953 13.4
5 953 13.4
1 954 12.83333
2 954 12.83333
3 954 12.83333
4 954 12.83333
5 954 12.83333
6 954 12.83333
1 957 3.5
2 957 3.5
3 957 3.5
4 957 3.5
1 961 10
2 961 10
3 961 10
4 961 10
5 961 10
6 961 10
7 961 10
1 962 5.5
2 962 5.5
3 962 5.5
4 962 5.5
5 962 5.5
1 964 8.333333
2 964 8.333333
3 964 8.333333
4 964 8.333333
1 965 13.33333
2 965 13.33333
3 965 13.33333
1 966 4.875
2 966 4.875
3 966 4.875
4 966 4.875
1 969 8.083333
2 969 8.083333
3 969 8.083333
4 969 8.083333
5 969 8.083333
6 969 8.083333
7 969 8.083333
1 972 5.625
2 972 5.625
3 972 5.625
4 972 5.625
5 972 5.625
1 973 4
2 973 4
3 973 4
1 974 10.75
2 974 10.75
3 974 10.75
4 974 10.75
5 974 10.75
6 974 10.75
7 974 10.75
1 975 1.875
2 975 1.875
3 975 1.875
4 975 1.875
5 975 1.875
6 975 1.875
1 978 2
2 978 2
3 978 2
1 983 6.726191
2 983 6.726191
3 983 6.726191
4 983 6.726191
5 983 6.726191
6 983 6.726191
7 983 6.726191
8 983 6.726191
1 1006 16.41667
2 1006 16.41667
3 1006 16.41667
4 1006 16.41667
5 1006 16.41667
6 1006 16.41667
7 1006 16.41667
1 1008 14.45455
2 1008 14.45455
3 1008 14.45455
4 1008 14.45455
5 1008 14.45455
6 1008 14.45455
7 1008 14.45455
8 1008 14.45455
9 1008 14.45455
10 1008 14.45455
11 1008 14.45455
1 1010 12.8
2 1010 12.8
3 1010 12.8
4 1010 12.8
5 1010 12.8
6 1010 12.8
7 1010 12.8
8 1010 12.8
9 1010 12.8
10 1010 12.8
1 1012 8.166667
2 1012 8.166667
3 1012 8.166667
1 1013 14.83333
2 1013 14.83333
3 1013 14.83333
4 1013 14.83333
1 1014 11.07143
2 1014 11.07143
3 1014 11.07143
4 1014 11.07143
5 1014 11.07143
6 1014 11.07143
7 1014 11.07143
1 1015 1.361111
2 1015 1.361111
3 1015 1.361111
1 1018 14.33333
2 1018 14.33333
3 1018 14.33333
1 1020 4.6875
2 1020 4.6875
3 1020 4.6875
4 1020 4.6875
5 1020 4.6875

[Andrew Musau]

This is really not a reshape, it's more of a contract.

contract schoolid avg_exp, freq(count) nomiss

see

help contract

[Uwalaka ikechukwu]

thanks for your response ,the code works as you describe .thanks .but i couldnt proceed with the objective .

i think i didnt ask the right questions

so here is what am trying to achieve

1. Compute the average years of experience by school. If any of the values for experience are missing, leave the average value missing for that entire school.
2. Reshape the data so that observations are uniquely identified by school.
3. i am to merge the dataset with another dataset then rerun the regression , controlling for the gender (which is a "treatment ") of the pupil and for the average years of experience of teachers (which is yrstaught)

here is the real dataset

what did was to calculate the average by egen avg_exp = mean(yrstaught), by(schoolid)

teacherid schoolid yrstaught
1 430 32
2 430 30
3 430 19
4 430 23
5 430
6 430
7 430 2
1 432 24
2 432 31
3 432 25
4 432 12
5 432 2
1 436 24
2 436 20
3 436 25
4 436 25
5 436 1
1 443 24
2 443 13
3 443 1.5
1 451 28
2 451 20
3 451 20
4 451 28
5 451
6 451 2
1 452 30
2 452 18
3 452 2
1 456 24
2 456 1
3 456 26
4 456 2
5 456 3
1 480 22
2 480 1.5
3 480 .33333333
1 483 22
2 483 32
3 483 25
4 483 32
5 483 1
6 483 30
7 483 .5
8 483 1
1 484 14
2 484 23
3 484 18
4 484 14
5 484 1.5
6 484 10
7 484 .5
8 484 2
1 485 5
2 485 15
3 485 14
4 485 8
5 485 10
6 485 .5
1 488 19
2 488 17
3 488 3
4 488 4
1 490 30
2 490 34
3 490 33
4 490
5 490
6 490 4
7 490 4
8 490
1 492 17
2 492 22
3 492 16
4 492 7
5 492 2
1 494 1.5
2 494 32
3 494 29
4 494
5 494
6 494 2
1 495 30
2 495 20
3 495 27
4 495 1
5 495 7
6 495 7
7 495
8 495 2
1 496 5
2 496 24
3 496 23
4 496 20
5 496 10
6 496 24
7 496 3
1 498 22
2 498 21
3 498 1.5
4 498 20
5 498 3
1 500 6
2 500 0
3 500 1
1 666 20
2 666 18
3 666 2.5
4 666 1
5 666 15
6 666 19
7 666 2
8 666 2
1 668 13
2 668 3
3 668 3
4 668 .5
1 670 1.5
2 670 18
3 670 17
4 670 4
5 670 2
6 670 1
1 675
2 675 1.5
3 675
4 675 .5
5 675 2
1 676 14
2 676 33
3 676 2
4 676 16
5 676 21
6 676 15
7 676 1.5
8 676 1
1 678 3
2 678 1
3 678 24
4 678 3
5 678 .5
1 680 16
2 680 17
3 680 18
4 680 1
1 685 30
2 685 28
3 685 29
4 685 23
5 685 19
6 685 13
7 685
8 685 1.5
1 687 2
2 687 10
3 687 10
4 687 22
5 687 .08333333
6 687 1
7 687 1
1 691 14
2 691 15
3 691 13
4 691 24
5 691
6 691 .5
7 691 .8333333
1 692 33
2 692 33
3 692 24
4 692 21
5 692 33
6 692 2
7 692 .41666667
1 693 26
2 693 28
3 693 26
4 693 30
5 693 .33333333
6 693 2
1 695 19
2 695 22
3 695 27
4 695
5 695
6 695 1.5
1 699
2 699 9
3 699 28
4 699 15
5 699 .5
1 708 22
2 708 8
3 708 16
4 708 1.5
5 708 10
6 708 24
7 708 3
1 709 11
2 709 30
3 709 24
4 709 0
5 709 22
6 709 11
7 709 .25
1 710 26
2 710 15
3 710 3
4 710
5 710 1
6 710 0
1 711 29
2 711 1.5
3 711 10
4 711 3
5 711 24
1 712 27
2 712 32
3 712 1.5
4 712 13
5 712 1
1 715 25
2 715 10
3 715 22
4 715 4
5 715 18
6 715 11
7 715 24
8 715 25
9 715 1.5
1 716
2 716
3 716 5
1 717 25
2 717 1.5
3 717 1.5
4 717 1.5
5 717 1.5
6 717 33
7 717 14
8 717 2
9 717 5
1 719 23
2 719 18
3 719 13
4 719 2
5 719 1
1 720 3
2 720 11
3 720 4
4 720 18
5 720 2
1 722 24
2 722 1.5
3 722 27
4 722 1
1 723 22
2 723 24
3 723 2
4 723 2
1 725
2 725 12
3 725 12
4 725 3
5 725 2
6 725 1
1 732 11
2 732 28
3 732 11
4 732 13
5 732 1.5
1 734 18
2 734 4
3 734 11
4 734 12
5 734 .25
6 734 .08333333
1 735 13
2 735 6
3 735 15
4 735 3
5 735 1
6 735 2
1 739 10
2 739 1
3 739 27
4 739 .58333333
1 740 30
2 740 28
3 740 1.5
4 740 18
5 740 1
6 740 0
1 767 1.5
2 767 30
3 767 31
4 767 31
5 767 1
1 769
2 769 16
3 769 31
4 769 1.5
5 769 3
6 769
7 769 1
1 770
2 770 27
3 770 1.5
4 770 22
5 770
1 771 14
2 771 16
3 771 20
4 771 2
5 771 25
6 771 1
1 777 6
2 777 1
3 777 25
4 777 14
5 777 1
6 777
1 778 25
2 778 30
3 778 35
4 778 12
5 778 26
6 778 .5
1 786 22
2 786 21
3 786 15
4 786 16
5 786 2
1 789 12
2 789 30
3 789 4
4 789 23
5 789 15
6 789 3
7 789 2
1 790
2 790 11
3 790 12
4 790 1
1 793 10
2 793 29
3 793 10
4 793 6
5 793 6
6 793 .5
7 793 1
1 795 20
2 795 32
3 795 29
4 795 21
5 795
6 795 2
1 856 2
2 856 1.5
3 856 1.5
4 856 3
5 856 .33333333
1 857 30
2 857 4
3 857 1.5
4 857 .41666667
5 857
6 857 .5
1 858 10
2 858 20
3 858 16
4 858 2
5 858 21
6 858 2
7 858 .41666667
1 859 16
2 859 16
3 859 28
4 859 4
5 859 12
6 859 4
7 859 2
8 859 3
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1 860 28
2 860 13
3 860 16
4 860 1
1 861 15
2 861 3
3 861 .5
4 861
1 862 3
2 862 22
3 862 27
4 862 3
5 862 23
6 862 1
7 862 9
8 862 1
1 863 11
2 863 11
3 863 31
4 863 16
5 863 2
6 863
1 864 1.5
2 864
1 866 25
2 866 23
3 866 8
4 866 16
5 866 2
1 867 14
2 867 8
3 867 11
4 867 23
5 867 12
6 867 30
7 867 3
8 867 1.5
1 871 7
2 871 11
3 871 3
4 871 3
5 871 3
6 871 3
7 871 2
1 872 7
2 872 9
3 872 3
4 872 21
5 872 16
6 872
7 872 .5
1 873 29
2 873 26
3 873 1
1 878 8
2 878 15
3 878 3
4 878 3
5 878 2
1 881 14
2 881 4
3 881 13
4 881 25
5 881
6 881
7 881 2
8 881 .41666667
1 883 13
2 883 2
3 883 26
4 883 25
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1 886 26
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3 893 33
4 893 3
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1 895 13
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3 896 .58333333
4 896 3
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4 897 .04166667
1 898 16
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[Andrew Musau]

I am not sure that I follow, but they following will do 1 and 2 below:

so here is what am trying to achieve

1. Compute the average years of experience by school. If any of the values for experience are missing, leave the average value missing for that entire school.
2. Reshape the data so that observations are uniquely identified by school.
3. i am to merge the dataset with another dataset then rerun the regression , controlling for the gender (which is a "treatment ") of the pupil and for the average years of experience of teachers (which is yrstaught)

clear
input float(teacherid schoolid yrstaught)
1 430 32
2 430 30
3 430 19
4 430 23
5 430 .
6 430 .
7 430 2
1 432 24
2 432 31
3 432 25
4 432 12
5 432 2
1 436 24
2 436 20
3 436 25
4 436 25
5 436 1
1 443 24
2 443 13
3 443 1.5
1 451 28
2 451 20
3 451 20
4 451 28
5 451 .
6 451 2
1 452 30
2 452 18
3 452 2
1 456 24
2 456 1
3 456 26
4 456 2
5 456 3
1 480 22
2 480 1.5
3 480 .33333333
1 483 22
2 483 32
3 483 25
4 483 32
5 483 1
6 483 30
7 483 .5
8 483 1
1 484 14
2 484 23
3 484 18
4 484 14
5 484 1.5
6 484 10
7 484 .5
8 484 2
1 485 5
2 485 15
3 485 14
4 485 8
5 485 10
6 485 .5
1 488 19
2 488 17
3 488 3
4 488 4
1 490 30
2 490 34
3 490 33
4 490 .
5 490 .
6 490 4
7 490 4
8 490 .
1 492 17
2 492 22
3 492 16
4 492 7
5 492 2
1 494 1.5
2 494 32
3 494 29
4 494 .
5 494 .
6 494 2
1 495 30
2 495 20
3 495 27
4 495 1
5 495 7
6 495 7
7 495 .
8 495 2
end

bys schoolid: egen missing= max(missing(yrstaught))
gen experience= cond(missing, ., yrstaught)
collapse experience, by(schoolid)

Res.:

. l, sep(0)

     +---------------------+
     | schoolid   experi~e |
     |---------------------|
  1. |      430          . |
  2. |      432       18.8 |
  3. |      436         19 |
  4. |      443   12.83333 |
  5. |      451          . |
  6. |      452   16.66667 |
  7. |      456       11.2 |
  8. |      480   7.944445 |
  9. |      483    17.9375 |
 10. |      484     10.375 |
 11. |      485       8.75 |
 12. |      488      10.75 |
 13. |      490          . |
 14. |      492       12.8 |
 15. |      494          . |
 16. |      495          . |
     +---------------------+

The criterion to mark average experience as missing, if at least one teacher experience value is missing, seems too strict. Consider setting a threshold, for instance, if 75% of values are missing. Otherwise, you might be discarding valuable information.