I have a dataset A that has 96 hourly observations (var_A). I also have a dataset B that has 24 average hourly observations (var_B).
I want to combine the datasets A & B to make dataset C (attached).
How do I repeat the datalines of var_B from dataset B so that dataset C shows the same 24 average hourly values over and over until the end of the dataset A?
data c;
set c;
input avg_delivered_kw @@;
datalines;
1.2075434357
1.1069760113
1.0151325226
0.9682368384
1.0312725859
1.0515738245
0.8532573946
0.6135882055
0.4793938309
0.4483874046
0.5043064344
0.5824541904
0.7168952859
0.9070687706
1.1672568734
1.6091569573
2.1673370093
2.5960530086
2.5846035421
2.4253723858
2.2624625265
1.9173588172
1.5982197997
1.3700097842
;
run;
My excel file attachment may explain better the desired end result for dataset C.
Dataset A:
Date | Hour | var_A |
6/1/2018 | 100 | 1 |
6/1/2018 | 200 | 2 |
6/1/2018 | 300 | 3 |
6/1/2018 | 400 | 4 |
6/1/2018 | 500 | 5 |
6/1/2018 | 600 | 6 |
6/1/2018 | 700 | 7 |
6/1/2018 | 800 | 8 |
6/1/2018 | 900 | 9 |
6/1/2018 | 1000 | 10 |
6/1/2018 | 1100 | 11 |
6/1/2018 | 1200 | 12 |
6/1/2018 | 1300 | 13 |
6/1/2018 | 1400 | 14 |
6/1/2018 | 1500 | 15 |
6/1/2018 | 1600 | 16 |
6/1/2018 | 1700 | 17 |
6/1/2018 | 1800 | 18 |
6/1/2018 | 1900 | 19 |
6/1/2018 | 2000 | 20 |
6/1/2018 | 2100 | 21 |
6/1/2018 | 2200 | 22 |
6/1/2018 | 2300 | 23 |
6/1/2018 | 2400 | 24 |
6/4/2018 | 100 | 25 |
6/4/2018 | 200 | 26 |
6/4/2018 | 300 | 27 |
6/4/2018 | 400 | 28 |
6/4/2018 | 500 | 29 |
6/4/2018 | 600 | 30 |
6/4/2018 | 700 | 31 |
6/4/2018 | 800 | 32 |
6/4/2018 | 900 | 33 |
6/4/2018 | 1000 | 34 |
6/4/2018 | 1100 | 35 |
6/4/2018 | 1200 | 36 |
6/4/2018 | 1300 | 37 |
6/4/2018 | 1400 | 38 |
6/4/2018 | 1500 | 39 |
6/4/2018 | 1600 | 40 |
6/4/2018 | 1700 | 41 |
6/4/2018 | 1800 | 42 |
6/4/2018 | 1900 | 43 |
6/4/2018 | 2000 | 44 |
6/4/2018 | 2100 | 45 |
6/4/2018 | 2200 | 46 |
6/4/2018 | 2300 | 47 |
6/4/2018 | 2400 | 48 |
6/5/2018 | 100 | 49 |
6/5/2018 | 200 | 50 |
6/5/2018 | 300 | 51 |
6/5/2018 | 400 | 52 |
6/5/2018 | 500 | 53 |
6/5/2018 | 600 | 54 |
6/5/2018 | 700 | 55 |
6/5/2018 | 800 | 56 |
6/5/2018 | 900 | 57 |
6/5/2018 | 1000 | 58 |
6/5/2018 | 1100 | 59 |
6/5/2018 | 1200 | 60 |
6/5/2018 | 1300 | 61 |
6/5/2018 | 1400 | 62 |
6/5/2018 | 1500 | 63 |
6/5/2018 | 1600 | 64 |
6/5/2018 | 1700 | 65 |
6/5/2018 | 1800 | 66 |
6/5/2018 | 1900 | 67 |
6/5/2018 | 2000 | 68 |
6/5/2018 | 2100 | 69 |
6/5/2018 | 2200 | 70 |
6/5/2018 | 2300 | 71 |
6/5/2018 | 2400 | 72 |
6/6/2018 | 100 | 73 |
6/6/2018 | 200 | 74 |
6/6/2018 | 300 | 75 |
6/6/2018 | 400 | 76 |
6/6/2018 | 500 | 77 |
6/6/2018 | 600 | 78 |
6/6/2018 | 700 | 79 |
6/6/2018 | 800 | 80 |
6/6/2018 | 900 | 81 |
6/6/2018 | 1000 | 82 |
6/6/2018 | 1100 | 83 |
6/6/2018 | 1200 | 84 |
6/6/2018 | 1300 | 85 |
6/6/2018 | 1400 | 86 |
6/6/2018 | 1500 | 87 |
6/6/2018 | 1600 | 88 |
6/6/2018 | 1700 | 89 |
6/6/2018 | 1800 | 90 |
6/6/2018 | 1900 | 91 |
6/6/2018 | 2000 | 92 |
6/6/2018 | 2100 | 93 |
6/6/2018 | 2200 | 94 |
6/6/2018 | 2300 | 95 |
6/6/2018 | 2400 | 96 |
Dataset B:
Hour | var_B |
100 | 2.1 |
200 | 3.1 |
300 | 4.1 |
400 | 5.1 |
500 | 6.1 |
600 | 7.1 |
700 | 8.1 |
800 | 9.1 |
900 | 10.1 |
1000 | 11.1 |
1100 | 12.1 |
1200 | 13.1 |
1300 | 14.1 |
1400 | 15.1 |
1500 | 16.1 |
1600 | 17.1 |
1700 | 18.1 |
1800 | 19.1 |
1900 | 20.1 |
2000 | 21.1 |
2100 | 22.1 |
2200 | 23.1 |
2300 | 24.1 |
2400 | 25.1 |
Dataset C (goal dataset):
Date | Hour | var_A | var_B |
6/1/2018 | 100 | 1 | 2.1 |
6/1/2018 | 200 | 2 | 3.1 |
6/1/2018 | 300 | 3 | 4.1 |
6/1/2018 | 400 | 4 | 5.1 |
6/1/2018 | 500 | 5 | 6.1 |
6/1/2018 | 600 | 6 | 7.1 |
6/1/2018 | 700 | 7 | 8.1 |
6/1/2018 | 800 | 8 | 9.1 |
6/1/2018 | 900 | 9 | 10.1 |
6/1/2018 | 1000 | 10 | 11.1 |
6/1/2018 | 1100 | 11 | 12.1 |
6/1/2018 | 1200 | 12 | 13.1 |
6/1/2018 | 1300 | 13 | 14.1 |
6/1/2018 | 1400 | 14 | 15.1 |
6/1/2018 | 1500 | 15 | 16.1 |
6/1/2018 | 1600 | 16 | 17.1 |
6/1/2018 | 1700 | 17 | 18.1 |
6/1/2018 | 1800 | 18 | 19.1 |
6/1/2018 | 1900 | 19 | 20.1 |
6/1/2018 | 2000 | 20 | 21.1 |
6/1/2018 | 2100 | 21 | 22.1 |
6/1/2018 | 2200 | 22 | 23.1 |
6/1/2018 | 2300 | 23 | 24.1 |
6/1/2018 | 2400 | 24 | 25.1 |
6/4/2018 | 100 | 25 | 2.1 |
6/4/2018 | 200 | 26 | 3.1 |
6/4/2018 | 300 | 27 | 4.1 |
6/4/2018 | 400 | 28 | 5.1 |
6/4/2018 | 500 | 29 | 6.1 |
6/4/2018 | 600 | 30 | 7.1 |
6/4/2018 | 700 | 31 | 8.1 |
6/4/2018 | 800 | 32 | 9.1 |
6/4/2018 | 900 | 33 | 10.1 |
6/4/2018 | 1000 | 34 | 11.1 |
6/4/2018 | 1100 | 35 | 12.1 |
6/4/2018 | 1200 | 36 | 13.1 |
6/4/2018 | 1300 | 37 | 14.1 |
6/4/2018 | 1400 | 38 | 15.1 |
6/4/2018 | 1500 | 39 | 16.1 |
6/4/2018 | 1600 | 40 | 17.1 |
6/4/2018 | 1700 | 41 | 18.1 |
6/4/2018 | 1800 | 42 | 19.1 |
6/4/2018 | 1900 | 43 | 20.1 |
6/4/2018 | 2000 | 44 | 21.1 |
6/4/2018 | 2100 | 45 | 22.1 |
6/4/2018 | 2200 | 46 | 23.1 |
6/4/2018 | 2300 | 47 | 24.1 |
6/4/2018 | 2400 | 48 | 25.1 |
6/5/2018 | 100 | 49 | 2.1 |
6/5/2018 | 200 | 50 | 3.1 |
6/5/2018 | 300 | 51 | 4.1 |
6/5/2018 | 400 | 52 | 5.1 |
6/5/2018 | 500 | 53 | 6.1 |
6/5/2018 | 600 | 54 | 7.1 |
6/5/2018 | 700 | 55 | 8.1 |
6/5/2018 | 800 | 56 | 9.1 |
6/5/2018 | 900 | 57 | 10.1 |
6/5/2018 | 1000 | 58 | 11.1 |
6/5/2018 | 1100 | 59 | 12.1 |
6/5/2018 | 1200 | 60 | 13.1 |
6/5/2018 | 1300 | 61 | 14.1 |
6/5/2018 | 1400 | 62 | 15.1 |
6/5/2018 | 1500 | 63 | 16.1 |
6/5/2018 | 1600 | 64 | 17.1 |
6/5/2018 | 1700 | 65 | 18.1 |
6/5/2018 | 1800 | 66 | 19.1 |
6/5/2018 | 1900 | 67 | 20.1 |
6/5/2018 | 2000 | 68 | 21.1 |
6/5/2018 | 2100 | 69 | 22.1 |
6/5/2018 | 2200 | 70 | 23.1 |
6/5/2018 | 2300 | 71 | 24.1 |
6/5/2018 | 2400 | 72 | 25.1 |
6/6/2018 | 100 | 73 | 2.1 |
6/6/2018 | 200 | 74 | 3.1 |
6/6/2018 | 300 | 75 | 4.1 |
6/6/2018 | 400 | 76 | 5.1 |
6/6/2018 | 500 | 77 | 6.1 |
6/6/2018 | 600 | 78 | 7.1 |
6/6/2018 | 700 | 79 | 8.1 |
6/6/2018 | 800 | 80 | 9.1 |
6/6/2018 | 900 | 81 | 10.1 |
6/6/2018 | 1000 | 82 | 11.1 |
6/6/2018 | 1100 | 83 | 12.1 |
6/6/2018 | 1200 | 84 | 13.1 |
6/6/2018 | 1300 | 85 | 14.1 |
6/6/2018 | 1400 | 86 | 15.1 |
6/6/2018 | 1500 | 87 | 16.1 |
6/6/2018 | 1600 | 88 | 17.1 |
6/6/2018 | 1700 | 89 | 18.1 |
6/6/2018 | 1800 | 90 | 19.1 |
6/6/2018 | 1900 | 91 | 20.1 |
6/6/2018 | 2000 | 92 | 21.1 |
6/6/2018 | 2100 | 93 | 22.1 |
6/6/2018 | 2200 | 94 | 23.1 |
6/6/2018 | 2300 | 95 | 24.1 |
6/6/2018 | 2400 | 96 | 25.1 |
This was my solution:
proc sort data=dataset_a;
by Hour;
run;
data dataset_c;
merge dataset_a dataset_b;
by hour;
run;
proc sort data=dataset_c;
by Date;
run;
This was my solution:
proc sort data=dataset_a;
by Hour;
run;
data dataset_c;
merge dataset_a dataset_b;
by hour;
run;
proc sort data=dataset_c;
by Date;
run;
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