Identifying paired samples in panel data

[Sarah Haynes]

Hello,

I am using panel data to compare two types of analyzing milk samples (the two methods are "24" and "MD") - see dataex example below. I am wanting to compare values of "energy" between the two methods. I am hoping for help with two things:
1. Some of the samples have been analyzed twice and need to be combined together to make an average value for energy. For example, person 1 has two values for type "MD" and two values for type "24." I can tell these are just reanalysis of the same sample because they occur on the same date. So what I want to do is generate a variable that is the mean value of "energy" of all MD samples taken on the same date. And the same for the 24 samples.
2. I then want to identify paired samples in the dataset (samples that are taken on the same date but are different types). I imagine this would be a new variable that indicates whether the samples are from day 1, 2, 3, etc.

Any advice is much appreciated!

Sarah

* Example generated by -dataex-. For more info, type help dataex
clear
input float pid str2 samplename1 str8 date float energy
 1 "24" "7/21/22"   82.57
 1 "MD" "7/21/22"   84.83
 1 "MD" "7/21/22"    84.5
 1 "24" "7/21/22"   82.17
 2 "24" "7/21/22"   89.45
 2 "MD" "7/21/22"   92.39
 2 "MD" "7/21/22"   94.01
 2 "24" "7/21/22"   89.03
 2 "MD" "7/21/22"   93.22
 3 "MD" "9/27/22"  110.39
 3 "24" "10/13/22"   90.6
 3 "MD" "10/21/22"  88.45
 3 "MD" "10/21/22"  89.04
 3 "24" "10/13/22"  87.95
 3 "MD" "10/6/22"   79.42
 3 "MD" "9/27/22"  111.39
 3 "24" "10/21/22"  93.42
 4 "MD" "10/6/22"   67.57
 4 "MD" "10/21/22"  71.95
 4 "MD" "10/6/22"   67.69
 4 "24" "10/6/22"   68.17
 4 "24" "10/21/22"  73.24
 4 "24" "10/21/22"   73.5
 4 "24" "10/6/22"   68.72
 4 "24" "10/27/22"  72.35
 4 "MD" "11/3/22"   73.92
 4 "MD" "11/3/22"   73.15
 4 "MD" "10/27/22"  67.92
 4 "24" "11/3/22"   70.93
 4 "24" "10/27/22"  72.13
 4 "MD" "10/21/22"  71.41
 4 "MD" "10/27/22"  67.23
 4 "24" "10/27/22"  71.49
 4 "24" "11/3/22"   69.91
 5 "24" "10/21/22"  67.28
 5 "MD" "10/21/22"  72.03
 5 "MD" "10/21/22"  72.08
 5 "24" "10/21/22"  67.62
 6 "24" "9/8/22"    88.57
 6 "MD" "9/8/22"   102.56
 6 "24" "9/8/22"    88.37
 6 "MD" "9/8/22"   102.76
 7 "24" "9/8/22"    83.72
 7 "MD" "9/8/22"   121.31
 7 "24" "9/8/22"    84.04
 7 "24" "10/21/22"  82.37
 7 "MD" "10/11/22" 139.59
 7 "24" "11/3/22"  125.37
 7 "24" "11/3/22"  124.58
 7 "MD" "9/22/22"  124.52
 7 "24" "10/11/22"  98.67
 7 "24" "9/27/22"   98.94
 7 "MD" "10/21/22"  80.34
 7 "24" "10/21/22"  82.31
 7 "MD" "10/11/22" 139.73
 7 "24" "9/27/22"   98.74
 7 "MD" "9/22/22"  120.98
 7 "MD" "11/1/22"  114.13
 7 "MD" "10/21/22"   80.5
 7 "24" "10/11/22"  98.33
 7 "MD" "9/8/22"   114.94
 7 "MD" "11/1/22"  114.66
 8 "24" "11/16/22"  80.43
 8 "24" "11/16/22"  80.36
 8 "MD" "11/16/22"  80.78
 8 "MD" "11/16/22"  80.89
 9 "MD" "11/10/22"   80.5
 9 "MD" "11/10/22"  80.84
 9 "24" "11/10/22"  75.96
 9 "24" "11/10/22"  74.96
10 "24" "12/17/22"  76.99
10 "24" "12/17/22"  76.91
10 "MD" "12/17/22"  92.19
10 "MD" "12/17/22"  91.52
11 "24" "11/30/22"  72.42
11 "MD" "11/10/22"  90.26
11 "MD" "11/10/22"  89.63
11 "24" "11/30/22"  71.48
12 "MD" "7/7/22"    82.67
12 "24" "6/23/22"   84.36
12 "24" "6/23/22"   84.66
12 "MD" "7/7/22"    80.79
13 "MD" "10/6/22"   75.35
13 "MD" "10/21/22"  84.06
13 "24" "10/21/22"  80.47
13 "24" "10/21/22"  89.09
13 "24" "10/21/22"   89.6
13 "24" "10/21/22"  80.59
13 "24" "10/6/22"   84.18
13 "MD" "10/21/22"  79.25
13 "MD" "10/21/22"  78.54
13 "MD" "10/6/22"   75.52
13 "MD" "10/21/22"  84.31
13 "24" "10/6/22"   83.67
14 "MD" "11/30/22"  79.12
14 "MD" "11/10/22"  75.78
14 "MD" "11/10/22"   75.8
14 "MD" "11/30/22"  79.54
14 "24" "11/30/22"  81.16
14 "24" "11/30/22"  80.27
end

[Daniel Shin]

First, collapse and average by pid, sample type, and date. Then reshape wide to pair your sample types by pid and date.

collapse (mean) mean_energy_=energy, by(pid samplename1 date)
reshape wide mean_energy_, i(pid date) j(samplename1) str

[Sarah Haynes]

Thank you Daniel! Is it possible to collapse more than one variable at a time? I also have a fat and carbs variable as well that I would like to explore. Or do I need to do them all separately?

[Clyde Schechter]

collapse (mean) mean_energy = energy mean_fat = fat mean_carbs = carbs, by(pid samplename1 date)

will work just fine. Do read -help collapse- for more information on this versatile command that is a "bread and butter" piece of Stata.