Ulbricht trial exhibit GX-940.pdf (mirror) is a 7-page spreadsheet recording "SILK ROAD SALES DATA: 02/06/2011 - 10/02/2013". It list total transaction count and Bitcoin turnover for all categories (ranging from the hyper-specific "4-AcO-Met" with 0 sales to the general "Custom Orders" to a catch-all null category with 190,803 transactions which apparently there is no data on or the data was perhaps lost?).

While interesting, GX-940 is hard to use because the copy-paste is useless and it cannot be parsed. I have taken the data and reformatted it into a CSV file which can be analyzed more easily.

For example, if you wanted to know the top 20 drug sales on SR1, you could write in R:

srSales <- read.csv("~/gx-940-sr1salesdata-060211-02102013.csv")
categorySales <- aggregate(Payment.BTC ~ Parent.Name, sum, data=srSales)
categorySales[order(categorySales$Payment.BTC, decreasing=TRUE),][1:20,]
     Parent.Name Payment.BTC
20      Cannabis  1350442.74
35       Ecstasy  1327712.14
80    Stimulants  1163084.11
72  Prescription   370747.51
34         Drugs   318762.78
73  Psychedelics   250638.86
63       Opioids   229782.41
55          MDMA   227276.68
87          Weed   193671.25
53           LSD   163894.33
74     Relaxants   148088.99
30 Dissociatives   143852.47
68   Pain Relief   120276.79
77       Shrooms   102323.69
61         NBOMe   101860.20
47        Heroin    90260.01
1      2C family    60410.03
43     Forgeries    34711.31
25  Concentrates    26482.40
6     Alprazolam    25283.54

We can compute percentages too; of recorded sales, LSD & marijuana seem to make up ~22% of SR1's sales:

R> sum(categorySales[categorySales$Parent.Name=="Cannabis" | categorySales$Parent.Name=="LSD",]$Payment.USD.equivalent) / sum(categorySales$Payment.USD.equivalent)
[1] 0.2179536975

Interpretation of the numbers is not quite clear. I think that the conversion to USD may not be as simple as summing Bitcoin revenue and multiplying by a post-October-2013 exchange rate, because if I redo the ranking with USD instead, the rankings change:

R> categorySales <- aggregate(Payment.USD.equivalent ~ Parent.Name, sum, data=srSales)
R> categorySales[order(categorySales$Payment.USD.equivalent, decreasing=TRUE),][1:20,]
     Parent.Name Payment.USD.equivalent
35       Ecstasy               35814820
20      Cannabis               33489389
80    Stimulants               31066989
34         Drugs                8482343
87          Weed                8300721
63       Opioids                7028938
72  Prescription                5589656
73  Psychedelics                4840689
53           LSD                4667347
30 Dissociatives                4321799
68   Pain Relief                4131742
55          MDMA                3955539
74     Relaxants                2887472
61         NBOMe                2706007
47        Heroin                2309606
77       Shrooms                1964256
6     Alprazolam                1277981
78         Speed                1195298
1      2C family                1193402
25  Concentrates                1162722

The USD equivalent fields may be converted at each sales' exchange rate, which is more competent than I expected.

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I converted the PDF to text using pdftotext; this left each entry on its own line, each run of columns separated by 1 blank line, and after 9 runs, another group has begun so eg the first batch is 268 lines long, 9 blank lines. I puzzled over some solutions but wound up using an elaborate Emacs macro to copy each of the 9 runs to separate files and then merging them, fixing errors that popped up along the way. The totals I calculate in R match the totals provided at the bottom of the spreadsheet, within 0.1btc (probably rounding) so I think my version is accurate.