Hi all,
I'm hoping for some guidance and advice.
The data I work with are normally realtively large datasets with small number of rows and large number of columns..
They tipically consist in samples (rows) and features (columns).
However, the samples have specific relationships and can be grouped together in different ways (normally these groupings are defined by an acillary file we call metadata)
I will tipically carryout operations which will transform the data and at each step the new and old matrix need to be recorded (create a new variables).
In the past I have done this by using new variables, but it get quite confusing as the number of variables in the workspace increase quite dramatically.
In an attempt to improve this I started using structures, but these can be hard to index at times.
My latest attempt is to store these data in Tables, which works well, but again indexing starts getting tricky (we do alot of indexing in both directions (columns and rows)
).
So My hope is that someone who works with these type of data has found a good design/system to store and operate on these.
Some attached screenshots.
