The core language is still a confusing mess (I'm still never sure when to use a matrix, a dataframe, a list..), but if you use their tools you can ignore it for the most part.
In under 10 lines you can massage data and generate fantastic graphics.
A little off topic: but does anyone know what their business model is? Are they going to run out of money and burnout in a year or two?
And no, we're not planning on burning out. We currently sell three things:
* RStudio Server Pro. An commercial version of the open-source server version that provides stuff that corporate IT wants (e.g. monitoring, more auth options, ...)
* Shiny Server Pro. A more flexible version of the open-source shiny server that offers more configurability (e.g. number of R processes per app), and again other stuff that corporate IT wants.
* Right to use the RStudio desktop IDE to companies who don't want to use AGPL software
matrix - If you have data that would make sense to be in a spreadsheet-type format and all your data are numbers.
dataframe - If you have data that would make sense to be in a spreadsheet-type format and some columns are numbers but other columns are something else (character strings, dates, TRUE/FALSE); but each column is only one thing. That is, you have one column that's all dates, another column that's all numbers, yet another column that's all character strings, etc.
list - if you need to mix data types within a certain entity (vector or column of data).
One useful heuristic worth asking is "Does it make sense to sort this data by something". In that case, you have a data frame. Whereas if you want to perform matrix math on something (inverting it, multiplying it by another matrix, reducing it, etc.), you have a matrix. Things that I use a matrix for can generally also be expressed as a data frame with columns rowId, colId, and value. If it doesn't make sense in that format, a matrix is generally not the appropriate structure.