[id_ris]

I've been using R extensively for the past 12 months and have achieved a high level of comfort with the language. Now I find myself at a wall because of my lack of math and statistics background. I've taken R as far as I can, or put more properly, R has taken me as far as I can go without learning more math.

With that said, I have little reason to use R right now except for it's excellent plotting ability with ggplot2. Otherwise for data munging, wrangling, connecting to databases, doing unit testing, etc - R is a giant PITA. Better to stick to Python for that. And as I learn D3, I think I'll use R even less for visualization.

Therefore R will only be valuable to me once I can harness it's power for data mining and machine learning, which is it's killer feature, IMHO.

[hadley]

Would love to hear what you find most painful about data munging/wrangling and unit testing. It's something that I've been trying to improve in R (e.g. http://vita.had.co.nz/papers/tidy-data.html and http://journal.r-project.org/archive/2011-1/RJournal_2011-1_...)

[tanyaM]

Do you think the love it/hate it dichotomy over R for data 'munging' stems from different ways of thinking about data. I'm slowly getting comfortable in R since returning to work in a sort of freelance arrangement that makes me highly motivated to use free or affordable tools. I started out, however, in clinical epidemiology data analysis using MS Access and SAS. I still think of data in terms of rectangular data sets, RDBMS and sql. I have a hard time with vector and matrix related terminology. I think I'm going to end up using reshape2 and data.table a lot since sqldf is noticeably slower even with my small data sets (compared with web analytics, finance, etc). The problem with sqldf and variable names containing a dot is a real drag as I try to adopt good coding style. I am missing the clarity and familiarity of sql statements, though, as I try to find my new workflow in R. I hope a more unified approach to data munging emerges soon. BTW, I totally espouse the reproducible research (RR) method of documenting study design, analysis, interpretation... I am loving knitr and latex for RR so I can no longer imagine using different tools for data munging and analysis.

[tanyaM]

Correction: I should have said 'literate programming' instead of 'reproducible research', since I'm not in a position to follow all components of RR.