bug report:
Matrices
> plank[, 4]
[1] 7 8
This should be
> plank[, 4]
[1] 0 8 // 0 here
for plank[1, 4] http://tryr.codeschool.com/levels/3/challenges/17
Try R — A new online course, for free
121–128 of 128 posts
Re: Try R — A new online course, for free
#122Earlier quoted context omitted.
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_... )
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 set…
Correction: I should have said 'literate programming' instead of 'reproducible research', since I'm not in a position to follow all components of RR.
Re: Try R — A new online course, for free
#123Earlier quoted context omitted.
I've found Python + Pandas much better in this regard than R. Maybe it's just me, but for grouping, indexing, and manipulating tabular data, Python syntax just makes more sense. That said, R is better for stats and matrix operations.
I would agree with you if it wasn't for the data.table package in R. It is a game changer. Really.
Can you elaborate on data.table being 'a game changer'. I am inclined to agree, but I'm am just starting to get a handle on it. I am still hesitant and switching between sqldf, reshape2, base::merge and data.table more than I would like. Do you think it could become a dominant method for data preparation?
Re: Try R — A new online course, for free
#124Earlier quoted context omitted.
I would agree with you if it wasn't for the data.table package in R. It is a game changer. Really.
Python has PyTables which complements Pandas nicely and seems to offer the same sort of features as data.table (note, I've not actually used data.table)
I am using R to analyse and document (knitr and latex) epidemiologic data which does not involve parsing a lot of text to extract my analysis data set. Data preparation for this type of research involves more combining data from different source tables, restructuring repeated measures, etc. I only know how to do that using R. Can Python be incorporated into the knitr literate programming framework and is it worth learning another language?
Re: Try R — A new online course, for free
#125Earlier quoted context omitted.
Python has PyTables which complements Pandas nicely and seems to offer the same sort of features as data.table (note, I've not actually used data.table)
I am using R to analyse and document (knitr and latex) epidemiologic data which does not involve parsing a lot of text to extract my analysis data set. Data preparation for this type of research involves more combining data from different source tables, restructuring repeated measures, etc. I only know how to do that using R. Can Python be incorporated into the knitr literate programming framework and is it worth lea…
Python will be better supported in knitr in future; for now it only has preliminary support: http://yihui.name/knitr/demo/engines/
Re: Try R — A new online course, for free
#126chisq.test (arbre,simulate.p.value=TRUE)
Re: Try R — A new online course, for free
#127chisq.test([14,25,1,8,25,4,7,15,23,6,0,5,26,8,6],simulate.p.value=TRUE)
Re: Try R — A new online course, for free
#128Earlier quoted context omitted.
I took that course when it was running on Coursera, and I honestly can't recommend it (in its current state, at least) to anyone looking to learn basic statistics. It covered a lot of material, but the quality and order of coverage was very inconsistent. The first couple weeks were fine, but it felt really odd to jump from correlations and scatterplots into regression, then come back to t-tests and AOV afterwards. Th…
Hi, thanks a ton for the detailed response. Luckily I don't really need a Stats 101, so I don't think I'll mind him jumping around. If, of course, it does get a bother I know which course is right for me. Till then I'm also doing a bit of Thrun's Udacity Stats course on the side. I would actually appreciate a list of resources in Python, that's what I like using most! I have downloaded a copy of "Think Stats", but ha…
Sorry for the late response, I completely forgot about this post!
Looks like you're on the right track though, "Think Stats" and Udacity's stats class were the main things I was going to recommend. I'd also recommend checking out IPython's web notebook for inline charts and general awesomeness, and the Pandas library for an R-style data frame built on top of NumPy. The best resources for learning about IPython are probably screencasts, and the author of Pandas has a book out named "Python for Data Analysis" that covers IPython, NumPy, Pandas and some matplotlib.