How the R-project is taking over statistical analysis software
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How the R-project is taking over statistical analysis software
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Re: How the R-project is taking over statistical analysis software
#2https://sites.google.com/site/r4statistics/_/rsrc/1318535062...
Re: How the R-project is taking over statistical analysis software
#3Re: How the R-project is taking over statistical analysis software
#4Anecdotally, NumPy (Python) has some traction. Similarly they don't consider SQL libraries. And I'm sure there are statistical analysis libraries for Java. According to the bar chart below R is mentioned by 45%, SQL by 32%, Python by 25%, Java by 24%. This seems a more reasonable comparison to me than the graphs earlier (higher up) in the post. https://sites.google.com/site/r4statistics/_/rsrc/1318535062...
Re: How the R-project is taking over statistical analysis software
#5Re: How the R-project is taking over statistical analysis software
#6Can someone point me to a good introduction/resources to R? Especially for web stuff?
Re: How the R-project is taking over statistical analysis software
#7Re: How the R-project is taking over statistical analysis software
#8love R.. but have to say because it's open source, you do have to watch for the quality of libraries
Re: How the R-project is taking over statistical analysis software
#9My only complaint is the awful default IDE, which can be mitigated to a large extent by scripting elsewhere and source()ing the script, and some odd edge behaviors including the mystifying row names of dataframes, the difficulty of dropping unused factor levels from aggregated or sliced data (another dataframe issue), and the perhaps unnecessary obscurity of some of the plotting functions (although holding R responsible for the lattice library is unfair).
All that said, for a free tool, it's extraordinary, and the authors of the base language and the many packages that I use have my gratitude.
Re: How the R-project is taking over statistical analysis software
#10I use R as my primary data-analysis tool for almost all of my work, with occasional recourse to SAS for certain specialized models (e.g., PROC GLIMMIX for generalized mixed models). My only complaint is the awful default IDE, which can be mitigated to a large extent by scripting elsewhere and source()ing the script, and some odd edge behaviors including the mystifying row names of dataframes, the difficulty of droppi…