Reasons I prefer R to python: - Rmarkdown. I prefer a text document over a web notebook for exploratory research - The standard library is for statistics and

Reasons I prefer R to python: - Rmarkdown. I prefer a text document over a web notebook for exploratory research - The standard library is for statistics and data: dataframe, lm, anova, etc. are builtin - A huge range of probability distributions are built-in. I don't need to import extra libs to do simulations. - Between functional programming techniques and vectorization, I can write very clean and concise code - Tidyverse and data.table are lovely and coherent approaches to data management. Data.table is fast and memory efficient. - Advanced models are trustworthy: For example, glmnet, mgcv, nlme, rms are authored by statistical heavyweights, and have accompanying books that are excellent. I don't have the same confidence in python's statsmodels. - CRAN is easy to use, I can access it from my R session, and there are rarely problems (big thanks to Uwe Ligges) - Libraries for design of experiments and surveys are available. R supports the entire design -> data management -> model cycle. - base graphics/lattice/ggplot2 are excellent for plotting. If I need something advanced, I can use grid. If I need vector graphics, I can use tikzDevice for latex. - Rstudio is a an excellent IDE, and Emacs Speaks Statistics is an excellent Emacs plugin - It is very easy to get help without going to google. (?foo, ??bar, etc) Documentation is well organized, and the documents often contain citations and relevant links. - Lots of advanced models can't be found outside of R. Today I fit a splines-on-a-sphere model using mgcv (https://stat.ethz.ch/R-manual/R-patched/library/mgcv/html/smooth.construct.sos.smooth.spec.html https://stat.ethz.ch/R-manual/R-patched/library/mgcv/html/sm...) - Rapid iteration in modeling using Wilkinson notation formulas. Built-in formulas are the actual killer app of R, IMO. - Things are generally fast, but if you need extra horsepower, plugging into c++ is easy using Rcpp. - R feels like lisp. Experimentation is easy, and I don't feel forced into any particular paradigm while using R. I have a lot of ways to evaluate code (https://ess.r-project.org/Manual/ess.html#Evaluating-code https://ess.r-project.org/Manual/ess.html#Evaluating-code)