## R Throwdown Challenge 185

theodp (442580) writes

*"'R beats Python!' screams the headline at Prof. Norm Matloff's Mad (Data) Scientist blog. 'R beats Julia! Anyone else wanna challenge R?' Not that he has anything against Python, Matloff adds, but he just doesn't believe that Python or Julia will become 'the new R' anytime soon, or ever. Why? 'R is written by statisticians, for statisticians,' explains Matloff. 'It matters. An Argentinian chef, say, who wants to make Japanese sushi may get all the ingredients right, but likely it just won't work out quite the same. Similarly, a Pythonista could certainly cook up some code for some statistical procedure by reading a statistics book, but it wouldn't be quite same. It would likely be missing some things of interest to the practicing statistician. And R is Statistically Correct.'"*
## Meh (Score:5, Informative)

Python, C, Mathematica and R all have different strengths for mathematical work / numerical calculations though, and using the best tool for the job is what it's about. As always, what the best tool actually is, is also rather subjective, as which tool will best solve a specific task is always dependent on your skill with the different tools. I do agree with professor though, even though there's quite abit of Python hype (python + scipy/matplotlib is amazing) R is not being replaced anytime soon. It's too good at what it's good at.

## Re:Bad analogy (Score:4, Informative)

You're just getting a plot. I'm talking about output that looks like this:

Call:

lm(formula = new_day_return ~ prior_day_return + rsi_under_10 +

rsi_under_20 + rsi_under_30 + rsi_over_70 + rsi_over_80 +

rsi_over_90 + fourteen_day_rsi, data = mydata5)

Residuals:Min 1Q Median 3Q Max

-100 -1 0 1 205700

Coefficients:Estimate Std. Error t value Pr(>|t|)

(Intercept) -9.845e+01 3.742e+02 -0.263 0.792

prior_day_return -4.143e-04 3.434e-03 -0.121 0.904

rsi_under_10 -1.916e-01 3.798e+00 -0.050 0.960

rsi_under_20 2.195e-02 1.447e+00 0.015 0.988

rsi_under_30 -2.291e-01 6.915e-01 -0.331 0.740

rsi_over_70 -2.364e-01 3.348e-01 -0.706 0.480

rsi_over_80 5.135e-03 4.820e-01 0.011 0.991

rsi_over_90 7.162e-03 8.650e-01 0.008 0.993

fourteen_day_rsi 4.193e-04 3.434e-03 0.122 0.903

Residual standard error: 163.7 on 1581663 degrees of freedom

(137 observations deleted due to missingness)

Multiple R-squared: 5.397e-07, Adjusted R-squared: -4.518e-06

F-statistic: 0.1067 on 8 and 1581663 DF, p-value: 0.999

## If you're going to use R (Score:5, Informative)

For a nice video on using ipython notebook in data analysis: https://www.youtube.com/watch?... [youtube.com]

For a nice selection of ipython notebooks for doing various type of data analysis: https://github.com/ipython/ipy... [github.com]