Julia for R users (written in Rstudio)

• a high-level, high-performance dynamic programming language

• for technical computing, with syntax that is familiar to other computing environments

• The library, mostly written in Julia itself, also integrates mature, best-of-breed C and Fortran libraries for linear algebra, random number generation, FFTs, and string processing.

• It provides a sophisticated compiler, distributed parallel execution, numerical accuracy, and an extensive mathematical function library.

• Free

• The future

• FAST (~2–3x C speed)

• Well thoughtout (is JIT compiled & everthing in Julia is an expression)

• Uses syntax almost identical to Matlab (very similar to R)

• Written by mathematicians, statisticians and computer scientists for mathematicians, statisticians and computer scientists

• Overall, deals with the two-language problem

• The architecture has been so well though through

• 1. Write code as you code up your research e.g. mu <- sum(x)/length(x)
• 1. Delve down a level to see how the computer is implementing, then delve some more, and more and even to the binary 0, 1's of your hardware
• 1. To allow high level programming with all of the deep, dark aspects some of which are seen using C++, Fortran

• You are in control when you want to be, and when you don't you can rest assured Julia will be quicker than R

• Oh and you can call R from Julia

• JuliaBox allows you to use Julia without installing on your computer/can use on your ipad

• Using the ; key switches from Julia in the command line to my bash shell

• Structure similar to python (Pkg.add(“X”) and using X)

• JuliaBox allows you to use Julia without installing on your computer/can use on your ipad

library(lme4)
head(Dyestuff)

  Batch Yield
1     A  1545
2     A  1440
3     A  1440
4     A  1520
5     A  1580
6     B  1540

lmer(Yield ~ 1|Batch, Dyestuff, REML=FALSE)

Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: Yield ~ 1 | Batch
   Data: Dyestuff
      AIC       BIC    logLik  deviance  df.resid 
 333.3271  337.5307 -163.6635  327.3271        27 
Random effects:
 Groups   Name        Std.Dev.
 Batch    (Intercept) 37.26   
 Residual             49.51   
Number of obs: 30, groups:  Batch, 6
Fixed Effects:
(Intercept)  
       1527  

library(survival)
data("ovarian")
head(ovarian)

  futime fustat     age resid.ds rx ecog.ps
1     59      1 72.3315        2  1       1
2    115      1 74.4932        2  1       1
3    156      1 66.4658        2  1       2
4    421      0 53.3644        2  2       1
5    431      1 50.3397        2  1       1
6    448      0 56.4301        1  1       2

ovarian$group <- ovarian$rx - 1
coxph(Surv(futime, fustat) ~ age + group, data=ovarian)

Call:
coxph(formula = Surv(futime, fustat) ~ age + group, data = ovarian)

         coef exp(coef) se(coef)     z      p
age    0.1473    1.1587   0.0461  3.19 0.0014
group -0.8040    0.4475   0.6320 -1.27 0.2034

Likelihood ratio test=15.9  on 2 df, p=0.000355
n= 26, number of events= 12 

Douglas Bates “Julia provides the best of both worlds (Fast development vs. fast execution) and is the technical programming language of the future”

• http://www.stat.wisc.edu/~bates/JuliaForRProgrammers.pdf
• http://julialang.org
• https://juliabyexample.helpmanual.io
• Simon Bryne (Statistician) https://www.youtube.com/watch?v=UFAolnvTcsk&list=PL1iFv_5jgWukgNxgdvaZ1zdG5p23b3rta&index=3