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% R语言春令营（1）：导论 % \href{mailto:[email protected]}{陈堰平} % April 14, 2013

R的安装

Reproducibility

  • Same script
  • Same results
  • Anywhere
    • Single thread
    • Multi-core
    • Cloud Scale

Everything starts with a seed.

Simulation is based off Pseudo-random number generation (PRNG).

  • PRNG is sequential, next number depends on the last state.
  • Seeds are used to store the state of a random number generator
  • by 'Setting a seed' one can place a PRNG into any exact state.

Parallel Random Number Generation

Simulation is complicated in new parallel environments.

  • PRNG is sequential,
  • parallel execution is not,
  • and order of execution is not guaranteed.
Right Left Default Center
12 12 12 12
123 123 123 123
1 1 1 1

: Demonstration of simple table syntax.

This is where parallel pseudo-random number generators help out.

Parallel PRNG

Parallel pseudo-random number generators start with a singe state that can spawn additional streams as well as streams of random numbers.

  1. SPRNG
  2. L'Ecuyer combined multiple-recursive generator

Centered Default Right Left Header Aligned Aligned Aligned

First row 12.0 Example of a row that spans multiple lines.

Second row 5.0 Here's another one. Note the blank line between rows.

Table: Here's the caption.

R包的使用

R package harvestr

https://github.com/halpo/harvestr

What harvestr does:

  • Reproducibility
  • Caching
  • Under parallelized environments.

How harvestr works

  • Analytical elements are separated into work-flows of dependent elements.
    • Set up environment/seed
    • Generate Data
    • Perform analysis
      • Stochastic
      • Non-Stochastic
    • Summarize
  • Results from one step carry to another by carrying the seed with the results.

Primary work-flow for harvestr

  • gather(n) - generate n random number streams.
  • farm(seeds, expr) - evaluate expr with each seed in seeds.
  • harvest(x, fun) - for each data in x call the function fun

数据管理

Building blocks

Some building blocks that might might be helpful.

  • plant- for setting up copies of an object with given seeds.
  • sprout - for obtaining the sub-streams used with graft.
  • reap - single object version of harvest

In case you are wondering

  • Yes it works with Rcpp code,
  • provided the compiled code uses the RNGScope for RNG in C++.
  • But take care to not carry C++ reference objects across parallel calls.
seeds <- gather(10, seed=20120614)
data <- farm(seeds, {
x1 <- rnorm(400)
x2 <- rnorm(400)
g <- rep(rnorm(4), each=100)
trt <- rep(1:4, each=100)
y <- rnorm(n=400, mean=3*x1 + x2 + g)
data.frame(y, x1, x2, trt)
})