vizdraws is available on cran. The goal of this package is to help communicate findings in a better way. For example, instead of communicating a point estimate you can communicate the probability that something is better or worse that its alternative:
library(vizdraws) vizdraws(prior = 'normal(0.05, 0.2)', posterior = rnorm(n = 10000, mean = 0.3, sd = 0.5))
However, in some cases 0 is not a relevant threshold for decision making.
Why? Shiny will scale really well as the data is not duplicated in memory for each user The data is housed in one location (plumber API) The underlying data can be VERY large, which may not be suitable for a shiny app, but is ok for an R process to solve. The API inner workings can be updated without redeploying the shiny application. The docker-compose.yml version: '3.
Inspired by this thread I decided to document how you can use Stan on a AWS instance with remoter.
Creating an AMI The simplest way of doing this is to start with an Ubuntu VM and install a docker container with Remoter and Rstan. I wrote a simple bash script that does that. Just ssh into Ubuntu and run:
wget -O - https://link.ignacio.website/remoter | bash Now ssh back into the instance and modify the password in my docker-compose.
Quick how to: # drat::addRepo(account = "Ignacio", alturl = "https://drat.ignacio.website/") # install.packages("IMS3") library("IMS3") ## Loading required package: aws.s3 set.enviroment() bucketlist() ## c..ignacios.test.bucket....2019.03.19T13.21.52.000Z.. ## 1 ignacios-test-bucket ## 2 2019-03-19T13:21:52.000Z # save an in-memory R object into S3 s3save(mtcars, bucket = "ignacios-test-bucket", object = "mtcars.Rdata") # `load()` R objects from the file s3load("mtcars.Rdata", bucket = "ignacios-test-bucket") Video talking about this: Using S3 from within an EC2 instance I added the aws.
Why? I want to make sure that when I make a change to some complicated code nothing breaks. Moreover, I want to make sure that nothing breaks in a clean install.
Getting gitlab up and running If you are reading this you probably know that I like gitlab better than bitbucket, github, and that awfaul thing that you are probably using. You can skip this and the next section and just use the hosted version of gitlab which gives you 2000 minutes per month to do this stuff.
The following code shows how to write some simple code to draw random numbers from a normal and a binomial distribution. Notice that instead of declaring A as a numeric matri
Serial Double loop #include <Rcpp.h>using namespace Rcpp; // [[Rcpp::export]] NumericMatrix my_matrix(int I) { NumericMatrix A(I,2); for(int i = 0; i < I; i++){ A(i,0) = R::rnorm(2,1) ; A(i,1) = R::rbinom(1,0.5) ; } colnames(A) = CharacterVector::create("Normal", "Bernoulli"); return A; } set.
This past weekend I discovered the wonders of c++ thanks to this datacamp course. Although c++ syntax is different, knowing Fortran made this much easier.
Filling a matrix with c++ The following code creates a function that can be called from R to fill a matrix. Something that is different than in Fortran is that to make loops more efficient you have to do right (j) to left (i) instead of left to right.
Why? I want to fill up a big matrix and I care about speed and to a lesser degree memory efficiency. In practice the matrix will have 4000 rows and K columns where K is the number of observations for which I want to run my predictive model. For this exercise I will keep K to just 500 because my R approach eats a ton of memory. For this simple exercise, I will \(A_{ik} = 1 / (1 + exp(i^2 + i^3 + k^2 + k^3))\) in practice the operation that I need to do is much more complicated which will make the difference is run time even bigger.
Why? You want to run R code on the cloud. For whatever reason, you don’t want to use google nor azure. Credit I took most of the code from this gist
The code This function takes a list with your instances, the path to your private key, and returns a cluster object that can be used with the future package. I was told that this function will be part of a new package soon.
