Initial commit

# Base image https://hub.docker.com/u/rocker/

FROM rocker/shiny:latest

# system libraries of general use

## install debian packages

RUN apt-get update -qq && apt-get -y --no-install-recommends install \

    libxml2-dev \

    libcairo2-dev \

    libsqlite3-dev \

    libmariadbd-dev \

    libpq-dev \

    libssh2-1-dev \

    unixodbc-dev \

    libcurl4-openssl-dev \

    libssl-dev

## update system libraries

RUN apt-get update && \

    apt-get upgrade -y && \

    apt-get clean

USER root

# install renv & restore packages

RUN Rscript -e 'install.packages(c("renv","tidyverse","plotly"))'

# expose port

EXPOSE 3838

# run app on container start

CMD ["R", "-e", "shiny::runApp('/app', host = '0.0.0.0', port = 3838)"]

logs/

upstream shinyservice {

 server localhost:3838;

}

server {

 listen 80;

 #server_name localhost;

 location / {

   # This would be the directory where your React app's static files are stored at

   root /usr/share/nginx/html;

   try_files $uri /index.html;

 }

 location /CLT {

   proxy_set_header X-Real-IP $remote_addr;

   proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;

   proxy_set_header X-NginX-Proxy true;

   proxy_pass http://shinyservice;

   proxy_ssl_session_reuse off;

   proxy_set_header Host $http_host;

   proxy_cache_bypass $http_upgrade;

   proxy_redirect off;

 }

}

# 0. git clone <repo URL>

# 1. cd <repo>

# 2. Change IP Address in assets/config/nginx.conf

# 3. change folder in docker-compose to shiny app folder

# 4. docker-compose up -d

get_my_sample <- function(nn,NN,theseed,correlated,pos_correlated){

    set.seed(theseed)

    # Draw nn observations from a standard normal distribution

    Sample_list <- lapply(1:NN,function(x) rnorm(nn))

    # Concatenate the lists to a data frame then cast the data.frame into a matrix

    samples_indep <- as.matrix(do.call("rbind",Sample_list))

    if(correlated){

        # Generate a random correlation matrix:

        # runif(nn^2)*2-1 generates nn² draws from the uniform distribution and

        # changes the support to -1 to 1 by multiplying by 2 and subtracting 1

        # One could also us another distribution it really doesn't matter for our purpose.

        # BB%*%BB will be the covariance variance matrix of the joint distribution of our realizations.

        # Nonetheless, if we would like to have only positive correlations then we would need to change the support

        # here to some positive interval (i.e. 0 to 1)

        if(!pos_correlated){

            BB <- matrix(runif(nn^2)*2-1, ncol=nn)

        }else{

            # Comment this line in if you would like to see only positive correlations

            BB <- matrix(runif(nn^2), ncol=nn)

        }

        Sigma <- t(BB)%*%BB

        # In any case, we need to standardise the variables so that the variance is between 0 and 1

        CC <- BB/matrix(sqrt(diag(Sigma)),nn,nn,byrow = TRUE)

        Sigma_std <- t(CC)%*%CC

        # Transform the iid standard normally observations into correlated normal distributions

        samples_corr <- (samples_indep%*%CC)

        return(samples_corr)

    }

    return(samples_indep)

}