cast-web-api in a snap

cast-web-api in a snap

After the sd card on my raspberry pi died the prospect of creating a new one so I could connect my google cast devices to smartthings did not sound like fun. Installing cast-web-api is not trivial. So, I decided this was a good opportunity to create my first snap. Creating a snap to wrap a nodejs app was super easy. I shared my code on github, but this is the whole thing:

nvidia-docker + greta

Goal: Use greta with nvidia-docker Docker file: ## Based on work by ## ## rocker ## FROM nvidia/cuda:9.0-cudnn7-runtime MAINTAINER "Ignacio Martinez" RUN echo 'debconf debconf/frontend select Noninteractive' | debconf-set-selections ## Prepare R installation from RUN sh -c 'echo "deb xenial-cran35/" >> /etc/apt/sources.list' \ && apt-key adv --keyserver --recv-keys E298A3A825C0D65DFD57CBB651716619E084DAB9 RUN apt-get update \ && apt-get upgrade -y -q \ && apt-get install -y --no-install-recommends \ libapparmor1 \ r-base \ r-base-dev \ littler \ r-cran-littler \ libxml2-dev \ libxt-dev \ libssl-dev \ libcurl4-openssl-dev \ imagemagick \ python-pip \ libpython2.

Cloud computing with R and AWS

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.

Embarrassingly Parallel Computing with doAzureParallel

Why? You want to run 100 regressions, they each take one hour, and the only difference is the data set they are using. This is an embarrassingly parallel problem. For whatever reason, you want to use Azure instead of google compute engine… Before you start I will assume that: you have an Azure account, you have correctly installed, and configured doAzureParallel Create some fake data library(dplyr) library(stringr) set.

Embarrassingly Parallel Computing with googleComputeEngineR

Why? You want to run 100 regressions, they each take one hour, and the only difference is the data set they are using. This is an embarrassingly parallel problem. Before you start I will assume that: you have a Google compute engine account, you have correctly installed, and configured googleComputeEngineR Create some fake data library(googleComputeEngineR) library(dplyr) library(stringr) library(future) library(future.apply) set.seed(12618) n<-10000 fakeData <- list() for(ii in 1:100){ fakeData[[ii]] <- future({ fakeDF <- data.

My highlights from StanCon 2018

Source: THE JUMPING RIVERS BLOG During my econ PhD I learned a lot about frequentist statistics. Alas, my training of Bayesian statistics was limited. Three years ago, I joined @MathPolResearch and started delving into this whole new world. Two weeks ago, thanks to @jumping_uk, I was able to attend StanCon. This was an amazing experience, which allowed me to meet some great people and learn a lot from them. These are my highlights from the conference:
Send emails from R with mailgun

Send emails from R with mailgun

Why? Until now I’ve been sending emails with R using my Gmail account. This works, but configuring mailR for the first time is always a pain. A few days ago @marked told me about mailgun and how to use it. The great thing is that you only need httr to use it. How? Using @marked as my base, I created a tiny R package to make using mailgun even easier:
Watson Conversation + R

Watson Conversation + R

Update 10/20/2018: IBM changed something on their api that broke my package. I might try to fix this at some point, for now you can take a look at the video bellow. I just discovered IBM’s Watson conversation, and this free online course shows you how to build your first chatbot. Alas, the course did not cover how to use the API with R. I think chatbots can be very useful when creating shiny apps.

Installing mailR

In order to get mailR I had to create a symlink with: #ln -s /usr/lib/jvm/java-1.7.0-openjdk-amd64/jre/lib/amd64/server/ /usr/lib/R/lib/ ln -s /usr/lib/jvm/java-9-openjdk-amd64/lib/amd64/server/ /usr/lib To make life easier, I created a wrapper around it using secret to store my password. To send an email notification, I just have to run: IMSecrets::email_me()

RStudio server with synology and docker

SSH into my synology and run: sudo su docker run -d -p 8787:8787 -e USER=<username> -e PASSWORD=<password> andrewheiss/tidyverse-rstanarm nano /usr/syno/share/nginx/Portal.mustache add the following to the Location section: proxy_set_header Upgrade $http_upgrade; proxy_set_header Connection "upgrade"; proxy_read_timeout 86400; run: sudo synoservicecfg --restart nginx