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.
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 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? 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’s gist as my base, I created a tiny R package to make using mailgun even easier:
Update 03/30/2019: IMWatson is working again!
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. Therefore, I started working on IMWatson. The goal of this R package is to make integrating a chatbot into a shiny app as easy as possible.
Goal: Create multiple, and trackable, short URLs that point to the same place Tools: YOURLS, R Steps: Installing YOURLS is very easy, particularly if you do it with docker.
A container for the database: docker run --name yourlsmysql -e MYSQL_ROOT_PASSWORD=mysecretpassword -d mysql A container for YOURLS: docker run --name yourls -d \ -e YOURL_USER='admin' \ -e YOURL_PASSWORD='mysecretpassword' \ -e YOURLS_SITE='http://sh.tld' \ --link yourlsmysql:mysql bios/docker-yourls To use the web-interface, you need to go to your site.
