Real-World Machine Learning

I find this book very helpful. The introduction chapter is freely available.

Sparklyr

The new sparklyr package from rstudio provides a convenient interface between R/Rstudio and Spark. It runs well on Linux; it also works on Windows for Spark 1.6.2 and lower. For some reasons, it does not work with Spark 2.0 on Windows. I assume it will get fixed in subsequent releases.

LaplacesDemon is back

Looks like LaplacesDemon package is back. Now we have a pure R-based Bayesian computation platform.

Microsoft Analytics in 2016

Here is a thorough introduction of data science solution offered by Microsoft.

Making Causal Impact Analysis Easy

Very helpful blog post regarding the CausalImpact package and MarketMatching package.

Setting Up New R Notebook

This is extremely cool!

Microsoft and Linux: True Romance or Toxic Love?

Insightful article.

tm reading in data frame and keep text's id

Here are some useful discussions.

Multiple imputation using R

R has a long list of packages for multiple imputation. The main problem is integration: statistical procedures in other packages may or may not work with the imputation procedures. I have been using Amelia together with Zelig. Because they were written by the same group, they work well together. However, I have been having trouble with making multiple imputation to work with the plm package. After searching the internet, here comes the solution:

1. Impute the missing data using Amelia or Mice.
2. Estimate the model on each imputed data.
3. Use the mitools package to extract and combine results. 

For example, here is a simple example:

...

imp <- mice(d)

mydata <- imputationList(lapply(1:5, complete, x = imp))

fit <- lapply(mydata$imputations, function(x){

plm(cog3pl ~ oc + grade9 + boy + han + ruralbirth, data = x,

index = c("schids"), model = "pooling")})

betas <- MIextract(fit, fun = coef)

vars <- MIextract(fit, fun = vcov)

summary(MIcombine(betas, vars))

I bet this will work for most, if not all, estimation procedures in R.