Azure Machine Learning - Python Query Script with Power BI dataset

This post started with how to code Python to consume Azure ML predictive model. I ended up with scripting in "R", as i found good examples.
1: Have a Azure ML predictive model
2: Want to use Power BI to consume and get the prediction from azure ML (web service)

3: Add the prediction to the original dataset in Power BI.

1. Added datasource in Power BI.

2. Edit Queries

3. Transform, and choose "Run R- script"

Added this code (dataset is Power BI data, you get it directly):

# 'dataset' holds the input data for this script
library("RCurl")
library("rjson")
library("bitops")
# Accept SSL certificates issued by public Certificate Authorities
options(RCurlOptions = list(cainfo = system.file("CurlSSL", "cacert.pem", package = "RCurl")))
dataset <- dataset[complete.cases(dataset), ] ##remove NaN
h = basicTextGatherer()
hdr = basicHeaderGatherer()
req =  list(
  Inputs = list(
    input1 = setNames(lapply(split(dataset, seq(nrow(dataset))), FUN = as.list), NULL)
  ),
  GlobalParameters = setNames(fromJSON('{}'), character(0))
)
body = enc2utf8(toJSON(req))
api_key = "<replace key>" # Replace this with the API key for the web service
authz_hdr = paste('Bearer', api_key, sep=' ')
h$reset()
curlPerform(url = "<replace url>", # Replace this with the URL for the web service
            httpheader=c('Content-Type' = "application/json", 'Authorization' = authz_hdr),
            postfields=body,
            writefunction = h$update,
            headerfunction = hdr$update,
            verbose = TRUE
)
headers = hdr$value()
httpStatus = headers["status"]
if (httpStatus >= 400){
  result.error <- data.frame("Error")
}else{
  result.json = h$value()
  result.list = fromJSON(result.json)$Results$output1
  result.list.final <- lapply(result.list, FUN = function(x) lapply(x, FUN = function(y) if( is.null(y) ){y <- NA}else{y <- y}))
  result.df <- data.frame(do.call(rbind.data.frame, result.list.final))
  result.df = merge(dataset,result.df,by.x="row_id",by.y="row_id") #to combine original dataset with prediction
}