Rest API in R using Restrserve & Plumber

Following the last post on how to make an API in python, we will make an API in R using two methods. Restrserve and Plumber.

The model of the API

We have made a machine learning model from the iris dataset that predicts the species. The code of the model is the following.

library(datasets)
library("caret")
library(dplyr)

ir_data<- iris
levels(ir_data$Species)
x <- ir_data[,1:4] 
y<-ir_data$Species

fit.lda <- train(Species~., data=ir_data, method="lda")

#save the model
saveRDS(fit.lda, "./final_model.rds")

Now that we have created and saved the model we can make an API.

Restrserve

The first method is Restrserve. Basically we have to create a function that gets the requests and gives the response back. In this example, the request$body is a JSON containing “Sepal.Length”, “Sepal.Width”, “Petal.Length”, “Petal.Width”. So using the jsonlite library we are getting these values and we are creating a data frame to feed our model to get the prediction. Then, we are setting the response$body with the result.

We only have to set the Restrserve application settings which are to add the endpoint “/iris_predict” and map it with our function. The full code of API is the following

library(RestRserve)
library("caret")
library(dplyr)
library(jsonlite)

#loading the model
super_model <- readRDS("./final_model.rds")



iris_predict=function(request, response){
  x = fromJSON(rawToChar(request$body))
  test <- data.frame("Sepal.Length" = x["Sepal.Length"], "Sepal.Width" = x["Sepal.Width"], "Petal.Length" = x["Petal.Length"],"Petal.Width"=x["Petal.Width"])
  result<-predict(super_model, test)
  response$body = result
  response$content_type = "application/json"
}



app = RestRserve::Application$new()
app$add_post(path = "/iris_predict", FUN = iris_predict)

backend = BackendRserve$new()
backend$start(app, 8080)

Running the above locally, it will run on the URL: http://0.0.0.0:8080/iris_predict. As an example we can set the request body with the following JSON:

{
    "Sepal.Length": 5,
    "Sepal.Width": 3,
    "Petal.Length": 6,
    "Petal.Width": 1
}

We are getting the following result:

"virginica"

Plumber

The second method is using Plumber. Now we have to add the parameters in a comment starting with @param and to set the endpoint with @post for a post method. Then in a function, we are adding the parameters as input and we continue as before.

library(RestRserve)
library("caret")
library(dplyr)
library(jsonlite)


super_model <- readRDS("./final_model.rds")

#' @param Sepal.Length Sepal.Width Petal.Length Petal.Width
#' @post /iris_predict


function(Sepal.Length, Sepal.Width, Petal.Length, Petal.Width){
  test <- data.frame("Sepal.Length" = Sepal.Length, "Sepal.Width" =Sepal.Width, "Petal.Length" = Petal.Length,"Petal.Width"=Petal.Width)
  predict(super_model, test)
}

Now, to run this API we have to run the following code in R:

library(plumber)
r <- plumb("plumber.R")
r$run(port=8080, host="0.0.0.0", swagger=TRUE)

The plumber.R is our code and for the port, you can add anything you want.

If you run the code above, the output should be the following:

Starting server to listen on port 8080
Running the swagger UI at http://127.0.0.1:8080/__swagger__/

That means that we are live and we can use our model!