├── .gitignore
├── dockerfile
├── main.R
├── make_model.R
├── readme.md
└── rest_controller.R


/.gitignore:
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1 | .Rproj.user
2 | .Rhistory
3 | .RData
4 | .Ruserdata


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/dockerfile:
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 1 | # start from the rocker/r-ver:3.5.0 image
 2 | FROM rocker/r-ver:3.5.0
 3 | 
 4 | # install the linux libraries needed for plumber
 5 | RUN apt-get update -qq && apt-get install -y \
 6 |   libssl-dev \
 7 |   libcurl4-gnutls-dev
 8 | 
 9 | # install plumber
10 | RUN R -e "install.packages('plumber')"
11 | 
12 | # copy everything from the current directory into the container
13 | COPY / /
14 | 
15 | # open port 80 to traffic
16 | EXPOSE 80
17 | 
18 | # when the container starts, start the main.R script
19 | ENTRYPOINT ["Rscript", "main.R"]


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/main.R:
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1 | library(plumber)
2 | 
3 | r <- plumb("rest_controller.R")
4 | r$run(port=80, host="0.0.0.0")


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/make_model.R:
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1 | # load the dataset
2 | dataset <- iris
3 | 
4 | # create the model
5 | model <- lm(Petal.Length ~ Petal.Width, data = dataset)
6 | 
7 | # # example: run the model once
8 | # prediction_data <- data.frame(Petal.Width=1)
9 | # predict(model,prediction_data)


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/readme.md:
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1 | # R API Tutorial (with docker!)
2 | 
3 | This code is what was used in our blog posts on [using R to make an API](https://medium.com/@heathernolis/r-can-api-c184951a24a3) and [using R with docker](https://medium.com/@skyetetra/using-docker-to-deploy-an-r-plumber-api-863ccf91516d).
4 | 
5 | The three R files are used to create a simple API with the plumber R library. The dockerfile can be used to take the API and create a docker container with it.
6 | 
7 | To run the API directly, run the `main.R` script (or deploy the container). Then go to http://127.0.0.1/predict_petal_length?petal_width=1 to run the model on a petal width of 1.
8 | 


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/rest_controller.R:
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 1 | 
 2 | # build the model
 3 | source("make_model.R")
 4 | 
 5 | #* @get /predict_petal_length
 6 | get_predict_length <- function(petal_width){
 7 |   
 8 |   # convert the input to a number
 9 |   petal_width <- as.numeric(petal_width)
10 |   
11 |   #create the prediction data frame
12 |   prediction_data <- data.frame(Petal.Width=petal_width)
13 |   
14 |   # create the prediction
15 |   predict(model,prediction_data)
16 | }
17 | 


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