diff --git a/examples/plumber/Dockerfile b/examples/plumber/Dockerfile
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+FROM rstudio/plumber
+
+WORKDIR /app
+
+COPY . /app
+
+RUN R -e "install.packages('caret', repos='http://cran.rstudio.com/')"
+
+EXPOSE 80
+
+ENTRYPOINT ["Rscript", "main.R"]
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diff --git a/examples/plumber/README.md b/examples/plumber/README.md
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+# REST API with Plumber 
+
+A RESTful API built with the Plumber package in R.
+The API provides three main functions:
+- Checking the server status of the API with a GET request.
+- Predicting iris petal length based on various parameters with a POST request.
+- Displaying a plot that compares actual to predicted petal lengths with a GET request.
+
+## Steps for Testing Locally
+
+To run the API locally, use the following command in your terminal:
+```sh
+Rscript main.R
+```
+
+Now you can explore your Swagger Docs at `http://127.0.0.1/__docs__/`.
+
+### Health Check (GET)
+Check the API's server status with:
+```sh
+curl -X 'GET' 'http://127.0.0.1/health_check'
+```
+
+### Predict Petal Length (POST)
+
+To predict petal length with only the petal width:
+```sh
+curl -X 'POST' 'http://127.0.0.1/predict_petal_length' -d 'petal_width=10'
+```
+
+To include petal width, sepal length, sepal width, and species of the flower:
+```sh
+curl -X 'POST' 'http://127.0.0.1/predict_petal_length' -d "petal_width=1.2" -d "sepal_length=3.5" -d "sepal_width=2.1" -d "species=setosa"
+```
+
+### Plot Actual vs Predicted (GET)
+To download a plot comparing actual and predicted petal lengths:
+```sh
+curl -X 'GET' 'http://127.0.0.1/plot_actual_vs_predicted' --output plot.png
+```
+
+## Steps for Deploying on Ploomber Cloud
+### Prerequisites
+- [Ploomber Cloud account](https://www.platform.ploomber.io/applications)
+- A Dockerfile
+- Your code
+
+Note: Docker deployment option is available exclusively to Pro, Teams, and Enterprise users. Start your 10-day free trial [here.](https://ploomber.io/pricing/)
+
+There are two ways you can deploy it on Ploomber Cloud: via (1) [Graphical User Interface](https://docs.cloud.ploomber.io/en/latest/quickstart/app.html), and (2) [Command Line Interface](https://docs.cloud.ploomber.io/en/latest/user-guide/cli.html). Let's take a look at the CLI method.
+
+### Command Line Interface
+
+If you haven't installed `ploomber-cloud`, run
+```sh
+pip install ploomber-cloud
+```
+
+Then, set your API key following to [this documentation](https://docs.cloud.ploomber.io/en/latest/quickstart/apikey.html).
+```sh
+ploomber-cloud key YOURKEY
+```
+
+Navigate to your project directory where your Dockerfile is located and initialize the project. Confirm the inferred project type (Docker) when prompted.
+```sh
+cd <project-name>
+ploomber-cloud init
+```
+
+Now, deploy your application.
+```sh
+ploomber-cloud deploy
+```
+
+Once its deployment is complete, access your endpoints deployed on Ploomber Cloud using your app's URL. For example, you can send a `GET` request with
+```sh
+curl -X 'GET' 'https://<id>.ploomberapp.io/health_check'
+```
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diff --git a/examples/plumber/main.R b/examples/plumber/main.R
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+library(plumber)
+pr <- plumb("plumber.R")
+pr$run(port = 80, host = "0.0.0.0")
diff --git a/examples/plumber/plot.png b/examples/plumber/plot.png
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diff --git a/examples/plumber/plumber.R b/examples/plumber/plumber.R
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+#* @apiTitle Iris Petal Length Prediction API
+#* @apiDescription This API allows users to interact with a linear regression model predicting iris petal length based on petal width, and optionally, sepal length, sepal width, and species.
+#* It provides endpoints for health checks, petal length predictions, and visualizations comparing actual to predicted lengths.
+
+library(caret)
+# Prepare the model
+dataset <- iris
+
+# Set seed for reproducibility
+set.seed(42)
+
+# Split data into training and testing sets
+train_index <- createDataPartition(iris$Petal.Length, p = 0.8, list = FALSE)
+train_data <- iris[train_index, ]
+test_data <- iris[-train_index, ]
+
+# Train the model using all parameters on the training data
+model_all <- lm(Petal.Length ~ Sepal.Length + Sepal.Width + Petal.Width + Species, data = train_data)
+
+# Train the model using only petal width on the training data
+model_petal_width <- lm(Petal.Length ~ Petal.Width, data = iris)
+
+#* Health check - Returns the API status and the current server time
+#* @get /health_check
+function() {
+  list(
+    status = "The API is running",
+    time = Sys.time()
+  )
+}
+
+#* Predict petal length - Returns a predicted petal length based on available parameters
+#* @param petal_width Numeric: Width of the petal (required)
+#* @param sepal_length Numeric: Length of the sepal
+#* @param sepal_width Numeric: Width of the sepal
+#* @param species Character: Species of the iris (setosa, versicolor, virginica)
+#* @post /predict_petal_length
+function(petal_width, sepal_length = NA, sepal_width = NA, species = NA) {
+  # Validate petal_width
+  if (is.na(petal_width) || is.na(as.numeric(petal_width))) {
+    return(list(error = "Invalid or missing parameter: petal_width"))
+  }
+  # Check which parameters are provided and create the input data frame accordingly
+  if (!is.na(sepal_length) && !is.na(sepal_width) && !is.na(species)) {
+    input_data <- data.frame(
+      Sepal.Length = as.numeric(sepal_length),
+      Sepal.Width = as.numeric(sepal_width),
+      Petal.Width = as.numeric(petal_width),
+      Species = as.factor(species)
+    )
+    prediction <- predict(model_all, input_data)
+  } else {
+    input_data <- data.frame(Petal.Width = as.numeric(petal_width))
+    prediction <- predict(model_petal_width, input_data)
+  }
+  list(petal_width = petal_width, predicted_petal_length = prediction)
+}
+
+#* Plot actual vs predicted - Displays a plot comparing actual vs predicted petal lengths for model_all
+#* @serializer png
+#* @get /plot_actual_vs_predicted
+function() {
+  predictions <- predict(model_all, test_data)
+  plot(test_data$Petal.Length, predictions,
+    xlab = "Actual Petal Length", ylab = "Predicted Petal Length",
+    main = "Actual vs Predicted Petal Length"
+  )
+  abline(0, 1, col = "red") # 1:1 line
+}