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Requirements

R blocks are only supported when running Mage using Docker.

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Add R block to pipeline

  1. Create a new pipeline or open an existing pipeline.
  2. Add a data loader, transformer, or data exporter block.
  3. Select R.

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Example pipeline

  1. Data loader 
    load_data <- function() {
    # Specify your data loading logic here
    # Return value: loaded dataframe
    df <- read.csv(url('https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv'))
    df
}
  2. Transformer 
    library("pacman")
p_load(dplyr)

transform <- function(df_1, ...) {
    # Specify your transformation logic here
    # Return value: transformed dataframe.
    df_1 <- filter(df_1, Pclass < 3)
    df_1
}
  3. Data exporter 
    export_data <- function(df_1, ...) {
    # Specify your data exporting logic here
    # Return value: exported dataframe
    write.csv(df_1, "titanic_filtered.csv", row.names = FALSE)
}

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Install R packages

Add the following at the start of your code in your R block: 
pacman::p_load(package1, package2, package3)
Or 
library("pacman")
p_load(dplyr)
Note When you run the R block for the 1st time, the package will be installed. The 2nd time you run the R block, the package won’t need to be installed again.

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What is pacman?

pacman is an R package management tool. You can use p_library() to view all the available packages. Here is the documentation for pacman where you can find more useful methods: https://www.rdocumentation.org/packages/pacman/versions/0.5.1

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Runtime variables

Runtime variables can be accessed via global_vars vector, like global_vars['execution_date']. Example code: 
load_data <- function() {
    df <- read.csv(file='titanic_clean.csv')
    df['date'] <- global_vars['execution_date']
    df
}