R blocks

You can write R language to transform data in blocks.

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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
}