blockr

{blockr} is Shiny’s WordPress (John Coene, 2024)

Why blockr?

{blockr} is an R package designed to democratize data analysis by providing a flexible, intuitive, and code-free approach to building data pipelines.

Who is it for?

{blockr} has 2 main user targets:

1. On the one hand, it empowers non technical users to create insightful data workflows using pre-built blocks that can be easily connected, all without writing a single line of code.

Below is a simple pre-built case study involving {blockr}. We use the palmerpenguins dataset to find out which femal species has the largest flippers. This tiny dashboard is composed of 4 steps: import the data, filter, create the plot and chose the geometry (points). Within each step (block), the user can change inputs and see the changes propagate in real time. Notice that the filter step requires to press a submit button before moving forward, which prevents the plot from appearing first. This is to prevent long running task from being run unecessarily. You can find more in other vignettes.

Penguins app demo

You can of course start with a totally empty dashboard and create your own analysis from scratch.

2. On the other hand, it provides developers with a set of tools to seamlessly create new blocks, thereby enhancing the entire framework and fostering collaboration within organizations teams. For instance, regarding the previous example, below is what it takes to create such dashboard.

library(blockr)
library(palmerpenguins)
library(ggplot2)

new_ggplot_block <- function(col_x = character(), col_y = character(), ...) {

  data_cols <- function(data) colnames(data)

  new_block(
    fields = list(
      x = new_select_field(col_x, data_cols, type = "name"),
      y = new_select_field(col_y, data_cols, type = "name")
    ),
    expr = quote(
      ggplot(mapping = aes(x = .(x), y = .(y)))
    ),
    class = c("ggplot_block", "plot_block"),
    ...
  )
}

new_geompoint_block <- function(color = character(), shape = character(), ...) {

  data_cols <- function(data) colnames(data$data)

  new_block(
    fields = list(
      color = new_select_field(color, data_cols, type = "name"),
      shape = new_select_field(shape, data_cols, type = "name")
    ),
    expr = quote(
      geom_point(aes(color = .(color), shape = .(shape)), size = 2)
    ),
    class = c("plot_layer_block", "plot_block"),
    ...
  )
}

stack <- new_stack(
  data_block = new_dataset_block("penguins", "palmerpenguins"),
  filter_block = new_filter_block("sex", "female"),
  plot_block = new_ggplot_block("flipper_length_mm", "body_mass_g"),
  layer_block = new_geompoint_block("species", "species")
)
serve_stack(stack)

Note that the {blockr.ggplot2} package exposes some ready to use blocks as shown above.

How to get started?

To get started, we invite you to read this vignette.

To get a better idea of {blockr} capabilities in various data context, you can look at this vignette.

Key features

1. User-Friendly Interface: Build data pipelines with intuitive interface.
2. Flexibility: Easily add, remove, or rearrange blocks in your pipeline.
3. Extensibility: Developers can create custom blocks to extend functionality.
4. Reproducibility: Pipelines created with blockr are easily shareable and reproducible, with exportable code.
5. Interactivity: Real-time feedback as you build and modify your pipeline.

Installation

You can install the development version of blockr from GitHub with:

pak::pak("BristolMyersSquibb/blockr")

Contribute

Easiest is to run make, otherwise:

1. Install npm dependencies with packer::npm_install()
2. Build CSS by running the script in dev/sass.R