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| ^\.eslintrc\.js$ | ||
| ^CODEOWNERS$ | ||
| ^index\.md$ | ||
| ^index\.Rmd$ | ||
| ^wiki | ||
| ^\.vscode$ | ||
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| --- | ||
| output: github_document | ||
| always_allow_html: true | ||
| --- | ||
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| ```{r, include = FALSE} | ||
| knitr::opts_chunk$set( | ||
| collapse = TRUE, | ||
| comment = "#>", | ||
| fig.path = "man/figures/README-" | ||
| ) | ||
| library(blockr) | ||
| library(bslib) | ||
| library(DiagrammeR) | ||
| ``` | ||
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| <!-- index.md is generated from index.Rmd. Please edit that file --> | ||
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| # blockr | ||
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| <!-- badges: start --> | ||
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| [](https://github.com/blockr-org/blockr/actions/workflows/ci.yml) | ||
| [](https://codecov.io/github/blockr-org/blockr) | ||
| <!-- badges: end --> | ||
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| > {blockr} is Shiny's WordPress (John Coene, 2024) | ||
| ## Why blockr? | ||
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| `{blockr}` is an R package designed to democratize data analysis by providing a flexible, intuitive, and __code-free__ approach to building data pipelines. It allows users to create __powerful__ data workflows using pre-built __blocks__ that can be easily __connected__, all without writing a single line of code. | ||
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| ```{r, echo=FALSE} | ||
| mermaid(" | ||
| flowchart TD | ||
| subgraph LR workspace[Workspace] | ||
| subgraph stack1[Stack] | ||
| direction LR | ||
| subgraph input_block[Block 1] | ||
| input(Data: dataset, browser, ...) | ||
| end | ||
| subgraph transform_block[Block 2] | ||
| transform(Transform block: filter, select ...) | ||
| end | ||
| subgraph output_block[Block 3] | ||
| output(Result/transform: plot, filter, ...) | ||
| end | ||
| input_block --> |data| transform_block --> |data| output_block | ||
| end | ||
| subgraph stack2[Stack 2] | ||
| stack1_data[Stack 1 data] --> |data| transform2[Transform] | ||
| end | ||
| stack1 --> |data| stack2 | ||
| subgraph stackn[Stack n] | ||
| stacki_data[Stack i data] --> |data| transformn[Transform] --> |data| Visualize | ||
| end | ||
| stack2 ---> |... data| stackn | ||
| end | ||
| ", | ||
| width = "100%" | ||
| ) |> | ||
| htmlwidgets::onRender( | ||
| "function(el, x) { | ||
| el.classList.add('text-center') | ||
| } | ||
| " | ||
| ) | ||
| ``` | ||
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| ## Key features | ||
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| 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. | ||
|
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| ## Installation | ||
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| You can install the development version of blockr from [GitHub](https://github.com/) with: | ||
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| ```r | ||
| pak::pak("blockr-org/blockr") | ||
| ``` | ||
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| ## Example: palmer penguins case study | ||
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| Below is a simple case study involving `{blockr}`. We use the palmerpenguins dataset to find out which | ||
| femal species has the largest flippers. We create 2 custom blocks allowing to create our plot block (see the plot vignette for more details). Note that the `{blockr.ggplot2}` package exposes some ready to use blocks. | ||
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| <div class="card bslib-card bslib-mb-spacing html-fill-item html-fill-container" data-bslib-card-init data-full-screen="false" data-require-bs-caller="card()" data-require-bs-version="5"> | ||
| <div class="card-body bslib-gap-spacing html-fill-item html-fill-container" style="margin-top:auto;margin-bottom:auto;flex:1 1 auto;"> | ||
| <iframe class="html-fill-item" src="https://shinylive.io/r/editor/#code=NobwRAdghgtgpmAXGKAHVA6ASmANGAYwHsIAXOMpMAdzgCMAnRRASwgGdSoAbbgCgA6YOtyIEA1gyG4ABAzioi7GQF4ZBQWAAWpUqnaIA9IZFjJAWiIMA5hmstSWgK50MLIodqNz8xe2kyQjp6Bsa+RBgM5tRQ7DAYVtZCAJTJAhDcLIxQDACefKYSDGkZWQw5+ag88AyoFNZObOwlmdl5fNbWqKKkAEwl6RBw1AD6nd1EpCOF4jIAPOYyAGZOEASk7hB8xNwjAB6q6lo5UOtwDHzJsjsjuYcEx+VnF1cyGO-JMiDp6TIyACZQLgjHbKBbLVbrTZ8QFcT47aDwdgwoFQAYQP5DUYzQQYv7LFhwbj-ZRqTKcXH4-EHNRYkbsIlwdYjJaE4nbIi7PayWFQEGc9iyUi5OqHISIuApXC-KkyO604b0xnM1lE-4c3a5Hmo-ncQUyYWitTi2CSsAlKlXGV-OB7VAMQ4ARyckzglNl4x6fBgaFQbGshygcGRNLefD2r3lYdyqQt+KteL+BG4sVJ6k0nsm01EEgCQgmUxmUutb3eMpKAF8fhA6dY4EQYIo2IWc7NwSs1hsSBqrPdHqdyC9ZOxjkajidnpdZO8MJ9vhAZbzdWDFh2od3efDORLkbyACSb6uYxU4kuq4lp8mkd1Una9hWjBncJlTc-qu8MbXA0FCkVwMWQKaxaJviI5oP+D5Ks+KpsuqYF1F+fI-gaf4ARKKQlgmVK2vaTouuQN74nWDYjE2ZB8EGyIfocGA9sUw6jhBYbwXAqTDiwABeTH9Jh0ogcmqb3JoBYjCmuTnNmZh5mAIlFuafFUjO5bpFWC4cFwEjzIsdKcKc4jukuMyHHSvIMi2ZjCfUjQcNJVTcDUdQQA0TTAX8smtsZiqZuZEiaEsmToBJz5OY4IwwDA0l0EQ-y5GFqZjK5MhiRJRmQcRjZEM2km+UI7B1AQhL+HggRgHlTKFRhEAlAyDAAG5wPSGn6bpEjJGAFYALpAA" height="700" width="100%" style="border: 1px solid rgba(0,0,0,0.175); border-radius: .375rem;" allowfullscreen="" allow="autoplay" data-external="1"></iframe> | ||
| </div> | ||
| <bslib-tooltip placement="auto" bsOptions="[]" data-require-bs-version="5" data-require-bs-caller="tooltip()"> | ||
| <template>Expand</template> | ||
| <span class="bslib-full-screen-enter badge rounded-pill"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" style="height:1em;width:1em;fill:currentColor;" aria-hidden="true" role="img"><path d="M20 5C20 4.4 19.6 4 19 4H13C12.4 4 12 3.6 12 3C12 2.4 12.4 2 13 2H21C21.6 2 22 2.4 22 3V11C22 11.6 21.6 12 21 12C20.4 12 20 11.6 20 11V5ZM4 19C4 19.6 4.4 20 5 20H11C11.6 20 12 20.4 12 21C12 21.6 11.6 22 11 22H3C2.4 22 2 21.6 2 21V13C2 12.4 2.4 12 3 12C3.6 12 4 12.4 4 13V19Z"/></svg></span> | ||
| </bslib-tooltip> | ||
| <script data-bslib-card-init>bslib.Card.initializeAllCards();</script> | ||
| </div> | ||
|
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| ```{r, eval=FALSE} | ||
| 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) | ||
| ``` | ||
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| ## Contribute | ||
|
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| Easiest is to run `make`, otherwise: | ||
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| 1. Install npm dependencies with `packer::npm_install()` | ||
| 2. Build CSS by running the script in `dev/sass.R` |
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