> ## Documentation Index
> Fetch the complete documentation index at: https://www.canva.dev/opensource/flourish-charts/llms.txt
> Use this file to discover all available pages before exploring further.

# Extending the Flourish base visualization ID

<Tabs storageKey="code-language">
  <Tab name="Python">
    ```python
    from flourishcharts import Flourish
    from flourishcharts.datasets import gapminder
    ```
  </Tab>

  <Tab name="R">
    ```r
    library(flourishcharts)
    ```
  </Tab>
</Tabs>

Flourish allows users to extend visualizations created in the app online via the API. This feature uses a base visualization ID to call the original chart.

The original chart must be published via the app. Every Flourish URL pointing to a chart made in the editor cotains a unique ID - this is the base visualization ID. As an example, we published this chart using the default data source that Flourish provides: [https://public.flourish.studio/visualisation/17833871/](https://public.flourish.studio/visualisation/17833871/).

The base visualization ID is 17833871.

We are able to recreate this graph inside R or Python by passing the base visualization ID to the `flourish()` function in R or the `Flourish()` class in Python.

<Tabs storageKey="code-language">
  <Tab name="Python">
    ```python
    graph = Flourish(base_visualisation_id = 17833871)
    ```
  </Tab>

  <Tab name="R">
    ```r
    graph <- flourish(base_visualisation_id = 17833871)
    ```
  </Tab>
</Tabs>

In addition to *recreating* it, we can also *extend* the graph by writing over data, data bindings, or chart settings.

<Tabs storageKey="code-language">
  <Tab name="Python">
    ```python
    df = (
        gapminder[gapminder['year'] == gapminder['year'].max()]
            .assign(lifeExp=lambda x: x['lifeExp'].round())
            .sort_values(by='lifeExp')
            .head(10)
    )

    Flourish(
        base_visualisation_id = 17833871
    ).bind_line_bar_pie_data(
        data = df,
        label = "country",
        value = "lifeExp"
    ).set_line_bar_pie_details(
        chart_y_axis_title = "Bottom 10 Life Expectancy in 2007"
    )
    ```
  </Tab>

  <Tab name="R">
    ```r
    df <- gapminder |>
      filter(year == max(year)) |>
      mutate(lifeExp = round(lifeExp)) |>
      arrange(lifeExp) |>
      slice_head(n = 10)

    graph |>
      bind_line_bar_pie_data(
        data = df,
        label = "country",
        value = "lifeExp"
      ) |>
      set_line_bar_pie_details(
      chart_y_axis_title = "Bottom 10 Life Expectancy in 2007"
      )
    ```
  </Tab>
</Tabs>

This is a powerful notion because we can publish graphs with dummy data and write over them with *real* data locally. This is important for conserving the privacy of enterprise data.

Additionally, as the Flourish team note in their documentation - the base visualization ID enables non-technical stakeholders to build and style graphs to which technical stakeholders can then add data. Moreover, if a non-technical stakeholder were to update the graph inside the website, the changes will flow down into the API.