> ## Documentation Index
> Fetch the complete documentation index at: https://docs.vizly.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Interactive Visualizations

> Create dynamic and interactive data visualizations with Vizly, using popular libraries like Plotly, Matplotlib, Seaborn, and ggplot2.

## Key Features

* **Popular Libraries**: Use familiar libraries such as Matplotlib and Seaborn for Python, and ggplot2 for R, to create static and publication-ready charts.
* **Interactive Visualizations**: Leverage Plotly for both Python and R to create interactive charts that allow zooming, panning, and exporting.
* **Real-Time Rendering**: Generate visualizations instantly as you analyze your data.
* **Customizable Dashboards**: Combine multiple visualizations into shareable dashboards for comprehensive data storytelling.

## How to Create Visualizations

1. **Upload Your Data**: Start by uploading a CSV, Excel file, or connecting to a database in the [Vizly app](https://vizly.ai/app).
2. **Select Your Language**: Choose Python or R to build your visualization.
3. **Write Your Code**: Use Vizly’s editor to input your code, leveraging the visualization library of your choice.
4. **Render Your Chart**: Run your code, and Vizly will generate the chart in real-time.

## Supported Libraries

### Python

* **Matplotlib**: Create static plots for detailed data visualization.
* **Seaborn**: Build elegant statistical plots with ease.
* **Plotly**: Develop interactive visualizations with advanced customization and export options.

### R

* **ggplot2**: Craft high-quality static visualizations.
* **Plotly**: Build interactive plots that support real-time data exploration.

## Example: Python Visualization with Plotly

```python theme={null}
import plotly.express as px
import pandas as pd

# Load your data
df = pd.read_csv("uploaded_file.csv")

# Create an interactive scatter plot
fig = px.scatter(df, x="sales", y="profit", color="region")
fig.show()
```

## Example: R Visualization with ggplot2

```R theme={null}
library(ggplot2)

# Load your data
data <- read.csv("uploaded_file.csv")

# Create a static bar chart
ggplot(data, aes(x = region, y = profit, fill = sales)) +
  geom_bar(stat = "identity")
```

## Example: R Visualization with Plotly

```R theme={null}
library(plotly)

# Load your data
data <- read.csv("uploaded_file.csv")

# Create an interactive scatter plot
plot_ly(data, x = ~sales, y = ~profit, type = "scatter", mode = "markers")
```

## Learn More

Explore how Vizly integrates with [Python and R](../features/python-r-support) to supercharge your analysis, or learn about [AI-Powered Suggestions](../features/ai-powered-suggestions) to guide your next steps in visualization.