Residual Plot in Python, R, Julia and JavaScript

Source code in 15 libraries — Python: Altair, Bokeh, lets-plot, Matplotlib, Plotly, plotnine, Pygal, Seaborn; R: ggplot2; Julia: Makie.jl; JavaScript: Chart.js, D3.js, Apache ECharts, Highcharts, MUI X Charts.

A residual plot displays the difference between observed and predicted values (residuals) against fitted values in regression analysis. This diagnostic visualization helps identify violations of regression assumptions including non-linearity, heteroscedasticity (non-constant variance), and outliers. A well-fitting model shows residuals randomly scattered around zero with no discernible pattern.

Renders

• Full-resolution render, light theme
• Full-resolution render, dark theme
• Interactive version, light theme
• Interactive version, dark theme

Preview render from the Bokeh implementation.

Implementations

• Residual Plot in Altair (Python)
• Residual Plot in Bokeh (Python)
• Residual Plot in lets-plot (Python)
• Residual Plot in Matplotlib (Python)
• Residual Plot in Plotly (Python)
• Residual Plot in plotnine (Python)
• Residual Plot in Pygal (Python)
• Residual Plot in Seaborn (Python)
• Residual Plot in ggplot2 (R)
• Residual Plot in Makie.jl (Julia)
• Residual Plot in Chart.js (JavaScript)
• Residual Plot in D3.js (JavaScript)
• Residual Plot in Apache ECharts (JavaScript)
• Residual Plot in Highcharts (JavaScript)
• Residual Plot in MUI X Charts (JavaScript)