interactive-viz-guide

Interactive Visualization Guide

Create interactive, publication-ready visualizations using Plotly, ECharts, Altair, and Bokeh for academic papers, presentations, and supplementary materials.

When to Use Interactive Visualizations

Scenario	Static	Interactive
Journal PDF figure	Preferred	Not supported
Supplementary materials	Optional	Excellent
Conference poster (digital)	Common	Increasingly popular
Presentation slides	Standard	Engaging
Online appendix / project website	Limited	Ideal
Exploratory data analysis	Quick	Detailed exploration

Plotly (Python)

Plotly produces interactive HTML charts with hover tooltips, zoom, pan, and export capabilities.

Scatter Plot with Hover Details

import plotly.express as px
import pandas as pd

# Example: visualize paper citations vs. year
df = pd.DataFrame({
    "title": ["Paper A", "Paper B", "Paper C", "Paper D", "Paper E"],
    "year": [2019, 2020, 2021, 2022, 2023],
    "citations": [150, 320, 89, 450, 210],
    "field": ["NLP", "CV", "NLP", "RL", "CV"],
    "venue": ["ACL", "CVPR", "EMNLP", "NeurIPS", "ICCV"]
})

fig = px.scatter(
    df, x="year", y="citations",
    color="field", size="citations",
    hover_data=["title", "venue"],
    title="Citation Counts by Year and Field",
    labels={"citations": "Citation Count", "year": "Publication Year"}
)
fig.update_layout(
    template="plotly_white",
    font=dict(size=14),
    width=800, height=500
)
fig.write_html("citations_interactive.html")
fig.show()

Grouped Bar Chart

import plotly.graph_objects as go

methods = ["Baseline", "Method A", "Method B", "Ours"]
accuracy = [82.1, 85.3, 87.0, 89.4]
f1_score = [79.8, 83.1, 85.2, 87.9]

fig = go.Figure(data=[
    go.Bar(name="Accuracy", x=methods, y=accuracy,
           text=[f"{v}%" for v in accuracy], textposition="auto"),
    go.Bar(name="F1 Score", x=methods, y=f1_score,
           text=[f"{v}%" for v in f1_score], textposition="auto")
])
fig.update_layout(
    barmode="group",
    title="Model Performance Comparison",
    yaxis_title="Score (%)",
    yaxis_range=[70, 95],
    template="plotly_white"
)
fig.write_html("comparison.html")