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<!DOCTYPE html>
<html lang="en">
<head>
<title>ggplot2 + plotly: Add Hover Tooltips and Zoom to Any Chart in One Line</title>
<meta charset="utf-8">
<meta name="Description" content="ggplotly() wraps any ggplot2 chart in an interactive plotly widget. Learn to customise tooltips, control hover modes, handle geom gotchas, and embed in reports.">
<meta name="Keywords" content="ggplotly, ggplot2 plotly, interactive ggplot2, ggplotly tooltip, plotly hover R, ggplot2 interactive chart, ggplotly zoom, plotly layout R, ggplotly customize, plotly R tutorial">
<meta name="Distribution" content="Global">
<meta name="Author" content="Selva Prabhakaran">
<meta name="Robots" content="index, follow">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<meta name="referrer" content="strict-origin-when-cross-origin">
<link rel="icon" href="/screenshots/iconb-64.png" type="image/x-icon" />
<link rel="canonical" href="https://r-statistics.co/Combining-ggplot2-with-plotly.html">
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<div id="sidebar-nav"><div class="continue-chip" data-continue-chip><span class="chip-label">Continue reading</span><a href="#" data-continue-link></a></div><div class="sidebar-tabs" role="tablist"><button class="sidebar-tab active" data-tab="posts" type="button" role="tab" onclick="var n=this.dataset.tab;document.querySelectorAll('.sidebar-tab').forEach(function(x){x.classList.toggle('active',x.dataset.tab===n)});document.querySelectorAll('.sidebar-panel').forEach(function(p){p.classList.toggle('active',p.dataset.panel===n)});try{localStorage.setItem('rstat_sidebar_tab',n)}catch(e){}">Posts</button><button class="sidebar-tab" data-tab="tools" type="button" role="tab" onclick="var 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data-subkey="sec0sub4"><a href="/R-Cheat-Sheet.html"><span class="progress-dot"></span>R Cheat Sheet</a></li><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec0sub5" data-collapsed="false"><span class="subsec-chevron">▼</span> Professional R</li><li data-subkey="sec0sub5"><a href="/Data-Ethics-in-R.html"><span class="progress-dot"></span>Data Ethics</a></li><li data-subkey="sec0sub5"><a href="/Bias-in-Data-and-Models.html"><span class="progress-dot"></span>Bias in Data & Models</a></li><li data-subkey="sec0sub5"><a href="/Reproducibility-Crisis.html"><span class="progress-dot"></span>Reproducibility</a></li><li data-subkey="sec0sub5"><a href="/Data-Privacy-in-R.html"><span class="progress-dot"></span>Data Privacy</a></li><li data-subkey="sec0sub5"><a href="/Communicating-Uncertainty.html"><span class="progress-dot"></span>Communicating Uncertainty</a></li></ul></li><li class="sidebar-section"><div class="sidebar-section-header"><span class="sidebar-chevron">▸</span> Data Wrangling<span class="section-meta" data-section-meta></span></div><ul class="sidebar-section-items list-unstyled"><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec1sub1" data-collapsed="false"><span class="subsec-chevron">▼</span> Import & Setup</li><li data-subkey="sec1sub1"><a href="/Importing-Data-in-R.html"><span class="progress-dot"></span>Importing Data</a></li><li data-subkey="sec1sub1"><a href="/R-Pipe-Operator.html"><span class="progress-dot"></span>Pipe Operator</a></li><li data-subkey="sec1sub1"><a href="/Tidy-Data-in-R.html"><span class="progress-dot"></span>Tidy Data</a></li><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec1sub2" data-collapsed="false"><span class="subsec-chevron">▼</span> dplyr Essentials</li><li data-subkey="sec1sub2"><a href="/dplyr-filter-select.html"><span class="progress-dot"></span>dplyr filter & select</a></li><li data-subkey="sec1sub2"><a href="/dplyr-mutate-rename.html"><span class="progress-dot"></span>dplyr mutate & rename</a></li><li data-subkey="sec1sub2"><a href="/dplyr-group-by-summarise.html"><span class="progress-dot"></span>dplyr group_by & summarise</a></li><li data-subkey="sec1sub2"><a href="/dplyr-arrange-slice.html"><span class="progress-dot"></span>dplyr arrange & slice</a></li><li data-subkey="sec1sub2"><a href="/dplyr-across.html"><span class="progress-dot"></span>dplyr across()</a></li><li data-subkey="sec1sub2"><a href="/dplyr-case-when.html"><span class="progress-dot"></span>dplyr case_when()</a></li><li data-subkey="sec1sub2"><a href="/dplyr-Exercises-in-R-quiz.html"><span class="progress-dot"></span>dplyr Quiz</a></li><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec1sub3" data-collapsed="false"><span class="subsec-chevron">▼</span> Join & Reshape</li><li data-subkey="sec1sub3"><a href="/R-Joins.html"><span class="progress-dot"></span>R Joins</a></li><li data-subkey="sec1sub3"><a href="/pivot_longer-pivot_wider-Reshape-Data-in-R.html"><span class="progress-dot"></span>pivot_longer & pivot_wider</a></li><li data-subkey="sec1sub3"><a href="/tidyr-separate-unite-Split-Combine-Columns-in-R.html"><span class="progress-dot"></span>separate() & unite()</a></li><li data-subkey="sec1sub3"><a href="/tidyr-Exercises-in-R-quiz.html"><span class="progress-dot"></span>tidyr Quiz</a></li><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec1sub4" data-collapsed="false"><span class="subsec-chevron">▼</span> Clean & Quality</li><li data-subkey="sec1sub4"><a href="/Missing-Values-in-R-Detect-Count-Remove-Impute-NA.html"><span class="progress-dot"></span>Missing Values (NA)</a></li><li data-subkey="sec1sub4"><a href="/Data-Quality-Checking-in-R.html"><span class="progress-dot"></span>Data Quality Checking</a></li><li data-subkey="sec1sub4"><a href="/janitor-Package-in-R.html"><span class="progress-dot"></span>janitor Package</a></li><li class="sidebar-divider 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(rvest)</a></li><li data-subkey="sec1sub6"><a href="/REST-APIs-in-R-with-httr2.html"><span class="progress-dot"></span>REST APIs (httr2)</a></li></ul></li><li class="sidebar-section expanded"><div class="sidebar-section-header"><span class="sidebar-chevron">▸</span> Visualization<span class="section-meta" data-section-meta></span></div><ul class="sidebar-section-items list-unstyled"><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec2sub1" data-collapsed="false"><span class="subsec-chevron">▼</span> ggplot2 Foundations</li><li data-subkey="sec2sub1"><a href="/ggplot2-Grammar-of-Graphics.html"><span class="progress-dot"></span>Grammar of Graphics</a></li><li data-subkey="sec2sub1"><a href="/ggplot2-Getting-Started.html"><span class="progress-dot"></span>ggplot2 Getting Started</a></li><li data-subkey="sec2sub1"><a href="/ggplot2-Aesthetics-aes-Map-Data.html"><span class="progress-dot"></span>ggplot2 Aesthetics (aes)</a></li><li data-subkey="sec2sub1"><a href="/ggplot2-Colours.html"><span class="progress-dot"></span>ggplot2 Colours</a></li><li data-subkey="sec2sub1"><a href="/ggplot2-Scales.html"><span class="progress-dot"></span>ggplot2 Scales</a></li><li data-subkey="sec2sub1"><a href="/ggplot2-Themes-in-R.html"><span class="progress-dot"></span>ggplot2 Themes</a></li><li data-subkey="sec2sub1"><a href="/ggplot2-Labels-and-Annotations.html"><span class="progress-dot"></span>Labels & Annotations</a></li><li data-subkey="sec2sub1"><a href="/ggplot2-Facets.html"><span class="progress-dot"></span>ggplot2 Facets</a></li><li data-subkey="sec2sub1"><a href="/ggplot2-Exercises-in-R-quiz.html"><span class="progress-dot"></span>ggplot2 Quiz</a></li><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec2sub2" data-collapsed="false"><span class="subsec-chevron">▼</span> Core Charts</li><li data-subkey="sec2sub2"><a href="/ggplot2-Scatter-Plots.html"><span class="progress-dot"></span>Scatter Plots</a></li><li data-subkey="sec2sub2"><a href="/ggplot2-Line-Charts.html"><span class="progress-dot"></span>Line Charts</a></li><li data-subkey="sec2sub2"><a href="/ggplot2-Bar-Charts.html"><span class="progress-dot"></span>Bar Charts</a></li><li data-subkey="sec2sub2"><a href="/ggplot2-Distribution-Charts.html"><span class="progress-dot"></span>Distribution Charts</a></li><li data-subkey="sec2sub2"><a href="/Error-Bars-in-R.html"><span class="progress-dot"></span>Error Bars</a></li><li data-subkey="sec2sub2"><a href="/geom_smooth-in-R.html"><span class="progress-dot"></span>geom_smooth()</a></li><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec2sub3" data-collapsed="false"><span class="subsec-chevron">▼</span> Distributions & Groups</li><li data-subkey="sec2sub3"><a href="/Violin-Plot-in-R.html"><span class="progress-dot"></span>Violin Plot</a></li><li data-subkey="sec2sub3"><a href="/Ridgeline-Plot-in-R.html"><span class="progress-dot"></span>Ridgeline Plot</a></li><li data-subkey="sec2sub3"><a href="/Lollipop-Chart-in-R.html"><span class="progress-dot"></span>Lollipop Chart</a></li><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec2sub4" data-collapsed="false"><span class="subsec-chevron">▼</span> Relationships</li><li data-subkey="sec2sub4"><a href="/Bubble-Chart-in-R.html"><span class="progress-dot"></span>Bubble Chart</a></li><li data-subkey="sec2sub4"><a href="/Heatmap-in-R.html"><span class="progress-dot"></span>Heatmap in R</a></li><li data-subkey="sec2sub4"><a href="/Correlation-Matrix-Plot-in-R.html"><span class="progress-dot"></span>Correlation Matrix</a></li><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec2sub5" data-collapsed="false"><span class="subsec-chevron">▼</span> Advanced Charts</li><li data-subkey="sec2sub5"><a href="/Pie-Donut-Chart-in-R.html"><span class="progress-dot"></span>Pie & Donut Chart</a></li><li data-subkey="sec2sub5"><a href="/Treemap-in-R.html"><span class="progress-dot"></span>Treemap</a></li><li data-subkey="sec2sub5"><a href="/Waffle-Chart-in-R.html"><span class="progress-dot"></span>Waffle Chart</a></li><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec2sub6" data-collapsed="false"><span class="subsec-chevron">▼</span> Exploratory Analysis</li><li data-subkey="sec2sub6"><a href="/Exploratory-Data-Analysis-in-R.html"><span class="progress-dot"></span>EDA (7-Step Framework)</a></li><li data-subkey="sec2sub6"><a href="/Univariate-EDA-in-R.html"><span class="progress-dot"></span>Univariate EDA</a></li><li data-subkey="sec2sub6"><a href="/Bivariate-EDA-in-R.html"><span class="progress-dot"></span>Bivariate EDA</a></li><li data-subkey="sec2sub6"><a href="/Descriptive-Statistics-in-R.html"><span class="progress-dot"></span>Descriptive Statistics</a></li><li data-subkey="sec2sub6"><a href="/Correlation-Analysis-in-R.html"><span class="progress-dot"></span>Correlation Analysis</a></li><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec2sub7" data-collapsed="false"><span class="subsec-chevron">▼</span> Interactive & Maps</li><li data-subkey="sec2sub7"><a href="/Combining-ggplot2-with-plotly.html" class="active"><span class="progress-dot"></span>ggplot2 + plotly Interactive</a></li><li data-subkey="sec2sub7"><a href="/Interactive-Maps-in-R-with-leaflet.html"><span class="progress-dot"></span>Leaflet Interactive Maps</a></li><li data-subkey="sec2sub7"><a href="/Spatial-Data-in-R-with-sf.html"><span class="progress-dot"></span>Spatial Data (sf)</a></li><li data-subkey="sec2sub7"><a href="/Choropleth-Maps-in-R.html"><span class="progress-dot"></span>Choropleth Maps (sf)</a></li><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec2sub8" data-collapsed="false"><span class="subsec-chevron">▼</span> Customization & Reference</li><li data-subkey="sec2sub8"><a href="/ggplot2-Legends-in-R.html"><span class="progress-dot"></span>ggplot2 Legends</a></li><li data-subkey="sec2sub8"><a href="/ggplot2-Secondary-Axis.html"><span class="progress-dot"></span>Secondary Axis</a></li><li data-subkey="sec2sub8"><a href="/ggplot2-Log-Scale.html"><span class="progress-dot"></span>Log Scale</a></li><li data-subkey="sec2sub8"><a href="/patchwork-Package.html"><span class="progress-dot"></span>patchwork (Combine Plots)</a></li><li data-subkey="sec2sub8"><a href="/Publication-Quality-Figures-in-R.html"><span class="progress-dot"></span>Publication-Ready Figures</a></li><li data-subkey="sec2sub8"><a href="/ggplot2-cheatsheet.html"><span class="progress-dot"></span>ggplot2 Quickref</a></li></ul></li><li class="sidebar-section"><div class="sidebar-section-header"><span class="sidebar-chevron">▸</span> Statistics<span class="section-meta" data-section-meta></span></div><ul class="sidebar-section-items list-unstyled"><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec3sub1" data-collapsed="false"><span class="subsec-chevron">▼</span> EDA & Data Quality</li><li data-subkey="sec3sub1"><a href="/Automated-EDA-in-R.html"><span class="progress-dot"></span>Automated EDA</a></li><li data-subkey="sec3sub1"><a href="/Missing-Data-Visualization-in-R-naniar.html"><span class="progress-dot"></span>Missing Data Viz (naniar)</a></li><li data-subkey="sec3sub1"><a href="/Outlier-Detection-in-R.html"><span class="progress-dot"></span>Outlier Detection</a></li><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec3sub2" data-collapsed="false"><span class="subsec-chevron">▼</span> Probability</li><li data-subkey="sec3sub2"><a href="/Sample-Spaces-Events-and-Probability-Axioms-in-R-With-Monte-Carlo-Proof.html"><span class="progress-dot"></span>Probability Axioms</a></li><li data-subkey="sec3sub2"><a href="/Conditional-Probability-in-R.html"><span class="progress-dot"></span>Conditional Probability</a></li><li data-subkey="sec3sub2"><a href="/Random-Variables-in-R.html"><span class="progress-dot"></span>Random Variables</a></li><li data-subkey="sec3sub2"><a href="/Binomial-and-Poisson-Distributions-in-R.html"><span class="progress-dot"></span>Binomial vs Poisson</a></li><li data-subkey="sec3sub2"><a href="/Normal-t-F-and-Chi-Squared-Distributions-in-R.html"><span class="progress-dot"></span>Normal, t, F, Chi-Squared</a></li><li data-subkey="sec3sub2"><a href="/Central-Limit-Theorem-in-R.html"><span class="progress-dot"></span>Central Limit Theorem</a></li><li data-subkey="sec3sub2"><a href="/Sampling-Distributions-in-R.html"><span class="progress-dot"></span>Sampling Distributions</a></li><li data-subkey="sec3sub2"><a href="/Law-of-Large-Numbers-vs-CLT-in-R.html"><span class="progress-dot"></span>LLN vs CLT</a></li><li data-subkey="sec3sub2"><a href="/What-Is-Probability-Simulation-First-Intuition-in-R-Before-the-Formulas.html"><span class="progress-dot"></span>Probability (Simulation-First)</a></li><li data-subkey="sec3sub2"><a href="/Expected-Value-and-Variance-in-R.html"><span class="progress-dot"></span>Expected Value and Variance</a></li><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec3sub3" data-collapsed="false"><span class="subsec-chevron">▼</span> Inference & Estimation</li><li data-subkey="sec3sub3"><a href="/Maximum-Likelihood-Estimation-in-R.html"><span class="progress-dot"></span>Maximum Likelihood Estimation</a></li><li data-subkey="sec3sub3"><a href="/Hypothesis-Testing-in-R.html"><span class="progress-dot"></span>Hypothesis Testing</a></li><li data-subkey="sec3sub3"><a href="/Sample-Size-Planning-in-R.html"><span class="progress-dot"></span>Sample Size Planning</a></li><li data-subkey="sec3sub3"><a href="/Which-Statistical-Test-in-R.html"><span class="progress-dot"></span>Choosing the Right Test</a></li><li data-subkey="sec3sub3"><a href="/Statistical-Tests-in-R.html"><span class="progress-dot"></span>Statistical Tests</a></li><li data-subkey="sec3sub3"><a href="/Measures-of-Association-in-R.html"><span class="progress-dot"></span>Measures of Association</a></li><li data-subkey="sec3sub3"><a href="/Point-Estimation-in-R.html"><span class="progress-dot"></span>Point Estimation</a></li><li data-subkey="sec3sub3"><a href="/Confidence-Intervals-in-R.html"><span class="progress-dot"></span>Confidence Intervals</a></li><li data-subkey="sec3sub3"><a href="/Type-I-and-Type-II-Errors-in-R.html"><span class="progress-dot"></span>Type I and II Errors</a></li><li data-subkey="sec3sub3"><a href="/Statistical-Power-Analysis-in-R.html"><span class="progress-dot"></span>Power Analysis</a></li><li data-subkey="sec3sub3"><a href="/Effect-Size-in-R.html"><span class="progress-dot"></span>Effect Size</a></li><li data-subkey="sec3sub3"><a href="/t-Tests-in-R.html"><span class="progress-dot"></span>t-Tests</a></li><li data-subkey="sec3sub3"><a href="/Proportion-Tests-in-R.html"><span class="progress-dot"></span>Proportion Tests</a></li><li data-subkey="sec3sub3"><a href="/Normality-and-Variance-Tests-in-R.html"><span class="progress-dot"></span>Normality & Variance Tests</a></li><li data-subkey="sec3sub3"><a href="/Chi-Square-Tests-in-R.html"><span class="progress-dot"></span>Chi-Square Tests</a></li><li data-subkey="sec3sub3"><a href="/Wilcoxon-Mann-Whitney-and-Kruskal-Wallis-in-R.html"><span class="progress-dot"></span>Wilcoxon, Mann-Whitney & Kruskal-Wallis</a></li><li data-subkey="sec3sub3"><a href="/Multiple-Comparisons-in-R.html"><span class="progress-dot"></span>Multiple Testing Correction</a></li><li data-subkey="sec3sub3"><a href="/Hypothesis-Testing-Exercises-in-R-quiz.html"><span class="progress-dot"></span>Hypothesis Testing Quiz</a></li><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec3sub4" data-collapsed="false"><span class="subsec-chevron">▼</span> Regression</li><li data-subkey="sec3sub4"><a href="/Linear-Regression.html"><span class="progress-dot"></span>Linear Regression</a></li><li data-subkey="sec3sub4"><a href="/Logistic-Regression-With-R.html"><span class="progress-dot"></span>Logistic Regression</a></li><li data-subkey="sec3sub4"><a href="/Variable-Selection-and-Importance-With-R.html"><span class="progress-dot"></span>Feature Selection</a></li><li data-subkey="sec3sub4"><a href="/Model-Selection-in-R.html"><span class="progress-dot"></span>Model Selection</a></li><li data-subkey="sec3sub4"><a href="/Missing-Value-Treatment-With-R.html"><span class="progress-dot"></span>Missing Value Treatment</a></li><li data-subkey="sec3sub4"><a href="/Outlier-Treatment-With-R.html"><span class="progress-dot"></span>Outlier Analysis</a></li><li data-subkey="sec3sub4"><a href="/adv-regression-models.html"><span class="progress-dot"></span>Advanced Regression Models</a></li><li data-subkey="sec3sub4"><a href="/Linear-Regression-Exercises-in-R-quiz.html"><span class="progress-dot"></span>Linear Regression Quiz</a></li><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec3sub5" data-collapsed="false"><span class="subsec-chevron">▼</span> Reporting</li><li data-subkey="sec3sub5"><a href="/Statistical-Consulting-in-R.html"><span class="progress-dot"></span>Statistical Consulting</a></li><li data-subkey="sec3sub5"><a href="/Statistical-Report-Writing-in-R.html"><span class="progress-dot"></span>Statistical Report Writing</a></li><li data-subkey="sec3sub5"><a href="/Bootstrap-Confidence-Intervals-in-R.html"><span class="progress-dot"></span>Bootstrap Confidence Intervals</a></li><li data-subkey="sec3sub5"><a href="/Reporting-Statistics-in-R.html"><span class="progress-dot"></span>Reporting Statistics</a></li><li data-subkey="sec3sub5"><a href="/Correlation-in-R.html"><span class="progress-dot"></span>Correlation (Pearson, Spearman, Kendall)</a></li><li data-subkey="sec3sub5"><a href="/Linear-Regression-Assumptions-in-R.html"><span class="progress-dot"></span>Linear Regression Assumptions</a></li><li data-subkey="sec3sub5"><a href="/Dummy-Variables-in-R.html"><span class="progress-dot"></span>Dummy Variables in R</a></li><li data-subkey="sec3sub5"><a href="/Interaction-Effects-in-R.html"><span class="progress-dot"></span>Interaction Effects</a></li><li data-subkey="sec3sub5"><a href="/Regression-Diagnostics-in-R.html"><span class="progress-dot"></span>Regression Diagnostics</a></li><li data-subkey="sec3sub5"><a href="/Logistic-Regression-in-R.html"><span class="progress-dot"></span>Logistic Regression (glm + ROC)</a></li><li data-subkey="sec3sub5"><a href="/Variable-Selection-in-R.html"><span class="progress-dot"></span>Variable Selection</a></li><li data-subkey="sec3sub5"><a href="/Poisson-Regression-in-R.html"><span class="progress-dot"></span>Poisson Regression</a></li><li data-subkey="sec3sub5"><a href="/Ridge-and-Lasso-Regression-in-R.html"><span class="progress-dot"></span>Ridge & Lasso Regression</a></li><li data-subkey="sec3sub5"><a href="/Polynomial-and-Spline-Regression-in-R.html"><span class="progress-dot"></span>Polynomial & Splines</a></li><li data-subkey="sec3sub5"><a href="/Regression-Tables-in-R.html"><span class="progress-dot"></span>Regression Tables (3 packages)</a></li><li data-subkey="sec3sub5"><a href="/One-Way-ANOVA-in-R.html"><span class="progress-dot"></span>One-Way ANOVA</a></li><li data-subkey="sec3sub5"><a href="/Post-Hoc-Tests-After-ANOVA.html"><span class="progress-dot"></span>Post-Hoc Tests After ANOVA</a></li><li data-subkey="sec3sub5"><a href="/Two-Way-ANOVA-in-R.html"><span class="progress-dot"></span>Two-Way ANOVA</a></li><li data-subkey="sec3sub5"><a href="/Repeated-Measures-ANOVA-in-R.html"><span class="progress-dot"></span>Repeated Measures ANOVA</a></li><li data-subkey="sec3sub5"><a href="/ANCOVA-in-R.html"><span class="progress-dot"></span>ANCOVA</a></li><li data-subkey="sec3sub5"><a href="/Experimental-Design-Principles-in-R.html"><span class="progress-dot"></span>Experimental Design in R</a></li><li data-subkey="sec3sub5"><a href="/Factorial-Experiments-in-R.html"><span class="progress-dot"></span>Factorial Designs (2^k)</a></li><li data-subkey="sec3sub5"><a href="/AB-Testing-in-R.html"><span class="progress-dot"></span>A/B Testing</a></li><li data-subkey="sec3sub5"><a href="/MANOVA-in-R.html"><span class="progress-dot"></span>MANOVA</a></li><li data-subkey="sec3sub5"><a href="/Mixed-ANOVA-in-R.html"><span class="progress-dot"></span>Mixed ANOVA</a></li><li data-subkey="sec3sub5"><a href="/Multivariate-Statistics-in-R.html"><span class="progress-dot"></span>Multivariate Distances & Hotelling's T²</a></li><li data-subkey="sec3sub5"><a href="/PCA-in-R.html"><span class="progress-dot"></span>PCA with prcomp()</a></li><li data-subkey="sec3sub5"><a href="/Interpreting-PCA-Results-in-R.html"><span class="progress-dot"></span>Interpreting PCA Output</a></li><li data-subkey="sec3sub5"><a href="/Exploratory-Factor-Analysis-in-R.html"><span class="progress-dot"></span>Exploratory Factor Analysis</a></li><li 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href="/Robust-Regression-in-R.html"><span class="progress-dot"></span>Robust Regression (rlm)</a></li><li data-subkey="sec3sub5"><a href="/factoextra-and-FactoMineR.html"><span class="progress-dot"></span>factoextra (PCA + Clusters)</a></li><li data-subkey="sec3sub5"><a href="/Categorical-Data-in-R.html"><span class="progress-dot"></span>Categorical Data (Tables & Mosaic)</a></li><li data-subkey="sec3sub5"><a href="/Chi-Square-Test-of-Independence-in-R.html"><span class="progress-dot"></span>Chi-Square Test of Independence</a></li><li data-subkey="sec3sub5"><a href="/Chi-Square-Goodness-of-Fit-Test-in-R.html"><span class="progress-dot"></span>Chi-Square Goodness-of-Fit</a></li><li data-subkey="sec3sub5"><a href="/Fishers-Exact-Test-in-R.html"><span class="progress-dot"></span>Fisher's Exact Test</a></li><li data-subkey="sec3sub5"><a href="/Odds-Ratios-and-Relative-Risk-in-R.html"><span class="progress-dot"></span>Odds Ratios & Relative Risk</a></li><li data-subkey="sec3sub5"><a href="/Logistic-Regression-in-R-2.html"><span class="progress-dot"></span>Logistic Regression (Diagnostics)</a></li><li data-subkey="sec3sub5"><a href="/Poisson-and-Negative-Binomial-Regression.html"><span class="progress-dot"></span>Poisson & Negative Binomial Regression</a></li><li data-subkey="sec3sub5"><a href="/Multinomial-and-Ordinal-Logistic-Regression-in-R.html"><span class="progress-dot"></span>Multinomial & Ordinal Logistic Regression</a></li><li data-subkey="sec3sub5"><a href="/When-to-Use-Nonparametric-Tests-in-R.html"><span class="progress-dot"></span>When to Use Nonparametric Tests</a></li><li data-subkey="sec3sub5"><a href="/Wilcoxon-Signed-Rank-Test-in-R.html"><span class="progress-dot"></span>Wilcoxon Signed-Rank Test</a></li><li data-subkey="sec3sub5"><a href="/Mann-Whitney-U-Test-in-R.html"><span class="progress-dot"></span>Mann-Whitney U Test</a></li><li data-subkey="sec3sub5"><a href="/Kruskal-Wallis-Test-in-R-2.html"><span class="progress-dot"></span>Kruskal-Wallis Test</a></li><li data-subkey="sec3sub5"><a href="/Friedman-Test-in-R.html"><span class="progress-dot"></span>Friedman Test</a></li><li data-subkey="sec3sub5"><a href="/Spearman-and-Kendall-Correlation-in-R.html"><span class="progress-dot"></span>Spearman & Kendall Correlation</a></li><li data-subkey="sec3sub5"><a href="/Bootstrap-in-R.html"><span class="progress-dot"></span>Bootstrap (boot package)</a></li><li data-subkey="sec3sub5"><a href="/Quantile-Regression-in-R-2.html"><span class="progress-dot"></span>Quantile Regression</a></li><li data-subkey="sec3sub5"><a href="/Matrix-Operations-in-R.html"><span class="progress-dot"></span>Matrix Operations in R</a></li><li data-subkey="sec3sub5"><a href="/Solving-Linear-Systems-in-R.html"><span class="progress-dot"></span>Solving Linear Systems in R</a></li><li data-subkey="sec3sub5"><a href="/Eigenvalues-and-Eigenvectors-in-R.html"><span class="progress-dot"></span>Eigenvalues & Eigenvectors in R</a></li><li data-subkey="sec3sub5"><a href="/Singular-Value-Decomposition-in-R.html"><span class="progress-dot"></span>Singular Value Decomposition in R</a></li><li data-subkey="sec3sub5"><a href="/Projections-and-the-Hat-Matrix-in-R.html"><span class="progress-dot"></span>Projections & the Hat Matrix</a></li><li data-subkey="sec3sub5"><a href="/QR-Decomposition-in-R.html"><span class="progress-dot"></span>QR Decomposition in R</a></li><li data-subkey="sec3sub5"><a href="/Quadratic-Forms-in-R.html"><span class="progress-dot"></span>Quadratic Forms</a></li><li data-subkey="sec3sub5"><a href="/Matrix-Derivatives-and-the-Hessian-in-R.html"><span class="progress-dot"></span>Matrix Derivatives & Hessian</a></li><li data-subkey="sec3sub5"><a href="/Exponential-Family-Distributions-in-R.html"><span class="progress-dot"></span>Exponential Family Distributions</a></li><li data-subkey="sec3sub5"><a href="/Sufficient-Statistics-in-R.html"><span class="progress-dot"></span>Sufficient Statistics</a></li><li data-subkey="sec3sub5"><a href="/Complete-and-Ancillary-Statistics-in-R.html"><span class="progress-dot"></span>Complete & Ancillary Statistics</a></li><li data-subkey="sec3sub5"><a href="/UMVUE-in-R-2.html"><span class="progress-dot"></span>UMVUE (Rao-Blackwell & Lehmann-Scheffé)</a></li><li data-subkey="sec3sub5"><a href="/Cramer-Rao-Lower-Bound-in-R-2.html"><span class="progress-dot"></span>Cramér-Rao Lower Bound</a></li><li data-subkey="sec3sub5"><a href="/Asymptotic-Theory-in-R-2.html"><span class="progress-dot"></span>Asymptotic Theory</a></li><li data-subkey="sec3sub5"><a href="/Neyman-Pearson-Lemma-in-R-2.html"><span class="progress-dot"></span>Neyman-Pearson Lemma</a></li><li data-subkey="sec3sub5"><a href="/Likelihood-Ratio-Tests-and-Pivotal-Methods.html"><span class="progress-dot"></span>Likelihood Ratio & Pivotal Methods</a></li><li data-subkey="sec3sub5"><a href="/Decision-Theory-in-R.html"><span class="progress-dot"></span>Decision Theory</a></li><li data-subkey="sec3sub5"><a href="/Asymptotic-Relative-Efficiency-in-R.html"><span class="progress-dot"></span>Asymptotic Relative Efficiency</a></li><li data-subkey="sec3sub5"><a href="/Bayes-Theorem-in-R.html"><span class="progress-dot"></span>Bayes' Theorem</a></li><li data-subkey="sec3sub5"><a href="/Bayesian-Statistics-in-R.html"><span class="progress-dot"></span>Bayesian Statistics</a></li><li data-subkey="sec3sub5"><a href="/Conjugate-Priors-in-R.html"><span class="progress-dot"></span>Conjugate Priors</a></li><li data-subkey="sec3sub5"><a href="/Grid-Approximation-in-R.html"><span class="progress-dot"></span>Grid Approximation</a></li><li data-subkey="sec3sub5"><a href="/MCMC-in-R.html"><span class="progress-dot"></span>MCMC in R</a></li><li data-subkey="sec3sub5"><a href="/Gibbs-Sampling-in-R.html"><span class="progress-dot"></span>Gibbs Sampling</a></li><li data-subkey="sec3sub5"><a href="/Hamiltonian-Monte-Carlo-in-R.html"><span class="progress-dot"></span>Hamiltonian Monte Carlo</a></li><li data-subkey="sec3sub5"><a href="/Stan-in-R.html"><span class="progress-dot"></span>Stan</a></li><li data-subkey="sec3sub5"><a href="/brms-in-R.html"><span class="progress-dot"></span>brms</a></li><li data-subkey="sec3sub5"><a href="/Choosing-Priors-in-R.html"><span class="progress-dot"></span>Choosing Priors</a></li><li data-subkey="sec3sub5"><a href="/Prior-Predictive-Checks-in-R.html"><span class="progress-dot"></span>Prior Predictive Checks</a></li><li data-subkey="sec3sub5"><a href="/Compare-Bayesian-Models-in-R.html"><span class="progress-dot"></span>Compare Bayesian Models</a></li><li data-subkey="sec3sub5"><a href="/Posterior-Predictive-Checks-in-R.html"><span class="progress-dot"></span>Posterior Predictive Checks</a></li><li data-subkey="sec3sub5"><a href="/Bayesian-Linear-Regression-in-R.html"><span class="progress-dot"></span>Bayesian Linear Regression</a></li><li data-subkey="sec3sub5"><a href="/Bayesian-Logistic-Regression-in-R.html"><span class="progress-dot"></span>Bayesian Logistic Regression</a></li><li data-subkey="sec3sub5"><a href="/Bayesian-Hierarchical-Models-in-R.html"><span class="progress-dot"></span>Bayesian Hierarchical Models</a></li><li data-subkey="sec3sub5"><a href="/Multilevel-Models-in-R.html"><span class="progress-dot"></span>Multilevel Models</a></li><li data-subkey="sec3sub5"><a href="/Bayesian-ANOVA-in-R.html"><span class="progress-dot"></span>Bayesian ANOVA</a></li><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec3sub6" data-collapsed="false"><span class="subsec-chevron">▼</span> Machine Learning</li><li data-subkey="sec3sub6"><a href="/Machine-Learning-Exercises-in-R-quiz.html"><span class="progress-dot"></span>Machine Learning Quiz</a></li></ul></li><li class="sidebar-section"><div class="sidebar-section-header"><span class="sidebar-chevron">▸</span> Time Series<span class="section-meta" data-section-meta></span></div><ul class="sidebar-section-items list-unstyled"><li data-subkey="sec4sub0"><a href="/Time-Series-Analysis-With-R.html"><span class="progress-dot"></span>Time Series Analysis</a></li><li data-subkey="sec4sub0"><a href="/Time-Series-Forecasting-With-R.html"><span class="progress-dot"></span>Time Series Forecasting</a></li><li data-subkey="sec4sub0"><a href="/Time-Series-Forecasting-With-R-part2.html"><span class="progress-dot"></span>More Time Series Forecasting</a></li><li data-subkey="sec4sub0"><a href="/Time-Series-Exercises-in-R-quiz.html"><span class="progress-dot"></span>Time Series Quiz</a></li></ul></li><li class="sidebar-section"><div class="sidebar-section-header"><span class="sidebar-chevron">▸</span> Advanced R<span class="section-meta" data-section-meta></span></div><ul class="sidebar-section-items list-unstyled"><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec5sub1" data-collapsed="false"><span class="subsec-chevron">▼</span> Functional Programming</li><li data-subkey="sec5sub1"><a href="/Functional-Programming-in-R.html"><span class="progress-dot"></span>Functional Programming</a></li><li data-subkey="sec5sub1"><a href="/R-Functional-Programming-Exercises-quiz.html"><span class="progress-dot"></span>Functional Programming Quiz</a></li><li data-subkey="sec5sub1"><a href="/purrr-map-Variants.html"><span class="progress-dot"></span>purrr map() Variants</a></li><li data-subkey="sec5sub1"><a href="/R-Anonymous-Functions.html"><span class="progress-dot"></span>R Anonymous Functions</a></li><li data-subkey="sec5sub1"><a href="/R-Function-Factories.html"><span class="progress-dot"></span>R Function Factories</a></li><li data-subkey="sec5sub1"><a href="/R-Function-Operators.html"><span class="progress-dot"></span>R Function Operators</a></li><li data-subkey="sec5sub1"><a href="/Reduce-Filter-Map-in-R.html"><span class="progress-dot"></span>Reduce, Filter, Map</a></li><li data-subkey="sec5sub1"><a href="/Memoization-in-R.html"><span class="progress-dot"></span>Memoization in R</a></li><li data-subkey="sec5sub1"><a href="/Writing-Composable-R-Code.html"><span class="progress-dot"></span>Composable R Code</a></li><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec5sub2" data-collapsed="false"><span class="subsec-chevron">▼</span> OOP in R</li><li data-subkey="sec5sub2"><a href="/OOP-in-R.html"><span class="progress-dot"></span>OOP in R: S3/S4/R6</a></li><li data-subkey="sec5sub2"><a href="/S3-Classes-in-R.html"><span class="progress-dot"></span>S3 Classes</a></li><li data-subkey="sec5sub2"><a href="/S3-Method-Dispatch-in-R.html"><span class="progress-dot"></span>S3 Method Dispatch</a></li><li data-subkey="sec5sub2"><a href="/S4-Classes-in-R.html"><span class="progress-dot"></span>S4 Classes</a></li><li data-subkey="sec5sub2"><a href="/S4-Methods-in-R.html"><span class="progress-dot"></span>S4 Methods & Dispatch</a></li><li data-subkey="sec5sub2"><a href="/R6-Classes-in-R.html"><span class="progress-dot"></span>R6 Classes</a></li><li data-subkey="sec5sub2"><a href="/R6-Advanced.html"><span class="progress-dot"></span>R6 Advanced</a></li><li data-subkey="sec5sub2"><a href="/Operator-Overloading-in-R.html"><span class="progress-dot"></span>Operator Overloading</a></li><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec5sub3" data-collapsed="false"><span class="subsec-chevron">▼</span> How R Works</li><li data-subkey="sec5sub3"><a href="/R-Names-and-Values.html"><span class="progress-dot"></span>R Names & Values</a></li><li data-subkey="sec5sub3"><a href="/R-Assignment-Deep-Dive.html"><span class="progress-dot"></span>R Assignment Deep Dive</a></li><li data-subkey="sec5sub3"><a href="/R-Memory-lobstr.html"><span class="progress-dot"></span>R Memory & lobstr</a></li><li data-subkey="sec5sub3"><a href="/R-Environments.html"><span class="progress-dot"></span>R Environments</a></li><li data-subkey="sec5sub3"><a href="/R-Lexical-Scoping.html"><span class="progress-dot"></span>Lexical Scoping</a></li><li data-subkey="sec5sub3"><a href="/R-Closures.html"><span class="progress-dot"></span>R Closures</a></li><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec5sub4" data-collapsed="false"><span class="subsec-chevron">▼</span> Debugging & Performance</li><li data-subkey="sec5sub4"><a href="/R-Conditions-System.html"><span class="progress-dot"></span>Conditions System</a></li><li data-subkey="sec5sub4"><a href="/R-Debugging.html"><span class="progress-dot"></span>Debugging R Code</a></li><li data-subkey="sec5sub4"><a href="/R-Common-Errors.html"><span class="progress-dot"></span>50 Common R Errors</a></li><li data-subkey="sec5sub4"><a href="/Parallel-Computing-With-R.html"><span class="progress-dot"></span>Parallel Computing</a></li><li data-subkey="sec5sub4"><a href="/Strategies-To-Improve-And-Speedup-R-Code.html"><span class="progress-dot"></span>Speedup R Code</a></li><li data-subkey="sec5sub4"><a href="/Shiny-Exercises-in-R-quiz.html"><span class="progress-dot"></span>Shiny Quiz</a></li></ul></li><li class="sidebar-section"><div class="sidebar-section-header"><span class="sidebar-chevron">▸</span> Classic Tutorials<span class="section-meta" data-section-meta></span></div><ul class="sidebar-section-items list-unstyled"><li data-subkey="sec6sub0"><a href="/R-Tutorial.html"><span class="progress-dot"></span>R Tutorial (Classic)</a></li><li data-subkey="sec6sub0"><a href="/ggplot2-Tutorial-With-R.html"><span class="progress-dot"></span>ggplot2 Short Tutorial</a></li><li data-subkey="sec6sub0"><a href="/Complete-Ggplot2-Tutorial-Part1-With-R-Code.html"><span class="progress-dot"></span>ggplot2 Tutorial 1 - Intro</a></li><li data-subkey="sec6sub0"><a href="/Complete-Ggplot2-Tutorial-Part2-Customizing-Theme-With-R-Code.html"><span class="progress-dot"></span>ggplot2 Tutorial 2 - Theme</a></li><li data-subkey="sec6sub0"><a href="/Top50-Ggplot2-Visualizations-MasterList-R-Code.html"><span class="progress-dot"></span>ggplot2 Tutorial 3 - Masterlist</a></li><li data-subkey="sec6sub0"><a href="/Association-Mining-With-R.html"><span class="progress-dot"></span>Association Mining</a></li><li data-subkey="sec6sub0"><a href="/Multi-Dimensional-Scaling-With-R.html"><span class="progress-dot"></span>Multi Dimensional Scaling</a></li><li data-subkey="sec6sub0"><a href="/Optimization-With-R.html"><span class="progress-dot"></span>Optimization</a></li><li data-subkey="sec6sub0"><a href="/Information-Value-With-R.html"><span class="progress-dot"></span>InformationValue Package</a></li></ul></li><li class="sidebar-section"><div class="sidebar-section-header"><span class="sidebar-chevron">▸</span> Practice Exercises<span class="section-meta" data-section-meta></span></div><ul class="sidebar-section-items list-unstyled"><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec7sub1" data-collapsed="false"><span class="subsec-chevron">▼</span> Mastery Quizzes (Certificate)</li><li data-subkey="sec7sub1"><a href="/R-Beginner-Exercises-quiz.html"><span class="progress-dot"></span>R Fundamentals Quiz</a></li><li data-subkey="sec7sub1"><a href="/dplyr-Exercises-in-R-quiz.html"><span class="progress-dot"></span>dplyr Quiz</a></li><li data-subkey="sec7sub1"><a href="/ggplot2-Exercises-in-R-quiz.html"><span class="progress-dot"></span>ggplot2 Quiz</a></li><li data-subkey="sec7sub1"><a href="/Hypothesis-Testing-Exercises-in-R-quiz.html"><span class="progress-dot"></span>Hypothesis Testing Quiz</a></li><li data-subkey="sec7sub1"><a href="/Linear-Regression-Exercises-in-R-quiz.html"><span class="progress-dot"></span>Linear Regression Quiz</a></li><li data-subkey="sec7sub1"><a href="/Machine-Learning-Exercises-in-R-quiz.html"><span class="progress-dot"></span>Machine Learning Quiz</a></li><li data-subkey="sec7sub1"><a href="/tidyr-Exercises-in-R-quiz.html"><span 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class="tool-icon"></span><span class="tool-label">Bayes Theorem</span></a></li><li><a href="/tools/bayes-factor-calculator.html"><span class="tool-icon"></span><span class="tool-label">Bayes Factor</span></a></li><li class="sidebar-divider"><span class="subsec-chevron">▼</span> Interpreters</li><li><a href="/tools/lm-output-interpreter.html"><span class="tool-icon"><svg viewBox="0 0 16 16" width="14" height="14" fill="none" stroke="currentColor" stroke-width="1.4" stroke-linecap="round" stroke-linejoin="round" aria-hidden="true"><path d="M2 13.5L14 3"/><circle cx="4" cy="11.5" r="1" fill="currentColor" stroke="none"/><circle cx="7.5" cy="9" r="1" fill="currentColor" stroke="none"/><circle cx="11" cy="6.5" r="1" fill="currentColor" stroke="none"/></svg></span><span class="tool-label">lm() Output</span></a></li><li><a href="/tools/glm-output-interpreter.html"><span class="tool-icon"><svg viewBox="0 0 16 16" width="14" height="14" fill="none" stroke="currentColor" stroke-width="1.4" stroke-linecap="round" stroke-linejoin="round" aria-hidden="true"><path d="M2 13C5 13 5 3 8 3s3 10 6 10"/><path d="M2 13.5h12"/></svg></span><span class="tool-label">glm() Output</span></a></li><li><a href="/tools/anova-output-interpreter.html"><span class="tool-icon"></span><span class="tool-label">ANOVA Output</span></a></li><li><a href="/tools/vif-interpreter.html"><span class="tool-icon"></span><span class="tool-label">VIF / Multicollinearity</span></a></li><li><a href="/tools/confusion-matrix-interpreter.html"><span class="tool-icon"><svg viewBox="0 0 16 16" width="14" height="14" fill="none" stroke="currentColor" stroke-width="1.4" stroke-linecap="round" stroke-linejoin="round" aria-hidden="true"><rect x="2.5" y="2.5" width="4.5" height="4.5"/><rect x="9" y="2.5" width="4.5" height="4.5"/><rect x="2.5" y="9" width="4.5" height="4.5"/><rect x="9" y="9" width="4.5" height="4.5"/></svg></span><span class="tool-label">Confusion Matrix</span></a></li><li><a href="/tools/diagnostic-plot-interpreter.html"><span class="tool-icon"></span><span class="tool-label">Diagnostic Plots</span></a></li><li class="sidebar-divider"><span class="subsec-chevron">▼</span> Pickers</li><li><a href="/tools/normality-test-picker.html"><span class="tool-icon"><svg viewBox="0 0 16 16" width="14" height="14" fill="none" stroke="currentColor" stroke-width="1.4" stroke-linecap="round" stroke-linejoin="round" aria-hidden="true"><path d="M2 13c2 0 3-1 4-3s1.5-6 2-6 .5 6 2 6 2.5 0 4 0"/><circle cx="13" cy="4" r="1.6" fill="currentColor" stroke="none"/></svg></span><span class="tool-label">Normality Test</span></a></li><li><a href="/tools/nonparametric-test-picker.html"><span class="tool-icon"></span><span class="tool-label">Non-Parametric Test</span></a></li><li><a href="/tools/multiple-testing-correction.html"><span class="tool-icon"><svg viewBox="0 0 16 16" width="14" height="14" fill="none" stroke="currentColor" stroke-width="1.4" stroke-linecap="round" stroke-linejoin="round" aria-hidden="true"><path d="M2.5 4l2 2 3-3M2.5 8l2 2 3-3M2.5 12l2 2 3-3"/><path d="M10.5 4h3M10.5 8h3M10.5 12h3"/></svg></span><span class="tool-label">Multiple Testing</span></a></li><li class="sidebar-divider"><span class="subsec-chevron">▼</span> Time series</li><li><a href="/tools/ts-stationarity-calculator.html"><span class="tool-icon"></span><span class="tool-label">TS Stationarity</span></a></li><li class="sidebar-divider"><span class="subsec-chevron">▼</span> Utilities</li><li><a href="/tools/dag-confounder-picker.html"><span class="tool-icon"></span><span class="tool-label">DAG Confounder Picker</span></a></li><li><a href="/tools/reprex-builder.html"><span class="tool-icon"></span><span class="tool-label">Reprex Builder</span></a></li></ul></div></div>
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<h1>ggplot2 + plotly: Add Hover Tooltips and Zoom to Any Chart in One Line</h1>
<p class="lead"><code>ggplotly()</code> converts any ggplot2 chart into an interactive plotly widget with hover tooltips, zoom, pan, and click-to-filter, in a single function call.</p>
<div class="post-byline" style="color:#6b7280;font-size:14px;margin:2px 0 18px 0;line-height:1.5;">By <strong>Selva Prabhakaran</strong> · Published May 12, 2026 · Last updated May 12, 2026</div>
<div class="engagement-header" data-difficulty="Intermediate" data-time="40" data-exercises="10" data-xp="150"></div>
<h2>How Does ggplotly() Turn a Static Chart Interactive?</h2>
<p>You have a polished <a class="auto-link" href="ggplot2-geom_point-in-R.html" title="ggplot2 geom_point() in R: Scatter Plots With Examples">ggplot2 scatter plot</a> and your stakeholder asks "can I hover over the points to see which car that is?" With base ggplot2, the answer is a flat PNG. With plotly, the answer is one function call. <code>ggplotly()</code> reads every aesthetic, axis, and layer from your ggplot object and rebuilds it as a JavaScript widget the reader can explore.</p>
<div class="webr-container" data-block-title="Build a static mpg scatter">
<div class="webr-code-block">
<div class="webr-header"><div class="webr-header-left"><span class="webr-header-badge">R</span><span class="webr-header-label">Build a static mpg scatter</span></div><div class="webr-header-right"><button type="button" class="webr-copy-btn" aria-label="Copy code" title="Copy code"><svg width="14" height="14" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" aria-hidden="true"><rect x="9" y="9" width="13" height="13" rx="2" ry="2"/><path d="M5 15H4a2 2 0 0 1-2-2V4a2 2 0 0 1 2-2h9a2 2 0 0 1 2 2v1"/></svg></button><button class="btn btn-sm btn-primary webr-run-btn" onclick="runWebR(this)">▶ Run <span class="webr-run-shortcut">Ctrl+Enter</span></button></div></div>
<div class="webr-editor" data-language="r"><span class="cl"><span class="nf">library</span>(ggplot2)</span>
<span class="cl"><span class="nf">library</span>(plotly)</span>
<span class="cl"></span>
<span class="cl"><span class="nf">library</span>(htmlwidgets)</span>
<span class="cl"><span class="nf">library</span>(tidyr)</span>
<span class="cl">p <span class="o"><-</span> <span class="nf">ggplot</span>(mpg, <span class="nf">aes</span>(x <span class="o">=</span> displ, y <span class="o">=</span> hwy, color <span class="o">=</span> class)) <span class="o">+</span></span>
<span class="cl"> <span class="nf">geom_point</span>(size <span class="o">=</span> <span class="m">2</span>) <span class="o">+</span></span>
<span class="cl"> <span class="nf">labs</span>(title <span class="o">=</span> <span class="s">"Engine Size vs Highway Mileage"</span>,</span>
<span class="cl"> x <span class="o">=</span> <span class="s">"Displacement (L)"</span>, y <span class="o">=</span> <span class="s">"Highway MPG"</span>)</span>
<span class="cl">p</span>
<span class="cl"><span class="c1">#> A scatter plot with 234 points colored by vehicle class</span></span>
<span class="cl"><span class="c1">#> Larger engines cluster at lower highway MPG</span></span></div>
<div class="webr-buttons">
<button class="btn btn-sm btn-primary webr-run-btn" onclick="runWebR(this)">▶ Run</button>
<button class="btn btn-sm btn-default webr-reset-btn" onclick="resetWebR(this)">↺ Reset</button>
</div>
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<p>That static chart already tells a story. Now make it interactive with a single line.</p>
<div class="webr-container" data-block-title="Convert ggplot to interactive">
<div class="webr-code-block">
<div class="webr-header"><div class="webr-header-left"><span class="webr-header-badge">R</span><span class="webr-header-label">Convert ggplot to interactive</span></div><div class="webr-header-right"><button type="button" class="webr-copy-btn" aria-label="Copy code" title="Copy code"><svg width="14" height="14" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" aria-hidden="true"><rect x="9" y="9" width="13" height="13" rx="2" ry="2"/><path d="M5 15H4a2 2 0 0 1-2-2V4a2 2 0 0 1 2-2h9a2 2 0 0 1 2 2v1"/></svg></button><button class="btn btn-sm btn-primary webr-run-btn" onclick="runWebR(this)">▶ Run <span class="webr-run-shortcut">Ctrl+Enter</span></button></div></div>
<div class="webr-editor" data-language="r"><span class="cl"><span class="nf">ggplotly</span>(p)</span>
<span class="cl"><span class="c1">#> An interactive plotly widget:</span></span>
<span class="cl"><span class="c1">#> - Hover over any point to see class, displ, and hwy</span></span>
<span class="cl"><span class="c1">#> - Click-drag to zoom into a region</span></span>
<span class="cl"><span class="c1">#> - Double-click to reset the view</span></span>
<span class="cl"><span class="c1">#> - Click legend entries to show/hide a class</span></span></div>
<div class="webr-buttons">
<button class="btn btn-sm btn-primary webr-run-btn" onclick="runWebR(this)">▶ Run</button>
<button class="btn btn-sm btn-default webr-reset-btn" onclick="resetWebR(this)">↺ Reset</button>
</div>
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<p>Every aesthetic you mapped in ggplot2, colour, size, shape, carries over. The tooltip shows those mapped values by default, and the toolbar in the top-right corner lets users zoom, pan, and download the chart as a PNG.</p>
<div class="callout callout-note"><div class="callout-label">Note</div><div class="callout-body"><strong>Interactive plotly output renders in RStudio and HTML documents, not in the browser code runner.</strong> The code blocks here run the R code and show text output. Copy the code into RStudio or an R Markdown document to see the full interactive chart with hover, zoom, and click features.</div></div>
<section class="tryit-block">
<p><strong>Try it:</strong> Build a bar chart counting the number of cars per <code>class</code> in <code>mpg</code> using <code>geom_bar()</code>, then convert it with <code>ggplotly()</code>.</p>
<div class="webr-container" data-block-title="Exercise: interactive bar chart">
<div class="webr-code-block">
<div class="webr-header"><div class="webr-header-left"><span class="webr-header-badge">R</span><span class="webr-header-label">Exercise: interactive bar chart</span></div><div class="webr-header-right"><button type="button" class="webr-copy-btn" aria-label="Copy code" title="Copy code"><svg width="14" height="14" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" aria-hidden="true"><rect x="9" y="9" width="13" height="13" rx="2" ry="2"/><path d="M5 15H4a2 2 0 0 1-2-2V4a2 2 0 0 1 2-2h9a2 2 0 0 1 2 2v1"/></svg></button><button class="btn btn-sm btn-primary webr-run-btn" onclick="runWebR(this)">▶ Run <span class="webr-run-shortcut">Ctrl+Enter</span></button></div></div>
<div class="webr-editor" data-language="r"><span class="cl"><span class="c1"># Try it: bar chart → interactive</span></span>
<span class="cl">ex_bar <span class="o"><-</span> <span class="nf">ggplot</span>(mpg, <span class="nf">aes</span>(x <span class="o">=</span> class)) <span class="o">+</span></span>
<span class="cl"> <span class="nf">geom_bar</span>(fill <span class="o">=</span> <span class="s">"steelblue"</span>) <span class="o">+</span></span>
<span class="cl"> <span class="nf">labs</span>(title <span class="o">=</span> <span class="s">"Cars by Class"</span>)</span>
<span class="cl"></span>
<span class="cl"><span class="c1"># Convert to interactive:</span></span>
<span class="cl"><span class="c1"># your code here</span></span></div>
<div class="webr-buttons">
<button class="btn btn-sm btn-primary webr-run-btn" onclick="runWebR(this)">▶ Run</button>
<button class="btn btn-sm btn-default webr-reset-btn" onclick="resetWebR(this)">↺ Reset</button>
</div>
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<details>
<summary>Click to reveal solution</summary>
<div class="webr-container" data-block-title="Exercise solution">
<div class="webr-code-block">
<div class="webr-header"><div class="webr-header-left"><span class="webr-header-badge">R</span><span class="webr-header-label">Exercise solution</span></div><div class="webr-header-right"><button type="button" class="webr-copy-btn" aria-label="Copy code" title="Copy code"><svg width="14" height="14" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" aria-hidden="true"><rect x="9" y="9" width="13" height="13" rx="2" ry="2"/><path d="M5 15H4a2 2 0 0 1-2-2V4a2 2 0 0 1 2-2h9a2 2 0 0 1 2 2v1"/></svg></button><button class="btn btn-sm btn-primary webr-run-btn" onclick="runWebR(this)">▶ Run <span class="webr-run-shortcut">Ctrl+Enter</span></button></div></div>
<div class="webr-editor" data-language="r"><span class="cl">ex_bar <span class="o"><-</span> <span class="nf">ggplot</span>(mpg, <span class="nf">aes</span>(x <span class="o">=</span> class)) <span class="o">+</span></span>
<span class="cl"> <span class="nf">geom_bar</span>(fill <span class="o">=</span> <span class="s">"steelblue"</span>) <span class="o">+</span></span>
<span class="cl"> <span class="nf">labs</span>(title <span class="o">=</span> <span class="s">"Cars by Class"</span>)</span>
<span class="cl"><span class="nf">ggplotly</span>(ex_bar)</span>
<span class="cl"><span class="c1">#> Interactive bar chart, hover over each bar to see the count</span></span></div>
<div class="webr-buttons">
<button class="btn btn-sm btn-primary webr-run-btn" onclick="runWebR(this)">▶ Run</button>
<button class="btn btn-sm btn-default webr-reset-btn" onclick="resetWebR(this)">↺ Reset</button>
</div>
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</div>
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</div>
<p><strong>Explanation:</strong> <code>ggplotly()</code> works on any ggplot2 object, including bar charts. Hover shows the x category and count.</p>
</details>
</section>
<h2>How Do You Control What Appears in the Tooltip?</h2>
<p>By default, <code>ggplotly()</code> shows every mapped aesthetic in the tooltip, x, y, colour, size, and anything else in <code>aes()</code>. That's often too much. The <code>tooltip</code> argument lets you pick exactly which aesthetics appear.</p>
<p>Let's start by limiting the tooltip to just the x and y values.</p>
<div class="webr-container" data-block-title="Limit tooltip to x and y">
<div class="webr-code-block">
<div class="webr-header"><div class="webr-header-left"><span class="webr-header-badge">R</span><span class="webr-header-label">Limit tooltip to x and y</span></div><div class="webr-header-right"><button type="button" class="webr-copy-btn" aria-label="Copy code" title="Copy code"><svg width="14" height="14" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" aria-hidden="true"><rect x="9" y="9" width="13" height="13" rx="2" ry="2"/><path d="M5 15H4a2 2 0 0 1-2-2V4a2 2 0 0 1 2-2h9a2 2 0 0 1 2 2v1"/></svg></button><button class="btn btn-sm btn-primary webr-run-btn" onclick="runWebR(this)">▶ Run <span class="webr-run-shortcut">Ctrl+Enter</span></button></div></div>
<div class="webr-editor" data-language="r"><span class="cl">p2 <span class="o"><-</span> <span class="nf">ggplot</span>(mpg, <span class="nf">aes</span>(x <span class="o">=</span> displ, y <span class="o">=</span> hwy, color <span class="o">=</span> class)) <span class="o">+</span></span>
<span class="cl"> <span class="nf">geom_point</span>(size <span class="o">=</span> <span class="m">2</span>) <span class="o">+</span></span>
<span class="cl"> <span class="nf">labs</span>(x <span class="o">=</span> <span class="s">"Displacement (L)"</span>, y <span class="o">=</span> <span class="s">"Highway MPG"</span>)</span>
<span class="cl"></span>
<span class="cl"><span class="nf">ggplotly</span>(p2, tooltip <span class="o">=</span> <span class="nf">c</span>(<span class="s">"x"</span>, <span class="s">"y"</span>))</span>
<span class="cl"><span class="c1">#> Tooltip now shows only:</span></span>
<span class="cl"><span class="c1">#> Displacement (L): 1.8</span></span>
<span class="cl"><span class="c1">#> Highway MPG: 29</span></span></div>
<div class="webr-buttons">
<button class="btn btn-sm btn-primary webr-run-btn" onclick="runWebR(this)">▶ Run</button>
<button class="btn btn-sm btn-default webr-reset-btn" onclick="resetWebR(this)">↺ Reset</button>
</div>
<pre class="webr-output"></pre>
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</div>
<p>That cleans up the hover, but sometimes you want a completely custom label, the car's manufacturer and model, plus formatted numbers. The <code>text</code> aesthetic is your tool for that.</p>
<div class="webr-container" data-block-title="Build custom tooltip with paste">
<div class="webr-code-block">
<div class="webr-header"><div class="webr-header-left"><span class="webr-header-badge">R</span><span class="webr-header-label">Build custom tooltip with paste</span></div><div class="webr-header-right"><button type="button" class="webr-copy-btn" aria-label="Copy code" title="Copy code"><svg width="14" height="14" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" aria-hidden="true"><rect x="9" y="9" width="13" height="13" rx="2" ry="2"/><path d="M5 15H4a2 2 0 0 1-2-2V4a2 2 0 0 1 2-2h9a2 2 0 0 1 2 2v1"/></svg></button><button class="btn btn-sm btn-primary webr-run-btn" onclick="runWebR(this)">▶ Run <span class="webr-run-shortcut">Ctrl+Enter</span></button></div></div>
<div class="webr-editor" data-language="r"><span class="cl">p3 <span class="o"><-</span> <span class="nf">ggplot</span>(mpg, <span class="nf">aes</span>(x <span class="o">=</span> displ, y <span class="o">=</span> hwy, color <span class="o">=</span> class,</span>
<span class="cl"> text <span class="o">=</span> <span class="nf">paste</span>(<span class="s">"Model:"</span>, manufacturer, model,</span>
<span class="cl"> <span class="s">"\nYear:"</span>, year,</span>
<span class="cl"> <span class="s">"\nMPG:"</span>, hwy))) <span class="o">+</span></span>
<span class="cl"> <span class="nf">geom_point</span>(size <span class="o">=</span> <span class="m">2</span>) <span class="o">+</span></span>
<span class="cl"> <span class="nf">labs</span>(x <span class="o">=</span> <span class="s">"Displacement (L)"</span>, y <span class="o">=</span> <span class="s">"Highway MPG"</span>)</span>
<span class="cl"></span>
<span class="cl"><span class="nf">ggplotly</span>(p3, tooltip <span class="o">=</span> <span class="s">"text"</span>)</span>
<span class="cl"><span class="c1">#> Tooltip now shows:</span></span>
<span class="cl"><span class="c1">#> Model: audi a4</span></span>
<span class="cl"><span class="c1">#> Year: 1999</span></span>
<span class="cl"><span class="c1">#> MPG: 29</span></span></div>
<div class="webr-buttons">
<button class="btn btn-sm btn-primary webr-run-btn" onclick="runWebR(this)">▶ Run</button>
<button class="btn btn-sm btn-default webr-reset-btn" onclick="resetWebR(this)">↺ Reset</button>
</div>
<pre class="webr-output"></pre>
</div>
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<p>When you set <code>tooltip = "text"</code>, plotly ignores all the default aesthetics and only shows your custom label. This gives you full control over what readers see on hover.</p>
<div class="callout callout-tip"><div class="callout-label">Tip</div><div class="callout-body"><strong>Use HTML tags inside the text aesthetic for rich formatting.</strong> plotly renders a subset of HTML, <code><b></code> for bold, <code><br></code> for line breaks, <code><i></code> for italic. Write <code>paste0("<b>", manufacturer, "</b><br>MPG: ", hwy)</code> for a polished tooltip.</div></div>
<p>Let's try HTML-formatted tooltips for a cleaner look.</p>
<div class="webr-container" data-block-title="Format tooltip with HTML tags">
<div class="webr-code-block">
<div class="webr-header"><div class="webr-header-left"><span class="webr-header-badge">R</span><span class="webr-header-label">Format tooltip with HTML tags</span></div><div class="webr-header-right"><button type="button" class="webr-copy-btn" aria-label="Copy code" title="Copy code"><svg width="14" height="14" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" aria-hidden="true"><rect x="9" y="9" width="13" height="13" rx="2" ry="2"/><path d="M5 15H4a2 2 0 0 1-2-2V4a2 2 0 0 1 2-2h9a2 2 0 0 1 2 2v1"/></svg></button><button class="btn btn-sm btn-primary webr-run-btn" onclick="runWebR(this)">▶ Run <span class="webr-run-shortcut">Ctrl+Enter</span></button></div></div>
<div class="webr-editor" data-language="r"><span class="cl">p4 <span class="o"><-</span> <span class="nf">ggplot</span>(mpg, <span class="nf">aes</span>(x <span class="o">=</span> displ, y <span class="o">=</span> hwy, color <span class="o">=</span> class,</span>
<span class="cl"> text <span class="o">=</span> <span class="nf">paste0</span>(<span class="s">"<b>"</span>, manufacturer, <span class="s">" "</span>, model, <span class="s">"</b>"</span>,</span>
<span class="cl"> <span class="s">"<br>Engine: "</span>, displ, <span class="s">"L"</span>,</span>
<span class="cl"> <span class="s">"<br>Highway: "</span>, hwy, <span class="s">" mpg"</span>,</span>
<span class="cl"> <span class="s">"<br>City: "</span>, cty, <span class="s">" mpg"</span>))) <span class="o">+</span></span>
<span class="cl"> <span class="nf">geom_point</span>(size <span class="o">=</span> <span class="m">2</span>) <span class="o">+</span></span>
<span class="cl"> <span class="nf">labs</span>(x <span class="o">=</span> <span class="s">"Displacement (L)"</span>, y <span class="o">=</span> <span class="s">"Highway MPG"</span>)</span>
<span class="cl"></span>
<span class="cl"><span class="nf">ggplotly</span>(p4, tooltip <span class="o">=</span> <span class="s">"text"</span>)</span>
<span class="cl"><span class="c1">#> Tooltip now renders with bold model name and clean line breaks</span></span></div>
<div class="webr-buttons">
<button class="btn btn-sm btn-primary webr-run-btn" onclick="runWebR(this)">▶ Run</button>
<button class="btn btn-sm btn-default webr-reset-btn" onclick="resetWebR(this)">↺ Reset</button>
</div>
<pre class="webr-output"></pre>
</div>
<div class="webr-plot-output"></div>
</div>
<p>The HTML approach makes tooltips readable even when you pack four or five fields into them.</p>
<section class="tryit-block">
<p><strong>Try it:</strong> Using the <code>iris</code> dataset, create a scatter plot of <code>Sepal.Length</code> vs <code>Petal.Length</code> coloured by <code>Species</code>. Build a custom tooltip showing the species name and petal length. Show only that tooltip.</p>
<div class="webr-container" data-block-title="Exercise: formatted iris tooltip">
<div class="webr-code-block">
<div class="webr-header"><div class="webr-header-left"><span class="webr-header-badge">R</span><span class="webr-header-label">Exercise: formatted iris tooltip</span></div><div class="webr-header-right"><button type="button" class="webr-copy-btn" aria-label="Copy code" title="Copy code"><svg width="14" height="14" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" aria-hidden="true"><rect x="9" y="9" width="13" height="13" rx="2" ry="2"/><path d="M5 15H4a2 2 0 0 1-2-2V4a2 2 0 0 1 2-2h9a2 2 0 0 1 2 2v1"/></svg></button><button class="btn btn-sm btn-primary webr-run-btn" onclick="runWebR(this)">▶ Run <span class="webr-run-shortcut">Ctrl+Enter</span></button></div></div>
<div class="webr-editor" data-language="r"><span class="cl"><span class="c1"># Try it: custom tooltip with iris</span></span>
<span class="cl">ex_iris <span class="o"><-</span> <span class="nf">ggplot</span>(iris, <span class="nf">aes</span>(x <span class="o">=</span> Sepal.Length, y <span class="o">=</span> Petal.Length, color <span class="o">=</span> Species,</span>
<span class="cl"> text <span class="o">=</span> <span class="s">"your tooltip here"</span>)) <span class="o">+</span></span>
<span class="cl"> <span class="nf">geom_point</span>()</span>
<span class="cl"></span>
<span class="cl"><span class="c1"># Convert with tooltip = "text":</span></span>
<span class="cl"><span class="c1"># your code here</span></span></div>
<div class="webr-buttons">
<button class="btn btn-sm btn-primary webr-run-btn" onclick="runWebR(this)">▶ Run</button>
<button class="btn btn-sm btn-default webr-reset-btn" onclick="resetWebR(this)">↺ Reset</button>
</div>
<pre class="webr-output"></pre>
</div>
<div class="webr-plot-output"></div>
</div>
<details>
<summary>Click to reveal solution</summary>
<div class="webr-container" data-block-title="Exercise solution">
<div class="webr-code-block">
<div class="webr-header"><div class="webr-header-left"><span class="webr-header-badge">R</span><span class="webr-header-label">Exercise solution</span></div><div class="webr-header-right"><button type="button" class="webr-copy-btn" aria-label="Copy code" title="Copy code"><svg width="14" height="14" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" aria-hidden="true"><rect x="9" y="9" width="13" height="13" rx="2" ry="2"/><path d="M5 15H4a2 2 0 0 1-2-2V4a2 2 0 0 1 2-2h9a2 2 0 0 1 2 2v1"/></svg></button><button class="btn btn-sm btn-primary webr-run-btn" onclick="runWebR(this)">▶ Run <span class="webr-run-shortcut">Ctrl+Enter</span></button></div></div>
<div class="webr-editor" data-language="r"><span class="cl">ex_iris <span class="o"><-</span> <span class="nf">ggplot</span>(iris, <span class="nf">aes</span>(x <span class="o">=</span> Sepal.Length, y <span class="o">=</span> Petal.Length, color <span class="o">=</span> Species,</span>
<span class="cl"> text <span class="o">=</span> <span class="nf">paste0</span>(<span class="s">"<b>"</span>, Species, <span class="s">"</b>"</span>,</span>
<span class="cl"> <span class="s">"<br>Petal Length: "</span>, Petal.Length))) <span class="o">+</span></span>
<span class="cl"> <span class="nf">geom_point</span>()</span>
<span class="cl"></span>
<span class="cl"><span class="nf">ggplotly</span>(ex_iris, tooltip <span class="o">=</span> <span class="s">"text"</span>)</span>
<span class="cl"><span class="c1">#> Hover shows: Species name (bold) and Petal Length</span></span></div>
<div class="webr-buttons">
<button class="btn btn-sm btn-primary webr-run-btn" onclick="runWebR(this)">▶ Run</button>
<button class="btn btn-sm btn-default webr-reset-btn" onclick="resetWebR(this)">↺ Reset</button>
</div>
<pre class="webr-output"></pre>
</div>
<div class="webr-plot-output"></div>
</div>
<p><strong>Explanation:</strong> Mapping <code>text</code> in <code>aes()</code> and passing <code>tooltip = "text"</code> to <code>ggplotly()</code> replaces the default tooltip with your custom label.</p>
</details>
</section>
<h2>How Do You Style the Tooltip Box Itself?</h2>
<p>Controlling <em>what</em> appears in the tooltip is half the story. The other half is <em>how</em> it looks. The <code>layout()</code> function controls the tooltip's background colour, font, and border. You chain it after <code>ggplotly()</code> using the pipe or the plotly-style <code>%>%</code>.</p>
<div class="webr-container" data-block-title="Style the hover label box">
<div class="webr-code-block">
<div class="webr-header"><div class="webr-header-left"><span class="webr-header-badge">R</span><span class="webr-header-label">Style the hover label box</span></div><div class="webr-header-right"><button type="button" class="webr-copy-btn" aria-label="Copy code" title="Copy code"><svg width="14" height="14" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" aria-hidden="true"><rect x="9" y="9" width="13" height="13" rx="2" ry="2"/><path d="M5 15H4a2 2 0 0 1-2-2V4a2 2 0 0 1 2-2h9a2 2 0 0 1 2 2v1"/></svg></button><button class="btn btn-sm btn-primary webr-run-btn" onclick="runWebR(this)">▶ Run <span class="webr-run-shortcut">Ctrl+Enter</span></button></div></div>
<div class="webr-editor" data-language="r"><span class="cl">p5 <span class="o"><-</span> <span class="nf">ggplot</span>(mpg, <span class="nf">aes</span>(x <span class="o">=</span> displ, y <span class="o">=</span> hwy, color <span class="o">=</span> class)) <span class="o">+</span></span>
<span class="cl"> <span class="nf">geom_point</span>(size <span class="o">=</span> <span class="m">2</span>)</span>
<span class="cl"></span>
<span class="cl"><span class="nf">ggplotly</span>(p5) <span class="o">|></span></span>
<span class="cl"> <span class="nf">layout</span>(hoverlabel <span class="o">=</span> <span class="nf">list</span>(</span>
<span class="cl"> bgcolor <span class="o">=</span> <span class="s">"white"</span>,</span>
<span class="cl"> font <span class="o">=</span> <span class="nf">list</span>(family <span class="o">=</span> <span class="s">"Arial"</span>, size <span class="o">=</span> <span class="m">13</span>, color <span class="o">=</span> <span class="s">"black"</span>),</span>
<span class="cl"> bordercolor <span class="o">=</span> <span class="s">"gray"</span></span>
<span class="cl"> ))</span>
<span class="cl"><span class="c1">#> Tooltip now has a white background, Arial font at 13px, gray border</span></span></div>
<div class="webr-buttons">
<button class="btn btn-sm btn-primary webr-run-btn" onclick="runWebR(this)">▶ Run</button>
<button class="btn btn-sm btn-default webr-reset-btn" onclick="resetWebR(this)">↺ Reset</button>
</div>
<pre class="webr-output"></pre>
</div>
<div class="webr-plot-output"></div>
</div>
<p>The <code>hoverlabel</code> argument accepts <code>bgcolor</code>, <code>font</code> (with <code>family</code>, <code>size</code>, <code>color</code>), and <code>bordercolor</code>. These apply globally to every trace in the chart.</p>
<p><img src="screenshots/Combining-ggplot2-with-plotly-tooltip-layers.webp" alt="Four layers of tooltip customisation" class="img-responsive img-zoomable" loading="lazy" width="2248" height="268" /></p>
<p><em>Figure 2: Four layers of tooltip customisation, from basic text aesthetic to trace-level styling.</em></p>
<p>For charts with multiple traces, like a line chart with several groups, the default "closest" hover mode shows the tooltip for whichever point is nearest to the cursor. That works for scatter plots, but for <a class="auto-link" href="Time-Series-Analysis-With-R.html" title="Time Series Analysis with R">time series</a> you often want to compare all y-values at the same x.</p>
<div class="webr-container" data-block-title="Unified x axis hover mode">
<div class="webr-code-block">
<div class="webr-header"><div class="webr-header-left"><span class="webr-header-badge">R</span><span class="webr-header-label">Unified x axis hover mode</span></div><div class="webr-header-right"><button type="button" class="webr-copy-btn" aria-label="Copy code" title="Copy code"><svg width="14" height="14" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" aria-hidden="true"><rect x="9" y="9" width="13" height="13" rx="2" ry="2"/><path d="M5 15H4a2 2 0 0 1-2-2V4a2 2 0 0 1 2-2h9a2 2 0 0 1 2 2v1"/></svg></button><button class="btn btn-sm btn-primary webr-run-btn" onclick="runWebR(this)">▶ Run <span class="webr-run-shortcut">Ctrl+Enter</span></button></div></div>
<div class="webr-editor" data-language="r"><span class="cl">economics_long <span class="o"><-</span> tidyr<span class="o">::</span><span class="nf">pivot_longer</span>(economics, cols <span class="o">=</span> <span class="nf">c</span>(unemploy, pop),</span>
<span class="cl"> names_to <span class="o">=</span> <span class="s">"metric"</span>, values_to <span class="o">=</span> <span class="s">"value"</span>)</span>
<span class="cl"></span>
<span class="cl">p6 <span class="o"><-</span> <span class="nf">ggplot</span>(economics_long, <span class="nf">aes</span>(x <span class="o">=</span> date, y <span class="o">=</span> value, color <span class="o">=</span> metric)) <span class="o">+</span></span>
<span class="cl"> <span class="nf">geom_line</span>() <span class="o">+</span></span>
<span class="cl"> <span class="nf">labs</span>(x <span class="o">=</span> <span class="s">"Date"</span>, y <span class="o">=</span> <span class="s">"Value"</span>)</span>
<span class="cl"></span>
<span class="cl"><span class="nf">ggplotly</span>(p6) <span class="o">|></span></span>
<span class="cl"> <span class="nf">layout</span>(hovermode <span class="o">=</span> <span class="s">"x unified"</span>)</span>
<span class="cl"><span class="c1">#> One tooltip appears per x-position showing both unemploy and pop values</span></span>
<span class="cl"><span class="c1">#> Makes it easy to compare the two metrics at any date</span></span></div>
<div class="webr-buttons">
<button class="btn btn-sm btn-primary webr-run-btn" onclick="runWebR(this)">▶ Run</button>
<button class="btn btn-sm btn-default webr-reset-btn" onclick="resetWebR(this)">↺ Reset</button>
</div>
<pre class="webr-output"></pre>
</div>
<div class="webr-plot-output"></div>
</div>
<div class="callout callout-insight"><div class="callout-label">Key Insight</div><div class="callout-body"><strong>"x unified" hover mode is the single biggest readability upgrade for multi-line charts.</strong> Instead of chasing individual points, readers see all values at the same x in one tooltip. Set <code>hovermode = "x unified"</code> for any chart with two or more overlapping traces.</div></div>
<section class="tryit-block">
<p><strong>Try it:</strong> Take the scatter plot from the first section (engine size vs highway MPG) and style the tooltip with a light yellow background (<code>"#FFFFF0"</code>) and a font size of 14.</p>
<div class="webr-container" data-block-title="Exercise: style hover label">
<div class="webr-code-block">
<div class="webr-header"><div class="webr-header-left"><span class="webr-header-badge">R</span><span class="webr-header-label">Exercise: style hover label</span></div><div class="webr-header-right"><button type="button" class="webr-copy-btn" aria-label="Copy code" title="Copy code"><svg width="14" height="14" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" aria-hidden="true"><rect x="9" y="9" width="13" height="13" rx="2" ry="2"/><path d="M5 15H4a2 2 0 0 1-2-2V4a2 2 0 0 1 2-2h9a2 2 0 0 1 2 2v1"/></svg></button><button class="btn btn-sm btn-primary webr-run-btn" onclick="runWebR(this)">▶ Run <span class="webr-run-shortcut">Ctrl+Enter</span></button></div></div>
<div class="webr-editor" data-language="r"><span class="cl"><span class="c1"># Try it: style the tooltip box</span></span>
<span class="cl">ex_style <span class="o"><-</span> <span class="nf">ggplot</span>(mpg, <span class="nf">aes</span>(x <span class="o">=</span> displ, y <span class="o">=</span> hwy, color <span class="o">=</span> class)) <span class="o">+</span></span>
<span class="cl"> <span class="nf">geom_point</span>(size <span class="o">=</span> <span class="m">2</span>)</span>
<span class="cl"></span>
<span class="cl"><span class="c1"># Add layout(hoverlabel = ...) to ggplotly():</span></span>
<span class="cl"><span class="c1"># your code here</span></span></div>
<div class="webr-buttons">
<button class="btn btn-sm btn-primary webr-run-btn" onclick="runWebR(this)">▶ Run</button>
<button class="btn btn-sm btn-default webr-reset-btn" onclick="resetWebR(this)">↺ Reset</button>
</div>
<pre class="webr-output"></pre>
</div>
<div class="webr-plot-output"></div>
</div>
<details>
<summary>Click to reveal solution</summary>
<div class="webr-container" data-block-title="Exercise solution">
<div class="webr-code-block">
<div class="webr-header"><div class="webr-header-left"><span class="webr-header-badge">R</span><span class="webr-header-label">Exercise solution</span></div><div class="webr-header-right"><button type="button" class="webr-copy-btn" aria-label="Copy code" title="Copy code"><svg width="14" height="14" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" aria-hidden="true"><rect x="9" y="9" width="13" height="13" rx="2" ry="2"/><path d="M5 15H4a2 2 0 0 1-2-2V4a2 2 0 0 1 2-2h9a2 2 0 0 1 2 2v1"/></svg></button><button class="btn btn-sm btn-primary webr-run-btn" onclick="runWebR(this)">▶ Run <span class="webr-run-shortcut">Ctrl+Enter</span></button></div></div>
<div class="webr-editor" data-language="r"><span class="cl">ex_style <span class="o"><-</span> <span class="nf">ggplot</span>(mpg, <span class="nf">aes</span>(x <span class="o">=</span> displ, y <span class="o">=</span> hwy, color <span class="o">=</span> class)) <span class="o">+</span></span>
<span class="cl"> <span class="nf">geom_point</span>(size <span class="o">=</span> <span class="m">2</span>)</span>
<span class="cl"></span>
<span class="cl"><span class="nf">ggplotly</span>(ex_style) <span class="o">|></span></span>
<span class="cl"> <span class="nf">layout</span>(hoverlabel <span class="o">=</span> <span class="nf">list</span>(</span>
<span class="cl"> bgcolor <span class="o">=</span> <span class="s">"#FFFFF0"</span>,</span>
<span class="cl"> font <span class="o">=</span> <span class="nf">list</span>(size <span class="o">=</span> <span class="m">14</span>)</span>
<span class="cl"> ))</span>
<span class="cl"><span class="c1">#> Tooltip now has light yellow background and larger 14px font</span></span></div>
<div class="webr-buttons">
<button class="btn btn-sm btn-primary webr-run-btn" onclick="runWebR(this)">▶ Run</button>
<button class="btn btn-sm btn-default webr-reset-btn" onclick="resetWebR(this)">↺ Reset</button>
</div>
<pre class="webr-output"></pre>
</div>
<div class="webr-plot-output"></div>
</div>
<p><strong>Explanation:</strong> <code>layout(hoverlabel = list(...))</code> accepts <code>bgcolor</code> for background colour and <code>font</code> for text styling. Both apply to every trace.</p>
</details>
</section>
<h2>Which ggplot2 Geoms Translate Well to plotly?</h2>
<p>Not every geom survives the conversion equally. Most common geoms, points, lines, bars, boxplots, translate perfectly. A few lose features or need manual adjustment. Knowing which is which saves you debugging time.</p>
<p>Here's a quick demo. All three geoms below convert cleanly.</p>
<div class="webr-container" data-block-title="Scatter line and bar basics">
<div class="webr-code-block">
<div class="webr-header"><div class="webr-header-left"><span class="webr-header-badge">R</span><span class="webr-header-label">Scatter line and bar basics</span></div><div class="webr-header-right"><button type="button" class="webr-copy-btn" aria-label="Copy code" title="Copy code"><svg width="14" height="14" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" aria-hidden="true"><rect x="9" y="9" width="13" height="13" rx="2" ry="2"/><path d="M5 15H4a2 2 0 0 1-2-2V4a2 2 0 0 1 2-2h9a2 2 0 0 1 2 2v1"/></svg></button><button class="btn btn-sm btn-primary webr-run-btn" onclick="runWebR(this)">▶ Run <span class="webr-run-shortcut">Ctrl+Enter</span></button></div></div>
<div class="webr-editor" data-language="r"><span class="cl">p_point <span class="o"><-</span> <span class="nf">ggplot</span>(mtcars, <span class="nf">aes</span>(x <span class="o">=</span> wt, y <span class="o">=</span> mpg)) <span class="o">+</span> <span class="nf">geom_point</span>()</span>
<span class="cl">p_line <span class="o"><-</span> <span class="nf">ggplot</span>(economics, <span class="nf">aes</span>(x <span class="o">=</span> date, y <span class="o">=</span> unemploy)) <span class="o">+</span> <span class="nf">geom_line</span>()</span>
<span class="cl">p_bar <span class="o"><-</span> <span class="nf">ggplot</span>(mpg, <span class="nf">aes</span>(x <span class="o">=</span> class)) <span class="o">+</span> <span class="nf">geom_bar</span>()</span>
<span class="cl"></span>
<span class="cl"><span class="nf">ggplotly</span>(p_point)</span>
<span class="cl"><span class="c1">#> Scatter plot, hover shows wt and mpg for each car</span></span>
<span class="cl"><span class="nf">ggplotly</span>(p_line)</span>
<span class="cl"><span class="c1">#> Line chart, hover shows date and unemployment count</span></span>
<span class="cl"><span class="nf">ggplotly</span>(p_bar)</span>
<span class="cl"><span class="c1">#> Bar chart, hover shows class and count</span></span></div>
<div class="webr-buttons">
<button class="btn btn-sm btn-primary webr-run-btn" onclick="runWebR(this)">▶ Run</button>
<button class="btn btn-sm btn-default webr-reset-btn" onclick="resetWebR(this)">↺ Reset</button>
</div>
<pre class="webr-output"></pre>
</div>
<div class="webr-plot-output"></div>
</div>
<p>All three behave exactly as you'd expect. Now let's look at a geom that needs a small fix: <code>geom_smooth()</code>.</p>
<div class="webr-container" data-block-title="Skip hover on smoothing layer">
<div class="webr-code-block">
<div class="webr-header"><div class="webr-header-left"><span class="webr-header-badge">R</span><span class="webr-header-label">Skip hover on smoothing layer</span></div><div class="webr-header-right"><button type="button" class="webr-copy-btn" aria-label="Copy code" title="Copy code"><svg width="14" height="14" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" aria-hidden="true"><rect x="9" y="9" width="13" height="13" rx="2" ry="2"/><path d="M5 15H4a2 2 0 0 1-2-2V4a2 2 0 0 1 2-2h9a2 2 0 0 1 2 2v1"/></svg></button><button class="btn btn-sm btn-primary webr-run-btn" onclick="runWebR(this)">▶ Run <span class="webr-run-shortcut">Ctrl+Enter</span></button></div></div>
<div class="webr-editor" data-language="r"><span class="cl">p_smooth <span class="o"><-</span> <span class="nf">ggplot</span>(mtcars, <span class="nf">aes</span>(x <span class="o">=</span> wt, y <span class="o">=</span> mpg)) <span class="o">+</span></span>
<span class="cl"> <span class="nf">geom_point</span>() <span class="o">+</span></span>
<span class="cl"> <span class="nf">geom_smooth</span>(method <span class="o">=</span> <span class="s">"lm"</span>, se <span class="o">=</span> <span class="kc">TRUE</span>)</span>
<span class="cl"></span>
<span class="cl"><span class="nf">ggplotly</span>(p_smooth) <span class="o">|></span></span>
<span class="cl"> <span class="nf">style</span>(hoverinfo <span class="o">=</span> <span class="s">"skip"</span>, traces <span class="o">=</span> <span class="nf">c</span>(<span class="m">2</span>, <span class="m">3</span>))</span>
<span class="cl"><span class="c1">#> The smooth line and CI band no longer produce hover tooltips</span></span>
<span class="cl"><span class="c1">#> Only the scatter points show tooltips</span></span></div>
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<p>Without the <code>style()</code> fix, hovering over the <a class="auto-link" href="ggplot2-geom_ribbon-in-R.html" title="ggplot2 geom_ribbon() in R: Filled Area Between Two Lines">confidence band</a> shows unhelpful "upper" and "lower" traces. The <code>style(hoverinfo = "skip", traces = c(2, 3))</code> call tells plotly to skip hover on the smooth line and its CI band (traces 2 and 3), so only the scatter points respond to hover.</p>
<p><img src="screenshots/Combining-ggplot2-with-plotly-geom-compat.webp" alt="Geom compatibility with ggplotly()" class="img-responsive img-zoomable" loading="lazy" width="1852" height="1472" /></p>
<p><em>Figure 3: Geom compatibility spectrum, which <a class="auto-link" href="ggplot2-Cheat-Sheet.html" title="ggplot2 Cheat Sheet: Quick Reference for Geoms, Scales, Themes and More">ggplot2 geoms</a> work best with ggplotly().</em></p>
<p>Here's the full compatibility table:</p>
<table class="table table-striped">
<thead>
<tr>
<th>Geom</th>
<th>Status</th>
<th>Notes</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>geom_point()</code></td>
<td>Perfect</td>
<td>All aesthetics carry over</td>
</tr>
<tr>
<td><code>geom_line()</code></td>
<td>Perfect</td>
<td>Hover shows x and y at each point</td>
</tr>
<tr>
<td><code>geom_bar()</code> / <code>geom_col()</code></td>
<td>Perfect</td>
<td>Hover shows category and count/value</td>
</tr>
<tr>
<td><code>geom_boxplot()</code></td>
<td>Perfect</td>
<td>Hover shows quartiles, median, outliers</td>
</tr>
<tr>
<td><code>geom_histogram()</code></td>
<td>Perfect</td>
<td>Hover shows bin range and count</td>
</tr>
<tr>
<td><code>geom_smooth()</code></td>
<td>Partial</td>
<td>CI band creates extra hover traces, use <code>style()</code> to suppress</td>
</tr>
<tr>
<td><code>geom_tile()</code></td>
<td>Partial</td>
<td>Works, but large heatmaps can be slow</td>
</tr>
<tr>
<td><code>geom_density()</code></td>
<td>Partial</td>
<td>Hover shows density values, sometimes noisy</td>
</tr>
<tr>
<td><code>geom_text()</code></td>
<td>Needs tweaks</td>
<td>Labels may overlap differently, use plotly's <code>textposition</code></td>
</tr>
<tr>
<td><code>geom_ribbon()</code></td>
<td>Needs tweaks</td>
<td>Splits into upper/lower traces</td>
</tr>
<tr>
<td><code>geom_sf()</code></td>
<td>Needs tweaks</td>
<td>Consider plotly's native <code>plot_geo()</code> for maps</td>
</tr>
</tbody>
</table>
<div class="callout callout-warning"><div class="callout-label">Warning</div><div class="callout-body"><strong>Large <a class="auto-link" href="Heatmap-in-R.html" title="Heatmap in R: Build and Customize with ggplot2 geom_tile()">geom_tile()</a> heatmaps (1000+ cells) can freeze the browser.</strong> plotly renders every cell as a separate SVG element. For heatmaps over ~500 cells, use plotly's native <code>plot_ly(type = "heatmap")</code> instead of converting a ggplot2 tile grid.</div></div>
<p>Let's also look at <code>geom_text()</code>. The labels convert, but plotly sometimes positions them differently than ggplot2.</p>
<div class="webr-container" data-block-title="Label points with geom text">
<div class="webr-code-block">
<div class="webr-header"><div class="webr-header-left"><span class="webr-header-badge">R</span><span class="webr-header-label">Label points with geom text</span></div><div class="webr-header-right"><button type="button" class="webr-copy-btn" aria-label="Copy code" title="Copy code"><svg width="14" height="14" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" aria-hidden="true"><rect x="9" y="9" width="13" height="13" rx="2" ry="2"/><path d="M5 15H4a2 2 0 0 1-2-2V4a2 2 0 0 1 2-2h9a2 2 0 0 1 2 2v1"/></svg></button><button class="btn btn-sm btn-primary webr-run-btn" onclick="runWebR(this)">▶ Run <span class="webr-run-shortcut">Ctrl+Enter</span></button></div></div>
<div class="webr-editor" data-language="r"><span class="cl">top_cars <span class="o"><-</span> <span class="nf">head</span>(mtcars[<span class="nf">order</span>(<span class="o">-</span>mtcars<span class="o">$</span>mpg), ], <span class="m">5</span>)</span>
<span class="cl">top_cars<span class="o">$</span>name <span class="o"><-</span> <span class="nf">rownames</span>(top_cars)</span>
<span class="cl"></span>
<span class="cl">p_text <span class="o"><-</span> <span class="nf">ggplot</span>(top_cars, <span class="nf">aes</span>(x <span class="o">=</span> wt, y <span class="o">=</span> mpg, label <span class="o">=</span> name)) <span class="o">+</span></span>
<span class="cl"> <span class="nf">geom_point</span>(color <span class="o">=</span> <span class="s">"steelblue"</span>, size <span class="o">=</span> <span class="m">3</span>) <span class="o">+</span></span>
<span class="cl"> <span class="nf">geom_text</span>(nudge_y <span class="o">=</span> <span class="m">0.8</span>, size <span class="o">=</span> <span class="m">3.5</span>)</span>
<span class="cl"></span>
<span class="cl"><span class="nf">ggplotly</span>(p_text)</span>
<span class="cl"><span class="c1">#> Labels appear but may overlap differently than in the static ggplot2</span></span>
<span class="cl"><span class="c1">#> Use plotly's layout(annotations = ...) for precise label control</span></span></div>
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<p>For precise label positioning after conversion, plotly's <code>layout(annotations = ...)</code> gives you pixel-level control. But for most use cases, <code>geom_text()</code> plus a <code>nudge_y</code> works well enough.</p>
<section class="tryit-block">
<p><strong>Try it:</strong> Create a scatter plot of <code>mtcars</code> (wt vs mpg) with a <code>geom_smooth(method = "lm")</code> layer. Convert it with <code>ggplotly()</code> and suppress hover on the smooth trace using <code>style()</code>.</p>
<div class="webr-container" data-block-title="Exercise: add smooth with hover skipped">
<div class="webr-code-block">
<div class="webr-header"><div class="webr-header-left"><span class="webr-header-badge">R</span><span class="webr-header-label">Exercise: add smooth with hover skipped</span></div><div class="webr-header-right"><button type="button" class="webr-copy-btn" aria-label="Copy code" title="Copy code"><svg width="14" height="14" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" aria-hidden="true"><rect x="9" y="9" width="13" height="13" rx="2" ry="2"/><path d="M5 15H4a2 2 0 0 1-2-2V4a2 2 0 0 1 2-2h9a2 2 0 0 1 2 2v1"/></svg></button><button class="btn btn-sm btn-primary webr-run-btn" onclick="runWebR(this)">▶ Run <span class="webr-run-shortcut">Ctrl+Enter</span></button></div></div>
<div class="webr-editor" data-language="r"><span class="cl"><span class="c1"># Try it: suppress smooth line hover</span></span>
<span class="cl">ex_smooth <span class="o"><-</span> <span class="nf">ggplot</span>(mtcars, <span class="nf">aes</span>(x <span class="o">=</span> wt, y <span class="o">=</span> mpg)) <span class="o">+</span></span>
<span class="cl"> <span class="nf">geom_point</span>() <span class="o">+</span></span>
<span class="cl"> <span class="nf">geom_smooth</span>(method <span class="o">=</span> <span class="s">"lm"</span>)</span>
<span class="cl"></span>
<span class="cl"><span class="c1"># Convert and use style(hoverinfo = "skip", traces = ...):</span></span>
<span class="cl"><span class="c1"># your code here</span></span></div>