Steps with Plotly in R

Interactive Data Visualization with plotly

Welcome! 🎨

Note

Interactive Plotting with Plotly

Learn to create beautiful, interactive visualizations in R

What You’ll Learn:

• Installing and setting up Plotly
• Creating interactive scatter plots, box plots, and histograms
• Understanding binwidths and their impact
• Customizing interactive plots
• Building publication-ready visualizations

Part 0: Cool Features of Plotting

Grassy Area Chart Art 🌱

# ref: https://www.r-graph-gallery.com/138-green-world-data-art/

library(ggplot2)
library(RColorBrewer)
ngroup=20
names=paste("G_",seq(1,ngroup),sep="")
DAT=data.frame()

for(i in seq(1:50)){
  data=data.frame( matrix(0, ngroup , 3))
  data[,1]=i
  data[,2]=sample(names, nrow(data))
  data[,3]=prop.table(sample(
    c(rep(0,100),
    c(1:ngroup)),
    nrow(data)))
  DAT=rbind(DAT,data)
}
colnames(DAT)=c("Year","Group","Value")
DAT=DAT[order( DAT$Year, DAT$Group) , ]

ggplot(DAT, aes(x=Year, y=Value, fill=Group )) + 
  geom_area(alpha=1 , color="transparent" )+
  theme_bw() +
  scale_fill_brewer(palette="greens", breaks=rev(levels(DAT$Group)))+
  theme(line = element_blank(),
        text = element_blank(),
       # line = element_blank(),
        title = element_blank(),
        legend.position="none",
        panel.border = element_blank(),
        panel.background = element_blank())

Tip

Read more about it at Data to Art

Grassy Area Chart Art Output 🌱

Chart Gallery 🎨

Explore a variety of chart types and styles available for all kinds of data visualization applications.

Find code examples to edit at; R-Graph-Gallery

Part 1: Getting Started With Plotly

What is Plotly? 🚀

Plotly is a powerful graphing library that creates interactive, publication-quality visualizations

Key Features:

• Interactive: Hover for details, zoom, pan, download
• Web-based and shareable: Works in RStudio, R Markdown, and Shiny
• Versatile: 40+ chart types
• Beautiful: Professional styling out-of-the-box

Plotly vs ggplot2: ggplot2 excels at static graphics; Plotly adds interactivity for web dashboards and exploration.

Clean Session Setup 🧹

Tip

Start with a clean workspace for reproducible R scripts

# Clear objects, plots, and console
remove_existing_objects <- function() {
  rm(list = ls())
  graphics.off()
  cat("\014")
}

remove_existing_objects()

Install and Load Packages 📦

Note

List the packages your script needs, then install any that are missing.

required_packages <- c('plotly', 'dplyr', 'tidyverse')

install_and_load <- function(packages) {
  for(pkg in packages) {
    if(!require(pkg, character.only = TRUE, quietly = TRUE)) {
      install.packages(pkg)
      library(pkg, character.only = TRUE)
    }
  }
}

install_and_load(required_packages)

Your First Plotly Plot 🎯

Note

Simple Scatter Plot — The easiest way to start with Plotly

library(plotly)

# Create a simple scatter plot
plot_ly(data = mtcars, 
        x = ~wt, 
        y = ~mpg, 
        type = 'scatter',
        mode = 'markers')

Result: Your First Plot 🎯

Note

Static ggplot2 version of the plotly output; run the plotly code for the interactive version.

Plotly Syntax: Key Components 🎯

Note

The first example uses the same core arguments in plot_ly().

• plot_ly(): Main plotting function
• data: Your data frame
• x, y: Variables (use ~ notation)
• type: Chart type (‘scatter’, ‘bar’, ‘box’, etc.)
• mode: For scatter plots (‘markers’, ‘lines’, ‘markers+lines’)

Interactive Features 🖱️

Tip

Every Plotly plot comes with built-in interactivity!

Explore the plot:

1. Hover to inspect data values
2. Zoom by clicking and dragging
3. Pan with Shift + drag
4. Box or lasso select points
5. Reset with a double-click

Plotly Toolbar Controls 🖱️

• 📷 Download the plot as PNG
• 🔍 Zoom, pan, and select points
• 🏠 Reset axes to original view
• ⚙️ Toggle spike lines and axis hover

Part 2: Scatter Plots

Basic Scatter Plot 📊

Exploring Relationships Between Variables

library(plotly)

# Basic scatter plot
fig <- plot_ly(data = iris,
               x = ~Sepal.Length,
               y = ~Petal.Length,
               type = 'scatter',
               mode = 'markers')

fig

Result: Basic Scatter Plot 📊

Customize Scatter Plot Markers 🎨

Tip

Adjust marker size, fill, and outline to make points easier to distinguish.

Customizing Markers:

# Add color, size, and opacity
fig <- plot_ly(data = iris,
               x = ~Sepal.Length,
               y = ~Petal.Length,
               type = 'scatter',
               mode = 'markers',
               marker = list(size = 10,
                           color = 'rgba(255, 182, 193, .9)',
                           line = list(color = 'rgba(152, 0, 0, .8)',
                                      width = 2)))
fig

Result: Custom Markers 🎨

Color Points by Category 🎨

Adding Color Dimension

Visualize a third variable with color

fig <- plot_ly(iris, x = ~Sepal.Length, y = ~Petal.Length,
               color = ~Species, type = 'scatter', mode = 'markers')
fig

Result: Color by Species 🎨

Add Labels and Choose Colors 🎨

fig <- fig %>% layout(
  title = "Iris Dataset: Sepal vs Petal Length",
  xaxis = list(title = "Sepal Length (cm)"),
  yaxis = list(title = "Petal Length (cm)")
)

fig

Color Options:

• color = ~variable: map a variable to color
• colors = 'Set1': use an RColorBrewer palette
• colors = c(...): provide custom colors

Result: Labeled Scatter Plot 🏷️

Scatter with Custom Hover 🔍

Tip

Customize Tooltip Information

# Custom hover text
fig <- plot_ly(data = iris,
               x = ~Sepal.Length,
               y = ~Petal.Length,
               color = ~Species,
               type = 'scatter',
               mode = 'markers',
               text = ~paste("Species:", Species,
                           "<br>Sepal Width:", Sepal.Width,
                           "<br>Petal Width:", Petal.Width),
               hoverinfo = 'text')

fig

Choose What Appears on Hover 🔍

Note

Use hoverinfo to show only the details that help explain the plot.

Hover Options:

• hoverinfo = 'x': Show only x value
• hoverinfo = 'y': Show only y value
• hoverinfo = 'x+y': Show both coordinates
• hoverinfo = 'text': Show custom text
• hoverinfo = 'none': Disable hover

Scatter with Trend Line 📈

Adding Statistical Models

library(plotly)
library(dplyr)

# Calculate linear model
model <- lm(Petal.Length ~ Sepal.Length, data = iris)
iris$predicted <- predict(model)

# Plot with trend line
fig <- plot_ly(data = iris, x = ~Sepal.Length) %>%
  add_markers(y = ~Petal.Length, name = 'Actual',
              marker = list(size = 8, opacity = 0.6)) %>%
  add_lines(y = ~predicted, name = 'Trend',
            line = list(color = 'red', width = 2)) %>%
  layout(title = "Scatter Plot with Linear Regression",
         xaxis = list(title = "Sepal Length"),
         yaxis = list(title = "Petal Length"))

fig

Result: Scatter with Trend Line 📈

Part 3: Box Plots

Basic Box Plot 📦

Note

Visualizing Distributions — Box plots show median, quartiles, and outliers

# Simple box plot
fig <- plot_ly(data = iris,
               y = ~Sepal.Length,
               type = 'box',
               name = 'Sepal Length')

fig

Result: Basic Box Plot 📦

Read a Box Plot 📦

Note

The box summarizes the distribution; points beyond the whiskers are outliers.

• Box: Interquartile range (IQR) - middle 50% of data
• Line in Box: Median (50th percentile)
• Whiskers: Extend to 1.5 × IQR
• Points: Outliers beyond whiskers
• Hover to inspect exact values

Grouped Box Plots 📊

Comparing Multiple Groups

# Box plot by species
fig <- plot_ly(data = iris,
               y = ~Sepal.Length,
               x = ~Species,
               color = ~Species,
               type = 'box')

fig <- fig %>% layout(
  title = "Sepal Length Distribution by Species",
  xaxis = list(title = "Species"),
  yaxis = list(title = "Sepal Length (cm)")
)

fig

Result: Grouped Box Plots 📊

Show Individual Values on Box Plots 🔎

Tip

Jitter spreads points horizontally so overlapping observations are easier to see.

# Show points on box plot
fig <- plot_ly(data = iris,
               y = ~Sepal.Length,
               x = ~Species,
               color = ~Species,
               type = 'box',
               boxpoints = 'all',        # Show all points
               jitter = 0.3,             # Spread points horizontally
               pointpos = -1.8)          # Position of points

fig

Result: Box Plot with Points 🔎

Horizontal Box Plots 🔄

Tip

Better for Long Category Names

# Horizontal orientation
fig <- plot_ly(data = iris,
               x = ~Sepal.Length,        # x for horizontal
               y = ~Species,             # y for categories
               color = ~Species,
               type = 'box',
               orientation = 'h')        # h for horizontal

fig <- fig %>% layout(
  title = "Sepal Length by Species (Horizontal)",
  xaxis = list(title = "Sepal Length (cm)"),
  yaxis = list(title = "")
)

fig

Result: Horizontal Box Plots 🔄

Multiple Box Plots 📈

Note

Comparing Different Measurements

# Create subplot with multiple box plots
fig <- plot_ly(data = iris, x = ~Species) %>%
  add_trace(y = ~Sepal.Length, name = 'Sepal Length',
            type = 'box', boxmean = 'sd') %>%
  add_trace(y = ~Sepal.Width, name = 'Sepal Width',
            type = 'box', boxmean = 'sd') %>%
  add_trace(y = ~Petal.Length, name = 'Petal Length',
            type = 'box', boxmean = 'sd') %>%
  add_trace(y = ~Petal.Width, name = 'Petal Width',
            type = 'box', boxmean = 'sd') %>%
  layout(title = "All Iris Measurements by Species",
         yaxis = list(title = "Measurement (cm)"))

fig

Result: Multiple Box Plots 📈

Show the Mean on a Box Plot 📈

Note

Set boxmean = 'sd' to show the mean and standard deviation.

Part 4: Histograms

Basic Histogram 📊

Distribution of a Single Variable

# Simple histogram
fig <- plot_ly(x = ~iris$Sepal.Length,
               type = 'histogram',
               marker = list(color = 'steelblue',
                           line = list(color = 'white', width = 1)))

fig <- fig %>% layout(
  title = "Distribution of Sepal Length",
  xaxis = list(title = "Sepal Length (cm)"),
  yaxis = list(title = "Count")
)

fig

Result: Basic Histogram 📊

Histogram Bin Controls 📊

Note

Choose the number of bins or specify exact bin boundaries.

• nbinsx: Number of bins (auto-calculated if not specified)
• xbins: Manual bin configuration

Understanding Binwidth ⚖️

Warning

Binwidth Matters! The choice of binwidth dramatically affects interpretation

Too Few Bins (Large Binwidth):

# Very few bins - loses detail
fig <- plot_ly(x = ~iris$Sepal.Length,
               type = 'histogram',
               nbinsx = 5)  # Only 5 bins
fig

Advantages: ✅ Clear overall pattern, less noise
Disadvantages: ❌ Loses fine-grained patterns, may hide bimodality

Result: 5 Bins 📊

Binwidth: Finding Balance 🎯

Too Many Bins (Small Binwidth):

# Too many bins - too noisy
fig <- plot_ly(x = ~iris$Sepal.Length,
               type = 'histogram',
               nbinsx = 50)  # 50 bins
fig

Advantages: ✅ Shows fine detail, reveals minor peaks
Disadvantages: ❌ Noisy, harder to see overall pattern, sparse bins

Result: 50 Bins 📊

Choosing a Binwidth 🎨

Tip

Rules of Thumb for Choosing Bins

Common Rules:

1. Sturges’ Rule: bins ≈ log₂(n) + 1
2. Square Root: bins ≈ √n
3. Scott’s Rule: binwidth ≈ 3.5σ / ∛n
4. Freedman-Diaconis: binwidth ≈ 2IQR / ∛n

Try an Automatic Binwidth 🎨

Tip

Plotly chooses a default binning; compare it with other choices to see which best tells the data’s story.

# Good balance - auto-calculated
fig <- plot_ly(x = ~iris$Sepal.Length,
               type = 'histogram',
               marker = list(color = 'lightgreen',
                           line = list(color = 'darkgreen', width = 1)))
fig  # Plotly uses smart defaults!

Result: Automatic Binwidth 🎨

Custom Binwidth 🔧

Note

Manual Control Over Bins

# Specify exact bin size
fig <- plot_ly(x = ~iris$Sepal.Length,
               type = 'histogram',
               xbins = list(
                 start = 4.0,     # First bin starts at 4
                 end = 8.0,       # Last bin ends at 8
                 size = 0.2       # Each bin is 0.2 cm wide
               ))

fig <- fig %>% layout(
  title = "Sepal Length with 0.2 cm Bins",
  xaxis = list(title = "Sepal Length (cm)"),
  yaxis = list(title = "Frequency")
)

fig

Result: Custom Binwidth 🔧

Overlaid Histograms 📊

Comparing Distributions

# Create separate data for each species
setosa <- iris %>% filter(Species == "setosa")
versicolor <- iris %>% filter(Species == "versicolor")
virginica <- iris %>% filter(Species == "virginica")

# Overlaid histograms
fig <- plot_ly(alpha = 0.6) %>%
  add_histogram(x = ~setosa$Sepal.Length, name = "Setosa") %>%
  add_histogram(x = ~versicolor$Sepal.Length, name = "Versicolor") %>%
  add_histogram(x = ~virginica$Sepal.Length, name = "Virginica") %>%
  layout(barmode = "overlay",          # Overlay instead of stack
         title = "Sepal Length by Species",
         xaxis = list(title = "Sepal Length (cm)"),
         yaxis = list(title = "Count"))

fig

Other barmode options: 'stack', 'group'

Result: Overlaid Histograms 📊

Stacked Histograms 📚

Tip

Showing Composition

# Stacked histogram
fig <- plot_ly() %>%
  add_histogram(x = ~setosa$Sepal.Length, 
                name = "Setosa",
                marker = list(color = '#e74c3c')) %>%
  add_histogram(x = ~versicolor$Sepal.Length,
                name = "Versicolor",
                marker = list(color = '#3498db')) %>%
  add_histogram(x = ~virginica$Sepal.Length,
                name = "Virginica",
                marker = list(color = '#2ecc71')) %>%
  layout(barmode = "stack",
         title = "Species Distribution Across Sepal Lengths",
         xaxis = list(title = "Sepal Length (cm)"),
         yaxis = list(title = "Total Count"))

fig

Result: Stacked Histograms 📚

Part 5: Other Plot Types

Bar Charts 📊

Note

Categorical Data Visualization

# Count by species
species_count <- iris %>%
  group_by(Species) %>%
  summarise(Count = n())

fig <- plot_ly(data = species_count,
               x = ~Species,
               y = ~Count,
               type = 'bar',
               marker = list(color = c('#e74c3c', '#3498db', '#2ecc71')))

fig <- fig %>% layout(
  title = "Sample Count by Species",
  xaxis = list(title = "Species"),
  yaxis = list(title = "Number of Samples")
)

fig

Result: Bar Chart 📊

Line Charts 📈

Time Series and Trends

iris_sorted <- iris %>% arrange(Sepal.Length)
fig <- plot_ly(iris_sorted, x = ~seq_along(Sepal.Length),
               y = ~Sepal.Length, color = ~Species,
               type = 'scatter', mode = 'lines+markers',
               line = list(width = 2), marker = list(size = 6)) %>%
  layout(title = "Sepal Length Sorted Values",
         xaxis = list(title = "Sample Index"),
         yaxis = list(title = "Sepal Length (cm)"))
fig

Result: Line Chart 📈

Violin Plots 🎻

Tip

Density + Box Plot Combined

# Violin plot shows distribution shape
fig <- plot_ly(data = iris,
               x = ~Species,
               y = ~Sepal.Length,
               color = ~Species,
               type = 'violin',
               box = list(visible = TRUE),     # Show box plot inside
               meanline = list(visible = TRUE)) # Show mean line

fig <- fig %>% layout(
  title = "Sepal Length Distribution (Violin Plot)",
  xaxis = list(title = "Species"),
  yaxis = list(title = "Sepal Length (cm)")
)

fig

Violin plots show the full probability density, making them superior to box plots for multimodal distributions!

Result: Violin Plot 🎻

Heatmaps 🔥

Note

Correlation Matrix Visualization

iris_numeric <- iris %>% select(-Species)
cor_matrix <- cor(iris_numeric)
fig <- plot_ly(x = colnames(cor_matrix), y = rownames(cor_matrix),
               z = cor_matrix, type = "heatmap",
               colors = colorRamp(c("blue", "white", "red"))) %>%
  layout(title = "Iris Feature Correlation Matrix",
         xaxis = list(title = ""), yaxis = list(title = ""))
fig

Result: Heatmap 🔥

3D Scatter Plots 🌐

Three-Dimensional Data

fig <- plot_ly(iris, x = ~Sepal.Length, y = ~Sepal.Width,
               z = ~Petal.Length, color = ~Species,
               type = 'scatter3d', mode = 'markers',
               marker = list(size = 5)) %>%
  layout(scene = list(
    xaxis = list(title = 'Sepal Length'),
    yaxis = list(title = 'Sepal Width'),
    zaxis = list(title = 'Petal Length')),
    title = "3D Iris Visualization")
fig

Interactivity is KEY! Rotate the plot by clicking and dragging!

Part 6: Customization & Styling

Layout Customization 🎨

Tip

Fine-Tuning Your Plots

fig <- plot_ly(iris, x = ~Sepal.Length, y = ~Petal.Length,
               color = ~Species, type = 'scatter', mode = 'markers')
fig <- fig %>% layout(
  title = list(text = "Beautiful Iris Plot",
               font = list(size = 24, color = '#2c3e50')),
  xaxis = list(title = "Sepal Length (cm)", gridcolor = '#E0E0E0',
               showline = TRUE, linecolor = '#2c3e50', linewidth = 2),
  yaxis = list(title = "Petal Length (cm)", gridcolor = '#E0E0E0'),
  plot_bgcolor = '#F8F9FA', paper_bgcolor = '#FFFFFF')
fig

Result: Layout Customization 🎨

Color Scales 🌈

Note

Using Color Effectively

# Color by continuous variable
fig <- plot_ly(data = iris,
               x = ~Sepal.Length,
               y = ~Petal.Length,
               color = ~Sepal.Width,      # Continuous color
               type = 'scatter',
               mode = 'markers',
               marker = list(size = 10,
                           colorbar = list(title = "Sepal Width")))

fig <- fig %>% layout(title = "Color by Sepal Width")
fig

Result: Continuous Color Scale 🌈

Choose a Color Scale 🌈

Note

Use a palette that helps readers distinguish values without distracting from the data.

Built-in Color Scales:

• colors = 'Viridis', 'Blues', 'Reds', 'RdBu'
• Or use custom: colors = colorRamp(c("blue", "yellow", "red"))

Add an Annotation 📝

Add a text label and arrow to call attention to a data point.

fig <- plot_ly(data = iris,
               x = ~Sepal.Length,
               y = ~Petal.Length,
               type = 'scatter',
               mode = 'markers') %>%
  layout(
    title = "Annotated Plot",
    annotations = list(
      list(x = 6.5, y = 5.5,
           text = "Cluster of large flowers",
           showarrow = TRUE,
           arrowhead = 2,
           arrowcolor = "red"))
  )

fig

Result: Annotated Plot 📝

Highlight a Region with a Shape 🟥

Tip

Use a transparent rectangle to mark an area of the plot without hiding the data.

fig <- plot_ly(data = iris,
               x = ~Sepal.Length,
               y = ~Petal.Length,
               type = 'scatter',
               mode = 'markers') %>%
  layout(
    shapes = list(
      list(type = "rect",
           x0 = 6, x1 = 7, y0 = 5, y1 = 6,
           line = list(color = "red", width = 2),
           fillcolor = "rgba(255, 0, 0, 0.1)")
    )
  )

fig

Result: Highlighted Region 🟥

Subplots 📊📊

Tip

Multiple Plots in One Figure

library(plotly)

# Create individual plots
p1 <- plot_ly(iris, x = ~Sepal.Length, type = "histogram",
              name = "Sepal Length")
p2 <- plot_ly(iris, x = ~Petal.Length, type = "histogram",
              name = "Petal Length")
p3 <- plot_ly(iris, x = ~Sepal.Length, y = ~Petal.Length,
              color = ~Species, type = "scatter", mode = "markers")
p4 <- plot_ly(iris, x = ~Species, y = ~Sepal.Length,
              color = ~Species, type = "box")

Combine Plots in a Grid 📊

Tip

After defining p1 through p4, arrange them in a 2 × 2 layout.

# Combine into subplot
fig <- subplot(p1, p2, p3, p4,
               nrows = 2, ncols = 2,
               shareX = FALSE, shareY = FALSE,
               titleX = TRUE, titleY = TRUE)

fig <- fig %>% layout(title = "Iris Dataset Overview",
                     showlegend = FALSE)
fig

Part 7: Interactive Code Generator

Plotly Code Generator 🛠️

Build Your Own Plotly Visualization!

Interactive tool to generate Plotly code for different plot types

Plotly Code Generator

Dataset: iris mtcars X Variable: Sepal.Length Sepal.Width Petal.Length Petal.Width Y Variable: Petal.Length Sepal.Length Sepal.Width Petal.Width

Plot Type: Scatter Plot Box Plot Histogram Bar Chart Line Chart Violin Plot Heatmap 3D Scatter Color By: None Species Sepal.Width Color Scale: Default Viridis Blues Reds RdBu

Show Markers Custom Hover Add Title

Click "Generate Code" to create your Plotly code!

Try the Generator! 🎮

Tip

Interactive Learning — Experiment with the controls and inspect the generated code.

Try these options:

• Switch datasets and plot types
• Add a color variable and choose a color scale
• Generate and copy the code

Try it: Generate code for a 3D scatter plot of iris data colored by Species!

Part 8: Best Practices

When to Use Plotly 🤔

Note

Choosing the Right Tool

Choose Plotly for:

✅ Interactive dashboards and web apps
✅ Exploratory data analysis
✅ Presentations where you can click and zoom
✅ Sharing HTML reports
✅ 3D visualizations

When to Use ggplot2 🤔

Note

Choose a static plot when publication control matters most.

✅ Publication-quality static graphics
✅ Fine-grained control over every element
✅ Complex multi-layered plots
✅ PDF/PNG exports for papers
✅ Faceted plots with many panels

Need both? Convert a ggplot with ggplotly().

Converting ggplot2 to Plotly 🔄

The Power of ggplotly()

library(ggplot2)
library(plotly)

# Create a ggplot
p <- ggplot(iris, aes(x = Sepal.Length, y = Petal.Length, 
                     color = Species)) +
  geom_point(size = 3) +
  theme_minimal() +
  labs(title = "Iris Dataset",
       x = "Sepal Length (cm)",
       y = "Petal Length (cm)")

# Convert to interactive Plotly plot
ggplotly(p)

Advantages:

• Keeps all your ggplot2 customization
• Automatically adds interactivity
• One line of code!

Performance Tips 🚀

Warning

Making Plotly Fast

For Large Datasets:

# 1. Use webgl for better performance
fig <- plot_ly(data = large_dataset,
               x = ~x, y = ~y,
               type = 'scattergl',  # Note: scattergl instead of scatter
               mode = 'markers')

# 2. Reduce marker count with sampling
sampled <- large_dataset %>% sample_n(1000)
fig <- plot_ly(data = sampled, ...)

# 3. Disable hover for thousands of points
fig <- plot_ly(..., hoverinfo = 'skip')

Keep Large Plots Responsive ⚡

Note

Choose a rendering strategy that matches the size of your data.

Tips:

• Use scattergl for >1000 points
• Consider aggregation or binning for huge datasets
• Disable unnecessary interactive features

Saving Plotly Plots 💾

Tip

Export Options

library(plotly)
library(htmlwidgets)

# Create a plot
fig <- plot_ly(iris, x = ~Sepal.Length, y = ~Petal.Length,
               color = ~Species, type = 'scatter', mode = 'markers')

# Save as interactive HTML
htmlwidgets::saveWidget(fig, "my_plot.html")

Export a Static Image 🖼️

Tip

Static PNG export requires a configured image engine such as Kaleido.

# install.packages("reticulate")
# reticulate::py_install("kaleido")
library(orca)
orca(fig, "my_plot.png")

Embed a Plot in a Report 📄

Note

In an R Markdown or Quarto document, print the plot object to include it.

fig

Key Takeaways: Core Ideas 🎯

Note

What We Learned

1. Installation: Use install.packages('plotly') and setup functions

2. Basic Syntax:

plot_ly(data = df, x = ~var1, y = ~var2, type = 'scatter')

3. Plot Types: - Scatter, Box, Histogram, Bar, Line, Violin, Heatmap, 3D

4. Binwidths: - Too few bins → lose detail - Too many bins → too noisy - Use Plotly’s smart defaults or calculate optimal bins

Key Takeaways: Customize and Extend 🎨

Note

Make plots clearer and shareable

5. Customization: - layout() for axes, titles, colors - add_trace() for multiple layers - Annotations and shapes for context

Practice Challenge 🏆

Tip

Try This on Your Own!

Challenge: Create an interactive Plotly visualization using the mtcars dataset

Requirements:

1. Create a scatter plot of mpg vs wt (weight)
2. Color points by cyl (cylinders)
3. Add custom hover text showing car name and horsepower
4. Include a title and axis labels
5. Customize the color scheme

Practice Challenge: Bonus Ideas 🏆

Tip

Try an extension after the required scatter plot is complete.

Bonus:

• Add a trend line
• Create a subplot with a histogram of mpg
• Make it 3D by adding disp as z-axis

Time: 5-10 minutes

Resources 📚

Continue Learning

Documentation: Plotly R · Graph reference · Community forum

Tutorials: Getting started · Plotly book · R Graph Gallery

Cheat sheet: Plotly R PDF

So What Did We Cover, Again?

Note

Keep Exploring!

You now know how to:

✅ Install and set up Plotly
✅ Create interactive scatter, box, and histogram plots
✅ Choose appropriate binwidths
✅ Customize plots with colors, layouts, and themes
✅ Use the code generator for quick starts
✅ Convert ggplot2 plots to interactive Plotly plots

What to Try Next 🚀

Note

Keep exploring with your own data

Next Steps:

• Practice with your own datasets
• Explore advanced plot types (sunburst, sankey, etc.)
• Build interactive dashboards with Shiny + Plotly
• Share your visualizations on the web!

Happy Plotting! 📊✨