4 Chapter 3: Interactive Data Visualization with ggplot2 and Plotly

4.0.1 Key Topics

Basics of ggplot2 for data visualization.
Using Plotly to add interactivity to charts.
Hands-on exercises to create static visualizations with ggplot2 and enhance them with Plotly.

4.0.2 Outcome

Participants will create and embed interactive visualizations in Quarto using R.

4.1 Introduction to ggplot2

ggplot2 is a powerful R package for creating static visualizations. It implements the Grammar of Graphics, allowing you to build complex plots from simple components.

4.1.1 Basic ggplot2 Example

Here is a simple example of creating a scatter plot using ggplot2:

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library(ggplot2)

# Basic scatter plot
ggplot(data = mtcars, aes(x = wt, y = mpg)) +
  geom_point(size = .8, col="firebrick") + xlab("Vehicle Weight") +
  ylab("Miles per Gallon (MPG)") +
  theme_bw()

4.1.2 Customizing Plots

You can customize the appearance of your plots by adjusting point size, shape, and color:

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# Customized scatter plot
ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point(size = .8, shape = 21, color = "steelblue", fill = "lightblue")  + xlab("Vehicle Weight") +
  ylab("Miles per Gallon (MPG)") +
  theme_bw()

4.2 Introduction to Plotly

Plotly is a library that allows you to create interactive charts. You can convert static ggplot2 plots into interactive plots using the ggplotly() function.

4.2.1 Converting ggplot2 to Plotly

Below is an example of how to convert a static ggplot2 plot into an interactive Plotly plot:

Show the code

library(plotly)


Attaching package: 'plotly'

The following object is masked from 'package:ggplot2':

    last_plot

The following object is masked from 'package:stats':

    filter

The following object is masked from 'package:graphics':

    layout

Show the code

# Create a ggplot object
p <- ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point(size = .8, shape = 21, color = "steelblue", fill = "lightblue")  + xlab("Vehicle Weight") + 
  theme_bw()

# Convert the ggplot object to an interactive plot
ggplotly(p)

4.3 Hands-On Exercise

4.3.1 Exercise 1: Create a Static Bar Chart with ggplot2

Use the diamonds dataset from the ggplot2 package.
Create a bar chart showing the count of diamonds by cut.
Can you change color to the bars?

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# Bar chart of diamond cuts
ggplot(diamonds, aes(x = cut)) +
  geom_bar(fill = "skyblue") +
  theme_bw()

4.3.2 Exercise 2: Add Interactivity with Plotly

Convert the static bar chart into an interactive chart using Plotly:

Show the code

# Convert ggplot bar chart to interactive plot
p_bar <- ggplot(diamonds, aes(x = cut)) +
  geom_bar(fill = "skyblue") +
  theme_bw()

ggplotly(p_bar)

4.4 Dashboard

Try creating a dashboard using the Plotly package, with example data from Plotly and Gapminder data to illustrate interactive charts. Note the use of external fonts from Google Fonts, color choices and animation button and slider control.

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# install.packages(c("plotly","tidyverse","RColrBrewer"))
library(plotly)
library(tidyverse)
# Reading in example dataset
df <- read.csv("https://plotly.com/~public.health/17.csv", skipNul = TRUE, encoding = "UTF-8")

# Font management
library(showtext)
font_add_google("EB Garamond","ebgaramond")
t <- list(
  family = "ebgaramond",
  size = 12)

# Create label function for animation button
labels <- function(size, label) {
  list(
    args = c("xbins.size", size), 
    label = label, 
    method = "restyle"
  )
}

# Create Figure object
fig <- df %>%
  plot_ly(
    x = ~date,
    autobinx = FALSE, 
    autobiny = TRUE, 
    marker = list(color = "steelblue"), 
    name = "date", 
    type = "histogram", 
    xbins = list(
      end = "2016-12-31 12:00", 
      size = "M1", 
      start = "1983-12-31 12:00"
    )
  )

# Configure dropdown menu
fig <- fig %>% layout(
  paper_bgcolor = "white", 
  plot_bgcolor = "white", 
  title = "<b>Shooting Incidents</b><br>use dropdown to change bin size", # HTML to separate line
  xaxis = list(
    type = 'date'
  ),
  yaxis = list(
    title = "Incidents"
  ),
  updatemenus = list(
    list(
      x = 0.1, 
      y = 1.15,
      active = 1, 
      showactive = TRUE,
      buttons = list(
        labels("D1", "Day"),
        labels("M1", "Month"),
        labels("M6", "Half Year"),
        labels("M12", "Year")
      )
    )
  )
)

fig <- fig %>% layout(font = t) # Add font to text

fig

Show the code

#install.packages("gapminder")
library(gapminder)
library(RColorBrewer)
library(plotly)

# Read in Gapminder data
df <- gapminder 

# Font management

t <- list(
  family = "corgaramond",
  size = 12)

# Create figure object
fig <- df %>%
  plot_ly(
    x = ~gdpPercap, 
    y = ~lifeExp,
    size = ~pop, 
    color = ~continent, 
    colors = c("slateblue3", "steelblue", "firebrick", "forestgreen", "turquoise1"), 
        # manually select colar 
    alpha=.5, # translucent glyth
    frame = ~year, 
    text = ~country, 
    hoverinfo = "text",
    type = 'scatter',
    mode = 'markers',
    fill = ~''
  )

# Add font to text
fig <- fig %>% layout(
  xaxis = list(
    type = "log"
  ), font = t
)


fig <- fig %>% layout(legend = list(orientation = "h",   # show entries horizontally
                     xanchor = "center",  # use center of legend as anchor
                     x = 0.5, y = 100))             # put legend in center of x-axis

fig <- fig %>% animation_button(
  x = 1, xanchor = "right", y = 0, yanchor = "bottom"
)
fig <- fig %>% animation_slider(
  currentvalue = list(prefix = "YEAR ", font = list(color="red"))
)
fig

By following these examples and exercises, participants will gain practical experience in creating both static and interactive visualizations using R. This session will enhance their ability to communicate data insights effectively through engaging graphics.

4.4.1 Recap

Static Visualizations: Introduces basic plotting with ggplot2, demonstrating how to create and customize scatter plots.
Interactive Visualizations: Shows how to use Plotly to add interactivity to existing ggplot2 plots.
Dashboard: Demonstrates how to create a dashboard with interactive charts using Plotly and external fonts.
Exercises: Provides hands-on practice for participants to reinforce learning by creating both static and interactive visualizations.

# Chapter 3: Interactive Data Visualization with *ggplot2* and *Plotly* ### Key Topics - Basics of `ggplot2` for data visualization. - Using `Plotly` to add interactivity to charts. - Hands-on exercises to create static visualizations with `ggplot2` and enhance them with `Plotly`. ### Outcome Participants will create and embed interactive visualizations in Quarto using R. ## Introduction to ggplot2 `ggplot2` is a powerful R package for creating static visualizations. It implements the Grammar of Graphics, allowing you to build complex plots from simple components. ### Basic ggplot2 Example Here is a simple example of creating a scatter plot using `ggplot2`: ```{r} library(ggplot2) # Basic scatter plot ggplot(data = mtcars, aes(x = wt, y = mpg)) + geom_point(size = .8, col="firebrick") + xlab("Vehicle Weight") + ylab("Miles per Gallon (MPG)") + theme_bw() ``` ### Customizing Plots You can customize the appearance of your plots by adjusting point size, shape, and color: ```{r} # Customized scatter plot ggplot(mtcars, aes(x = wt, y = mpg)) + geom_point(size = .8, shape = 21, color = "steelblue", fill = "lightblue") + xlab("Vehicle Weight") + ylab("Miles per Gallon (MPG)") + theme_bw() ``` ## Introduction to Plotly `Plotly` is a library that allows you to create interactive charts. You can convert static `ggplot2` plots into interactive plots using the `ggplotly()` function. ### Converting ggplot2 to Plotly Below is an example of how to convert a static `ggplot2` plot into an interactive Plotly plot: ```{r} library(plotly) # Create a ggplot object p <- ggplot(mtcars, aes(x = wt, y = mpg)) + geom_point(size = .8, shape = 21, color = "steelblue", fill = "lightblue") + xlab("Vehicle Weight") + theme_bw() # Convert the ggplot object to an interactive plot ggplotly(p) ``` ## Hands-On Exercise ### Exercise 1: Create a Static Bar Chart with ggplot2 1. Use the `diamonds` dataset from the `ggplot2` package. 2. Create a bar chart showing the count of diamonds by cut. 3. Can you change color to the bars? ```{r} # Bar chart of diamond cuts ggplot(diamonds, aes(x = cut)) + geom_bar(fill = "skyblue") + theme_bw() ``` ### Exercise 2: Add Interactivity with Plotly Convert the static bar chart into an interactive chart using Plotly: ```{r} # Convert ggplot bar chart to interactive plot p_bar <- ggplot(diamonds, aes(x = cut)) + geom_bar(fill = "skyblue") + theme_bw() ggplotly(p_bar) ``` ## Dashboard Try creating a dashboard using the **Plotly** package, with example data from Plotly and Gapminder data to illustrate interactive charts. Note the use of external fonts from Google Fonts, color choices and animation button and slider control. ```{r setup, include=FALSE} # Set theme for dashboard # install.packages(c("bslib","thematic")) library(thematic) if (requireNamespace("thematic")) thematic::thematic_rmd(font = "auto") ``` ```{r message=FALSE} # install.packages(c("plotly","tidyverse","RColrBrewer")) library(plotly) library(tidyverse) # Reading in example dataset df <- read.csv("https://plotly.com/~public.health/17.csv", skipNul = TRUE, encoding = "UTF-8") # Font management library(showtext) font_add_google("EB Garamond","ebgaramond") t <- list( family = "ebgaramond", size = 12) # Create label function for animation button labels <- function(size, label) { list( args = c("xbins.size", size), label = label, method = "restyle" ) } # Create Figure object fig <- df %>% plot_ly( x = ~date, autobinx = FALSE, autobiny = TRUE, marker = list(color = "steelblue"), name = "date", type = "histogram", xbins = list( end = "2016-12-31 12:00", size = "M1", start = "1983-12-31 12:00" ) ) # Configure dropdown menu fig <- fig %>% layout( paper_bgcolor = "white", plot_bgcolor = "white", title = "<b>Shooting Incidents</b><br>use dropdown to change bin size", # HTML to separate line xaxis = list( type = 'date' ), yaxis = list( title = "Incidents" ), updatemenus = list( list( x = 0.1, y = 1.15, active = 1, showactive = TRUE, buttons = list( labels("D1", "Day"), labels("M1", "Month"), labels("M6", "Half Year"), labels("M12", "Year") ) ) ) ) fig <- fig %>% layout(font = t) # Add font to text fig ``` ```{r message=FALSE} #install.packages("gapminder") library(gapminder) library(RColorBrewer) library(plotly) # Read in Gapminder data df <- gapminder # Font management t <- list( family = "corgaramond", size = 12) # Create figure object fig <- df %>% plot_ly( x = ~gdpPercap, y = ~lifeExp, size = ~pop, color = ~continent, colors = c("slateblue3", "steelblue", "firebrick", "forestgreen", "turquoise1"), # manually select colar alpha=.5, # translucent glyth frame = ~year, text = ~country, hoverinfo = "text", type = 'scatter', mode = 'markers', fill = ~'' ) # Add font to text fig <- fig %>% layout( xaxis = list( type = "log" ), font = t ) fig <- fig %>% layout(legend = list(orientation = "h", # show entries horizontally xanchor = "center", # use center of legend as anchor x = 0.5, y = 100)) # put legend in center of x-axis fig <- fig %>% animation_button( x = 1, xanchor = "right", y = 0, yanchor = "bottom" ) fig <- fig %>% animation_slider( currentvalue = list(prefix = "YEAR ", font = list(color="red")) ) fig ``` By following these examples and exercises, participants will gain practical experience in creating both static and interactive visualizations using R. This session will enhance their ability to communicate data insights effectively through engaging graphics. ### Recap - **Static Visualizations:** Introduces basic plotting with `ggplot2`, demonstrating how to create and customize scatter plots. - **Interactive Visualizations:** Shows how to use `Plotly` to add interactivity to existing `ggplot2` plots. - **Dashboard:** Demonstrates how to create a dashboard with interactive charts using `Plotly` and external fonts. - **Exercises:** Provides hands-on practice for participants to reinforce learning by creating both static and interactive visualizations.