How to Create an Animated Line Graph using Plotly
Last Updated : 26 Apr, 2025
An animated line graph is a visual representation of data that changes over time or over a categorical variable. It can be a powerful tool for visualizing trends and patterns in data and can help to communicate complex ideas in a clear and concise way. In this tutorial, we will learn how to create an animated line graph using the Plotly library in R Programming Language.
Before we dive into the steps for creating an animated line graph, it's important to understand some of the key concepts and terminology related to this type of visualization.
- Frame: In an animated line graph, each frame represents a different point in time or a different category. When the frame changes, the data points on the graph are updated to reflect the new data.
- Animation Attributes: The animation attributes are the settings that control how the animation behaves. For example, you can specify the duration of each frame, the easing function used to transition between frames, and whether to start the animation from the current frame or from the beginning.
Now that we've covered the basic concepts, let's take a look at the steps needed to create an animated line graph using Plotly in R. Load the required libraries: To use the Plotly library in R, you will need to install it first and then load it.
R # Load the Plotly and data.table libraries library(plotly) library(data.table)
Before you can create an animated line graph, you will need to have some data to visualize. You can use your own data, or you can load one of the built-in datasets available in R.
R # Load the mpg dataset data(mpg)
Convert the data to a data table: Plotly works best with data stored in a data table format. To convert your data to a data table, you can use the as.data.table() function. For example dt <- as.data.table(mtcars).
R # Convert the mpg data to a data table dt <- as.data.table(mpg)
To create a Plotly line graph, you can use the plot_ly() function. You will need to specify the x and y variables, as well as the type and mode of the graph.
R # Create a Plotly line graph p <- plot_ly(dt, x = ~displ, y = ~hwy, type = "scatter", mode = "lines", frame = ~class)
To make the line graph animated, you will need to add the animation_frame and animation_opts attributes to the graph. The animation_frame attribute specifies which column to use as the animation frame, and the animation_opts attribute contains a list of options that control the behaviour of the animation.
R # Add the animation attributes p <- layout(p, animation_opts = list(frame = list(duration = 500, redraw = TRUE))) # Display the graph p
Output:
Animated Line Graph using Plotly- A graph or plot object is represented by the variable p. The layout(p,...) instruction in the code first applies a layout to the graph. The... stands for more inputs or choices that may be made for the layout function.
- An animation_opts argument, which is set to a list containing another list of options, is located inside the layout function. The choices listed are:
- Frame: It appears to be in charge of the animation frames.
- duration: A value of 500 milliseconds for each frame's duration. This indicates that each frame of the animation will last 30 frames.
- Redraw: A boolean value indicating whether or not the graph needs to be redrawn for every animation frame. The graph will be redrawn for each frame if the value is set to TRUE.
Example 2:
R # Load the Plotly and data.table libraries library(plotly) library(data.table) # Load the mtcars dataset data(mtcars) # Convert the mtcars data to a data table dt <- as.data.table(mtcars) # Set custom colors for each frame colors <- c("#FF0000", "#00FF00", "#0000FF") # Create a Plotly line graph p <- plot_ly(dt, x = ~wt, y = ~mpg, type = "scatter", mode = "lines", frame = ~cyl, color = ~cyl) %>% style(marker = list(color = ~cyl, colors = colors)) # Add the animation attributes p <- layout(p, animation_opts = list(frame = list(duration = 5000, redraw = TRUE))) # Display the graph p
Output:
Animated Line Graph using Plotly with color changes- mode = "lines" instructs the plot to display lines rather than specific markers.
- frame = cyl: This specifies that the animation frames should be based on the data's cyl column, which implies that each different value of cyl will result in a single frame.
- The colour of the plot elements (lines or markers) is determined by the cyl column using the formula colour = cyl.
- Style: list (marker = colour = cyl, colours = colours).
- colour = cyl, colours = colours, marker = list Based on the cyl column, it determines the colour of the marks. A colour scheme should be present in the colours variable.
- layout(p, animation_opts = list) using the formula (frame = list(duration = 5000, redraw = TRUE)).
- layout: The plot's layout can be changed using this function.
- animation_opts: Options for animation can be set up.
frame = list(redraw = TRUE, time = 5000): It outlines the options for the animation frames. Each frame's duration is set to 5000 milliseconds (5 seconds) using the duration option.
Similar Reads
Create an Animated GIF Using Python Matplotlib
In this article, we will discuss how to create an animated GIF using Matplotlib in Python. Matplotlib can be used to create mathematics-based animations only. These can include a point moving on the circumference of the circle or a sine or cosine wave which are just like sound waves. In Matplotlib w
3 min read
How to Create Grouped Line Chart Using ggplot and plotly in R
Creating grouped line charts in R allows you to visualize multiple trends or series in the same plot. By using the combination of ggplot2 plotting and plotly for interactivity, you can create rich, dynamic visualizations that let you explore your data in depth. In this article, we will explore how t
4 min read
How to Create Pie Chart Using Plotly in R
The pie chart is a circular graphical representation of data that is divided into some slices based on the proportion of it present in the dataset. In R programming this pie chart can be drawn using Plot_ly() function which is present in the Plotly package. In this article, we are going to plot a p
3 min read
How to Add Constant Line to Animated Plot in Plotly?
Animated plots are a useful way to visualize data over time or to highlight the relationship between different variables. In this article, we will learn how to create animated plots using the plot_ly() function in R Programming Language. To create an animated plot in R, we will use the plot_ly() fun
2 min read
Create a Graph Plotter using HTML CSS and JavaScript
In this article, we will create an interactive and user-friendly graph plotter using HTML, CSS, and JavaScript. This project allows users to enter mathematical equations and instantly see the corresponding graphs in a web browser, providing a user-friendly and interactive experience. The application
3 min read
How to Create an Ogive Graph in Python?
In this article, we will create an Ogive Graph. An ogive graph can also be called as cumulative histograms, this graph is used to determine the number of values that lie above or below a particular value in a data set. The class interval is plotted on the x-axis whereas the cumulative frequency is p
4 min read
How To Create A Multiline Plot Using Seaborn?
Data visualization is a crucial component of data analysis, and plotting is one of the best ways to visualize data. The Python data visualization package Seaborn offers a high-level interface for making visually appealing and educational statistics visuals. The multiline plot, which lets you see num
4 min read
How to Plot a Dataframe using Pandas
Pandas plotting is an interface to Matplotlib, that allows to generate high-quality plots directly from a DataFrame or Series. The .plot() method is the core function for plotting data in Pandas. Depending on the kind of plot we want to create, we can specify various parameters such as plot type (ki
8 min read
How to Create Animations in Python?
Animations are a great way to make Visualizations more attractive and user-appealing. It helps us to demonstrate Data Visualization in a Meaningful Way. Python helps us to create Animation Visualization using existing powerful Python libraries. Matplotlib is a very popular Data Visualisation Library
5 min read
Create Plotly Express charts in less lines of code using Python and Datamallet
In this article, we will see how to create all possible Plotly express charts in very less lines of code using a new Python Module known as datamallet. Introduction to the datamallet Library According to the PyPi website datamallet library is made by Data Scientists to help fellow Data Scientists ea
5 min read