Course
Data Science
Continuing Education

Data Visualization with R

12 Hours

Estimated learning time

Self-Paced

Progress at your own speed

Popular course

A popular course among students

About the Course

Description

In this course, you will learn the Grammar of Graphics, a system for describing and building graphs, and how the ggplot2 data visualization package for R applies this concept to basic bar charts, histograms, pie charts, scatter plots, line plots, and box plots. You will also learn how to further customize your charts and plots using themes and other techniques. You will then learn how to use another data visualization package for R called Leaflet to create map plots, a unique way to plot data based on geolocation data. Finally, you will be introduced to creating interactive dashboards using the R Shiny package. You will learn how to create and customize Shiny apps, alter the appearance of the apps by adding HTML and image components, and deploy your interactive data apps on the web.

You will practice what you learn and build hands-on experience by completing labs in each module and a final project at the end of the course.

Watch the videos, work through the labs, and watch your data science skill grow. Good luck!

NOTE: This course requires knowledge of working with R and data. If you do not have these skills, it is highly recommended that you first take the Introduction to R Programming for Data Science as well as the Data Analysis with R courses from IBM prior to starting this course. Note: The pre-requisite for this course is basic R programming skills.

This Course is part of a program

You can only buy it along with program.

Sections

Schedule

Asynchronous

Delivery method

Online

Deliverables

  • 0 Credits

    Academic Excellence

    Earn necessary number of credit hours for completing this content

  • Hone Important Skills

    Total Upgrade

    Such as Data Visualization, R Programming, Plot (Graphics), Statistical Visualization, Data Analysis, Interactive Data Visualization, Visualization (Computer Graphics), Visual Design, Computer Programming, Statistical Programming, Ggplot2, Data Science