Applied Data Visualization with R and ggplot2
- 3.9
-
![]()
![]()
![]()
![]()
Brief Introduction
Create useful, elaborate, and visually-appealing plots using this open source packageDescription
Applied Data Visualization with R and ggplot2 introduces you to the world of data visualization by taking you through the basic features of ggplot2. To start with, you'll learn how to set up the R environment, followed by getting insights into the grammar of graphics and geometric objects before you explore the plotting techniques.
You'll discover what layers, scales, coordinates, and themes are, and study how you can use them to transform your data into aesthetical graphs. Once you've grasped the basics, you'll move on to studying simple plots such as histograms and advanced plots such as superimposing and density plots. You'll also get to grips with plotting trends, correlations, and statistical summaries.
By the end of this course, you'll have created data visualizations that will impress your clients.
About the Author
Chris DallaVilla is the founder and CEO of VALID., an independent marketing consulting practice specializing in providing data-driven solutions that help chief marketing officers and their teams strengthen their planning and execution, and drive results. Chris has expertise in digital and social media marketing, as well as certifications in Agile, Google AdWords, and Google Analytics. He studied computer science at Harvard University, design technology at Massachusetts College of Art and Design, and advertising and marketing communications at the Questrom School of Business at Boston University.
Tania Moulik has a PhD in particle physics. She has worked at CERN, the European Organization for Nuclear Research, and on the Tevatron at Fermi National Accelerator Laboratory in IL, USA. She has years of programming experience in C++, Python, and R. She has also worked in the feld of big data and has worked with technologies such as grid computing. She has a passion for data analysis and would like to share her passion with others who would like to delve into the world of data analytics. She especially likes R and ggplot2 as a powerful analytics package.
Requirements
- Requirements
- It is assumed that you know the basics of the R language and its commands and objects.
Packt Publishing
3.9 Raiting
Applied Statistical Modeling for Data Analysis in R
- 4.2
-
![]()
![]()
![]()
![]()
![]()
Your Complete Guide to Statistical Data Analysis and Visualization For Practical Applications in R COMPLETE GUIDE TO STATISTICAL DATA ANALYSIS & VISUALIZATION FOR PRACTICAL APPLICATIONS IN R...
Learning Path: Python: Predictive Analysis with Python
- 3.9
-
![]()
![]()
![]()
![]()
![]()
Manipulate, analyze, and visualize your data with powerful Python libraries - Pandas and scikit-learn Predictive analytics is the process of analyzing historical data to estimate the future results....
Market Basket Analysis & Linear Discriminant Analysis with R
- 4.1
-
![]()
![]()
![]()
![]()
![]()
Master: Association rules (MBA) & it's usage, Linear Discriminant Analysis (LDA) for classification & variable selection This course has two parts....
Data Analysis with R Programming
- 4.8
-
![]()
![]()
![]()
![]()
![]()
This course is the seventh course in the Google Data Analytics Certificate. These courses will equip you with the skills needed to apply to introductory-level data analyst jobs....
