4.5 hours







Packt Publishing

Learn to design and implement data from scratch

Expected learning & outcomes

  • Set up the R environment, RStudio, and understand the structure of ggplot2
  • Use basic programming concepts of R such as loading packages, arithmetic functions, data structures, and flow control
  • Import data to R from various formats, such as CSV, Excel, and SQL
  • Clean data by handling missing values and standardizing fields
  • Perform univariate and bivariate analysis using ggplot2
  • Create statistical summary and advanced plots, such as histograms, scatter plots, box plots, and interaction plots
  • Apply data management techniques, such as factors, pivots, aggregation, merging, and dealing with missing values, on the example data sets
  • Distinguish variables and use best practices to visualize them
  • Build complex and aesthetic visualizations with ggplot2 analysis methods

    Skills you will learn

    Advertising, Advising, Agile, Analysis, Arithmetic, Arts, Consulting, Data Analysis, Data management, Data visualization, Execution, Finance, Google AdWords, Google Analytics, Google+

    About this course

    Data analysis is crucial to accurately predict the performance of an application. When data is presented to you in a graphical or pictorial format, you can analyze it more effectively. This Learning Path introduces you to the tools for working with data. To start with, you'll understand you how to set up R and RStudio, followed by exploring R packages, functions, data structures, control flow, and loops.

    Once you have grasped the basics, you'll move on to studying data visualization and graphics. You'll learn how to build statistical and advanced plots using the powerful ggplot2 library. In addition to this, you'll discover data management concepts such as factoring, pivoting, aggregating, merging, and dealing with missing values. You'll discover what layers, scales, coordinates, and themes are, and study how you can use them to transform your data into aesthetical graphs. Next, you'll study 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 Learning Path, you'll become master in data visualization techniques using the powerful R libraries.

    About the Author

    Samik Sen is currently working with R on machine learning. He has done his PhD in Theoretical Physics. He has tutored classes for high performance computing postgraduates and lecturer at international conferences. He has experience of using Perl on data, producing plots with gnuplot for visualization and latex to produce reports. He, then, moved to finance/football and online education with videos.

    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.


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