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Learning R (2013)

Course by: Barton Poulson
Learning R (2013) Watch preview
  • Course details

    Join author Barton Poulson as he introduces the R statistical processing language, including how to install R on your computer, read data from SPSS and spreadsheets, and use packages for advanced R functions.

    The course continues with examples on how to create charts and plots, check statistical assumptions and the reliability of your data, look for data outliers, and use other data analysis tools. Finally, learn how to get charts and tables out of R and share your results with presentations and web pages.

    Instructor

    • Barton Poulson

      Barton Poulson

      datalab.cc founder & data do-gooder
      View on LinkedIn

      Barton Poulson is a professor, designer, and data analytics expert.

      Barton has bridged the analytic and aesthetic for most of his life, with a background in industrial design, a Ph.D. in social and personality psychology, and a deep love for data analysis and data visualization as well as the visual and performing arts. Bart is an associate professor of psychology at Utah Valley University and has studied at Brigham Young University, the University of Utah, and the City University of New York. In his role as a teacher of research and analysis, he has conducted hundreds of studies with his students on topics from social identity and aspirations to implicit preferences for poetry and architecture. Bart and his wife, Jacque Lynn Bell, a modern dance choreographer, live with their three children in beautiful Salt Lake City.

      View all courses by Barton Poulson

    Skills covered in this course

  • Welcome

    (light, bright tones) - Hi, I'm Bart Poulson, and I'd like to welcome you to Up and Running with R. R is an open source program and programming language that has become one of the most powerful choices available for statistical analysis. In this course, I'll teach you to use charts, such as histograms, bar charts, scatter plots, and box plots, to get the big picture of your data; descriptive statistics such as means, standard deviations, and correlations for a more precise depiction; inferential statistics like regression, t-tests, the analysis of variance, and the chi-square test to help you determine the reliability of your results. Finally, I'll demonstrate how you can create beautiful charts for presentations and share your results with other people. If you're ready to get going, let's get started with Up and Running with R.

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Course Contents