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CBB Jamboree - Intro to R

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Course Description

This course is designed for the Yale University Computational Biology & Bioinformatics (CBB) program's Jamboree. It provides a quick introduction to basic R commands while assuming familiarity with programming concepts. The majority of the course focuses on tidy data analysis using tidyr/dplyr and visualization using ggplot2
  1. 1

    Intro to basics

    Free

    In this chapter, based on DataCamp's Intro To R course, you will take your first steps with R. You will learn how to use the console as a calculator and how to assign variables. You will also get to know the basic data types in R. Let's get started!

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    How it works
    100 xp
    Variable assignment
    100 xp
    Basic data types in R
    100 xp
    What's a data frame?
    100 xp
    Quick, have a look at your data
    100 xp
    A second look, data structure
    100 xp
    Slicing data frames
    100 xp
  2. 2

    Data the Tidy Way

    Free

    In this chapter, you will learn some of the fundamentals of taking raw data and cleaning it to form tidy data, the fundamental currency of data science and visualization tools such as ggplot2.

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    Intro to Data Science
    0 xp
    Some (Un)Tidy Data
    0 xp
    A Tidy Sample
    0 xp
    Real (Messy) Data: Income Distribution by Religion
    100 xp
    Refactoring Columns Into a Variable
    100 xp
    Joining Multiple Data Sets
    100 xp
    Filtering Down To The Most Interesting Data
    100 xp
  3. 3

    Beautiful Data

    Free

    From tidy data comes great visualization - learn how to visualize your data using the grammer of graphics.

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    Introduction to ggplot2
    0 xp
    The grammar of ggplot2
    0 xp
    Exploring ggplot2
    0 xp
    Adding New Aesthetics
    0 xp
    Refining our Aesthetics Layer by Layer
    0 xp
    Adding Additional Geometries
    0 xp
    When Layering, Order Matters
    0 xp
    A Spoonful of Color Makes the Plot Rock
    0 xp
    Theming our Plots
    0 xp
    The Tools for Beautiful Plots
    0 xp
  4. 4

    Application to Expression Data

    Free

    In this chapter, you will combine what you learned in the first three chapters to read in, tidy, and visualize gene expression data. Let's get started!

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    Putting it all together
    25 xp
    Reading in the data
    25 xp
    ExpressionSet Objects
    100 xp
    Filter the Data
    100 xp
    Combine the data
    100 xp
    Tidy the data
    100 xp
    Join the data
    100 xp
    Plotting Gene Expression
    200 xp
    Plotting Gene Expression (2)
    200 xp
    Plotting Gene Expression (3)
    200 xp
  5. 5

    Extra Credit - Summarising Data

    Free

    Now at the end of our course, use what you know to take your data science investigations to the next level!

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    Summarising Data
    0 xp
    Summarising Data (2)
    0 xp
    Summarising Data (3)
    0 xp
    Summarising Data (4)
    0 xp
    Summarising Data (5): Conclusion
    0 xp
Stefan Avey HeadshotStefan Avey

Bioinformatics PhD Candidate at Yale University passionate about data science and data visualization in HealthTech and EdTech
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Robert Amezquita HeadshotRobert Amezquita

PhD Candidate at Yale University Dept. of Immunobiology, with a fondness for (epi)genomics, cooking, dogs, biking, and Rocket League.
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