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Key Concepts R for Data Science.

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Presentation on theme: "Key Concepts R for Data Science."— Presentation transcript:

1 Key Concepts R for Data Science

2 Grammar of Graphics Chapter 3
Data Mapping of aesthetics Geometric objects Statistical calculations Position Coordinate system Facets Grammar of Graphics Chapter 3

3 Data Transformation Chapter 5
Rows Filter Arrange Columns Select Mutate and transmute Collapsing Summarise Group_by Data Transformation Chapter 5

4 Exploratory Data Analysis Chapter 7
Tools of EDA Visualization Transformation Modeling Distribution (Single Variable) Location or central tendency Scale or spread Symmetry Outliers or tail thickness relative to normal Relationships and patterns (Among Variables) Between variables Covariation Covariance Correlation Models Response and explanatory variables Extract patterns Explained variation Unexplained variation

5 Manipulation of Data Frames and Tibbles Chapters 12 and 13
Single Data Frame or tibble (Reshape) relational Creation and Combination of Columns Gather (Wide to Long) Spreading (Long to Wide) Operations on values in a variable or column Separate Unite Keys Primary Foreign Surrogate Mutating Joins Inner Full Left Right Filtering Joints Semi Anti

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