R Data Science Training Course

Learn the fundamentals of R Data Science and Apply it


  • 5 Days

What do I need?

  • Webinar : A laptop, and a stable internet connection. The recommended minimum speed is around 10 Mbps.
  • Classroom Training : A laptop, please notify us if you are not bringing your own laptop. Please see the calendar below for the schedule


  • Attendance : If you have attended 80% of the sessions and completed all the class work, you qualify for the Attendance Certificate. (Course Price : R14 995)
  • Competency : If you have also enrolled for the additional competency projects, you qualify for the Competency Certificate. (Course Price : R18 995)


Who will benefit

  • Coders who want to move into the Data Science space with R
  • Learn how to use R to turn raw data into insight, knowledge, and understanding
  • Learn R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun
  • Get yourself into data science as quickly as possible
  • We take you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results
  • Get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details

What you will learn

  • Wrangle—transform your datasets into a form convenient for analysis
  • Program—learn powerful R tools for solving data problems with greater clarity and ease
  • Explore—examine your data, generate hypotheses, and quickly test them
  • Model—provide a low-dimensional summary that captures true "signals" in your dataset
  • Communicate—learn R Markdown for integrating prose, code, and results

Day One


1 Data Visualization with ggplot2

    First Steps

  • Aesthetic Mappings
  • Common Problems
  • Facets
  • Geometric Objects
  • Statistical Transformations
  • Postition Adjustments
  • Coordinate systems
  • The layered Grammar of Graphics

2 Workflow :Basics

  • Coding Basics
  • What’s in a Name?
  • Calling Functions

3 Data Transformation with dplyr


  • Prerequisites
  • nycflights13
  • dplyr Basics

    Filter Rows with filter()

  • Comparisons
  • Logical Operators
  • Missing Values

    Arrange Rows with arrange ()

    Select Columns with select ()

    Add new Variables with mutate ()

    Grouped Summaries with summarize ()

  • Combining Multiple Operations with the Pipe
  • Values
  • Counts
  • Useful Summary Functions
  • Grouping by Multiple Variables
  • Ungrouping

    Grouped Mutates (and Filters)

4 Workflow:Scripts

  • Running Code
  • RStudion Diagnostics
  • Day Two

    1. Exploratory Data Analysis


    • Visualizing Distributions
    • Typical Values
    • Unusual Values

      Missing Values


    • A Categorical and Continuous Variable
    • Two Categorical Variables
    • Two Continuous Variables
    • Patterns and Models

      ggplot2 Calls

      Learning More

    2.Workflow; Projects

    • What Is Real
    • Where Does Your Analysis Live?
    • Paths and Directories
    • RStudio Projects


    3.Tibbles with tibble

    • Creating Tibbles
    • Tibbles versus data.frame



    • Interacting with Older Code

    4. Data Import with readr

    Getting Started

    • Compared to Base R

    Parsing a Vector

    • Numbers
    • Strings
    • Factors
    • Dates, Date-Times, and Times

    Parsing a File

    • Strategy
    • Problems
    • Other Strategies

    Writing to a File

    Other Types of Data

    5.Tidy Data with tidyr

    Tidy Data

    Spreading and Gathering

    • Gathering
    • Spreading

    Separating and Pull

    • Seperate
    • Unite

    Missing Values

    Case Study

    Nontidy Data

    Day Three

    1. Relational Data with dplyr



    Mutating Joins

    • Understanding Joins
    • Inner Join
    • Outer Join
    • Duplicate Keys
    • Defining the key Columns

    Filtering Joins

    Join Problems

    Set Operations

    2. Strings with stringr>

    String Basics

    • String Length
    • Combining Strings
    • Subsetting Strings
    • Locales

    Matching Patterns with Regular Expressions

    • Basic Matches
    • Excercises
    • Anchors
    • Excercises
    • Character Classes and Alternatives
    • Excercises
    • Repetition
    • Grouping and backreferences


    • Detect Matches
    • Exercises
    • Extract Matches
    • Exercises
    • Grouped Matches
    • Exercises
    • Replacing Matches
    • Exercises
    • Splitting
    • Exercises
    • Find Matches

    Other types of pattern

    Other Uses of Regular Expressions


    3. Factors with forcats

    • Creating factors
    • General Social Survey
    • Modifying Factor Order
    • Modifying Factor Levels

    4. Dates and Times with lubridate

    Creating Date/Times
    • From Strings
    • From Individual Components
    • From Other Types
    Date-Time Components
    • Getting Components
    • Rounding
    • Setting Components
    Time Spans
    • Durations
    • Periods
    • Intervals
    Time Zones


    Pipes with magrittr

    Piping Alternatives
    • Intermediate Steps
    • Overwrite the Original
    • Function Composition
    • Use the Pipe

    When Not to Use the Pipe

    Other Tools from magrittr

    Day Four

    1. Functions

    When Should You Write a Function? Functions Are for Humans and Computers Conditional Execution
    • Conditions
    • Multiple Conditions
    • Code Style
    Function Arguments
    • Choosing Names
    • Checking Values
    • Dot-Dot-Dot (…)
    • Lazy Evaluation
    Return Values
    • Explicit Return Statements
    • Writing Pipeable Functions

    2. Vectors

    Vector Basics Important Types of Atomic Vector
    • Logical
    • Numeric
    • Character
    • Missing Values
    Using Atomic Vectors
    • Coercion
    • Test Functions
    • Scalars and Recycling Rules
    • Naming Vectors
    • Subsetting
    Recursive Vectors (Lists)
    • Visualizing Lists
    • Subsetting
    • Lists of Condiments
    Attributes Augmented Vectors
    • Factors
    • Dates and Date-Times
    • Tibbles

    3. iteration with purrr

    For Loops For Loop Variations
    • Modifying an Existing Object
    • Looping Patterns
    • Unknown Output Length
    • Unknown Sequence Length
    For Loops Versus Functionals The Map Functions
    • Shortcuts
    • Base R
    Dealing with Failure Mapping over Multiple Arguments
    • Invoking Different Functions
    Walk Other Patterns of For Loops
    • Predicate Functions
    • Reduce and Accumulate


    4. Model Basics with modelr

    A Simple Model Visualizing Models
    • Predictions
    • Residuals
    Formulas and Model Families
    • Categorical Variables
    • Interactions (Continuous and Categorical)
    • Interactions (Two Continuous)
    • Transformations
    Missing Values Other Model Families

    5. Model Building Why Are Low-Quality Diamonds More Expensive?

    • Price and Carat
    • A More Complicated Model
    What Affects the Number of Daily Flights?
    • Day of Week
    • Seasonal Saturday Effect
    • Computed Variables
    • Time of Year: An Alternative Approach
    Learning More About Models

    Day Five

    1.Many Models with purr and broom

    • Nested Data
    • List-Columns
    • Unnesting
    • Model Quality
    List-Columns Creating List-Columns
    • With Nesting
    • From Vectorized Functions
    • From Multivalued Summaries
    • From a Named List
    Simplifying List-Columns
    • List to Vector
    • Unnesting
    Making Tidy Data with broom


    2. R Markdown

    R Markdown Basics Text Formatting with Markdown Code Chunks
    • Chunk Name
    • Chunk Options
    • Table
    • Caching
    • Global Options
    • Inline Code
    Troubleshooting YAML Header
    • Parameters
    • Bibliographies and Citations
    Learning More

    3. Graphics for Communication with ggplot2

    Label Annotations Scales
    • Axis Ticks and Legend Keys
    • Legend Layout
    • Replacing a Scale
    Exercises Zooming Themes Saving Your Plots
    • Figure Sizing
    • Other Important Options
    Learning More

    4. R Markdown Formats

    Output Options Documents Notebooks Presentations Dashboards Interactivity
    • htmlwidgets
    • Shiny
    Websites Other Formats

    5. R Markdown Workflow

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