Data Analytics with R Training

Data Analytics with R Training

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Course Length: 3 days
Delivery Methods: Available as private class only
Course Overview

R is a very popular, open source environment for statistical computing, data analytics and graphics. This Data Analytics with R Training class introduces R programming language to students. It covers language fundamentals, libraries, and advanced concepts and advanced data analytics and graphing with real world data.

Course Benefits
  • Learn the basics of R and Rstudio. 
  • Import and manipulate tabular data with R.
  • Conduct exploratory analysis.
  • Generate rich graphics with GGPlot2.
  • Test for group differences using inferential methods.
  • Build statistical regression models using R.
Course Outline
  1. Course Overview 
    1. Why R? Advantages and disadvantages
    2. Downloading and installing
    3. How to find documentation
  2. Introduction 
    1. Using the R console/RStudio
    2. Getting help
    3. Learning about the environment
    4. Writing and executing scripts
    5. Introduction to vectorized calculations
    6. Introduction to data frames
    7. Installing packages
    8. Working directory
    9. Saving your work
  3. Variable types and data structures 
    1. Variables and assignment
    2. Data types
    3. Numeric, character, boolean, and factors
    4. Data structures
    5. Vectors, matrices, arrays, dataframes, lists
    6. Indexing, subsetting
    7. Assigning new values
    8. Viewing data and summaries
    9. Naming conventions
    10. Objects
  4. Manipulating Data with R
    1. Getting data into the R environment and understanding dataframes 
    2. Built-in data
    3. Overview of dataframes
    4. Reading data from structured text files
    5. Reading data using ODBC
    6. Dataframe manipulation with dplyr 
    7. Renaming columns
    8. Adding new columns
    9. Managing data types
    10. Binning data (continuous to categorical)
    11. Combining categorical values
    12. Transforming variables
    13. Handling missing data
    14. Long to wide and back
    15. Merging datasets together
    16. Stacking datasets together (concatenation)
    17. Handling dates in R 
    18. Date and date-time classes in R
    19. Formatting dates for modeling
  5. Exploratory data analysis (descriptive statistics) including base graphics 
    1. Continuous data
    2. Distributions
    3. Quantiles, mean
    4. Bi-modal distributions
    5. Histograms, box-plots
    6. Categorical data
    7. Tables
    8. Barplots
    9. Group by calculations with dplyr
    10. Split-apply-combine
    11. Long to wide and back, tidy data structures
  6. Advanced graphics in R: using GGPlot 
    1. Understanding the grammar of graphics
    2. Quick plots (qplot function)
    3. Building graphics by pieces (ggplot function)
    4. Understanding geoms (geometries)
    5. Linking chart elements to variable values
    6. Controlling legends and axes
    7. Exporting graphics
  7. Testing for Group differences
    1. Traditional Inferential Statistics, A/B testing
    2. Null hypothesis testing and p-values
    3. Comparing Groups
    4. P-Values, summary statistics, sufficient statistics, inferential targets
    5. T-Tests (equal and unequal variances)
    6. ANOVA
    7. Chi-Square Tests
    8. Correlation
  8. Modeling with R
    1. Frequentist Approaches to multivariable Statistics:
    2. Linear Regression
    3. Multivariate linear regression
    4. Capturing Non-linear Relationships
    5. Comparing Model Fits
    6. Scoring new data
Class Materials

Each student will receive a comprehensive set of materials, including course notes and all the class examples.

Class Prerequisites

Experience in the following is required for this R Programming class:

  • Basic programming background.
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  • Expert Instructors
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