
Data Analytics with R Training
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
- Course Overview
- Why R? Advantages and disadvantages
- Downloading and installing
- How to find documentation
- Introduction
- Using the R console/RStudio
- Getting help
- Learning about the environment
- Writing and executing scripts
- Introduction to vectorized calculations
- Introduction to data frames
- Installing packages
- Working directory
- Saving your work
- Variable types and data structures
- Variables and assignment
- Data types
- Numeric, character, boolean, and factors
- Data structures
- Vectors, matrices, arrays, dataframes, lists
- Indexing, subsetting
- Assigning new values
- Viewing data and summaries
- Naming conventions
- Objects
- Manipulating Data with R
- Getting data into the R environment and understanding dataframes
- Built-in data
- Overview of dataframes
- Reading data from structured text files
- Reading data using ODBC
- Dataframe manipulation with dplyr
- Renaming columns
- Adding new columns
- Managing data types
- Binning data (continuous to categorical)
- Combining categorical values
- Transforming variables
- Handling missing data
- Long to wide and back
- Merging datasets together
- Stacking datasets together (concatenation)
- Handling dates in R
- Date and date-time classes in R
- Formatting dates for modeling
- Exploratory data analysis (descriptive statistics) including base graphics
- Continuous data
- Distributions
- Quantiles, mean
- Bi-modal distributions
- Histograms, box-plots
- Categorical data
- Tables
- Barplots
- Group by calculations with dplyr
- Split-apply-combine
- Long to wide and back, tidy data structures
- Advanced graphics in R: using GGPlot
- Understanding the grammar of graphics
- Quick plots (qplot function)
- Building graphics by pieces (ggplot function)
- Understanding geoms (geometries)
- Linking chart elements to variable values
- Controlling legends and axes
- Exporting graphics
- Testing for Group differences
- Traditional Inferential Statistics, A/B testing
- Null hypothesis testing and p-values
- Comparing Groups
- P-Values, summary statistics, sufficient statistics, inferential targets
- T-Tests (equal and unequal variances)
- ANOVA
- Chi-Square Tests
- Correlation
- Modeling with R
- Frequentist Approaches to multivariable Statistics:
- Linear Regression
- Multivariate linear regression
- Capturing Non-linear Relationships
- Comparing Model Fits
- 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.
Request a Private Class
- Private Class for your Team
- Online or On-location
- Customizable
- Expert Instructors