Beginning R programming

Beginning R programming

This is a two day beginner level course designed for learners without any prior programming knowledge in R. Although this is a programming course, it will not delve deep into the field of programming, except for what is absolutely necessary within the context of R.

The primary focus of this course will be gaining the ability to program in R for Statistics and Data Science.

Learning Outcome

As with any training, the learning outcome cannot be guaranteed as everyone has different learning abilities. That being said, this course is intended to cover the following bread topics and the learner mat be expected to be familiarized and skilled in implementing them:

• Systematically explore data in R
• Understand the techniques of working with various forms of data sources
• Index, slice, and subset data
• Learn the fundamentals of programming in R
• Work with R’s conditional statements, functions, and loops
• UDFs in R
• Data I/O in R
• Data Science Fundamentals R
• Learn the fundamentals of statistics and apply them in practice using R
• External packages in R
• Visualizations in R
• Transformation techniques in R
• Understand and carry out regression analysis in R

Prerequisites

Software:

• R Studio
• OS (either one): Windows / MacOS / Linux (must be a debian build)
• Web Browser
• Access to external URL (for data source)

Hardware:

• PC / Laptop with at least 8GB RAM and 20GB HDD free (for data)
• Dual Screen
• Webcam and microphone

Outline

This is a 2-day course.

1. Introduction
   1. Environment
   2. Workspace
   3. Data Source
2. Basics of programming in R
   1. Data types
   2. Operations
   3. Coercion
   4. Functions
3. Composite data objects
   1. Vectors
   2. Vector operations
   3. Recycling
   4. Dimensions
   5. Matrices
   6. Indexing
   7. Slicing
   8. Arithmetics and operations
   9. Lists
4. Programming in R
   1. Operators
   2. Conditions
   3. Loops
   4. UDFs
5. Data
   1. Data Frames
   2. Import from data source
   3. Export to data destination
   4. Functions and operations with Data Frames
   5. Data cleaning
   6. Transformations
   7. Sampling
   8. Tidying
6. Visualization
   1. Using ggplot2
   2. Histogram
   3. Box plot
   4. Box & Whiskers plot
   5. Bar chart
   6. Scatter plot
7. Analytics
   1. Exploratory analytics
   2. Population vs. Sample
   3. Three M’s
   4. Skewness
   5. Variance, standard deviation, and coefficient of variability
   6. Covariance and correlation
   7. The linear regression model
   8. Correlation vs regression
   9. Geometrical representation
   10. Practical regression in R
   11. How to interpret the regression table
   12. Decomposition of variability: SST, SSR, SSE
   13. R-squared
8. Assessment

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