R Programming training

R Programming  Training Course Content

R programming training builds proficiency in using R programming language for statistical computing and graphics. R, a language and environment, is gaining popularity in getting insight into complex data. The business analyst and other professionals dealing in a large amount of data can derive results using the ready-made functions available in R.

R programming training course introduces the R environment and basic statistical analysis. It extends the learning curve by teaching techniques used for data manipulation and the overview of basic data structures. Statistical applications using R programming and exploration of data using box plots, histograms, correlation coefficients will also be illustrated.

By the end of R programming classes, you will inculcate the following skillset:

• The clear understanding of Statistical programming and R environment
• In-depth knowledge of basic features, functions, operators available with R
• Comprehensive information about programming statistical graphics
• Ways of using simulation and numerical optimization
• Extract data from R objects, perform reading and writing of Data and handle databases
• Use subscription, character manipulation, and reshaping of data
• Find probability, distributions, regression, and correlation
• Significance of sample size and its calculation
• Advance data handling techniques

Prerequisites

Programming background like C, C++, Python will be an added advantage but not mandatory to learn R, but introductory statistics is a prerequisite.

 Overview

• History of R
• Advantages and disadvantages
• Downloading and installing
• How to find documentation

 Introduction

• Using the R console
• Getting help
• Learning about the environment
• Writing and executing scripts
• Object-oriented programming
• 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
• Data structures
• Indexing, subsetting
• Assigning new values
• Viewing data and summaries
• Naming conventions
• Objects

 Getting data into the R environment

• Built-in data
• Reading data from structured text files
• Reading data using ODBC

 Dataframe manipulation with deeply

• Renaming columns
• Adding new columns
• 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

 Control flow

• Truth testing
• Branching
• Looping

 Functions in depth

• Parameters
• Return values
• Variable scope
• Exception handling

 Applying functions across dimensions

• Supply, apply, apply

 Exploratory data analysis (descriptive statistics)

• Continuous data
• Categorical data
• Group by calculations with deeply
• Melting and casting data

 Inferential statistics

• Bivariate correlation
• T-test and non-parametric equivalents
• Chi-squared test

 Base graphics

• Base graphics system in R
• Scatter plots, histograms, bar charts, box, and whiskers, dot plots
• Labels, legends, titles, axes
• Exporting graphics to different formats

 Advanced R graphics: ggplot2

• Understanding the grammar of graphics
• Quick plots (plot function)
• Building graphics by pieces (plot function)

 General linear regression

• Linear and logistic models
• Regression Plots
• Confounding / interaction in regression
• Scoring new data from models (prediction)

 Conclusion