Intermediate and Advance R Programming

R programming along with a substantial knowledge of statistics can help candidates to have a great career in data Analytics. R is also an widely used tool in many big firms like top Banks, IT, Retail, Healthcare, Pharma, Supply chain and logistics firms. Analyzing large data-sets can be done in a shorter period with the help of R programming. There is a huge shortage in the market for professionals with skills in R programming which makes it more interesting to pursue. Since R is a free software it is being widely used which creates a lot of opportunities for professional who are looking to pursue a career in R Programming.
In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples.

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INR 999

Intermediate and Advance R Programming

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 dplyr

  • 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

  • Sapply, lapply, apply

Exploratory data analysis (descriptive statistics)

  • Continuous data
    • Distributions
    • Quantiles, mean
    • Bi-modal distributions
    • Histograms, box-plots
  • Categorical data
    • Tables
    • Barplots
  • Group by calculations with dplyr
    • Split-apply-combine
  • Melting and casting data

Inferential statistics

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

Base graphics

  • Base graphics system in R
  • Scatterplots, histograms, barcharts, box and whiskers, dotplots
  • Labels, legends, titles, axes
  • Exporting graphics to different formats

Advanced R graphics: ggplot2

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

General linear regression

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

Conclusion

 

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