R Programming Training Program

Data frame manipulation:

• Introduction to tables, enhanced data frames

• Renaming columns

• Adding new columns

• Binning data (continuous to categorical)

• Combining categorical values

• Transforming variables

• Handling missing data

• 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):

• Continuous data

• Distributions

• Quantiles, mean

• Bi-modal distributions

• Histograms, box-plots

• Categorical data

• Tables

• Bar plots

• Group by calculations

• Split-apply-combine

• Reshaping and pivoting data in R (long to wide with aggregation)

• Melt and cast

Working with text data:

• Finding and matching patterns in text

• Stringer package for text manipulation

• Introduction to regular expressions in R

• Categorical data wrangling with forcats

Control flow & functions:

• Truth testing

• Branching

• Looping

• Functions

• Parameters

• Return values

• Variable scope

• Exception handling

• Applying functions across dimensions

• Sapply, lapply, apply

• Programming with map and purr

Graphics in R Overview:

• Base graphics system in R

• Scatterplots, histograms, bar charts, box and whiskers, dot plots

• Labels, legends, titles, axes

• Exporting graphics to different formats

Advanced R graphics:

• 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

Inferential Statistics:

• Bivariate correlation

• T-test and non-parametric equivalents

• Chi-squared test

General Linear Regression Models in R:

• Understanding formulas

• Linear and logistic regression models

• Regression plots

• Confounding / interaction in regression

• Evaluating residuals

• Scoring new data from models (prediction)

• Useful plots from regression models

Placement Guide:

• Tips to clear an Interview

• Common Interview questions and answers

• R programming Interview Questions and Answers

• Resume Building Guide

• Attempt for the related Global Certification Exam

• Earn Credentials and Start applying for Jobs

History of R

Advantages and disadvantages

Downloading and installing

How to find documentation

Using the R console and R Studio

Getting help

Learning about the environment

Writing and executing scripts

Object oriented programming

Introduction to vectorised calculations

Introduction to data frames

Installing and loading packages

Working directory

Saving your work

Variables and assignment

Data types

Numeric, character, Boolean, and factors

Data structures

Vectors, matrices, arrays, data frames, lists

Indexing, sub-setting

Assigning new values

Viewing data and summaries

Naming conventions

Objects

Built-in data

Reading data from structured text files

Reading data using ODBC

Introduction to tables, enhanced data frames

Renaming columns

Adding new columns

Binning data (continuous to categorical)

Combining categorical values

Transforming variables

Handling missing data

Merging datasets together

Stacking datasets together (concatenation)

Date and date-time classes in R

Formatting dates for modeling

Continuous data

Distributions

Quantiles, mean

Bi-modal distributions

Histograms, box-plots

Categorical data

Tables

Bar plots

Group by calculations

Split-apply-combine

Reshaping and pivoting data in R (long to wide with aggregation)

Melt and cast

Finding and matching patterns in text

Stringer package for text manipulation

Introduction to regular expressions in R

Categorical data wrangling with forcats

Truth testing

Branching

Looping

Functions

Parameters

Return values

Variable scope

Exception handling

Applying functions across dimensions

Sapply, lapply, apply

Programming with map and purr

Base graphics system in R

Scatterplots, histograms, bar charts, box and whiskers, dot plots

Labels, legends, titles, axes

Exporting graphics to different formats

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

Bivariate correlation

T-test and non-parametric equivalents

Chi-squared test

Understanding formulas

Linear and logistic regression models

Regression plots

Confounding / interaction in regression

Evaluating residuals

Scoring new data from models (prediction)

Useful plots from regression models

Tips to clear an Interview

Common Interview questions and answers

R programming Interview Questions and Answers

Resume Building Guide

Attempt for the related Global Certification Exam

Earn Credentials and Start applying for Jobs