Introduction to R Programming training course teaches attendees how to use R programming to explore data from a variety of sources by building inferential models and generating charts, graphs, and other data representations.

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✓ 180 days of access to high-quality, self-paced learning content designed by industry experts ✓ Flexible pricing options ✓ Enterprise grade Learning Management System (LMS) ✓ Enterprise dashboards for individuals and teams	✓ Pricing for - Course, Category, and all-course-access ✓ Enterprise-class learning management system (LMS) ✓ Enhanced reporting for individuals and teams ✓ 24x7 teaching assistance and support

Corporate Training

✓ 180 days of access to high-quality, self-paced learning content designed by industry experts

✓ Flexible pricing options

✓ Enterprise grade Learning Management System (LMS)

✓ Enterprise dashboards for individuals and teams

✓ Pricing for - Course, Category, and all-course-access

✓ Enterprise-class learning management system (LMS)

✓ Enhanced reporting for individuals and teams

✓ 24x7 teaching assistance and support

Contact to our training advisor

R Training Course Curriculum

Module 1 Overview
✅History of R ✅Advantages and disadvantages ✅Downloading and installing ✅How to find documentation

Module 1

Overview

✅History of R

✅Advantages and disadvantages

✅Downloading and installing

✅How to find documentation

Module 2 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

Module 2

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

Module 3 Variable types and data structures
✅Variables and assignment ✅Data types ✅Data structures ✅Indexing, subsetting ✅Assigning new values ✅Viewing data and summaries ✅Naming conventions ✅Objects

Module 3

Variable types and data structures

✅Variables and assignment

✅Data types

✅Data structures

✅Indexing, subsetting

✅Assigning new values

✅Viewing data and summaries

✅Naming conventions

✅Objects

Module 4 Getting data into the R environment
✅Built-in data ✅Reading data from structured text files ✅Reading data using ODBC

Module 4

Getting data into the R environment

✅Built-in data

✅Reading data from structured text files

✅Reading data using ODBC

Module 5 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)

Module 5

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)

Module 6 Handling dates in R
✅Date and date-time classes in R ✅Formatting dates for modeling

Module 6

Handling dates in R

✅Date and date-time classes in R

✅Formatting dates for modeling

Module 7 Control flow
✅Truth testing ✅Branching ✅Looping

Module 7

Control flow

✅Truth testing

✅Branching

✅Looping

Module 8 Functions in depth
✅Parameters ✅Return values ✅Variable scope

Module 8

Functions in depth

✅Parameters

✅Return values

✅Variable scope

Module 9 Applying functions across dimensions
✅Sapply, lapply, apply

Module 9

Applying functions across dimensions

✅Sapply, lapply, apply

Module 10 Exploratory data analysis (descriptive statistics)
✅Continuous data ✅Categorical data ✅Group by calculations with dplyr ✅Melting and casting data

Module 10

Exploratory data analysis (descriptive statistics)

✅Continuous data

✅Categorical data

✅Group by calculations with dplyr

✅Melting and casting data

Module 11 Inferential statistics
✅Bivariate correlation ✅T-test and non-parametric equivalents ✅Chi-squared test

Module 11

Inferential statistics

✅Bivariate correlation

✅T-test and non-parametric equivalents

✅Chi-squared test

Module 12 Base graphics
✅Base graphics system in R ✅Scatterplots, histograms, barcharts, box and whiskers, dotplots ✅Labels, legends, titles, axes ✅Exporting graphics to different formats

Module 12

Base graphics

✅Base graphics system in R

✅Scatterplots, histograms, barcharts, box and whiskers, dotplots

✅Labels, legends, titles, axes

✅Exporting graphics to different formats

Module 13 Advanced R graphics: ggplot2
✅Understanding the grammar of graphics ✅Quick plots (qplot function) ✅Building graphics by pieces (ggplot function)

Module 13

Advanced R graphics: ggplot2

✅Understanding the grammar of graphics

✅Quick plots (qplot function)

✅Building graphics by pieces
(ggplot function)

Module 14 General linear regression
✅Linear and logistic models ✅Regression plots ✅Confounding / interaction in regression ✅Scoring new data from models (prediction)

Module 14

General linear regression

✅Linear and logistic models

✅Regression plots

✅Confounding / interaction in regression

✅Scoring new data from models (prediction)

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