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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.

In this introduction to R, you will master the basics of this beautiful open source language, including factors, lists and data frames. With the knowledge gained in this course, you will be ready to undertake your first very own data analysis. With over 2 million users worldwide R is rapidly becoming the leading programming language in statistics and data science. Every year, the number of R users grows by 40% and an increasing number of organizations are using it in their day-to-day activities

R Languages Training

Introduction to R Languages

• R as a language
• Working with data in R

The R ecosystem

• Why use R?
• Getting started
• Installation and setup
• Packages

Data types

• Character
• Factor
• Integer
• Float
• Date and time

Data structures

• Vectors
• Matrices
• Lists
• Data frames

Data handling

• Importing data from multiple sources/formats like .csv, .txt, .xlsx, SAS and SPSS files
• Exporting data to multiple formats
• Handling data frames: filtering, sorting, merging
• PLYR package for easy data manipulation

Functions

• Commonly used built in functions
• Writing user defined functions
• Installing packages
• Looping functions
• The "apply" family of functions
• Basic visualization

Basic statistics in R

• TDistributions
• Testing
• Modeling

Graphics in R

• Graphics for exploratory data analysis
• Standard graphic displays

The R environment

• R in the cloud

Statistical analysis with R

• Linear models
• Generalized linear models

Advanced statistical modeling with R

• Density estimation
• Survival analysis
• Classification
• Clustering

Introduction to Writing R Packages Integrating with other tools.

• Tableau
• Python
• CPP

Real time problems. Data manipulation with data table package. Interview questions.

Jobs in R Programming

R programming skills are listed as a job requirement on thousands of jobs in the fields of statistics and data analysis. You’ll find R experience either required or recommended in job postings for data scientists, machine learning engineers, big data engineers, IT specialists, database developers and much more. Adding R skills to your CV will help you in any one of these data specializations requiring mastery of statistical techniques.