# R PROGRAMMING TRAINING – GTR1

## Course Description

Over the past few years, R has been steadily gaining popularity with business analysts, statisticians and data scientists as a tool of choice for conducting statistical analysis of data as well as supervised and unsupervised machine learning. |

This intensive training course helps students learn the practical aspects of the R programming language. The course is supplemented by many hands-on labs which allow attendees to immediately apply their theoretical knowledge in practice.
High octane introduction to R programming Learning about R data structures Working with R functions Statistical data analysis with R Supervised and unsupervised machine learning with R ## Duration2 Days ## Target AudienceBusiness Analysts, Technical Managers, and Programmers ## PrerequisitesParticipants should have the general knowledge of statistics and programming. ## Suggested Follow on CoursesThere are a number of options, please contact us for further details. ## Course Content
Installing R Character Terminal and GUI Interfaces to R Other GUI Integrated Development Environments
Running R Learning GUI Integrated Development Environment Interacting with R Interpreter R Sessions and Workspaces Saving Your Workspace Loading Your Workspace Removing Objects in Workspace Getting Help Getting System Information Standard R Packages Loading Packages CRAN (The Comprehensive R Archive Network) Extending R
General Notes on R Commands and Statements Variables Assignment Operators Arithmetic Operators Logical Operators
R Objects Vectors Logical Vectors Character Vectors Creating and Working with Vectors Lists Creating and Working with Lists Matrices Creating and Working with Matrices Data Frames Creating and Working with Data Frames Interactive Creation of Data Frames Getting Info about a Data Frame Sorting Data in Data Frames Matrices vs Data Frames
Using R Common Functions Numeric Functions Character / String Functions Date and Time Functions Other Useful Functions Applying Functions to Matrices and Data Frames Type Conversion Creating and Using User-Defined Functions
Conditional Execution Repetitive Execution
Creating Scripts Loading and Executing Scripts Batch Execution Mode
Reading Data from Files Writing Data to Files Getting the List of Files in a Directory Diverting System Output to a File
Import and Export Operations in R Working with CSV Files Reading Data from Excel Exporting Data in SPSS Data Format
Basic Statistical Functions Writing Your Own skew and kurtosis Functions Generating Normally Distributed Random Numbers Generating Uniformly Distributed Random Numbers Using the summary() Function Math Functions Used in Data Analysis Correlations Testing Correlation Coefficient for Significance Regression Analysis Types of Regression Simple Linear Regression Model Least-Squares Method (LSM) LSM Assumptions Fitting Linear Regression Models in R Confidence Intervals for Model Parameters Multiple Regression Analysis Finding the Best-Fitting Regression Model Comparing Regression Models with anova and AIC
R Graphics Graphics Export Options Creating Bar Plots in R Using barplot() with Matrices Stacked vs Juxtaposed Layouts Customizing Plots Histograms Building Histograms with hist() Pie Charts Generic X-Y Plotting Dot Plots
Supervised and Unsupervised Machine Learning Algorithms k-Nearest Neighbors Monte Carlo Simulation |