Allied Health Training Programs
Admin / 09 Mar 2017
Please enter keywords
FOR POWERFUL PEOPLE
Certification Training is our specialty. Courses are for certificate preparation purposes only. These courses are NOT associated with WIOA/WDP or any other State or Federal sponsored employment training.
Admin / 09 Mar 2017
Admin / 09 Mar 2017
Admin / 09 Mar 2017
R is a powerful and widely used open source software and programming environment for data analysis and test relationships between large amounts of data. R is one of the most popular programming language used by data scientists. R can run on any type of hardware and software. R itself is a powerful language that performs wide variety of functions from data manipulation, statistical modeling and graphics. Major advantage of R is its extensibility which makes developers to easily write their own software and distribute inn their own packages. R user community is growing every day.
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.
Section 1
Introduction to R
Install R
RStudio Overview
R Built in Help
R Variables and Operators
Arithmetic, Logical and Vectorized
Section 2
Data Structures and Data Frames
Atomic Vector Operations
Matrices Operations and Arrays
Functional Components in R
Argument Matching
Section 3
Flow Control Using if, if-else, switch, while, For
Packages in R
Install and Load R packages
Manage Packages
Section 4
Importing Data into R
Excel/CSV, Table, data from web, Database
Data Analysis
Mean, Median Tendency
Spread (Range, Quartiles, Histogram)
Categorical Data
Section 5
Descriptive Statistics Demo
Data Visualization Demo
Handling Big Data
Section 6
Data Visualization
Spatial
Hierarchial
Textual
Graph and Network Data
Section 7
Deep Dive with Data Frame
Filtering a Data Frame
Introduction to qplot
Merging Data frames
Section 8
Exporting Data out of R
Text Files
Graphics