Course Description
R programming becomes more and more
popular since it is fully open source and reacts very dynamic to new
developments. Learn programming language R, from the very basics to
complex modeling. This course covers regression, classification,
clustering, reading data, programming basics, visualization, data
munging, modern machine learning and more. This course is meant to give
you an introductory understanding of the R language.
Nowadays it
is vital in many scientific or other analytical fields to have a good
understanding of the R language. With this course you can build a very
solid foundation to later on branch out to the various applications R
has to offer. This course is designed for beginners that have no
previous R programming experience. You will require a fundamental
understanding of statistics to get the most out of this course.
This
course ensure quick learning in a simplified way. It explains the most
important aspects of working on data and conduct analysis through
example. You will start by learning how to install and navigate R
studio. Learn Data/Object Types and Operations, Importing into R, and
Loops and Conditions. you will be introduced to the use of R in
Analytics, where you will learn a little about each object type in R and
use that in Data Mining/Analytical Operations. learn the use of R in
Statistics, using R to evaluate Descriptive Statistics, Probability
Distributions, Hypothesis Testing, Linear Modeling, Generalized Linear
Models, Non-Linear Regression, and Trees. Learn to create 2-dimensional
Univariate and Multi-variate plots. You will also learn about formatting
various parts of a plot, covering a range of topics like Plot Layout,
Region, Points, Lines, Axes, Text, Color and so on.
Once you have
completed this computer based training course, you will be fully capable
of using R for developing statistical software and data analysis tools.
Curriculum
What are GGPLOT2?
Adding a GEOM
Annotation
apply in Looping
Calling Methods
Classes and Methods Overview
Classes illustration
colorRampPalette
Date and Time Overview in R
Debugging Overview and Introduction
Dumping R Objects
Evaluation
Explicit Coercion and Mixing Objects
Generic/Method Mechanism
GGPLOT2 in Details
How do you Debug?
Introduction and Objectives
Introduction to Plotting
Introduction to Simulation
Linear Models and Random Numbers
Lists
Loop Functions
Metacharacters
Missing Values
More Metacharacters
Operations in Date and Time
Overview and History of R
Plotting and Color
read.table
read.table for larger datasets
Reading Data
Scoping Rules
Sebsetting Nested elements of a List
Section 1: Introduction
Section 10: Regular Expressions
Section 11: Debugging
Section 2: Datatypes and Basic Operations
Section 3: Reading and Writing Data
Section 4: Simulation
Section 5: Plotting in R
Section 6: Scoping Rules
Section 7: Looping
Section 8: Classes and Methods
Section 9: Date and Time
split in Looping
Subsetting Partial Matching
Summery
System Capacity
Useful Graphics Devices
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