Beginning Using R For Data Analysis

You will be skillful with core concepts and basic data management methods in R, e.g. data structures, variables, sorting, meg, subsetting datasets.
You will get enough knovledge in plotting statistical graphs in R, using ggplot2 package in particular.
You will get touch with some addvanced data management methods in R, e.g. writing functions, using control flow, working with strings, reshaping datasets, etc.
You will be able to get started working with basic statistical and data analysis in R.
Requirements
No programming experience is needed, at least you can operating your computer in Windows.
Knowledge of statistics in elementary level is a prerequisite, e.g. mean, standard deviation, correlation, etc.
Description
R is one of the most popular programming languages and framework for statistical computing and data science application currently available. The goal of this course is to bring you up to start this comprehensive software as quickly as possible, such that you can apply it effectively to your own data analysis.This course is built for people of any age who trie to start programming in R at first or even have never programmed at all. If you want to learn the basics of programming quickly so you can focus on interesting projects, and you like to test yourunderstanding of new concepts by solving meaningful problems, then this course is appropriate for you.The course contents are divided into five main sections. The first section -Get started with R environmentprovides a brief overview of R programming language as well as the installation of R and RStudio, so will make you become familiar with the R environment.Section 2 - R data structure and datasetcovers mainly the basics of R data structure, and how to import data files to create useful format for further analysis in R.Section 3 provides a brief overview of some useful basic data management methods in R, which includes mainly data type conversions,recoding variables, handling dates and missing values, Selecting and dropping variables, Sorting, meg, and subsetting datasets.Section 4, some of the more addvanced data management methods in R are explained. The main topics in this section include mathematical and statistical functions, functions for string and character, control flow in R, writing your own functions, and reshaping and aggregating datasets as well as summary statistics. R graphics is covered in section 5. In this section, we will introduce the ggplot2 package, and how to creat some simple bivariate (two-variable) graphs, as well as using grouping and faceting to create multivariate graphs, how to save graphs in multiple formats, and how to plot some basic statistical plots.Several virtual datasets suitable for R framework are followed during the course.Each section has its own source code file written in R-format, which can be loaded into RStudio. you can the source files from the course page.By the end of the course you will be given an exercise which you can utilize all the knowledge learned to practice and evalutate what you have learned in this course.

Overview

Section 1: Get started with R environment

Lecture 1 Course Introduction and Outline

Lecture 2 Installation of R and R-Studio

Lecture 3 Get started working R

Lecture 4 Working with R packages

Lecture 5 A simple example

Section 2: R data structure, create datasets

Lecture 6 Vector

Lecture 7 Matrix

Lecture 8 Array

Lecture 9 Data frame

Lecture 10 Factor

Lecture 11 List

Lecture 12 Create datasets , inputing csv file

Lecture 13 Using with

Lecture 14 Object functions

Section 3: Basic data management

Lecture 15 A working example - Basic data management

Lecture 16 Create new variables

Lecture 17 Recoding variables

Lecture 18 Renaming variables

Lecture 19 Handling missing value

Lecture 20 Working with Date value

Lecture 21 Type conversion

Lecture 22 Sorting data

Lecture 23 Meg datasets

Lecture 24 Subsetting datasets

Section 4: Advanced data management

Lecture 25 An working example - Advanced data management

Lecture 26 R mathematical functions

Lecture 27 R statistical functions

Lecture 28 R probability functions

Lecture 29 R character functions

Lecture 30 Using Apply functions

Lecture 31 Solution to working example-Advanced data management

Lecture 32 Control flow

Lecture 33 Creating your own functions

Lecture 34 Transposing data objects

Lecture 35 Aggregating data

Lecture 36 Descriptive statistics

Section 5: Using ggplot2 for R graphic

Lecture 37 Start building a graph with ggplot2

Lecture 38 Add geoms in ggplot2

Lecture 39 Using grouping

Lecture 40 Using scales

Lecture 41 Using facets

Lecture 42 Formulating labels

Lecture 43 Formulating themes

Lecture 44 Using graph as objects

Lecture 45 Saving graphs

Lecture 46 Bar charts

Lecture 47 Pie charts

Lecture 48 Histograms

Section 6: Final exercise

Lecture 49 Final exercise

For R programming bner, or someone who have experiences with other statistical software, and is interested in learning R.,Suitable for jobb-seeker who needs an R-course certificate at bning level, or people who are working in social science, management, natural science who will use R programming in statistical and data analysis.

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