Data Analysis and Visualization with R for beginners

Data Analysis and Visualization with R for beginners, available at $27.99, has an average rating of 3.95, with 28 lectures, based on 13 reviews, and has 1741 subscribers.

You will learn about Install R and RStudio Create new R Projects Create R variables (objects) Install and load R Packages Import data into R Studio Perform data wrangling on your data Pipe several functions into a dataset using the Pipe function Create data visualizations using graphs Read data into a variable This course is ideal for individuals who are Beginners to data analysis and visualization using R It is particularly useful for Beginners to data analysis and visualization using R.

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Summary

Title: Data Analysis and Visualization with R for beginners

Price: $27.99

Average Rating: 3.95

Number of Lectures: 28

Number of Published Lectures: 28

Number of Curriculum Items: 28

Number of Published Curriculum Objects: 28

Original Price: $27.99

Quality Status: approved

Status: Live

What You Will Learn

Install R and RStudio

Create new R Projects

Create R variables (objects)

Install and load R Packages

Import data into R Studio

Perform data wrangling on your data

Pipe several functions into a dataset using the Pipe function

Create data visualizations using graphs

Read data into a variable

Who Should Attend

Beginners to data analysis and visualization using R

Target Audiences

Beginners to data analysis and visualization using R

R is a programming language. R is often used for statistical computing and graphical presentation to analyse and visualize data.

R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, …) and graphical techniques, and is highly extensible.

One of R’s strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed.

R is available as Free Software under the terms of the Free Software Foundation’s GNU General Public License in source code form. It compiles and runs on a wide variety of UNIX platforms and similar systems (including FreeBSD and Linux), Windows and MacOS.

R is an integrated suite of software facilities for data manipulation, calculation and graphical display. It includes

an effective data handling and storage facility,

a suite of operators for calculations on arrays, in particular matrices,

a large, coherent, integrated collection of intermediate tools for data analysis,

graphical facilities for data analysis and display either on-screen or on hardcopy, and

a well-developed, simple and effective programming language which includes conditionals, loops, user-defined recursive functions and input and output facilities.

R can be extended (easily) via packages. There are about eight packages supplied with the R distribution and many more are available through the CRAN family of Internet sites covering a very wide range of modern statistics.

RStudiois an integrated development environment for R, a programming language for statistical computing and graphics. It is available in two formats: RStudio Desktop is a regular desktop application while RStudio Server runs on a remote server and allows accessing RStudio using a web browser.

Course Curriculum

Chapter 1: Getting Started

Lecture 1: Introduction

Lecture 2: What is R

Lecture 3: Installing R on Windows

Lecture 4: Installing R on Mac

Lecture 5: What is R Studio

Lecture 6: Installing R studio on Windows

Lecture 7: Installing R Studio on Mac

Lecture 8: Exploring R Studio Default Interface

Lecture 9: Creating a new project in R Studio

Lecture 10: What are Packages

Lecture 11: How to install Packages

Lecture 12: Data sets vs Data frames

Lecture 13: Loading Packages

Chapter 2: Data Analysis & Visualization

Lecture 1: Importing data into R Studio

Lecture 2: How to read data in a csv file with R

Lecture 3: Installing Janitor Package

Lecture 4: Selecting a subset of data

Lecture 5: Performing multiple operations using Pipe operator

Lecture 6: Cleaning columns

Lecture 7: Creating new columns from existing columns

Lecture 8: Create a new R Project

Lecture 9: Load data into new project

Lecture 10: What is Data Wrangling

Lecture 11: Data Wrangling steps

Lecture 12: Importance of data wrangling

Lecture 13: Perform Data Wrangling on Data

Lecture 14: Create a scatter plot

Lecture 15: Create a bar graph

Instructors

Bluelime Learning Solutions
Making Learning Simple

Rating Distribution

1 stars: 1 votes

2 stars: 2 votes

3 stars: 1 votes

4 stars: 3 votes

5 stars: 6 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

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