English | MP4 | AVC 1920×1080 | AAC 44KHz 2ch | 4 Hours | 1.95 GB
Starter guide for mastering data analysis and data visualization using R
The importance of data is undeniable with companies fighting over the right to your data. The power of data has exceptionally grown in today’s world where data offers everything you need to know about a person or a potential future trend. This has companies scouring to find amazing data analysts that can help them make sense of the large sets of data available. Companies are using this data to make decisions that can change the direction of the world. This is why R is currently one of the most important languages on the tech market. So, if you want to master R – you’ve come to the right place! Our R course is taking you back to the basics to help you breakdown the dynamic and easy R programming language. The course doesn’t simply focus on the theory, but also on how to actually work with data by showing you the step-by-step process and helping you build your own experimental programs. These will also help you get some insight into how you can actually write programs using R and how you can analyze data sets to create graphical representations of the data that you have. At the end of this course, you’ll have the knowledge as well as the confidence to start working on analyzing large data sets and turning them into data that makes sense. Enroll now and become a master analyst with this basics course!
In this course, we will cover the fundamentals you need to learn the R programming language, including the syntax, rules, how to write in R, the benefits of the R programming language, how to work with large data sets and so much more!
What You Will Learn
- Learn to setup R in your computer
- Learn Data visualization with RLearn data manipulation and analysis with R
Table of Contents
Introduction to R
1 Introduction
2 Installation of R programming
3 Basic Fundamentals
4 Data sets and packages in R tool
Charts
5 Bar charts for one variable
6 Pie charts for one variable
7 Histograms
8 Boxplots
Statistics
9 Descriptive Statistics
10 Modifying Data
11 Data Structures
12 Data Frames
13 Activity Line Chart Plot
14 Activity Scattered Plots (2D and 3D)
Data Simulation
15 Working with the Data file
16 Grouped charts in R
17 Scattered Plots for associations
18 Loop functions
19 Simulation
20 Clustered Bar chart
Grouping of Data
21 Scatter Plots by groups
22 Profiling R code
23 Correlations
24 Bivariate regression
25 Two-sample t-test and Paired t-test
26 Activity Cluster Analysis
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