Bestseller | h264, yuv420p, 1280x720 |ENGLISH, 44100 Hz, 2 Channels | 20h 40mn | 9.55 GB

R Programming Language for Data Visualization.

GGplot2, Data Analysis, Data Preparation, Data Sciene Tools, RStudio
What you'll learn

Visualize data

Foundations of data visualization (Grammar of Graphics and ggplot2)

Transform data before visualization is applied (data wrangling libraries)

Apply exploratory data analysis techniques with R and ggplot2

Wrap up analysis using RMarkdown reports

Use ggplot2 for creating many different standard statistical plots

Requirements

R and RStudio already installed on your computer.

Basic knowledge of statistics is a plus.

Basic to intermediate R knowledge is a plus.

Complete R bners will find course more challeg.

For complete R bners I recommend first taking one of the R bners courses, to get proper foundations needed.

To be fond of data transformation libraries (dplyr, tidyr) is a plus. However course includes a quick overview of dplyr and tidyr.

Interest in data science and data visualization.

Please update R or R's libraries if necessary. List of versions ( R and all R's libraries used in the exercises) provided at the end of course material.

Description

Today we live in a world where tons of data is generated every second. We need to analyze data to get some useful insight. One of the strongest weapons for data insight is data visualization. Probably you have heard this one before: "A picture tells more than a thousand words combined ". Therefore to tell stories from the data we need tools for producing adequate and amazing graphics. Here R as one of the most rapidly growing tools in the fields of data science and statistics provides needed assistance. If you combine R with its library ggplot2 you get one of the deadliest tools for data visualization, which grows every day and is freely accessible to anyone.

This course is designed to first give you quick and proper theoretical foundations for creating statistical plots. Then you dive into the world of exploratory data analysis where you are confronted with different datasets and creating a wide variety of statistical plots.



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