7.1.Programming and data analytics - sj50179/Google-Data-Analytics-Professional-Certificate GitHub Wiki

In this course, you will learn how to use the R programming language to work with your data without tool limitations. You will get plenty of practice using R for statistical analysis, and RStudio—an integrated developer environment (IDE) for R that you will use to create advanced data visualizations with lots of detail. R makes it easier to present your data with beautiful, artistic style. A few other advantages of R include its:

Popularity: R is frequently used for data analysis
Tools: R has a convenient library of ready-to-use tools for data cleaning and analysis
Focus: R was created with statistics in mind; data analysts can conveniently use a rich library of statistical routines
Adaptability: R adapts well for use in both machine learning and data analysis projects
Availability: R is an open source programming language

After you get comfortable and more confident using R and RStudio, you might find that you are curious to learn and add even more programming languages to your skillset (and resume). Pretty exciting, right?

Course content

Course 7 – Data Analysis with R Programming

Understanding the basics of R: R is a programming language that can be used to perform tasks in every phase of the data analysis process. In this part of the course, you will learn about R and RStudio, an integrated developer environment (IDE) for R. You will explore the benefits of using RStudio to work with R. RStudio enables you to easily leverage the features and functionality of R.
Programming using RStudio: In this part of the course, you will explore the fundamental concepts associated with R. You will learn about functions and variables that you can use in your calculations and other programming. You will also learn about R packages, which are collections of R functions, code, and sample data that you can use in RStudio.
Working with data in R: The R programming language was designed to work with data at all stages of the data analysis process. In this part of the course, you will examine how R can help you structure, organize, and clean your data through functions and other processes. You will learn about data frames and how to work with them in R. You will also revisit the concept of data bias and how you can use R to address it.
Visualizations, aesthetics, and annotations: R is a great tool for creating detailed visualizations. In this part of the course, you will learn how to use R to generate and troubleshoot visualizations. You will also explore the features of R and RStudio that can help you improve the aesthetics of your visualizations. You will learn how to annotate visualizations and save the changes.
Documentation and reports: R has a number of different options to explore when you are ready to save and present your analysis. In this part of the course, you will explore R Markdown, a file format for making dynamic documents with R. You will learn how to format and export R Markdown and incorporate R code chunks in your documents.
Course challenge: At the end of the course you will apply everything you have learned in the Course Challenge. The Course Challenge will ask you questions about the key skills you have been practicing and will give you an opportunity to demonstrate those skills in three scenarios.

R is a programming language that can help you in your data analysis process. In this part of the course, you’ll learn about R and RStudio, the environment you’ll use to work in R. You’ll explore the benefits of using R and RStudio as well as the components of RStudio that will help you get started.

Learning Objectives

Compare and contrast the R programming environment and the RStudio programming environment
Describe the RStudio programming environment including its components and benefits
Describe the R programming language and its programming environment
Describe programming languages and appropriate use including examples
Download and install R assets to a computer
Open R and execute a command
Differentiate between the R Console and R programming environments
Execute operations in R using mathematical operators such as +, -, *, and /
Download and use RStudio Desktop
Demonstrate how to complete basic tasks in R