Sách Exploring Data Science with R and the Tidyverse, A Concise Introduction

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Sách Exploring Data Science with R and the Tidyverse, A Concise Introduction

Sách Exploring Data Science with R and the Tidyverse, A Concise Introduction

Sách Exploring Data Science with R and the Tidyverse, A Concise Introduction

Sách Exploring Data Science with R and the Tidyverse, A Concise Introduction

Sách Exploring Data Science with R and the Tidyverse, A Concise Introduction

Sách Exploring Data Science with R and the Tidyverse, A Concise Introduction

Sách Exploring Data Science with R and the Tidyverse, A Concise Introduction

Sách Exploring Data Science with R and the Tidyverse, A Concise Introduction

Sách Exploring Data Science with R and the Tidyverse, A Concise Introduction

Sách Exploring Data Science with R and the Tidyverse, A Concise Introduction

Sách Exploring Data Science with R and the Tidyverse, A Concise Introduction

Sách Exploring Data Science with R and the Tidyverse, A Concise Introduction

Sách Exploring Data Science with R and the Tidyverse, A Concise Introduction

Sách Exploring Data Science with R and the Tidyverse, A Concise Introduction

Sách Exploring Data Science with R and the Tidyverse, A Concise Introduction

Sách Exploring Data Science with R and the Tidyverse, A Concise Introduction

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Sách Exploring Data Science with R and the Tidyverse, A Concise Introduction

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Mô tả

Sách Exploring Data Science with R and the Tidyverse, A Concise Introduction

Sách Exploring Data Science with R and the Tidyverse, A Concise Introduction (sách keo gáy, bìa mềm)

Thể loại:Computers - Algorithms and Data Structures

Năm:2023

Ngôn ngữ:english

Trang:492

This book introduces the reader to data science using R and the

tidyverse. No prerequisite knowledge is needed in college-level

programming or mathematics (e.g., calculus or statistics). The book is

self-contained so readers can immediately begin building data science

workflows without needing to reference extensive amounts of external

resources for onboarding. The contents are targeted for undergraduate

students but are equally applicable to students at the graduate level

and beyond. The book develops concepts using many real-world examples to

motivate the reader.

Upon completion of the text, the reader will be able to:

Gain proficiency in R programming

Load and manipulate data frames, and "tidy" them using tidyverse tools

Conduct statistical analyses and draw meaningful inferences from them

Perform modeling from numerical and textual data

Generate data visualizations (numerical and spatial) using ggplot2 and understand what is being represented

An

accompanying R package "edsdata" contains synthetic and real datasets

used by the textbook and is meant to be used for further practice. An

exercise set is made available and designed for compatibility with

automated grading tools for instructor use.

As you develop

familiarity with processing data, you learn how to develop intuition

from the data at hand by glancing at its values. Unfortunately, there is

only so much you can do with glancing at values. There is a substantial

limitation to what you can obtain when the data at hand is so large.

Visualization is a powerful tool in such cases. In this chapter we

introduce another key member of the tidyverse, the ggplot2 package, for

visualization. R provides many facilities for creating visualizations.

The most sophisticated of them, and perhaps the most elegant, is

ggplot2. In this section we introduce generating visualizations using

ggplot2.

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