掌握R语言：应用于统计分析与可视化实战

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"《学习R语言应用于统计》是一本旨在帮助读者掌握R编程基础，并将其应用于数据科学、数据挖掘领域中的实际统计分析的指南。作者Eric Goh Ming Hui以其深入浅出的方式，引领读者探索R的世界，包括统计学、大数据、数据科学和数据挖掘的基础概念。本书的核心内容涵盖了R语言的基本语法，如变量和算术运算、向量、列表、数据框、条件语句、循环以及函数的使用。通过学习，读者将能够熟练处理和理解数据，从描述性统计出发，掌握如何创建各类图表，如条形图、线图、散点图、箱线图、直方图和散点图，这些都是数据分析中的重要工具。此外，书中还重点讲解了推断性统计方法，如t检验、卡方检验、方差分析（ANOVA）、非参数测试以及线性和多元线性回归等，这些都是深入理解数据背后含义的关键步骤。这些统计技术对于验证假设、预测模型和理解变量间关系具有重要意义。《Learn R for Applied Statistics with Data Visualizations, Regressions, and Statistics》这本书不仅提供了理论知识，而且还通过实践案例让读者亲自动手操作，确保理论与实践的紧密结合。该书的出版日期为2019年，符合当前数据科学领域的技能需求，是提升个人在R语言统计分析能力的理想教材。本书的版权信息表明，所有权利归作者所有，未经许可不得复制、重印、再利用或通过电子方式传播，无论是通过微缩胶片还是其他物理手段，或者是通过计算机软件或类似或未来开发的方法。书中可能包含商标名、标志和图像，它们仅在编辑上下文中被提及，以尊重知识产权。《Learn R for Applied Statistics》是一本实用的资源，对于希望在R语言环境中开展实际统计工作的专业人士和初学者来说，都是不可或缺的参考资料。通过阅读和实践，读者将建立起坚实的R编程基础，并能有效地进行数据探索和分析，为数据驱动决策提供强有力的支持。"

between languages before execution. Thus, a low-level programming

language can be faster than a high-level programming language. Low-level

programming languages like the assembly language are more inclined

towards machine language that deals with bits 0 and 1.

R is a HLL because it shares many similarities to human languages. For

example, in R programming code,

> var1 <- 1;

> var2 <- 2;

> result <- var1 + var2;

> print(result)

[1] 3

The R programming code is more like human language. A low-level

programming language like the assembly language is more towards the

machine language, like 0011 0110:

0x52ac87: movl7303445 (%ebx), %eax

0x52ac78: calll 0x6bfb03

What Is Statistics?

Statistics is a collection of mathematics to deal with the organization,

analysis, and interpretation of data. Three main statistical methods are

used in the data analysis: descriptive statistics, inferential statistics, and

regressions analysis.

Descriptive statistics summarizes the data and usually focuses on

the distribution, the central tendency, and the dispersion of data. The

distribution can be normal distribution or binomial distribution, and the

central tendency is to describe the data with respect to the central of the

data. The central tendency can be the mean, median, and mode of the

Chapter 1 IntroduCtIon

The term “data science” has become a buzzword and is now used to

represent many areas like data analytics, data mining, text mining, data

visualizations, prediction modeling, and so on.

The history of data science started in November 1997, when C.F.

Jeff Wu characterized statistical work as data collection, analysis, and

decision making, and presented his lecture called “Statistics = Data

Science?” In 2001, William S.Cleveland introduced data science as a field

that comprised statistics and some computing in his article called “Data

Science: An Action Plan for Expanding the Technical Area of the Field of

Statistics.”

DJ Patil, who claims to have coined the term “data science” with Jeff

Hammerbacher and who wrote the “Data Scientist: The Sexiest Job of the

21st Century” article published in the Harvard Business Review, says that

there is a data scientist shortage in many industries, and data science is

important in many companies because data analysis can help companies

make many decisions. Every company needs to make decisions in strategic

directions.

Statistics is important in data science because it can help analysts or

data scientists analyze and understand data. Descriptive statistics assists in

summarizing the data, inferential statistics tests the relationship between

two data sets or samples, and regression analysis explores the relationships

between multiple variables. Data visualizations can explore the data

with charts, graphs, and dashboards. Regressions and machine learning

algorithms can be used in predictive analytics to train a model and predict

a variable.

Linear regression has the formula y = mx + c. You use historical data

to train the formula to get the m and c. Y is the output variable and x is the

input variable. Machine learning algorithms and regression or statistical

learning algorithms are used to predict a variable like this approach.

Domain expertise is the knowledge of the data set. If the data set

is business data, then the domain expertise should be business; if it

is university data, education is the domain expertise; if the data set is

Chapter 1 IntroduCtIon

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