Chapter 1 Preface

1.1 Story at the beginning

作为一名有过高校经历的年轻学者，我深刻体会到数据分析在科研工作中的重要性。数据分析往往占据了我们至少 50% 的时间，这项工作既枯燥又容易出错。尤其是在食品科学等领域，很多研究生和青年科研工作者并非统计学或数学背景，他们从网上随便找些教程，却不知道方法是否正确；导师通常也没有时间一一核对，这就导致许多团队重视实验操作，却忽视了数据分析的重要性。这种现象在国内外高校都非常普遍，博士生在这方面缺乏系统培养是一个遗憾。

我亲身经历过这些问题，也深知自学数据分析的痛苦——时间消耗巨大，容易走弯路。尽管现在有像 ChatGPT 这样的强大工具辅助，但如果连最基本的统计学概念和数据分析关键词都不了解，就无法正确提问，更谈不上得到有效的解答。因此，我总结了自己在科研工作中的经验，撰写了这本面向硕士、博士以及青年教师的 《R Cookbook in Food Science》。

这本书特别针对食品科学领域，同时也欢迎农业、化学、生物等“天坑”专业的同行阅读。这些领域的数据量庞大，数据分析和可视化的难度也与日俱增。很多高校和科研机构购买了昂贵的软件来降低学习门槛，但学生们依然需要花费大量时间掌握统计学基础知识。相比之下，R 语言作为为统计学家设计的软件，结合丰富的扩展包，为年轻科研工作者提供了一种灵活且高效的解决方案。

从我的个人经历出发，许多网上的学习资源主要以生物学为中心，例如基因组学和遗传学相关的案例。这些内容对于食品科学领域的学习者来说并不合适，反而增加了学习成本。因此，我希望通过这本书，提供食品科学领域的实际案例，帮助学生和科研人员掌握统计学概念，提高 R 语言编程能力，节省宝贵的时间和精力。

本书主要内容涵盖基于 R 编程的统计分析、数据可视化和机器学习方法，我相信，这本书将为食品科学领域的学生和科研工作者提供实用的帮助，也希望它能成为你们科研路上的一块坚实基石。

As a young researcher, I have deeply realized the importance of data analysis in scientific research. It often consumes at least 50% of our time, and the process is both tedious and prone to errors. Particularly in fields like food science, many graduate students and early-career researchers lack a background in statistics or mathematics. They frequently rely on scattered tutorials found online without knowing whether the methods are correct. Supervisors often don’t have the time to verify these details, resulting in many research teams prioritizing experimental work while neglecting data analysis. Unfortunately, this issue is common in universities worldwide, where doctoral programs often fail to provide systematic training in this area.

I have been through these struggles myself and know how painful self-teaching data analysis can be. It takes a lot of time and effort and is easy to go astray. While tools like ChatGPT have become invaluable, their effectiveness is limited if you lack a basic understanding of key statistical concepts and data analysis terminology. Without this foundation, you won’t even know how to ask the right questions. To address these challenges, I wrote this book, R Cookbook for Scientific Research, specifically for master’s students, doctoral candidates, and young faculty members.

This book focuses on the field of food science but is also highly relevant to researchers in “challenging” disciplines such as agriculture, chemistry, and biology. In these fields, the massive volume of data has significantly increased the complexity of data analysis and visualization. Many institutions invest in expensive software to lower the learning curve, but students still need to spend substantial time mastering statistical knowledge. R, as a software designed for statisticians, combined with its extensive package ecosystem, provides an ideal solution for young researchers to perform data analysis and visualization effectively.

From my personal experience, much of the material available online is tailored toward biological sciences, with examples in genomics and genetics. These are often not suitable for food science and further increase the learning barrier. With this book, I aim to provide practical examples from food science research to help students understand statistical concepts and improve their coding abilities, saving time and energy for those passionate about food science.

The book aims to provide a basic overview of data science for statistical analysis in Food Science. This book is intended to save students and young scientists from confusions as a starter in data science application.

With the development of food science, this major has became a comprehensive discipline including analytical chemistry, biochemistry, nutrition and even basic medicine. The large amount of data greatly increases the difficulty of data analysis and visualization. Many universities and research institutes have to buy expensive software to lower the threshold. At the same time, students also need to spend a lot of extra time to learn many basic statistical knowledge. R as a software designed for statisticians, combined with a wide variety of packages, is ideal for young students and scientists for data analysis and visualization.

Therefore, this book is going to provide solid example in Food Science to help student understand the statistical concepts and improve the coding ability. This should be helpful to save time and energy for most of people love food science.

I believe this book will serve as a practical guide for students and researchers in food science, helping them build a solid foundation in data science and paving the way for more efficient and impact research.

1.2 Topic covered

Section 1 Basic skills in R: data import, package management, data clean
Section 2 Statistician: Descriptive statistics, Normality test, Regression
Section 3 Modeling: Classification, Correlation, PCA
Section 4 Data visualization, ggplot2

Figure 1.1: Key stages of a metabolomics study. By Ycyc0927 - Own work, CC BY-SA 4.0, https://en.wikipedia.org/w/index.php?curid=57237638