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<h1><span class="header-section-number">第 1 章</span> 简介<a href="tools-intro.html#tools-intro" class="anchor-section" aria-label="Anchor link to header"></a></h1>
<p>本书以R语言为基础,以数据分析为导向,简单地介绍了R语言在数据分析、机器学习等领域的应用,未来可能涉及到信号处理。尽可能以简洁的语言表达,辅以部分实际案例,希望为R语言和数据分析感兴趣的初学者提供一些帮助。</p>
<p>主要参考以下两本书籍:</p>
<p><a href="https://r4ds.had.co.nz/">《<strong>R for Data Science</strong>》</a> by Hadley Wickham</p>
<p><a href="https://www.math.pku.edu.cn/teachers/lidf/docs/Rbook/html/_Rbook/index.html">《<strong>R语言教程</strong>》</a> by 李东风</p>
<p>Tidyverse是一系列R包的集合,包含了dplyr、ggplot2、tidyr、stringr等,从数据导入预处理,再到高级转化、可视化、建模和展示;除此之外,Tidyverse同样可以加深我们对于数据,甚至于数据科学的理解,希望我们能够更多深入学习,鉴于能够看到本书的读者基本都有一定的R语言基础,本书不在介绍R所涉及到的基础知识。 本书暂且安排包括tidyverse、statistical analysis、bayes theorem、machine learning和missing data。前期主要在对tidyverse的详细介绍和范例,案例主要是对r4ds的代码解读和实际工作中遇到的问题解决方案,后期对statistical analysis、machine learning与causal inference,特别是对于MCMC,纵向数据,时序数据和孟德尔随机化的深入学习,如果精力与时间允许,也将对部分生信分析进行一定的学习和交流。</p>
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