Statistics and R for Linguists - a reading list [轉帖]
原文是Tempfli Péter寫在blogspot上，所以我只好貼在下面了：http://szamitogepesnyelveszet.blogspot.hu/2012/08/statistics-and-r-for-linguists-reading.html
所有的鏈接我都沒貼，一個個弄有點麻煩，自己動手搜搜就好了。
我在前面標註了類型，大致是[Book], [online course], [tool], [PDF], 和[R package]這幾個類型。
Linguists and students of linguistics can find themselves in tough situations since statistics and computing are usually not an integral part of their education, or they only get a crash course on these subjects. I think R is good choice for self study because of the available high quality books and other resources.
The basics
[PDF] Verzani: simpleR - short, free and very beginner friendly intro into R and stats. The author wrote Using R for Introductory Statistics [book] which is a more elaborated version of simpleR.
[Online Course] Udacity Intro to Statistics (ST101) - Sebastian Thrun's free Udacity course is a general introduction to statistics. It is very nice and you can work on the course at your own speed. It contains optional material on statistical programming with Python.
[Online Course] Coursera: Statistics One - Andrew Conway's Princeton course is now on Coursera. Just like the Udacity course, it is a general intro to stats, so it doesn't deal with linguistics applications but it will also provide an intro to R
[Online Course] Coursera: Computing for Data Analysis - Roger D. Peng's introductory course at John Hopkins is now adapted to Coursera. According to the course description, this is course is focusing on using R for basic tasks.
Linguistics and statistics
[Book], [PDF] Cristopher Butler: Statistics in Linguistics - this classic is freely available
[Book] Marcus Kracht: Introduction to Probability Theory and Statistics for Linguistics - a very comprehensive free ebook, but it assumes a good grounding in maths
[Book] McEnry - Hardie: Corpus Linguistics - corpus linguistics answers the question "why bother with stats"
[Book] Michael P. Oakes: Statistics for Corpus Linguistics - an oldie-but-goodie book （這本書謹慎使用，據說有不少bug）
[Book] Bod - Hay - Jannedy: Probabilistic Linguistics - a pretty good collection of papers on the applications of probabilistic methods in various fields of linguistics
Lx and R
[Book] Gries: Quantitative Corpus Linguistics with R - Gries' book is well written and assumes no prior knowledge of stats, R and corpus linguistics. I love this title as it simply does its job and teach the basics of manipulating and analyzing linguistic data with R.
[Book] Gries: Statistics for Linguistics - a general intro into stats and R for linguists. It is very readable and i think it is perfect for self-study.
[Book] Baayen: Analyzing Linguistic Data - this book can be a tremendous resource for upper-level undergrads and graduate students. It assumes that you are not new to linguistics and shows you how to use R in your investigations. However it goes through its topics very fast and sometimes gives very limited explanations to the R code fragments. I don't think this title is an ideal first book on statistical methods and R.
[Book] Johnson: Quantitative Methods in Linguistics - this book is about quantitative methods and it is using data sets drawn from various fields of linguistics. This is the more advanced book in this list, it is more about the methods, and less about programming in R, so don't pick it up as you first book in this subject. Later, when you have some background in stats and R, you can learn a lot from this title.
[Book] Vasishth - Broe: The Foundations of Statistics - this book is using a simulation based approach to teach statistics (and R). I think this book is targeted towards psycholinguists, but others also can find it useful as it assumes no knowledge of stats and/or R.
[Book] Levy: Probabilistic Models in the Study of Language - a work in progress textbook. It is free, but the author hasn't finished it yet. As the intended audience is graduate students of linguistics and cognate disciplines, it is not a gentle introduction.
Advanced materials
The above-mentioned books are pretty good and if you work hard on them you can make considerable progress. At some point, you'll realize that they are pretty good at analyzing data, but tells you nothing about dealing with data and doing data analysis. As your data analysis projects are getting more complex, you have to learn about these topics.
[Tool] git and github - version control helps you to track your work and revert back to previous stages if something wrong happens
[R package] ProjectTemplate - helps you to organize your scripts and data
[R package] RUnit - unit testing is a good software engineering practice that helps you to write more reliable code
[Book] Phil Spector: Data Manipulation in R - this short book gives you tips and trick that helps you to use R for manipulating your data
[Book] John Chambers: Software for Data Analysis - an advanced book on R that helps you to put the pieces together and become an advanced R user
所有的鏈接我都沒貼，一個個弄有點麻煩，自己動手搜搜就好了。
我在前面標註了類型，大致是[Book], [online course], [tool], [PDF], 和[R package]這幾個類型。
Linguists and students of linguistics can find themselves in tough situations since statistics and computing are usually not an integral part of their education, or they only get a crash course on these subjects. I think R is good choice for self study because of the available high quality books and other resources.
The basics
[PDF] Verzani: simpleR - short, free and very beginner friendly intro into R and stats. The author wrote Using R for Introductory Statistics [book] which is a more elaborated version of simpleR.
[Online Course] Udacity Intro to Statistics (ST101) - Sebastian Thrun's free Udacity course is a general introduction to statistics. It is very nice and you can work on the course at your own speed. It contains optional material on statistical programming with Python.
[Online Course] Coursera: Statistics One - Andrew Conway's Princeton course is now on Coursera. Just like the Udacity course, it is a general intro to stats, so it doesn't deal with linguistics applications but it will also provide an intro to R
[Online Course] Coursera: Computing for Data Analysis - Roger D. Peng's introductory course at John Hopkins is now adapted to Coursera. According to the course description, this is course is focusing on using R for basic tasks.
Linguistics and statistics
[Book], [PDF] Cristopher Butler: Statistics in Linguistics - this classic is freely available
[Book] Marcus Kracht: Introduction to Probability Theory and Statistics for Linguistics - a very comprehensive free ebook, but it assumes a good grounding in maths
[Book] McEnry - Hardie: Corpus Linguistics - corpus linguistics answers the question "why bother with stats"
[Book] Michael P. Oakes: Statistics for Corpus Linguistics - an oldie-but-goodie book （這本書謹慎使用，據說有不少bug）
[Book] Bod - Hay - Jannedy: Probabilistic Linguistics - a pretty good collection of papers on the applications of probabilistic methods in various fields of linguistics
Lx and R
[Book] Gries: Quantitative Corpus Linguistics with R - Gries' book is well written and assumes no prior knowledge of stats, R and corpus linguistics. I love this title as it simply does its job and teach the basics of manipulating and analyzing linguistic data with R.
[Book] Gries: Statistics for Linguistics - a general intro into stats and R for linguists. It is very readable and i think it is perfect for self-study.
[Book] Baayen: Analyzing Linguistic Data - this book can be a tremendous resource for upper-level undergrads and graduate students. It assumes that you are not new to linguistics and shows you how to use R in your investigations. However it goes through its topics very fast and sometimes gives very limited explanations to the R code fragments. I don't think this title is an ideal first book on statistical methods and R.
[Book] Johnson: Quantitative Methods in Linguistics - this book is about quantitative methods and it is using data sets drawn from various fields of linguistics. This is the more advanced book in this list, it is more about the methods, and less about programming in R, so don't pick it up as you first book in this subject. Later, when you have some background in stats and R, you can learn a lot from this title.
[Book] Vasishth - Broe: The Foundations of Statistics - this book is using a simulation based approach to teach statistics (and R). I think this book is targeted towards psycholinguists, but others also can find it useful as it assumes no knowledge of stats and/or R.
[Book] Levy: Probabilistic Models in the Study of Language - a work in progress textbook. It is free, but the author hasn't finished it yet. As the intended audience is graduate students of linguistics and cognate disciplines, it is not a gentle introduction.
Advanced materials
The above-mentioned books are pretty good and if you work hard on them you can make considerable progress. At some point, you'll realize that they are pretty good at analyzing data, but tells you nothing about dealing with data and doing data analysis. As your data analysis projects are getting more complex, you have to learn about these topics.
[Tool] git and github - version control helps you to track your work and revert back to previous stages if something wrong happens
[R package] ProjectTemplate - helps you to organize your scripts and data
[R package] RUnit - unit testing is a good software engineering practice that helps you to write more reliable code
[Book] Phil Spector: Data Manipulation in R - this short book gives you tips and trick that helps you to use R for manipulating your data
[Book] John Chambers: Software for Data Analysis - an advanced book on R that helps you to put the pieces together and become an advanced R user
