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R programming for bioinformatics / Robert Gentleman. p. cm. There are other devices, such as pdf, for producing documents in the portable document format. The Monk Who Sold His Ferrari A Fable About Fulfilling Your Dreams and Reaching Your Destin y Robin R Programming - Tutorialspoint. raw/master/_build/latex/lesforgesdessalles.info If you like this To use R, you first need to install the R program on your computer. How to check if R.
No specific grant was needed for the work in this paper; the paper summarises work performed as part of my regular teaching duties. Help me, please. Published online Aug Example vectorization problem Given a vector of event times e, write a function to return the interval between successive events, e. For a general introduction to R, Introductory Statistics with R  provides a nice balance of introducing R and showing its application to classical statistical testing; Introduction to Probability with R  goes further into aspects of probability. Finally, a general text for biological modelling is Dynamical Models in Biology . Scientific American.
Conway's game of life  provides a nice example of studying cellular automata.
As well as learning about particular concepts useful in computational biology e. R generates high-quality graphical output. It is worth providing simple examples for generating graphs that can be used as templates as given in the lecture notes for their work. Students often fail to realise the difference between vector and bitmap formats, and this is worth discussing in class to suggest they generate graphs using either PDF or Postscript devices, rather than bitmap formats.
R currently has two systems for generating graphs: The base system is much simpler and easier to use, and so we recommend students learn this system and most introductory books and resources also use base graphics.
However, students should be made aware of the grid package, which allows for much more flexibility over generating graphics. In particular, the lattice package  uses the grid package to allow the user to quickly generate sophisticated and flexible graphics.
The idea of reproducible research is quite simple: R provides infrastructure for this in the form of Sweave documents. The document is processed to extract and run the R code; output either textual or graphical is then inserted back into the document which is then typeset.
Students should be taught about the idea of reproducible research, and the idea should be reinforced by asking them to submit their coursework in the form of Sweave documents.
Reproducible research also encourages students to run their code in batch mode whereas most students initially prefer working interactively with R. Larger pieces of reproducible research are likely to be released in an R package containing both data and code , but teaching students how to build packages is beyond the scope of our current course. Writing Sweave documents takes much longer than writing R scripts, but it leads to self-documenting work that is likely to be understandable by many researchers long after it has been written.
The R website currently lists over 80 books, together with short descriptions that may help the reader decide which books to select http: Here I give a short, non-exhaustive list of books that I recommend to students to complement lecture notes and to show applications of R in computational biology.
Some of the books are quite advanced and are likely to be useful for students only after they have gained sufficient experience. I also take these books to lab sessions so that students can see which book would be most useful for them. For a general introduction to R, Introductory Statistics with R  provides a nice balance of introducing R and showing its application to classical statistical testing; Introduction to Probability with R  goes further into aspects of probability.
A First Course in Statistical Programming with R  introduces R as a programming language; those already familiar with programming may wish to consult S Programming .
Finally, for students wishing to explore the graphing facilities of R, R Graphics  is recommended. Several texts focus on aspects of computational biology.
First, the introductory text on Computational Genome Analysis  provides worked examples in R throughout the book. Stochastic Modelling for Systems Biology  uses R to demonstrate modelling in systems biology. An advanced book for those already familiar with R is R Programming for Bioinformatics . Finally, a general text for biological modelling is Dynamical Models in Biology . Although the book does not describe R, the online supplementary information provides a comprehensive introduction to R and shows how to use R to simulate the models discussed in the book, along with numerous exercises http: R has numerous online resources that students should be encouraged to explore.
Here are some additional sites that we have found useful:. Powered by Google, this site searches numerous online R resources, including documentation, source code, and books. It also searches the numerous email lists hosted by the R project; R-help in particular is a useful list for people to learn about R.
A very useful guide for students who know Matlab; it provides a comprehensive list of Matlab functions and the corresponding functions in R. This site provides a gallery of advanced graphic examples, along with the R code used to generate those plots.
Students with previous programming experience usually find learning R quite straightforward. It has a rich set of online documentation for each function, complete with examples, to help learn the language. However, there are some common problems that occur when learning R, described briefly below, along with suggestions for helping students. The syntax of R can be difficult for students to acquire, and students often report that they spend many hours debugging simple problems.
We encourage students to ask a colleague for help, as often these errors are simple, yet frustrating to spot. We use a wiki to allow students to post questions or exchange tips and example code.
Furthermore, although R has a rich set of documentation for inbuilt functions, students often report that it is hard to discover these functions, as they do not know what to search for. With this in mind, our introductory lecture notes were written to describe most core R functions with which we would expect a student eventually to become familiar during the year. Of course, it is infeasible to provide a complete list, especially given the vast number of numerical routines that come with R, and for this we suggest using the Rseek internet search tool, described above.
Lecturers should also give hints as to which functions might be of use for particular assignments. In R, variables do not need to be defined before use; they are simply created when required. A common problem with this is demonstrated in the following code:. A key problem here is that on line 3, y has been initialised to be the value zero, which is a vector of length one. Within each cycle of the loop, the length of y needs to increase by one, and so R silently reallocates the vector y to be long enough to store the new result.
The code works, but is inefficient, especially when looping over many values. A simple solution is to pre-allocate the vector when the length of the vector is known in advance. In this case, we can change line 3 to read:.
Many operations in R process entire vectors at once. This is called vectorization, and students familiar with other programming languages, such as C, often use slow and inefficient for loops to perform these calculations. It is worth reminding students at several stages while they are learning R that they should try to think about how to vectorize their code.
Sometimes this requires them to learn new R functions, such as the apply family of commands. For example, if we wish to compute the mean of each column of a matrix mat, rather than writing an explicit loop over each column, we can do:. The apply family of functions are powerful, but require careful explanation of how they work.
Continuing the example in the previous section, at first glance it may not seem suitable for vectorizing, given the if-then test operating on each element. However, R has the function ifelse, which simplifies the threshold example to:. In this case, as well as avoiding the for loop, the problem about allocating the size of the resulting vector y has gone. Vectorized solutions are often shorter, too, implying that there is less code to maintain. Even when students are familiar with vectorization, a common question asked is how to recognise which code might benefit from vectorization.
The answer, unfortunately, is that it requires accumulating experience at applying various tricks. Students can be helped by giving them examples, such as the one in the following paragraph, and asked to study it so that they understand exactly how it works. Warnings should be given, however, that even simple problems, such as computing the Fibonacci series, are impossible to vectorize.
It is better to get the code working correctly and then worry about efficiency later: Given a vector of event times e, write a function to return the interval between successive events, e. Given that the vector e and the result are of different lengths, it may seem that vectorized solutions are not possible.
A common concern raised by students is that they are not sure when to use the different data types e. Part of the problem is caused by the flexibility in R for functions to transparently handle different data types. Again, such issues normally resolve themselves by continued exposure to R, but instructors can help by showing how the type of an object can be determined and how objects can be converted from one type to another.
Relationships among data types should also be highlighted e. In this article I have summarised our experience to date on teaching R.
As the last section has shown, there are several difficulties with learning R, but I believe that they are fairly minor compared to the benefits in using such a powerful environment. Learning R is an ongoing process, and once students have mastered the basics, they should be encouraged to explore the wealth of contributed packages on the Comprehensive R Archive Network CRAN http: Thanks also to the R core team of developers for their ongoing work in maintaining and developing R.
The author has declared that no competing interests exist. No specific grant was needed for the work in this paper; the paper summarises work performed as part of my regular teaching duties. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. National Center for Biotechnology Information , U. PLoS Comput Biol. Published online Aug Stephen J. Fran Lewitter, Editor. Author information Copyright and License information Disclaimer.
Copyright Eglen. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
This article has been cited by other articles in PMC. Lecture notes for programming in R. Text S2: Text S3: PDF output from the example Sweave document. Why Use R in Computational Biology? How to Teach R to Students This brief article is an introduction to teaching R, based on my experience in teaching computational biology graduate students.
Lecture material Most students starting our master's programme have not previously seen R; at first, we assumed that students would self-learn R during the course. There are other devices, such as pdf, for producing documents in the portable document If you like this To use R, you first need to install the R program on your computer. How to check if R This is a very excellent book. As far as I can see there is only one single thing wrong with it, and that is its title. The book is about R Bioinformatics with R Cookbook - Bioinformatics, Pondicherry University ; Besides R, he has experience in various other programming languages, which As for bioinformatics, the author is not only a co-creator of R, he is a leading R language, as the most popular programming language for Why Use R in Computational Biology?
Bioinformatics and Computational Biology Solutions using R and Opaque this. Bioinformatics and R Programming for Detecting independent and recurrent copy number aberrations using Microarray R-based analysis of complex lysate experiments with Bioinformatics, Volume 30, Issue 17, 1 September , Pages i—i, The experimental protocol can be of any file type e.
R and Data Mining: Discussion forum: Please join our discussions on R and data mining at the RDataMining group It is always a good practice to begin R programming with an We developed an R package DiscoverSL to predict and visualize synthetic lethality in Supplementary data are available at Bioinformatics online.