R and MATLAB

 
 
Chapman & Hall/CRC (Verlag)
  • erschienen am 3. September 2018
  • |
  • 233 Seiten
 
E-Book | PDF mit Adobe DRM | Systemvoraussetzungen
978-1-315-36272-4 (ISBN)
 

The First Book to Explain How a User of R or MATLAB Can Benefit from the Other

In today's increasingly interdisciplinary world, R and MATLAB® users from different backgrounds must often work together and share code. R and MATLAB® is designed for users who already know R or MATLAB and now need to learn the other platform. The book makes the transition from one platform to the other as quick and painless as possible.

Enables R and MATLAB Users to Easily Collaborate and Share Code

The author covers essential tasks, such as working with matrices and vectors, writing functions and other programming concepts, graphics, numerical computing, and file input/output. He highlights important differences between the two platforms and explores common mistakes that are easy to make when transitioning from one platform to the other.

  • Englisch
  • London
  • |
  • Großbritannien
Taylor & Francis Ltd
  • Für höhere Schule und Studium
10 schwarz-weiße Abbildungen, 13 schwarz-weiße Tabellen
978-1-315-36272-4 (9781315362724)

David E. Hiebeler is an associate professor in the Department of Mathematics & Statistics at the University of Maine. He earned a PhD in applied mathematics from Cornell University. His research involves mathematical and computational stochastic spatial models in population ecology and epidemiology.

<strong>Installing and Running R and MATLAB
</strong>Obtaining and installing
Commands for getting help
Demos
Quitting
Additional resources


<b>
</b>

<b>Getting Started: Variables and Basic Computations</b>
Variable names
Assignment statements
Basic computations
Formatting of output
Other computations
Complex numbers
Strange variable names in R
Data types


<b>
</b>

<b>Matrices and Vectors</b>
Overview
Creating vectors
Working with vectors
Creating matrices
Working with matrices
Reshaping matrices, and higher-dimensional arrays
Sparse matrices
Names with vectors and matrices/arrays
Miscellaneous

<i>
</i>

<i><b>
</b></i>

<i><b>Matrix/Vector Calculations and Functions</b>
Applying a function to rows or columns of a matrix
Applying a function to all elements of a matrix
Linear algebra calculations with vectors and matrices
Statistical calculations
Vectorized logical tests
Other calculations </i>


<i><b>
</b></i>

<i><b>Lists and Cell Arrays</b>
Creating lists and cell arrays
Using lists and cell arrays
Applying functions to all elements of lists and cell arrays
Converting other data types to lists and cell arrays
Converting lists and cell arrays to other data types</i>


<i><b>
</b></i>

<i><b>Flow Control</b>
Conditional ("if") statements
"If/else" statements
"for" loops
"while" loops
Breaking out of loops
"switch" statements
"ifelse" statements in R </i>


<i><b>
</b></i>

<i><b>Running Code from Files: Scripts</b>
Current working directory
The MATLAB search path
Executing code from a file
Creating a new script document in the editor
Comments in script files
Executing code from the editor window
Summary of differences </i>


<i><b>
</b></i>

<i><b>Writing Your Own Functions</b>
R
MATLAB
Summary of main differences </i>


<i><b>
</b></i>

<i><b>Probability and Random Numbers</b>
Basic random values, permutations, and samples
Random number seed
Random variates from probability distributions
PDFs, CDFs, and inverse CDFs </i>


<i><b>
</b></i>

<i><b>Graphics
</b>Creating, selecting, and closing figure windows
Basic 2-D scatterplots
Adding additional plots to a figure
Axis ranges
Logarithmic axis scales
Background grid
Plotting multiple data sets simultaneously
Axis labels and figure titles
Adding text to figures
Greek letters and mathematical symbols
Arrows
Figure legends
Size and font adjustments
Two <i>y</i> axes
Plotting functions
Image plots and contours
Colormaps
3-D plotting
Multiple subplots in one figure
Saving figures
Other types of plots
Final notes about graphics </i>


<i><b>
</b></i>

<i><b>Numerical Computing</b>
Root-finding
Univariate optimization
Multivariate optimization
Numerical integration
Curve fitting
Differential equations </i>

<i><i>
</i></i>

<i><i><b>
</b></i></i>

<i><i><b>File Input and Output
</b>Opening files
Reading a table of numbers
Reading numeric data with a different comment character
Reading numbers from a file where different lines have varying numbers of values
Reading numbers and strings
Reading the raw character data in, a line at a time
Writing a table of numbers
Writing a set of strings
Saving and loading variables in binary format
Images
URLs
Excel files </i></i>


<i><i><b>
</b></i></i>

<i><i><b>Miscellaneous </b>
Working with variables
Character strings
Reading user input
Recording a copy of commands and output
Date calculations
Miscellaneous
Debugging
Startup and shutdown sequences
Add-ons: packages and toolboxes
Object-oriented programming
Other interfaces
Efficiency/performance </i></i>


<i><i><b>
</b></i></i>

<i><i><b>Calling C
</b>R
MATLAB </i></i>


<i><i><b>
</b></i></i>

<i><i><b>Bibliography </b></i></i>


<i><i><b>Index of R commands, variables, and symbols </b></i></i>


<i><i><b>Index of MATLAB commands, variables, and symbols</b></i></i>

"... there is more to the book than simply a guide for translating from one computing environment to the other. The author also provides many useful suggestions for effective use of both R and MATLAB. Furthermore, in many places, the author explains what each environment is actually doing when a command or routine is called. This is useful because it can serve as an indicator as to whether R or MATLAB is the more appropriate choice for a given computing task. ... a highly valuable resource for anyone currently using or intending to use either. ... I personally welcome the existence of this book and am very grateful to the author for putting in the work to write it. R and MATLAB is well written and should be accessible to students and researchers alike."
-MAA Reviews, December 2015


"I find this text to be an important reference for many researchers (especially those in highly collaborative environments) that only know one of these two languages. In this situation, I believe this text to be an essential reference as it will make working with your collaborators more efficient. Even if you are well versed in both of these languages, I think this text can help you save time converting between the two. " (The American Statistician)
 

"... there is more to the book than simply a guide for translating from one computing environment to the other. The author also provides many useful suggestions for effective use of both R and MATLAB. Furthermore, in many places, the author explains what each environment is actually doing when a command or routine is called. This is useful because it can serve as an indicator as to whether R or MATLAB is the more appropriate choice for a given computing task. ... a highly valuable resource for anyone currently using or intending to use either. ... I personally welcome the existence of this book and am very grateful to the author for putting in the work to write it. <strong>R and MATLAB</strong> is well written and should be accessible to students and researchers alike."
-<em>MAA Reviews</em>, December 2015


"I find this text to be an important reference for many researchers (especially those in highly collaborative environments) that only know one of these two languages. In this situation, I believe this text to be an essential reference as it will make working with your collaborators more efficient. Even if you are well versed in both of these languages, I think this text can help you save time converting between the two. " (The American Statistician)

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