Advanced R: Data Programming and the Cloud - download pdf or read online

By Matt Wiley

ISBN-10: 1484220765

ISBN-13: 9781484220764

ISBN-10: 1484220773

ISBN-13: 9781484220771

Software for facts research utilizing R and examine sensible abilities to make your paintings extra effective. This publication covers tips to automate working code and the production of stories to percentage your effects, in addition to writing features and applications. complex R isn't designed to educate complex R programming nor to educate the idea in the back of statistical techniques. really, it really is designed to be a pragmatic consultant relocating past in simple terms utilizing R to programming in R to automate projects. This publication will assist you manage facts in sleek R buildings and comprises connecting R to information bases equivalent to SQLite, PostgeSQL, and MongoDB. The ebook closes with a hands-on part to get R working within the cloud. each one bankruptcy additionally features a certain bibliography with references to analyze articles and different assets that disguise appropriate conceptual and theoretical themes.

Show description

Read Online or Download Advanced R: Data Programming and the Cloud PDF

Best object-oriented software design books

New PDF release: COBOL Programmers Swing with Java

That includes the advance of graphical person interfaces (GUI's) utilizing the most recent in Java swing parts, this re-creation of Java for the COBOL Programmer (Cambridge, 1999) offers COBOL programmers a transparent, effortless transition to Java programming by means of drawing at the a variety of similarities among COBOL and Java.

Download e-book for iPad: Pattern-Oriented Analysis and Design: Composing Patterns to by Sherif M. Yacoub

Software program specialists agree: the main tricky element of creating software program isn't really coding; it's the judgements the dressmaker makes within the early levels. these judgements reside with the approach for the remainder of its lifetime. sturdy designs beget strong software program. undesirable designs beget hassle. Designers are confronted with a difficult query: how do they recognize no matter if their designs are stable or undesirable?

Download e-book for kindle: Variational Object-Oriented Programming Beyond Classes and by Mira Mezini

Objective of the ebook This booklet provides an method of increase the normal object-oriented professional­ gramming version. The inspiration is aimed toward helping a bigger variety of incre­ psychological habit diversifications and therefore can provide to be more desirable in gaining knowledge of the complexity of cutting-edge software program. the power of facing the evolutionary nature of software program is one among major benefits of object-oriented info abstraction and inheritance.

Additional resources for Advanced R: Data Programming and the Cloud

Sample text

In contrast, many functions are written as part of R packages, in which case the function’s environment is defined by the R package. Therefore, before jumping in to writing functions, it is helpful to understand scoping. ) UseMethod("plot") plot <- 5 plot [1] 5 In the first instance, R finds plot in the graphics package. In the second instance, R finds plot in the global environment. Note that assigning 5 to the variable, plot, does not overwrite the plot() function.

An example is shown in the second use case of the little function in the following code. An error causes the function to terminate, and once that happens, the expression in on. exit() is executed. exit() is particularly valuable when a function modifies any values outside itself. For example, sometimes a plotting function modifies the default plot parameters and returns them to whatever their original state was on completion. exit(print("Game over")) x + 5 } f(3) [1] "Game over" [1] 8 f("a") Error in x + 5 (from #3) : non-numeric argument to binary operator [1] "Game over" The last set of function-specific functions we cover is related to giving the software or user a signal.

The following little function calculates the coefficient of variation, the sample standard deviation divided by the sample mean. call() captures exactly the call to the function, though it adds explicit argument names. This can sometimes be useful for keeping a record of exactly what the call was that created particular output. Perhaps the most common place where this is used is in the output from regression models in R. 06823 The return() function is typically used at the end of functions to return a specific object, as you have already seen in some of the previous examples, though it was not explicitly discussed.

Download PDF sample

Advanced R: Data Programming and the Cloud by Matt Wiley

by John

Rated 4.85 of 5 – based on 6 votes