Muhammad Imdad Ullah

Student and Instructor of Statistics and business mathematics. Completed my Ph.D. in Statistics from the Department of Statistics, Bahauddin Zakariya University, Multan, Pakistan. l like Applied Statistics, Mathematics and Statistical Computing. Statistical and Mathematical software used is SAS, STATA, GRETL, EVIEWS, R, SPSS, VBA in MS-Excel. Like to use type-setting LaTeX for composing Articles, thesis, etc.

Debugging Tools in R Language

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The built-in debugging tools (programs: functions) in the R statistical computing environment are traceback(), debug(), browser(), trace(), and recover(). The purpose of the debugging tools is to help the programmer find unforeseen problems quickly and efficiently.

The R system has two main ways of reporting a problem in executing a function. One of them is a warning message while the other one is a simple error. The purpose of the warning is to tell the user (programmer) that “something unusual happened during the execution of the function, but the function was nevertheless able to execute to completion”. Writing a robust code (code that checks for imputing errors) is important for larger programs.

log(-1)     #produce a warning (NaN)
message <- function(x){
             if(x > 0)
               print("Hello")
            else
               print("Goodbye")
}

The log(-1) will result in a fatal error, not a warning. The first thing one should do is to print the call stack (print the sequence of function calls that led to the error). The traceback() function can be used which prints the list of functions that were called before the error occurred. However, this can be uninteresting if the error occurred at a top-level function.

Debugging Tools in R

Debugging Tools in R

The debugging tools in R are:

The traceback() Function

The traceback() function prints the sequence of function calls in reverse order from the top.

The debug() Function

The debug() function takes a single argument (the name of a function) and steps through the function line-by-line to identify the specific location of a bug, that function is flagged for debugging. To unflag a function, undebug() function is used. A function flagged for debugging does not execute in a usual way, rather, each statement in the function is executed one at a time and the user can control when each statement gets executed. After a statement is executed, the function suspends and the user is free to interact with the environment.

The browser() Function

The browser() function can be used to suspend the execution of a function so that the user can browse the local environment.

The trace() Function

The trace() function is very useful for making minor modifications to function “on the fly” without having to modify functions and re-sourcing them. It is especially useful if you need to track down an error that occurs in a base function.

trace("mean", quote( if( any(is.nan(x) ) ){ browser() }), print = FALSE)

The trace() function copies the original function code into a temporary location and replaces the original function with a new function containing the insert code.

The recover() Function

The recover() function can help to “jump up” to a higher level in the function call stack.

options(error = recover)

The error option tells R what to do in a situation where a function must halt the execution.

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Namespaces in R Language Made Easy

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The packages can have namespaces in R Language, and currently, all of the base and recommended packages do except the dataset packages. Understanding the use of namespaces is vital if one plans to submit a package to CRAN because CRAN requires that the package plays nicely with other submitted packages on CRAN.

Namespaces in R Language

Namespaces in R Language are essential tools for organizing code and preventing naming conflicts.
They become especially important when dealing with multiple packages, each potentially containing functions or objects with the same names.

Namespaces in R Language ensure that other packages will not interfere with your code and that the package works regardless of the environment in which it’s run. In R Language, the namespace environment is the internal interface of the package. It includes all objects in the package, both exported and non-exported to ensure that every function can find every other function in the package.

For example, plyr and Hmisc both provide a function namely summarize(). Loading plyr package and then Hmise, the summarize() function will refer to the Hmisc. However, loading the package in the opposite order, the summarize() function will refer to the plyr package version.

To avoid confusion, one can explicitly refer to the specific function, for example,

Hmisc::summarize

and

plyr::summarize
Namespaces in R Language

Now, the order in which the packages are loaded would not matter.

The Namespaces in R Language do three things:

  • Namespaces allow the package writer to hide functions and data that are meant only for internal use,
  • Namespaces prevent functions from breaking when a user (or other package writers) picks a name that clashes with one in the package, and
  • Namespaces in R provide a way to refer to an object within a particular package

Namespace Operators

In R language, two operators work with namespaces.

  • Doule-Colon Operator
    The double-colon operator:: selects definitions from a particular namespace. The transpose function t() will always be available as the base::t because it is defined in the base package. Only functions exported from the package can be retrieved this way.
  • Triple-Colon Operator
    The triple-colon operator ::: acts like the double-colon operator but also allows access to hidden objects. Users are more likely to use the getAnywhere() function, which searches multiple packages.

Packages are often interdependent, and loading one may cause others to be automatically loaded. The colon operators will also cause automatic loading of the associated package. When packages with namespaces are loaded automatically they are not added to the search list.

Benefits of using namespaces:

  • Clarity: Namespaces clarify the code by avoiding ambiguity when using common function names across different packages.
  • Fewer conflicts: Namespaces prevent errors that might arise if a user accidentally overwrites an object from another package with the same name.
  • Modular design: Namespaces promotes a modular approach to code organization, making managing and reusing code across projects easier.

FAQs about Namespaces in R Language

  1. What is namespace in R Language?
  2. What do namespaces in R language ensure?
  3. List and discuss namespace operators.
  4. Write a note on the benefits of using namespaces in R Language.
  5. What is the purpose of getAnywhere() function in R?
  6. Discuss double colon and triple colon operators.

R Language Basics: Frequently Asked Questions

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Vectors in R Language

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Introduction to Vectors in R Language

Vectors in the R Language are the simplest data structures. A vector in R is also an object containing elements of the same data type. To create a vector (say ‘x’) of the same type (that is data type is double) of elements consisting of five elements one can use the c() function. For example,

Creating Vectors in R using the c() Function

x <- c(10, 7, 3, 2, 1)

The c() function can be used to combine a different number of vectors into a single vector. A single number is regarded as a vector of length one. For example, a vector (say ‘y’) is created by combining the existing vector(s) with a single number.

Appending a Number to an Existing Vector(s)

One can append a number to an existing vector or even append a vector with another vector. For example, vectors in R Language can be appended like:

y <- c(x, .55)
z <- c(x, y)
Vectors in R Language

Extracting Vector Element(s)

The simplest example to select a particular element of a vector can be performed by using a subscription mechanism. That is, use the name of the vector with a square ([ ]) bracket with a number in it indicating the position of a vector element. For example,

# shows first element of vector x
> x[1:2]   # shows first two elements of vector 'x'
> x[3:5]   # shows elements of vector 'x' from index 3 to 5

Note that a positive number is used as a subscript index in a square bracket. A positive subscript indicates the index (position) of a number to extract from the vector. A negative number as the index can also be used, which is used to select all the elements except the number(s) that are used in the square bracket ([ ]).

An example of a negative index is;

x[-1]       # shows all elements of vector 'x' except first element
x[-(1:2)]   # shows elements of vector 'x' except first two elements

Also note that if the number exceeds the number of elements in a vector, then it will result in NA (not available). For example,

x[7] 
x[1:10]

Updating Vector Elements

One or more elements of a vector can be changed by the subsetting mechanism. For example, to change the 4th element of a vector, one can proceed as follows;

x[4] <- 15     # 4th position of vector 'x' is updated to 15
x[1:3] <- 4    # first three numbers are updated to 4
x[1:3] <- c(1,2,3) # first three numbers are updated to 1, 2, and 3

Learn about R Workspace, Objects, and .RData File

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