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.

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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Reading and Writing Data in R: A Quick Guide

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For dealing with data, one may need “reading and writing data in R”. Therefore, it is important to discuss reading and writing data in R. It is also important to learn about reading and writing data files in the R environment.

Reading and Writing Data in R Language

Reading Data in R

  • read.table(), and read.csv(), for reading tabular data
  • readLines() for reading lines of a text file
  • source() for reading in R code files (inverse of dump)
  • dget() for reading in R code files (inverse of dput)
  • load() for reading in saved workspaces.

Writing Data to File in R

Following are a few functions for writing (exporting) data to files in R Language.

  • write.table() in R, and write.csv() export data to a wider range of file formats including CSV and tab-delimited.
  • writeLines() write text lines to a text-mode connection.
  • dump() takes a vector of names of R objects and produces text representations of the objects on a file (or connection). A dump file can usually be sourced into another R session.
  • dput() writes an ASCII text representation of an R object to a file (or connection) or uses one to recreate the object.
  • save() writes an external representation of R objects to the specified file.

Reading Data Files with read.table()

The read.table() function in R is one of the most commonly used functions for reading data into R. It has a few important arguments.

  • file, the name of a file, or a connection
  • header, logical indicating if the file has a header line
  • sep, a string indicating how the columns are separated
  • colClasses, a character vector indicating the class of each column in the data set
  • nrows, the number of rows in the dataset
  • comment.char, a character string indicating the comment character
  • skip, the number of lines to skip from the beginning
  • stringsAsFactors, should character variables be coded as factors?

read.table() and read.csv() Examples

data <- read.table("foo.txt")
data <- read.table("D:\\datafiles\\mydata.txt")
data <- read.csv("D:\\datafiles\\mydata.csv")

R will automatically skip lines that begin with a symbol # and figure out how many rows there are (and how much memory needs to be allocated). R also figures out what type of variable is in each table column.

Writing Data Files with write.table() in R

A few important arguments are usually used in function write.table() in R.

  • x, the object to be written, typically a data frame
  • file, the name of the file that the data are to be written to
  • sep, the field separator string
  • col.names, a logical value indicating whether the column names of x are to be written along with x, or a character vector of column names to be written
  • row.names, a logical value indicating whether the row names of x are to be written along with x, or a character vector of row names to be written
  • na, the string to use for missing values in the data

write.table() and write.csv() Examples

The following are few examples about saving data in CSV or TXT files.

x <- data.frame(a = 5, b = 10, c = pi)

write.table(x, file = "data.csv", sep = ",")
write.table(x, "c:\\mydata.txt", sep = "\t")
write.csv(x, file = "data.csv")
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