R Language Basics

The post is about R Language Job Interview Questions. It contains some basic questions that are usually asked in job interviews and examinations vivas.

R Language Job Interview Questions

The following are R FAQs Interview Questions with their detailed answers:

What are the Capabilities of the R Language?

The following are the capabilities of the R Language:

• Data Handling Capabilities: Good data handling capabilities and options for parallel computation.
• Availability/Cost: R languages is an open source, one can use it anywhere and free of cost.
• Advancement in Tool: R gets the latest features and updates frequently.
• Ease of Learning: R language has a steep learning curve. On the other hand, R is a low-level programming language, as a result, simple procedures can take long codes.
• Graphical capabilities: R has the most advanced graphical capabilities.
• Job Scenario: It is a better option for start-ups and companies looking for cost efficiency.
• Customer Service support and community: R is the biggest online growing community.

Explain a Few Features to Write R Code that Runs Faster.

R Language is a popular and famous statistical software for its enormous amount of packages. The syntax of R language is very flexible making it convenient at the cost of performance. R is indeed slow compared to many other scripting languages, but there are a few tricks that can make our R code run faster.

• Use a matrix instead of a data frame whenever possible. Data frames cause problems in many cases. Only use data frame when necessary.
• Use double(n) to create a vector of length n instead of using code rep(0,n), and similar to others.
• Split big data objects (e.g., big data frame or matrix) into smaller ones, and operate on these smaller objects.
• Use vector and matrix operations if possible. These *apply functions are very helpful for this purpose.
• Use for each(i=1:n) %dopar% {} to do parallel computing if applicable. Even if a for loop is not parallelizable, for each(i=1:n) %do% {} is a better alternative.
• Avoid changing the type and size of an object in R language. Though one can use R objects as if they are typeless, they have type actually. In R, changing the type and size of an R object forces it to reallocate a memory space which is of course insufficient.
• Avoid creating too many objects in each working environment. Not having enough memory can not only make the code run slower but also make it fail to run if have to allocate big vectors. One way to do this is to write small functions and run the functions instead of running everything directly in a working environment.

What is Visualization in R?

Visualization is a technique for creating images, diagrams, or animations to communicate a message. Visualization through visual imagery has been an effective way to communicate both abstract and concrete ideas since the dawn of humanity. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data.

Why R Language?

R is popular among researchers, data scientists, and statisticians. It is also used in finance, which relies heavily on statistical data. The R programming language is used for many reasons, including:

• Data analysis: R is a statistical programming tool well-suited for data analysis, mining, and modeling. It is also used for data cleaning and importing.
• Open-source and free: R is free and open-source and works on different platforms, including Windows, Mac, and Linux.
• Data visualization: R is a powerful tool for creating publication-ready graphics and visualizations, such as cluster bar charts, pie charts, histograms, box plots, and scatter plots.
• Machine learning: R is an effective tool for machine learning algorithms.
• Specialized focus on analysis: R handles specialized data science projects better than general-purpose software development languages like Python.
• Many packages: R has many packages (libraries of functions) that can be used to solve different problems.
• Large community support: R has a large community support.

What is SAS and SPSS in R?

SAS: SAS stands for Statistical Analysis System. It was primarily developed to be able to analyze large quantities of agriculture data.
SPSS: SPSS stands for Statistical Package for the Social Sciences and was developed for the social sciences.
R Language: R language was the first statistical programming language for the PC.

Computer MCQs Online Test, Chi-Square Distribution $\chi^2$

The post is about R Interview Questions. It contains some basic R Interview Questions that are usually asked in Job and university admissions Interviews.

R Interview Questions

The following are R FAQs Interview Questions with their detailed answers:

Why Should One Adopt the R Programming Language?

• R programming language is the best software for statistical data analysis and machine learning. By using R language software, one can create objects, functions, and R packages.
• R is an open-source programming language.
• Using R one can create any form of statistical analysis and data manipulation.
• It can be used in almost every field of finance, marketing, sports, etc.
• R Programming is extensible and hence, R contributor groups are noted for their energetic contributions.
• A lot of R’s typical features can be written in R Language itself and hence, R has gotten faster over time and serves as a glue language.

What are the Programming Features of R?

• Packages are part of R programming. R Packages are useful in collecting sets of R functions into a single unit.
• R’s programming features include database input, exporting data, viewing data, variable labels, missing data, etc.
• R is an interpreted language, so one can access it through a command line interpreter.
• R supports matrix arithmetic.
• R supports procedural programming with functions and object-oriented programming with generic functions.
• Procedural programming includes procedures, records, modules, and procedure calls while object-oriented programming language includes classes, objects, and functions.

Is R is a Slow Language?

• R programs can be slow, however, well-written R code/programs are usually fast enough.
• In R language, Speed was not the primary design criterion.
• R language is designed to make programming easier.
• Slow programs are often a result of bad programming practices or not understanding how R works.
• There are various options for calling C or C++ functions from R.

Why is R important for data science?

• One can run the R code without any Compiler because R language is an interpreted language. Hence one can run Code without any compiler.
• R interprets the Code and makes the development of code easier.
• Many calculations are done with vectors because R is a vector language, so anyone can add functions to a single Vector without putting it in a loop. Hence, the R language is more powerful and faster than other languages.
• R language is a Language widely used in biology, genetics as well as in Statistics. R is to a turning complete language where any type of task can be performed.

Why is R Good for Business?

• The most important reason why R is good for business is that it is open-source and Free. R language is great for data visualization. As per new research, R has far more capabilities as compared to earlier tools and computing languages.
• For data-driven decisions in businesses, data science talent shortage is a very big problem. Companies are using R programming as their platform and recruit trained users of R.

What are the Statistical and Programming Features of the R Language?

1. Statistical Features

• Basic Statistics: Measures of central tendencies (Mean, variance, median, etc.), measures of dispersion (range, standard deviation, variance), Quartiles, etc.
• Static graphics: Basic plots, graphic maps, scatter plots, line charts, etc.
• Probability distributions: Normal, Poisson, Binomial, t, F, Beta, Gamma, etc.
• Inferential Statistics: Comparison tests (one sample, two samples, ANOVA, etc.), correlation and regression analysis, non-parametric tests, etc.
• Multivariate Analysis: Principal Component Analysis (PCA), Factor Analysis, Canonical Correlation, etc.

2. Programming Features

• Distributed Computing: Distributed computing is an open-source, high-performance platform for the R language. It splits tasks between multiple processing nodes to reduce execution time and analyze large datasets.
• R packages: R packages are a collection of R functions, compiled code, documentation, and sample data. By default, R installs a set of packages during installation.
• R is an interpreted language: R language does not need a compiler to make a program from the code. R directly interprets provided code into lower-level calls and pre-compiled code.
• Compatible Programming Language: Most R language functions are written in R itself, C, C++, or FORTRAN, and can be used for computationally heavy tasks. Java, .NET, Python, C, C++, and FORTRAN can also be used to manipulate objects directly.

https://itfeature.com, https://gmstat.com

R language has a number of facilities for generating commonly used sequences of numbers. There are a number of functions for generating regular sequences in R to perform data analysis tasks:

• Colon Operator (:)
• seq() Function
• rep() Function

Generating Regular Sequences in R Language

Usually, the functions related to generating regular sequences in R are used to create index vectors, vectors of evenly spaced numbers, repeating the patterns, and creating sequences for plotting.

Colon Operator (:)

The colon operator generates a sequence of integers, for example, 1:30 is the vector c(1, 2, …, 29, 30). The colon operator has a high priority within an expression, for example, 2*1:15 is the vector c(2, 4, …, 28, 30).

Let set $n=10$ and then compare the sequences $1:n-1$ and $1:(n-1)$:

n = 10
1:n-1
1:(n-1)

The 30:1 may be used to generate a sequence backward.

30:1
## Output
 [1] 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16 15 14 13 12 11 10  9  8  7  6
[26]  5  4  3  2  1

The seq() Function

The seq() functions offer more flexibility and control over generating sequences. The seq() functions have five arguments, some of which may be specified in any call. The first two arguments of the function specify the beginning and end of the sequence.

Like other R functions, the arguments to seq() can also given in named form, in which case the order in which they appear is irrelevant. The first two arguments of seq() functions may be named from=value and to=value. Therefore seq(1, 30), seq(from = 1, to = 30) and seq(to = 30, from = 1) are all the same as 1:30. The other two arguments may be named by = value and length = value, which specify a step size and a length for the sequence, respectively. By default the by argument is set to 1, that is, by = 1. The examples of seq() functions are

seq(1, 20)
## Output
[1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20

seq(from = 1, to = 20)
## Output 
[1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20

seq(from = 1, to = 20, by = 1)
## OUtput
[1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20

seq(-5, 5, by = 0.2)
##
 [1] -5.0 -4.8 -4.6 -4.4 -4.2 -4.0 -3.8 -3.6 -3.4 -3.2 -3.0 -2.8 -2.6 -2.4 -2.2
[16] -2.0 -1.8 -1.6 -1.4 -1.2 -1.0 -0.8 -0.6 -0.4 -0.2  0.0  0.2  0.4  0.6  0.8
[31]  1.0  1.2  1.4  1.6  1.8  2.0  2.2  2.4  2.6  2.8  3.0  3.2  3.4  3.6  3.8
[46]  4.0  4.2  4.4  4.6  4.8  5.0

seq(length = 51, from = -5, by = 0.2)

Note that if only the first two arguments are given the result is the same as the colon operator. For example, seq(2, 10) results in the same output as 2:10.

The length.out argument may be used to generate a sequence of evenly spaced numbers, for example,

# generate a sequence of evenly spaced numbers between 0 and 1
seq(from = 0, to = 1, length.out = 11) 

## Output
[1] 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

The fifth argument may be named along = vector, which is normally used as the only argument to create the sequence 1,2, …., length(vector) or the empty sequence if the vector is empty. For example

x = rnorm(10)
seq(along = x)

## Output
[1]  1  2  3  4  5  6  7  8  9 10

The rep() Function

The rep function is used for replicating or repeating an object in various complicated ways. The simplest form of the rep() function is

rep(1:5, times = 5)

## Output
[1] 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5

The rep(1:5, times = 5) will put five copies of 1:5 end-to-end. The other useful version of rep() function is

rep(1:5, each = 5)

## Output
[1] 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 4 4 4 4 4 5 5 5 5 5

The rep(1:5, each = 5) repeats each element of 1:5 five times before moving on to the next number.

R Language Quiz

General Knowledge Quizzes

Statistics and Data Analysis

Frequently Asked Questions about Generating Sequences

• Describe R functions that are used to generate regular sequences.
• What is the use of seq() function in R?
• Give some examples of colon operators in R?
• Describe rep() function in R with examples.
• What is the length.out argument in seq() function?
• Write about important arguments of seq() function in R language.
• How one can generate a sequence backward, give an example.