r programming homework help

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R Programming Exercises, Practice Questions and Solutions

R Programming Language is an open-source language mostly used for machine learning, statistics, data visualization, etc. R was developed by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand. R is similar to S programming language which is a GNU project created by John Chambers and his team at Bell Laboratories.

It comes with a command-line interface and provides a vast list of packages for performing tasks. It is an interpreted language that supports both object-oriented and procedural programming and it is available on widely used platforms e.g. Windows , Linux and Mac. You might have seen various R tutorials explaining the concepts and the theoretical part with some examples, but that is not enough to understand this language. You need more practice to make yourself perfect as practice will make you perfect.

This R Programming Exercise article will cover all R programming practice Questions and learn R Language . You can sharpen your R programming Skills using sets of questions from basic to advance, containing a well-explained and detailed solution to each question.

Table of Content Basics – R Programming (14 exercises with solution) Lists – R Programming Data Types – R Programming Strings – R Programming Functions – R Programming Loops – R Programming If Else – R programming Variable – R programming Vector – R Programming Matrix – R Programming DataFrame – R Programming Factor – R Programming Data and Time – R Programming CSV – R Programming Excel – R Programming

List of R Exercises with Solutions :

R programming language – basic exercises with solution.

• Write an R Program for “Hello Geeks”.Write an R Program to Add Two Vectors
• Find the Sum, Mean and Product of the Vector in R Programming
• Create an R Program to Take Input From the User
• How to Generate Random Numbers from Standard Distributions in R
• R Program to Sample from a Population
• Create an R Program to Find the Minimum and Maximum
• R Program to Sort a Vector
• How to Find the Factorial of a Number
• How to create R Multiplication Table
• Write an R Program to Check Prime Number
• R Program to check Armstrong Number
• R Program to Print the Fibonacci Sequence
• R Program to Check for Leap Year
• Check if a Number is Odd or Even in R Programming

R Programming Language – List Exercises with Solution

• Count the Number of List Elements in R
• Create a list with random values in R
• How to add Key Value Pair to List in R?
• Access Index Names of List Using apply Function in R
• Convert matrix to list in R
• Convert the list to a data frame with specific column names in R
• Convert list to array in R

Also, check: More Programs on Lists

R Programming Language – Data Types Exercises with Solution

• R Data Types
• Data Type Conversion in R
• Getting different data types in R Programming – a type of the () Function .

R Programming Language – String Exercises with Solution

• Convert Character String to Variable Name in R
• Count the Number of Characters in the String in R
• Count Number of Occurrences of Certain Character in String in R
• Extract Numbers from the Character String Vector in R
• Count the Number of Words in a String using R
• How to calculate the number of occurrences of a character in each row of the R data frame?
• Write a Program to Concatenate Two Strings in R.
• R Program to Find the Length of a String
• How to Check if Characters are Present in a String in R.
• R Program to Extract n Characters From a String
• How to Replace Characters in a String in R
• Create a Program to Compare Two Strings in R.
• R Program to Convert Factors to Characters
• R Program to Trim Leading and Trailing Whitespaces

Also, check: More Programs on Strings

R Programming Language – Functions Exercises with Solution

• Types of Functions in R Programming
• Function Arguments in R Programming

R Programming Language – Looping Exercises with Solution

• for loop to print the elements of a vector?
• The sum of parts in a vector using a for loop?
• Finding the maximum value in a vector using a for loop?
• Reversing a vector using a for loop?
• Counting the number of even and odd elements in a vector using a for loop?
• while loop to print the elements of a vector?
• while loop to find the first occurrence of a specific element in a vector?
• while loop to calculate the factorial of a number?
• while loop to calculate the square of numbers?
• while loop to reverse a string?
• Looping over Objects in R Programming
• repeat to print the elements of a vector.
• Repeat loop to generate random numbers until a number greater than 0.9 is generated?
• repeat loop to generate a sequence of numbers?
• Nested for loop to print multiplication tables up to a certain number.
• Nested for loop to create a 2D matrix.
• Nested for loop to print a pattern.
• Nested for loop to calculate the transpose of a matrix.

R Programming Language – If … Else Exercises with Solution

• Check if a number is positive or negative using if-else a statement.
• if-else to find the maximum of two numbers.
• Create a programme to assign grades based on a student’s score using if-else .
• Create a programme to categorize numbers into odd or even.
• if-else to check if a number is divisible by another number.
• if-else to categorize ages into different groups.
• if-else to check if a character string contains a specific substring.
• Grade Classification Based on Multiple Conditions.
• Nested if-else for Temperature Classification.
• Quadrant Classification for Coordinates.

R Programming Language – Variable Exercises with Solution

• R Variables
• Scope of The Variables
• How to Create Categorical Variables in R?
• Accessing variables of a data frame in R Programming – attach() and detach() function
• Select variables (columns) in R using Dplyr
• Dummy Variables in R Programming

R Programming Language – Vector Exercises with Solution

• How to create an empty vector in R?
• Create empty vector and append values
• Find the Sum, Mean and Product of a Vector in R
• Find the product of vector elements in R
• Count the number of vector values in the range with R
• Count the specific value in a given vector in R
• Access the last value of a given vector in R
• Find the elements of a vector that are not in another vector in R
• Find the Nth highest value of a vector in R
• How to find Nth smallest value in vector in R?
• Extract every Nth element of a vector in R
• R Program to Concatenate a Vector of Strings
• How to Check if a Vector Contains the Given Element
• Write an R Program to Count the Number of Elements in a Vector
• R Program to Find Index of an Element in a Vector
• Write an R Program to Access Values in a Vector
• R Program to Add Leading Zeros to Vector

Also, check: More Programs on Vectors

R Programming Language – Matrix Exercises with Solution

• How to create an empty matrix in R?
• Fill an empty matrix in R
• Elementwise Matrix Multiplication in R
• Multiply Matrix by Vector in R
• Find the power of a matrix in R
• Raise a matrix to a fractional power in R
• Get the element at the specific position from the matrix in R
• Find the row and column index of maximum and minimum value in a matrix in R
• Select rows of a matrix in R that meet a condition
• Multiply a matrix by its transpose while ignoring missing values in R

Also, check: More Programs on Matrices

R Programming Language – DataFrame Exercises with Solution

• How to Convert a List to a Dataframe
• R Program to Create an Empty Dataframe
• How to Combine Two Dataframe into One
• Create an R Program to Change the Column Name of a Dataframe
• How to Extract Columns From a Dataframe
• R Program to Drop Columns in a Dataframe
• R Program to Reorder Columns in a Dataframe
• How to Split Dataframe
• R Program to Merge Multiple Dataframes
• R Program to Delete Rows From Dataframe
• R Program to Make a List of Dataframes
• How to create a data frame from given vectors in R?
• Create an empty DataFrame with only column names in R
• Insert multiple rows in R DataFrame
• How to add a column to the data frame in R?
• Extract the first N rows from the data frame in R
• How to select the row with the maximum value in each group in R Language?
• Remove rows with NA in one column of the R DataFrame
• How to remove empty rows from the R data frame?
• Find columns and rows with NA in R DataFrame
• Sort DataFrame by column name in R
• How To Merge Two DataFrames in R?
• Append one data frame to the end of another data frame in R
• How to find common rows and columns between two data frames in R?

Also, check: More Programs on DataFrame

R Programming Language – Factor Exercises with Solution

• How to count values per level in a factor in R
• Find the levels of a factor of a given vector in R
• How to change the order of levels of a factor in R?
• How to convert factor levels to list in R?
• Concatenate two given factors in a single factor in R
• Get All Factor Levels of the DataFrame Column in R

Also. check: More Programs on Factors

R Programming Language – Date and Time Exercises with Solution

• How to Add and Subtract Days to and from Date in R?
• How to subtract time in R?
• How to Extract time from the timestamp in R?
• How to calculate the number of days between two dates in R?
• How to calculate Years between Dates in R?
• How to convert a factor into a date format?

Also, check: More Programs on Date and Time

R Programming Language – File Handling Exercises with Solution

• How to check if a file already exists in R?
• R – Check if a Directory Exists and Create if It does not
• Add New Line to Text File in R
• How To Import Data from a File in R Programming
• How to export dataframe to RDATA file in R ?

Also, check: More Programs on File Handling

R Programming Language – CSV Exercises with Solution

• Reading the CSV file into Dataframes in R
• Export CSV File without Row Names in R
• How to write to CSV in R without index?
• Append row to CSV using R
• How to calculate the mean of a CSV file in R?

Also, check: More Programs on CSV

R Programming Language – Excel Exercises with Solution

• How to import an Excel File into R?
• How to export a DataFrame to Excel File in R?
• Convert an Excel column into a list of vectors in R
• How to convert an Excel column to a vector in R?
• How to convert Excel content into DataFrame in R?
• Delete rows with empty cells from Excel using R

Also, check: More Programs on Excel

R Programming Language – Data Visualization Exercises with Solution

• Adding Colors to Charts in R Programming
• How to show legend in heatmap in R?
• Display All X-Axis Labels of Barplot in R
• How to Create a Stacked Dot Plot in R?
• Change Spacing of Axis Tick Marks in Base R Plot
• Add legends without borders and with white backgrounds in R
• Plot Shaded Area between vertical lines in R
• How to add the Mean and Median to Histogram in R?
• Create a Scatter plot from CSV in R
• Customizing Colors in Plots .
• Adding Legends to Plots
• Creating Interactive Plots using Shiny
• Annotating Text and Labels in Plots
• Formatting Axis Labels and Ticks in Plots
• Working with Multiple Plots (Faceting)
• Plotting Time Series Data in R
• Visualizing Geographic Data with Maps
• Creating Animated Plots in R
• Creating 3D Plots in R
• Working with Plotly for Interactive Visualizations
• Creating Trellis (Lattice) Plots in R
• Plotting Large Datasets with ggplot2’s geom_point() and geom_bin2d()
• Visualizing Hierarchical Data with Dendrograms
• Creating Sunburst Charts for Hierarchical Data
• Working with Word Clouds in R
• Network Visualization in R using graph
• Creating Heatmaps with Hierarchical Clustering
• Plotting Multiple Data Series in a Single Plot
• Interactive Data Visualization with Plotly Express

In Conclusion, R programming exercises are a complete guide for practising R programming Exercise Questions. After theoritical reading, The best way to master anything is by practice and exercise questions. Here you have the opportunity to practice the R programming language concepts by solving the exercises starting from basic to more complex exercises. A sample solution is provided for each exercise. It is recommended to do these exercises by yourself first before checking the solution. we hope, these exercises help you to improve your R programming coding skills. At present, the following sections are accessible, and we are diligently striving to incorporate additional exercises. Keep coding with enthusiasm!

R Programming Exercises – FAQs

1. what is the r programming language used for.

R is a language for statistical computing and graphics, commonly used in data analysis and visualization.

2. Is R free to use?

Yes, R is open-source and freely available for anyone to use, modify, and distribute.

3. Can I create plots and charts in R?

Yes, R has powerful libraries like ggplot2 that allow you to create a wide variety of visualizations easily.

4. What are packages in R?

Packages are collections of R functions and data, designed to extend the capabilities of R and make specific tasks easier.

5. How can I install packages in R?

You can install packages using the install.packages(“package_name”) command in the R console.

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R programming existed as one of the oldest programming languages in the world. With the sudden boom in data science, it has gained immense popularity and students want to understand the subject thoroughly. But R is not an easy language and students need a lot of time to get command over this language. That is the reason students are always on the lookout for expert R help online.

If you are worried about completing your R assignment or homework, you can connect with us at FavTutor. We have a team of experts who are professionals in R programming and have years of experience in working on any problem related to R. Since our inception, we have been helping hundreds of students so you could be the next one to get our R programming homework help.

R is a programming language and software package setting for applied mathematics analysis, graphics illustration, and coverage. The core of R is an interpreted computer language that permits branching and iteration like programming exploitation functions. R permits integration with the procedures written within the C, C++, .Net, Python, or algebraic language. R is a well-developed, easy and effective programming language with good data handling and storage facility It also provides a collection of operators for calculations on arrays, lists, vectors, and matrices.

R is not only used in academics, but many large companies like Google, Uber, Airbnb, Facebook use an R programming language in their products. The most frequent use of R programming is data analysis. Data analysis with R is done in a step-by-step manner like programming, transforming, discovering, modeling, and communicating with the result. Apart from data analysis, R programming is also used for statistical inference and machine learning algorithms.

Being a complex programming language, students always face challenges while working with the R language. It is very tough to learn and understand complicated statistical tools and techniques used by R as a coding language. Let us go through some of the key topics in R where students find a hard time working with.

Key Topics in R

Let us understand some of the key topics of R programming language below:

• Function: A function could be a set of statements organized along to perform a particular task. R contains a sizable amount of in-built functions and also the user will produce their own functions.
• Matrices: Matrices are the R objects during which the element is organized during a two-dimensional rectangular layout. They contain parts of equivalent data types. Although we will produce a matrix containing solely characters or solely logical values, they're not of a lot of use.
• Vectors and Lists: Vectors in R are homogeneous data structures that contain elements of the same data type mostly integer, character, numeric or logical. Lists are heterogeneous data structures containing elements of mixed data types.
• Data Frames: A Data frame could be a table or a two-dimensional array-like structure during which every column contains values of 1 variable row contains one set of values from each column.
• Factors: Factors in R are data structures that signify levels and are the most appropriate for categorical variables
• R packages: Packages in R are basically libraries containing a set of library functions. For example: dplyr is an R package that contains library functions, mutate(), select(), filter(), summarise() and arrange(). In a nutshell, dplyr is the data manipulation package of R. Some other common packages of R include stats, superml, tree, MASS, ggplot2, etc.

ML Algorithms in R Programming

Below are the 6 common machine learning algorithms which are applied using R programming language-

• Linear Regression: lm() under the stats package is used for training a Linear Regression Model on Training Data in R. It models a linear relationship between features, X, and continuous target y.
• Logistic Regression: glm() under the stats package is used for training a Logistic Regression Model in R. It models a linear decision boundary for the classification of data points.
• Naive Bayes: naive_bayes() under the naive Bayes package is used for training a Naive Bayes Model in R. It is a classification algorithm that is based on Prior and Posterior Probabilities.
• SVM: SVM() under the e1071 package is for training a Support Vector Machine Model in R. Both regression and classification can be performed along with density estimation.
• Decision Tree: tree() under the tree package is for training a Decision Tree in R. Like SVM, both regression and classification can be performed by binary recursive partitioning.
• KNN: kNN() under the DMwR package is for training a k-nearest neighbor model in R. It also performs Data Normalization before training the model with Training Data.
• Clustering: kmeans() under the stats package performs k-Means Clustering on the given data matrix in R. It is an Unsupervised ML Algorithm that is used for segmentation and data-grouping on unlabelled data.

Which R Programming Concepts are Most Difficult for Students?

The following are some subtopics with which students usually face problems, and our R programming experts can help you with them:

• Complex Coding Structures: R programming involves intricate coding structures that can be intimidating for newcomers. Understanding the unique syntax and structure of R can be a significant challenge, especially for those new to programming.
• Statistical Complexity:  R is widely used for statistical analysis, which means students must grapple with complex statistical concepts. Learning to apply these concepts effectively within the R environment can be demanding.
• Data Manipulation Hurdles: While R is known for its data manipulation capabilities, students may struggle to harness this power. Handling and transforming data, especially with large datasets, can be perplexing for beginners.
• Package Puzzles: R relies on packages and libraries to extend its functionality. Selecting the right packages and understanding how to install and utilize them can be a source of confusion for students.
• Debugging Dilemmas: Just like any programming language, R can produce errors and bugs in code. Troubleshooting and deciphering error messages can pose challenges for students.
• Time-Consuming Tasks: R assignments, particularly those involving extensive data analysis or intricate statistical models, can be time-consuming. Balancing these assignments with other coursework can be demanding.
• Limited Practical Experience:  While students might grasp the theoretical aspects of R, applying it to real-world problems can be challenging. Practical experience is crucial for bridging this gap.
• Scarcity of Helpful Resources:  Finding accessible and informative documentation and support resources for R can be a struggle. Students often need reliable sources to clarify doubts and enhance their learning.
• Visualization Vexation: R excels in data visualization, but crafting meaningful and visually appealing plots and graphs can be a daunting task for students.
• Keeping Up with Updates: R is a dynamic language with frequent updates and new packages. Staying current with the latest features and best practices can be a demanding task for students.

Advantages and Features of R programming language

R is one of the most used programming languages for statistical and analysis purposes. But just like every other programming language, R has its own set of advantages. Check out some of this advantages in detail below:

• R language is very flexible and hence it is prominently used in the field of data science and statistics. Apart from this, it is also used in the field of biology and genetics to draw out predictions and analyses.
• As R language is a vector programming language, it is easy to add functions to a single vector without putting it in a loop.
• It is the best option for business as it is one of the affordable programming languages. It provides amazing visualizations and graphics.
• It enables us to perform virtual statistical computation in a short amount of time and provide error-free content.
• R programming language provides the best and most effective data handling and storage facilities.
• It allows the user to perform multiple calculations at the same time using a single command.
• R programming language can work on multiple operating systems like Mac, Windows, Linux, etc.
• It supports the data frames, arrays, and various other data types at the same time and it is also easily compatible with other programming languages.

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The help() function and ? help operator in R provide access to the documentation pages for R functions, data sets, and other objects, both for packages in the standard R distribution and for contributed packages. To access documentation for the standard lm (linear model) function, for example, enter the command help(lm) or help("lm") , or ?lm or ?"lm" (i.e., the quotes are optional).

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Many packages include vignettes , which are discursive documents meant to illustrate and explain facilities in the package. You can discover vignettes by accessing the help page for a package, or via the browseVignettes() function: the command browseVignettes() opens a list of vignettes from all of your installed packages in your browser, while browseVignettes(package=package-name) (e.g., browseVignettes(package="survival") ) shows the vignettes, if any, for a particular package. vignette() is employed similarly, but displays a list of vignettes in text form.

You can also use the vignette("vignette-name") command to view a vignette (possibly specifying the name of the package in which the vignette resides, if the vignette name is not unique): for example, vignette("timedep") or vignette("timedep", package="survival") (which are, in this case, equivalent).

Vignettes may also be accessed from the CRAN page for the package (e.g.  survival ), if you wish to review the vignette for a package prior to installing and/or using it.

Packages may also include extended code demonstrations (“demos”). The command demo() lists all demos for all packages in your library, while demo(package="package-name") (e.g., demo(package="stats") ) lists demos in a particular package. To run a demo, call the demo() function with the quoted name of the demo (e.g., demo("nlm") ), specifying the name of the package if the name of the demo isn’t unique (e.g., demo("nlm", package="stats") , where, in this case, the package name need not be given explicitly).

Searching for Help Within R

The help() function and ? operator are useful only if you already know the name of the function that you wish to use. There are also facilities in the standard R distribution for discovering functions and other objects. The following functions cast a progressively wider net. Use the help system to obtain complete documentation for these functions: for example, ?apropos .

The apropos() function searches for objects, including functions, directly accessible in the current R session that have names that include a specified character string. This may be a literal string or a regular expression to be used for pattern-matching (see ?"regular expression" ). By default, string matching by apropos() is case-insensitive. For example, apropos("^glm") returns the names of all accessible objects that start with the (case-insensitive) characters "glm" .

help.search() and ??

The help.search() function scans the documentation for packages installed in your library. The (first) argument to help.search() is a character string or regular expression. For example, help.search("^glm") searches for help pages, vignettes, and code demos that have help “aliases,” “concepts,” or titles that begin (case-insensitively) with the characters "glm" . The ?? operator is a synonym for help.search() : for example, ??"^glm" .

RSiteSearch()

RSiteSearch() uses an internet search engine (also see below ) to search for information in function help pages and vignettes for all CRAN packages, and in CRAN task views (described below ). Unlike the apropos() and help.search() functions, RSiteSearch() requires an active internet connection and doesn’t employ regular expressions. Braces may be used to specify multi-word terms; otherwise matches for individual words are included. For example, RSiteSearch("{generalized linear model}") returns information about R functions, vignettes, and CRAN task views related to the term "generalized linear model" without matching the individual words "generalized" , "linear" , or "model" .

findfn() and ??? in the sos package, which is not part of the standard R distribution but is available on CRAN, provide an alternative interface to RSiteSearch() .

help.start()

help.start() starts and displays a hypertext based version of R’s online documentation in your default browser that provides links to locally installed versions of the R manuals, a listing of your currently installed packages and other documentation resources.

R Help on the Internet

There are internet search sites that are specialized for R searches, including search.r-project.org (which is the site used by RSiteSearch ) and Rseek.org .

It is also possible to use a general search site like Google , by qualifying the search with “R” or the name of an R package (or both). It can be particularly helpful to paste an error message into a search engine to find out whether others have solved a problem that you encountered.

CRAN Task Views

CRAN Task Views are documents that summarize R resources on CRAN in particular areas of application, helping your to navigate the maze of thousands of CRAN packages. A list of available Task Views may be found on CRAN.

R FAQs (Frequently Asked Questions)

There are three primary FAQ listings which are periodically updated to reflect very commonly asked questions by R users. There is a Main R FAQ , a Windows specific R FAQ and a Mac OS (OS X) specific R FAQ .

Asking for Help

If you find that you can’t answer a question or solve a problem yourself, you can ask others for help, either locally (if you know someone who is knowledgeable about R) or on the internet. In order to ask a question effectively, it helps to phrase the question clearly, and, if you’re trying to solve a problem, to include a small, self-contained, reproducible example of the problem that others can execute. For information on how to ask questions, see, e.g., the R mailing list posting guide , and the document about how to create reproducible examples for R on Stack Overflow.

Stack Overflow

Stack Overflow is a well organized and formatted site for help and discussions about programming. It has excellent searchability. Topics are tagged, and “r” is a very popular tag on the site with almost 150,000 questions (as of summer 2016). To go directly to R-related topics, visit http://stackoverflow.com/questions/tagged/r . For an example both of the value of the site’s organization and information that is very useful to R users, see “How to make a great R reproducible example?” , which is also mentioned above.

R Email Lists

The R Project maintains a number of subscription-based email lists for posing and answering questions about R, including the general R-help email list, the R-devel list for R code development, and R-package-devel list for developers of CRAN packages; lists for announcements about R and R packages ; and a variety of more specialized lists. Before posing a question on one of these lists, please read the R mailing list instructions and the posting guide .