r programming assignments

Data Visualization
Statistics in R
Machine Learning in R
Data Science in R
Packages in R
Solve Coding Problems
Explicit Coercion in R Programming
Convert string from lowercase to uppercase in R programming - toupper() function
Calculate arc tangent of a value in R programming - atan2(y, x) function
Replicate elements of vector in R programming - rep() Method
Adding elements in a vector in R programming - append() method
Slot machine project using R programming
How to find SubString in R programming?
Finding the length of string in R programming - nchar() method
Convert String from Uppercase to Lowercase in R programming - tolower() method
Poisson Functions in R Programming
Concatenate Two Strings in R programming - paste() method
Calculate the absolute value in R programming - abs() method
Splitting Strings in R programming - strsplit() method
Difference Between & and && in R
Bootstrapping in R Programming
Rounding off values in R Language - round() Function
Calculate arc cosine of a value in R programming - acos() function
Performing F-Test in R programming - var.test() Method
Multiline Plot using Plotly in R

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.

R Programming Exercises, Practice Questions and Solutions

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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Improve your Coding Skills with Practice

What kind of Experience do you want to share?

This course is not yet live; some links might not yet work. Coming July 1, 2024.

An introduction to programming using a language called R, a popular language for statistical computing and graphics in data science and other domains. Learn to use RStudio, a popular integrated development environment (IDE). Learn to represent real-world data with vectors, matrices, arrays, lists, and data frames. Filter data with conditions, via which you can analyze subsets of data. Apply functions and loops, via which you can manipulate and summarize data sets. Write functions to modularize code and raise exceptions when something goes wrong. Tidy data with R’s tidyverse and create colorful visualizations with R’s grammar of graphics. By course’s end, learn to package, test, and share R code for others to use. Assignments inspired by real-world data sets.

Whereas CS50x itself focuses on computer science more generally as well as programming with C, Python, SQL, and JavaScript, this course, aka CS50R, is entirely focused on R. You can take CS50R before CS50x, during CS50x, or after CS50x. But for an introduction to computer science itself, you should still take CS50x!

How to Take this Course

Even if you are not a student at Harvard, you are welcome to “take” this course for free via this OpenCourseWare by working your way through the course’s seven weeks of material. If you’d like to submit the course’s problem sets and final project for feedback, be sure to create an edX account , if you haven’t already. Ask questions along the way via any of the course’s communities !

If interested in a verified certificate from edX , enroll at cs50.edx.org/r instead.
If interested in transfer credit and accreditation from Harvard Extension School , register at web.dce.harvard.edu/extension/csci/e/5a instead.
If interested in transfer credit and accreditation from Harvard Summer School , register at web.dce.harvard.edu/summer/csci/s/5a instead.

How to Teach this Course

If you are a teacher, you are welcome to adopt or adapt these materials for your own course, per the license .

Lab 1: Introduction to R

Professor noli brazil, january 11, 2023.

You will be using R to complete all data analysis tasks in this class. For those who have never used R or it has been a long time since you’ve used the program, welcome to what will be a life fulfilling journey !

The objectives of the guide are as follows

Install and set up R and RStudio.
Understand R data types
Understand R data structures
Understand R functions
Introduction to tidyverse and its suite of data wrangling functions
Understand R Markdown

This lab guide follows closely and supplements the material presented in Chapters 2, 4, 5, 7 and 21 in the textbook R for Data Science (RDS).

R is a free, open source statistical programming language. It is useful for data cleaning, analysis, and visualization. R is an interpreted language, not a compiled one. This means that you type something into R and it does what you tell it. It is both a command line software and a programming environment. It is an extensible, open-source language and computing environment for Windows, Macintosh, UNIX, and Linux platforms, which allows for the user to freely distribute, study, change, and improve the software. It is basically a free, super big, and complex calculator. You will be using R to accomplish all data analysis tasks in this class. You might be wondering “Why in the world do we need to know how to use a statistical software program?” Here are the main reasons:

You will be learning about abstract concepts in lecture and the readings. Applying these concepts using real data is an important form of learning. A statistical software program is the most efficient (and in many cases the only) means of running data analyses, not just in the cloistered setting of a university classroom, but especially in the real world. Applied data analysis will be the way we bridge statistical theory to the “real world.” And R is the vehicle for accomplishing this.

In order to do applied data analysis outside of the classroom, you need to know how to use a statistical program. There is no way around it as we don’t live in an exclusively pen and paper world. If you want to collect data on soil health, you need a program to store and analyze that data. If you want to collect data on the characteristics of recent migrants, you need a program to store and analyze that data.

The next question you may have is “I love Excel [or insert your favorite program]. Why can’t I use that and forget your stupid R?” Here are some reasons

it is free. Most programs are not;
it is open source. Which means the software is community supported. This allows you to get help not from some big corporation (e.g. Microsoft with Excel), but people all around the world who are using R. And R has a lot of users, which means that if you have a problem, and you pose it to the user community, someone will help you;
it is powerful and extensible (meaning that procedures for analyzing data that don’t currently exist can be readily developed);
it has the capability for mapping data, an asset not generally available in other statistical software;
If it isn’t already there, R is becoming the de-facto data analysis tool in the social sciences

R is different from Excel in that it is generally not a point-and-click program. You will be primarily writing code to clean and analyze data. What does writing or sourcing code mean? A basic example will help clarify. Let’s say you are given a dataset with 10 rows representing people living in Davis, CA. You have a variable in the dataset representing individual income. Let’s say this variable is named inc . To get the mean income of the 10 people in your dataset, you would write code that would look something like this

The command tells the program to get the mean of the variable inc . If you wanted the sum, you write the command sum(inc) .

Now, where do you write this command? You write it in a script. A script is basically a text file. Think of writing code as something similar to writing an essay in a word document. Instead of sentences to produce an essay, in a programming script you are writing code to run a data analysis. We’ll go through scripting in more detail later in this lab, but the basic process of sourcing code to run a data analysis task is as follows.

Write code . First, you open your script file, and write code or various commands (like mean(inc) ) that will execute data analysis tasks in this file.
Send code to the software program . You then send some or all of your commands to the statistical software program (R in our case).
Program produces results based on code . The program then reads in your commands from the file and executes them, spitting out results in its console screen.

I am skipping over many details, most of which are dependent on the type of statistical software program you are using, but the above steps outline the general work flow. You might now be thinking that you’re perfectly happy pointing and clicking your mouse in Excel (or wherever) to do your data analysis tasks. So, why should you adopt the statistical programming approach to conducting a data analysis?

Your script documents the decisions you made during the data analysis process. This is beneficial for many reasons.
It allows you to recreate your steps if you need to rerun your analysis many weeks, months or even years in the future.
It allows you to share your steps with other people. If someone asks you what were the decisions made in the data analysis process, just hand them the script.
Related to the above points, a script promotes transparency (here is what i did) and reproducibility (you can do it too). When you write code, you are forced to explicitly state the steps you took to do your research. When you do research by clicking through drop-down menus, your steps are lost, or at least documenting them requires considerable extra effort.
If you make a mistake in a data analysis step, you can go back, change a few lines of code, and poof, you’ve fixed your problem.
It is more efficient. In particular, cleaning data can encompass a lot of tedious work that can be streamlined using statistical programming.

Hopefully, I’ve convinced you that statistical programming and R are worthwhile to learn.

R can be downloaded from one of the “CRAN” (Comprehensive R Archive Network) sites. In the US, the main site is at http://cran.us.r-project.org/ . Look in the “Download and Install R” area. Click on the appropriate link based on your operating system.

If you already have R on your computer, make sure you have the most updated version of R on your personal computer (4.2.2 “Innocent and Trusting”).

On the “R for Mac OS X” page, there are multiple packages that could be downloaded. If you are running High Sierra or higher, click on R-4.2.2.pkg; if you are running an earlier version of OS X, download the appropriate version listed under “Binary for legacy OS X systems.”

After the package finishes downloading, locate the installer on your hard drive, double-click on the installer package, and after a few screens, select a destination for the installation of the R framework (the program) and the R.app GUI. Note that you will have to supply the Administrator’s password. Close the window when the installation is done.

An application will appear in the Applications folder: R.app.

Browse to the XQuartz download page . Click on the most recent version of XQuartz to download the application.

Run the XQuartz installer. XQuartz is needed to create windows to display many types of R graphics: this used to be included in MacOS until version 10.8 but now must be downloaded separately.

On the “R for Windows” page, click on the “base” link, which should take you to the “R-4.2.2 for Windows (32/64 bit)” page

On this page, click “Download R 4.2.2 for Windows”, and save the exe file to your hard disk when prompted. Saving to the desktop is fine.

To begin the installation, double-click on the downloaded file. Don’t be alarmed if you get unknown publisher type warnings. Window’s User Account Control will also worry about an unidentified program wanting access to your computer. Click on “Run”.

Select the proposed options in each part of the install dialog. When the “Select Components” screen appears, just accept the standard choices

Note: Depending on the age of your computer and version of Windows, you may be running either a “32-bit” or “64-bit” version of the Windows operating system. If you have the 64-bit version (most likely), R will install the appropriate version (R x64 3.5.2) and will also (for backwards compatibility) install the 32-bit version (R i386 3.5.2). You can run either, but you will probably just want to run the 64-bit version.

What is RStudio?

If you click on the R program you just downloaded, you will find a very basic user interface. For example, below is what I get on a Mac

We will not use R’s direct interface to run analyses. Instead, we will use the program RStudio. RStudio gives you a true integrated development environment (IDE), where you can write code in a window, see results in other windows, see locations of files, see objects you’ve created, and so on. To clarify which is which: R is the name of the programming language itself and RStudio is a convenient interface.

Getting RStudio

To download and install RStudio, follow the directions below

Navigate to RStudio’s download site
Click on the appropriate link based on your OS (Windows, Mac, Linux and many others). Do not download anything from the “Zip/Tarballs” section.
Click on the installer that you downloaded. Follow the installation wizard’s directions, making sure to keep all defaults intact. After installation, RStudio should pop up in your Applications or Programs folder/menu.

The RStudio Interface

Open up RStudio. You should see the interface shown in the figure below which has three windows.

The RStudio Interface.

Console (left) - The way R works is you write a line of code to execute some kind of task on a data object. The R Console allows you to run code interactively. The screen prompt > is an invitation from R to enter its world. This is where you type code in, press enter to execute the code, and see the results.

History - shows a list of executed commands in the current session.
Connections - you can connect to a variety of data sources, and explore the objects and data inside the connection. I typically don’t use this window, but you can .
Files - shows all the files and folders in your current working directory (more on what this means later).
Plots - shows any charts, graphs, maps and plots you’ve successfully executed.
Packages - tells you all the R packages that you have access to (more on this later).
Help - shows help documentation for R commands that you’ve called up.
Viewer - allows you to view local web content (won’t be using this much).

There is actually fourth window. But, we’ll get to this window a little later (if you read the assignment guidelines you already know what this fourth window is).

Setting RStudio Defaults

While not required, I strongly suggest that you change preferences in RStudio to never save the workspace so you always open with a clean environment. See Ch. 8.1 of R4DS for some more background

From the Tools menu on RStudio, open the Tools menu and then select Global Options.
If not already highlighted, click on the General button from the left panel.
Uncheck the following Restore boxes
Restore most recently opened project at startup
Restore previously open source documents at startup
Restore .RData into workspace at startup
Set Save Workspace to .RData on exit to Never
Click OK at the bottom to save the changes and close the preferences window. You may need to restart RStudio.

The reason for making these changes is that it is preferable for reproducibility to start each R session with a clean environment. You can restore a previous environment either by rerunning code or by manually loading a previously saved session.

The R Studio environment is modified when you execute code from files or from the console. If you always start fresh, you do not need to be concerned about things not working because of something you typed in the console, but did not save in a file.

You only need to set these preferences once.

R Data Types

Let’s now explore what R can do. R is really just a big fancy calculator. For example, type in the following mathematical expression in the R console (left window)

Note that spacing does not matter: 1+1 will generate the same answer as 1 + 1 . Can you say hello to the world?

Nope. What is the problem here? We need to put quotes around it.

“hello world” is a character and R recognizes characters only if there are quotes around it. This brings us to the topic of basic data types in R. There are four basic data types in R: character, logical, numeric, and factors (there are two others - complex and raw - but we won’t cover them because they are rarely used in practice).

Characters are used to represent words or letters in R. We saw this above with “hello world”. Character values are also known as strings. You might think that the value "1" is a number. Well, with quotes around, it isn’t! Anything with quotes will be interpreted as a character. No ifs, ands or buts about it.

A logical takes on two values: FALSE or TRUE. Logicals are usually constructed with comparison operators, which we’ll go through more carefully in Lab 2. Think of a logical as the answer to a question like “Is this value greater than (lower than/equal to) this other value?” The answer will be either TRUE or FALSE. TRUE and FALSE are logical values in R. For example, typing in the following

Gives you a true. What about the following?

Numerics are separated into two types: integer and double. The distinction between integers and doubles is usually not important. R treats numerics as doubles by default because it is a less restrictive data type. You can do any mathematical operation on numeric values. We added one and one above. We can also multiply using the * operator.

And even take the logarithm!

Uh oh. What is -Inf ? Well, you can’t take the logarithm of 0, so R is telling you that you’re getting a non numeric value in return. The value -Inf is another type of value type that you can get in R.

Think of a factor as a categorical variable. It is sort of like a character, but not really. It is actually a numeric code with character-valued levels. Think of a character as a true string and a factor as a set of categories represented as characters. We won’t use factors too much in this course.

R Data Structures

You just learned that R has four basic data types. Now, let’s go through how we can store data in R. That is, you type in the character “hello world” or the number 3, and you want to store these values. You do this by using R’s various data structures.

A vector is the most common and basic R data structure and is pretty much the workhorse of the language. A vector is simply a sequence of values which can be of any data type but all of the same type. There are a number of ways to create a vector depending on the data type, but the most common is to insert the data you want to save in a vector into the command c() . For example, to represent the values 4, 16 and 9 in a vector type in

You can also have a vector of character values

The above code does not actually “save” the values 4, 16, and 9 - it just presents it on the screen in a vector. If you want to use these values again without having to type out c(4, 16, 9) , you can save it in a data object . At the heart of almost everything you will do (or ever likely to do) in R is the concept that everything in R is an object. These objects can be almost anything, from a single number or character string (like a word) to highly complex structures like the output of a plot, a map, a summary of your statistical analysis or a set of R commands that perform a specific task.

You assign data to an object using the arrow sign <- . This will create an object in R’s memory that can be called back into the command window at any time. For example, you can save “hello world” to a vector called b by typing in

You can pronounce the above as “b becomes ‘hello world’”.

Note that R is case sensitive, if you type in B instead of b , you will get an error.

Similarly, you can save the numbers 4, 16 and 9 into a vector called v1

You should see the objects b and v1 pop up in the Environment tab on the top right window of your RStudio interface.

Environment window

Note that the name v1 is nothing special here. You could have named the object x or crd230 or your pet’s name (mine was charlie ). You can’t, however, name objects using special characters (e.g. !, @, $) or only numbers (although you can combine numbers and letters, but a number cannot be at the beginning e.g. 2d2 ). For example, you’ll get an error if you save the vector c(4,16,9) to an object with the following names

Also note that to distinguish a character value from a variable name, it needs to be quoted. “v1” is a character value whereas v1 is a variable. One of the most common mistakes for beginners is to forget the quotes.

The error occurs because R tries to print the value of object brazil , but there is no such variable. So remember that any time you get the error message object 'something' not found , the most likely reason is that you forgot to quote a character value. If not, it probably means that you have misspelled, or not yet created, the object that you are referring to. I’ve included the common pitfalls and R tips in this class resource .

Every vector has two key properties: type and length . The type property indicates the data type that the vector is holding. Use the command typeof() to determine the type

Note that a vector cannot hold values of different types. If different data types exist, R will coerce the values into the highest type based on its internal hierarchy: logical < integer < double < character. Type in test <- c("r", 6, TRUE) in your R console. What is the vector type of test ?

The command length() determines the number of data values that the vector is storing

You can also directly determine if a vector is of a specific data type by using the command is.X() where you replace X with the data type. For example, to find out if v1 is numeric, type in

There is also is.logical() , is.character() , and is.factor() . You can also coerce a vector of one data type to another. For example, save the value “1” and “2” (both in quotes) into a vector named x1

To convert x1 into a numeric, use the command as.numeric()

There is also as.logical() , as.character() , and as.factor() .

An important practice you should adopt early is to keep only necessary objects in your current R Environment. For example, we will not be using x2 any longer in this guide. To remove this object from R forever, use the command rm()

The data frame object x2 should have disappeared from the Environment tab. Bye bye!

Also note that when you close down R Studio, the objects you created above will disappear for good. Unless you save them onto your hard drive (we’ll touch on saving data in Lab 2), all data objects you create in your current R session will go bye bye when you exit the program.

Data Frames

We learned that data values can be stored in data structures known as vectors. The next step is to learn how to store vectors into an even higher level data structure. The data frame can do this. Data frames store vectors of the same length. Create a vector called v2 storing the values 5, 12, and 25

We can create a data frame using the command data.frame() storing the vectors v1 and v2 as columns

Store this data frame in an object called df1

to bring the worksheet up. You can’t edit this worksheet directly, but it allows you to see the values that a higher level R data object contains.

We can store different types of vectors in a data frame. For example, we can store one character vector and one numeric vector in a single data frame.

For higher level data structures like a data frame, use the function class() to figure out what kind of object you’re working with.

We can’t use length() on a data frame because it has more than one vector. Instead, it has dimensions - the number of rows and columns. You can find the number of rows and columns that a data frame has by using the command dim()

Here, the data frame df1 has 3 rows and 2 columns. Data frames also have column names, which are characters.

In this case, the data frame used the vector names for the column names.

We can extract columns from data frames by referring to their names using the $ sign.

We can also extra data from data frames using brackets [ , ]

The value before the comma indicates the row, which you leave empty if you are not selecting by row, which we did above. The value after the comma indicates the column, which you leave empty if you are not selecting by column. The above line of code selected the first column. Let’s select the 2nd row.

What is the value in the 2nd row and 1st column?

Let’s take a step back and talk about functions (also known as commands). An R function is a packaged recipe that converts one or more inputs (called arguments) into a single output. You execute all of your tasks in R using functions. We have already used a couple of functions above including typeof() and colnames() . Every function in R will have the following basic format

functionName(arg1 = val1, arg2 = val2, ...)

In R, you type in the function’s name and set a number of options or parameters within parentheses that are separated by commas. Some options need to be set by the user - i.e. the function will spit out an error because a required option is blank - whereas others can be set but are not required because there is a default value established.

Let’s use the function seq() which makes regular sequences of numbers. You can find out what the options are for a function by calling up its help documentation by typing ? and the function name

The help documentation should have popped up in the bottom right window of your RStudio interface. The documentation should also provide some examples of the function at the bottom of the page. Type the arguments from = 1, to = 10 inside the parentheses

You should get the same result if you type in

The code above demonstrates something about how R resolves function arguments. When you use a function, you can always specify all the arguments in arg = value form. But if you do not, R attempts to resolve by position. So in the code above, it is assumed that we want a sequence from = 1 that goes to = 10 because we typed 1 before 10. Type in 10 before 1 and see what happens. Since we didn’t specify step size, the default value of by in the function definition is used, which ends up being 1 in this case.

Each argument requires a certain type of data type. For example, you’ll get an error when you use character values in seq()

Functions do not exist in a vacuum, but exist within R packages . Packages are the fundamental units of reproducible R code. They include reusable R functions, the documentation that describes how to use them, and sample data. At the top left of a function’s help documentation, you’ll find in curly brackets the R package that the function is housed in. For example, type in your console ? seq . At the top right of the help documentation, you’ll find that seq() is in the package base . All the functions we have used so far are part of packages that have been pre-installed and pre-loaded into R.

In order to use functions in a new package, you first need to install the package using the install.packages() command. For example, we will be using commands from the package tidyverse in this lab. If you are working on a campus lab computer, you will likely not need to install this package.

You should see a bunch of gobbledygook roll through your console screen. Don’t worry, that’s just R downloading all of the other packages and applications that tidyverse relies on. These are known as dependencies . Unless you get a message in red that indicates there is an error (like we saw when we typed in “hello world” without quotes), you should be fine.

Next, you will need to load packages in your working environment (every time you start RStudio). We do this with the library() function. Notice there are no quotes around tidyverse this time.

The Packages window at the lower-right of your RStudio shows you all the packages you currently have installed. If you don’t have a package listed in this window, you’ll need to use the install.packages() function to install it. If the package is checked, that means it is loaded into your current R session

For example, here is a section of my Packages window

The only packages loaded into my current session is methods , a package that is loaded every time you open an R session. Let’s say I use install.packages() to install the package matrixStats . The window now looks like

When you load it in using library() , a check mark appears next to matrixStats

To uninstall a package, use the function remove.packages() .

Note that you only need to install packages once ( install.pacakges() ), but you need to load them each time you relaunch RStudio ( library() ) . Repeat after me: Install once, library every time. If you need to reinstall R or update to a new version of R, you will need to reinstall all packages. And as noted earlier, R has several packages already preloaded into your working environment. These are known as base packages and a list of their functions can be found here .

In most labs, we will be using commands from the tidyverse package. Tidyverse is a collection of high-powered, consistent, and easy-to-use packages developed by a number of thoughtful and talented R developers.The consistency of the tidyverse , together with the goal of increasing productivity, mean that the syntax of tidy functions is typically straightforward to learn. You can read more about tidyverse principles in Chapter 9, pages 147-151 in RDS.

Excited about entering the tidyverse ? I bet you are, so here is a badge to show your excitement!

Although the tidyverse works with all data objects, its fundamental object type is the tibble. Tibbles are data frames, but they tweak some older behaviors to make life a little easier. There are two main differences in the usage of a data frame vs a tibble: printing and subsetting. Let’s be clear here - tibbles are just a special kind of data frame. They just makes things a little “tidier.” Let’s bring in some data to illustrate the differences and similarities between data frames and tibbles. Install the package nycflights13

Make sure you also load the package.

There is a dataset called flights included in this package. It includes information on all 336,776 flights that departed from New York City in 2013. Let’s save this file in the local R environment

This dataset is a tibble. Let’s also save it as a regular data frame by using the as.data.frame() function

The first difference between data frames and tibbles is how the dataset looks. Tibbles have a refined print method that shows only the first 10 rows, and only the columns that fit on the screen. In addition, each column reports its name and type.

Tibbles are designed so that you don’t overwhelm your console when you print large data frames. Compare the print output above to what you get with a data frame

Ugly, right? You can bring up the Excel like worksheet of the tibble (or data frame) using the View() function

You can identify the names of the columns (and hence the variables in the dataset) by using the function names()

Finally, you can convert a regular data frame to a tibble using the as_tibble() function

Not all functions work with tibbles, particularly those that are specific to spatial data. As such, we’ll be using a combination of tibbles and regular data frames throughout the class, with a preference towards tibbles where possible. Note that when you search on Google for how to do something in R, you will likely get non tidy ways of doing things. Most of these suggestions are fine, but some are not and may screw you up down the road. My advice is to try to stick with tidy functions to do things in R.

Anyway, you earned another badge. Yes!

Data Wrangling

It is rare that the data work on are in exactly the right form for analysis. For example, you might want to discard certain variables from the dataset to reduce clutter. Or you need to create new variables from existing ones. Or you encounter missing data. The process of gathering data in its raw form and molding it into a form that is suitable for its end use is known as data wrangling . What’s great about the tidyverse package is its suite of functions make data wrangling relatively easy, straight forward, and transparent.

In this lab, we won’t have time to go through all of the methods and functions in R that are associated with the data wrangling process. We will cover more in later labs and many methods you will have to learn on your own given the specific tasks you will need to accomplish. In the rest of this guide, we’ll go through some of the basic data wrangling techniques using the functions found in the package dplyr , which was automatically installed and loaded when you brought in the tidyverse package. These functions can be used for either tibbles or regular data frames.

Reading in data

The dataset nycflights13 was included in an R package. In most cases, you’ll have to read it in. Most data files you will encounter are comma-delimited (or comma-separated) files, which have .csv extensions. Comma-delimited means that columns are separated by commas. We’re going to bring in two csv files lab1dataset1.csv and lab1dataset2.csv . The first file is a county-level dataset containing median household income. The second file is also a county-level dataset containing Non-Hispanic white, Non-Hispanic black, non-Hispanic Asian, and Hispanic population counts. Both data sets come from the 2014-2018 American Community Survey (ACS). We’ll cover the Census, and how to download Census data, in the next lab.

To read in a csv file, use the function read_csv() , which is a part of the tidyverse package, and plug in the name of the file in quotes inside the parentheses. Make sure you include the .csv extension. I uploaded the two files on GitHub, so you can read them in directly from there. We’ll name these objects ca1 and ca2

You should see two tibbles ca1 and ca2 pop up in your Environment window (top right). Every time you bring a dataset into R for the first time, look at it to make sure you understand its structure. You can do this a number of ways. One is to use the function glimpse() , which gives you a succinct summary of your data.

If you like viewing your data through an Excel style worksheet, type in View(ca1) , and ca1 should pop up in the top left window of your R Studio interface. Scroll up and down, left and right.

We’ll learn how to summarize your data using descriptive statistics and graphs in the next lab.

By learning how to read in data the tidy way, you’ve earned another badge! Hooray!

Renaming variables

You will likely encounter a variable with a name that is not descriptive. The more descriptive the variable names, the more efficient your analysis will be and the less likely you are going to make a mistake. To see the names of variables in your dataset, use the names() command.

The name Estimated median income of a household, between 2014-2018. is super duper long! Use the command rename() to - what else? - rename a variable! Let’s rename Estimated median income of a household, between 2014-2018. to medinc .

Note that you can rename multiple variables within the same rename() command. For example, we can also rename Formatted FIPS to GEOID . Make this permanent by assigning it back to ca1 using the arrow operator <-

Selecting variables

In practice, most of the data files you will download will contain variables you don’t need. It is easier to work with a smaller dataset as it reduces clutter and clears up memory space, which is important if you are executing complex tasks on a large number of observations. Use the command select() to keep variables by name. Visually, we are doing the following (taken from the RStudio cheatsheet )

Let’s take a look at the variables we have in the ca2 dataset

We’ll go into more detail what these variables mean next lab when we cover the U.S. Census, but we only want to keep the variables GEOID , which is the county FIPS code (a unique numeric identifier), and tpoprE , nhwhiteE , nhblkE , nhasnE , and hispE , which are the total, white, black, Asian and Hispanic population counts.

Here, we provide the data object first, followed by the variables we want to keep separated by commas.

Let’s keep County , GEOID , and medinc from the ca1 dataset. Rather than listing all the variables we want to keep like we did above, a shortcut way of doing this is to use the : operator.

The : operator tells R to select all the variables from County to medinc . This operator is useful when you’ve got a lot of variables to keep and they all happen to be ordered sequentially.

You can use also use select() command to keep variables except for the ones you designate. For example, to keep all variables in ca1 except FIPS Code and save this back into ca1 , type in

The negative sign tells R to exclude the variable. Notice we need to use quotes around FIPS Code because it contains a space. You can delete multiple variables. For example, if you wanted to keep all variables except FIPS Code and County , you would type in select(ca1, -"FIPS Code", -County) .

Take a glimpse to see if we got what we wanted.

Do the same for ca2 .

Creating new variables

The mutate() function allows you to create new variables within your dataset. This is important when you need to transform variables in some way - for example, calculating a ratio or adding two variables together. Visually, you are doing this

You can use the mutate() command to generate as many new variables as you would like. For example, let’s construct four new variables in ca2 - the percent of residents who are non-Hispanic white, non-Hispanic Asian, non-Hispanic black, and Hispanic. Name these variables pwhite , pasian , pblack , and phisp , respectively.

Note that you can create new variables based on the variables you just created in the same line of code. For example, you can create a categorical variable yielding “Majority” if the tract is majority Hispanic and “Not Majority” otherwise after creating the percent Hispanic variable within the same mutate() command. Let’s save these changes back into ca2 .

We used the function case_when() to create mhisp - the function tells R that if the condition phisp > 0.5 is met, the tract’s value for the variable mhisp will be “Majority”, otherwise (designated by TRUE ) it will be “Not Majority”.

Take a look at our data

Joining tables

Rather than working on two separate datasets, we should join the two datasets ca1 and ca2 , because we may want to examine the relationship between median household income, which is in ca1 , and racial/ethnic composition, which is in ca2 . To do this, we need a unique ID that connects the tracts across the two files. The unique Census ID for a county combines the county and state IDs. The Census ID is named GEOID in both files. The IDs should be the same data class, which is the case.

If they are not the same class, we can coerce them using the as.numeric() or as.character() function described earlier.

To merge the datasets together, use the function left_join() , which matches pairs of observations whenever their keys or IDs are equal. We match on the variable GEOID and save the merged data set into a new object called cacounty .

We want to merge ca2 into ca1 , so that’s why the sequence is ca1, ca2 . The argument by tells R which variable(s) to match rows on, in this case GEOID . You can match on multiple variables and you can also match on a single variable with different variable names (see the left_join() help documentation for how to do this). The number of columns in cacounty equals the number of columns in ca1 plus the number of columns in ca2 minus the ID variable you merged on.

Note that if you have two variables with the same name in both files, R will attach a .x to the variable name in ca1 and a .y to the variable name in ca1 . For example, if you have a variable named Robert in both files, cacounty will contain both variables and name it Robert.x (the variable in ca1 ) and Robert.y (the variable in ca1 ). Try to avoid having variables with the same names in the two files you want to merge.

Let’s use select() to keep the necessary variables.

Filtering means selecting rows/observations based on their values. To filter in R, use the command filter() . Visually, filtering rows looks like.

The first argument in the parentheses of this command is the name of the data frame. The second and any subsequent arguments (separated by commas) are the expressions that filter the data frame. For example, we can select Sacramento county using its FIPS code

The double equal operator == means equal to. We can also explicitly exclude cases and keep everything else by using the not equal operator != . The following code excludes Sacramento county.

What about filtering if a county has a value greater than a specified value? For example, counties with a percent white greater than 0.5 (50%).

What about less than 0.5 (50%)?

Both lines of code do not include counties that have a percent white equal to 0.5. We include it by using the less than or equal operator <= or greater than or equal operator >= .

In addition to comparison operators, filtering may also utilize logical operators that make multiple selections. There are three basic logical operators: & (and), | is (or), and ! is (not). We can keep counties with phisp greater than 0.5 and medinc greater than 50000 percent using & .

Use | to keep counties with a GEOID of 06067 (Sacramento) or 06113 (Yolo) or 06075 (San Francisco)

You’ve gone through some of the basic data wrangling functions offered by tidyverse . As such, you’ve earned another Tidy badge. Congratulations!

In running the lines of code above, we’ve asked you to work directly in the R Console and issue commands in an interactive way. That is, you type a command after > , you hit enter/return, R responds, you type the next command, hit enter/return, R responds, and so on. Instead of writing the command directly into the console, you should write it in a script. The process is now: Type your command in the script. Run the code from the script. R responds. You get results. You can write two commands in a script. Run both simultaneously. R responds. You get results. This is the basic flow.

One way to do this is to use the default R Script, which is covered in the assignment guidelines . In your homework assignments, we will be asking you to submit code in another type of script: the R Markdown file. R Markdown allows you to create documents that serve as a neat record of your analysis. Think of it as a word document file, but instead of sentences in an essay, you are writing code for a data analysis.

When going through lab guides, I would recommend not copying and pasting code directly into the R Console, but saving and running it in an R Markdown file. This will give you good practice in the R Markdown environment. Now is a good time to read through the class assignment guidelines as they go through the basics of R Markdown files.

To open an R Markdown file, click on File at the top menu in RStudio, select New File , and then R Markdown . A window should pop up. In that window, for title , put in “Lab 1”. For author , put your name. Leave the HTML radio button clicked, and select OK. A new R Markdown file should pop up in the top left window.

Don’t change anything inside the YAML (the stuff at the top in between the --- ). Also keep the grey chunk after the YAML.

Delete everything else. Save this file (File -> Save) in an appropriate folder. It’s best to set up a clean and efficient file management structure as described in the assignment guidelines . For example, below is where I would save this file on my Mac laptop (I named the file “Lab 1”).

This is what file organization looks like

Follow the directions in the assignment guidelines to add this lab’s code in your Lab 1 R Markdown file. Then knit it as an html, word or pdf file. You don’t have to turn in the Rmd and its knitted file, but it’s good practice to create an Rmd file for each lab.

Although the lab guides and course textbooks should get you through a lot of the functions that are needed to successfully accomplish tasks for this class, there are a number of useful online resources on R and RStudio that you can look into if you get stuck or want to learn more. We outline these resources here . If you ever get stuck, check this resource out first to troubleshoot before immediately asking a friend or the instructor.

Practice makes perfect

Here are a few practice questions. You don’t need to submit these, but it’s good practice to answer these questions in R Markdown, producing a knitted file (html, pdf or docx).

Look up the help documentation for the function rep() . Use this function to create the following 3 vectors.
[1] 0 0 0 0 0
[1] 1 2 3 1 2 3 1 2 3 1 2 3
[1] 4 5 5 6 6 6
Explain what is the problem in each line of code below. Fix the code so it will run properly.
my variable <- 3
seq(1, 10 by = 2)
Library(cars)
Look up the help documentation for the function cut() .

Describe the purpose of this function. What kind of data type(s) does this function accept? Which arguments/options are required? Which arguments are not required and what are their default value(s)?

Create an example vector and use the cut() function on it. Explain your results.

Load the mtcars dataset by using the code data(mtcars) . Find the minimum, mean, median and maximum of the variable mpg in the mtcars dataset using just one line of code. We have not covered a function that does this yet, so the main point of this question is to get you used to using the resources you have available to find an answer. Describe the process you used (searched online? use the class textbook?) to find the answer.

Look up the functions arrange() and relocate() . Input the variable phisp from cacounty in each function. What are the functions doing?

Use the function bind_rows() to create a new dataset called cacounty_brows that combines ca1 and ca2 . Describe the structure of this new dataset. Do the same for the function bind_cols() (name the new dataset cacounty_bcols ). How is bind_cols() different from left_join() ?

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Mastering R Programming Assignments: A Step-by-Step Guide

A Step-by-Step Guide for Success When Solving R Programming Assignments

Understanding R Basics

Understanding R basics is crucial for anyone venturing into R programming. It lays the foundation for all subsequent learning and ensures a smooth journey in the language. With a solid grasp of R basics, individuals can confidently write code, manipulate data, and conduct statistical analyses. Learning about data types, variables, and functions helps beginners become familiar with R's core components. Furthermore, understanding how to execute R scripts and use it as a calculator facilitates quick experimentation and problem-solving. Armed with these fundamental skills, aspiring R programmers can progress to more complex tasks and unlock the language's full potential in data analysis and visualization.

Topics to Master Before Starting an R Programming Assignment:

Before tackling an R programming assignment, mastering fundamental topics is essential. Understanding R basics, data import and cleaning, visualization, and statistical analysis lays a strong foundation for successful data-driven projects.

Data Import and Cleaning

Data import and cleaning are critical steps before embarking on any data analysis project in R. Properly importing data from various sources ensures that you have the necessary information to work with. Understanding data formats and handling missing or inconsistent data prevents skewed results. Cleaning the data involves removing duplicates, correcting errors, and transforming data into a suitable format for analysis. By mastering these techniques, you ensure data integrity and reliability, enabling you to derive accurate insights and make informed decisions. A well-organized and clean dataset sets the stage for successful data manipulation, visualization, and statistical analysis in R programming assignments.

Data Visualization

Data visualization is a critical skill for any data analyst or researcher working with R programming. Visualizing data allows one to present complex information in a clear and intuitive manner, enabling easier understanding and communication of insights. Learning R's powerful visualization packages like ggplot2 empowers users to create a wide range of compelling graphs, charts, and plots. Effective data visualization helps identify patterns, trends, and outliers in the data, aiding decision-making processes. Whether it's bar plots, scatter plots, or heatmaps, mastering data visualization in R enhances the ability to extract meaningful information from data and makes R programming assignments more impactful and visually appealing.

Statistical Analysis in R

Statistical analysis in R is a critical skill for drawing meaningful insights from data. Mastering hypothesis testing, regression analysis, and ANOVA allows researchers to make data-driven decisions with confidence. R's extensive range of statistical functions and packages, such as stats and tidyverse, empowers users to perform advanced analyses effortlessly. Whether investigating relationships between variables, detecting patterns, or predicting outcomes, R provides a robust environment for statistical modeling. Additionally, R's graphical capabilities enable the creation of visually compelling plots, facilitating better communication of findings. Proficiency in statistical analysis equips data analysts with the tools needed to solve complex problems and make informed decisions.

Programming Control Structures

Programming control structures are vital in R programming as they allow for efficient and automated data processing. By mastering loops (for and while), if-else statements, and switch-case constructs, programmers can manipulate data iteratively and make decisions based on specific conditions. These control structures facilitate code reusability, reduce redundancy, and enhance the overall code readability. Moreover, they enable users to create complex algorithms and implement iterative processes with ease. Proficiency in programming control structures empowers R programmers to write efficient, structured, and scalable code, making them well-equipped to handle challenging assignments and data analysis tasks.

Functions and Custom Packages

Mastering functions and custom packages in R takes your programming skills to the next level. Functions allow you to encapsulate complex logic into reusable blocks of code, enhancing efficiency and readability. By writing custom packages, you contribute to R's vibrant ecosystem and share your work with others. Learning how to document functions properly ensures seamless collaboration and code maintenance. Custom packages enable you to extend R's functionality and tailor it to specific needs, making your codebase more organized and manageable. Embracing functions and custom packages empowers you to solve intricate problems and become a more proficient R programmer with a broader impact on the community.

Collaborating with R Markdown

Collaborating with R Markdown is a game-changer for reproducible research and project documentation. R Markdown seamlessly integrates R code, text, and visualizations, allowing collaborators to understand the entire workflow at a glance. By sharing the R Markdown file, team members can easily reproduce the analysis, ensuring transparency and credibility. Additionally, R Markdown promotes effective communication, enabling researchers to present findings coherently with interactive and dynamic reports. Its versatility extends to exporting documents to various formats like PDF, HTML, and Word, making it suitable for different audiences. Embracing R Markdown streamlines collaboration, enhances productivity, and fosters a collaborative and inclusive data analysis environment.

How to Solve Assignments in R Programming

Now that you have a strong foundation in essential R topics, let's discuss a step-by-step approach to solving assignments in R programming:

Step 1: Read and Understand the Assignment

This step ensures clarity on the objectives, requirements, and dataset involved. Taking the time to grasp the assignment's nuances allows you to plan your approach effectively and avoid costly mistakes. Understanding the specific goals will also help you prioritize tasks, identify essential tools or functions, and ensure that your solution aligns precisely with what is expected. This foundational step sets the stage for a well-structured and successful R programming assignment.

Step 2: Plan Your Approach

In any R programming assignment, planning your approach is the key to success. Breaking down the problem into smaller tasks helps in organizing your thoughts and setting a clear direction. By outlining the sequence of operations and identifying the relevant R functions and packages to use, you can ensure efficiency and accuracy. A well-thought-out plan acts as a roadmap, guiding you through the assignment, and prevents you from getting lost in complexities. Taking the time to plan upfront can save valuable time and effort in the long run, leading to a more effective and satisfactory solution.

Step 3: Import and Clean the Data

Step 3 involves the critical process of importing and cleaning data before analysis. Accurate data is crucial for reliable insights, and R provides powerful tools to handle various file formats seamlessly. Cleaning data involves dealing with missing values, outliers, and inconsistencies, ensuring a robust dataset for analysis. This step is fundamental as it lays the groundwork for the subsequent analysis, making it imperative to master data import and cleaning techniques to yield accurate and meaningful results in R programming assignments.

Step 4: Data Exploration and Visualization

In Step 4, data exploration and visualization play a crucial role in gaining insights from the dataset. By summarizing the data with descriptive statistics and creating informative plots, patterns and trends become apparent. Visualizations like scatter plots, histograms, and boxplots provide a clear understanding of data distributions and relationships between variables. These exploratory techniques aid in identifying outliers and potential issues with the data, guiding subsequent analysis and ensuring a solid foundation for data-driven decision-making.

Step 5: Conduct Statistical Analysis

This step involves conducting statistical analysis, a critical phase in R programming assignments. By applying appropriate statistical methods using R's vast array of packages, analysts can gain valuable insights from data. These analyses provide evidence for drawing conclusions, making data-driven decisions, and understanding relationships within the dataset. Whether it's hypothesis testing, regression modeling, or ANOVA, conducting statistical analysis in R empowers researchers to uncover meaningful patterns and trends, contributing to the overall success of the assignment and its relevance in real-world applications.

Step 6: Implement Functions and Custom Code

This step involves implementing custom functions and code, which enhances the efficiency and organization of R programming assignments. By creating reusable functions, programmers can avoid redundant code and streamline complex tasks. Well-documented functions also improve code readability and facilitate collaboration among team members. Moreover, writing custom code allows for tailored solutions to specific problems, empowering programmers to tackle unique challenges creatively. Implementing functions and custom code not only simplifies the assignment but also contributes to building a comprehensive R programming skill set.

Step 7: Compile Your Report

Compiling your report is a crucial step in the R programming assignment process. It involves organizing your analysis, code, and results into a comprehensive document. Using R Markdown, you can create professional reports with text explanations, code chunks, and embedded visualizations. This approach ensures that your work is well-documented, easy to understand, and reproducible. Additionally, presenting your findings in a structured report enhances your ability to communicate complex analyses effectively, making it easier for others to grasp your insights and conclusions.

Step 8: Review and Test Your Code

Reviewing and testing your code, is a crucial checkpoint before submitting any R programming assignment. It helps catch potential errors, ensuring the code runs smoothly and produces accurate results. By reviewing the code meticulously, you can identify logical flaws or inconsistencies and make necessary improvements. Testing the code with various scenarios helps verify its robustness and efficiency, reassuring that it meets the assignment's requirements. Thorough review and testing foster confidence in the final solution and ensure the assignment's success.

Step 9: Seek Feedback and Improve

Receiving input from peers or instructors helps identify blind spots and areas for improvement in your R programming assignment. Constructive criticism refines your coding practices and analytical approaches, making your solutions more robust. Embracing feedback fosters a learning environment, encouraging continuous development and refining your skills. Utilizing feedback to improve your work is a hallmark of a proactive learner, setting you on a path towards becoming an accomplished R programmer.

In conclusion, mastering essential topics before starting an R programming assignment is pivotal for success. A solid understanding of R basics, data import, cleaning, visualization, statistical analysis, control structures, functions, and R Markdown lays a strong foundation for tackling data-driven tasks with confidence. Embracing a structured approach to solving assignments ensures efficiency and clarity in code development and analysis. Seeking feedback and collaboration fosters growth and improvement, allowing for continuous learning and skill refinement. R's versatility and powerful capabilities make it an indispensable tool for data analysis and research. With dedication and practice, aspiring R programmers can unlock endless possibilities in data exploration and analysis, fostering a rewarding journey in the world of R programming.

R Programming

Getting started.

Resources for R Programming
References for R Programming
Data Science Specialization Value Proposition
R Onboarding for SAS Users

Programming Assignments

Strategy for Coding the Programming Assignments
Tutorial for those struggling with Programming Assignment 1
Breaking Down pollutantmean
Assignment 1: A More Elegant Solution
A SAS Version of pollutantmean?
Tutorial for those struggling with Programming Assignment 2
Tutorial for those struggling with Programming Assignment 3
PA1-test: testthat , Unit Tests for Programming Assignment 1
PA3-test: testthat , Unit Tests for Programming Assignment 3
Alternative submit script for Programming Assignment 1 that makes submitting more convenient by allowing selection of multiple parts plus prompting if user wants to submit another part before exiting
Grading the SHA-1 Hash Code
Assignment 2: Demystifying makeVector
Assignment 2: makeCacheMatrix as an Object
Some notes on the R Language
A Data Frame is Also a List
S Objects, R Objects, and Lexical Scoping
Common R Mistakes: Overwriting Functions with Data Objects
Forms of the Extract Operator
Functions to Sort Data Frames
Creative Use of R: Downloading Course Lectures Article illustrating how to use R to automate the download of lectures from Data Science Specialization courses, such as R Programming . Techniques used in this article are helpful to make research reproducible, as required for courses like Getting and Cleaning Data and Reproducible Research .
Lexical Scoping and Statistical Computing Article by Robert Gentleman and Ross Ihaka at the University of Auckland describing how lexical scoping works, and why it is valuable in statistical computing.
Data Science Job Report 2017: R Passes SAS, But Python Leaves Them Both Behind Bob Muenchen’s take on the job market for various data science langauges.

R language cheatsheet

R cheatsheet covering all lectures

R and Commercial Statistics Packages

R Onboarding for SAS Users Provides an overview and links to a variety of resources to help people with SAS experience make the transition to R
Commercial Statistics Packages: An Historical Perspective
Why is R More Difficult than SAS?
Thinking in R versus Thinking in SAS

Comprehensive Notes

Complete notes for R Programming

R Programming Assignment

Introduction

Table of Contents

Understanding R Programming

Tips and tricks, common error-prone cases and how to avoid them.

As an online educator with more than a decade of experience in R programming, I am excited to take you through this enlightening journey of R Programming Assignment. We'll dive deep into the world of R, a language built specifically for statistical computing and graphics.

R is an open-source programming language that is extensively used for statistical computing and graphics. It provides a wide variety of statistical techniques such as regression modeling, classical statistical tests, time-series analysis, data mining, classification, clustering, and more.
Furthermore, R is highly extensible. It allows users to write their own functions. In addition, R works well with other programming languages like C, C++, and Fortran.
In the context of R programming, assignments typically involve working with data sets, conducting statistical analysis, creating visual representations of data, and writing code to solve specific problems.
For instance, an assignment might involve analyzing a given dataset and using regression methods to build a predictive model. Here is a simple example of how to read a CSV file in R:
Always start your assignment by understanding what is required. Analyze the problem, break it down into smaller tasks and tackle each one systematically.
Use comments in your code. Comments help you and others understand the purpose of your code. In R, you can add a comment by using the # symbol. For example:
Use meaningful variable names. This makes your code more readable and maintainable.
Keep your code DRY (Don't Repeat Yourself). If you are using the same code multiple times, consider turning it into a function.
One common error in R programming involves incorrect data types. Be careful when you are working with different data types. If you try to perform an operation that isn't allowed for the data type you're using, you'll get an error.
Another common error involves missing values. R uses the special value NA to represent missing data, and many functions in R can't handle NA values. To avoid errors, always check your data for NA values before using it.
Finally, be careful with indexing. In R, indexing starts from 1, not 0. This can lead to off-by-one errors if you're not careful.

As we conclude, it's important to remember that proficiency in R programming is achieved through continued practice and curiosity. The more you work with R, the more comfortable you'll become with its nuances and capabilities. Keep exploring, keep learning and keep coding.

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Assignment Operators in R (3 Examples) | Comparing = vs. ＜- vs. ＜＜-

On this page you’ll learn how to apply the different assignment operators in the R programming language .

The content of the article is structured as follows:

Let’s dive right into the exemplifying R syntax!

Example 1: Why You Should Use ＜- Instead of = in R

Generally speaking, there is a preference in the R programming community to use an arrow (i.e. ＜-) instead of an equal sign (i.e. =) for assignment.

In my opinion, it makes a lot of sense to stick to this convention to produce scripts that are easy to read for other R programmers.

However, you should also take care about the spacing when assigning in R. False spacing can even lead to error messages .

For instance, the following R code checks whether x is smaller than minus five due to the false blank between ＜ and -:

A properly working assignment could look as follows:

However, this code is hard to read, since the missing space makes it difficult to differentiate between the different symbols and numbers.

In my opinion, the best way to assign in R is to put a blank before and after the assignment arrow:

As mentioned before, the difference between ＜- and = is mainly due to programming style . However, the following R code using an equal sign would also work:

In the following example, I’ll show a situation where ＜- and = do not lead to the same result. So keep on reading!

Example 2: When ＜- is Really Different Compared to =

In this Example, I’ll illustrate some substantial differences between assignment arrows and equal signs.

Let’s assume that we want to compute the mean of a vector ranging from 1 to 5. Then, we could use the following R code:

However, if we want to have a look at the vector x that we have used within the mean function, we get an error message:

Let’s compare this to exactly the same R code but with assignment arrow instead of an equal sign:

The output of the mean function is the same. However, the assignment arrow also stored the values in a new data object x:

This example shows a meaningful difference between = and ＜-. While the equal sign doesn’t store the used values outside of a function, the assignment arrow saves them in a new data object that can be used outside the function.

Example 3: The Difference Between ＜- and ＜＜-

So far, we have only compared ＜- and =. However, there is another assignment method we have to discuss: The double assignment arrow ＜＜- (also called scoping assignment).

The following code illustrates the difference between ＜- and ＜＜- in R. This difference mainly gets visible when applying user-defined functions .

Let’s manually create a function that contains a single assignment arrow:

Now, let’s apply this function in R:

The data object x_fun1, to which we have assigned the value 5 within the function, does not exist:

Let’s do the same with a double assignment arrow:

Let’s apply the function:

And now let’s return the data object x_fun2:

As you can see based on the previous output of the RStudio console, the assignment via ＜＜- saved the data object in the global environment outside of the user-defined function.

Video & Further Resources

I have recently released a video on my YouTube channel , which explains the R syntax of this tutorial. You can find the video below:

The YouTube video will be added soon.

In addition to the video, I can recommend to have a look at the other articles on this website.

R Programming Examples

In summary: You learned on this page how to use assignment operators in the R programming language. If you have further questions, please let me know in the comments.

assignment-operators-in-r How to use different assignment operators in R – 3 R programming examples – R programming language tutorial – Actionable R programming syntax in RStudio

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[Programming Assignment 1] R Programming
by Anderson Hitoshi Uyekita
Last updated almost 2 years ago
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UC Business Analytics R Programming Guide

Assignment & evaluation.

The first operator you’ll run into is the assignment operator. The assignment operator is used to assign a value. For instance we can assign the value 3 to the variable x using the <- assignment operator. We can then evaluate the variable by simply typing x at the command line which will return the value of x . Note that prior to the value returned you’ll see ## [1] in the command line. This simply implies that the output returned is the first output. Note that you can type any comments in your code by preceding the comment with the hashtag ( # ) symbol. Any values, symbols, and texts following # will not be evaluated.

Interestingly, R actually allows for five assignment operators:

The original assignment operator in R was <- and has continued to be the preferred among R users. The = assignment operator was added in 2001 primarily because it is the accepted assignment operator in many other languages and beginners to R coming from other languages were so prone to use it. However, R uses = to associate function arguments with values (i.e. f(x = 3) explicitly means to call function f and set the argument x to 3. Consequently, most R programmers prefer to keep = reserved for argument association and use <- for assignment.

The operators <<- is normally only used in functions which we will not get into the details. And the rightward assignment operators perform the same as their leftward counterparts, they just assign the value in an opposite direction.

Overwhelmed yet? Don’t be. This is just meant to show you that there are options and you will likely come across them sooner or later. My suggestion is to stick with the tried and true <- operator. This is the most conventional assignment operator used and is what you will find in all the base R source code…which means it should be good enough for you.

Lastly, note that R is a case sensitive programming language. Meaning all variables, functions, and objects must be called by their exact spelling:

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R Programming by Johns Hopkins University on Coursera Assignment 2

byron-data/R-Programming-Assignment-2

Folders and files, repository files navigation, r programming assignment 2.

R Programming by Johns Hopkins University on Coursera

Introduction

This second programming assignment will require you to write an R function that is able to cache potentially time-consuming computations. For example, taking the mean of a numeric vector is typically a fast operation. However, for a very long vector, it may take too long to compute the mean, especially if it has to be computed repeatedly (e.g. in a loop). If the contents of a vector are not changing, it may make sense to cache the value of the mean so that when we need it again, it can be looked up in the cache rather than recomputed. In this Programming Assignment you will take advantage of the scoping rules of the R language and how they can be manipulated to preserve state inside of an R object.

Example: Caching the Mean of a Vector

In this example we introduce the <<- operator which can be used to assign a value to an object in an environment that is different from the current environment. Below are two functions that are used to create a special object that stores a numeric vector and caches its mean.

The first function, makeVector creates a special "vector", which is really a list containing a function to

set the value of the vector
get the value of the vector
set the value of the mean
get the value of the mean

The following function calculates the mean of the special "vector" created with the above function. However, it first checks to see if the mean has already been calculated. If so, it get s the mean from the cache and skips the computation. Otherwise, it calculates the mean of the data and sets the value of the mean in the cache via the setmean function.

Assignment: Caching the Inverse of a Matrix

Matrix inversion is usually a costly computation and there may be some benefit to caching the inverse of a matrix rather than computing it repeatedly (there are also alternatives to matrix inversion that we will not discuss here). Your assignment is to write a pair of functions that cache the inverse of a matrix.

Write the following functions:

makeCacheMatrix : This function creates a special "matrix" object that can cache its inverse.
cacheSolve : This function computes the inverse of the special "matrix" returned by makeCacheMatrix above. If the inverse has already been calculated (and the matrix has not changed), then cacheSolve should retrieve the inverse from the cache.

Computing the inverse of a square matrix can be done with the solve function in R. For example, if X is a square invertible matrix, then solve(X) returns its inverse.

For this assignment, assume that the matrix supplied is always invertible.

In order to complete this assignment, you must do the following:

Fork the GitHub repository containing the stub R files at https://github.com/rdpeng/ProgrammingAssignment2 to create a copy under your own account.
Clone your forked GitHub repository to your computer so that you can edit the files locally on your own machine.
Edit the R file contained in the git repository and place your solution in that file (please do not rename the file).
Commit your completed R file into YOUR git repository and push your git branch to the GitHub repository under your account.
Submit to Coursera the URL to your GitHub repository that contains the completed R code for the assignment.

This assignment will be graded via peer assessment.

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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.

How our experts provide R help online?

At FavTutor, our R experts help you in teaching any complex R concept and completing your homework or assignments on time. With many years of experience, they are experts in providing best R homework help to college or school students. We value your time and hence help you in completing your assignments on time. You can also connect with our R experts for any query related to the assignment or homework. Moreover, our services can be availed at affordable prices, and students do not feel the pinch of paying through their pocket money. If you are stuck at homework, get Instant R help online and secure better grades.

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Evaluating Detection Probabilities for the Endangered Growling Grass Frog in Southern Victoria using r programming.

R language is believed to be the very best tool for data visualization.

Retail Relay

We also adjust the transition probability into the retention rate (R). Below mentioned tables, we calculated the M and R to find the customer life time value by using the CLV function in RStudio.

Example No1 Data-Analytic

Due to severe lack of examining doctors at the hospital, hospital needs to outsource their special needs clients to OC's. These OC's are highly costly for the company, for that reason Fargo group has decided to carry out predictive analysis for the company's approaching clients.

Example No2 R studio

As we all know that wildlife exists in this world, the same world where we (human kind) exist as well. They (wild animals) live independently of man and characteristically try to resist interference with human beings either by avoidance or by aggression. The mounting pressure of the global increments of population of either side causes a decline in the presence of resources necessary for the existence of both, ultimately will lead to extinction of either mankind or wildlife. We know mankind as the most superior being existing in nature and has known to survive any situation for many ages but unluckily, same cannot be said for the other side (wild life). There are many examples of animals that have gone into extinction due to humans or other catalyst, such as Western Black Rhinoceros, Tasmanian tiger and many others. Therefore we have conducted this analysis to detect probabilities of survival of endangered Growling Grass Frog in different environments and environmental factors .

Example No4 R Data

Example No5 Retail Relay using R Coding

As mentioned in the case that transition study are not convincing to every one because it was providing the more extensive study that using the samples containing many individuals who had recently became customers of Retail Relay, which led to the biasness factor in analysis . The customers who had recently became the customers of Retail Relay, can lead to the highest decrease in the percent of second and more purchases from the new customers because here we would not be able to observe anything other than the one and more purchases from the side of new customers. That is why we are using the Pilot Transition study to avoid any type of biasness and involvement of others unusual factors in the data set for the calculation of customer life time value (CLV).

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R is one of the most popular programming languages for solving a range of statistical needs. It finds a wide set of applications in Machine Learning, Scientific Computing, Statistical analysis, and Graphics visualization. If you are pursuing a course in one of these fields and are not able to complete R assignments on your own, they have landed at the right place! We offer instant yet affordable R assignment help on different sets of topics. Our pool of 100+ programmers cum statisticians has in-depth knowledge and experience in delivering R assignment solutions.

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What is r programming.

R is an open-source language that is popularly known as R and is developed for statistical computing. It creates a statistical environment to carry out statistical computing and graphics. The R programming language is used by data miners and statisticians to analyze data and develop statistical software. Conducting polls, studying the literature databases, and doing surveys have increased the demand for the R language in the past few years.

The key features that make R as an important aspect of your programming curriculum:

It is an effective tool to carry out virtual statistical computations.
R is efficient in handling data and offers an ample storage facility
R is an interpreted language. There is no need to use a compiler to create a program from the code

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Students: How do you get help with R programming Assignments?

For students who are yearning to learn any language, the R programming language would be perfect. This language is easy to learn and accessible and used in different programs. However, students without a basic understanding of the subject will struggle to complete the assignments on their own. If you are one such student and do not have adequate time to solve the assignments, then seek help from our R programming Assignment Help experts.

Completing the R assignments would need an in-depth understanding of statistical techniques and tools. Students often commit structural mistakes, which would impact the quality of the assignment. Hence, avail help in R Programming from our Statistics experts and earn A+ grades.

Types of R Assignment Help

R finds applications for many cases. Some of the important types of R coding help are below:

R Programming assignment help | R Coding Help

R programming language is covered in the academic curriculum of many institutions across the world. Students look for help in completing the R assignments due to a lack of coding skills. Our team of R programmers strictly adheres to the project timelines and shares the R codes well before your deadlines. Our R experts are well versed in all the concepts in R and hence help you on any topic regardless of the complexity.

R Studio assignment help | R Studio homework help

R studio is an integrated environment that is used for R programming language. It is an agile and powerful language that is used in different applications such as computation, data science , AI, statistical data analysis and so on. It also has a huge collection of powerful analytical tools and good data visualization facilities. Submit your assignment today to seek quality R Studio assignment help.

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Students use R Markdown file to save as well as execute the code. It is also used for generating high-quality reports, which you can also share with the audience. The R Markdown documents that are produced are reproducible and give enough support to dynamic output formats. If you identify any of the data transcription errors or you want to do data analysis, you can easily recompile the report without making any kind of changes to the actual document. Learn the usage of R Markdown by seeking help from our R tutors.

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R programming is widely used in data analytics tools to do visual analytics. It is easy to learn, share and visualize the data. Ggplot2 is the popularly used data visualization package by the R community. It is good to be used for visualizing the data that is broken into graphs and later into semantic components such as layers and scales. When you use this tool, it also offers you the data, calls the specific function and let you map variables to aesthetics. Avail the instant and affordable gglplot2 assignment help from The Programming Assignment Help.

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R Studio has different packages to carry out data management tasks and integrate them easily with the analysis activities. There are many processing tasks that are packaged in a consistent way to make the code efficient. It is also easy to remember the syntax and read it. Ddplyr is a package that is built with the purpose of simplifying, manipulating, sorting, summarizing, and joining various data frames. Submit your assignment and get the best quality R assignment help online from us.

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Exploratory data analysis in short EDA is a statistical approach and a technique that is used to analyze the data sets to summarize critical characteristics with the help of certain visual aids. The EDA approach used will help you acquire knowledge and follow various data aspects such as traits and features of the data, variables along with the relationships, and critical variables to use in the problem. The iterative approach followed by EDA will also help you generate questions related to the data.

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R Workflow is used for data analysis and reporting. R workflow will have many examples related to the code, output, and graphics. There are many graphics that are highly interactive. There are different methods available in R workflow that allows you to thoroughly analyze the data, be it you are working with business, manufacturing system, finance, science, journalism, experimental research, or observational research.

Critical Topics in R Programming Assignments & Homework

A few of the important areas in which students should get hold to develop their R coding skills and boost knowledge include:

Mapping: Apart from statistics, R is also used in geographical information systems to plot data on maps. Understanding the concepts of mapping will let students prepare publication-ready maps. However, if you are feeling stressed to write the assignment on this topic, you can seek our R programming assignment help from our skilled programmers

Graphics: This is the most challenging area where students would need practical exposure to write the assignment. Students should focus on pictures rather than numbers. Graphics are widely preferred over data. Our R statistics help experts are well-acquainted with this topic and have ample experience working on it. If you need practical assistance, we are always available for you.

T-test Statistics: This is the most challenging area in statistics that would compare two sets of data to find out the differences in two sets of data.

R packages: It offers a compilation of R data, code, documentation, and various functions in an organized way so that it would become easy to share with other people. Every package comprises one or multiple functions. There are various data operations that are carried out using R. These include - statistical operations, plotting, graphical representation, and machine learning.

R Studio: This is the key component of the whole R ecosystem. This is the main development environment that is available in R. You can get the free version or paid edition of this studio. R studio is compatible with various operating systems and browsers that are connected to it. This is used in different applications and to specialize in different subjects taught in universities.

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Machine Learning Algorithms in R Programming

Learn Machine Learning algorithms in a simple, easy-to-understand manner from our experienced tutors. Some o the important Machine Learning concepts on which R Programming assignments are based are listed below"

1. Correlation analysis using R

Correlation analysis is done to evaluate the relationship between one or multiple variables. For example, if you would like to know the relationship between the height of a father or a son, the coefficient of correlation can be used to get the answer to this query. Pearson correlation and Kendall tau are two different types of correlation analyses. Seek such quality online R Programming assignment help from us and de-stress yourself from the worries of solving the assignment on your own.

2. Multiple Linear Regression using R:

Multiple Linear Regression is used to predict the variable outcome with ease based on the multiple distinct predictor variables. It is done using the fit model, and diagnostic plots. This gives you normality, influential observations, and heteroscedasticity. It also compares various nested models with the help of the ANOVA() function. The cross-validation is done using cv.lm() function. Avail of the R Regression assignment help from the best programming experts.

3. ANOVA; One and two sample tests:

It is a statistical test that is done to estimate the dependent variables, which are quantitative in nature. It changes based on the levels of one or multiple categorical variables. It also helps you to learn the difference in means of groups at every level of variable that is independent. It is a one-way and two-way ANOVA test that you can do.

4. Logistic Regression in R :

Logistic regression is used to predict the class of people based on one or multiple predictor variables. This can be yes or no or diseased or non-diseased. The linear regression is called a generalized linear model that is an extension of the linear regression model, which is used in various other situations. This type of regression is widely used to get the probability for a specific membership class.

5. Clustering using R Programming:

It is a type of unsupervised machine-learning technique where the data is divided into parts and groups known as clusters. It is done based on the similarity of data. There are different clusters produced after segmenting the information. All the objects in a specific cluster will hold the same traits. When you perform data mining and analysis, you can use clustering to get similar datasets. It is widely used in marketing, medical sciences, games, and the internet.

6. Decision Tree in R :

A decision tree is a kind of graph that allows you to show the graphs and results, which are portrayed in the form of a tree. The nodes in the graph show the event whereas the edges would show the decision rules. It is widely used in machine learning and data mining apps. The best example of a decision tree is to predict whether or not the email is spam. It also helps you to predict the tumour and predict if the bank loan is good or bad based on the risk factors. This model is made based on observational data also known as training data.

7. Support Vector Machine (SVM):

The support vector machine is a supervised learning model that is linked to various learning algorithms to analyze data. The analysis is carried out using regression and classification analysis. It is good to use this SVM to solve various classification problems. The algorithm has data items to plot the point in the n-dimensional space. The value of a feature would be mapped to the value of a specific coordinate.

K-nearest neighbor or KNN is a kind of supervised non-linear algorithm that is also known as a non-parametric algorithm that is not done based on assumptions about the data or its distribution. It is a simple algorithm that makes use of the k value and it is used in different applications and various industries such as healthcare and finance.

9. Survival analysis using R :

Survival analysis is done in R which will allow you to thoroughly analyze the occurrence of events over a period of time without doing any assumptions. Basically, this allows you to have a model until the event happens. You can also compare time to the event between various groups and co-relate the time to event with different quantitative variables. There are two different methods to do survival analysis. One is the Kaplan-Meier method and the other is the Cox Proportional hazard model.

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Real World Applications of R Programming

Here are a few real-world applications of R programming

Research and academics: You can use R programming to carry out statistical analysis and calculations. It is widely used by students as a statistical research tool. There are various techniques available in this programming for doing linear and non-linear modelling, time-series analysis, and classification.
IT sector : It is used by small, medium, and large IT companies for business intelligence. It is used to generate statistical data. It helps the companies to gain insights and get administration functions. R is also used to make measurable figuring devices.
Banking: R programming is used by the banking sector to display credit chances and identify different hazards. The mortgage haircut model is used by banks to assume the control they can have over the property when there is a credit default. This is used along with the SAS model to thoroughly do money-related revealing.
E-commerce: R can be used in E-commerce to analyze the user experience they have on the websites. The sites can also improve cross-selling abilities with R. The previous customers can also reap benefits by receiving recommendations based on their previous purchases. R is used to target ads, financial data processing, and sales models.
Social media: R is used to thoroughly evaluate the behaviour and sentiments of users based on the posts they are viewing and the content they are going through. The ads will be customized based on their history and feelings.

One needs to understand the subject well to apply R in such real-world use cases. So, do not wait any further. Reach out to us to seek the best-in-class R assignment help and R 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.
CS50's Introduction to Programming with R
By course's end, learn to package, test, and share R code for others to use. Assignments inspired by real-world data sets. Whereas CS50x itself focuses on computer science more generally as well as programming with C, Python, SQL, and JavaScript, this course, aka CS50R, is entirely focused on R. You can take CS50R before CS50x, during CS50x ...
Introduction to R Programming for Data Science
Manipulate primitive data types in the R programming language using RStudio or Jupyter Notebooks. Control program flow with conditions and loops, write functions, perform character string operations, write regular expressions, handle errors. ... Access to lectures and assignments depends on your type of enrollment. If you take a course in audit ...
R Examples
R Program to Access Values in a Vector. R Program to Find the Statistical Mode. R Program to Add Leading Zeros to Vector. R Program to Write to a File. R Program to Trim Leading and Trailing Whitespaces. R Program to Format Decimal Places. This page contains examples of basic concepts of Python programming like loops, functions, native ...
R Programming Course (Johns Hopkins)
We also introduce the first programming assignment for the course, which is due at the end of the week. What's included. 13 videos 3 readings 2 quizzes 3 programming assignments. Show info about module content. ... We have now entered the third week of R Programming, which also marks the halfway point. The lectures this week cover loop ...
Lab 1: Introduction to R
R is a free, open source statistical programming language. It is useful for data cleaning, analysis, and visualization. R is an interpreted language, not a compiled one. This means that you type something into R and it does what you tell it. It is both a command line software and a programming environment.
R Projects that Inspire and Empower [2024]
Develop Your R Programming Skills. Explore our R Projects for practical assignments in data manipulation, statistical computing, machine learning, and data visualization. These projects are designed to hone your skills and prepare you for a vibrant career in data analysis and statistics, using the powerful R programming language.
PDF R Programming Fundamentals
R is a free programming language and software environment used for statistical computing and graphics. R is widely used by data analysts, statisticians, and data scientists around the world. This beginner-friendly course is a comprehensive introduction to R, covering everything from installation to basic statistical functions.
R programming Exercises, Practice, Solution
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.
Mastering R Programming Assignments: A Step-by-Step Guide
This step involves implementing custom functions and code, which enhances the efficiency and organization of R programming assignments. By creating reusable functions, programmers can avoid redundant code and streamline complex tasks. Well-documented functions also improve code readability and facilitate collaboration among team members.
R Programming
R Language. Creative Use of R: Downloading Course Lectures Article illustrating how to use R to automate the download of lectures from Data Science Specialization courses, such as R Programming. Techniques used in this article are helpful to make research reproducible, as required for courses like Getting and Cleaning Data and Reproducible ...
Mastering R Programming: A Comprehensive Guide for Assignments
R Programming Assignment. Upskill yourself, get started with AI courses 🚀 Explore All Courses Explore All Courses. In the context of R programming, assignments typically involve working with data sets, conducting statistical analysis, creating visual representations of data, and writing code to solve specific problems. ...
Assignment Operators in R (3 Examples)
On this page you'll learn how to apply the different assignment operators in the R programming language. The content of the article is structured as follows: 1) Example 1: Why You Should Use ＜- Instead of = in R. 2) Example 2: When ＜- is Really Different Compared to =. 3) Example 3: The Difference Between ＜- and ＜＜-. 4) Video ...
Learning Path: Introduction to R
Learning path: Introduction to R. From the basis of the R syntax to professional development in a team, for solid programming foundations in R. R basics - objects, functions and operations (free online); Data analysis with tidyverse ; Programming R ; Build an R Package ; Becoming an R developer ; The 2nd and 3rd workshops are available on demand for those teams wishing to follow the full ...
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The R programming language provides you with all the tools you need to conduct powerful data analysis, providing the conduit between your data and the real-world problems you want to solve. ... Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials ...
RPubs
RPubs. by RStudio. Sign inRegister. [Programming Assignment 1] R Programming. by Anderson Hitoshi Uyekita. Last updatedalmost 2 years ago. HideComments(-)ShareHide Toolbars. ×.
Assignment & Evaluation · UC Business Analytics R Programming Guide
The original assignment operator in R was <-and has continued to be the preferred among R users. The = assignment operator was added in 2001 primarily because it is the accepted assignment operator in many other languages and beginners to R coming from other languages were so prone to use it. However, R uses = to associate function arguments with values (i.e. f(x = 3) explicitly means to call ...
byron-data/R-Programming-Assignment-2
This second programming assignment will require you to write an R function that is able to cache potentially time-consuming computations. For example, taking the mean of a numeric vector is typically a fast operation.
R Programming Homework Help (24x7 R help online)
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.
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Week 1 Quiz • 30 minutes. 7 programming assignments • Total 1,260 minutes. swirl Lesson 1: Basic Building Blocks • 180 minutes. swirl Lesson 2: Workspace and Files • 180 minutes. swirl Lesson 3: Sequences of Numbers • 180 minutes. swirl Lesson 4: Vectors • 180 minutes. swirl Lesson 5: Missing Values • 180 minutes.
How do you use "<<-" (scoping assignment) in R?
This makes it possible to maintain a counter that records how many times a function has been called, as the following example shows. Each time new_counter is run, it creates an environment, initialises the counter i in this environment, and then creates a new function. new_counter <- function() {. i <- 0.
R Programming Assignment Help
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Data Analysis with R Programming Course (Google)
The R programming language was designed to work with data at all stages of the data analysis process. In this part of the course, you'll examine how R can help you structure, organize, and clean your data using functions and other processes. ... To access graded assignments and to earn a Certificate, you will need to purchase the Certificate ...
R Assignment Help
Seek such quality online R Programming assignment help from us and de-stress yourself from the worries of solving the assignment on your own. 2. Multiple Linear Regression using R: Multiple Linear Regression is used to predict the variable outcome with ease based on the multiple distinct predictor variables. It is done using the fit model, and ...