What is R?
R is a free software environment and a language used by programmers for statistical computing. The R programming language is famously used for data analysis by data scientists.
How is R used?
Zippia reviewed thousands of resumes to understand how r is used in different jobs. Explore the list of common job responsibilities related to r below:
- Develop and streamline R code for data evaluation and statistical analyses performed on data collected from experiments and from outside sources.
- Managed all R and D projects associated with OSI technology.
- Helped win the company's largest R &D Federal contract at DHS (CanScan - Domestic Nuclear Detection Office).
- Analyzed final data collected using R statistical software.
- Used Excel and R to analyze population, housing, employment and land use conditions as well as make projections.
- Performed statistical data analysis using R and SAS, which involved cleaning raw data and displaying relevant graphs.
Are R skills in demand?
Yes, r skills are in demand today. Currently, 43,216 job openings list r skills as a requirement. The job descriptions that most frequently include r skills are vice president of research and development, co-author, and recording artist.
How hard is it to learn R?
Based on the average complexity level of the jobs that use r the most: vice president of research and development, co-author, and recording artist. The complexity level of these jobs is challenging.
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What jobs can you get with R skills?
You can get a job as a vice president of research and development, co-author, and recording artist with r skills. After analyzing resumes and job postings, we identified these as the most common job titles for candidates with r skills.
Vice President Of Research And Development
Job description:
A vice president of research and development will lead a team of engineers in developing innovative products on time and on budget. This role will require you to perform a variety of tasks that include executing the company's overall technology vision, managing the appropriate development methodologies, and creating an organizational structure that will drive a high performing development team to deliver high-quality solutions to the market. In addition, you will be responsible for attracting, developing, and retaining top talent for the research and development function.
- R
- Product Development
- Project Management
- Strategic Plan
- Intellectual Property
- Regulatory Affairs
Manager, Product Research And Development
- R
- Product Development
- Market Research
- Manage Cross
- Product Quality
- Product Management
Programming Development Project Manager
- R
- Project Management
- Delivery Methodology
- Market Research
- Process Improvement
- Project Portfolio
Research And Development Director
Job description:
A research and development director spearheads and oversees the research and development initiatives and projects in a company. It is their duty to set goals and guidelines, establish timelines and budgets, direct and manage different departments, liaise with internal and external parties, gather and analyze data to implement solutions against problem areas, and utilize expertise in developing strategies to optimize company operations. Moreover, as a director, it is essential to lead and encourage the workforce to reach goals, all while promoting the company's policies and regulations, creating new ones as needed.
- R
- Product Development
- Project Management
- FDA
- Oversight
- Business Development
Chief Scientific Officer
Job description:
Chief scientific officers are executives who manage a company's scientific, technological, and research operations. They are professionals who ensure that an organization's scientific and research facilities' primary concern aligns with the mission and vision they agreed on. These officers meet with other branches of the company to maintain their connections within the government and industry. To be successful in this position, these officers hone their scientific expertise and leadership skills. They also make formal presentations at medical or scientific meetings on behalf of their company.
- R
- Chemistry
- Business Development
- NIH
- Molecular Biology
- Clinical Studies
Research And Development Project Leader
- R
- Data Collection
- FDA
- GMP
- Product Development
- Technical Support
Distinguished Member Of The Technical Staff
- R
- System Architecture
- Product Development
- RF
- IP
- API
Research And Development Chemist
Job description:
A research and development chemist primarily works at laboratories to conduct extensive tests and experiments aiming to develop new products and technologies. Although the extent of their duties may vary, it typically revolves around conducting research and studies, observing chemical reactions, maintaining records and databases, collaborating with fellow experts, and identifying the strengths and weaknesses of existing components or mixtures. They can find employment in different areas, such as manufacturing companies, private laboratories, government agencies, and even education.
- R
- Product Development
- Laboratory Equipment
- Analytical Methods
- HPLC
- Synthesis
Chief Science Officer
Job description:
Chief Science Officers are responsible for leading the scientific operations of an organization. Their duties include developing scientific strategies, directing clinical trial designs, implementing research processes, and communicating the scientific vision to investors and senior management. Besides that, they are involved in managing the scientific budget, identifying research opportunities, and fostering scientific partnerships with key stakeholders. Chief Science Officers are also involved in creating research programs, track research milestones, and source for funding channels. They produce research and development reports and provide mentorship to the research team.
- R
- Business Strategy
- Oversight
- Program Development
- Partnerships
- Professional Development
Research And Development Program Manager
Job description:
Research and development program managers are responsible for research, planning, and implementing new programs and protocols into their company or organization and overseeing the development of new products. Their duties and responsibilities also include assessing the scope of the project and ensuring the project is going according to budget. They also develop and implement research and development procedures and techniques.
- R
- Project Management
- Program Management
- Portfolio
- Product Development
- FDA
Heavy Line Technician
- R
- ASE
- Automotive Repair
- Repair Orders
- Manual Transmission
- Customer Vehicles
Engineering Program/Project Manager
- R
- Product Development
- Program Management
- Project Management
- Product Design
- CAD
Research And Development Manager
Job description:
A research and development manager is responsible for supervising project development procedures to support business operations and identify business opportunities that would pave the way for more revenue resources and profits. Research and development managers monitor the production plans from the conceptualization to the final outputs, inspecting inconsistencies and flaws in every phase and revising strategies as needed to achieve the required specifications and requirements. They delegate tasks to the staff, oversee progress, and conduct research and development programs to maximize productivity and team efforts.
- R
- Customer Service
- Project Management
- Patients
- Product Development
- C++
Senior Research Chemist
- R
- Chemistry
- Product Development
- Analytical Laboratory
- Organic Synthesis
- Polymer
How much can you earn with R skills?
You can earn up to $170,226 a year with r skills if you become a vice president of research and development, the highest-paying job that requires r skills. Co-authors can earn the second-highest salary among jobs that use Python, $70,759 a year.
Job Title  | Average Salary | Hourly Rate |
|---|---|---|
| Vice President Of Research And Development | $170,226 | $82 |
| CO-Author | $70,759 | $34 |
| Recording Artist | $53,291 | $26 |
| Statistical Consultant | $90,428 | $43 |
| Manager, Product Research And Development | $107,581 | $52 |
Companies using R in 2025
The top companies that look for employees with r skills are Takeda Pharmaceuticals U.S.A., Inc., Johnson & Johnson, and Boeing. In the millions of job postings we reviewed, these companies mention r skills most frequently.
| Rank | Company | % Of All Skills | Job Openings |
|---|---|---|---|
| 1 | Takeda Pharmaceuticals U.S.A., Inc. | 14% | 1,208 |
| 2 | Johnson & Johnson | 13% | 1,009 |
| 3 | Boeing | 8% | 1,654 |
| 4 | Deloitte | 7% | 19,051 |
| 5 | Intel | 6% | 1,357 |
20 courses for R skills
1. Programming for Data Science with R
Prepare for a data science career by learning the fundamental data programming tools: R, SQL, command line, and git...
2. Introduction to R: Basic R syntax
This guided project is for beginners interested in taking their first steps with coding in the statistical language R. It assumes no previous knowledge of R, introduces the RStudio environment, and covers basic concepts, tools, and general syntax. By the end of the exercise, learners will build familiarity with RStudio and the fundamentals of the statistical coding language R...
3. Advanced R
This course is intended for R and data science professionals aiming to master R. Intermediate and advanced users, will both find that this course will separate them from the rest of people doing analytics with R. We don't recommend this course on beginners. We start by explaining how to work with closures, environments, dates, and more advanced topics. We then move into regex expressions and parsing html data. We explain how to write R packages, and write the proper documentation that the CRAN team expects if you want to upload your code into R's libraries. After that we introduce the necessary skills for profiling your R code. We then move into C++ and Rcpp, and we show how to write super fast C++ parallel code that uses OpenMP. Understanding and mastering Rcpp will allow you to push your R skills to another dimension. When your colleagues are writing R functions, you will be able to get Rcpp+OpenMP equivalent code running 4-8X times faster. We then move into Python and Java, and show how these can be called from R and vice-versa. This will be really helpful for writing code that leverages the excellent object oriented features from this pair of languages. You will be able to build your own classes in Java or Python that store the data that you get from R. Since the Python community is growing so fast, and producing so wonderful packages, it's great to know that you will be able to call any function from any Python package directly from R. We finally explain how to use sqldf, which is a wonderful package for doing serious, production grade data processing in R. Even though it has its limitations, we will be able to write SQL queries directly in R. We will certainly show how to bypass those limitations, such as its inability to write full joins using specific tricks. All the code (R, JAVA, C++,. csv) used in this course is available for download, and all the lectures can be downloaded as well. Our teaching strategy is to present you with examples carrying the minimal complexity, so we hope you can easily follow each lecture. In case you have doubts or comments, feel free to send us a message...
4. R Programming
Data science is an exciting discipline that allows you to turn raw data into understanding, insight, and knowledge. The goal of "R for Data Science" is to help you learn the most important tools in R that will allow you to do data science. After going through this course, you'll have the tools to tackle a wide variety of data science challenges, using the best parts of R. What you will learnData science is a huge field, and there's no way you can master it by going through a single course. The goal of this course is to give you a solid foundation in the most important toolsFirst, you must import your data into R. This typically means that you take data stored in a file, database, or web API, and load it into a data frame in R. If you can't get your data into R, you can't do data science on it! Once you've imported your data, it is a good idea to tidy it. Tidying your data means storing it in a consistent form that matches the semantics of the dataset with the way it is stored. In brief, when your data is tidy, each column is a variable, and each row is an observation. Tidy data is important because the consistent structure lets you focus your struggle on questions about the data, not fighting to get the data into the right form for different functions. Once you have tidy data, a common first step is to transform it. Transformation includes narrowing in on observations of interest (like all people in one city, or all data from the last year), creating new variables that are functions of existing variables (like computing speed from distance and time), and calculating a set of summary statistics (like counts or means). Together, tidying and transforming are called wrangling, because getting your data in a form that's natural to work with often feels like a fight! Once you have tidy data with the variables you need, there are two main engines of knowledge generation: visualization and modelling. These have complementary strengths and weaknesses so any real analysis will iterate between them many times. Prerequisites: You should be generally numerically literate, and it's helpful if you have some programming experience already. Testimonials: i really need this type of teaching style.. this is superb ~ Nitish kumar giriIt dives right into advanced R concepts related to Data Science ~ Rainer RodriguesI am into revision.. its good. ~ Jagannath ChaudharyHonestly it's a good match for me and I'm hoping to know more ~ Salim AdamsIt was a good experience. Find it really helpful. ~ Shafia AminIts is really helpful for R programming building. ~ Muhammad Nazimclass was really informative and got new learning experience. ~ Gayathry Harilal...
5. Data Analysis with R
In this Specialization, you will learn to analyze and visualize data in R and create reproducible data analysis reports, demonstrate a conceptual understanding of the unified nature of statistical inference, perform frequentist and Bayesian statistical inference and modeling to understand natural phenomena and make data-based decisions, communicate statistical results correctly, effectively, and in context without relying on statistical jargon, critique data-based claims and evaluated data-based decisions, and wrangle and visualize data with R packages for data analysis...
6. Data Analysis with R
The R programming language is purpose-built for data analysis. R is the key that opens the door between the problems that you want to solve with data and the answers you need to meet your objectives. This course starts with a question and then walks you through the process of answering it through data. You will first learn important techniques for preparing (or wrangling) your data for analysis. You will then learn how to gain a better understanding of your data through exploratory data analysis, helping you to summarize your data and identify relevant relationships between variables that can lead to insights. Once your data is ready to analyze, you will learn how to develop your model and evaluate and tune its performance. By following this process, you can be sure that your data analysis performs to the standards that you have set, and you can have confidence in the results. You will build hands-on experience by playing the role of a data analyst who is analyzing airline departure and arrival data to predict flight delays. Using an Airline Reporting Carrier On-Time Performance Dataset, you will practice reading data files, preprocessing data, creating models, improving models, and evaluating them to ultimately choose the best model. Watch the videos, work through the labs, and add to your portfolio. Good luck! Note: The pre-requisite for this course is basic R programming skills. For example, ensure that you have completed a course like Introduction to R Programming for Data Science from IBM...
7. Data Visualization with R
In this course, you will learn the Grammar of Graphics, a system for describing and building graphs, and how the ggplot2 data visualization package for R applies this concept to basic bar charts, histograms, pie charts, scatter plots, line plots, and box plots. You will also learn how to further customize your charts and plots using themes and other techniques. You will then learn how to use another data visualization package for R called Leaflet to create map plots, a unique way to plot data based on geolocation data. Finally, you will be introduced to creating interactive dashboards using the R Shiny package. You will learn how to create and customize Shiny apps, alter the appearance of the apps by adding HTML and image components, and deploy your interactive data apps on the web. You will practice what you learn and build hands-on experience by completing labs in each module and a final project at the end of the course. Watch the videos, work through the labs, and watch your data science skill grow. Good luck! NOTE: This course requires knowledge of working with R and data. If you do not have these skills, it is highly recommended that you first take the Introduction to R Programming for Data Science as well as the Data Analysis with R courses from IBM prior to starting this course. Note: The pre-requisite for this course is basic R programming skills...
8. R Crash Course - Learn R-programming in 2 hours: R & RStudio
'R Crash Course - a short and concise introduction to R and R Studio, R-programming for the Beginners'This is an R crash course for anyone who previously had no or very little contact with script-based programming in R. The main goal is to establish the basic understanding needed for more advanced courses that use the R language, RStudio, and R-programming for example, for data science, machine learning, or statistical analysis in R. This is also a baseline course that I will recommend to my students to take to refresh their knowledge on learning R-programming language for my upcoming data science courses in R. The best about this course that is in a very concise manner (2 hours!) you will be able to learn all the fundamentals of R-programming that will enable you to get started with R! What will you learn in this course:§ Package Management§ Calculate with R§ Variables§ Vectors§ Matrices§ Lists§ Data frames§ Missing values§ Functions§ Control Structures§ For loopsAll the R-scripts used in this course will be also provided to you. The course is ideal for professionals such as data scientists, statisticians, geographers, programmers, social scientists, geologists, and all other experts who need to use statistics & data science in their field. This course is NOT for you if you an intermediate or advanced user of R and don't need an introduction to R programming! Let's get started!...
9. R Programming - R Language for Absolute Beginners
So, you've decided that you want to learn R or you want to get familiar with it, but don't know where to start? Or are you a data/business analyst or data scientist that wants to have a smooth transition into R programming?Then, this course was designed just for you! This course was designed to be your first step into the R programming world! We will delve deeper into the concepts of R objects, understand the R user interface and play around with several datasets. This course contains lectures around the following groups: Introductory slides lectures with the most well-known commands for each type of R object. Code along lectures where you will see how we can implement the stuff we will learn! Test your knowledge with questions and practical exercises with different levels of difficulty! Analyze real datasets and understand the thought process from question to R code solution! This course was designed to be focused on the practical side of coding in R - instead of teaching you every function and method out there, I'll show you how you can read questions and examples and get to the answer by yourself, compounding your knowledge on the different R objects. At the end of the course you should be able to use R to analyze your own datasets. Along the way you will also learn what R vectors, arrays, matrixes and lists are and how you can combine the knowledge of those objects to power up your analysis. Here are some examples of things you will be able to do after finishing the course: Load CSV and Excel files into R;Do interesting line plots that enable you to draw conclusions from data. Plot histograms of numerical data. Create your own functions that will enable you to reutilize code. Slice and dice Data Frames, subsetting data for specific domains. Join thousands of professionals and students in this R journey and discover the amazing power of this statistical open-source language. This course will be constantly updated based on students feedback...
10. R For Beginners: Learn R Programming from Scratch
Hi there, Welcome to my "R For Beginners: Learn R Programming from Scratch" course. R, r programming, r language, data science, machine learning, r programming language, r studio, data analytics, statistics, data science, data mining, machine learningR Programming in R and R Studio, analyze data with R (programming language) and become professional at data miningMachine learning and data analysis are big businesses. The former shows up in new interactive and predictive smartphone technologies, while the latter is changing the way businesses reach customers. Learning R from a top-rated OAK Academy's instructor will give you a leg up in either industry. R is the programming language of choice for statistical computing. Machine learning, data visualization, and data analysis projects increasingly rely on R for its built-in functionality and tools. And despite its steep learning curve, R pays to know. In this course, you will learn how to code with R Programming Language, manage and analyze data with R programming and report your findings. R programming language is a leading data mining technology. To learn data science, if you don't know which high return programming language to start with. The answer is R programming. This R programming course is for: Students in statistical courses R (programming language), Analysts who produce statistical reports, Professional programmers on other languages, Academic researchers developing the statistical methodology, Specialists in the various area who need to develop sophisticated graphical presentations of data, and anyone who is particularly interested in big data, machine learning and data intelligence. No Previous Knowledge is needed! This course will take you from a beginner to a more advanced level. If you are new to data science, no problem, you will learn anything you need to start with R. If you are already used to r statics and you just need a refresher, you are also in the right place. Here is the list of what you'll learn by the end of the course,· Installation for r programming language· R Console Versus R Studio· R and R Studio Installation in r shiny· Basic Syntax in r statistics· Data Types in R shiny· Operators and Functions in R· R Packages in data analytics· Managing R Packages in r language· Data Management in R· Getting Data into R in machine learning· Computation and Statistics in data science· Hands-on Projects Experimental Learning in r programmingR programming languageRR languageAfter every session, you will have a strong set of skills to take with you into your Data Science career. So, This is the right course for anyone who wants to learn R from scratch or for anyone who needs a refresher. Fresh ContentWhat is R and why is it useful?The R programming language was created specifically for statistical programming. Many find it useful for data handling, cleaning, analysis, and representation. R is also a popular language for data science projects. Much of the data used for data science can be messy and complex. The programming language has features and libraries available geared toward cleaning up unorganized data and making complex data structures easier to handle that can't be found in other languages. It also provides powerful data visualization tools to help data scientists find patterns in large sets of data and present the results in expressive reports. Machine learning is another area where the R language is useful. R gives developers an extensive selection of machine learning libraries that will help them find trends in data and predict future events. What careers use R?R is a popular programming language for data science, business intelligence, and financial analysis. Academic, scientific, and non-profit researchers use the R language to glean answers from data. R is also widely used in market research and advertising to analyze the results of marketing campaigns and user data. The language is used in quantitative analysis, where its data analysis capabilities give financial experts the tools they need to manage portfolios of stocks, bonds, and other assets. Data scientists use R in many industries to turn data into insights and predict future trends with its machine learning capabilities. Data analysts use R to extract data, analyze it, and turn it into reports that can help enterprises make better business decisions. Data visualization experts use R to turn data into visually appealing graphs and charts. Is R difficult to learn?Whether R is hard to learn depends on your experience. After all, R is a programming language designed for mathematicians, statisticians, and business analysts who may have no coding experience. For some beginning users, it is relatively simple to learn R. It can have a learning curve if you are a business analyst who is only familiar with graphical user interfaces since R is a text-based programming language. But compared to other programming languages, users usually find R easier to understand. R also may have an unfamiliar syntax for programmers who are used to other programming languages, but once they learn the syntax, the learning process becomes more straightforward. Beginners will also find that having some knowledge of mathematics, statistics, and probabilities makes learning R easier. It's no secret how technology is advancing at a rapid rate. New tools are released every day, and it's crucial to stay on top of the latest knowledge. You will always have up-to-date content to this course at no extra charge. What is Python?Python is a general-purpose, object-oriented, high-level programming language. Whether you work in artificial intelligence or finance or are pursuing a career in web development or data science, Python is one of the most important skills you can learn. Python's simple syntax is especially suited for desktop, web, and business applications. Python's design philosophy emphasizes readability and usability. Python was developed on the premise that there should be only one way (and preferably, one obvious way) to do things, a philosophy that resulted in a strict level of code standardization. The core programming language is quite small and the standard library is also large. In fact, Python's large library is one of its greatest benefits, providing different tools for programmers suited for a variety of tasks. Python vs. R: what is the Difference?Python and R are two of today's most popular programming tools. When deciding between Python and R, you need to think about your specific needs. On one hand, Python is relatively easy for beginners to learn, is applicable across many disciplines, has a strict syntax that will help you become a better coder, and is fast to process large datasets. On the other hand, R has over 10,000 packages for data manipulation, is capable of easily making publication-quality graphics, boasts superior capability for statistical modeling, and is more widely used in academia, healthcare, and finance. Video and Audio Production QualityAll our contents are created/produced as high-quality video/audio to provide you with the best learning experience. You will be,· Seeing clearly· Hearing clearly· Moving through the course without distractionsYou'll also get: Lifetime Access to The CourseFast & Friendly Support in the Q & A sectionUdemy Certificate of Completion Ready for DownloadDive in now! R For Beginners: Learn R Programming from ScratchWe offer full support, answering any questions. See you in the course!...