John Hopkins DS Specialization Series
What is R, R vs Python and the best way to learn R?
complete seriesPart 1- What is data science, big data and the data science processPart 2- The origin of R, why use R, R vs. Python and resources for learningpart 3- Version control, Git & GitHub and best practices for sharing code.part 4- The 6 types of data analysispart 5- The ability to design experiments to answer your Ds questionspart 6- Valor P e P-hackpart 7- Big data, its advantages, challenges and future
This series is based onData Science Specializationoffered by Johns Hopkins University on Coursera. The articles in this series are course-based notes with additional research and topics for my own study purposes. for the first courseData scientist toolbox, the notes are divided into 7 parts. You can also find notes related to the series.Here.
If you are new to the R programming language or want to learn more about it, then this article is for you, inspired by the DS Toolbox course on Coursera. I'll do a brief review of R's use in data science, detailing its importance and how it compares to Python. At the end of the article, I'll also share some resources for learning R. Hope you enjoy reading this article!
The creation of many programming languages is inspired by a specific problem and, in general, to make programming more intuitive. A more accessible and simpler language for statisticians should be developed for R. At that time there was a very complex language called SAS that was created for statisticians, but the syntax and functions were very difficult to use. Two brilliant developers saw an opportunity and created R, a free and open source alternative to SAS that is easier to write and optimize.
A quick look at Wikipedia makes that clear.
The R language is widely used by statisticians and data miners to develop statistical software and data analysis.
The definition is self-explanatory, R is a simple language geared towards statistical analysis, like Flutter for building mobile apps and React for beautiful websites. In general, R provides an easy way to take data and turn it into useful statistics. Stunning graphs and statistical learning models for predictions and inferences.
Today R is not a language for statisticians in academic environments, it has numerous extensions to serve different purposes for different domains. For example, it can be used in engineering, marketing, finance, insurance, etc.
The course mainly gives 4 reasons, namely:
R is the fundamental language for statistical analysis, and with its increasing functionality and sophistication and the rise of data science, it has become a common language for data scientists.
2. Free and open source
Like most languages, it is open source and free to use. There are statistical software like IBM SPSS that cost money, so making R accessible to everyone is great.
3. Extensive functionality
R is really versatile. In addition to statistics and graphs, the wide range of functions includes website creation, language analysis and much more. With the right package, you can do almost anything you want with R.
4. Great community
Like Python, it is open source and has a wide range of packages that extend the functionality. One advantage of this is that you can get help if you run into problems in R. You can go to the forums to sort out your answers.
Installation of R
You can download R fromFAUCET, what does theCfriendlyR AfilenorteRot
The official IDE for R isRStudioGenericName, which makes programming in R very easy and fun.
Given the rise of data science,reverse tidewas raised. It is a collection of R packages for use in data science, extending the capabilities of the base. If you have previous programming experience and are learning R for data science, using TidyVerse is ideal. However, without any programming knowledge, you must first learn the basics of R before proceeding with TidyVerse.
R and Python are the two giants of data science that are hotly debated. I will briefly cover the main points and make more detailed comparisons later in the references.
R was built for statisticians, so R will have an advantage in statistical modeling and analysis, especially with the TidyVerse package: data manipulation and plotting with ggplot2 and reporting with R Notebook and Markdown in RStudio. However, it has a steep learning curve and
Python is a more general language, a jack of all trades. It has an advantage in production and in other aspects such as software development, website building, machine learning and deep learning (PyTorch).
As both languages have their own advantages and disadvantages, most people often use both languages for different purposes depending on their specializations and use cases.
So which ones should you learn?
There are many different opinions that you should learn about. But I think the most important thing isunderstand the fundamentals of programmingOnly because all programming languages work according to the underlying concepts in programming and computer science.
And even after that, it doesn't really matter which language you want to learn first, both R and Python are widely used and used, and which one you would use depends on your specialization and the projects you are doing. so it's a good ideajust start learningSo don't waste time researching, thinking and acquiring skills to secure your future.
One way to narrow it down is to decide if your preferred role is in data science. When searching for your dream job, you will be able to learn which languages your preferred company prefers. Assuming you want to be a data analyst, you need to focus on R which has cutting edge packages. However, if you prefer to create templates and output your code, you should focus on Python.
ANDData Science Specializationoffered by Johns Hopkins University is a great starting point that provides everything you need to know about the fundamentals of data science and how to go from scratch to hero in R. Along with this course, you can get started with the bookR for data science.
To better understand machine learning and statistics, you can also read the bookIntroduction to Statistical LearningHere you will learn how to create all kinds of statistical learning models in R, but only after you have made significant progress in the Data Science specialization.
In addition, you should also always make use of the myriad of resources available on the web - like YouTube tutorials, articles, etc. and always experiment to really reinforce and pragmatize what you've learned and take notes (handwritten notes for better preservation) and code what you learn after your study sessions.
The most important takeaway for me about learning online is that your goals shouldn't be about certificates or showing new skills on your resume, it's about this.ability to learnjlearn to learnThis is extremely important in this fast-paced world where the latest data science tools and techniques can change in the coming years.
This guide to learning R is notthe bestOf course, learning R is just my personal plan and I hope you'll find it a good way to get the skill of writing R code too. Hope you enjoy learning.
In short, R is a great language for modeling and statistical analysis, and many people still use it to this day. Deciding to learn R and persevere in your learning can be one of the best decisions of your life. I'm currently learning R and I think it's a great language to learn and I can't wait to get good enough to create beautiful models and diagrams.
Learning data science can be incredibly difficult sometimes, you feel like you're not good enough (imposter syndrome). Personally, one way to stay motivated and determined is to remember why you're learning: to secure your future in this age of data science and AI, while gaining amazing skills in the process.
You must also understand that learning is a lifelong journey and process, it is not a 4 year college degree that you can get. Notes on a piece of paper do not show your mindset and philosophy, nor do they prove your skills and abilities (in the real world). The field is constantly changing and it is more important to be adaptable and focus on how to learn rather than textbook definitions and methods.
"Education lights a flame, it doesn't fill a vessel."
Like thisAlbert Einstein:
"Intellectual growth must begin at birth and end only at death."
So if you've thought about becoming a data scientist, learning R is a great place to start. I wish you all the best.
Thanks for reading and I hope this article was educational and gave you some insight into the R programming language.
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R is considered one of the more difficult programming languages to learn due to how different its syntax is from other languages like Python and its extensive set of commands. It takes most learners without prior coding experience roughly four to six weeks to learn R.What is the easiest way to learn R? ›
- Install , RStudio, and R packages like the tidyverse. ...
- Spend an hour with A Gentle Introduction to Tidy Statistics In R. ...
- Start coding using RStudio. ...
- Publish your work with R Markdown. ...
- Learn about some power tools for development.
- Google. Data Analysis with R Programming. ...
- Johns Hopkins University. Data Science: Foundations using R. ...
- Duke University. Data Analysis with R. ...
- Johns Hopkins University. ...
- University of Colorado Boulder. ...
- Coursera Project Network. ...
- Johns Hopkins University. ...
High-Quality Instruction. With R in 3 Months, you'll get high-quality instruction that will guide you from R newbie to R expert. Over the three months, you'll go through Getting Started with R, Fundamentals of R, and Going Deeper with R, courses that have helped thousands of people around the world learn R.Can I learn R on my own? ›
Yes. At Dataquest, we've had many learners start with no coding experience and go on to get jobs as data analysts, data scientists, and data engineers. R is a great language for programming beginners to learn, and you don't need any prior experience with code to pick it up.Why R is so difficult? ›
R is known for being hard to learn. This is in large part because R is so different from many programming languages. The syntax of R, unlike languages like Python, is very difficult to read. Basic operations like selecting, naming, and renaming variables are more confusing in R than they are in other languages.Can I learn R in 2 weeks? ›
Those who have programming knowledge may be able to learn how to use the language within two weeks. R online courses commonly offer instruction in the following topics: R syntax. Set-up.Is R difficult than Python? ›
Both Python and R are considered fairly easy languages to learn. Python was originally designed for software development. If you have previous experience with Java or C++, you may be able to pick up Python more naturally than R. If you have a background in statistics, on the other hand, R could be a bit easier.Can you learn R in a week? ›
It depends on your programming experience. If you have prior knowledge of any programming language, then it will take one week to learn R programming. Otherwise, for a complete beginner, it will take one week to clear the basics, provided you are practicing 3 hours per day.Is R difficult to learn? ›
R is considered by most to be a relatively difficult programming language to learn. One factor contributing to this difficulty is the sheer number of commands R users must learn.
A good data scientist is a passionate coder-slash-statistician, and there's no better programming language for a statistician to learn than R. The standard among statistical programming languages, R is sometimes called the “golden child” of data science.Is learning R enough for data science? ›
Yes, both Python and R are good options for data science, but they have their pros and cons. This means that If you're new to data science, one option might be more suitable than the other and if you already know one of them, learning the other might still be worth it.Is R harder than Excel? ›
R and Excel are beneficial in different ways. Excel starts off easier to learn and is frequently cited as the go-to program for reporting, thanks to its speed and efficiency. R is designed to handle larger data sets, to be reproducible, and to create more detailed visualizations.How many hours does it take to learn R programming? ›
The time it takes to learn R depends on the time you devote to learning and what you want to do with the language. A beginner-friendly course like Learn R takes about 20 hours to complete. So if you have an hour a day to devote to learning R, then you can complete the course in less than a month.Which website is best for learning R programming? ›
- R Programming by John Hopkins University: Coursera.
- Data Science R Basics Certificate by Harvard University: edX.
- R Training Course: Lynda.
- R Programming A - Z: R for Data Science: Udemy.
- R Programming Course and Tutorial Online: Pluralsight.
In the context of biomedical data science, learn Python first, then learn enough R to be able to get your analysis done, unless the lab that you're in is R-dependent, in which case learn R and fill in the gaps with enough Python for easier scripting purposes. If you learn both, you can R code into Python using rpy.Where can I practice R programming? ›
- DataCamp's free introduction to R tutorial and the follow-up course Intermediate R programming. ...
- The swirl package, a package with offline interactive R coding exercises. ...
- On edX you can take Introduction to R Programming by Microsoft.
- Data Science: Foundations using R: Johns Hopkins University.
- Data Analysis with R: Duke University.
- Google Data Analytics: Google.
- Data Analysis with R Programming: Google.
- Getting Started with R: Coursera Project Network.
- R Programming: Johns Hopkins University.
It's slow. R is slower than other programming languages like Python or MATLAB. It takes up a lot of memory. Memory management isn't one of R's strong points.What is the hardest programming language? ›
- C++ C++ is an object-oriented programming language and is considered the fastest language out there. ...
- Prolog. Prolog stands for Logic Programming. ...
- LISP. LISP stands for List Processing. ...
- Haskell. ...
- Assembly Language (ASM) ...
- Rust. ...
- Esoteric Languages.
as of August 2021, making it a favourite among data analysts and research programmers. It's also a crucial tool in finance, which heavily relies on statistical data. Developers tired of mainstream programming languages should learn R.Is R better than Python for statistics? ›
R is an open-source programming language widely used for statistical analysis and visual representation of data. Python appears to be faster with a simpler syntax. R is relatively slower than python or other programming languages with poorly written code.Which pays more R or Python? ›
According to a Dice Tech Salary Survey, the average salary for professionals skilled in R and Python is $115,531 and $94,139, respectively.Which is better SQL or R? ›
If you are interested in doing statistical analysis and data visualization, then R would be a good choice. If you are interested in working with databases, then SQL would be a better choice. If you are unsure which one to choose, you could consider learning both, as they can be used together in many different ways.What tool do most R developers use? ›
RStudio is the primary choice for development in the R programming language.How do I start programming in R? ›
- Learn the basics. Visit Try R to learn how to write basic R code. ...
- Broaden your skills. Work through The Beginner's Guide to R by Computerworld Magazine. ...
- Practice good habits. ...
- Look up help. ...
- Ask questions. ...
- Keep tabs on the R community. ...
- Deepen your expertise. ...
- Got R down?
- R Programming by Johns Hopkins University. ...
- R Basics — R Programming Language Introduction. ...
- Learn Data Science With R. ...
- Learn R for Business Analytics from Basics.
In general, you shouldn't be choosing between R and Python, but instead should be working towards having both in your toolbox. Investing your time into acquiring working knowledge of the two languages is worthwhile and practical for multiple reasons.How long does it take to learn R after Python? ›
R and Python have become the most preferred languages for data analytics and machine learning. It would ideally take 12–15 weeks to learn R and Python if you are a complete beginner. But, if you know the basics of R and Python, then it should take nearly 8 weeks to master both Python and R.Is 3 months enough to learn data science? ›
In conclusion, I would say that it is hard to become a Data Scientist, especially in three months. This is because: Some Bootcamp is not qualified enough to teach you the necessary data science skills. Not every student are talented enough to catch up with the learning material in a short time.
R is the winner for this section. The factors that affected this decision were the speed of data cleaning, packages available for data cleaning, and the ease of which the database connections are established. Packages used in R for data cleaning are well-established and, once learned, easy to use.How do I master R programming? ›
- Learn R: Introduction. Learn the basics of R Syntax and jumpstart your journey into data analysis.
- Learn R: Data Frames. ...
- Learn R: Data Cleaning. ...
- Learn R: Fundamentals of Data Visualization with ggplot2. ...
- Learn R: Aggregates. ...
- Learn R: Joining Tables. ...
- Learn R: Mean, Median, and Mode.
R is hands down the best option when you focus on statistics and probabilities. It has a large community of statisticians that can answer your questions. But, if you want to develop applications that process enormous amounts of data, Python is your best option.Is CSV or Excel better for R? ›
While there are R packages designed to access data from Excel spreadsheets (e.g., gdata, RODBC, XLConnect, xlsx, RExcel), users often find it easier to save their spreadsheets in comma-separated values files (CSV) and then use R's built in functionality to read and manipulate the data.Can R do everything Excel can? ›
R can handle very large datasets
Excel is limited in that there are only so many rows and columns per spreadsheet. So when you run out of rows/columns, you're forced to move to a new tab or a new file.
Python Vs R: Full Comparison
Python is better suitable for machine learning, deep learning, and large-scale web applications. R is suitable for statistical learning having powerful libraries for data experiment and exploration. Python has a lot of libraries. However, it can be complex to understand all of them.
How much do R Programming developers make? The salaries of candidates in this role range from a low of $160,000 to a high of $200,000, with a median salary of $189,750.Should I download R or R studio? ›
R and RStudio install in the standard manner on each of Windows, macOS, and Linux systems. System-specific instructions for installing R are given below. Regardless of your operating system, you should install R before installing RStudio.Is R or Python harder to learn? ›
R is easier to learn when you start out, but gets more difficult when using advanced functionalities. Python is a beginner-friendly language with English-like syntax. RStudio.Do data analysts use R or Python more? ›
While Python dominates the business environment, R is dominant in research. This is an important factor to consider when choosing your first programming language for data analysis – are you looking for a career in business or academia? Let's take a look at other important factors to take into account.
If you are interested in doing statistical analysis and data visualization, then R would be a good choice. If you are interested in working with databases, then SQL would be a better choice. If you are unsure which one to choose, you could consider learning both, as they can be used together in many different ways.Should I learn R if I know Python? ›
Conclusion — it's better to learn Python before you learn R
There are still plenty of jobs where R is required, so if you have the time it doesn't hurt to learn both, but I'd suggest that these days, Python is becoming the dominant programming language for data scientists and the better first choice to focus on.
R and Excel are beneficial in different ways. Excel starts off easier to learn and is frequently cited as the go-to program for reporting, thanks to its speed and efficiency. R is designed to handle larger data sets, to be reproducible, and to create more detailed visualizations.