Programming Language R: 1993 / Ross Ihaka & Robert Gentleman
R is an open source programming language for graphics and statistical calculation.
One could say that the programming language R is an implementation of S, combining a semantics of lexical scope.
The software is mainly written in C, R and Fortran.
R offers a wide variety of statistical and graphic techniques in addition to being highly scalable.
It includes conventional statistical tests, linear and non-linear modelling, time series analysis, aggregation and classification.
One of the main strengths of the R language is to effortlessly produce a well-designed quality publication plot, including mathematical formulas and symbols.
How does R help with data analysis?
Transform: the organization of data occurs by transforming a column into a variable during a row in observation. Observe your interests, create a new variable based on current variables, and discover observation statistics. Visualization: graphical representations of data to easily recognize trends, patterns, and data exceptions.
Models: these are complementary visualization tools such as computer or mathematical tools to answer observation questions.
Communication: Communicate results with others, from visualization to modeling, using easy-to-produce plots and print quality to share with anyone in the world.
Who uses R and why?
R has the trust not only of academics, but also of large companies, including Google, Facebook, Airbnb, Uber, etc.
On top of that, data analytics are definitely shaping today’s businesses. Even though there are many tools available, R stands out. That’s because you can have:
Excel and PowerBI but lack modeling capabilities Python is ideal for AI and ML but does not have communication features;
Table is excellent for graphical representations, but it needs to do better in decision making and statistics. However, R fills the void by offering an excellent learning curve with a good balance between data implementation and analysis.
Therefore, it makes sense to learn R for data manipulation and analysis and even become a data scientist.
And that’s why data scientists use R to understand data, perform manipulations, take the best approach, and communicate with others through reports, dashboards, or web applications. In this way, only one platform performs all the work.
You now know how R works and why you should go there, but where to learn R?
Is it that hard to learn?
If you had asked me those questions a few years ago, I would have said yes, it’s a bit difficult because of its complex structure. But now packages are introduced to solve this problem, which has made data handling easier and intuitive, and creating graphics is rather easy.
Packages such as TensorFlow and Keras allow you to create high-end Machine Learning techniques; you can call Python, C++ and Java in R and connect with Hadoop or Spark. And R has also evolved in terms of computational speed.
So, do you want to learn R?
I guess a YES!
Data Scientist with R:
Acquire the R skills that can help you build your career as a Data Scientist with Data Camp. To start the course, you do not need any previous knowledge or experience in this field.
They will teach you the versatile R language and how you can use it to import, manipulate, visualize and clean data, which are the basic integral skills you need. With interactive exercises, gain hands-on experience with the famous R packages such as ggplot2 as well as Tidyverse packages such as readr and dplyr.
The course will also introduce you to real-world datasets that will help you learn the machine learning and statistical techniques needed to write functions and perform cluster analysis on your own.
All you have to do is start this course, develop your R skills and continue your way to become a successful data scientist. They offer over 75 hours of learning resources. It includes the introduction of language to master the bases of data analysis with typical data structures such as matrices, vectors, data frames, etc. R Programming A-Z:
They teach you R step by step and you will learn valuable concepts that are applicable immediately after each course. And another great thing is that they teach you concepts using live examples. All the training is full of real-world analytical challenges that you will solve during your lecture and homework.
Anyone with any skill set can learn this course, but you need to learn the R language and take on exciting challenges. The course material teaches you its basic principles and how to create variables, vectors, loops and functions.
You will also learn normal distribution and practice with financial data, statistical data and sports data. In addition, you will learn how to use the R Studio and customize it according to your preferences.
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