A powerful language in the world of Data Science and statistical computing, R is getting increasingly popular among students. After it was developed at the beginning of the 1990s, there have been endless efforts made to improve the user interface of the programming language.
In its journey from being a rudimentary text editor to becoming an interactive R Studio and then going on to be Jupyter Notebooks, R has kept the world Data Science communities engaged.
But learning R could be frustrating if it is not approached the right way. You are probably familiar with student reviews documenting the struggle with the language. There would be some who gave up midway, and there are still some who feel stuck and are desperately looking for a more structured way to approach it.
Whether you fall in these categories or are a fresher, you may be relieved to know that the language does have some inherent issues. So quit being harsh on yourself if you find it difficult. Usually, there is a clear mismatch between the source of your motivation and what you are learning.
Nobody wants to engage with dry practice problems and coding syntax because they love these rather boring activities. Absolutely not! People want to bear with this long, arduous process of mastering the syntax because it will allow them to graduate to the good stuff. However, the mountain of complicated and lengthy topics that you have to cover to be able to do something with it can be painful.
And if you have arrived here to find out if there is a more natural way to reach your goal, you are where you should be.
There is a more structured way to learn R and believe me it is worth learning! For anyone interested, there are some definite advantages of learning R over the other programming languages. Most importantly, the everyday tasks in Data Science can be conducted straightforwardly with the tidyverse ecosystem of R. The Data Visualisation in R programming language is both simple and powerful. It also has one of the friendliest and most inclusive online community which you will find very helpful.
If you want to learn R, you need to be very clear about what you are dealing with and get a comprehensive view of the big picture. That is exactly what we will be doing here. For starters you are expected to have a lot of doubts regarding R, starting from the basics of what it means and Why learn R? it to the more complex areas of data analysis, data manipulation and machine learning. Let us tackle the aspects one by one as we guide you towards the right way of learning R.
What is R?
The R Foundation has described r as “a language and environment for statistical computing and graphics.” That is to put it very simply because R is clearly a lot more than that.
Below is a list of characteristics that have become definitive of R as a programming language:
- A data analysis software: For anyone wanting to make sense of data, R can be used for Data Visualisation, statistical analysis, and predictive modeling.
- A programming language: R is an object-oriented language that provides operators, functions, and objects to make it possible to explore, visualize, and model data.
- An open-source software project: Although free, the numerical accuracy and standard of quality in R is very high. The open interfaces of the language allow its easy integration with other systems and applications.
- A statistical analysis environment: R is where some of the most cutting-edge research happens in predictive modeling and statistics. This is why R is often the first platform to offer a newly developed technique after it arrives. Even for the standard statistical methods, implementation in R is really easy.
- A community: With a large online community, R has about two million users! It should not be surprising that the R project leadership includes leading computer scientists and statisticians.
Read: R Tutorial for Beginners
Why should you learn R?
It is a common belief that learning Data Science requires you to learn Python or R. The reason why most people choose R is because it has some clear advantages over other programming languages.
- R has an easy style of coding.
- As it is open-source, you do not have to worry about paying any subscription fee or additional charges.
- It offers instant access to more than 7800 customized packages for different computation tasks.
- There is overwhelming community support and numerous forums if you need any help.
- It promises a high-performance computing experience only a few other platforms can offer.
- Most Data Science companies and analytics around the world view R as a valuable skill in an employee.
What is your motivation for learning R?
Before you even begin with R, it is important to be clear at least to yourself about why you would want to do it. It will be interesting to find out what your motivation is and what expectations you have from this journey. Believe it or not, this exercise might act as a necessary anchor for you when the going gets tough and in this case, even boring. Find out what kind of data you want to work with and the kind of projects you would want to build.
Do you want to analyze language? Computer vision? Predict stock market? Deal with sports statistics? What does the future scope of data science look like? As you may have noticed, these aspects require you to delve a little deeper than just “being a data scientist”. It is not about becoming a data scientist as much as what you want to do as a data scientist.
Defining your end goal will be crucial in laying down your path. When you already know what you are looking to do with the knowledge, chances of getting distracted with anything you will not need are bleak. You will be able to stay focused on the aspects that are crucial to your goal and in the process and filter out the necessary from the unnecessary on your own.
Learn the basics in R
There is no learning R skipping this. Your first task would be to get familiar with the coding environment.
R Studio Interface
The first area is the R Console which displays the output of the code that is run. The next is R Script. This is the space where the codes have to be entered. The next is the R environment. It shows the additional set of external elements. It includes data sets, functions, vectors, variables, and so on. The last is the Graphical Output. These graphs are the result of exploratory data analysis.
It is best, to begin with some simple calculations. You can also use the R console as an interactive calculator. You can perform experiments with combinations of different calculations and match their results. As you move forward, you can also access previous calculations.
Pressing the Up and Down arrow after clicking on R console will take you to your previous calculation by activating the commands previously executed. However, if there are too many calculations involved, you can simply create variables. Remember though that these variables have to be alphanumeric or just alphabets but not numeric.
Considered the building block of programming language, the better you get at this, the less trouble you will face in debugging. The five atomic or basic classes of objects in R are character, integer or whole numbers, numeric or real numbers, complex and logical (true or false). These objects can have different attributes like names or names of dimensions, dimensions, length, and class.
Also read: R Interview Questions & Answers
The various data types in R includes vector (integer, numeric, etc.), data frames, list and matrices. Vector is the most basic object in this programming language. In order to create an empty vector, you will have to use vector(). Vector will consist of an object of the same class. It is also possible to create a vector by mixing objects of different classes.
It results in different types of objects being converted into one class. The list is a term used for a special type of vector. The list includes elements of various data types. Matrix is a name for a vector with a dimension attribute, i.e. introduced with a row and column. In the family of data types; however, the data frame is the most commonly used. This is because it stores tabular data.
A control structure is used to monitor the flow of commands or codes entailed within the function. A function is a command set created to automate a coding task that is repetitive. Students often find this section difficult to understand. Fortunately, there are many packages in R which compliment the task performed by these control structures.
Out of some 7800 packages or more, there are surely some that you will need more than the others. Life in Data Science is much easier when you know them. Among the many packages available to import data readr, jsonlite, data.table, sqldf and RMySQL are more useful. When it comes to data visualization, ggplot2 is best for advanced graphics.
R truly boasts a fantastic collection of data manipulation packages and some of the exceptional ones are plyr, stringr, lubridate, dplyr and tidyr. Now, everything you need to create a machine learning model can be provided by caret. But you can also install packages by algorithms like gbm, rpart, randomForest and so on.
Get acquainted with Data Exploration and Data Manipulation
This is the section where you deep dive into the different stages of predictive modeling. The deep-diving necessitates that you pay attention to understanding this section exceptionally well. The only way you can learn to build practical models that will be both great and accurate is by exploring the data from start to finish.
It is this stage that forms the foundation of data manipulation, which follows data exploration. Data manipulation is data exploration at a more advanced level. Under this section, you will get acquainted with feature engineering, label encoding and one hot encoding.
Also learn about: Python vs R for Data Science
Learn Predictive Modeling and Machine Learning
Mostly for starters, Machine Learning defines Data Science. It is where you deal with the topic, and it includes Decision Trees in R, Regression and Random Forest. This part will require you to deal very deeply with Regression, hence make sure you are clear with the basics.
You will come across Linear or Multiple Regression, Logistic Regression and related concepts. A decision tree is a term for a decisions and consequences model that is arranged in a tree-like fashion. It is a decision support tool that includes utility, event outcomes and resource costs. Random forests are also known as random decision forests, and they are created by multiple decision trees.
Move on to Structured Projects
Once you are equipped with the necessary knowledge covered under these broad categories, you will be able to move on to structured projects. It is probably the only way to master an art. When you apply your knowledge, your experience broadens as you encounter practical problems and device solutions on the go. This will also help you build a portfolio that you can present to your future employers regarding your practical experience in the field.
Remember, it is not uncommon to get frustrated at this stage as you face one hurdle after another. It is the part you have been preparing yourself for and do not be surprised if this seems more challenging than everything you have done till now. It usually happens because candidates cannot control their excitement to take up challenges and often dive into unique projects. Honestly, at this stage, you may not be ready for something like that, and it is best to stick to more structured projects that you are familiar with.
Build projects and continue learning
After working with some structured projects falling within the zone of familiarity, you can now venture into unknown territories. The expertise will only come with practice, and the idea is that once you have practiced with elements you were comfortable with, it is time to move beyond the comfort zone. It is where you test how much you have learned. This experience will not only show you how far you have come, but it will also reveal your strengths and weaknesses.
As you take up interesting Data Science projects, you will understand which are the areas you are still struggling with and need to focus on. Referring to resources for guidance and seeking the help of your mentors and field experts will only add to your knowledge of new methods, approaches, and techniques. This is where you benefit from upGrad because we see you through your journey from obtaining practical and theoretical knowledge to becoming a skilled Data Scientist.
Hence, if you get stuck, all you have to do is reach out. As you take up unique Data Science projects, you will understand which are the areas you are still struggling with and need to focus on. Referring to resources for guidance and seeking the help of your mentors and field experts will only add to your knowledge of new methods, approaches, and techniques.
It is where you benefit from upGrad because we see you through your journey from obtaining practical and theoretical knowledge to becoming a skilled Data Scientist. Hence, if you get stuck, all you have to do is reach out.
Usually in R, learning to work on a new project often means that you are learning to use a new package because mostly there will be packages exclusively meant for the kind of work you are doing. This is the knowledge you get with experience, which eventually makes you an expert. You can select the projects you want to work on based on your preferences which we asked you to settle at the very beginning.
Ramp up the level of difficulty as you progress because the secret to success with a programming language is never to stop learning. Just like a spoken language, you can reach a place where you are fluent and comfortable, but there will still be a lot to learn.
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Why is R considered to be a good choice for data science?
R is a highly preferred programming language for data science because it provides the users with an environment for analyzing, processing, transforming, and also visualizing the available information. R language also provides extensive support for statistical modeling.
Earlier, R was only used for academic purposes, but it became widely used in industries as well because of its sea of packages that can help out in different forms of disciplines like biology, astronomy, and much more. Other than that, R also provides plenty of options of advanced data analytics for the development of machine learning algorithms and prediction models, along with different packages for image processing. This is why R is considered to be a preferred choice by data scientists.
What are the key differences between R and Python?
Both R and Python are considered to be really useful in data science. Python provides a more general approach in data science, while R is usually utilized for statistical analysis. On one hand, the primary objective of R is statistics and data analysis, while the main work of Python is production and deployment.
Python is pretty simple and easy to learn because of its libraries and simple syntax, while R will be difficult in the beginning. The users of the R programming language are usually R&D professionals and scholars, while those of Python are developers and programmers.
Which one is easier to learn – R or Python?
Both R and Python are considered to be pretty easy to learn when it comes to programming languages. If you are familiar with the concepts of Java and C++, then you will find it pretty easy to adapt with Python, while if you are more on the side of math and statistics, then R will be a bit easier for you to learn.
In general, we can say that Python is a bit easier to learn and adapt to because of its easy-to-read syntax.