For a long time now, R has stayed at the top on the list of the best programming languages. It's a statistical programming tool with the prowess to handle gigantic amounts of data. Hence, it enjoys great worldwide popularity and various use cases. This R programming tutorial is for everyone interested in learning its basics.
This R language tutorial will help beginners find the direction needed to begin learning it. We'll begin with 'what is R', moving on to its terminologies, uses, possible career paths and much more. So, without any further ado, let's get started!
This R programming tutorial is for anyone who's interested in beginning their learning journey. R is apt for wrangling gigantic amounts of information and consequently producing ready-to-publish visualizations and graphics. In short, it's suitable for all kinds of data mining, modeling and analysis tasks.
There are many more benefits associated with this language, which will be explored further into this tutorial. Let's begin with an introduction to what it actually is and does.
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R is a widely used graphics and statistical computing system. There are two parts of this system. One is the R language itself and the other is a run-time environment. This interpreted language enables users to access its function via a command-line interpreter.
R a domain-specific language (DSL), which makes it different from its competitors like Python and Java. The latter two are tagged as general-purpose languages, making them usable in various domains. R, on the other hand, has functions and use cases that are applicable for a set domain. It's said to be best used for data science tasks.
It has a huge set of functions that render is useful for data visualizations. Users can thus analyze data, model it according to their needs and consequently create graphics.
Created by Ross Ihaka and Robert Gentleman in 1997, R takes its name from its creators' first letter. The R Foundation, which is a non-profit organization, was founded in April 2003 to support the R project. It was switched to an open-source and free software in 1995 under the GNU General Public Licence.
Both Ross and Robert were statisticians at the University of Auckland. Both these statistics professors saw the need to have a better software environment. This need they saw led them to commence developing R.
R programming language is supported on various operating systems including Windows, Mac OS X and Ubuntu. One can install and use R studio on any of them according to their preference. In this tutorial, we are exploring how to install R on Windows:
Step 1. First, ensure that your system is compatible with the RStudio package you want to install. Here are the basic software and hardware requirements for Windows:
| Category | Requirement |
|---|---|
| Operating System | Windows 10 or later (64-bit recommended) |
| Processor | 1.6 GHz or faster (dual-core or better recommended) |
| Memory (RAM) | At least 4 GB (8 GB or more recommended) |
| Disk Space | Minimum 500 MB (more needed for packages/data) |
| Display | Minimum 1024×768 resolution |
Step 2. Go to the official website and download the required packages.
Step 3. Now, navigate the files that you have just downloaded and double click on it to start the installation process.
Step 4. Now, you have to check if RStudio is working efficiently by running a basic Hello World program.
# Input
print('Hello world')
# Output
[1] "Hello world!"
Step 5. Lastly, install all the necessary packages with the following command:
install.packages(c("dplyr","ggplot2"))
This R programming tutorial section covers the key terminologies of this language. Understanding these terminologies gives better insights about what this language can do and what its capabilities are. Here are the fundamental terminologies that set this language apart from the others.
R's core strength comes from its amazing data handling capabilities. It provides multiple data structures like matrices, lists, data frames and vectors, enabling users to analyze, store and manipulate data effectively.
Vectors are R's building blocks and one-dimensional arrays that empower users to simultaneously perform operations on various data points. Matrices are two-dimensional arrays produced by amalgamating various vectors of one length. Data frames are employees for data analysis and manipulation since they store different data types. Lists are versatile data structures that hold different element types.
Prior to analysis, data should certainly be clear and error-free. R contains a rich suite of packages and functions to carry out tasks like data cleaning and preprocessing. Missing values are handled with functions like complete.cases, na.omit and na.fill. Functions for data transformations, which are vital of many Machine Learning algorithms, are also present.
Another reason for the wide popularity of this language is the robust statistical toolkit it offers. An elaborate range of statistical packages and functions is offered. These make it a favorite amongst analysts, researchers and statisticians. It's capable of handling different statistical tasks, from the basic descriptive ones to advanced inferential analysis ones.
Data visualization makes up a huge aspect of data analysis, for which, this language provides various amazing libraries and tools. ggplot2 is a top data visualization library by R that helps in creating informative and compelling charts and plots.
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These steps will certainly be useful for those who are wondering how to learn R programming to leverage it optimally. Here are a few steps to take into consideration-
First things first and that means covering the basics of this language. To begin with the basics, it's important to install R and RStudio (an integrated development environment for R). R is downloadable from CRAN (Comprehensive R Archive Network) and RStudio is downloadable from RStudio.
It's also important to gain familiarity with the RStudio interface. The interface includes various aspects including-
Since it's a programming language, it includes learning and understanding the basic syntax for a strong foundation. There are various things to learn in this. This includes-
R is certainly a more complex language than a lot of its counterparts like Python or Java. It has a steeper learning curve, which means it takes more time to learn and perfect it. Thus, it is imperative to take an online course, training program or publications related to R.
There are many options in training courses today. For instance, live, interactive courses are best for those who are ready to spend a dedicated amount of time every day. Alternatively, self-paced courses are better for those looking to learn but at their own pace and comfort. Each has their own perks and when opted from a leading learning platform, the outcome is sure to be beneficial.
It's important to get started by practicing basic data manipulation and operations. This hands-on learning will imbibe more confidence in the learners about their skills and knowledge. Aspirants must write and execute scripts with RStudio's script editor. '#' should also be used to add documents and comments to the code.
Learn to work with factors- 'factor()', matrices- 'matric()', vectors- 'rep()', 'seq()', 'c()', etc., data frames- 'data.frame()' and lists- 'list()'.
Exploring packages is an integral aspect of becoming a pro in this field. Begin by installing packages with 'install.packages("package_name")' and loading them with 'library(package_name)'.
The top packages to learn about are-
Working on data analysis projects ensures that aspirants gain hands-on knowledge. The kind of skill enhancement that happens with this step is exceptional. There are plenty of things to work upon and these include-
Being an expert in R programming language means having a sea of skills and a wide knowledge span. Work on getting an advanced skill set in relation to statistical analysis, interactive visualizations and machine learning.
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As already discussed, R language is powerful and is capable of performing various data analysis, modeling and visualization tasks. It has found its usage in various industries, some already huge and some still in their growing phase. Here are some top applications of R language.
R has rapidly become a leading name in the finance sector because of its advanced statistical suite. R enables organizations to adjust risk performance, use different visualizations and perform downside risk measurement. Many tools for time-series analysis, autoregression, and moving averages are provided. These hold a great value in financial applications.
R is used extensively in healthcare aspects like bioinformatics, epidemiology, and genetics. Companies working primarily in these fields are enabled to crunch the data in hand and process gathered information. This renders them a suitable backdrop and environment for data processing and further analysis. It's also being used for advanced processes like drug discovery.
The e-commerce industry is certainly a leading one that extensively utilizes R in for data science. R is highly effective in this sector as internet-based organizations deal with different data forms and sources. R is also used to analyze cross-selling products for the clients. It's a great language for analyzing suggestions and recommendations.
Banking sector uses the R programming language for credit risk modeling and additional kinds of risk analytics. R, along with Hadoop, plays a big role in analyzing customer segmentation, retention and quality. It's used by big names like Bank of America.
Many beginners in R and data science prefer commencing in the field of social media. There are different social media data mining forms like sentiment analysis wherein R has proven to be a leading statistical tool. R helps in segmenting potential clients and making them targets to sell products.
Various manufacturing companies like Ford employ statistical tools like R for analyzing customer sentiment. This ultimately helps in optimizing their product as per the customer interests and trends. Companies are also able to match their production speed and quantity to the changing market demand.
Here is a list of some of the top trending names that are using the R programming language in their everyday working. It's a great language that is being used by top companies on a global level. Its statistical analytics properties come especially handy.
The R programming tutorial brings another important aspect for learners and that includes understanding what can be done with R. Here are the five awesome things that users can easily accomplish with R.
Data visualizations are helpful in understanding data. With R's ggplot2 package, almost any plot can be drawn easily. Hence, mastering this brilliant data visualization tool enables users in harnessing the maximum power of R. ggplot2's elegant syntax can be combined with plotly's interactivity to create an interactive dashboard.
Statistics are made to ensure that the data analysis are scientifically correct. To guarantee this, R is probably the best language with the finest suite of statistical tools. R gives users a lot of power with models like logistic regression and linear regression.
Outputs are structured in a report to communicate the results of the analysis in the best way possible. R Markdown offers a simple to use and highly comprehensive syntax for report generation in various formats.
The first step upon receiving new data is performing exploratory data analysis. This helps in understanding what the data is all about. R offers the 'tidyverse', which is a suite of packages including tidyr and dplyr. Both these and more packages help in manipulating and calculating on the data.
R has gained a lot of popularity since its first release. This R programming tutorial is for everyone looking for the best way to commence their journey in this language. While it's not as easy to use or learn as Python, it's certainly a language with many perks. Its advanced statistical capabilities ensure anyone using this language enjoys many additional career perks.
It depends on the content of the tutorial. It will be easy if the tutorial begins with the very fundamental concepts like basic syntax and data structures and then goes to core concepts. An average non-programer can learn it in about 6 weeks.
To learn R programming with a tutorial you must first install the RStudio. Then start with a suitable tutorial and follow each and every session of it. Alongside the tutorial use some additional sources like online communities and practice daily to speed up your learning.
Key features of R programming language includes open-source availability, cross-platform capability, data analysis and visualization, integration and strong community support.
You definitely can use R coding language for ML operations. It has many packages and libraries including caret, mlr and h2o for tasks like regression, classification, clustering and deep learning.
R is mainly focused on statistics and data analysis, while Python is a general-purpose language used for web development, automation and machine learning.
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