R is a programming language utilized to compute statistics, interpret data, and display data graphically. Built in the 1990s by Ross Ihaka and Robert Gentleman, R was developed as an efficient data handling, data cleaning, analysis, and statistical representation platform. R was not a very common tool back then, but now it has gained enormous applications and popularity as a tool for projects in data science.
According to data science developers, 40 percent of all data scientists surveyed prefer R, 34 percent prefer SAS, and 26 percent prefer Python. R is the second most common language in data science, according to data science experts. You are likely to be confused between R and Python when you decide on the language to use for your next data science project. Yes, a conflict of ages in the data science world! Though each of these is equally competent and has its pros and cons, each is associated with certain distinct advantages. The strengths of R in data science are addressed here and why it proves to be an ideal choice in this space.
Blog Contents
- Benefits of R
- Academics
- Data Wrangling
- Visualizing Data
- Specificity
- Machine Learning
- Availability
- Conclusion
Here are some reasons why people choose R as their partner in the journey of Data Science.
Benefits of R
As we all already know, the R language has a unique place for itself in the programming world, which shows its importance. Let’s have a look at why R is so popular and why you should consider using it.
1. Academics
In academia, R is a very common language. In an attempt to experiment with data science, many researchers and scholars use R. Many popular data science books and learning resources use R for statistical analysis as well. This creates a wide pool of people who have a strong working knowledge of R programming, as it is a language favored by academicians. If many individuals study R programming in their academic years, putting it differently would build a wide pool of professional statisticians who can use this expertise when moving to the field. Thus this language is contributing to increased momentum.
2. Data Wrangling
The cycle of cleaning messy and complex data sets to allow easy consumption and further study is data wrangling. This is a very crucial step in data science, which requires time. R has a comprehensive library of resources for manipulating and wrangling data and databases.
Some of the common data manipulation packages in R include:
Dplyr Kit- Dplyr is best known for its data discovery, and transformation capabilities and highly adaptive chaining syntax developed and maintained by Hadley Wickham.
Data.table Package- It enables the data set with minimal coding to be manipulated more easily. This simplifies the aggregation of data and significantly decreases the calculation time.
Readr Package- ‘Readr’ helps to read different data types into R. It performs the task at a 10x faster speed by not translating characters into variables.
3. Visualizing data
The visual depiction of data in graphical form is data visualization. This makes it possible to examine data from angles that are not apparent in unorganized or tabulated data. In data visualization, analysis, and representation, R has several resources that can support it. Ggplot2 and credit for R packages have become the traditional packages for plotting. Although the ggplot2 kit focuses on data visualization, ggedit lets users cross the gap between making a plot and precisely correcting all those pesky plot aesthetics.
4. Specificity
R is a language designed particularly for the study of statistics and reconfiguration of data. To make data analysis simpler, more open, and detailed, all the R libraries concentrate on making one thing certain. Via R libraries, any new statistical method is allowed first. For data analysis and prediction, this makes R a great choice. Members of the R community are very involved and welcoming and have an outstanding understanding of both statistics and programming. This all gives a special edge to R, making it a great choice for projects in data science.
5. Machine learning
A programmer may be required to train the algorithm at some stage in data science and bring in the capabilities of automation and learning to make predictions possible. R offers developers enough tools to train and test an algorithm and forecast future events. Thus, R makes machine learning much simpler and more accessible (a branch of data science). The list of machine learning R packages is very comprehensive. MICE (to take care of missing values), rpart & PARTY (for creating data partitions), CARET (for classification and regression training), randomFOREST (for creating decision trees), and much more are included in R machine learning packages.
6. Availability
R is an open-source programming language and is not severely limited to operating systems. R, being open-source, is protected by the General Public License Agreement of GNU. For a project of any scale, this makes it highly cost-efficient. Since it is open-source, R inventions occur at a rapid scale, and the developer community is massive. All this, combined with a massive amount of learning tools, makes R programming a great option for data science to begin learning R programming. Since there are many new developers exploring the R programming landscape, hiring or outsourcing R developers is simpler and more cost-effective.
ConclusionIt is eventually concluded that R is an ideal programming method in Data Science for research. An attempt was made in this article to enlist the features of R that make it a successful Data Science tool. R is worth its popularity, and it will scale higher. R enables a broad range of statistical and graphical techniques to be applied, such as linear and nonlinear modeling, study of time series, classification, classical statistical tests, clustering, etc. It deals with broader scopes of data handling and, thus, is very popular in the field of Data Science. Obtain a data science certification, enroll in a data science certification course.
