Machine Learning (ML): The ability of a machine to learn independently without any kind of explicit programming for a given scenario is called Machine Learning. Because there are many new developers exploring the landscape of R programming it is easier and cost-effective to recruit or outsource to R developers.Īrtificial intelligence is pertained to a system by two main ways: All of this, along with a tremendous amount of learning resources makes R programming a perfect choice to begin learning R programming for data science. Since it is open-source, developments in R happen at a rapid scale and the community of developers is huge. This makes it highly cost-effective for a project of any size.
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Being open-source, R is covered under the GNU General Public License Agreement. R programming language is open source and is not severely restricted to operating systems. Read More: 5 Machine Learning Trends to Follow R machine learning packages include 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. The list of R packages for machine learning is really extensive. Thus, R makes machine learning (a branch of data science) a lot more easy and approachable. R provides ample tools to developers to train and evaluate an algorithm and predict future events. This all gives R a special edge, making it a perfect choice for data science projects.Īt some point in data science, a programmer may need to train the algorithm and bring in automation and learning capabilities to make predictions possible. Members of the R community are very active and supportive and they have a great knowledge of statistics as well as programming. This makes R a perfect choice for data analysis and projection. Any new statistical method is first enabled through R libraries. All the R libraries focus on making one thing certain – to make data analysis easier, more approachable, and detailed. R is a language designed especially for statistical analysis and data reconfiguration. While the ggplot2 package is focused on visualizing data, ggedit helps users bridge the gap between making a plot and getting all of those pesky plot aesthetics precisely correct. The R packages ggplot2 and ggedit for have become the standard plotting packages. R has many tools that can help in data visualization, analysis, and representation. This allows analyzing data from angles that are not clear in unorganized or tabulated data. By not converting characters into factors it performs the task at 10x faster speed.ĭata visualization is the visual representation of data in graphical form. Readr Package – ‘readr’ helps in reading various forms of data into R. It simplifies data aggregation and drastically reduces the compute time. Some of the popular packages for data manipulation in R include:ĭplyr Package – Created and maintained by Hadley Wickham, dplyr is best known for its data exploration and transformation capabilities and highly adaptive chaining syntax.ĭata.table Package – It allows for faster manipulation of data set with minimum coding.
R has an extensive library of tools for data and database manipulation and wrangling. This is a very important and time taking process in data science. Read More: Suitability of Python for Artificial Intelligenceĭata wrangling is the process of cleaning messy and complex data sets to enable convenient consumption and further analysis. Thus, leading to increased traction towards this language. Putting it differently, if many people study R programming in their academic years then this will create a large pool of skilled statisticians who can use this knowledge when they move to the industry. Since it is a language preferred by academicians, this creates a large pool of people who have a good working knowledge of R programming. Many popular books and learning resources on data science use R for statistical analysis as well.
Many researchers and scholars use R for experimenting with data science. R is a very popular language in academia. R or Python? Why use R for Data Science?1.
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