R is a free open source programming language used for data analytics and statistical analysis. Learning to build and use these AI applications with R will quickly enable you to develop custom AI apps to deploy within your own organization: applications for predictive modeling, for deep learning, for extracting mission-critical information from reams of text, and more.

R is one of the most efficient and effective tools for analyzing and manipulating data for statistical purpose. In addition R is inexpensive and beautiful.

R is a general purpose environment for data analytics. While it might not be the fastest language for every use, it is very fast and can help with nearly every AI task one can think of:

R AI Pros

Gather and clean up data-sets
Use matrix transformations and linear algebra to process the data
Randomly split a large data-set into training and test sets
Apply various statistical and machine learning functions.
Provide an analysis of the reliability and goodness of fit of the results
Use the results to develop predictions about new data
Monitor for changes in the nature of incoming data
The entire development process can be captured in a markdown document to facilitate the publication of findings and new techniques discovered.
The language has been ported to big data environments.
The language can even provide a experimental web interface so that users try their hand at data analysis creating a synergy between man and machine to tackle big data problems.

While the R language has a few unusual ways of doing common things, a lot of work can be done with just a little code that is developed in an interactive environment. This makes it easy to develop and test new idea and verify them with the wide range of graphic functions provided.

On top of it all, this clever tool is available for free, runs on common and server hardware using an interpreter that is open source and supported by a professional community of committed users and data scientists.

Tools for AI with R

Tool Description
C50 C50 finds application in building decision tree algorithms. Decision tree models have a structure similar to the flowcharts with decision node indicating the decision to be made on a particular attribute. This algorithm has widespread application for processes which needs to maintain transparency at all levels. For instance, Airtel may try to predict a set of customers who are likely to churn out from their network. Such analysis may help Airtel understand the reasons for the churn and so, Airtel would be able to attract the customers with some lucrative offers or act upon the reasons of dissatisfaction among such customers. C50 does such predictions fair justice.
Class ‘class’ contains the knn( ) function which provides the food for constructing the k-nearest neighbours algorithm- an easy machine learning algorithm. The knn( ) function uses the Euclidean distance method to identify the k-nearest neighbors; k is a user-specified number.
e1071 Provides the function naiveBayes( ) based on the simple application of conditional probability. Let us try to analyse a situation where retailers need to find out what is the probability of a customer to buy bread when he has already bought butter. Such type of analysis requires conditional probability which can be made available using e1071 package which in turn helps in finding effective business solutions. In our example, if the probability of buying bread is high the retailer may formulate new strategies such as keeping bread and butter together or give some discount if bread and butter are bought together etc., to augment the store’s revenue.
Gmodels During statistical analysis, we may often want to compare relationship between two nominal variables. To explain this, let’s consider 2 nominal variables, one being ‘Income groups’ (Levels=High, Medium, Low), and the other being ‘Highest level of Education’ (Levels= Undegraduation, Graduation, Post-Graduation).We might be interested to find out whether the Income has a significant relationship with the affordability of the level of education. Such analysis can be done using CrossTable( ) function available in gmodels package, where the results are represented in a tabular format with rows indicating the levels of one variable and the columns indicating the levels of the other variable.
Kernlab OCR reads various characters using key dimensions. The typical machine has to be able to distinguish the letters accurately. Image processing is perhaps one of the most difficult tasks involved considering the amount of noise present, the positioning and orientation and how the image gets captured. Support Vector Machine(SVM) models finds extensive applications in pattern recognition fields as it is highly dexterous in learning the complex patterns efficiently.
Neuralnet Artificial Neural Network Algorithms (ANN) often referred to as ‘deep learning’ can be practised through the ‘neuralnet’ package. ANN builds a model based on the understanding of how the human brain works by establishing a relationship between the input and the output signals.
RODBC If the data is stored in SQL databases (Oracle, MySQL) or ODBC(Open Database Connectivity) and needs to be converted into R data frame, then nothing can be as effective as RODBC package to import this data frame.
rpart For building regression trees. Regression is a concept which involves establish relationship between a single dependant variable and independent variable(s).Suppose, a product company needs to determine how it’s sales have been due to promotions on TV, Out of Home (OOH), Newspapers, Magazines etc. The rpart package containing the rpart() function helps explain the variance in the dependant variable( eg. sales) caused by the independent variables(TV ads, newspaper ads, magazines).
Tm These days lots of statistical analysis requires thorough processing of text data, be it SMS’s or mails, which involves a lot of tedious efforts. This kind of analysis might even require removing punctuation marks, numbers and certain unwanted words like ‘but’,’or’ etc. depending upon the business requirement. The tm package contains flexible functions like corpus( ) which can read from pdf’s and word documents, and convert the text data into R vector and tm_map() which helps in cleaning the text data( removing blanks, conversion from upper to lower and viceversa etc.), thereby making the data ready for analysis.
Wordcloud The package ‘wordcloud’ helps to create a diagrammatic representation of words and a user can actually customize the ‘wordcloud’ such as place the high-frequency words closer together in the centre, arrange the words in a random fashion, specify the frequency of a particular word etc. thereby etching a long lasting impression in anyone’s mind.

Data science is driving the AI market, with organizations looking to leverage AI capabilities for predictive modeling. To leverage these capabilities, organizations need developers trained in developing Artificial intelligence applications using R. Businesses all over the world are looking for smarter tools and applications that help them reduce efforts and maximize profits.

R AI Jobs

These are some positions which can help you have an idea what recruiters are looking for in terms of experience, coding language, education, certificate … etc.

R AI Jobs
INDEED Indeed R AI Jobs
Monster Monster R AI Jobs

R AI Videos

These are some videos related to R and artificial intelligence. Enjoy!

R Code Practice

Would you like to practice your coding skills? Here are some great resources:

R Code Practice
R Statistics R Practice
R Bloggers Learn R with R Bloggers

More information: R offers a huge list of benefits to all. The usage of R is such that it cannot be limited to only one activity. Its growing popularity has allowed it to enter into some of the most popular and complex processes like Artificial Intelligence (AI), Machine Learning (ML), natural language processing, data science etc. We hope this page was helpful and provided you with some information about AI with R. Check out our main page for more components of artificial intelligence resources.