There are over 3,000 R packages and the list is growing day by day. It would be beyond the scope of any book to even attempt to explain all these packages. This book focuses only on the key features of R and the most frequently used and popular packages.
R packages are self-contained units of R functionality that can be invoked as functions. A good analogy would be a .jar file in Java. There is a vast library of R packages available for a very wide range of operations ranging from statistical operations and machine learning to rich graphic visualization and plotting. Every package will consist of one or more R functions. An R package is a re-usable entity that can be shared and used by others. R users can install the package that contains the functionality they are looking for and start calling the functions in the package. A comprehensive list of these packages can be found at http://cran.r-project.org/ called Comprehensive R Archive Network (CRAN).
R enables a wide range of operations. Statistical operations, such as mean, min, max, probability, distribution, and regression. Machine learning operations, such as linear regression, logistic regression, classification, and clustering. Universal data processing operations are as follows:
To build an effective analytics application, sometimes we need to use the online Application Programming Interface (API) to dig up the data, analyze it with expedient services, and visualize it by third-party services. Also, to automate the data analysis process, programming will be the most useful feature to deal with.
R has its own programming language to operate data. Also, the available package can help to integrate R with other programming features. R supports object-oriented programming concepts. It is also capable of integrating with other programming languages, such as Java, PHP, C, and C++. There are several packages that will act as middle-layer programming features to aid in data analytics, which are similar to sqldf, httr, RMongo, RgoogleMaps, RGoogleAnalytics, and google-prediction-api-r-client.
As the number of R users are escalating, the groups related to R are also increasing. So, R learners or developers can easily connect and get their uncertainty solved with the help of several R groups or communities.
The following are many popular sources that can be found useful:
R mailing list: This is an official R group created by R project owners.
R blogs: R has countless bloggers who are writing on several R applications. One of the most popular blog websites is http://www.r-bloggers.com/ where all the bloggers contribute their blogs.
Stack overflow: This is a great technical knowledge sharing platform where the programmers can post their technical queries and enthusiast programmers suggest a solution. For more information, visit http://stats.stackexchange.com/.
Groups: There are many other groups existing on LinkedIn and Meetup where professionals across the world meet to discuss their problems and innovative ideas.
Books: There are also lot of books about R. Some of the popular books are R in Action, by Rob Kabacoff, Manning Publications, R in a Nutshell, by Joseph Adler, O'Reilly Media, R and Data Mining, by Yanchang Zhao, Academic Press, and R Graphs Cookbook, by Hrishi Mittal, Packt Publishing.
Data modeling is a machine learning technique to identify the hidden pattern from the historical dataset, and this pattern will help in future value prediction over the same data. This techniques highly focus on past user actions and learns their taste. Most of these data modeling techniques have been adopted by many popular organizations to understand the behavior of their customers based on their past transactions. These techniques will analyze data and predict for the customers what they are looking for. Amazon, Google, Facebook, eBay, LinkedIn, Twitter, and many other organizations are using data mining for changing the definition applications.
The most common data mining techniques are as follows:
Regression: In statistics, regression is a classic technique to identify the scalar relationship between two or more variables by fitting the state line on the variable values. That relationship will help to predict the variable value for future events. For example, any variable y can be modeled as linear function of another variable x with the formula y = mx+c. Here, x is the predictor variable, y is the response variable, m is slope of the line, and c is the intercept. Sales forecasting of products or services and predicting the price of stocks can be achieved through this regression. R provides this regression feature via the
lmmethod, which is by default present in R.Classification: This is a machine-learning technique used for labeling the set of observations provided for training examples. With this, we can classify the observations into one or more labels. The likelihood of sales, online fraud detection, and cancer classification (for medical science) are common applications of classification problems. Google Mail uses this technique to classify e-mails as spam or not. Classification features can be served by
glm,glmnet,ksvm,svm, andrandomForestin R.Clustering: This technique is all about organizing similar items into groups from the given collection of items. User segmentation and image compression are the most common applications of clustering. Market segmentation, social network analysis, organizing the computer clustering, and astronomical data analysis are applications of clustering. Google News uses these techniques to group similar news items into the same category. Clustering can be achieved through the
knn,kmeans,dist,pvclust, andMclustmethods in R.Recommendation: The recommendation algorithms are used in recommender systems where these systems are the most immediately recognizable machine learning techniques in use today. Web content recommendations may include similar websites, blogs, videos, or related content. Also, recommendation of online items can be helpful for cross-selling and up-selling. We have all seen online shopping portals that attempt to recommend books, mobiles, or any items that can be sold on the Web based on the user's past behavior. Amazon is a well-known e-commerce portal that generates 29 percent of sales through recommendation systems. Recommender systems can be implemented via
Recommender()with therecommenderlabpackage in R.