In Introduction to R, you will master the basics of this widely used open source language, including factors, lists, and data frames. With the knowledge gained in this course, you will be ready to undertake your first very own data analysis.
Oracle estimated over 2 million R users worldwide in 2012, cementing R as a leading programming language in statistics and data science. Every year, the number of R users grows by about 40%, and an increasing number of organizations are using it in their day-to-day activities. Begin your journey to learn R with us today!
Program starts with basics of R Programming, evolving into How to work with Data in R; Importing the Data, Preparing the Data, Analysing the Data in R and Visualization of the results in R.
R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, …) and graphical techniques, and is highly extensible. The S language is often the vehicle of choice for research in statistical methodology, and R provides an Open Source route to participation in that activity.
✔ Learn the most popular tool for data analytics
✔ Start with the R basics, to advance Programming in R
✔ Hybrid Learning with Guided practice & Weekly Practice quiz questions on the app along with the classroom sessions
✔ Extensive Learning hours with 44 hours of classroom training including the classroom assessment with additional 42 hours of online guided practice for better learning and increased retention
R is the most popular language used by Data Scientists & Statisticians. It is estimated that there are approximately 2 million users of R. Thousands of people around the world are contributing to R with Data Science Course technology along with their team. It is considered to be as the game-changer because R programming has turned out to be the best data analytical tool.
The term “environment” is intended to characterize it as a fully planned and coherent system, rather than an incremental accretion of very specific and inflexible tools, as is frequently the case with other data analysis software.
R, like S, is designed around a true computer language, and it allows users to add additional functionality by defining new functions. Much of the system is itself written in the R dialect of S, which makes it easy for users to follow the algorithmic choices made. For computationally-intensive tasks, C, C++ and Fortran code can be linked and called at run time. Advanced users can write C code to manipulate R objects directly.
BRILLIANCE has been awarded as “Best IT and Education Training Company”. It delivers high-quality R Programming training. This course has been especially designed to prepare students for a job in the analytics space. This training is delivered on the short duration as well as long term basis. Any technical student or corporate person can join this training.
The company has designed training programs for both students and professionals separately. R Programming course with BRILLIANCE provides an extensive coverage of a number of advanced R Programming concepts and their implementation in real-time projects. The training offers hands-on experience with a number of common R Programming concepts.
With world-class infrastructure and a dynamic team of experienced faculty, BRILLIANCE provides the R Programming training in Jaipur. After completion of the R Programming program at BRILLIANCE, the students are awarded a global Certificate which is recognized as well as accepted not only in India but also in foreign countries. The training process at BRILLIANCE comprises of both classroom as well as practical sessions which will make students capable of handling complex and difficult situation when they would join the corporate world.
Training in R programming is available on weekdays as well as weekends. Special Classes can also be scheduled as per requirement. Its industry-focused curriculum is in line with the international standards to make sure that the training prepares our students for jobs not just in India but foreign countries too. Apart from that, it makes all its efforts to provide placement in top MNC’s to its certified students.
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R is being developed for the Unix-like, Windows and Mac families of operating systems. Support for Mac OS Classic ended with R 1.7.1..
The name is partly based on the (first) names of the first two R authors (Robert Gentleman and Ross Ihaka), and partly a play on the name of the Bell Labs language ‘S’
A package consists of a subdirectory containing a file DESCRIPTION and the subdirectories R, data, demo, exec, inst, man, po, src, and tests (some of which can be missing). The package subdirectory may also contain files INDEX, NAMESPACE, configure, cleanup, LICENSE, LICENCE, COPYING and NEWS. See section “Creating R packages” in Writing R Extensions, for details. This manual is included in the R distribution, and gives information on package structure, the configure and cleanup mechanisms, and on automated package checking and building. R version 1.3.0 has added the function package.skeleton() which will set up directories, save data and code, and create skeleton help files for a set of R functions and datasets.
x[i] <- list(NULL)
to set component i of the list x to NULL, similarly for named components. Do not set x[i] or x[[i]] to NULL, because this will remove the corresponding component from the list. For dropping the row names of a matrix x, it may be easier to use rownames(x) <- NULL, similarly for column names.
R has a special environment called .AutoloadEnv. Using autoload(name, pkg), where name and pkg are strings giving the names of an object and the package containing it, stores some information in this environment. When R tries to evaluate name, it loads the corresponding package pkg and reevaluates name in the new package’s environment. Using this mechanism makes R behave as if the package was loaded, but does not occupy memory (yet). See the help page for autoload() for a very nice example.
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