R is a programming language and free software environment used for multiple purposes such as statistical analysis, data manipulation, predicting and forecasting, etc. With R, well-designed publication plots can be produced. R runs on platforms like UNIX, LINX, Windows, and MacOS. The code for R is written in C, Fortran and R. R is an interpreted language that can implement a wide variety of statistical and graphical techniques. R makes it easier for the users to follow the algorithm choices as most of the functions are written in R itself.
Due to it interesting benefits, R is used by several companies such as Google, Facebook, Ford, etc. R is used by the Human Rights Data Analysis Group to gauge the impact of war. R is also used by Ford to revamp the designs of its vehicles. R has a promising future because of its open source nature. According to Gartner, the popularity of R will definitely grow further. So, it is the right time to move forward in your career with R. This article covers important R programming interview questions that you can take ideas from if you’re taking an interview.
R is a programming language and a software environment meant for statistical analysis and creating graphs. It is used by analysts, statisticians and data scientists for various purposes. R uses data objects for data calculations and it is an alternative to conventional statistical packages such as SAS, SPSS, etc. A lot of companies are incorporating R into their business models to proliferate their revenue. There is a huge career prospect in R such as data scientist, R programmer, Analyst consultant, etc.
The functions that R provides are
Other functions of R include Regression, GLM, mixed-effects, distribution, GAM, non-linear, etc.
Data structure is a form of organizing and storing data. It is imperative to have a strong understanding of various data types and data structures in order to make the best use of R languages. R programming supports five types of data structures namely vector, matrix, list, data frame and factor.
The following steps are followed to build and evaluate a linear regression model in R
R packages are the collection of R functions and sample data that are stored under a directory name called library. Initially, R adds a set of packages during installation. However, new packages are added as and when required for specific purposes. The different packages available in R are
The functions in dplyr package are-
The packages used for mining in R are-
Clustering refers to the group of objects that belongs to the same class. It is a process to make a group of abstract objects into the class of similar objects. Clustering is required in data analysis due to the following reasons-
Rattle gives statistical and visual summaries of data and is a popular GUI for data mining. It transforms data so it can be easily modelled and builds a supervised and unsupervised ML model from the data. It also gives the graphical presentation of the models. Rattle is also used as a teaching facility to learn R languages. The features of Rattle package include clustering, modelling, evaluation, statistical test, etc.
In R, a white noise model is a basic time series model which is also the basis for more elaborated and defined models. To stimulate the data from a variety of tie series model, Arima.sim() function is used. The white noise model has a fixed constant mean, fixed constant variance and no correlation over time.
In R programming, random walk model is an example of the non-stationary model. A random walk has no fixed mean or variance. It also has a strong dependence over time. There are two types of random walks namely random walk without drift and random walk with drift.
There are several ways to import data in R. You can use R commander to import data in R.
under the Principal Component Analysis, the data is transformed into a new space. The first principal component takes the maximum amount of variance from the original data. The second principal component captures the amount of variability left. This is true for each component element and they are all uncorrelated. In R programming, Principal Component Analysis can be done using the function prcomp().
The following are the few differences between Python and R language
R programming language | Python Programming language |
In R programming, model building is similar to python. | Model building is similar to R. |
It has good model interpretability. | It has comparatively low model interpretability. |
It has a steep learning curve. | In python, the learning curve is easier as compared with R. |
It has better data visualisation libraries. | Data visualisation is not better than R. |
Good commuting support. | Commuting support not better than R. |
Library()- If the desired package cannot be loaded, this function will display an error message. It loads the package whether it is already loaded or not.
Require()- When a particular package is not found, it gives warning messages. Require() is used inside a function. It checks whether it is loaded or not and loads if it is not loaded.
R language is currently the most sought-after programming languages. It offers several benefits to the users
In R, the following sorting algorithms are available
The following are the programming features of R
The scope of R as a programming language is high and it has varied applications in various verticals. The important applications are
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