R Programming Certification Training
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About Course
R programming is primarily used for statistical computing and data visualization. It’s a powerful tool for data analysis, machine learning, and creating statistical graphics, making it a popular choice in fields like data science, bioinformatics, and finance. Enroll today to get Certified.
R Programming Training Course Syllabus
Loading Packages Installing Packages Unloading Packages
Integer Double Logical Character
Vectors Lists Matrix Data Frames Arrays
CSV Files Excel Sheets Databases SAS Files SPSS Files STATA Files URL Etcs
Min, Max, Range Mean, Median variance, Standard Deviation Quantiles Categorical Values
DPLYR
TIDYVERSE TIDYR
ggplot2 Lattice
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R Programming Training FAQ'S
At the University of Auckland in New Zealand, Ross Ihaka and Robert Gentleman developed R, an interpreted programming language. It is a software environment used to examine graphical displays, reporting, and data modeling. The S programming language is implemented in R and combines with lexical scoping semantics.
A vector is a series of data elements of the same basic type. The members in the vector are known as a component.
The R object that contains elements of different types such as numbers, strings, vectors, or another list inside it, is known as List.
A two-dimensional data structure used to bind the vectors from the same length, known as the matrix. The matrix contains the same types of elements.
A Data frame is a generic form of a matrix. It is a combination of lists and matrices. In the Data frame, different data columns contain different data types.
There are the following packages that are used for data imputation
- MICE
- missFores
- Mi
- Hmisc
- Amelia
- imputeR
This function is used to initialize the private data members while declaring the object.
Get ahead in your career by learning R Programming through VISWA Online Trainings
A random walk is the simplest example of a non-stationary process. A random walk has no specified mean or variance, and strong dependence over time, and its changes or increments are white noise. Simulating random walk in R:
arima.sim(model=list(order=c(0,1,0)),n=40)->rw ts.plot(rw)
