Essential Fundamentals of R Assignment Help

Essential Fundamentals of R is a comprehensive programme that draws on a wide range of introductory topics and courses to provide participants with a solid foundation of knowledge for using R software for any intended purpose. There is no requirement for statistical knowledge, programming knowledge, or experience with R software. Installing R and RStudio; interactive versus batch use of R; reading data and datasets into R; essentials of scripting; getting help in R; primitive data types; important data structures; using functions in R; writing user-defined functions; the ‘apply’ family of functions in R; data set manipulation: and sub setting, and row and column manipulation Most sessions include “hands-on” material that shows how to run R “scripts” (sets of commands) and use many extended examples of R functions, applications, and packages for a variety of common purposes. The programme also makes use of RStudio, a popular open-source Integrated Development Environment (IDE) for developing and using R applications, supplemented with R-based direct scripts (e.g. ‘command-line prompts’) when necessary.

KEY FEATURE

R is a statistical computing and graphics programming language supported by the R Core Team and the R Foundation for Statistical Computing.

R, developed by statisticians Ross Ihaka and Robert Gentleman, is used for data analysis and statistical software development by data miners, bioinformaticians, and statisticians.

R is one of the most commonly used programming languages in data mining, according to user surveys and studies of scholarly literature databases.

The official R software environment is an open-source free software environment included in the GNU package that is distributed under the GNU General Public License.

Essential Fundamentals of R Assignment Help

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Key Topics

    Who should take this course:

    • Anyone interested in learning to use R software who is relatively new (or ‘brand new’) to R.
    • People who want to learn the fundamentals of R, such as data types and structures, data input and output, writing user-defined functions, and manipulating data sets.
    • Undergraduate and/or graduate students looking for an alternative to SAS or SPSS software
    • Professionals performing quantitative analyses and/or data analysis tasks who are looking for an alternative to SAS and/or SPSS software.

    Topics Covered By Our Essential Fundamentals of R Experts

    R is a programming language and free software that was created in 1993 by Ross Ihaka and Robert Gentleman. R has a large library of statistical and graphical methods. It includes, to name a few, machine learning algorithms, linear regression, time series, and statistical inference. Although most R libraries are written in R, C, C++, and Fortran codes are preferred for heavy computational tasks. R is trusted not only by academics, but also by many large corporations such as Uber, Google, Airbnb, Facebook, and others. Programming, transforming, discovering, modeling, and communicating the results are all steps in data analysis with R.

    a) Introduction And Orientation:

    • Workspace Management Controls
    • Workspace Management R Manuals
    • Hands-On Tutorial of R Basics
    • Tutorial with R Functions
    • Distributional Functions and Plotting

    b) Input And Output, Data And Data Structure

    • Data Input and Output
    • Accessing Data Sets in R
    • Basic Data Structures
    • Manipulating Dataframes
    • Input Output Exercises
    • Dataframe Manipulation Exercises

    c) Manipulating Data Frames In Depth

    • Input Output Exercises Solution
    • Data Manipulation Exercise Solution
    • Manipulating Dataframes

    d) Remaining Data Manipulation Exercises Solutions

    • User-Defined Function Exercise and Finish Manipulating Dataframes
    • Begin User-Defined Functions Demonstrations
    • The ‘Scope’ of a Function

    e) User defined functions in R

    • User-Defined Functions Exercise Solution
    • More on User-Defined Functions
    • Loops and Repeats
    • Control Statements
    • Anonymous Functions

    f) Writing Functions In R

    • Some Short Programs in R
    • The Apply family of Functions
    • The Reshape Package in R
    • Recoding Data in R
    • More Vector-Maker Exercises

    Marketing Analysis Project

    Several techniques for analysing survey data can be learned in marketing analysis. You can also learn about your market, which will help you identify your target audience and customers. There are several factors that influence marketing analysis:
    • Market Trends Market Size
    • Market Potential
    • Rate Of Market Growth
    • Profitability In The Market
    • Channels Of Distribution

    Installing Useful Packages and Using R and RStudio

    R is a programming language that is intended for statistical analysis. It can also be used in an object-oriented or functional context. It is, in particular, an implementation of S, an interactive statistical programming language. R was first released in August of 1993. It is still maintained by the R Development Core Team. RStudio is a fantastic Integrated Development Environment (IDE) for writing and working with R. Many data scientists write R with RStudio because it includes a console window, a code editor, tools for creating plots and graphics, and it can even be integrated with GitHub to support version control. While R shares some functionality with Microsoft Excel, it gives you more control over your data and allows you to add on a variety of packages that provide statistical functionality out of the box—you won’t have to build a formula to conduct a survival analysis; simply install the survival package and use it!

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