R Programming Assignment Help
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The main goal in all R programming project is to import a data set, clean and tidy the data, and perform basic exploratory data analysis; all while using R Markdown to produce an HTML report that is fully reproducible. The best way we learn anything is by practice and assignment tasks. We have started R programming assignment help service for those (beginner to intermediate) who are familiar with R Programming and want to improve their R Programming coding skills. Hire us and Get your assignment done by R Programming assignment expert or learn from R expert with team training & coaching experiences. Our R programming expert will provide help in any type of programming Help, tutoring, mentorship and in R project development
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What is R Programming?
R is a Programming language and environment for statistical computing and graphics. 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. One of R’s strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed. Great care has been taken over the defaults for the minor design choices in graphics, but the user retains full control..
R Programming Assignment Help topics
Codersarts is the trusted platform for students who are looking for programming assignment help. R is a programming language and environment for statistical computing and graphics and we cover almost every topics from small assignment task to large analytics project. We provide the best academic R programming assignment help . Our top programming expert will assist you with the R programming assignment help. they're available 24/7 for your help.
But there are some important topics that you need to learn and work on R Programming assignment. As student when you are learning machine learning with R to complete academic assignment or learning R as developer to build statistical application there are certain topics which you must know so that you can easily get started the things. Practice your R programming skills using R Assignments. Once you learn R, it is important to practice to understand R concepts. This will also help you to understand the code and complete php assignment by yourself .
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Statistical analysis, from descriptive to inferential, from time series to clustering.
Create statistical and machine learning models, some generic, some specific to very complex fields
Create production machine learning data products to interact with your applications.
Report statistical analysis (or whatever you want to) in professional looking reports using RMarkdown.
Statistical packages including Stata, SAS, SPSS, Mplus, G*Power and Sample_Power
R can be used for data mining, statistical computing and modelling, machine learning and even reporting upto some extent.
Computational and statistical methods for the analysis of genomic data.
Vectors, Matrices and Arrays
Factors and Tables
R Studio assignment help needed
Get Help In following R Programming Tools
Popular R programming editor are RStudio, Jupyter Notebook
Caret ( classification and regression training )
The caret package stands for Classification And Regression Training contains functions to streamline the model training process for complex regression and classification problems. It makes the process of training, tuning and evaluating machine learning models in R consistent and easy. The caret features are data splitting, data pre-processing, feature selection, feature importance, model tuning, parallel processing, and visualization.
The DataExplorer package is one of the most popular machine learning packages in R language for exploratory data analysis. This package has three main goals: Exploratory data analysis, data reporting and feature engineering. Automated data exploration process for analytic tasks and predictive modeling, so that users could focus on understanding data and extracting insights. The package scans and analyzes each variable, and visualizes them with typical graphical techniques. Common data processing methods are also available to treat and format data.
One of the core packages of the tidyverse in the R programming language, dplyr is primarily a set of functions designed to enable data frame manipulation in an intuitive, user-friendly way. Data analysts generally use dplyr in order to transform existing datasets into a format better suited for some particular type of analysis, or data visualization.
ggplot2 is one of the most popular open source data visualization packages for the statistical programming language R. This package is a plotting package that makes it simple to create a complex plot from data in a dataframe. It provides a more programmatic interface for specifying what variables to plot, how they are displayed, and general visual properties.
kernlab is an extensible package for kernel-based machine learning methods in R. It takes advantage of R’s new S4 object model and provides a framework for creating and using kernel based algorithms. The package contains dot product primitives (kernels), implementations of support vector machines and the relevance vector machine, Gaussian processes, a ranking algorithm, kernel PCA, kernel CCA, kernel feature analysis, online kernel methods and a spectral clustering algorithm. Moreover it provides a general purpose quadratic programming solver, and an incomplete Cholesky decomposition method.
The mice package implements a method to deal with missing data. The package creates multiple imputations (replacement values) for multivariate missing data. The method is based on Fully Conditional Specification, where each incomplete variable is imputed by a separate model. The MICE algorithm can impute mixes of continuous, binary, unordered categorical and ordered categorical data. In addition, MICE can impute continuous two-level data, and maintain consistency between imputations by means of passive imputation. Many diagnostic plots are implemented to inspect the quality of the imputations.
A modern object-oriented machine learning framework in R. The R package mlr3 and its associated ecosystem of extension packages implements a powerful, object-oriented and extensible framework for machine learning (ML) in R. It provides a unified interface to many learning algorithms available on CRAN, augmenting them with model-agnostic general-purpose functionality that is needed in every ML project, for example train-test-evaluation, resampling, preprocessing, hyperparameter tuning, nested resampling, and visualization of results from ML experiments.
Plotly's R graphing library makes interactive, publication-quality graphs. Examples of how to make line plots, scatter plots, area charts, bar charts, error bars, box plots, histograms, heatmaps, subplots, multiple-axes, and 3D (WebGL based) charts.
randomForest is a machine learning package in R Programming language. It is used for the Classification and regression based on a forest of trees using random inputs, based on the Breiman random forest algorithm.
Rpart stands for Recursive partitioning. It is a machine learning package in R programming language for classification, regression and survival trees or rpart helps in building classification or regression models of a very general structure using a two-stage procedure and the resulting models can be represented as binary trees. The package implements many of the ideas found in the CART (Classification and Regression Trees) books.
The SuperML R package is designed to unify the model training process in R like Python. It provides a standard interface to the users who can use both the programming languages Python and R for building machine learning models. This package basically provides the features of Scikit Learn and predicts the interface to train machine learning models in R.
Misc Functions of the Department of Statistics, Probability Theory Group. Functions for latent class analysis, short time Fourier transform, fuzzy clustering, support vector machines, shortest path computation, bagged clustering, naive Bayes classifier, generalized k-nearest neighbour etc.
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R Programming Assignment projects
Basic statistical modelling examples
Multiple Linear Regression
Multinomial Logistic Regression
Ordered Logistic Regression
Multiple Linear Regression with interaction terms
Data Manipulation Assignment In R
Tidy evaluation is one of the major feature of the latest versions of dplyr and tidyr
Tidy eval: Programming with dplyr, tidyr, and ggplot2
Data wrangling with R and RStudio
Data Processing with dplyr & tidyr (Rpubs)
Joins: Join Functions, Joining Data in R with dplyr
data.table Package: Wrangling with data.table, Data crunching with data.table
String manipulation and stringr package: String Manipulation in R with stringr, Regular Expression in R
R graphics with ggplot2
Business Analytics Using Statistical Modeling
Methodological focus: Analytics to explain business phenomena and inform decision making by: describing and visualizing data; creating statistical models from domain knowledge; testing our domain understanding against data; creating experiments; and guarding against fallacious use of statistics.
Statistical focus: Computational approach to statistics by using programming techniques to overcome limitations of data quality and quantity. We will learn to reshape data, simulate data and statistics, discover unseen dimensions in data, and create complex models of unobservable phenomena.
R Programming sample assignments
Interest to determine if experts perceive supermarket chocolate differently to non-experts (the
Forecast Inventory demand using historical sales data in R
Predict Churn for a Telecom company using Logistic Regression
Cross Industry Standard Process for Data Mining (CRISP-DM)
Thera Bank - Loan Purchase Modeling
IBM used the request hyper parameter (Differential privacy (DP) ) with Naive Bayes
Solve a multi-class classification problem of predicting new users first booking destination.
Clustering time series in R
Data analysis focusing on health problems
Call center statistics analysis.
Shiny Application for Tweets Analysis
Targeting calcium signaling in bone micrometastases
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