R Assignments help, R Homework help
Globalwebtutors provide premium R Assignment help services for complicated problems & questions. We provide customized help with R assignments & homework. Please send your assignment at firstname.lastname@example.org to get the instant help with R assignments.
Our online assignment help tutors are available 24/7 for students struggling with complex R problems. Get the 24/7 help & complete solutions for R assignments.
R is a programming language and software environment for statistical computing and graphics supported by the R Foundation for Statistical Computing.The R language is widely used among statisticians and data miners for developing statistical software and data analysis. Polls, surveys of data miners, and studies of scholarly literature databases show that R's popularity has increased substantially in recent years.
R is a GNU project.The source code for the R software environment is written primarily in C, Fortran, and R.R is freely available under the GNU General Public License, and pre-compiled binary versions are provided for various operating systems. While R has a command line interface, there are several graphical front-ends available.
Topics covered under R Programming Assignment are :
Introduction to programming, R Installation,Basic syntax,Scripts Documenting, commenting & sharing code,Creating and Manipulating objects in R,Objects in R,Vectors, Matrices Dates & Times,For Loops & Vectorization,Missing Values Objects in R, Arrays, Data Frames and Lists,Folder and File structure, Importing Data,Validating & Exploring Data Manipulating Data ,Summarizing, sorting, Sub-setting, Merging Visualizing ,Basic plotting ,Visualizing – 3D plotting, Histograms, Multi-panel plotting,Boxplots, ggplot2,Creating functions,Installing Packages,Introduction to Ecological Modeling,Mathematical modeling of e.g. individuals,populations, ecosystems,Maps & Charts Statistics
Univariate Analysis,Multivariate Analysis – Linear & Nonlinear Models.
Multivariate Analysis – Generalized Linear & Additive Models (GLMs & GAMs),Saving & Plotting Output,If Loops Model Comparison & Selection,Introduction to Time-series Analysis – Discontinuity analysis, detrending, smoothing, spectral analysis,Introduction to Clustering/Classification e.g.k-Means Partitioning, Partitioning Around Mediods (PAM).
Introduction to Unconstrained & Constrained (Simple & Canonical) Ordination –Principal Components Analysis (PCA), Redundancy Analysis (RDA),Interfacing with other languages (e.g. Python, Latex) Programming for parallel processing
Specific R packages for biological oceanography
R for fisheries management