The R Language

00 The R Language.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# The R Language  \n",
    "[01 Starter Demos](01_Starter_Demos.ipynb)  \n",
    "[02 Simple R Syntax](02_Simple_R_Syntax.ipynb)  \n",
    "[03 Atomic Classes](03_Atomic_Classes.ipynb)  \n",
    "[04 Control Structures](04_Control_Structures.ipynb)  \n",
    "[05 The Base Plotting System](05_The_Base_Plotting_System.ipynb)  \n",
    "[06 Vectors](06_Vectors.ipynb)  \n",
    "[07 The plotrix Graphics Package](07_The_plotrix_Graphics_Package.ipynb)  \n",
    "[08 Lists](08_Lists.ipynb)  \n",
    "[09 Factors](09_Factors.ipynb)  \n",
    "[10 Base R Sample Datasets](10_Base_R_Sample_Datasets.ipynb)  \n",
    "[11 Attributes](11_Attributes.ipynb)  \n",
    "[12 Data Frames](12_Data_Frames.ipynb)  \n",
    "[13 Date Time](13_Date_Time.ipynb)  \n",
    "[14 Manipulating Data](14_Manipulating_Data.ipynb)  \n",
    "[15 Mandelbrot Fractal](15_Mandelbrot_Fractal.ipynb)  \n",
    "[16 Matrix Transformations](16_Matrix_Transformations.ipynb)  \n",
    "[17 Linear Algebra](17_Linear_Algebra.ipynb)  \n",
    "[18 Probability Distributions](18_Probability_Distributions.ipynb)  \n",
    "[19 Statistical Probability Functions](19_Statistical_Probability_Functions.ipynb)  \n",
    "[20 Matrices](20_Matrices.ipynb)  \n",
    "[21 Functions](21_Functions.ipynb)  \n",
    "[22 Logistic Regression](22_Logistic_Regression.ipynb)  \n",
    "[23 Linear Regression](23_Linear_Regression.ipynb)  \n",
    "[24 Regression Correlation](24_Regression_Correlation.ipynb)  \n",
    "[25 Object Oriented Programming](25_Object_Oriented_Programming.ipynb)  \n",
    "[26 The ggplot2 Package](26_The_ggplot2_Package.ipynb)  \n",
    "[27 Fourier Transform.ipynb](27_Fourier_Transform.ipynb)  \n",
    "[28 The dplyr Package](28_The_dplyr_Package.ipynb)  \n",
    "[29 The tibble Package](29_The_tibble_Package.ipynb)  \n",
    "[30 The readr Package](30_The_readr_Package.ipynb)  \n",
    "[31 The tidyr Package](31_The_tidyr_Package.ipynb)  \n",
    "[32 K-Nearest Neighbors](32_K_Nearest_Neighbors.ipynb)  \n",
    "[33 Machine Learning Project](33_Machine_Learning_Project.ipynb)  \n",
    "[34 Artificial Neural Networks ???](34_Artificial_Neural_Networks.ipynb)  \n",
    "[35 Bayesian_Networks](35_Bayesian_Networks.ipynb)  \n",
    "[36 K-Means Clustering](36_K_Means_Clustering.ipynb)  \n",
    "[37 Support Vector Machines](37_Support_Vector_Machines.ipynb)  \n",
    "[38 MNIST Machine Learning](38_MNIST_Machine_Learning.ipynb)  \n",
    "\n",
    "## Good Reads\n",
    "[R for Data Science - Grolemund & Wickham](http://r4ds.had.co.nz)  \n",
    "[Exploratory Data Analysis with R - Roger Peng](https://bookdown.org/rdpeng/exdata)  \n",
    "[R Programming for Data Science - Roger Peng](https://bookdown.org/rdpeng/rprogdatascience)  \n",
    "[R tips: 16 HOWTO’s with examples for data analysts - Lingyun Zhang](https://bookdown.org/lyzhang10/lzhang_r_tips_book/)  \n",
    "[Cookbook for R - Winston Chang](http://www.cookbook-r.com/)  \n",
    "[UC Business Analytics R Programming Guide](http://uc-r.github.io/)  \n",
    "[WikiBooks: R Programming](https://en.wikibooks.org/wiki/R_Programming)  \n",
    "[CRAN: An Introduction to R](https://cran.r-project.org/doc/manuals/r-devel/R-intro.html)  \n",
    "[CRAN: R Language Definition](https://cran.r-project.org/doc/manuals/r-devel/R-lang.html)  \n",
    "[CRAN: R Language Definition](https://cran.r-project.org/doc/manuals/r-devel/R-lang.pdf)  \n",
    "\n",
    "## Install R and RStudio\n",
    "\n",
    "Download and install R from CRAN for your operating system: \n",
    "[Download and install R](https://cran.r-project.org)  \n",
    "\n",
    "Download and install RStudio for desktop \n",
    "[Download and install RStudio](https://www.rstudio.com/products/rstudio/download)  \n",
    "    \n",
    "\n",
    "\n",
    "## Overview\n",
    "                       \n",
    "- R was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand as an open source implementation of the S language.\n",
    "- R an open source interpreted programming language and environment for statistical analysis and graphics reporting.\n",
    "- R is available under the GNU General Public License, and as pre-compiled binary versions for various operating systems including Linux, Windows and Mac.\n",
    "- R is case sensitive.\n",
    "- R integrates with C, C++, .Net, Python, and FORTRAN languages.\n",
    "- The set of symbols which can be used in R names depends on the operating system and locale in use. All alphanumeric symbols are allowed, plus ‘.’ and ‘_’, with the restriction that a name must start with ‘.’ or a letter, and if it starts with ‘.’ the second character must not be a digit. Names length is unlimited.\n",
    "- Elementary commands consist of either expressions or assignments.\n",
    "- A command is evaluated and printed (unless specifically made invisible), and the value is lost. An assignment also evaluates an expression and passes the value to a variable but the result is not automatically printed.\n",
    "- Commands are separated either by a semi-colon (‘;’), or by a newline.\n",
    "- Elementary commands can be grouped into a compound expression by braces (‘{’ and ‘}’).\n",
    "- Comments can be put almost anywhere, starting with a hashmark ('#') up to the end of the line.\n",
    "- If a command is not complete at the end of a line, R will give a continuation prompt (‘+’), by default.\n",
    "\n",
    "## Data Science Workflow\n",
    "\n",
    "![Data Science Workflow](images/DataScienceWorkflow.png \"Data Science Workflow\")\n",
    "\n",
    "-  **Data Source** First the data must be imported from some data source, such as a file, database, or web API, and that data is usually loaded into a data frame. \n",
    "-  **Tidy** Tidying data means storing it in a consistent structure where each table column is a variable, and each row is an observation, and missing data and outliers may be handled in a useful way.\n",
    "-  **Transform** Transforming data includes activities that focus on specific variables in the data, or create new convenience variables that are computed from existing variables, and calculate summary statistics. Together, tidying and transforming data is known as data wrangling.\n",
    "-  **Visualize** Data visualisation provides insight into your data that can deepen understanding and guide further inquirey. Visualisation requires human or artificial intellegence to interpret the underlying meaning embedded in the data.\n",
    "-  **Model** A data model is a mathematical or algorithmic reprsentation of data relationships that can verify a hypothesis about the data, or make new predictions from the data.\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.4.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}