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December 2, 2017 09:15
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getting started with RStudio
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{ | |
"cells": [ | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"# Rstudio OverView" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"R-Studio overview \n", | |
"we have 4 panes \n", | |
"1) script pan - to write and save the programing script \n", | |
"2) Console pane - where all the code will get executed \n", | |
"3) Environment/history pane - displays all the variables created,functions used with in the current session \n", | |
"4) Helper pane - contains multiple tabs to install/display pacakges, view visualization plots, \n", | |
"locate files within the workspace \n" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 1, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"help(mean)" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"# getting and setting workspace" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 2, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/html": [ | |
"'C:/Users/Suresh/mlclassscripts'" | |
], | |
"text/latex": [ | |
"'C:/Users/Suresh/mlclassscripts'" | |
], | |
"text/markdown": [ | |
"'C:/Users/Suresh/mlclassscripts'" | |
], | |
"text/plain": [ | |
"[1] \"C:/Users/Suresh/mlclassscripts\"" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"# to display current working directory use getwd() function\n", | |
"getwd()" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"# to set up workspace or working directory use setwd() function\n", | |
"#syntax is shown below\n", | |
"setwd(\"path\")" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 6, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"setwd(\"C:\\\\Suresh\\\\R&D\\\\Projects\\\\ML classroom training\\\\sessions\")\n", | |
"setwd(\"C:/Suresh/R&D/Projects/ML classroom training/sessions\")" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"# getting help in R" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"To get help within R environment, we use help() function to get the documentation for any of the functions/packages available within R environment. \n", | |
"To see the arguments required for a function, we use args() function. \n", | |
"to see the example of a function, example() function is used. \n" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"help(\"stats\")\n", | |
"help(\"mean\")\n", | |
"args(\"mean\")\n", | |
"example(\"mean\")\n", | |
"\n", | |
"#getting help documentation for a package\n", | |
"help(package=\"caret\")" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"# online help for R programming" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"We can get online help on available packages in R from official website of R-Cran \n", | |
"https://cran.r-project.org/web/views/\n" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"We can also get online support for our day to day activities from below websites: \n", | |
"https://stackoverflow.com/ \n", | |
"https://stats.stackexchange.com \n" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"# Installing Packages" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"#install pacakges in R can be done in two ways,\n", | |
"#1) using install.packages() function and from the bottom right pane of Rstudio\n", | |
"install.packages(\"randomForest\")\n", | |
"\n", | |
"#loading of installed or downloaded packages can be done using library() function. Note that we can only load the package if\n", | |
"# we have installed the package already within our R environment\n", | |
"library(cluster)\n" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"#below code to first verify if the library is installed in the R environment, if it is not available\n", | |
"# then the package will get installed.\n", | |
"if(!library(cluster)){\n", | |
"install.pacakges(\"cluster\")\n", | |
"}" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"# basic operations in R" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"# Adding two numericals\n", | |
"1+1\n", | |
"\n", | |
"#multiplying two numericals\n", | |
"10*2\n", | |
"\n", | |
"#dividing two numericals\n", | |
"10/2\n", | |
"\n", | |
"#applying modulus operation on two numericals\n", | |
"10%%2" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"# printing results to R console" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"#printing the data on the console\n", | |
"print(10*2)\n", | |
"\n", | |
"print(\"data science\")\n", | |
"\n", | |
"print(pi^2)\n" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"# Variable declaration and assignment in R" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"variable assignment: In the below example, we are creating variable named z: " | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 8, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"z <- 100" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"we use left arrow or = symbol for variable assignment. Its always good practice to use left arrow for assignment. " | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 9, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"z = 10.009\n", | |
"z <- 10.009" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"# Loading existing or default datasets available in R environment" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"we can access default datasets avaiable in R using data() function. \n", | |
"data() function will displays all the avaiable datasets within R. " | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"data()" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"In order to load a specific dataset into R, we need to give the dataset name as argument to the data() function" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"data(AirPassengers)" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"# Viewing data of R objects" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"To view first 5 records of a R object (ex:dataframe), we use head() function. \n", | |
"head() function expects the data object as argument and prints the first 5 records on the R console. " | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"head(AirPassengers)" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"to view all the records in a nice tabular view" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"View(AirPassengers)" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"# Getting the decription and structure of R object" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"use str function to see the descriptions of the data object," | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"str(AirPassengers)" | |
] | |
} | |
], | |
"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": "3.4.1" | |
} | |
}, | |
"nbformat": 4, | |
"nbformat_minor": 2 | |
} |
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