b:r_cookbook

# R Cookbook

# Chapter 1 Getting Started and Getting Help

## Introduction

## Downloading and Installing R

## Starting R

## Entering Commands

## Exiting from R

## Interrupting R

## Viewing the Supplied Documentation

## Getting Help on a Function

## Searching the Supplied Documentation

## Getting Help on a Package

## Searching the Web for Help

## Finding Relevant Functions and Packages

## Searching the Mailing Lists

## Submitting Questions to the Mailing Lists

# Chapter 2 Some Basics

## Introduction

## Printing Something

## Setting Variables

## Listing Variables

## Deleting Variables

## Creating a Vector

## Computing Basic Statistics

## Creating Sequences

## Comparing Vectors

## Selecting Vector Elements

## Getting Operator Precedence Right

## Defining a Function

## Typing Less and Accomplishing More

## Avoiding Some Common Mistakes

# Chapter 3 Navigating the Software

## Introduction

## Getting and Setting the Working Directory

## Saving Your Workspace

## Viewing Your Command History

## Saving the Result of the Previous Command

## Displaying the Search Path

## Accessing the Functions in a Package

## Accessing Built-in Datasets

## Viewing the List of Installed Packages

## Installing Packages from CRAN

## Setting a Default CRAN Mirror

## Suppressing the Startup Message

## Running a Script

## Running a Batch Script

## Getting and Setting Environment Variables

## Locating the R Home Directory

## Customizing R

## Introduction

## Entering Data from the Keyboard

## Printing Fewer Digits (or More Digits)

## Redirecting Output to a File

## Listing Files

## Dealing with “Cannot Open File” in Windows

## Reading Fixed-Width Records

## Reading Tabular Data Files

## Reading from CSV Files

## Writing to CSV Files

## Reading Tabular or CSV Data from the Web

## Reading Data from HTML Tables

## Reading Files with a Complex Structure

## Reading from MySQL Databases

## Saving and Transporting Objects

# Chapter 5 Data Structures

## Introduction

## Appending Data to a Vector

## Inserting Data into a Vector

## Understanding the Recycling Rule

## Creating a Factor (Categorical Variable)

## Combining Multiple Vectors into One Vector and a Factor

## Creating a List

## Selecting List Elements by Position

## Selecting List Elements by Name

## Building a Name/Value Association List

## Removing an Element from a List

## Flatten a List into a Vector

## Removing NULL Elements from a List

## Removing List Elements Using a Condition

## Initializing a Matrix

## Giving Descriptive Names to the Rows and Columns of a Matrix

## Selecting One Row or Column from a Matrix

## Initializing a Data Frame from Column Data

## Initializing a Data Frame from Row Data

## Appending Rows to a Data Frame

## Preallocating a Data Frame

## Selecting Data Frame Columns by Position

## Selecting Data Frame Columns by Name

## Selecting Rows and Columns More Easily

## Changing the Names of Data Frame Columns

## Editing a Data Frame

## Removing NAs from a Data Frame

## Excluding Columns by Name

## Combining Two Data Frames

## Merging Data Frames by Common Column

## Accessing Data Frame Contents More Easily

## Converting One Atomic Value into Another

## Converting One Structured Data Type into Another

## Introduction

## Splitting a Vector into Groups

## Applying a Function to Each List Element

## Applying a Function to Every Row

## Applying a Function to Every Column

## Applying a Function to Groups of Data

## Applying a Function to Groups of Rows

## Applying a Function to Parallel Vectors or Lists

# Chapter 7 Strings and Dates

## Introduction

## Getting the Length of a String

## Concatenating Strings

## Splitting a String According to a Delimiter

## Replacing Substrings

## Seeing the Special Characters in a String

## Generating All Pairwise Combinations of Strings

## Getting the Current Date

## Converting a String into a Date

## Converting a Date into a String

## Converting Year, Month, and Day into a Date

## Getting the Julian Date

## Creating a Sequence of Dates

# Chapter 8 Probability

## Introduction

## Counting the Number of Combinations

## Generating Combinations

## Generating Random Numbers

## Generating Reproducible Random Numbers

## Generating a Random Sample

## Generating Random Sequences

## Randomly Permuting a Vector

## Calculating Probabilities for Discrete Distributions

## Calculating Probabilities for Continuous Distributions

## Converting Probabilities to Quantiles

## Plotting a Density Function

# Chapter 9 General Statistics

## Introduction

## Summarizing Your Data

## Calculating Relative Frequencies

## Tabulating Factors and Creating Contingency Tables

## Testing Categorical Variables for Independence

## Calculating Quantiles (and Quartiles) of a Dataset

## Inverting a Quantile

## Converting Data to Z-Scores

## Testing the Mean of a Sample (t Test)

## Testing a Sample Proportion

## Testing for Normality

## Testing for Runs

## Comparing the Means of Two Samples

## Comparing the Locations of Two Samples Nonparametrically

## Testing a Correlation for Significance

## Testing Groups for Equal Proportions

## Testing Two Samples for the Same Distribution

# Chapter 10 Graphics

## Introduction

## Creating a Scatter Plot

## Adding a Title and Labels

## Adding a Grid

## Creating a Scatter Plot of Multiple Groups

## Adding a Legend

## Plotting the Regression Line of a Scatter Plot

## Plotting All Variables Against All Other Variables

## Creating One Scatter Plot for Each Factor Level

## Creating a Bar Chart

## Adding Confidence Intervals to a Bar Chart

## Coloring a Bar Chart

## Plotting a Line from x and y Points

## Changing the Type, Width, or Color of a Line

## Plotting Multiple Datasets

## Adding Vertical or Horizontal Lines

## Creating a Box Plot

## Creating One Box Plot for Each Factor Level

## Creating a Histogram

## Adding a Density Estimate to a Histogram

## Creating a Discrete Histogram

## Creating a Normal Quantile-Quantile (Q-Q) Plot

## Creating Other Quantile-Quantile Plots

## Plotting a Variable in Multiple Colors

## Graphing a Function

## Pausing Between Plots

## Displaying Several Figures on One Page

## Opening Additional Graphics Windows

## Writing Your Plot to a File

## Changing Graphical Parameters

# Chapter 11 Linear Regression and ANOVA

## Introduction

## Getting Regression Statistics

## Understanding the Regression Summary

## Selecting the Best Regression Variables

## Regressing on a Subset of Your Data

## Regressing on a Polynomial

## Plotting Regression Residuals

## Diagnosing a Linear Regression

## Identifying Influential Observations

## Testing Residuals for Autocorrelation (Durbin?Watson Test)

## Predicting New Values

## Creating an Interaction Plot

## Finding Differences Between Means of Groups

## Comparing Models by Using ANOVA

# Chapter 12 Useful Tricks

## Introduction

## Peeking at Your Data

## Widen Your Output

## Printing the Result of an Assignment

## Summing Rows and Columns

## Printing Data in Columns

## Binning Your Data

## Finding the Position of a Particular Value

## Selecting Every nth Element of a Vector

## Finding Pairwise Minimums or Maximums

## Generating All Combinations of Several Factors

## Flatten a Data Frame

## Sorting a Data Frame

## Sorting by Two Columns

## Stripping Attributes from a Variable

## Revealing the Structure of an Object

## Timing Your Code

## Suppressing Warnings and Error Messages

## Taking Function Arguments from a List

## Defining Your Own Binary Operators

# Chapter 13 Beyond Basic Numerics and Statistics

## Introduction

## Minimizing or Maximizing a Single-Parameter Function

## Minimizing or Maximizing a Multiparameter Function

## Calculating Eigenvalues and Eigenvectors

## Finding Clusters in Your Data

## Predicting a Binary-Valued Variable (Logistic Regression)

## Bootstrapping a Statistic

## Factor Analysis

# Chapter 14 Time Series Analysis

## Introduction

## Representing Time Series Data

## Plotting Time Series Data

## Subsetting a Time Series

## Merging Several Time Series

## Filling or Padding a Time Series

## Lagging a Time Series

## Computing Successive Differences

## Computing a Moving Average

## Applying a Function by Calendar Period

## Applying a Rolling Function

## Plotting the Autocorrelation Function

## Testing a Time Series for Autocorrelation

## Plotting the Partial Autocorrelation Function

## Finding Lagged Correlations Between Two Time Series

## Detrending a Time Series

## Fitting an ARIMA Model

## Removing Insignificant ARIMA Coefficients

## Running Diagnostics on an ARIMA Model

## Making Forecasts from an ARIMA Model

## Testing for Mean Reversion

## Smoothing a Time Series

b/r_cookbook.txt · Last modified: 2018/02/02 02:33 by hkimscil