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    R Notes for Professionals book

    Amazing collection of free programming books

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    Amazing collection of free programming books

    The Notes for Professionals book is compiled from Stack Overflow Documentation, the content is written by the beautiful people at Stack Overflow. Text content is released under Creative Commons BY-SA. See credits at the end of this book whom contributed to the various chapters. Images may be copyright of their respective owners unless otherwise specified

    Book created for educational purposes and is not affiliated with group(s), company(s) nor Stack Overflow. All trademarks belong to their respective company owners

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    章節目錄

    • 1-1
      Content list
    • 1-2
      About
    • 1-3
      Chapter 1: Getting started with R Language
    • 1-4
      Section 1.1: Installing R
    • 1-5
      Section 1.2: Hello World!
    • 1-6
      Section 1.3: Getting Help
    • 1-7
      Section 1.4: Interactive mode and R scripts
    • 1-8
      Chapter 2: Variables
    • 1-9
      Section 2.1: Variables, data structures and basic Operations
    • 1-10
      Chapter 3: Arithmetic Operators
    • 1-11
      Section 3.1: Range and addition
    • 1-12
      Section 3.2: Addition and subtraction
    • 1-13
      Chapter 4: Matrices
    • 1-14
      Section 4.1: Creating matrices
    • 1-15
      Chapter 5: Formula
    • 1-16
      Section 5.1: The basics of formula
    • 1-17
      Chapter 6: Reading and writing strings
    • 1-18
      Section 6.1: Printing and displaying strings
    • 1-19
      Section 6.2: Capture output of operating system command
    • 1-20
      Section 6.3: Reading from or writing to a file connection
    • 1-21
      Chapter 7: String manipulation with stringi package
    • 1-22
      Section 7.1: Count pattern inside string
    • 1-23
      Section 7.2: Duplicating strings
    • 1-24
      Section 7.3: Paste vectors
    • 1-25
      Section 7.4: Splitting text by some fixed pattern
    • 1-26
      Chapter 8: Classes
    • 1-27
      Section 8.1: Inspect classes
    • 1-28
      Section 8.2: Vectors and lists
    • 1-29
      Section 8.3: Vectors
    • 1-30
      Chapter 9: Lists
    • 1-31
      Section 9.1: Introduction to lists
    • 1-32
      Section 9.2: Quick Introduction to Lists
    • 1-33
      Section 9.3: Serialization: using lists to pass information
    • 1-34
      Chapter 10: Hashmaps
    • 1-35
      Section 10.1: Environments as hash maps
    • 1-36
      Section 10.2: package:hash
    • 1-37
      Section 10.3: package:listenv
    • 1-38
      Chapter 11: Creating vectors
    • 1-39
      Section 11.1: Vectors from build in constants: Sequences of letters & month names
    • 1-40
      Section 11.2: Creating named vectors
    • 1-41
      Section 11.3: Sequence of numbers
    • 1-42
      Section 11.4: seq()
    • 1-43
      Section 11.5: Vectors
    • 1-44
      Section 11.6: Expanding a vector with the rep() function
    • 1-45
      Chapter 12: Date and Time
    • 1-46
      Section 12.1: Current Date and Time
    • 1-47
      Section 12.2: Go to the End of the Month
    • 1-48
      Section 12.3: Go to First Day of the Month
    • 1-49
      Section 12.4: Move a date a number of months consistently by months
    • 1-50
      Chapter 13: The Date class
    • 1-51
      Section 13.1: Formatting Dates
    • 1-52
      Section 13.2: Parsing Strings into Date Objects
    • 1-53
      Section 13.3: Dates
    • 1-54
      Chapter 14: Date-time classes (POSIXct and POSIXlt)
    • 1-55
      Section 14.1: Formatting and printing date-time objects
    • 1-56
      Section 14.2: Date-time arithmetic
    • 1-57
      Section 14.3: Parsing strings into date-time objects
    • 1-58
      Chapter 15: The character class
    • 1-59
      Section 15.1: Coercion
    • 1-60
      Chapter 16: Numeric classes and storage modes
    • 1-61
      Section 16.1: Numeric
    • 1-62
      Chapter 17: The logical class
    • 1-63
      Section 17.1: Logical operators
    • 1-64
      Section 17.2: Coercion
    • 1-65
      Section 17.3: Interpretation of NAs
    • 1-66
      Chapter 18: Data frames
    • 1-67
      Section 18.1: Create an empty data.frame
    • 1-68
      Section 18.2: Subsetting rows and columns from a data frame
    • 1-69
      Section 18.3: Convenience functions to manipulate data.frames
    • 1-70
      Section 18.4: Introduction
    • 1-71
      Section 18.5: Convert all columns of a data.frame to character class
    • 1-72
      Chapter 19: Split function
    • 1-73
      Section 19.1: Using split in the split-apply-combine paradigm
    • 1-74
      Section 19.2: Basic usage of split
    • 1-75
      Chapter 20: Reading and writing tabular data in plain-text files (CSV, TSV, etc.)
    • 1-76
      Section 20.1: Importing .csv files
    • 1-77
      Section 20.2: Importing with data.table
    • 1-78
      Section 20.3: Exporting .csv files
    • 1-79
      Section 20.4: Import multiple csv files
    • 1-80
      Section 20.5: Importing fixed-width files
    • 1-81
      Chapter 21: Pipe operators (%>% and others)
    • 1-82
      Section 21.1: Basic use and chaining
    • 1-83
      Section 21.2: Functional sequences
    • 1-84
      Section 21.3: Assignment with %<>%
    • 1-85
      Section 21.4: Exposing contents with %$%
    • 1-86
      Section 21.5: Creating side eects with %T>%
    • 1-87
      Section 21.6: Using the pipe with dplyr and ggplot2
    • 1-88
      Chapter 22: Linear Models (Regression)
    • 1-89
      Section 22.1: Linear regression on the mtcars dataset
    • 1-90
      Section 22.2: Using the 'predict' function
    • 1-91
      Section 22.3: Weighting
    • 1-92
      Section 22.4: Checking for nonlinearity with polynomial regression
    • 1-93
      Section 22.5: Plotting The Regression (base)
    • 1-94
      Section 22.6: Quality assessment
    • 1-95
      Chapter 23: data.table
    • 1-96
      Section 23.1: Creating a data.table
    • 1-97
      Section 23.2: Special symbols in data.table
    • 1-98
      Section 23.3: Adding and modifying columns
    • 1-99
      Section 23.4: Writing code compatible with both data.frame and data.table
    • 1-100
      Section 23.5: Setting keys in data.table
    • 1-101
      Chapter 24: Pivot and unpivot with data.table
    • 1-102
      Section 24.1: Pivot and unpivot tabular data with data.table - I
    • 1-103
      Section 24.2: Pivot and unpivot tabular data with data.table - II
    • 1-104
      Chapter 25: Bar Chart
    • 1-105
      Section 25.1: barplot() function
    • 1-106
      Chapter 26: Base Plotting
    • 1-107
      Section 26.1: Density plot
    • 1-108
      Section 26.2: Combining Plots
    • 1-109
      Section 26.3: Getting Started with R_Plots
    • 1-110
      Section 26.4: Basic Plot
    • 1-111
      Section 26.5: Histograms
    • 1-112
      Section 26.6: Matplot
    • 1-113
      Section 26.7: Empirical Cumulative Distribution Function
    • 1-114
      Chapter 27: boxplot
    • 1-115
      Section 27.1: Create a box-and-whisker plot with boxplot() {graphics}
    • 1-116
      Section 27.2: Additional boxplot style parameters
    • 1-117
      Chapter 28: ggplot2
    • 1-118
      Section 28.1: Displaying multiple plots
    • 1-119
      Section 28.2: Prepare your data for plotting
    • 1-120
      Section 28.3: Add horizontal and vertical lines to plot
    • 1-121
      Section 28.4: Scatter Plots
    • 1-122
      Section 28.5: Produce basic plots with qplot
    • 1-123
      Section 28.6: Vertical and Horizontal Bar Chart
    • 1-124
      Section 28.7: Violin plot
    • 1-125
      Chapter 29: Factors
    • 1-126
      Section 29.1: Consolidating Factor Levels with a List
    • 1-127
      Section 29.2: Basic creation of factors
    • 1-128
      Section 29.3: Changing and reordering factors
    • 1-129
      Section 29.4: Rebuilding factors from zero
    • 1-130
      Chapter 30: Pattern Matching and Replacement
    • 1-131
      Section 30.1: Finding Matches
    • 1-132
      Section 30.2: Single and Global match
    • 1-133
      Section 30.3: Making substitutions
    • 1-134
      Section 30.4: Find matches in big data sets
    • 1-135
      Chapter 31: Run-length encoding
    • 1-136
      Section 31.1: Run-length Encoding with `rle`
    • 1-137
      Section 31.2: Identifying and grouping by runs in base R
    • 1-138
      Section 31.3: Run-length encoding to compress and decompress vectors
    • 1-139
      Section 31.4: Identifying and grouping by runs in data.table
    • 1-140
      Chapter 32: Speeding up tough-to-vectorize code
    • 1-141
      Section 32.1: Speeding tough-to-vectorize for loops with Rcpp
    • 1-142
      Section 32.2: Speeding tough-to-vectorize for loops by byte compiling
    • 1-143
      Chapter 33: Introduction to Geographical Maps
    • 1-144
      Section 33.1: Basic map-making with map() from the package maps
    • 1-145
      Section 33.2: 50 State Maps and Advanced Choropleths with Google Viz
    • 1-146
      Section 33.3: Interactive plotly maps
    • 1-147
      Section 33.4: Making Dynamic HTML Maps with Leaflet
    • 1-148
      Section 33.5: Dynamic Leaflet maps in Shiny applications
    • 1-149
      Chapter 34: Set operations
    • 1-150
      Section 34.1: Set operators for pairs of vectors
    • 1-151
      Section 34.2: Cartesian or "cross" products of vectors
    • 1-152
      Section 34.3: Set membership for vectors
    • 1-153
      Section 34.4: Make unique / drop duplicates / select distinct elements from a vector
    • 1-154
      Section 34.5: Measuring set overlaps / Venn diagrams for vectors
    • 1-155
      Chapter 35: tidyverse
    • 1-156
      Section 35.1: tidyverse: an overview
    • 1-157
      Section 35.2: Creating tbl_df’s
    • 1-158
      Chapter 36: Rcpp
    • 1-159
      Section 36.1: Extending Rcpp with Plugins
    • 1-160
      Section 36.2: Inline Code Compile
    • 1-161
      Section 36.3: Rcpp Attributes
    • 1-162
      Section 36.4: Specifying Additional Build Dependencies
    • 1-163
      Chapter 37: Random Numbers Generator
    • 1-164
      Section 37.1: Random permutations
    • 1-165
      Section 37.2: Generating random numbers using various density functions
    • 1-166
      Section 37.3: Random number generator's reproducibility
    • 1-167
      Chapter 38: Parallel processing
    • 1-168
      Section 38.1: Parallel processing with parallel package
    • 1-169
      Section 38.2: Parallel processing with foreach package
    • 1-170
      Section 38.3: Random Number Generation
    • 1-171
      Section 38.4: mcparallelDo
    • 1-172
      Chapter 39: Subsetting
    • 1-173
      Section 39.1: Data frames
    • 1-174
      Section 39.2: Atomic vectors
    • 1-175
      Section 39.3: Matrices
    • 1-176
      Section 39.4: Lists
    • 1-177
      Section 39.5: Vector indexing
    • 1-178
      Section 39.6: Other objects
    • 1-179
      Section 39.7: Elementwise Matrix Operations
    • 1-180
      Chapter 40: Debugging
    • 1-181
      Section 40.1: Using debug
    • 1-182
      Section 40.2: Using browser
    • 1-183
      Chapter 41: Installing packages
    • 1-184
      Section 41.1: Install packages from GitHub
    • 1-185
      Section 41.2: Download and install packages from repositories
    • 1-186
      Section 41.3: Install package from local source
    • 1-187
      Section 41.4: Install local development version of a package
    • 1-188
      Section 41.5: Using a CLI package manager -- basic pacman usage
    • 1-189
      Chapter 42: Inspecting packages
    • 1-190
      Section 42.1: View Package Version
    • 1-191
      Section 42.2: View Loaded packages in Current Session
    • 1-192
      Section 42.3: View package information
    • 1-193
      Section 42.4: View package's built-in data sets
    • 1-194
      Section 42.5: List a package's exported functions
    • 1-195
      Chapter 43: Creating packages with devtools
    • 1-196
      Section 43.1: Creating and distributing packages
    • 1-197
      Section 43.2: Creating vignettes
    • 1-198
      Chapter 44: Using pipe assignment in your own package %<>%: How to ?
    • 1-199
      Section 44.1: Putting the pipe in a utility-functions file
    • 1-200
      Chapter 45: Arima Models
    • 1-201
      Section 45.1: Modeling an AR1 Process with Arima
    • 1-202
      Chapter 46: Distribution Functions
    • 1-203
      Section 46.1: Normal distribution
    • 1-204
      Section 46.2: Binomial Distribution
    • 1-205
      Chapter 47: Shiny
    • 1-206
      Section 47.1: Create an app
    • 1-207
      Section 47.2: Checkbox Group
    • 1-208
      Section 47.3: Radio Button
    • 1-209
      Section 47.4: Debugging
    • 1-210
      Section 47.5: Select box
    • 1-211
      Section 47.6: Launch a Shiny app
    • 1-212
      Section 47.7: Control widgets
    • 1-213
      Chapter 48: spatial analysis
    • 1-214
      Section 48.1: Create spatial points from XY data set
    • 1-215
      Section 48.2: Importing a shape file (.shp)
    • 1-216
      Chapter 49: sqldf
    • 1-217
      Section 49.1: Basic Usage Examples
    • 1-218
      Chapter 50: Code profiling
    • 1-219
      Section 50.1: Benchmarking using microbenchmark
    • 1-220
      Section 50.2: proc.time()
    • 1-221
      Section 50.3: Microbenchmark
    • 1-222
      Section 50.4: System.time
    • 1-223
      Section 50.5: Line Profiling
    • 1-224
      Chapter 51: Control flow structures
    • 1-225
      Section 51.1: Optimal Construction of a For Loop
    • 1-226
      Section 51.2: Basic For Loop Construction
    • 1-227
      Section 51.3: The Other Looping Constructs: while and repeat
    • 1-228
      Chapter 52: Column wise operation
    • 1-229
      Section 52.1: sum of each column
    • 1-230
      Chapter 53: JSON
    • 1-231
      Section 53.1: JSON to / from R objects
    • 1-232
      Chapter 54: RODBC
    • 1-233
      Section 54.1: Connecting to Excel Files via RODBC
    • 1-234
      Section 54.2: SQL Server Management Database connection to get individual table
    • 1-235
      Section 54.3: Connecting to relational databases
    • 1-236
      Chapter 55: lubridate
    • 1-237
      Section 55.1: Parsing dates and datetimes from strings with lubridate
    • 1-238
      Section 55.2: Dierence between period and duration
    • 1-239
      Section 55.3: Instants
    • 1-240
      Section 55.4: Intervals, Durations and Periods
    • 1-241
      Section 55.5: Manipulating date and time in lubridate
    • 1-242
      Section 55.6: Time Zones
    • 1-243
      Section 55.7: Parsing date and time in lubridate
    • 1-244
      Section 55.8: Rounding dates
    • 1-245
      Chapter 56: Time Series and Forecasting
    • 1-246
      Section 56.1: Creating a ts object
    • 1-247
      Section 56.2: Exploratory Data Analysis with time-series data
    • 1-248
      Chapter 57: strsplit function
    • 1-249
      Section 57.1: Introduction
    • 1-250
      Chapter 58: Web scraping and parsing
    • 1-251
      Section 58.1: Basic scraping with rvest
    • 1-252
      Section 58.2: Using rvest when login is required
    • 1-253
      Chapter 59: Generalized linear models
    • 1-254
      Section 59.1: Logistic regression on Titanic dataset
    • 1-255
      Chapter 60: Reshaping data between long and wide forms
    • 1-256
      Section 60.1: Reshaping data
    • 1-257
      Section 60.2: The reshape function
    • 1-258
      Chapter 61: RMarkdown and knitr presentation
    • 1-259
      Section 61.1: Adding a footer to an ioslides presentation
    • 1-260
      Section 61.2: Rstudio example
    • 1-261
      Chapter 62: Scope of variables
    • 1-262
      Section 62.1: Environments and Functions
    • 1-263
      Section 62.2: Function Exit
    • 1-264
      Section 62.3: Sub functions
    • 1-265
      Section 62.4: Global Assignment
    • 1-266
      Section 62.5: Explicit Assignment of Environments and Variables
    • 1-267
      Chapter 63: Performing a Permutation Test
    • 1-268
      Section 63.1: A fairly general function
    • 1-269
      Chapter 64: xgboost
    • 1-270
      Section 64.1: Cross Validation and Tuning with xgboost
    • 1-271
      Chapter 65: R code vectorization best practices
    • 1-272
      Section 65.1: By row operations
    • 1-273
      Chapter 66: Missing values
    • 1-274
      Section 66.1: Examining missing data
    • 1-275
      Section 66.2: Reading and writing data with NA values
    • 1-276
      Section 66.3: Using NAs of dierent classes
    • 1-277
      Section 66.4: TRUE/FALSE and/or NA
    • 1-278
      Chapter 67: Hierarchical Linear Modeling
    • 1-279
      Section 67.1: basic model fitting
    • 1-280
      Chapter 68: *apply family of functions (functionals)
    • 1-281
      Section 68.1: Using built-in functionals
    • 1-282
      Section 68.2: Combining multiple `data.frames` (`lapply`, `mapply`)
    • 1-283
      Section 68.3: Bulk File Loading
    • 1-284
      Section 68.4: Using user-defined functionals
    • 1-285
      Chapter 69: Text mining
    • 1-286
      Section 69.1: Scraping Data to build N-gram Word Clouds
    • 1-287
      Chapter 70: ANOVA
    • 1-288
      Section 70.1: Basic usage of aov()
    • 1-289
      Section 70.2: Basic usage of Anova()
    • 1-290
      Chapter 71: Raster and Image Analysis
    • 1-291
      Section 71.1: Calculating GLCM Texture
    • 1-292
      Section 71.2: Mathematical Morphologies
    • 1-293
      Chapter 72: Survival analysis
    • 1-294
      Section 72.1: Random Forest Survival Analysis with randomForestSRC
    • 1-295
      Section 72.2: Introduction - basic fitting and plotting of parametric survival models with the survival package
    • 1-296
      Section 72.3: Kaplan Meier estimates of survival curves and risk set tables with survminer
    • 1-297
      Chapter 73: Fault-tolerant/resilient code
    • 1-298
      Section 73.1: Using tryCatch()
    • 1-299
      Chapter 74: Reproducible R
    • 1-300
      Section 74.1: Data reproducibility
    • 1-301
      Section 74.2: Package reproducibility
    • 1-302
      Chapter 75: Fourier Series and Transformations
    • 1-303
      Section 75.1: Fourier Series
    • 1-304
      Chapter 76: .Rprofile
    • 1-305
      Section 76.1: .Rprofile - the first chunk of code executed
    • 1-306
      Section 76.2: .Rprofile example
    • 1-307
      Chapter 77: dplyr
    • 1-308
      Section 77.1: dplyr's single table verbs
    • 1-309
      Section 77.2: Aggregating with %>% (pipe) operator
    • 1-310
      Section 77.3: Subset Observation (Rows)
    • 1-311
      Section 77.4: Examples of NSE and string variables in dpylr
    • 1-312
      Chapter 78: caret
    • 1-313
      Section 78.1: Preprocessing
    • 1-314
      Chapter 79: Extracting and Listing Files in Compressed Archives
    • 1-315
      Section 79.1: Extracting files from a .zip archive
    • 1-316
      Chapter 80: Probability Distributions with R
    • 1-317
      Section 80.1: PDF and PMF for dierent distributions in R
    • 1-318
      Chapter 81: R in LaTeX with knitr
    • 1-319
      Section 81.1: R in LaTeX with Knitr and Code Externalization
    • 1-320
      Section 81.2: R in LaTeX with Knitr and Inline Code Chunks
    • 1-321
      Section 81.3: R in LaTex with Knitr and Internal Code Chunks
    • 1-322
      Chapter 82: Web Crawling in R
    • 1-323
      Section 82.1: Standard scraping approach using the RCurl package
    • 1-324
      Chapter 83: Creating reports with RMarkdown
    • 1-325
      Section 83.1: Including bibliographies
    • 1-326
      Section 83.2: Including LaTeX Preample Commands
    • 1-327
      Section 83.3: Printing tables
    • 1-328
      Section 83.4: Basic R-markdown document structure
    • 1-329
      Chapter 84: GPU-accelerated computing
    • 1-330
      Section 84.1: gpuR gpuMatrix objects
    • 1-331
      Section 84.2: gpuR vclMatrix objects
    • 1-332
      Chapter 85: heatmap and heatmap.2
    • 1-333
      Section 85.1: Examples from the ocial documentation
    • 1-334
      Section 85.2: Tuning parameters in heatmap.2
    • 1-335
      Chapter 86: Network analysis with the igraph package
    • 1-336
      Section 86.1: Simple Directed and Non-directed Network Graphing
    • 1-337
      Chapter 87: Functional programming
    • 1-338
      Section 87.1: Built-in Higher Order Functions
    • 1-339
      Chapter 88: Get user input
    • 1-340
      Section 88.1: User input in R
    • 1-341
      Chapter 89: Spark API (SparkR)
    • 1-342
      Section 89.1: Setup Spark context
    • 1-343
      Section 89.2: Cache data
    • 1-344
      Section 89.3: Create RDDs (Resilient Distributed Datasets)
    • 1-345
      Chapter 90: Meta: Documentation Guidelines
    • 1-346
      Section 90.1: Style
    • 1-347
      Section 90.2: Making good examples
    • 1-348
      Chapter 91: Input and output
    • 1-349
      Section 91.1: Reading and writing data frames
    • 1-350
      Chapter 92: I/O for foreign tables (Excel, SAS, SPSS, Stata)
    • 1-351
      Section 92.1: Importing data with rio
    • 1-352
      Section 92.2: Read and write Stata, SPSS and SAS files
    • 1-353
      Section 92.3: Importing Excel files
    • 1-354
      Section 92.4: Import or Export of Feather file
    • 1-355
      Chapter 93: I/O for database tables
    • 1-356
      Section 93.1: Reading Data from MySQL Databases
    • 1-357
      Section 93.2: Reading Data from MongoDB Databases
    • 1-358
      Chapter 94: I/O for geographic data (shapefiles, etc.)
    • 1-359
      Section 94.1: Import and Export Shapefiles
    • 1-360
      Chapter 95: I/O for raster images
    • 1-361
      Section 95.1: Load a multilayer raster
    • 1-362
      Chapter 96: I/O for R's binary format
    • 1-363
      Section 96.1: Rds and RData (Rda) files
    • 1-364
      Section 96.2: Enviromments
    • 1-365
      Chapter 97: Recycling
    • 1-366
      Section 97.1: Recycling use in subsetting
    • 1-367
      Chapter 98: Expression: parse + eval
    • 1-368
      Section 98.1: Execute code in string format
    • 1-369
      Chapter 99: Regular Expression Syntax in R
    • 1-370
      Section 99.1: Use `grep` to find a string in a character vector
    • 1-371
      Chapter 100: Regular Expressions (regex)
    • 1-372
      Section 100.1: Dierences between Perl and POSIX regex
    • 1-373
      Section 100.2: Validate a date in a "YYYYMMDD" format
    • 1-374
      Section 100.3: Escaping characters in R regex patterns
    • 1-375
      Section 100.4: Validate US States postal abbreviations
    • 1-376
      Section 100.5: Validate US phone numbers
    • 1-377
      Chapter 101: Combinatorics
    • 1-378
      Section 101.1: Enumerating combinations of a specified length
    • 1-379
      Section 101.2: Counting combinations of a specified length
    • 1-380
      Chapter 102: Solving ODEs in R
    • 1-381
      Section 102.1: The Lorenz model
    • 1-382
      Section 102.2: Lotka-Volterra or: Prey vs. predator
    • 1-383
      Section 102.3: ODEs in compiled languages - definition in R
    • 1-384
      Section 102.4: ODEs in compiled languages - definition in C
    • 1-385
      Section 102.5: ODEs in compiled languages - definition in fortran
    • 1-386
      Section 102.6: ODEs in compiled languages - a benchmark test
    • 1-387
      Chapter 103: Feature Selection in R -- Removing Extraneous Features
    • 1-388
      Section 103.1: Removing features with zero or near-zero variance
    • 1-389
      Section 103.2: Removing features with high numbers of NA
    • 1-390
      Section 103.3: Removing closely correlated features
    • 1-391
      Chapter 104: Bibliography in RMD
    • 1-392
      Section 104.1: Specifying a bibliography and cite authors
    • 1-393
      Section 104.2: Inline references
    • 1-394
      Section 104.3: Citation styles
    • 1-395
      Chapter 105: Writing functions in R
    • 1-396
      Section 105.1: Anonymous functions
    • 1-397
      Section 105.2: RStudio code snippets
    • 1-398
      Section 105.3: Named functions
    • 1-399
      Chapter 106: Color schemes for graphics
    • 1-400
      Section 106.1: viridis - print and colorblind friendly palettes
    • 1-401
      Section 106.2: A handy function to glimse a vector of colors
    • 1-402
      Section 106.3: colorspace - click&drag interface for colors
    • 1-403
      Section 106.4: Colorblind-friendly palettes
    • 1-404
      Section 106.5: RColorBrewer
    • 1-405
      Section 106.6: basic R color functions
    • 1-406
      Chapter 107: Hierarchical clustering with hclust
    • 1-407
      Section 107.1: Example 1 - Basic use of hclust, display of dendrogram, plot clusters
    • 1-408
      Section 107.2: Example 2 - hclust and outliers
    • 1-409
      Chapter 108: Random Forest Algorithm
    • 1-410
      Section 108.1: Basic examples - Classification and Regression
    • 1-411
      Chapter 109: RESTful R Services
    • 1-412
      Section 109.1: opencpu Apps
    • 1-413
      Chapter 110: Machine learning
    • 1-414
      Section 110.1: Creating a Random Forest model
    • 1-415
      Chapter 111: Using texreg to export models in a paper-ready way
    • 1-416
      Section 111.1: Printing linear regression results
    • 1-417
      Chapter 112: Publishing
    • 1-418
      Section 112.1: Formatting tables
    • 1-419
      Section 112.2: Formatting entire documents
    • 1-420
      Chapter 113: Implement State Machine Pattern using S4 Class
    • 1-421
      Section 113.1: Parsing Lines using State Machine
    • 1-422
      Chapter 114: Reshape using tidyr
    • 1-423
      Section 114.1: Reshape from long to wide format with spread()
    • 1-424
      Section 114.2: Reshape from wide to long format with gather()
    • 1-425
      Chapter 115: Modifying strings by substitution
    • 1-426
      Section 115.1: Rearrange character strings using capture groups
    • 1-427
      Section 115.2: Eliminate duplicated consecutive elements
    • 1-428
      Chapter 116: Non-standard evaluation and standard evaluation
    • 1-429
      Section 116.1: Examples with standard dplyr verbs
    • 1-430
      Chapter 117: Randomization
    • 1-431
      Section 117.1: Random draws and permutations
    • 1-432
      Section 117.2: Setting the seed
    • 1-433
      Chapter 118: Object-Oriented Programming in R
    • 1-434
      Section 118.1: S3
    • 1-435
      Chapter 119: Coercion
    • 1-436
      Section 119.1: Implicit Coercion
    • 1-437
      Chapter 120: Standardize analyses by writing standalone R scripts
    • 1-438
      Section 120.1: The basic structure of standalone R program and how to call it
    • 1-439
      Section 120.2: Using littler to execute R scripts
    • 1-440
      Chapter 121: Analyze tweets with R
    • 1-441
      Section 121.1: Download Tweets
    • 1-442
      Section 121.2: Get text of tweets
    • 1-443
      Chapter 122: Natural language processing
    • 1-444
      Section 122.1: Create a term frequency matrix
    • 1-445
      Chapter 123: R Markdown Notebooks (from RStudio)
    • 1-446
      Section 123.1: Creating a Notebook
    • 1-447
      Section 123.2: Inserting Chunks
    • 1-448
      Section 123.3: Executing Chunk Code
    • 1-449
      Section 123.4: Execution Progress
    • 1-450
      Section 123.5: Preview Output
    • 1-451
      Section 123.6: Saving and Sharing
    • 1-452
      Chapter 124: Aggregating data frames
    • 1-453
      Section 124.1: Aggregating with data.table
    • 1-454
      Section 124.2: Aggregating with base R
    • 1-455
      Section 124.3: Aggregating with dplyr
    • 1-456
      Chapter 125: Data acquisition
    • 1-457
      Section 125.1: Built-in datasets
    • 1-458
      Section 125.2: Packages to access open databases
    • 1-459
      Section 125.3: Packages to access restricted data
    • 1-460
      Section 125.4: Datasets within packages
    • 1-461
      Chapter 126: R memento by examples
    • 1-462
      Section 126.1: Plotting (using plot)
    • 1-463
      Section 126.2: Commonly used functions
    • 1-464
      Section 126.3: Data types
    • 1-465
      Chapter 127: Updating R version
    • 1-466
      Section 127.1: Installing from R Website
    • 1-467
      Section 127.2: Updating from within R using installr Package
    • 1-468
      Section 127.3: Deciding on the old packages
    • 1-469
      Section 127.4: Updating Packages
    • 1-470
      Section 127.5: Check R Version
    • 1-471
      Credits
    • 1-472
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