2.3 Create Vectors With Patterns | R Programming: Zero To Pro

• R Programming: Zero to Pro
• Preface
• Contributing to the Book
  • Typos or Grammar Issues
  • General Questions or Feedback
• Acknowledgement
• Building Environment
• 1 Introduction
  • 1.1 Installation of R, RStudio and R Packages
    • 1.1.1 Download and Install
    • 1.1.2 RStudio Interface
    • 1.1.3 Install and load R packages
    • 1.1.4 Exercises
  • 1.2 Use R as a Fancy Calculator
    • 1.2.1 Add comments using “#”
    • 1.2.2 Basic calculation
    • 1.2.3 Get help in R
    • 1.2.4 Approximation
    • 1.2.5 Power & logarithm
    • 1.2.6 Trigonometric function
    • 1.2.7 Exercises
  • 1.3 Object Assignment
    • 1.3.1 What is an R Object?
    • 1.3.2 Assignment Operation with <-
    • 1.3.3 Object Naming Rules
    • 1.3.4 Review objects in environment
    • 1.3.5 Object types
    • 1.3.6 Exercises
• 2 R Objects (I): Atomic Vectors
  • 2.1 Introduction to Numeric Vectors
    • 2.1.1 Creation and class
    • 2.1.2 Operations and recycling rule
    • 2.1.3 Storage types (doubles and integers)
    • 2.1.4 Printing
    • 2.1.5 Exercises
  • 2.2 Introduction to Character Vectors
    • 2.2.1 Creation, class and storage type
    • 2.2.2 Change case
    • 2.2.3 Review of getting help in R
    • 2.2.4 Exercises
  • 2.3 Introduction to Logical Vectors
    • 2.3.1 Logical vectors: creation by comparisons, class, and storage type
    • 2.3.2 Logical vectors: creation when testing for types
    • 2.3.3 Exercises
  • 2.4 Coercion Rule
    • 2.4.1 Coercion with c()
    • 2.4.2 Coercion in operators
    • 2.4.3 Explicit Coercion
    • 2.4.4 Exercises
  • 2.5 Vector Subsetting and Modifying Values
    • 2.5.1 Named Vectors
    • 2.5.2 Vector subsetting
    • 2.5.3 Access and modify values in vectors and sub-vectors
    • 2.5.4 Exercises
  • 2.6 Numeric Vectors: Creating numeric vectors with patterns
    • 2.6.1 The colon operator : – Equally-spaced numeric vectors with increment of 1 or -1
    • 2.6.2 The seq() function – Equally-spaced numeric vectors with any increment
    • 2.6.3 The sequence generator seq_along() – Matching numeric vectors
    • 2.6.4 The rep() Function – Numeric vectors with repeated values
    • 2.6.5 The unique() and table() Functions – Getting unique elements and their frequencies
    • 2.6.6 Exercises
  • 2.7 Numeric Vectors: Sort, Rank, Order
    • 2.7.1 Sort vectors with sort()
    • 2.7.2 Get ranks in vectors with rank()
    • 2.7.3 Get the ordering permutation via `order()
    • 2.7.4 Exercises
  • 2.8 Numeric vector: Summary Statistics
    • 2.8.1 Minimum and Maximum
    • 2.8.2 Sum and Product
    • 2.8.3 Mean and median
    • 2.8.4 Quantiles
    • 2.8.5 Summary statistics
    • 2.8.6 Exercises
  • 2.9 Character Vectors: Create with Repetition and Concatenate
    • 2.9.1 Create character vectors with repetition
    • 2.9.2 Concatenate strings with paste()
    • 2.9.3 Output via cat()
    • 2.9.4 Exercises
  • 2.10 Character Vectors: Sort, Rank, Order
    • 2.10.1 Ordering rules
    • 2.10.2 Sort vectors with sort()
    • 2.10.3 Get ranks in vectors with rank()
    • 2.10.4 Get the ordering permutation via order()
    • 2.10.5 Summary and comparisons
    • 2.10.6 Exercises
  • 2.11 Character Vectors, Factors & Ordered Factors
    • 2.11.1 Create a factor from a vector
    • 2.11.2 Set the factor levels and labels
    • 2.11.3 Ordered factors
    • 2.11.4 Exercises
  • 2.12 Logical Vectors: Create with Repetitions and Summary Functions
    • 2.12.1 Create logical vectors with repetitions
    • 2.12.2 Statistical Functions on Logical Vectors
    • 2.12.3 Exercises
  • 2.13 Logical Vectors: Logical Operators
    • 2.13.1 NOT operator by !
    • 2.13.2 AND operator by &
    • 2.13.3 OR operator by |
    • 2.13.4 Exclusive OR by xor
    • 2.13.5 Summary of Logical Operators
    • 2.13.6 Exercises
  • 2.14 Set Operations on Vectors
    • 2.14.1 Set operations on vectors
    • 2.14.2 Applicance of the coercion rule
    • 2.14.3 Summary
    • 2.14.4 Exercises
  • 2.15 Missing Values (NA)
    • 2.15.1 NA and NaN are contagious
    • 2.15.2 Work with NA values
    • 2.15.3 Exercises
  • 2.16 Dates and Times
    • 2.16.1 Dates
    • 2.16.2 Times
    • 2.16.3 Exercises
  • 2.17 Other Atomic Vector Types
    • 2.17.1 Complex Vectors
    • 2.17.2 Raw Vectors
    • 2.17.3 Full Coercion Ordering
    • 2.17.4 Exercises
• 3 R Objects (II): Other Types
  • 3.1 Matrix
    • 3.1.1 Create a matrix from a vector
    • 3.1.2 Combine vectors or matrices into a matrix
    • 3.1.3 Matrix subsetting
    • 3.1.4 Modify values in sub-matrices
    • 3.1.5 Operators and functions on matrices
    • 3.1.6 Apply functions on each row or each column
    • 3.1.7 Exercises
  • 3.2 Array
    • 3.2.1 Create an array from a vector
    • 3.2.2 Array subsetting
    • 3.2.3 Operators and functions on arrays
    • 3.2.4 Apply functions along certain dimension(s)
    • 3.2.5 Exercises
  • 3.3 Data Frame
    • 3.3.1 Introduction to Data Frames
    • 3.3.2 Adding Observations or Variables in Data Frames
    • 3.3.3 Subsetting and Modifying Data Frames
    • 3.3.4 Exercises
  • 3.4 Tibble
    • 3.4.1 Introduction to tibbles
    • 3.4.2 Adding Observations or Variables in Tibbles
    • 3.4.3 Tibble subsetting and modifying
    • 3.4.4 Exercises
  • 3.5 List
    • 3.5.1 Create a list
    • 3.5.2 Extract a list element and list subsetting
    • 3.5.3 List inside a list
    • 3.5.4 Unlist a list
    • 3.5.5 Apply functions for each element of a list
    • 3.5.6 List Matrix
    • 3.5.7 Exercises
  • 3.6 NULL, NaN, and Inf
    • 3.6.1 NULL
    • 3.6.2 NaN
    • 3.6.3 Inf
    • 3.6.4 A comparison of the four special values in R
    • 3.6.5 Exercises
  • 3.7 Summary of R Objects
    • 3.7.1 Exercises
• 4 Data Import and Export
  • 4.1 Introduction to Data Sets
    • 4.1.1 Gapminder Data Set
    • 4.1.2 Small Ames Housing Price Data Set
    • 4.1.3 Converting Data Types
    • 4.1.4 Recover Modified Values
  • 4.2 Exporting Data to Delimited Files
    • 4.2.1 Set the working directory
    • 4.2.2 Delimited files
    • 4.2.3 Write an object into a .csv file
    • 4.2.4 Write an object into a general delimited file
    • 4.2.5 Exercises
  • 4.3 Importing Data from Delimited Files
    • 4.3.1 Import .csv Files using read_csv()
    • 4.3.2 Read Inline .csv Files
    • 4.3.3 Handling Missing Values
    • 4.3.4 Importing Data From a Delimited File
    • 4.3.5 Import Data Using the Menu
    • 4.3.6 Exercises
  • 4.4 Exporting to and Importing from Excel Files
    • 4.4.1 Export data into Excel files
    • 4.4.2 Import Excel Files (.xls and .xlsx ) using read_excel()
    • 4.4.3 Import Excel file using the menu
    • 4.4.4 Exercises
  • 4.5 Exporting to and Importing from SPSS, SAS, and Stata Files
    • 4.5.1 Export and Import SPSS Files
    • 4.5.2 Export and Import SAS Files
    • 4.5.3 Export and Import Stata Files
    • 4.5.4 Import using the menu
    • 4.5.5 Exercise
  • 4.6 Save and Restore Objects and Workspace
    • 4.6.1 Save and Restore Objects using .RData
    • 4.6.2 Save and Restore a Single Object using saveRDS() and readRDS()
    • 4.6.3 Exercises
• 5 Basic Data Visualization
  • 5.1 Scatterplot
    • 5.1.1 Using the plot() function
    • 5.1.2 Interacting and Saving the plot from plot()
    • 5.1.3 Using the ggplot() function
    • 5.1.4 Interact and Save the plot from ggplot()
    • 5.1.5 Exercises
  • 5.2 ggplot Aesthetics (I): Constant-Valued Aesthetics
    • 5.2.1 Exercises
  • 5.3 ggplot Aesthetics (II): Local Aesthetics Mapping
    • 5.3.1 Basic Local Aesthetics Mapping
    • 5.3.2 Customize the Local Aesthetics Mapping
    • 5.3.3 Customize the legend order for discrete variables
    • 5.3.4 Create Logical Variables and Map to Aesthetics
    • 5.3.5 Exercises
  • 5.4 Customization in ggplot
    • 5.4.1 Customization of Labels and Titles
    • 5.4.2 Customizing the Breaks on the x and y Axes
    • 5.4.3 Zoom In to a Specific Region of the Data
    • 5.4.4 Generate Log-Scale Plot
    • 5.4.5 Coordinate Flip
    • 5.4.6 Exercises
  • 5.5 Line Plots
    • 5.5.1 Line Plots via plot()
    • 5.5.2 Line Plots via geom_line()
    • 5.5.3 Constant-Valued Aesthetics in Line Plots
    • 5.5.4 Mapping Variables to Aesthetics in Line Plots
    • 5.5.5 Exercises
  • 5.6 Smoothline Fits
    • 5.6.1 Creating Smoothline Fits using geom_smooth()
    • 5.6.2 Constant-Valued Aesthetics in Smoothline Fits
    • 5.6.3 Local Aesthetics Mapping in Smoothline Fits
    • 5.6.4 Exercises
  • 5.7 ggplot Aesthetics (III): Global Aesthetic Mapping
    • 5.7.1 Multiple geoms
    • 5.7.2 Global Aesthetic Mapping
    • 5.7.3 Exercises
  • 5.8 Bar Charts and Pie Charts via geom_bar()
    • 5.8.1 An Introduction to Bar Charts
    • 5.8.2 Reordering Bars in Bar Charts
    • 5.8.3 Aesthetics in Bar Charts
    • 5.8.4 Pie Charts
    • 5.8.5 Exercises
  • 5.9 Histograms
    • 5.9.1 Using the hist() function
    • 5.9.2 Using the geom_histogram() function
    • 5.9.3 Aesthetics in geom_histogram()
    • 5.9.4 Density Estimate Using Histograms
    • 5.9.5 Exercises
  • 5.10 Density Plots
    • 5.10.1 Aesthetics in Density Plots
    • 5.10.2 Exercises
  • 5.11 Boxplots
    • 5.11.1 Using the boxplot() function
    • 5.11.2 Using the geom_boxplot() function
    • 5.11.3 Compare distributions in different groups
    • 5.11.4 Aesthetics in Boxplots
    • 5.11.5 Notched Boxplots
    • 5.11.6 Exercises
  • 5.12 Arrange Multiple Plots
    • 5.12.1 Facet wrap
    • 5.12.2 Facet grid
    • 5.12.3 Exercises
  • 5.13 Review of Basic Visualization
    • 5.13.1 Summary of geoms
    • 5.13.2 The grammatical structure of ggplot()
    • 5.13.3 A complex ggplot() example
• 6 Advanced Data Visualization
  • 6.1 Jitter and Count Plots
    • 6.1.1 Overplotting
    • 6.1.2 Jittering
    • 6.1.3 Counts Plots
    • 6.1.4 Exercises
  • 6.2 Violin Plots
    • 6.2.1 The basic violin plot
    • 6.2.2 Violin plot with boxplot
  • 6.3 Line Segments
    • 6.3.1 Using abline() with plot()
    • 6.3.2 Using geom_hline(), geom_vline() and geom_abline()
    • 6.3.3 Exercises
  • 6.4 Heat Map
    • 6.4.1 Using the heatmap() function
    • 6.4.2 Using geom_tile() in ggplot2
  • 6.5 Error Bars
  • 6.6 Interactive Plot with plotly()
• 7 Data Manipulation
  • 7.1 Filter Observations, Objects Masking, and the Pipe
    • 7.1.1 Objects Masking
    • 7.1.2 Filter Observations
    • 7.1.3 The pipe operator
    • 7.1.4 Exercise
  • 7.2 Select, Rename, and Reorder Variables
    • 7.2.1 Select Variables via Names
    • 7.2.2 Rename Variables
    • 7.2.3 Select Variables with Patterns
    • 7.2.4 Reorder Variables via Names
    • 7.2.5 Exercise
  • 7.3 Reorder Observations
    • 7.3.1 Exercises
  • 7.4 Create New Variables via mutate() and transmute()
    • 7.4.1 Exercises
  • 7.5 Create Grouped Summaries via group_by() and summarize()
    • 7.5.1 Create Summaries
    • 7.5.2 Create Grouped Summaries
    • 7.5.3 Exercises
  • 7.6 Grouped Mutate and Filter via group_by()
    • 7.6.1 Grouped Filter
    • 7.6.2 Grouped Mutate
    • 7.6.3 Exercises
  • 7.7 Joins
    • 7.7.1 Inner Join
    • 7.7.2 Left Join
    • 7.7.3 Right Join
    • 7.7.4 Full Join
    • 7.7.5 Filtering Joins
    • 7.7.6 A Summary of All Joins
    • 7.7.7 Exercises
  • 7.8 Set Operations on Datasets
    • 7.8.1 intersect(x, y)
    • 7.8.2 union(x, y)
    • 7.8.3 union_all(x, y)
    • 7.8.4 setdiff(x, y)
    • 7.8.5 symdiff(x, y)
    • 7.8.6 setequal(x, y)
    • 7.8.7 Summary
    • 7.8.8 Exercises
• 8 Tidy Data
  • 8.1 Convert Between Names and Values
    • 8.1.1 Convert Values into Column Names
    • 8.1.2 Convert Column Names into Values
    • 8.1.3 Multiple Variables
    • 8.1.4 Exercises
  • 8.2 Separate and Combine Columns via separate() and unite()
    • 8.2.1 Combine Columns via unite()
    • 8.2.2 Separate One Column into Multiple Columns Using separate()
    • 8.2.3 Exercises
  • 8.3 More on Missing Values
    • 8.3.1 Missing Values in Tidy Data
    • 8.3.2 Filling Missing Values
• 9 Strings
  • 9.1 Concatenate and Subset Strings
    • 9.1.1 Concatenate Strings
    • 9.1.2 Create and Modify Substrings
    • 9.1.3 Exercises
  • 9.2 Matching Pattern with Regular Expressions
    • 9.2.1 Basic Pattern Matching
    • 9.2.2 Character Classes
    • 9.2.3 Combining Patterns with Logical Operators
    • 9.2.4 Conclusion
    • 9.2.5 Exercises
• 10 Statistics
  • 10.1 Normal Distribution
    • 10.1.1 Probability Density Function (PDF)
    • 10.1.2 Cumulative Distribution Function (CDF)
    • 10.1.3 The Empirical Rule (68-95-99.7)
    • 10.1.4 Quantile Function
    • 10.1.5 Random Number Generator
    • 10.1.6 Exercises
  • 10.2 Other Distributions
    • 10.2.1 Discrete vs. Continuous Distributions
    • 10.2.2 Binomial Distribution
    • 10.2.3 Poisson Distribution
    • 10.2.4 Exponential Distribution
    • 10.2.5 A Summary of Commonly Used Distributions
    • 10.2.6 Exercise
  • 10.3 Random Permutation and Random Sampling
    • 10.3.1 Random Permutation
    • 10.3.2 Random Sampling Without Replacement
    • 10.3.3 Random Sampling with Replacement
    • 10.3.4 Random Sampling with Unequal Probabilities
    • 10.3.5 Exercise
  • 10.4 Multivariate Normal Distribution
    • 10.4.1 Generate Multivate Normal Distribution
    • 10.4.2 Properties of Multivariate Normal Distribution
    • 10.4.3 Sample Mean and Sample Covariance Matrix
    • 10.4.4 Sample Correlation Matrix
    • 10.4.5 Exercise
• 11 Control Statements
  • 11.1 Conditional Statements via if, else, and switch()
    • 11.1.1 The Structure of Conditional Statements
    • 11.1.2 Example: Checking Even or Odd Numbers
    • 11.1.3 Using ifelse()
    • 11.1.4 Multiple Conditions with else if
    • 11.1.5 Using switch() for Multiple Choices
    • 11.1.6 Logical Operators in Conditional Statements
    • 11.1.7 Exercises
  • 11.2 Loops
    • 11.2.1 for loops with known sequence
    • 11.2.2 Combine the conditional statements with loops
    • 11.2.3 Control Loop Flow With next and break
    • 11.2.4 Nested Loops
    • 11.2.5 Nested Loop via the outer() Function
    • 11.2.6 Exercises
  • 11.3 While Loops and Repeat
    • 11.3.1 while loop
    • 11.3.2 repeat Loop
    • 11.3.3 Comparison of for, while, and repeat Loops
    • 11.3.4 Exercises
• 12 Functions
  • 12.1 Introduction to Functions
    • 12.1.1 What are Functions?
    • 12.1.2 Syntax of a Function
    • 12.1.3 Default Arguments in Functions
    • 12.1.4 Project: Dice Simulation
    • 12.1.5 Project: Get Fibonacci Sequence
    • 12.1.6 Exercises
  • 12.2 Going Beyond Basics: Advanced R Functions
    • 12.2.1 Returning Multiple Values Using Lists
    • 12.2.2 Nested Functions
    • 12.2.3 Higher-Order Functions
    • 12.2.4 Error Handling in Functions
    • 12.2.5 Exercise
  • 12.3 Anonymous Functions and Scoping
    • 12.3.1 Anonymous Functions
    • 12.3.2 Advanced Use Cases for Anonymous Functions
    • 12.3.3 Scoping in R
    • 12.3.4 Exercises
• 13 R Markdown
  • 13.1 Introduction to R Markdown
    • 13.1.1 Create a new R Markdown document
    • 13.1.2 Knit the R Markdown document
    • 13.1.3 Write an R Markdown document
  • 13.2 Text Formatting in R Markdown
    • 13.2.1 Headers: Your Document’s Signposts
    • 13.2.2 Emphasis: Making Your Point
    • 13.2.3 Inline Code: Displaying and Evaluating
    • 13.2.4 Lists: Ordered and Unordered
    • 13.2.5 Hyperlinks: Connecting to the World
    • 13.2.6 Blockquotes: Highlighting Wisdom
    • 13.2.7 Footnotes: Adding Extra Details
    • 13.2.8 The Language of the Universe: Writing Mathematics
    • 13.2.9 Tables: From Simple to Stunning
    • 13.2.10 Insert citations and manage bibliographies in R Markdown
    • 13.2.11 Your Turn: A Mini-Challenge
• 14 A Case Study: 24 Game Solver
  • 14.1 Permutation and Operator Sequence
    • 14.1.1 Breaking Down a Solution
    • 14.1.2 Getting All Permutations
    • 14.1.3 Getting All Operators
  • 14.2 Composite Operators and Check Solution
    • 14.2.1 Composite Operators
    • 14.2.2 Check if Solution is Found
    • 14.2.3 Looping Over All Number Sequences and All Operator Sequences
  • 14.3 Solve 24
• References
• Published with bookdown

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