Data wrangling with R
著者
書誌事項
Data wrangling with R
（Use R! / series editors, Robert Gentleman, Kurt Hornik, Giovanni Parmigiani）
Springer, c2016
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注記
Includes bibliographical references and index
内容説明・目次
内容説明
This guide for practicing statisticians, data scientists, and R users and programmers will teach the essentials of preprocessing: data leveraging the R programming language to easily and quickly turn noisy data into usable pieces of information. Data wrangling, which is also commonly referred to as data munging, transformation, manipulation, janitor work, etc., can be a painstakingly laborious process. Roughly 80% of data analysis is spent on cleaning and preparing data; however, being a prerequisite to the rest of the data analysis workflow (visualization, analysis, reporting), it is essential that one become fluent and efficient in data wrangling techniques.
This book will guide the user through the data wrangling process via a step-by-step tutorial approach and provide a solid foundation for working with data in R. The author's goal is to teach the user how to easily wrangle data in order to spend more time on understanding the content of the data. By the end of the book, the user will have learned:
How to work with different types of data such as numerics, characters, regular expressions, factors, and dates
The difference between different data structures and how to create, add additional components to, and subset each data structure
How to acquire and parse data from locations previously inaccessible
How to develop functions and use loop control structures to reduce code redundancy
How to use pipe operators to simplify code and make it more readable
How to reshape the layout of data and manipulate, summarize, and join data sets
目次
1. Preface 2. Introduction a. The Role of Data Wrangling i. Introduction to R 1. Open Source 2. Flexibility 3. Community ii. R Basics 1. Assignment & Evaluation 2. Vectorization 3. Getting help 4. Workspace 5. Working with packages 6. Style guide
3. Working with Different Types of Data in R a. Dealing with Numbers i. Integer vs. Double ii. Generating sequence of non-random numbers iii. Generating sequence of random numbers iv. Setting the seed for reproducible random numbers v. Comparing numeric values vi. Rounding numbers b. Dealing with Character Strings i. Character string basics ii. String manipulation with base R iii. String manipulation with stringr iv. Set operatons for character strings c. Dealing with Regular Expressions i. Regex Syntax ii. Regex Functions iii. Additional resources d. Dealing with Factors i. Creating, converting & inspecting factors ii. Ordering levels iii. Revalue levels iv. Dropping levels e. Dealing with Dates i. Getting current date & time ii. Converting strings to dates iii. Extract & manipulate parts of dates iv. Creating date sequences v. Calculations with dates vi. Dealing with time zones & daylight savings vii. Additional resources
<4. Managing Data Structures in R a. Data Structure Basics i. Identifying the Structure ii. Attributes b. Managing Vectors i. Creating ii. Adding on to iii. Adding attributes iv. Subsetting c. Managing Lists i. Creating iii. Adding attributes iv. Subsetting d. Managing Matrices i. Creating ii. Adding on to iii. Adding attributes iv. Subsetting e. Managing Data Frames i. Creating ii. Adding on to iii. Adding attributes iv. Subsetting f. Dealing with Missing Values i. Testing for missing values ii. Recoding missing values iii. Excluding missing values
5. Importing, Scraping, and Exporting Data with R a. Importing Data i. Reading data from text files ii. Reading data from Excel files iii. Load data from saved R object file iv. Additional resources b. Scraping Data i. Importing tabular and Excel files stored online ii. Scraping HTML text iii. Scraping HTML table data iv. Working with APIs v. Additional Resources c. Exporting Data i. Writing data to text files ii. Writing data to Excel files iii. Saving data as an R object file iv. Additional resources
6. Creating Efficient & Readable Code in R a. Functions i. Function Components ii. Arguments iii. Scoping Rules iv. Lazy Evaluation v. Returning Multiple Outputs from a Function vi. Dealing with Invalid Parameters vii. Saving and Sourcing Functions viii. Additional Resources b. Loop Control Statements i. Basic control statements (i.e. if, for, while, etc.) ii. Apply family iii. Other useful "loop-like" functions iv. Additional Resources c. Simplify Your Code with %>% i. Pipe (%>%) Operator ii. Additional Functions iii. Additional Pipe Operators iv. Additional Resources
7. Shaping & Transforming Your Data with R a. Reshaping Your Data with tidyr i. Making wide data long ii. Making long data wide iii. Splitting a single column into multiple columns iv. Combining multiple columns into a single column v. Additional tidyr functions vi. Sequencing your tidyr operations vii. Additional resources b. Transforming Your Data with dplyr i. Selecting variables of interest ii. Filtering rows iii. Grouping data by categorical variables iv. Performing summary statistics on variables v. Arranging variables by value vi. Joining datasets vii. Creating new variables viii. Additional resources
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