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Record Nr. |
UNINA9910300469103321 |
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Autore |
Pace Larry |
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Titolo |
R Recipes [[electronic resource] ] : A Problem-Solution Approach / / by Larry Pace |
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Pubbl/distr/stampa |
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Berkeley, CA : , : Apress : , : Imprint : Apress, , 2014 |
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ISBN |
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Edizione |
[1st ed. 2014.] |
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Descrizione fisica |
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1 online resource (253 p.) |
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Collana |
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The Expert's Voice in R Programming |
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Disciplina |
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Soggetti |
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Big data |
Software engineering |
R (Computer program language) |
Big Data |
Software Engineering/Programming and Operating Systems |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Nota di contenuto |
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Contents at a Glance; Contents; About the Author; About the Technical Reviewer; Acknowledgments; Introduction; Chapter 1: Migrating to R: As Easy As 1, 2, 3; Getting R Up and Running on Your System; Okay, So I Have R. What's Next?; Understanding the Data Types in R; Handling Missing Data in R; Working with Vectors in R; Working with Matrices in R; Looking Backward and Forward; Chapter 2: Input and Output; Recipe 2-1. Inputting and Outputting Data; Problem; Solution; Keyboard and Monitor Access; Reading and Writing Data Files; Reading Data Files; Writing Data Files |
Recipe 2-2. Cleaning Up DataProblem; Solution; Recipe 2-3. Dealing with Text Data; Problem; Solution; Recipe 2-4. Getting Data from the Internet; Problem; Solution; Chapter 3: Data Structures; Recipe 3-1. How to Work with Vectors; Problem; Solution; Recipe 3-2. How to Work with Matrices; Problem; Solution; Recipe 3-3. How to Work with Lists; Problem; Solution; Creating a List; Adding and Deleting List Components; Applying Functions to Lists; Recipe 3-4. Working with Data Frames; Problem; Solution; Creating a Data Frame and Accessing Data Frame Elements; Dealing with Missing Data, Take 2 |
Subsetting DataSaving Datasets; Chapter 4: Merging and Reshaping |
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Datasets; Recipe 4-1. Merging Datasets by a Common Variable; Problem; Solution; Recipe 4-2. Adding Rows and Columns; Problem; Solution; Recipe 4-3. Reshaping a Dataset; Problem; Solution; Recipe 4-4. Stacking and Unstacking Data; Problem; Solution; Chapter 5: Working with Dates and Strings; Recipe 5-1. Working with Dates and Times; Problem; Solution; Recipe 5-2. Working with Character Strings; Problem; Solution; Creating Character Strings; Finding Patterns and Matches in Strings; Using the stringr Package |
The str_length() and str_c() FunctionsThe str_sub() Function; The str_dup() Function; Padding, Wrapping, and Trimming Strings; Extracting Words; Chapter 6: Working with Tables; Recipe 6-1. Working with One-Way Tables; Problem; Solution; Recipe 6-2. Working with Two-Way Tables; Problem; Solution; Recipe 6-3. Analyzing One- and Two-Way Tables; Problem; Solution; Recipe 6-4. Working with Higher-Order Tables; Problem; Solution; Chapter 7: Summarizing and Describing Data; Recipe 7-1. Creating Simple Frequency Distributions; Problem; Solution; Recipe 7-2. Creating Grouped Frequency Distributions |
ProblemSolution; Recipe 7-3. Calculating Summary Statistics; Problem; Solution; Recipe 7-4. Working with Quantiles; Problem; Solution; Chapter 8: Graphics and Data Visualization; Recipe 8-1. Getting the Colors You Want; Problem; Solution; Recipe 8-2. Using the Standard Graphs; Problem; Solution; Histograms; Bar Charts; Line Graphs; Scatterplots; Recipe 8-3. Using Graphics for Exploratory Data Analysis; Problem; Solution; Recipe 8-4. Using Graphics for Data Visualization; Problem; Solution; Chapter 9: Probability Distributions; Recipe 9-1. Finding Areas Under the Standard Normal Curve; Problem |
Solution |
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Sommario/riassunto |
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R Recipes is your handy problem-solution reference for learning and using the popular R programming language for statistics and other numerical analysis. Packed with hundreds of code and visual recipes, this book helps you to quickly learn the fundamentals and explore the frontiers of programming, analyzing and using R. R Recipes provides textual and visual recipes for easy and productive templates for use and re-use in your day-to-day R programming and data analysis practice. Whether you're in finance, cloud computing, big or small data analytics, or other applied computational and data science - R Recipes should be a staple for your code reference library. |
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