- Find a freely available government dataset on the web. Read the dataset's description, and think about what assumptions you might make about the data when planning a certain analysis. Translate these into actual code so that R can check them for you. Were there any deviations from your expectations?
- An ISBN-10 is a unique identifier for books. It is 10 characters long. The last digit is called a check digit and is determinable from the preceding digits using modular arithmetic. When the check digit is "10", it is represented with an "X". To review, an ISBN-10 is either 10 digits or nine digits, with an "X" at the end. Write a function that uses regular expressions to check if an input string (or vector of strings) is in the forma
t of a valid ISBN-10. As a bonus, write code to check if the check digit matches what it is supposed to be after learning about how the check digit is computed on Wikipedia! Use this predicate function in an assertr construct, like
lib %>% assert(YOUR_FUNCTION_HERE...
Data Analysis with R, Second Edition - Second Edition
Data Analysis with R, Second Edition - Second Edition
Overview of this book
Frequently the tool of choice for academics, R has spread deep into the private sector and can be found in the production pipelines at some of the most advanced and successful enterprises. The power and domain-specificity of R allows the user to express complex analytics easily, quickly, and succinctly.
Starting with the basics of R and statistical reasoning, this book dives into advanced predictive analytics, showing how to apply those techniques to real-world data though with real-world examples.
Packed with engaging problems and exercises, this book begins with a review of R and its syntax with packages like Rcpp, ggplot2, and dplyr. From there, get to grips with the fundamentals of applied statistics and build on this knowledge to perform sophisticated and powerful analytics. Solve the difficulties relating to performing data analysis in practice and find solutions to working with messy data, large data, communicating results, and facilitating reproducibility.
This book is engineered to be an invaluable resource through many stages of anyone’s career as a data analyst.
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Current Title:
Data Analysis with R, Second Edition - Second Edition
Table of Contents (24 chapters)
Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
Free Chapter
RefresheR
The Shape of Data
Describing Relationships
Probability
Using Data To Reason About The World
Testing Hypotheses
Bayesian Methods
The Bootstrap
Predicting Continuous Variables
Predicting Categorical Variables
Predicting Changes with Time
Sources of Data
Dealing with Missing Data
Dealing with Messy Data
Dealing with Large Data
Working with Popular R Packages
Reproducibility and Best Practices
Other Books You May Enjoy
Index
Customer Reviews