- Write a function called
dt_table
similar to thetable
function in base R. The arguments will be adata.table
, and a string specifying a column name. Do the same thing fortibbles
and call itdp_table
. - Read some of the documentation for
data.table
anddplyr
/tidyr
. Of the former, learn about.SD
and.SDcols
. Of the later, learn about theseparate_
functions. These are life-savers. - Learn about some of the other popular packages of the tidyverse. What do all of these have in common, again?
- Describe the four major voting bloc "cleavages" defined by Lipset and Rokkan in Party systems and voter alignments. Since its publication in 1967, have any other potential cleavage points emerged in your nation or state? How do these cleavages manifest themselves in political party differences?
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.
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