Preface
1 Getting Started with Data in R
I Data Science via the tidyverse
2 Data Visualization
3 Data Wrangling
4 Data Importing & “Tidy” Data
II Data Modeling via moderndive
5 Basic Regression
6 Multiple Regression
III Statistical Inference via infer
7 Sampling
8 Bootstrapping & Confidence Intervals
9 Hypothesis Testing
10 Inference for Regression
11 Tell the Story with Data
Appendix
A Statistical Background
B Information about R packages Used
Bibliography
Index
• Chester Ismay is a Data Science Evangelist for DataRobot and is based in Portland, Oregon, USA.
•Albert Y. Kim is an Assistant Professor of Statistical and Data Sciences at Smith College in Northampton, Massachusetts, USA.
"Through apt use of analogies, hands-on exercises, and abundant
opportunities to get coding, this book delivers on its promise to
give a reader without a background in statistics or programming the
tools necessary for understanding and conducting real-world
statistical inference and data analysis. With an emphasis on
learning new concepts first "by hand," before turning to the code,
it would make a particularly useful classroom companion. However,
the "learning checks" provided throughout also make it a great
guide for self-study. Students and teachers alike will benefit from
this thoughtful introduction, as it addresses even the smallest of
details that can trip beginners up, and keep them from getting to
the more fruitful parts of data analysis."
- Mara Averick, Developer Advocate, RStudio, Inc."This is a
comprehensive, modern resource for teaching and learning data
science. ModernDive couples the introduction of core statistical
concepts directly with learning how to apply data science methods
to realistic data sets using the R programming language. The
pedagogical approach of ModernDive is thoughtful and highly
effective. The text engages learners early with tangible and
practical concepts, such as creating data visualizations, that
enable students to see early returns on their investment in
learning R. The authors have created a guide to learning data
science that increases students’ engagement and enthusiasm, while
simultaneously providing students with the depth of understanding
needed to conduct meaningful and reproducible data analyses.
ModernDive is my go-to resource for teaching data science. I use it
in all of my courses and workshops and I have found it to be the
most effective and comprehensive introduction to data science in R
available."
- Rich Majerus, Queens University of Charlotte"With its emphasis on
visualization, real world data, and simulation, along with clear
instructions about how to work with R and the Tidyverse, ModernDive
is the most accessible and student-friendly statistics textbook I
have taught from. The book's early chapters on data wrangling and
visualization provide students with hands-on experience with real
data and get them excited about making beautiful and informative
figures with modern statistical tools like R and the Tidyverse.
Where the book especially shines is its simulation-based approach
to modeling, confidence intervals, and hypothesis testing. Instead
of teaching a complicated flowchart with dozens of types of
statistical tests, the book is instead centered around linear
modeling and simulation. The chapters on hypothesis testing use
simulation to teach about p-values, an approach that students find
eminently intuitive. Overall, ModernDive is a phenomenal modern
introduction to statistical inference—it is an essential book for
any statistics instructor!"
-Dr. Andrew Heiss, Andrew Young School of Policy Studies, Georgia
State University
"My overall impression of the book is very positive. If you want to
learn R programming and statistics at the same time, this is a good
book for you. I like the intertwining of the two since I think
modern data analysis requires computing.
Focusing on resampling techniques for the creation of confidence
intervals and the conducting of hypothesis tests is a deviation
from typical introductory books. I think that focus helps solidify
a student’s understanding of sampling variability and its central
role in statistical inference."
- Adam L. Pintar, Journal of Quality Technology"Through apt use of
analogies, hands-on exercises, and abundant opportunities to get
coding, this book delivers on its promise to give a reader without
a background in statistics or programming the tools necessary for
understanding and conducting real-world statistical inference and
data analysis. With an emphasis on learning new concepts first "by
hand," before turning to the code, it would make a particularly
useful classroom companion. However, the "learning checks" provided
throughout also make it a great guide for self-study. Students and
teachers alike will benefit from this thoughtful introduction, as
it addresses even the smallest of details that can trip beginners
up, and keep them from getting to the more fruitful parts of data
analysis."
- Mara Averick, Developer Advocate, RStudio, Inc. "This is a
comprehensive, modern resource for teaching and learning data
science. ModernDive couples the introduction of core statistical
concepts directly with learning how to apply data science methods
to realistic data sets using the R programming language. The
pedagogical approach of ModernDive is thoughtful and highly
effective. The text engages learners early with tangible and
practical concepts, such as creating data visualizations, that
enable students to see early returns on their investment in
learning R. The authors have created a guide to learning data
science that increases students’ engagement and enthusiasm, while
simultaneously providing students with the depth of understanding
needed to conduct meaningful and reproducible data analyses.
ModernDive is my go-to resource for teaching data science. I use it
in all of my courses and workshops and I have found it to be the
most effective and comprehensive introduction to data science in R
available."
- Rich Majerus, Queens University of Charlotte"With its emphasis on
visualization, real world data, and simulation, along with clear
instructions about how to work with R and the Tidyverse, ModernDive
is the most accessible and student-friendly statistics textbook I
have taught from. The book's early chapters on data wrangling and
visualization provide students with hands-on experience with real
data and get them excited about making beautiful and informative
figures with modern statistical tools like R and the Tidyverse.
Where the book especially shines is its simulation-based approach
to modeling, confidence intervals, and hypothesis testing. Instead
of teaching a complicated flowchart with dozens of types of
statistical tests, the book is instead centered around linear
modeling and simulation. The chapters on hypothesis testing use
simulation to teach about p-values, an approach that students find
eminently intuitive. Overall, ModernDive is a phenomenal modern
introduction to statistical inference—it is an essential book for
any statistics instructor!"
-Dr. Andrew Heiss, Andrew Young School of Policy Studies, Georgia
State University"The monograph belongs to the The R series, and it
can serve as a convenient way for learning data science and
statistics simultaneously with the R language. The textbook
consists of four parts, eleven chapters, and each chapter contains
sections and subsections. In Preface, the authors describe the book
structure and illustrate it with a pipeline going from importing
data to making its tidy version, which is applied in a loop of
transforming-modeling-visualizing, and finally is used for
communication, or interpretation and reporting of the modeling
results...The monograph supplies multiple links to the websites of
the R packages and related statistical methods, and the online
version of the book with all the codes and outputs is available at
moderndive.com. The textbook presents to students and researchers a
very useful introduction to the data science and contemporary R
programing, with numerous examples of R implementation for solving
various problems of statistical estimation and inference."
- Stan Lipovetsky, Technometrics, Vol 62"One of the great things
about this textbook is that the authors provide great learning
checks and helpful hints scattered throughout the chapters, with
links in the text to references that can help the reader along if
they get stuck. Although this textbook sticks to the simpler world
of simple and multiple linear regression (foregoing the
complexities of other regressions like logistic and Poisson), the
take home messages really apply to all types of regression for
inference, especially considering the intended audience for this
book is for instructors teaching introductory statistical inference
courses (particularly those interested in using R).
If you are an instructor, and are teaching an introductory course
to statistical inference (and particularly want to teach it in R),
I highly recommend this text for its adaptability, availability,
and ease of use."
- Zachary Fusfeld, Biometrics"The new ModernDive (Statistical
Inference via Data Science) textbook is simply wonderful! It uses
accessible language to introduce the topics of data science and
statistics, as well as an intuitive simulation-based inference
first approach. Importantly, it does not stop there. It also places
great emphasis on how to do all of this in the R programming
language! True to the book's name, the R code taught and
demonstrated in the book uses a modern, tidy approach for data
wrangling, visualization and statistics. I have used it
successfully in an introductory statistics setting at both the
undergraduate-level and the professional Master's level.
Furthermore, I would choose to do this again."
- Tiffany Timbers, University of British Columbia"With the help of
visualization, the authors give examples of identifying outliers
and identifying relationships between continuous numerical data.
Based on this, we can conclude that the authors very well describe
one of the steps of data analysis – pre-processing. This step is
important because it is a main milestone in the identification of
the relationship between variables in the data...The authors also
provide a detailed review of the main methods of presenting the
classical results based on linear models. This part is very
important in the preparation of articles or books and greatly
simplifies the work on the preparation.
- Igor Malyk, ISCB News, December 2020“The forementioned book is a
successful attempt to help convert classical statisticians into
modern data scientists. This book aims and provides an excellent
exposition of data-driven statistical tools to draw statistical
inferences from data, all while using the R software and its
‘tidyverse’ package…This book is designed for those who want to
understand and know how to retrieve the information hidden inside
the provided data, using R software using the tools of classical
statistics. The authors have tried to keep the readers away from
in-depth mathematical details while presenting the material in this
book. The authors assume that the readers have a good grasp of the
statistical tools and methodologies…The topics are accompanied and
explained with data-based examples.”
- Shalabh, IIT Kanpur, India
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