ModernDive 1.1.0

• Typo fixes and clarifying wording tweaks
• With a big assist from @mariumtapal, we cleaned and refactored all R Markdown code to make it easier for future bookdown users to understand. However all code seen by readers of the print edition has been left intact.
• Appendix C (online only):
  • Renamed from "Reach for the Stars" to "Tips and Tricks".
  • Added Appendix C.1 on most common data wrangling questions we've encountered, mostly written by @smetzer180

ModernDive 1.0.0

• Version 1.0.0 corresponds to our CRC Press print edition.
• Changed word in title of book from "moderndive" to "ModernDive" for consistency with hex sticker.
• Added Foreword by Kelly S. McConville. Thanks, @mcconvil!
• Fixed various typos throughout the book and tried to make language consistent. For example, using "data sets" instead of "datasets" or "data-sets".
• Switched from gather() and spread() with tidyr to pivot_long() and pivot_wide() following this tidyverse article
• Added geom_parallel_slopes() user-defined geom extension to ggplot2. Thanks @echasnovski!

ModernDive 0.6.1

• Changed chapter numbers. Chapter "1. Introduction" is now "Preface", thus all Chapter numbers decreased by one.
• Moved discussions on normal distribution (Ch on sampling) and log-transformations (Ch on tell your data story) to Appendix A "Statistical Background"
• Updated images used in book
• Did a full scan of the book for typos
• Created greyscale versions of many images for the CRC Press printed version

ModernDive 0.6.0

We're only a few cosmetic edits away from v1.0.0, which will correspond to our print edition with CRC Press!

Done first pass of infer chapters

Completed major re-organization and clean-up of Chapters 9-11 using the infer package for "tidy and transparent" statistcal inference.

• Chapter 9: Bootstrapping & confidence intervals
  • Tactile exercise of sampling 50 pennies from bank and resampling from this sample.
  • Added sections on
    1. "Interpreting confidence intervals", in particular determinants of CI width.
    2. "Theory-based confidence intervals" using formula for SE of p-hat, thereby bridging gap between simulation and theory-based methods.
• Chapter 10: Hypothesis testing
  • Added promotions example on gender discrimination in promotions at a bank. Data source: openintro::gender.discrimination
  • Added section on "Theory-based hypothesis tests" using t-test, thereby bridging gap between simulation and theory-based methods.
• Chapter 11: Inference for regression.
  • Discussion on LINE conditions for inference. In particular using moderndive::get_regression_points() wrapper function to broom::augment() so that novices can do their own residual analyses.

Other changes

• Chapter 7: Multiple regression
  • Added Section 7.3.1 on model selection: choosing between "interaction" and "parallel slopes" models
• Chapter 8: Sampling
  • Added Section 8.5.3 with more in-depth discussion of normal distribution
• Chapter 12: Renamed to "Tell your story with data"

ModernDive 0.5.0

Highlights

• "Data wrangling" chapter now comes after "Tidy data" chapter.
• Improved explanations and examples of geom_histogram(), geom_boxplot(), and "tidy" data
• Moving residual analysis from regression Chapters 6 & 7 to Chap 11: Inference for regression
• Reorganized Chap 8 on Sampling
• All learning check solutions now in Appendix D
• PDF build re-added (still a work-in-progress)

All content changes

• Changed title
  • From: "Statistical Inference via Data Science in R"
  • To: "Statistical Inference via Data Science: A moderndive into R and the tidyverse"
• Chapter 2 - Getting Started
  • Added subsection 2.2.3 "Errors, warnings, and messages" by @andrewheiss
• Chapter 3 - Data visualization:
  • Added simpler introductory geom_histogram() and geom_boxplot() examples
  • Started downweighting the amount of data wrangling previews included in this chapter, in particular join.
  • Cleaned up conclusion section
  • Added cheatsheet
• Switched order of "Chap 4 Tidy Data" and "Chap 5 Data Wrangling": Data Wrangling now comes first
• Chapter 4 - Data wrangling:
  • Added cheatsheet
• Chapter 5 - Renamed to "Importing and tidy data"
  • Reordered sections: importing then tidying
  • Added fivethirtyeight::drinks example of "hitting the non-tidy wall", then using tidyr::gather()
  • Made Guatemala democracy score a case study.
  • Added discussion on what tidyverse package is.
  • Moved discussion on normal forms to Ch4: Data Wrangling - joins.
  • Moved discussion on identification vs measurement variables to Ch2: Getting started with data.
• Chapter 6 - Basic regression:
  • Moved residual analysis to Chapter 11
• Chapter 7 - Multiple regression:
  • Moved residual analysis to Chapter 11
• Chapter 8 - Sampling: Major refactoring of presentation/exposition; see below
• Chapter 11 - Inference for regression:
  • Moved residual analysis from Chapter 6 & 7 here
• Moved all Learning Check solutions to Appendix D

Chapter 8 Sampling Refactoring

Old chapter structure:

1. Introduction to sampling a) Concepts related to sampling b) Inference via sampling
2. Tactile sampling simulation a) Using the shovel once b) Using the shovel 33 times
3. Virtual sampling simulation a) Using the shovel once b) Using shovel 33 times c) Using shovel 1000 times d) Using different shovels
4. In real-life sampling: Polls
5. Conclusion a) Central Limit Theorem b) What’s to come? c) Script of R code

New chapter structure:

1. Activity: Sampling from a bowl a) Question: What proportion of this bowl is red? b) Using shovel once c) Using shovel 33 times
2. Computer simulation: a) What is a simulation? We just did a "tactile" one by hand, now let's do one using the the computer b) Using shovel once c) Using shovel 33 times d) Using shovel 1000 times e) Using different shovels
3. Goal: Study fluctuations due to sampling variation a) You probably already knew: Bigger sample size means "better" guess. b) Comparing shovels: Role of sample size
4. Framework: Sampling a) Terminology for sampling (population, sample, point estimate, etc) b) Statistical concepts: sampling distribution and standard error c) Computer's random number generator
5. Interpretation: a) Visual display of differences
6. Case study: Obama poll
7. Big picture: a) Table of inferential scenarios: Add bowl and obama poll (both p) b) Why does this work? Theoretial result: CLT c) There's a formula for that: SE formula that has sqrt(n) at the bottom d) Appendix: Normal distribution discuss

ModernDive 0.4.0

Highlights

1. The infer package is ready for prime-time! Thus we made a first pass at incorporating it into the book in Chapters 9 and 10 on confidence intervals and hypothesis testing!
2. Chapter 12 on "Thinking with Data" now includes a case study using the Seattle house prices dataset on Kaggle.com. Chapters 3 and 4 from new "Modeling with Data in the Tidyverse" DataCamp course by Albert Y. Kim are based on this analysis!
3. Speaking of DataCamp, we point readers to various DataCamp courses that directly align with various chapters in the book!
4. We significantly cleaned up Chapter 8 on sampling! In particular: adding a 2013 Obama approval rating poll example to tie in with our sampling bowl tactile and virtual simulations and making it very clear that ultimately we are performing statistical inference via sampling.

All content changes

• Introduction: Added section on correspondence of chapters to various DataCamp courses. Furthermore, links to relevant DataCamp course are included at the outset of each chapter.
• Chapter 3 - Data visualization:
  • Added simplified geom_jitter() example
  • More explanations for how whiskers and outliers are constructed in geom_boxplots
  • Added summary of table of all 5 named graphs
• Chapter 4 - Tidy data:
  • Added section on importing Excel data via RStudio
  • Added example of tidy vs non-tidy: fivethirtyeight::drinks
• Chapter 5 - Data wrangling:
  • Added computing available seat miles data wrangling case study
  • Abandoned "5 Main Verbs" 5MV notion
  • Added _join() and group_by() multiple variables
• Chapter 6 - Basic regression:
  • Clarified explanations of indicator/dummy variables when using categorical variable in regression.
  • Expanded "Correlation is not necessarily causation" subsection with example of "does sleeping with shoes on cause headaches?" including causal diagram
  • Introduced concept of a "wrapper function" when introducing moderndive::get_regression_table() function
  • Replaced all base::summary() with skimr::skim() for quick numerical summaries
• Chapter 7 - Multiple regression:
  • Changed all "everything else being equal" interpretation statements with "taking into account/controlling for all other variables in our model"
• Chapter 8 - Sampling:
  • Significantly cleaned up sampling terminology and definitions and made more clear that we are sampling for inference
  • Cleaned up section and subsection structure to be much cleaner:
    1. Tactile sampling simulation
    2. Virtual sampling simulation
    3. In real-life sampling: Introduced example of 2013 Obama approval rating poll and then tie everything with sampling bowl.
• Major overhaul: Chapter 9 - Confidence intervals
  • infer package now being ready for prime-time, we made first pass at incorporation into book.
• Major overhaul: Chapter 10 - Hypothesis testing
  • infer package now being ready for prime-time, we made first pass at incorporation into book.
  • Added discussion on Allan Downey's "There is only one test" ideas
• Chapter 11 - Inference for Regression
  • Added a simple linear regression example using the infer package
• Major overhaul: Chapter 12 - Thinking with data
  • Added case study of Seattle house prices dataset from Kaggle, which is now available in house_prices dataframe in moderndive package.
    1. Chapters 3 and 4 from new "Modeling with Data in the Tidyverse" DataCamp course are based on this analysis
    2. Includes a discussion on the importance of log10-transformations
    3. Introduces modeling/regression for prediction: predicting house prices
  • Laid outline for "effective data storytelling" using fivethirtyeight data and added one small example using US births data
  • At the beginning of chapter, we now come full circle and revisit the discussion on the ModernDive flowchart in the introduction.

Other changes

• Updated moderndive package on CRAN to 0.2.0. See NEWS.md

Content changes

• Reorganized chapter sequencing according to flowchart at top of Section 1.1
• Chapter 2 - Getting Started: Added more explanation on R packages, including analogy for install.packages() and library() (akin to downloading apps onto phone)
• Added "Data Modeling" portion to book
  • Chapter 6 - Basic regression: one numerical explanatory variable, correlation, one categorical explanatory variable)
  • Chapter 7 - Multiple regression: two numerical explanatory variables, one numerical and one categorical, interaction effects, Simpson's Paradox
  • Uses new moderndive package, which includes get_regression_table() and get_regression_points() wrapper functions to simplify outputing of clean regression tables and observed/fitted values + resisuals
• Added "statistical inference" portion to book
  • Added Chapter 8 - Sampling (still under construction) using sampling bowl
  • Chapters 9 and 10 on confidence intervals and hypothesis testing have not yet been updated, as we were awaiting the now launched package: infer: A tidyverse-friendly R package fo statistical inference
  • Added Chapter 11 - Inference for regression (still under construction), where we'll revisit the regression models fit in Chapters 6 & 7

Other changes

• Development version of book now available at http://moderndive.netlify.com/; deployed via travis-ci + netlify.
• Added wide ModernDive logo to top of each chapter and logos folder
• Added favicon (icon in browser tab)
• Moved home GitHub repository from https://github.com/ismayc/moderndiver-book/ to https://github.com/moderndive/moderndive_book

Content changes

• Incorporated feedback from consultations with Prof. Yana Weinstein, cognitive psychological scientist and co-founder of The Learning Scientists.
• Restructured/revamped chapters
  • Chapter 1: Introduction
    • Friendlier introduction targetted to students is first thing users see. Followed then by introduction for instructors, ways to connect/contribute, and technical details.
    • Added links to example student projects from two courses that have previously used ModernDive:
      • Middlebury College MATH 116 Introduction to Statistical and Data Sciences using student collected data.
      • Pacific University SOC 301 Social Statistics using data from the fivethirtyeight R package
  • Chapter 2: Getting Started New chapter added meant for new R users/coders, including
    • Discusions on R vs RStudio and how to install both (with support videos)
    • A "How do I code in R?" section with links to DataCamp.com courses that covers the console, data types, vectors, factors, data frames, boolean operators, functions etc
    • Thorough discussion on R packages
    • An end-to-end starter example analysis of the data frames in the nycflights13 package using the console, View(), glimpse() etc.
  • Chapter 3: Data Visualization via ggplot2 now first non-intro chapter.
    • Replaced Menard's "Napolean's March on Moscow" with Hans Rosling's (RIP) "Gapminder" plots as introductory example to Grammar of Graphics.
    • Added geom_col() for making barcharts when data is pre-tabulated, instead of using geom_bar(stat="identity")
  • Chapter 4: Tidy Data via tidyr bumped back. Added sections on converting from wide to long/tidy format and importing CSV's
  • Chapter 5: Data Manipulation Wrangling via dplyr
  • Chapter 6: Data Modeling using Regression via broom bumped up from end of book to here given its pedagogical importance, added notes on viewing regression in a prediction framework.
  • Chapter 7-9: Sampling, Hypothesis Testing, Confidence Intervals Mostly unchanged for now; see pending changes section below.

Technical changes

• Book is now hosted on ModernDive.com
• Development version now on original ModernDive site https://ismayc.github.io/moderndiver-book/
• Added links to digital copies and source code of all past versions of ModernDive in Chapter 1.
• Cut build/compilation time of book from ~20 minutes to ~1 minute
• Disabled gitbook PDF output

Pending changes for next version

• Chapter 6: Data Modeling using Regression via broom
  • Better treatment of experimental design and its effect on bias/causation than currently exists in chapter.
  • Examples of regression with categorical predictors with 3 or more levels.
  • Multivariate regression, in particular the following predictor scenarios: 2 numerical, 2 categorical, and 1 numerical + 1 categorical
  • Interaction effects
• Chapter 7-9: Sampling, Hypothesis Testing, Confidence Intervals have largely not been updated, pending developments of infer: A tidyverse-friendly R package fo statistical inference

• Attempting to fix Shiny app in Figure 6.2 appearing as white box in published site noted here
  • Reverted to using screenshot with link instead
• Updated link to dplyr cheatsheet and ggplot2 cheatsheet
• Began adding DataCamp chapters as Review Questions to the end of Chapters 3 and 4 (More to come)
• Updated link to MailChimp
• Fixed language in LC3.5

• Converted last updated in index.Rmd to inline instead of R chunk
• Fixed edit link to point to moderndive-book GitHub repo instead of moderndive-source repo
• Fixed broken links to script files at the end of Chapters 4-9
• Added purl=FALSE to chunks that do not contain useful code to the reader
• Attempting to fix Shiny app in Figure 6.2 appearing as white box in published site noted here
  • Decided to replace active shiny app with screenshot instead