Statistical inference via data science: a ModernDive into R and the tidyverse
Gespeichert in:
| Hauptverfasser: | , |
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Boca Raton ; London ; New York
CRC Press
[2020]
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| Schriftenreihe: | Chapman & Hall/CRC, the R series
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| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Klappentext |
| Beschreibung: | xxx, 430 Seiten Illustrationen, Diagramme |
| ISBN: | 9780367409876 9780367409821 0367409879 |
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Datensatz im Suchindex
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Contents Foreword Preface About the authors xv xvii xxix 1 Getting Started with Data in R 1.1 What are R and RStudio?. 1.1.1 Installing R and RStudio . 1.1.2 Using R via RStudio. 1.2 How do I code in R?. 1.2.1 Basic programming concepts and terminology. 1.2.2 Errors, warnings, and messages. 1.2.3 Tips on learning to code. 1.3 What are R packages?. 1.3.1 Package installation. 1.3.2 Package loading . 1.3.3 Package use. 1.4 Explore your first datasets . 1.4.1 nycflights13 package. 1.4.2 flights data frame. 1.4.3 Exploring data frames. 1.4.4 Identification and measurement variables. 1.4.5 Help files. 1.5 Conclusion . 1.5.1 Additional
resources. 1.5.2 What’s to come?. 1 1 2 3 4 4 6 7 8 9 11 11 12 12 13 14 17 18 19 19 20 1 Data Science with tidyverse 21 2 Data Visualization 2.1 The grammar of graphics . 2.1.1 Components of the grammar . 2.1.2 Gapminder data. 2.1.3 Other components. 23 24 24 25 26 vii
Contents vļ» 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 2.1.4 ggplot2 package. Five named graphs - the 5NG. 5NG#1: Scatterplots . 2.3.1 Scatterplots via geom_point. 2.3.2 Overplotting . 2.3.3 Summary. 5NG#2: Linegraphs . 2.4.1 Linegraphs via geom_line. 2.4.2 Summary. 5NG#3: Histograms. 2.5.1 Histograms via geom_histogram. 2.5.2 Adjusting the bins. 2.5.3 Summary. Facets. 5NG#4: Boxplots . 2.7.1 Boxplots via geom_boxplot . 2.7.2 Summary. 5NG#5: Barplots . 2.8.1 Barplots
via geom_bar or geom_col . 2.8.2 Must avoid pie charts!. 2.8.3 Two categorical variables. 2.8.4 Summary. Conclusion . 2.9.1 Summary table. 2.9.2 Function argument specification. 2.9.3 Additional resources. 2.9.4 What’s to come. 3 Data Wrangling 3.1 3.2 3.3 3.4 The pipe operator: % %. filter rows. summarize variables . group_by rows. 3.4.1 Grouping by more than one variable. 3.5 mutate existing variables. 3.6 arrange and sort rows . 3.7 join data frames . 3.7.1 Matching “key” variable names. 3.7.2 Different “key” variable names
. 3.7.3 Multiple “key” variables. 3.7.4 Normal forms. 3.8 Other verbs. 27 27 28 29 37 35 35 36 38 38 40 41 43 43 45 47 50 50 51 54 55 60 60 60 61 62 62 65 67 69 72 75 78 80 84 86 87 88 89 90 91
Contents 3.9 4 93 93 94 94 96 96 4.1 100 101 102 Importing data. 4.1.1 Using the console. 4.1.2 Using RStudio’s interface. “Tidy” data . 4.2.1 Definition of “tidy” data. 4.2.2 Converting to “tidy” data. 4.2.3 nycflightsl 3 package. Case study: Democracy in Guatemala . tidyverse package . Conclusion . 4.5.1 Additional resources. 4.5.2 What’s to come?. Data Modeling with moderndive 5.2 5.4 121 One numerical explanatory variable . 5.1.1 Exploratory data analysis. 5.1.2 Simple linear regression. 5.1.3 Observed/fitted values and residuals. One categorical explanatory variable. 5.2.1 Exploratory data analysis. 5.2.2
Linear regression. 5.2.3 5.3 Observed/fitted values and residuals. Related topics . 5.3.1 Correlation is not necessarily causation. 5.3.2 Best-fitting line. 5.3.3 get_regression_xO functions. Conclusion . 5.4.1 Additional resources. 5.4.2 What’s to come?. Multiple Regression 6.1 One numerical and one categorical explanatory variable 103 106 108 112 113 116 117 117 117 119 Basic Regression 5.1 6 91 99 4.3 4.4 4.5 5 3.8.1 select variables. 3.8.2 rename variables. 3.8.3 top_n values of a variable. Conclusion . 3.9.1 Summary table. 3.9.2 Additional resources. 3.9.3 What’s to come?. Data Importing and “Tidy” Data 4.2 II ix 123 124 133 137 139 140 147 151 152 152 154
157 160 160 160 161 . . . 162
x Contents 6.1.1 Exploratory data analysis. 6.1.2 Interaction model. 6.1.3 Parallel slopes model. 6.1.4 Observed/fitted values and residuals. 6.2 Two numerical explanatory variables. 6.2.1 Exploratory data analysis. 6.2.2 Regression plane. 6.2.3 Observed/fitted values and residuals. 6.3 Related topics . 6.3.1 Model selection. 6.3.2 Correlation coefficient. 6.3.3 Simpson’s Paradox. 6.4 Conclusion . 6.4.1 Additional resources. 6.4.2 What’s to come?. III 7 Statistical Inference with infer 193 Sampling 195 7.1 Sampling bowl activity . 7.1.1 What proportion of this bowl’s balls are red?. 7.1.2 Using the shovel once . 7.1.3 Using the shovel 33
times. 7.1.4 What did we just do? . 7.2 Virtual sampling. 7.2.1 Using the virtual shovel once. 7.2.2 Using the virtual shovel 33 times. 7.2.3 Using the virtual shovel 1000 times. 7.2.4 Using different shovels. 7.3 Sampling framework. 7.3.1 Terminology and notation. 7.3.2 Statistical definitions. 7.3.3 The moral of the story. 7.4 Case study: Polls. 7.5 Conclusion . 7.5.1 Sampling scenarios. 7.5.2 Central Limit Theorem . 7.5.3 Additional resources. 7.5.4 What’s to come?. 8 Bootstrapping and ConfidenceIntervals 8.1 Pennies activity 162 166 169 173 175 176 181 183 184 184 188 188 191 191 191 . 195 196 196 198 201
202 203 206 209 212 216 216 219 222 226 230 230 231 232 232 233 235
Contents xi 8.1.1 What is the average year on US pennies in 2019? . 8.1.2 Resampling once. 8.1.3 Resampling 35 times. 8.1.4 What did we just do? . 8.2 Computer simulation of resampling. 8.2.1 Virtually resampling once. 8.2.2 Virtually resampling 35 times. 8.2.3 Virtually resampling 1000 times. 8.3 Understanding confidence intervals. 8.3.1 Percentile method . 8.3.2 Standard error method. 8.4 Constructing confidence intervals. 8.4.1 Original workflow. 8.4.2 infer package workflow. 8.4.3 Percentile method with infer. 8.4.4 Standard error method with infer. 8.5 Interpreting confidence intervals . 8.5.1 Did the net capture the fish? . 8.5.2 Precise and shorthand interpretation. 8.5.3 Width of confidence intervals. 8.6 Case study: Is yawning
contagious?. 8.6.1 Mythbusters study data. 8.6.2 Sampling scenario . 8.6.3 Constructing the confidence interval. 8.6.4 Interpreting the confidence interval. 8.7 Conclusion . 8.7.1 Comparing bootstrap and sampling distributions . 8.7.2 Theory-based confidence intervals. 8.7.3 Additional resources. 8.7.4 What’s to come?. 9 Hypothesis Testing 9.1 9.2 9.3 Promotions activity . 9.1.1 Does gender affect promotions at a bank?. 9.1.2 Shuffling once. 9.1.3 Shuffling 16 times. 9.1.4 What did we just do? . Understanding hypothesis tests. Conducting hypothesis tests. 9.3.1 infer package workflow. 9.3.2 Comparison with confidence intervals. 9.3.3 “There is only one test”. 235 239 244 246 247 247
249 251 254 255 256 258 259 259 267 269 271 272 280 281 284 284 286 287 294 295 295 300 305 305 307 308 308 310 314 316 317 320 322 328 332
xii Contents 9.4 Interpreting hypothesis tests . 9.4.1 Two possible outcomes. 9.4.2 Types of errors. 9.4.3 How do we choose alpha?. 9.5 Case study: Are action or romance movies rated higher? 9.5.1 IMDb ratings data. 9.5.2 Sampling scenario . 9.5.3 Conducting the hypothesis test. 9.6 Conclusion . 9.6.1 Theory-based hypothesis tests. 9.6.2 When inference is not needed. 9.6.3 Problems with p-values . 9.6.4 Additional resources. 9.6.5 What’s to come. 10 Inference for Regression 10.1 Regression refresher . 10.1.1 Teaching evaluations analysis. 10.1.2 Sampling scenario . 10.2 Interpreting regression tables . 10.2.1 Standard error. 10.2.2 Test statistic. 10.2.3 p-value
. 10.2.4 Confidence interval. 10.2.5 How does R compute the table?. 10.3 Conditions for inference for regression . 10.3.1 Residuals refresher. 10.3.2 Linearity of relationship. 10.3.3 Independence of residuals. 10.3.4 Normality of residuals. 10.3.5 Equality of variance. 10.3.6 What’s the conclusion?. 10.4 Simulation-based inference forregression. 10.4.1 Confidence interval for slope. 10.4.2 Hypothesis test for slope. 10.5 Conclusion 333 333 335 336 337 338 340 341 347 347 356 358 359 359 361 361 362 364 365 366 367 368 369 370 371 371 373 374 375 376 378 379 380 384 . 386 10.5.1 Theory-based inference for regression. 10.5.2 Summary of statistical inference. 10.5.3 Additional resources.
10.5.4 What’s to come. 386 388 389 389
Contents IV xiii Conclusion 11 Tell Your Story with Data 11.1 Review . 11.2 Case study: Seattle house prices . 11.2.1 Exploratory data analysis: Part I. 11.2.2 Exploratory data analysis: Part II. 11.2.3 Regression modeling. 11.2.4 Making predictions. 11.3 Case study: Effective data storytelling. 11.3.1 Bechdel test for Hollywood gender representation . 11.3.2 US Births in 1999 . 11.3.3 Scripts of R code. Appendix A Statistical Background A.l Basic statistical terms. A. 1.1 Mean . A. 1.2 Median. A. 1.3 Standard deviation. A. 1.4 Five-number summary. A.1.5 Distribution. A. 1.6 Outliers. A.2 Normal distribution
. A.3 loglO transformations . Appendix В Versions of R Packages Used 391 393 393 396 397 404 407 409 410 411 411 414 417 417 417 417 417 418 418 418 418 421 423 Bibliography 425 Index 427
Statistical Inference via Data Science: A ModernDive into R and the Tidyverse provides a pathway for learning about statistical inference using data science tools widely used in industry, academia, and government. It introduces the tidyverse suite of R packages, including the ggplot2 package for data visualization, and the dplyr package for data wrangling. After equipping readers with just enough of these data science tools to perform effective exploratory data analyses, the book covers traditional introductory statistics topics like confidence intervals, hypothesis testing, and multiple regression modeling, while focusing on visualization throughout. Features • Assumes minimal prerequisites, notably, no prior calculus nor coding experience • Motivates theory using real-world data, including all domestic flights leaving New York City in 2013, the Gapminder project, and the data journalism website, FiveThirtyEight.com • Centers on simulation-based approaches to statistical inference rather than mathematical formulas • Uses the infer package for “tidy” and transparent statistical inference to construct confidence intervals and conduct hypothesis tests via the bootstrap and permutation methods • Provides all code and output embedded directly in the text; also available in the online version at moderndive.com This book is intended for individuals who would like to simultaneously start developing their data science toolbox and start learning about the inferential and modeling tools used in much of modern-day research. The book can be used in methods and data science courses and
first courses in statistics, at both the undergraduate and graduate levels. 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. |
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| spelling | Ismay, Chester Verfasser (DE-588)1220385859 aut Statistical inference via data science a ModernDive into R and the tidyverse Chester Ismay, Albert Y. Kim Boca Raton ; London ; New York CRC Press [2020] © 2020 xxx, 430 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Chapman & Hall/CRC, the R series Quantitative Methode (DE-588)4232139-6 gnd rswk-swf R Programm (DE-588)4705956-4 gnd rswk-swf Statistische Schlussweise (DE-588)4182963-3 gnd rswk-swf Statistische Schlussweise (DE-588)4182963-3 s Quantitative Methode (DE-588)4232139-6 s R Programm (DE-588)4705956-4 s b DE-604 Kim, Albert Y. 1980- Verfasser (DE-588)1203245866 aut Erscheint auch als Online-Ausgabe 978-0-367-40991-3 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=031586478&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=031586478&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | Ismay, Chester Kim, Albert Y. 1980- Statistical inference via data science a ModernDive into R and the tidyverse Quantitative Methode (DE-588)4232139-6 gnd R Programm (DE-588)4705956-4 gnd Statistische Schlussweise (DE-588)4182963-3 gnd |
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| title | Statistical inference via data science a ModernDive into R and the tidyverse |
| title_auth | Statistical inference via data science a ModernDive into R and the tidyverse |
| title_exact_search | Statistical inference via data science a ModernDive into R and the tidyverse |
| title_full | Statistical inference via data science a ModernDive into R and the tidyverse Chester Ismay, Albert Y. Kim |
| title_fullStr | Statistical inference via data science a ModernDive into R and the tidyverse Chester Ismay, Albert Y. Kim |
| title_full_unstemmed | Statistical inference via data science a ModernDive into R and the tidyverse Chester Ismay, Albert Y. Kim |
| title_short | Statistical inference via data science |
| title_sort | statistical inference via data science a moderndive into r and the tidyverse |
| title_sub | a ModernDive into R and the tidyverse |
| topic | Quantitative Methode (DE-588)4232139-6 gnd R Programm (DE-588)4705956-4 gnd Statistische Schlussweise (DE-588)4182963-3 gnd |
| topic_facet | Quantitative Methode R Programm Statistische Schlussweise |
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