Hands-on data science for librarians:
Librarians understand the need to store, use and analyze data related to their collection, patrons and institution, and there has been consistent interest over the last 10 years to improve data management, analysis, and visualization skills within the profession. However, librarians find it difficul...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Boca Raton
CRC Press
2023
|
| Ausgabe: | First editon |
| Schriftenreihe: | Data science series
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Zusammenfassung: | Librarians understand the need to store, use and analyze data related to their collection, patrons and institution, and there has been consistent interest over the last 10 years to improve data management, analysis, and visualization skills within the profession. However, librarians find it difficult to move from out-of-the-box proprietary software applications to the skills necessary to perform the range of data science actions in code. This book will focus on teaching R through relevant examples and skills that librarians need in their day-to-day lives that includes visualizations but goes much further to include web scraping, working with maps, creating interactive reports, machine learning, and others. While there’s a place for theory, ethics, and statistical methods, librarians need a tool to help them acquire enough facility with R to utilize data science skills in their daily work, no matter what type of library they work at (academic, public or special). By walking through each skill and its application to library work before walking the reader through each line of code, this book will support librarians who want to apply data science in their daily work. Hands-On Data Science for Librarians is intended for librarians (and other information professionals) in any library type (public, academic or special) as well as graduate students in library and information science (LIS).Key Features:- Only data science book available geared toward librarians that includes step-by-step code examples- Examples include all library types (public, academic, special)- Relevant datasets- Accessible to non-technical professionals- Focused on job skills and their applications. |
| Beschreibung: | 1. Introduction 2. Using RStudio’s IDE 3. Tidying data with dplyr 4. Visualizing your project with ggplot2 5. Webscraping with rvest 6. Mapping with tmap 7. Textual Analysis with tidytext 8. Creating Dynamic Documents with rmarkdown 9. Creating a flexdashboard 10. Creating an interactive dashboard with shiny 11. Using tidymodels to Understand Machine Learning 12. Conclusion Appendix A. Dependencies Appendix B. Additional Skills |
| Beschreibung: | xix, 180 Seiten Diagramme |
| ISBN: | 9781032080826 |
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| 520 | |a Librarians understand the need to store, use and analyze data related to their collection, patrons and institution, and there has been consistent interest over the last 10 years to improve data management, analysis, and visualization skills within the profession. However, librarians find it difficult to move from out-of-the-box proprietary software applications to the skills necessary to perform the range of data science actions in code. This book will focus on teaching R through relevant examples and skills that librarians need in their day-to-day lives that includes visualizations but goes much further to include web scraping, working with maps, creating interactive reports, machine learning, and others. While there’s a place for theory, ethics, and statistical methods, librarians need a tool to help them acquire enough facility with R to utilize data science skills in their daily work, no matter what type of library they work at (academic, public or special). By walking through each skill and its application to library work before walking the reader through each line of code, this book will support librarians who want to apply data science in their daily work. Hands-On Data Science for Librarians is intended for librarians (and other information professionals) in any library type (public, academic or special) as well as graduate students in library and information science (LIS).Key Features:- Only data science book available geared toward librarians that includes step-by-step code examples- Examples include all library types (public, academic, special)- Relevant datasets- Accessible to non-technical professionals- Focused on job skills and their applications. | ||
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Datensatz im Suchindex
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| adam_text | Contents List of Figures List of Tables Preface About the Authors xi xiii xv xix 1 Introduction 1.1 What Is Data Science?................................................................. 1.2 Why Learn Data Science? ........................................................... 1.3 Why Use Code?.............................................................................. 1.4 Vignette.......................................................................................... 1.5 Structure of This Book.................................................................. 1.6 Who This Book Is for..................................................................... 1 1 2 3 5 5 6 2 Using RStudio’s IDE 2.1 Learning Objectives........................................................................ 2.2 Terms You’ll Learn........................................................................ 2.3 Scenario.......................................................................................... 2.4 Introduction.................................................................................... 2.5 What is R?....................................................................................... 2.6 Introducing the Tidyverse............................................................ 2.7 Getting Started with theRStudio IDE........................................ 2.8 Packages Needed for thisBook....................................................... 2.9 Viewing Tabular Data inRStudio................................................. 2.10
Summary.......................................................................................... 2.11 Further Practice.............................................................................. 2.12 Additional Resources..................................................................... 7 7 7 7 8 8 10 11 15 16 19 20 20 3 Tidying Data withdplyr 21 3.1 Learning Objectives........................................................................ 21 3.2 Terms You’ll Learn........................................................................ 21 3.3 Scenario.......................................................................................... 21 3.4 Packages DatasetsNeeded.......................................................... 22 3.5 Introduction......................................................................... . . . 22 3.6 Getting Started with U.S.Census Data...................................... 22 vii
Contents viii 3.7 3.8 3.9 3.10 3.11 3.12 3.13 3.14 Tidy Data Tools from dplyr........................................................ Getting Started with dplyr Functions......................................... Occupation Data........................................................................... Clean Up Metadata........................................................................ Create a CSV of Unemployment Data......................................... Summary.......................................................................................... Further Practice.............................................................................. Additional Resources..................................................................... 26 26 29 34 38 38 39 39 4 Visualizing Your Project with ggplot2 41 4.1 Learning Objectives........................................................................ 41 4.2 Terms You’ll Learn.......................................................... 41 4.3 Scenario.......................................................................................... 41 4.4 Packages Datasets Needed........................................................ 42 4.5 Introduction to ggplot2................................................................. 42 4.6 Components of a plot.................................................................... 42 4.7 Coordinate Systems.................................. 43 4.8 Unemployment plots..................................................................... 44 4.9 Additional ggplot2
Concepts........................................................ 50 4.10 Summary.......................................................................................... 56 4.11 Additional Resources.................................................................... 57 4.12 Further Practice ........................................... 57 5 Webscraping with west 59 5.1 Learning Objectives....................................................................... 59 5.2 Terms You’ll Learn....................................................................... 59 5.3 Scenario.......................................................................................... 59 5.4 Packages Datasets Needed ........................................................ 60 5.5 Introduction.................................................................................... 60 5.6 Identifying Scraping Website Components............................. 61 5.7 Web Scraping Part 1: Wards Aidermen.................................. 63 5.8 Web Scraping Part 2: Email Addresses on Individual Pages . . 66 5.9 Create Reusable Data File........................................................... 70 5.10 Summary.......................................................................................... 71 5.11 Further Practice.............................................................................. 72 5.12 Additional Resources.................................................................... 72 6 Mapping with tmap 73 6.1 Learning Objectives........................................................................
73 6.2 Terms You’ll Learn....................................................................... 73 6.3 Scenario.......................................................................................... 74 6.4 Packages Datasets Needed........................................................ 74 6.5 An Overview of Spatial Data........................................................ 74 6.6 Loading in the Data........................................................................ 81 6.7 Summary.......................................................................................... 87
ix Contents 6.8 6.9 Further Practice........................................................................... Additional Resources.................................................................. 88 88 7 Textual Analysis with tidytext 89 7.1 Learning Objectives..................................................................... 89 7.2 Terms You’ll Learn..................................................................... 89 7.3 Scenario........................................................................................ 89 7.4 Packages Datasets Needed. . .................................................... 90 7.5 Introduction.................................................................................. 90 7.6 Query the NYT Article Database............................................. 91 7.7 Tokenization................................................................................. 97 7.8 Stop Words .................................................................................... 100 7.9 Sentiment Analysis......................................... 101 7.10 TF-IDF............................................................................................. 102 7.11 Summary.......................................................................................... 106 7.12 Further Practice.............................................................................. 106 7.13 Additional Resources.....................................................................106 8 Creating Dynamic Documents with rmarkdown 107 8.1 Learning
Objectives........................................................................ 107 8.2 Scenario.......................................................................................... 107 8.3 Introduction to R Markdown.........................................................107 8.4 Packages Datasets Needed......................................................... 108 8.5 Creating an R Markdown Document ..........................................109 8.6 R Markdown Document Structure................................................109 8.7 Adding Data Visualizations to Your R Markdown Document. . 113 8.8 Creating Your Output .................................................................. 115 8.9 Customizing Your R Markdown Document with Templates . . 117 8.10 Summary...........................................................................................120 8.11 Resources ....................................................................................... 120 8.12 Further Practice.............................................................................. 120 9 Creating a Flexdashboard 121 9.1 Learning Objectives........................................................................ 121 9.2 Terms You’ll Learn........................................................................ 121 9.3 Scenario...........................................................................................121 9.4 Overview of flexdashboard............................................................ 122 9.5 Packages and Datasets
Needed...................................................... 122 9.6 Initiating the flexdashboard......................................................... 123 9.7 Creating the Pages........................................................................ 124 9.8 Creating the Columns..................................................................... 124 9.9 Adding the Code Chunks............................................................... 126 9.10 Summary........................................................................................... 128 9.11 Further Practice.............................................................................. 129 9.12 Resources ........................................................................... 130
x Contents 10 Creating an Interactive Dashboard with Shiny 131 10.1 Learning Objectives......................................................................... 131 10.2 Terms You’ll Learn.........................................................................131 10.3 Scenario........................................................................................... 131 10.4 Packages and datasets needed...................................................... 132 10.5 What You Need to Know About Shiny....................................... 132 10.6 Creating a Shiny Web Map App................................................... 133 10.7 Creating a ggplot Shiny App......................................................... 138 10.8 Integrating Shiny Apps into a Flexdashboard.............................. 142 10.9 Summary..................... ;................................................................... 159 10.10 Further Practice............................................................................... 160 10.11 Resources ........................................................................................ 160 11 Using tidymodels to Understand Machine Learning 161 11.1 Learning Objectives......................................................................... 161 11.2 Terms You’ll Learn......................................................................... 161 11.3 Scenario................................................. ;....................................... 161 11.4 Packages Datasets Needed......................................................... 162 11.5
Introduction..................................................................................... 162 11.6 What ML is..................................................................................... 163 11.7 How ML Works in R (tidymodels)................................................ 163 11.8 Problems with Machine Learning................................................ 165 11.9 Machine Learning in Employment................................................ 167 11.10 Summary........................................................................................... 168 11.11 Further Practice............................................................................... 168 11.12 Additional Resources...................................................................... 168 12 Conclusion 169 12.1 Wrapping Up...for Now.................................................................. 169 12.2 Where Do You Go from Here? . . ................................................ 170 12.3 Data Management: Never Do Work Without It ........................ 171 12.4 Final Send-off on Your Data Science Journey.............................. 171 A Dependencies 173 A.l iOS Dependencies............................................................................ 173 A.2 Windows Dependencies.................................................................. 173 A.3 Package Dependencies for This Book.......................................... 174 В Additional Skills 175 B.l Using the Shell or Command Line on YourComputer.............. 175 B.2 Using GitHub Git
...................................................................... 175 B.3 Troubleshooting Package InstallationProblems ......................... 175 Bibliography 177 Index 179
|
| adam_txt |
Contents List of Figures List of Tables Preface About the Authors xi xiii xv xix 1 Introduction 1.1 What Is Data Science?. 1.2 Why Learn Data Science? . 1.3 Why Use Code?. 1.4 Vignette. 1.5 Structure of This Book. 1.6 Who This Book Is for. 1 1 2 3 5 5 6 2 Using RStudio’s IDE 2.1 Learning Objectives. 2.2 Terms You’ll Learn. 2.3 Scenario. 2.4 Introduction. 2.5 What is R?. 2.6 Introducing the Tidyverse. 2.7 Getting Started with theRStudio IDE. 2.8 Packages Needed for thisBook. 2.9 Viewing Tabular Data inRStudio. 2.10
Summary. 2.11 Further Practice. 2.12 Additional Resources. 7 7 7 7 8 8 10 11 15 16 19 20 20 3 Tidying Data withdplyr 21 3.1 Learning Objectives. 21 3.2 Terms You’ll Learn. 21 3.3 Scenario. 21 3.4 Packages DatasetsNeeded. 22 3.5 Introduction. . . . 22 3.6 Getting Started with U.S.Census Data. 22 vii
Contents viii 3.7 3.8 3.9 3.10 3.11 3.12 3.13 3.14 Tidy Data Tools from dplyr. Getting Started with dplyr Functions. Occupation Data. Clean Up Metadata. Create a CSV of Unemployment Data. Summary. Further Practice. Additional Resources. 26 26 29 34 38 38 39 39 4 Visualizing Your Project with ggplot2 41 4.1 Learning Objectives. 41 4.2 Terms You’ll Learn. 41 4.3 Scenario. 41 4.4 Packages Datasets Needed. 42 4.5 Introduction to ggplot2. 42 4.6 Components of a plot. 42 4.7 Coordinate Systems. 43 4.8 Unemployment plots. 44 4.9 Additional ggplot2
Concepts. 50 4.10 Summary. 56 4.11 Additional Resources. 57 4.12 Further Practice . 57 5 Webscraping with west 59 5.1 Learning Objectives. 59 5.2 Terms You’ll Learn. 59 5.3 Scenario. 59 5.4 Packages Datasets Needed . 60 5.5 Introduction. 60 5.6 Identifying Scraping Website Components. 61 5.7 Web Scraping Part 1: Wards Aidermen. 63 5.8 Web Scraping Part 2: Email Addresses on Individual Pages . . 66 5.9 Create Reusable Data File. 70 5.10 Summary. 71 5.11 Further Practice. 72 5.12 Additional Resources. 72 6 Mapping with tmap 73 6.1 Learning Objectives.
73 6.2 Terms You’ll Learn. 73 6.3 Scenario. 74 6.4 Packages Datasets Needed. 74 6.5 An Overview of Spatial Data. 74 6.6 Loading in the Data. 81 6.7 Summary. 87
ix Contents 6.8 6.9 Further Practice. Additional Resources. 88 88 7 Textual Analysis with tidytext 89 7.1 Learning Objectives. 89 7.2 Terms You’ll Learn. 89 7.3 Scenario. 89 7.4 Packages Datasets Needed. . . 90 7.5 Introduction. 90 7.6 Query the NYT Article Database. 91 7.7 Tokenization. 97 7.8 Stop Words . 100 7.9 Sentiment Analysis. 101 7.10 TF-IDF. 102 7.11 Summary. 106 7.12 Further Practice. 106 7.13 Additional Resources.106 8 Creating Dynamic Documents with rmarkdown 107 8.1 Learning
Objectives. 107 8.2 Scenario. 107 8.3 Introduction to R Markdown.107 8.4 Packages Datasets Needed. 108 8.5 Creating an R Markdown Document .109 8.6 R Markdown Document Structure.109 8.7 Adding Data Visualizations to Your R Markdown Document. . 113 8.8 Creating Your Output . 115 8.9 Customizing Your R Markdown Document with Templates . . 117 8.10 Summary.120 8.11 Resources . 120 8.12 Further Practice. 120 9 Creating a Flexdashboard 121 9.1 Learning Objectives. 121 9.2 Terms You’ll Learn. 121 9.3 Scenario.121 9.4 Overview of flexdashboard. 122 9.5 Packages and Datasets
Needed. 122 9.6 Initiating the flexdashboard. 123 9.7 Creating the Pages. 124 9.8 Creating the Columns. 124 9.9 Adding the Code Chunks. 126 9.10 Summary. 128 9.11 Further Practice. 129 9.12 Resources . 130
x Contents 10 Creating an Interactive Dashboard with Shiny 131 10.1 Learning Objectives. 131 10.2 Terms You’ll Learn.131 10.3 Scenario. 131 10.4 Packages and datasets needed. 132 10.5 What You Need to Know About Shiny. 132 10.6 Creating a Shiny Web Map App. 133 10.7 Creating a ggplot Shiny App. 138 10.8 Integrating Shiny Apps into a Flexdashboard. 142 10.9 Summary. ;. 159 10.10 Further Practice. 160 10.11 Resources . 160 11 Using tidymodels to Understand Machine Learning 161 11.1 Learning Objectives. 161 11.2 Terms You’ll Learn. 161 11.3 Scenario. ;. 161 11.4 Packages Datasets Needed. 162 11.5
Introduction. 162 11.6 What ML is. 163 11.7 How ML Works in R (tidymodels). 163 11.8 Problems with Machine Learning. 165 11.9 Machine Learning in Employment. 167 11.10 Summary. 168 11.11 Further Practice. 168 11.12 Additional Resources. 168 12 Conclusion 169 12.1 Wrapping Up.for Now. 169 12.2 Where Do You Go from Here? . . . 170 12.3 Data Management: Never Do Work Without It . 171 12.4 Final Send-off on Your Data Science Journey. 171 A Dependencies 173 A.l iOS Dependencies. 173 A.2 Windows Dependencies. 173 A.3 Package Dependencies for This Book. 174 В Additional Skills 175 B.l Using the Shell or Command Line on YourComputer. 175 B.2 Using GitHub Git
. 175 B.3 Troubleshooting Package InstallationProblems . 175 Bibliography 177 Index 179 |
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| illustrated | Not Illustrated |
| index_date | 2024-07-03T22:01:57Z |
| indexdate | 2024-07-10T09:51:31Z |
| institution | BVB |
| isbn | 9781032080826 |
| language | English |
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| record_format | marc |
| series2 | Data science series |
| spelling | Lin, Sarah Verfasser aut Hands-on data science for librarians Sarah Lin, Dorris Scott First editon Boca Raton CRC Press 2023 © 2023 xix, 180 Seiten Diagramme txt rdacontent n rdamedia nc rdacarrier Data science series 1. Introduction 2. Using RStudio’s IDE 3. Tidying data with dplyr 4. Visualizing your project with ggplot2 5. Webscraping with rvest 6. Mapping with tmap 7. Textual Analysis with tidytext 8. Creating Dynamic Documents with rmarkdown 9. Creating a flexdashboard 10. Creating an interactive dashboard with shiny 11. Using tidymodels to Understand Machine Learning 12. Conclusion Appendix A. Dependencies Appendix B. Additional Skills Librarians understand the need to store, use and analyze data related to their collection, patrons and institution, and there has been consistent interest over the last 10 years to improve data management, analysis, and visualization skills within the profession. However, librarians find it difficult to move from out-of-the-box proprietary software applications to the skills necessary to perform the range of data science actions in code. This book will focus on teaching R through relevant examples and skills that librarians need in their day-to-day lives that includes visualizations but goes much further to include web scraping, working with maps, creating interactive reports, machine learning, and others. While there’s a place for theory, ethics, and statistical methods, librarians need a tool to help them acquire enough facility with R to utilize data science skills in their daily work, no matter what type of library they work at (academic, public or special). By walking through each skill and its application to library work before walking the reader through each line of code, this book will support librarians who want to apply data science in their daily work. Hands-On Data Science for Librarians is intended for librarians (and other information professionals) in any library type (public, academic or special) as well as graduate students in library and information science (LIS).Key Features:- Only data science book available geared toward librarians that includes step-by-step code examples- Examples include all library types (public, academic, special)- Relevant datasets- Accessible to non-technical professionals- Focused on job skills and their applications. Datenanalyse (DE-588)4123037-1 gnd rswk-swf Data Science (DE-588)1140936166 gnd rswk-swf Datenanalyse (DE-588)4123037-1 s Data Science (DE-588)1140936166 s DE-604 Scott, Dorris Verfasser aut Äquivalent Druck-Ausgabe, Hardcover 978-1-032-10999-2 Erscheint auch als Online-Ausgabe 978-1-003-21801-2 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=034231053&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Lin, Sarah Scott, Dorris Hands-on data science for librarians Datenanalyse (DE-588)4123037-1 gnd Data Science (DE-588)1140936166 gnd |
| subject_GND | (DE-588)4123037-1 (DE-588)1140936166 |
| title | Hands-on data science for librarians |
| title_auth | Hands-on data science for librarians |
| title_exact_search | Hands-on data science for librarians |
| title_exact_search_txtP | Hands-on data science for librarians |
| title_full | Hands-on data science for librarians Sarah Lin, Dorris Scott |
| title_fullStr | Hands-on data science for librarians Sarah Lin, Dorris Scott |
| title_full_unstemmed | Hands-on data science for librarians Sarah Lin, Dorris Scott |
| title_short | Hands-on data science for librarians |
| title_sort | hands on data science for librarians |
| topic | Datenanalyse (DE-588)4123037-1 gnd Data Science (DE-588)1140936166 gnd |
| topic_facet | Datenanalyse Data Science |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034231053&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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