A primer in biological data analysis and visualization using R /:
R is the most widely used open-source statistical and programming environment for the analysis and visualization of biological data. Drawing on Gregg Hartvigsen's extensive experience teaching biostatistics and modeling biological systems, this text is an engaging, practical, and lab-oriented i...
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| Format: | Elektronisch E-Book |
| Sprache: | English |
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New York :
Columbia University Press,
[2014]
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| Online-Zugang: | Volltext |
| Zusammenfassung: | R is the most widely used open-source statistical and programming environment for the analysis and visualization of biological data. Drawing on Gregg Hartvigsen's extensive experience teaching biostatistics and modeling biological systems, this text is an engaging, practical, and lab-oriented introduction to R for students in the life sciences. Underscoring the importance of R and RStudio in organizing, computing, and visualizing biological statistics and data, Hartvigsen guides readers through the processes of entering data into R, working with data in R, and using R to visualize data using histograms, boxplots, barplots, scatterplots, and other common graph types. He covers testing data for normality, defining and identifying outliers, and working with non-normal data. Students are introduced to common one- and two-sample tests as well as one- and two-way analysis of variance (ANOVA), correlation, and linear and nonlinear regression analyses. This volume also includes a section on advanced procedures and a chapter introducing algorithms and the art of programming using R. |
| Beschreibung: | 1 online resource (ix, 234 pages) : illustrations |
| Bibliographie: | Includes bibliographical references (pages 229-230) and index. |
| ISBN: | 9780231537049 0231537042 |
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| 100 | 1 | |a Hartvigsen, Gregg, |e author. | |
| 245 | 1 | 2 | |a A primer in biological data analysis and visualization using R / |c Gregg Hartvigsen. |
| 264 | 1 | |a New York : |b Columbia University Press, |c [2014] | |
| 264 | 4 | |c ©2014 | |
| 300 | |a 1 online resource (ix, 234 pages) : |b illustrations | ||
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| 504 | |a Includes bibliographical references (pages 229-230) and index. | ||
| 505 | 0 | |a Machine generated contents note: 1. Introducing Our Software Team -- 1.1. Solving Problems with Excel and R -- 1.2. Install R and Rstudio -- 1.3. Getting Help with R -- 1.4.R as a Graphing Calculator -- 1.5. Using Script Files -- 1.6. Extensibility -- 1.7. Problems -- 2. Getting Data into R -- 2.1. Using C() for Small Datasets -- 2.2. Reading Data from an Excel Spreadsheet -- 2.3. Reading Data from a Website -- 2.4. Problems -- 3. Working with Your Data -- 3.1. Accuracy and Precision of Our Data -- 3.2. Collecting Data into Dataframes -- 3.3. Stacking Data -- 3.4. Subsetting Data -- 3.5. Sampling Data -- 3.6. Sorting an Array of Data -- 3.7. Ordering Data -- 3.8. Sorting a Dataframe -- 3.9. Saving a Dataframe to a File -- 3.10. Problems -- 4. Tell Me about My Data -- 4.1. What are Data? -- 4.2. Where's the Middle? -- 4.3. Dispersion about the Middle -- 4.4. Testing for Normality -- 4.5. Outliers -- 4.6. Dealing with Non-Normal Data -- 4.7. Problems -- 5. Visualizing Your Data -- 5.1. Overview. | |
| 505 | 0 | |a Contents note continued: 5.2. Histograms -- 5.3. Boxplots -- 5.4. Barplots -- 5.5. Scatterplots -- 5.6. Bump Charts (Before and After Line Plots) -- 5.7. Pie Charts -- 5.8. Multiple Graphs (Using Par and Pairs) -- 5.9. Problems -- 6. The Interpretation of Hypothesis Tests -- 6.1. What Do We Mean by "Statistics"? -- 6.2. How to Ask and Answer Scientific Questions -- 6.3. The Difference Between "Hypothesis" and "Theory" -- 6.4.A Few Experimental Design Principles -- 6.5. How to Set Up a Simple Random Sample for an Experiment -- 6.6. Interpreting Results: What is the "P-Value"? -- 6.7. Type I and Type II Errors -- 6.8. Problems -- 7. Hypothesis Tests: One- and Two-Sample Comparisons -- 7.1. Tests with One Value and One Sample -- 7.2. Tests with Paired Samples (Not Independent) -- 7.3. Tests with Two Independent Samples -- Samples are Normally Distributed -- Samples are not Normally Distributed -- 7.4. Problems -- 8. Testing Differences among Multiple Samples -- 8.1. Samples are Normally Distributed. | |
| 505 | 0 | |a Contents note continued: 8.2. One-Way Test for Non-Parametric Data -- 8.3. Two-Way Analysis of Variance -- 8.4. Problems -- 9. Hypothesis Tests: Linear Relationships -- 9.1. Correlation -- 9.2. Linear Regression -- 9.3. Problems -- 10. Hypothesis Tests: Observed and Expected Values -- 10.1. The X2 Test -- 10.2. The Fisher Exact Test -- 10.3. Problems -- 11.A Few More Advanced Procedures -- 11.1. Writing Your Own Function -- 11.2. Adding 95% Confidence Intervals to Barplots -- 11.3. Adding Letters to Barplots -- 11.4. Adding 95% Confidence Interval Lines for Linear Regression -- 11.5. Non-Linear Regression -- Get and Use the Derivative -- 11.6. An Introduction to Mathematical Modeling -- 11.7. Problems -- 12. An Introduction to Computer Programming -- 12.1. What is a "Computer Program"? -- An Example: The Central Limit Theorem -- 12.2. Introducing Algorithms -- 12.3.Combining Programming and Computer Output -- 12.4. Problems -- 13. Final Thoughts -- 13.1. Where Do I Go from Here? | |
| 588 | 0 | |a Print version record. | |
| 520 | |a R is the most widely used open-source statistical and programming environment for the analysis and visualization of biological data. Drawing on Gregg Hartvigsen's extensive experience teaching biostatistics and modeling biological systems, this text is an engaging, practical, and lab-oriented introduction to R for students in the life sciences. Underscoring the importance of R and RStudio in organizing, computing, and visualizing biological statistics and data, Hartvigsen guides readers through the processes of entering data into R, working with data in R, and using R to visualize data using histograms, boxplots, barplots, scatterplots, and other common graph types. He covers testing data for normality, defining and identifying outliers, and working with non-normal data. Students are introduced to common one- and two-sample tests as well as one- and two-way analysis of variance (ANOVA), correlation, and linear and nonlinear regression analyses. This volume also includes a section on advanced procedures and a chapter introducing algorithms and the art of programming using R. | ||
| 546 | |a In English. | ||
| 650 | 0 | |a R (Computer program language) |0 http://id.loc.gov/authorities/subjects/sh2002004407 | |
| 650 | 0 | |a Mathematical statistics |x Data processing. |0 http://id.loc.gov/authorities/subjects/sh85082137 | |
| 650 | 0 | |a Electronic books. |0 http://id.loc.gov/authorities/subjects/sh93007047 | |
| 650 | 6 | |a R (Langage de programmation) | |
| 650 | 6 | |a Statistique mathématique |x Informatique. | |
| 650 | 6 | |a Livres numériques. | |
| 650 | 7 | |a e-books. |2 aat | |
| 650 | 7 | |a HEALTH & FITNESS |x Holism. |2 bisacsh | |
| 650 | 7 | |a HEALTH & FITNESS |x Reference. |2 bisacsh | |
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| 776 | 0 | 8 | |i Print version: |a Hartvigsen, Gregg. |t Primer in biological data analysis and visualization using R. |d New York : Columbia University Press, [2014] |z 9780231166980 |w (DLC) 2013952140 |w (OCoLC)842878997 |
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| contents | Machine generated contents note: 1. Introducing Our Software Team -- 1.1. Solving Problems with Excel and R -- 1.2. Install R and Rstudio -- 1.3. Getting Help with R -- 1.4.R as a Graphing Calculator -- 1.5. Using Script Files -- 1.6. Extensibility -- 1.7. Problems -- 2. Getting Data into R -- 2.1. Using C() for Small Datasets -- 2.2. Reading Data from an Excel Spreadsheet -- 2.3. Reading Data from a Website -- 2.4. Problems -- 3. Working with Your Data -- 3.1. Accuracy and Precision of Our Data -- 3.2. Collecting Data into Dataframes -- 3.3. Stacking Data -- 3.4. Subsetting Data -- 3.5. Sampling Data -- 3.6. Sorting an Array of Data -- 3.7. Ordering Data -- 3.8. Sorting a Dataframe -- 3.9. Saving a Dataframe to a File -- 3.10. Problems -- 4. Tell Me about My Data -- 4.1. What are Data? -- 4.2. Where's the Middle? -- 4.3. Dispersion about the Middle -- 4.4. Testing for Normality -- 4.5. Outliers -- 4.6. Dealing with Non-Normal Data -- 4.7. Problems -- 5. Visualizing Your Data -- 5.1. Overview. Contents note continued: 5.2. Histograms -- 5.3. Boxplots -- 5.4. Barplots -- 5.5. Scatterplots -- 5.6. Bump Charts (Before and After Line Plots) -- 5.7. Pie Charts -- 5.8. Multiple Graphs (Using Par and Pairs) -- 5.9. Problems -- 6. The Interpretation of Hypothesis Tests -- 6.1. What Do We Mean by "Statistics"? -- 6.2. How to Ask and Answer Scientific Questions -- 6.3. The Difference Between "Hypothesis" and "Theory" -- 6.4.A Few Experimental Design Principles -- 6.5. How to Set Up a Simple Random Sample for an Experiment -- 6.6. Interpreting Results: What is the "P-Value"? -- 6.7. Type I and Type II Errors -- 6.8. Problems -- 7. Hypothesis Tests: One- and Two-Sample Comparisons -- 7.1. Tests with One Value and One Sample -- 7.2. Tests with Paired Samples (Not Independent) -- 7.3. Tests with Two Independent Samples -- Samples are Normally Distributed -- Samples are not Normally Distributed -- 7.4. Problems -- 8. Testing Differences among Multiple Samples -- 8.1. Samples are Normally Distributed. Contents note continued: 8.2. One-Way Test for Non-Parametric Data -- 8.3. Two-Way Analysis of Variance -- 8.4. Problems -- 9. Hypothesis Tests: Linear Relationships -- 9.1. Correlation -- 9.2. Linear Regression -- 9.3. Problems -- 10. Hypothesis Tests: Observed and Expected Values -- 10.1. The X2 Test -- 10.2. The Fisher Exact Test -- 10.3. Problems -- 11.A Few More Advanced Procedures -- 11.1. Writing Your Own Function -- 11.2. Adding 95% Confidence Intervals to Barplots -- 11.3. Adding Letters to Barplots -- 11.4. Adding 95% Confidence Interval Lines for Linear Regression -- 11.5. Non-Linear Regression -- Get and Use the Derivative -- 11.6. An Introduction to Mathematical Modeling -- 11.7. Problems -- 12. An Introduction to Computer Programming -- 12.1. What is a "Computer Program"? -- An Example: The Central Limit Theorem -- 12.2. Introducing Algorithms -- 12.3.Combining Programming and Computer Output -- 12.4. Problems -- 13. Final Thoughts -- 13.1. Where Do I Go from Here? |
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Overview.</subfield></datafield><datafield tag="505" ind1="0" ind2=" "><subfield code="a">Contents note continued: 5.2. Histograms -- 5.3. Boxplots -- 5.4. Barplots -- 5.5. Scatterplots -- 5.6. Bump Charts (Before and After Line Plots) -- 5.7. Pie Charts -- 5.8. Multiple Graphs (Using Par and Pairs) -- 5.9. Problems -- 6. The Interpretation of Hypothesis Tests -- 6.1. What Do We Mean by "Statistics"? -- 6.2. How to Ask and Answer Scientific Questions -- 6.3. The Difference Between "Hypothesis" and "Theory" -- 6.4.A Few Experimental Design Principles -- 6.5. How to Set Up a Simple Random Sample for an Experiment -- 6.6. Interpreting Results: What is the "P-Value"? -- 6.7. Type I and Type II Errors -- 6.8. Problems -- 7. Hypothesis Tests: One- and Two-Sample Comparisons -- 7.1. Tests with One Value and One Sample -- 7.2. Tests with Paired Samples (Not Independent) -- 7.3. Tests with Two Independent Samples -- Samples are Normally Distributed -- Samples are not Normally Distributed -- 7.4. Problems -- 8. Testing Differences among Multiple Samples -- 8.1. Samples are Normally Distributed.</subfield></datafield><datafield tag="505" ind1="0" ind2=" "><subfield code="a">Contents note continued: 8.2. One-Way Test for Non-Parametric Data -- 8.3. Two-Way Analysis of Variance -- 8.4. Problems -- 9. Hypothesis Tests: Linear Relationships -- 9.1. Correlation -- 9.2. Linear Regression -- 9.3. Problems -- 10. Hypothesis Tests: Observed and Expected Values -- 10.1. The X2 Test -- 10.2. The Fisher Exact Test -- 10.3. Problems -- 11.A Few More Advanced Procedures -- 11.1. Writing Your Own Function -- 11.2. Adding 95% Confidence Intervals to Barplots -- 11.3. Adding Letters to Barplots -- 11.4. Adding 95% Confidence Interval Lines for Linear Regression -- 11.5. Non-Linear Regression -- Get and Use the Derivative -- 11.6. An Introduction to Mathematical Modeling -- 11.7. Problems -- 12. An Introduction to Computer Programming -- 12.1. What is a "Computer Program"? -- An Example: The Central Limit Theorem -- 12.2. Introducing Algorithms -- 12.3.Combining Programming and Computer Output -- 12.4. Problems -- 13. Final Thoughts -- 13.1. Where Do I Go from Here?</subfield></datafield><datafield tag="588" ind1="0" ind2=" "><subfield code="a">Print version record.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">R is the most widely used open-source statistical and programming environment for the analysis and visualization of biological data. Drawing on Gregg Hartvigsen's extensive experience teaching biostatistics and modeling biological systems, this text is an engaging, practical, and lab-oriented introduction to R for students in the life sciences. Underscoring the importance of R and RStudio in organizing, computing, and visualizing biological statistics and data, Hartvigsen guides readers through the processes of entering data into R, working with data in R, and using R to visualize data using histograms, boxplots, barplots, scatterplots, and other common graph types. He covers testing data for normality, defining and identifying outliers, and working with non-normal data. Students are introduced to common one- and two-sample tests as well as one- and two-way analysis of variance (ANOVA), correlation, and linear and nonlinear regression analyses. 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| id | ZDB-4-EBA-ocn877868816 |
| illustrated | Illustrated |
| indexdate | 2024-11-27T13:25:55Z |
| institution | BVB |
| isbn | 9780231537049 0231537042 |
| language | English |
| oclc_num | 877868816 |
| open_access_boolean | |
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| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource (ix, 234 pages) : illustrations |
| psigel | ZDB-4-EBA |
| publishDate | 2014 |
| publishDateSearch | 2014 |
| publishDateSort | 2014 |
| publisher | Columbia University Press, |
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| spelling | Hartvigsen, Gregg, author. A primer in biological data analysis and visualization using R / Gregg Hartvigsen. New York : Columbia University Press, [2014] ©2014 1 online resource (ix, 234 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier text file Includes bibliographical references (pages 229-230) and index. Machine generated contents note: 1. Introducing Our Software Team -- 1.1. Solving Problems with Excel and R -- 1.2. Install R and Rstudio -- 1.3. Getting Help with R -- 1.4.R as a Graphing Calculator -- 1.5. Using Script Files -- 1.6. Extensibility -- 1.7. Problems -- 2. Getting Data into R -- 2.1. Using C() for Small Datasets -- 2.2. Reading Data from an Excel Spreadsheet -- 2.3. Reading Data from a Website -- 2.4. Problems -- 3. Working with Your Data -- 3.1. Accuracy and Precision of Our Data -- 3.2. Collecting Data into Dataframes -- 3.3. Stacking Data -- 3.4. Subsetting Data -- 3.5. Sampling Data -- 3.6. Sorting an Array of Data -- 3.7. Ordering Data -- 3.8. Sorting a Dataframe -- 3.9. Saving a Dataframe to a File -- 3.10. Problems -- 4. Tell Me about My Data -- 4.1. What are Data? -- 4.2. Where's the Middle? -- 4.3. Dispersion about the Middle -- 4.4. Testing for Normality -- 4.5. Outliers -- 4.6. Dealing with Non-Normal Data -- 4.7. Problems -- 5. Visualizing Your Data -- 5.1. Overview. Contents note continued: 5.2. Histograms -- 5.3. Boxplots -- 5.4. Barplots -- 5.5. Scatterplots -- 5.6. Bump Charts (Before and After Line Plots) -- 5.7. Pie Charts -- 5.8. Multiple Graphs (Using Par and Pairs) -- 5.9. Problems -- 6. The Interpretation of Hypothesis Tests -- 6.1. What Do We Mean by "Statistics"? -- 6.2. How to Ask and Answer Scientific Questions -- 6.3. The Difference Between "Hypothesis" and "Theory" -- 6.4.A Few Experimental Design Principles -- 6.5. How to Set Up a Simple Random Sample for an Experiment -- 6.6. Interpreting Results: What is the "P-Value"? -- 6.7. Type I and Type II Errors -- 6.8. Problems -- 7. Hypothesis Tests: One- and Two-Sample Comparisons -- 7.1. Tests with One Value and One Sample -- 7.2. Tests with Paired Samples (Not Independent) -- 7.3. Tests with Two Independent Samples -- Samples are Normally Distributed -- Samples are not Normally Distributed -- 7.4. Problems -- 8. Testing Differences among Multiple Samples -- 8.1. Samples are Normally Distributed. Contents note continued: 8.2. One-Way Test for Non-Parametric Data -- 8.3. Two-Way Analysis of Variance -- 8.4. Problems -- 9. Hypothesis Tests: Linear Relationships -- 9.1. Correlation -- 9.2. Linear Regression -- 9.3. Problems -- 10. Hypothesis Tests: Observed and Expected Values -- 10.1. The X2 Test -- 10.2. The Fisher Exact Test -- 10.3. Problems -- 11.A Few More Advanced Procedures -- 11.1. Writing Your Own Function -- 11.2. Adding 95% Confidence Intervals to Barplots -- 11.3. Adding Letters to Barplots -- 11.4. Adding 95% Confidence Interval Lines for Linear Regression -- 11.5. Non-Linear Regression -- Get and Use the Derivative -- 11.6. An Introduction to Mathematical Modeling -- 11.7. Problems -- 12. An Introduction to Computer Programming -- 12.1. What is a "Computer Program"? -- An Example: The Central Limit Theorem -- 12.2. Introducing Algorithms -- 12.3.Combining Programming and Computer Output -- 12.4. Problems -- 13. Final Thoughts -- 13.1. Where Do I Go from Here? Print version record. R is the most widely used open-source statistical and programming environment for the analysis and visualization of biological data. Drawing on Gregg Hartvigsen's extensive experience teaching biostatistics and modeling biological systems, this text is an engaging, practical, and lab-oriented introduction to R for students in the life sciences. Underscoring the importance of R and RStudio in organizing, computing, and visualizing biological statistics and data, Hartvigsen guides readers through the processes of entering data into R, working with data in R, and using R to visualize data using histograms, boxplots, barplots, scatterplots, and other common graph types. He covers testing data for normality, defining and identifying outliers, and working with non-normal data. Students are introduced to common one- and two-sample tests as well as one- and two-way analysis of variance (ANOVA), correlation, and linear and nonlinear regression analyses. This volume also includes a section on advanced procedures and a chapter introducing algorithms and the art of programming using R. In English. R (Computer program language) http://id.loc.gov/authorities/subjects/sh2002004407 Mathematical statistics Data processing. http://id.loc.gov/authorities/subjects/sh85082137 Electronic books. http://id.loc.gov/authorities/subjects/sh93007047 R (Langage de programmation) Statistique mathématique Informatique. Livres numériques. e-books. aat HEALTH & FITNESS Holism. bisacsh HEALTH & FITNESS Reference. bisacsh MEDICAL Alternative Medicine. bisacsh MEDICAL Atlases. bisacsh MEDICAL Essays. bisacsh MEDICAL Family & General Practice. bisacsh MEDICAL Holistic Medicine. bisacsh MEDICAL Osteopathy. bisacsh Mathematical statistics Data processing fast R (Computer program language) fast Print version: Hartvigsen, Gregg. Primer in biological data analysis and visualization using R. New York : Columbia University Press, [2014] 9780231166980 (DLC) 2013952140 (OCoLC)842878997 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=760980 Volltext |
| spellingShingle | Hartvigsen, Gregg A primer in biological data analysis and visualization using R / Machine generated contents note: 1. Introducing Our Software Team -- 1.1. Solving Problems with Excel and R -- 1.2. Install R and Rstudio -- 1.3. Getting Help with R -- 1.4.R as a Graphing Calculator -- 1.5. Using Script Files -- 1.6. Extensibility -- 1.7. Problems -- 2. Getting Data into R -- 2.1. Using C() for Small Datasets -- 2.2. Reading Data from an Excel Spreadsheet -- 2.3. Reading Data from a Website -- 2.4. Problems -- 3. Working with Your Data -- 3.1. Accuracy and Precision of Our Data -- 3.2. Collecting Data into Dataframes -- 3.3. Stacking Data -- 3.4. Subsetting Data -- 3.5. Sampling Data -- 3.6. Sorting an Array of Data -- 3.7. Ordering Data -- 3.8. Sorting a Dataframe -- 3.9. Saving a Dataframe to a File -- 3.10. Problems -- 4. Tell Me about My Data -- 4.1. What are Data? -- 4.2. Where's the Middle? -- 4.3. Dispersion about the Middle -- 4.4. Testing for Normality -- 4.5. Outliers -- 4.6. Dealing with Non-Normal Data -- 4.7. Problems -- 5. Visualizing Your Data -- 5.1. Overview. Contents note continued: 5.2. Histograms -- 5.3. Boxplots -- 5.4. Barplots -- 5.5. Scatterplots -- 5.6. Bump Charts (Before and After Line Plots) -- 5.7. Pie Charts -- 5.8. Multiple Graphs (Using Par and Pairs) -- 5.9. Problems -- 6. The Interpretation of Hypothesis Tests -- 6.1. What Do We Mean by "Statistics"? -- 6.2. How to Ask and Answer Scientific Questions -- 6.3. The Difference Between "Hypothesis" and "Theory" -- 6.4.A Few Experimental Design Principles -- 6.5. How to Set Up a Simple Random Sample for an Experiment -- 6.6. Interpreting Results: What is the "P-Value"? -- 6.7. Type I and Type II Errors -- 6.8. Problems -- 7. Hypothesis Tests: One- and Two-Sample Comparisons -- 7.1. Tests with One Value and One Sample -- 7.2. Tests with Paired Samples (Not Independent) -- 7.3. Tests with Two Independent Samples -- Samples are Normally Distributed -- Samples are not Normally Distributed -- 7.4. Problems -- 8. Testing Differences among Multiple Samples -- 8.1. Samples are Normally Distributed. Contents note continued: 8.2. One-Way Test for Non-Parametric Data -- 8.3. Two-Way Analysis of Variance -- 8.4. Problems -- 9. Hypothesis Tests: Linear Relationships -- 9.1. Correlation -- 9.2. Linear Regression -- 9.3. Problems -- 10. Hypothesis Tests: Observed and Expected Values -- 10.1. The X2 Test -- 10.2. The Fisher Exact Test -- 10.3. Problems -- 11.A Few More Advanced Procedures -- 11.1. Writing Your Own Function -- 11.2. Adding 95% Confidence Intervals to Barplots -- 11.3. Adding Letters to Barplots -- 11.4. Adding 95% Confidence Interval Lines for Linear Regression -- 11.5. Non-Linear Regression -- Get and Use the Derivative -- 11.6. An Introduction to Mathematical Modeling -- 11.7. Problems -- 12. An Introduction to Computer Programming -- 12.1. What is a "Computer Program"? -- An Example: The Central Limit Theorem -- 12.2. Introducing Algorithms -- 12.3.Combining Programming and Computer Output -- 12.4. Problems -- 13. Final Thoughts -- 13.1. Where Do I Go from Here? R (Computer program language) http://id.loc.gov/authorities/subjects/sh2002004407 Mathematical statistics Data processing. http://id.loc.gov/authorities/subjects/sh85082137 Electronic books. http://id.loc.gov/authorities/subjects/sh93007047 R (Langage de programmation) Statistique mathématique Informatique. Livres numériques. e-books. aat HEALTH & FITNESS Holism. bisacsh HEALTH & FITNESS Reference. bisacsh MEDICAL Alternative Medicine. bisacsh MEDICAL Atlases. bisacsh MEDICAL Essays. bisacsh MEDICAL Family & General Practice. bisacsh MEDICAL Holistic Medicine. bisacsh MEDICAL Osteopathy. bisacsh Mathematical statistics Data processing fast R (Computer program language) fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh2002004407 http://id.loc.gov/authorities/subjects/sh85082137 http://id.loc.gov/authorities/subjects/sh93007047 |
| title | A primer in biological data analysis and visualization using R / |
| title_auth | A primer in biological data analysis and visualization using R / |
| title_exact_search | A primer in biological data analysis and visualization using R / |
| title_full | A primer in biological data analysis and visualization using R / Gregg Hartvigsen. |
| title_fullStr | A primer in biological data analysis and visualization using R / Gregg Hartvigsen. |
| title_full_unstemmed | A primer in biological data analysis and visualization using R / Gregg Hartvigsen. |
| title_short | A primer in biological data analysis and visualization using R / |
| title_sort | primer in biological data analysis and visualization using r |
| topic | R (Computer program language) http://id.loc.gov/authorities/subjects/sh2002004407 Mathematical statistics Data processing. http://id.loc.gov/authorities/subjects/sh85082137 Electronic books. http://id.loc.gov/authorities/subjects/sh93007047 R (Langage de programmation) Statistique mathématique Informatique. Livres numériques. e-books. aat HEALTH & FITNESS Holism. bisacsh HEALTH & FITNESS Reference. bisacsh MEDICAL Alternative Medicine. bisacsh MEDICAL Atlases. bisacsh MEDICAL Essays. bisacsh MEDICAL Family & General Practice. bisacsh MEDICAL Holistic Medicine. bisacsh MEDICAL Osteopathy. bisacsh Mathematical statistics Data processing fast R (Computer program language) fast |
| topic_facet | R (Computer program language) Mathematical statistics Data processing. Electronic books. R (Langage de programmation) Statistique mathématique Informatique. Livres numériques. e-books. HEALTH & FITNESS Holism. HEALTH & FITNESS Reference. MEDICAL Alternative Medicine. MEDICAL Atlases. MEDICAL Essays. MEDICAL Family & General Practice. MEDICAL Holistic Medicine. MEDICAL Osteopathy. Mathematical statistics Data processing |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=760980 |
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