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The Resource A primer in biological data analysis and visualization using R, Gregg Hartvigsen
A primer in biological data analysis and visualization using R, Gregg Hartvigsen
Resource Information
The item A primer in biological data analysis and visualization using R, Gregg Hartvigsen represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Manitoba Libraries.This item is available to borrow from all library branches.
Resource Information
The item A primer in biological data analysis and visualization using R, Gregg Hartvigsen represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Manitoba Libraries.
This item is available to borrow from all library branches.
 Summary
 R is a popular programming language that statisticians use to perform a variety of statistical computing tasks. Rooted in Gregg Hartvigsen's extensive experience teaching biology, this text is an engaging, practical, and laboriented introduction to R for students in the life sciences.Underscoring the importance of R and RStudio to the organization, computation, and visualization of biological statistics and data, Hartvigsen guides readers through the processes of entering data into R, working with data in R, and using R to express data in histograms, boxplots, barplots, scatterpl
 Language
 eng
 Extent
 1 online resource (404 p.)
 Note
 Description based upon print version of record
 Contents

 Cover; Title Page; Copyright; Contents; Introduction; 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 data3.7 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 nonnormal data; 4.7 Problems; 5. Visualizing your data; 5.1 Overview; 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 "pvalue"?; 6.7 Type I and type II errors; 6.8 Problems; 7. Hypothesis Tests: One and twosample 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 Problems8. Testing differences among multiple samples; 8.1 Samples are normally distributed; 8.2 Oneway test for nonparametric data; 8.3 Twoway 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 χ2 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 regression11.5 Nonlinear 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?; Acknowledgments; Solutions to OddNumbered Problems; Bibliography; Index
 Isbn
 9780231537049
 Label
 A primer in biological data analysis and visualization using R
 Title
 A primer in biological data analysis and visualization using R
 Statement of responsibility
 Gregg Hartvigsen
 Language
 eng
 Summary
 R is a popular programming language that statisticians use to perform a variety of statistical computing tasks. Rooted in Gregg Hartvigsen's extensive experience teaching biology, this text is an engaging, practical, and laboriented introduction to R for students in the life sciences.Underscoring the importance of R and RStudio to the organization, computation, and visualization of biological statistics and data, Hartvigsen guides readers through the processes of entering data into R, working with data in R, and using R to express data in histograms, boxplots, barplots, scatterpl
 Cataloging source
 MiAaPQ
 http://library.link/vocab/creatorName
 Hartvigsen, Gregg
 Dewey number
 005.133
 Illustrations
 illustrations
 Index
 index present
 LC call number
 QA276.45.R3
 LC item number
 .H378 2014
 Literary form
 non fiction
 Nature of contents

 dictionaries
 bibliography
 http://library.link/vocab/subjectName

 R (Computer program language)
 Mathematical statistics
 Label
 A primer in biological data analysis and visualization using R, Gregg Hartvigsen
 Note
 Description based upon print version of record
 Bibliography note
 Includes bibliographical references (pages [229]230) and index
 Carrier category
 online resource
 Carrier category code
 cr
 Carrier MARC source
 rdacarrier
 Content category
 text
 Content type code
 txt
 Content type MARC source
 rdacontent
 Contents

 Cover; Title Page; Copyright; Contents; Introduction; 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 data3.7 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 nonnormal data; 4.7 Problems; 5. Visualizing your data; 5.1 Overview; 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 "pvalue"?; 6.7 Type I and type II errors; 6.8 Problems; 7. Hypothesis Tests: One and twosample 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 Problems8. Testing differences among multiple samples; 8.1 Samples are normally distributed; 8.2 Oneway test for nonparametric data; 8.3 Twoway 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 χ2 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 regression11.5 Nonlinear 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?; Acknowledgments; Solutions to OddNumbered Problems; Bibliography; Index
 Dimensions
 unknown
 Extent
 1 online resource (404 p.)
 Form of item
 online
 Isbn
 9780231537049
 Media category
 computer
 Media MARC source
 rdamedia
 Media type code
 c
 Specific material designation
 remote
 System control number

 (CKB)2560000000141420
 (EBL)1603504
 (MiAaPQ)EBC1603504
 (EXLCZ)992560000000141420
 Label
 A primer in biological data analysis and visualization using R, Gregg Hartvigsen
 Note
 Description based upon print version of record
 Bibliography note
 Includes bibliographical references (pages [229]230) and index
 Carrier category
 online resource
 Carrier category code
 cr
 Carrier MARC source
 rdacarrier
 Content category
 text
 Content type code
 txt
 Content type MARC source
 rdacontent
 Contents

 Cover; Title Page; Copyright; Contents; Introduction; 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 data3.7 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 nonnormal data; 4.7 Problems; 5. Visualizing your data; 5.1 Overview; 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 "pvalue"?; 6.7 Type I and type II errors; 6.8 Problems; 7. Hypothesis Tests: One and twosample 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 Problems8. Testing differences among multiple samples; 8.1 Samples are normally distributed; 8.2 Oneway test for nonparametric data; 8.3 Twoway 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 χ2 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 regression11.5 Nonlinear 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?; Acknowledgments; Solutions to OddNumbered Problems; Bibliography; Index
 Dimensions
 unknown
 Extent
 1 online resource (404 p.)
 Form of item
 online
 Isbn
 9780231537049
 Media category
 computer
 Media MARC source
 rdamedia
 Media type code
 c
 Specific material designation
 remote
 System control number

 (CKB)2560000000141420
 (EBL)1603504
 (MiAaPQ)EBC1603504
 (EXLCZ)992560000000141420
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