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

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
Creator
Author
Subject
Genre
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 lab-oriented 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
Instantiates
Publication
Copyright
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 non-normal 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 "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 Problems8. Testing differences among multiple samples; 8.1 Samples are normally distributed; 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 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 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?; Acknowledgments; Solutions to Odd-Numbered 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
Publication
Copyright
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 non-normal 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 "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 Problems8. Testing differences among multiple samples; 8.1 Samples are normally distributed; 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 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 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?; Acknowledgments; Solutions to Odd-Numbered 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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