A primer in biological data analysis and visualization using R /

A primer in biological data analysis and visualization using R /

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Bibliographic Details
Author / Creator:Hartvigsen, Gregg, author.
Imprint:New York : Columbia University Press, [2014]
©2014
Description:1 online resource (ix, 234 pages) : illustrations
Language:English
Subject:
R (Computer program language)
Mathematical statistics -- Data processing.
Mathematical statistics -- Data processing.
R (Computer program language)
Electronic books.
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/11225831
Hidden Bibliographic Details
ISBN:9780231537049
0231537042
9780231166980
0231166982
9780231166997
0231166990
Digital file characteristics:text file PDF
Notes:Includes bibliographical references (pages 229-230) and index.
In English.
Print version record.
Summary: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.
Other form:Print version: Hartvigsen, Gregg. Primer in biological data analysis and visualization using R. New York : Columbia University Press, [2014] 9780231166980
Standard no.:10.7312/hart16698
Table of 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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