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Practical R for Biologists: An Introduction [Pehme köide]

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(Chulalongkorn University, Thailand), (Professional biology and science tutor), (Chulalongkorn University, Thailand)
  • Formaat: Paperback / softback, 424 pages, kõrgus x laius x paksus: 244x172x21 mm, kaal: 1046 g
  • Ilmumisaeg: 15-Jan-2021
  • Kirjastus: CABI Publishing
  • ISBN-10: 1789245346
  • ISBN-13: 9781789245349
Teised raamatud teemal:
Practical R for Biologists: An IntroductionSuurem pilt
  • Formaat: Paperback / softback, 424 pages, kõrgus x laius x paksus: 244x172x21 mm, kaal: 1046 g
  • Ilmumisaeg: 15-Jan-2021
  • Kirjastus: CABI Publishing
  • ISBN-10: 1789245346
  • ISBN-13: 9781789245349
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781789245349.html
Märksõnad:
R is a freely available, open-source statistical programming environment which provides powerful statistical analysis tools and graphics outputs. R is now used by a very wide range of people; biologists (the primary audience of this book), but also all other scientists and engineers, economists, market researchers and medical professionals. R users with expertise are constantly adding new associated packages, and the range already available is immense. This text works through a set of studies that collectively represent almost all the R operations that biology students need in order to analyze their own data. The material is designed to serve students from first year undergraduates through to those beginning post graduate levels. Chapters are organized around topics such as graphing, classical statistical tests, statistical modelling, mapping, and text parsing. Examples are based on real scientific studies, and each one covers the use of more R functions than those simply necessary to get a p-value or plot. The book walks the reader through the data analysis process, starting with very simple plots, and continuing through more complex analyses and programming. It shows how to deal with issues such as error messages that can be confronting for beginners, in order to set students up for a successful scientific career using R. Prof. Dr Donald Quicke has had more than 40 years' experience teaching undergraduate and postgraduate biology students, initially at Sheffield University, UK and then at Imperial College London; Buntika Butcher gained her PhD at Imperial College and is currently Associate Professor in the Biology Department at Chulalongkorn University, Bangkok, with 20 years of teaching experience; Dr Rachel Kruft Welton did her master's degree at Imperial College, London and a PhD at University of Birmingham, UK before qualifying as a teacher. She has been a professional biology and science tutor for nearly 20 years, including mentoring undergraduates as part of Birmingham University's alumni scheme. Collectively the authors have a vast amount of teaching experience which they apply here to make the passage into R programming as gentle and easy as possible, whilst guiding the reader to tackle quite complicated programming.

Muu info

Suitable for: Undergraduate and beginning undergraduate students taking biology degrees, masters courses and graduate school courses.
About the Authors xv
Preface xix
Acknowledgements xxxi
1 How to Use This Book
1(2)
Setting Up Your Computer
1(1)
Running Code as You Go Along
1(1)
Chapter Structure
2(1)
2 Installing and Running R
3(6)
Downloading and Installing R onto Your Computer
3(4)
Installing Packages
7(2)
3 Very Basic R Syntax
9(4)
4 First Simple Programs and Graphics
13(18)
Basic R Features
13(1)
Commas, Brackets and Concatenation
14(1)
The Colon Character
15(1)
Raise to the Power of Symbol
15(1)
Exiting from R
16(1)
Help Pages
16(1)
Beginning with Simple R Code to Get Used to the Command Line System
16(3)
Playing with Graphics
19(4)
Working with Character Variables
23(4)
Built-in R Datasets
27(1)
The table Function
27(1)
Ragged Data
28(3)
5 The Dataframe Concept
31(6)
Combining Sets of Tables for Data Collected on Different Dates
34(1)
Converting Factors in a Dataframe to Numeric or Character
34(3)
6 Plotting Biological Data in Various Ways
37(42)
Example 1 Bryophytes up a Mountain
37(4)
Troubleshooting 1
41(2)
Adding a Legend to a Plot
43(2)
Troubleshooting 2 - Vector Lengths Differ
45(1)
Troubleshooting 3 - Missing Data and NAs
46(2)
Incorporating More Types of Data on the Same Graph
48(1)
Example 2 Tropical Forests, Rural Population, Logarithmic Axes and Installing Packages
49(3)
Example 3 Creating a Barplot: Bryophytes Side-by-side
52(1)
Example 4 Stacked Bar Chart, with Different Colours, Fills and Legends
53(4)
Example 5 Dietary Differences between Hornbill Species - Entering Data as a Table
57(3)
Example 6 Horizontal Bar Plot of Camera Trap Data and More Troubleshooting
60(2)
Example 7 Adding Error Bars to a Barplot or Plot: Fly Ommatidea
62(2)
Example 8 Creating Pie Charts Using pie and circlize
64(5)
Example 9 Fish Metacercarial Load and Box and Whisker Plots
69(4)
Adding Notches to a Boxplot
73(1)
Tukey's Honest Significant Difference Test
74(5)
7 The Grammar of Graphics Family of Packages
79(6)
8 Sets and Venn Diagrams
85(10)
9 Statistics: Choosing the Right Test
95(8)
Explanatory and Response Variables, Experiments and Surveys
97(1)
Parametric versus Non-parametric Tests
98(1)
Difference between Linear Models and Generalized Linear Models
98(4)
Our Basic Aim Is to Achieve a Near-linear QQ Plot and Even Variance
102(1)
10 Commonly Used Measures and Statistical Tests
103(16)
Normality, Skew and Kurtosis
103(1)
Testing Whether Proportions Agree with Null Expectations
104(2)
The Special Case of Contingency Tables
106(1)
Hardy-Weinberg Equilibrium
107(3)
Alternatives to the Chi-squared Test under Some Circumstances
110(1)
Testing Whether Two Means Are Significantly Different
111(1)
Single-sample t-test
111(1)
Two-sample t-test
112(1)
Paired t-test
113(1)
Testing Whether Three or More Means Differ from One Another
113(1)
Comparing Two Variances
114(1)
Non-normally Distributed Data with Small Sample Sizes - Mann-Whitney U Test
114(2)
Non-parametric Two-sample Tests
116(1)
Binomial Test
117(2)
11 Regression and Correlation Analyses
119(28)
Linear versus Non-linear Regression
120(1)
Log-log Plot Example Correlation of Numbers of Species with Area
121(2)
Linearizing Data with No Known Underlying Model
123(2)
Errant Points and Leverage
125(4)
QQ Model Plot from the car Library
129(1)
Comparing Regression Slopes and Intercepts Using t-test
130(4)
Non-linear Regression
134(3)
Multiple Regression
137(1)
Hairwise riots ot txplanatory Variables to Visually Inspect Interactions
138(2)
Polynomial Regression and Model Simplification
140(3)
Model Simplification
143(4)
12 Count Data as Response Variable
147(8)
Example 1 Fledgling Numbers in Relation to Clutch Initiation Date
148(3)
Example 2 Pollinator Flower Visits in Passiflora in Relation to Flower Size
151(4)
13 Analysis of Variance (ANOVA)
155(11)
Example 1 A One-way ANOVA, the InsectSprays Dataset
155(2)
Example 2 ANOVA with Proportion Data as Response Variable Using Arcsine Transformation
157(6)
Example 3 Analysis with Proportion Data as Response Variable Using Logit Transformation
163(3)
14 Analysis of Covariance (ANCOVA)
166(5)
Example 1 Growth of Tagged Gobies
166(2)
Example 2 Fitting through the Origin and Count Data as Response Variable
168(3)
15 More Generalized Linear Modelling
171(16)
Model Inspection
171(1)
Binary Response Variable with One Continuous Explanatory Variable
172(1)
Example 1 Logistic regression of gall former predation
172(4)
LD50s
176(1)
Example 2 Pollinator counts - showing importance of deviance
177(5)
Example 3 Proportion data with N known
182(5)
16 Monte Carlo Tests and Randomization
187(7)
Random Number Generator Code
187(1)
Example 1 Flower Visits by Thai Honey Bee Species
188(3)
Randomizing Cells in a Matrix
191(3)
17 Principal Components Analysis
194(6)
Example 1 Rock Oyster Allozymes
194(3)
Example 2-The Iris Dataset
197(3)
18 Species Abundance, Accumulation and Diversity Data
200(18)
Species Accumulation Data
200(2)
Species Accumulation Curves and Randomization
202(6)
Species Richness Estimation
208(1)
Species Diversity Indices
208(2)
A Note to Be Cautious about Logarithms in Functions
210(1)
Broken-stick Models
211(3)
A Much Faster Approach Using Vectorization
214(4)
19 Survivorship
218(9)
Example 1 Survival of Killdeer Nests
218(9)
20 Dates and Julian Dates
227(13)
Problem with Two-digit Dates and POSIX: A Date of Burial Example
232(2)
Phenology and the density Function
234(2)
Extracting Day and Month from Julian Days
236(2)
Seasonal Patterns and Other Smoothing Curves
238(2)
21 Mapping and Parsing Text Input for Data
240(17)
Creating Our Own Map from Digitized Coordinates
247(10)
22 More on Manipulating Text
257(18)
Example 1 Standardizing Names in a Phylogenetic Tree Description
257(2)
Method 1 With Wildcards
259(3)
Method 2 Based on Fixed Character String Length
262(1)
Method 3 Using a Vector of Positions
262(2)
Example 2 Substrings of Unknown Length
264(4)
Trimming White Spaces and/or Tabs
268(1)
Using Wildcards to Locate Internal Letter Strings
268(1)
Finding Suffixes, Prefixes and Specifying Letters, Numbers and Punctuation
269(2)
Manipulating Character Case
271(1)
Ignoring Character Case
272(1)
Specifying Particular and Modifiable Character Classes
273(2)
23 Phytogenies and Trees
275(9)
Branch Lengths
279(1)
Random Trees
280(1)
Different Types of Plots in ape
281(3)
24 Working with DNA Sequences and Other Character Data
284(13)
Sequential Runs of Base Types
288(2)
Downloading DNA Sequences from GenBank
290(2)
Translating DNA to Amino Acids
292(1)
Prettifying a Table
293(2)
Easy Ways to Extract Taxon Names from a Phylogenetic Matrix
295(1)
Replacing Specified Ambiguity Codes with a Question Mark
296(1)
25 Spacing in Two Dimensions
297(6)
26 Population Modelling Including Spatially Explicit Models
303(23)
Example 1 Ricker Population Growth Model, Plotting as You Go
303(3)
Example 2 Host-Parasitoid Population Modelling - Discrete Time Version
306(4)
Example 3 Spatial Host-Parasitoid Model
310(8)
Example 4 Genetic Drift, a Program Aimed at Teaching Students about Evolution
318(4)
27 More on apply Family of Functions - Avoid Loops to Get More Speed
322(1)
Using apply
323(1)
Using tapply to Calculate Values Based on Factors
324(2)
28 Food Webs and Simple Graphics
326(6)
A Parasitoid foodweb Example
326(2)
Foodweb and Community Packages
328(4)
29 Adding Photographs
332(3)
30 Standard Distributions in R
335(13)
The Normal Distribution
335(3)
Student's t Distribution
338(2)
Lognormal Distribution
340(1)
Logistic Distribution
341(1)
Poisson Distribution
342(1)
Gamma Distribution
343(1)
The Chi-squared Distribution
344(4)
31 Reading and Writing Data to and from Files
348(9)
Appending Data to an Existing File
349(1)
Using read.delim with Non-tab Separator
350(1)
Choosing a File to Read Interactively
350(1)
Using Excel for Data Entry
351(1)
The readxl Function and Tibbies
352(2)
Reading PDF Files for Data Mining
354(1)
Writing Graphics Directly to Disc
354(3)
Appendix 1 Summary of Graphical Parameters
357(3)
Arguments Passed Directly to par Function
357(1)
Arguments Applied Directly to the plot Function as well as in Some Others
357(1)
Arguments for the lines Function
358(1)
Having Multiple Graphics Windows Open at the Same Time
358(1)
Macintosh-specific Graphics
359(1)
Using the layout Function
359(1)
Using the split-screen Function
359(1)
Appendix 2 General Housekeeping R Functions and Others Not Covered in the Main Text
360(4)
General Housekeeping Functions
360(1)
Setting or Changing the Working Directory
360(1)
Finding What Files Are in a Directory
361(1)
Graphical Functions and Parameters
361(1)
Interaction with User
361(1)
Mathematical Functions
361(1)
Writing Concatenated Data Straight to File (in the Working Directory) Using cat
362(1)
Troubleshooting Package Installation
362(2)
Appendix 3 Some Useful Statistical and Mathematical Equations
364(5)
Logical Mathematical Operators
364(1)
Descriptive Statistics
364(1)
Distributions
365(1)
Correlation Coefficients
365(1)
Statistical Tests
365(1)
Logarithms and Exponents
366(1)
Logistic Functions
366(1)
Weibull and Compertz Equations
366(1)
Trigonometric Functions
367(1)
Convert Radians and Degrees Functions
367(2)
Bibliography 369(6)
Web Resources 375(2)
Index 377
Prof. Dr Donald Quicke has had more than 40 years' experience teaching undergraduate and postgraduate biology students, initially at Sheffield University, UK and then at Imperial College London. He retired in 2013 and is now a teaching research fellow at the Department of Biology, Chulalongkorn University, Bangkok, Thailand. He is one of the world's leading experts on the taxonomy and systematics and biology of parasitoid wasps. Buntika A. Butcher has worked parasitoid wasps (plus some forensic entomology) since gaining her PhD in the Department of Biology, Imperial College London in 2004. On returning to Thailand she was appointed to a lectureship at Chulalongkorn University Bangkok and was subsequently promoted to Associate Professor in 2015. She has published more than 60 papers and has supervised numerous entomology students to masters anddoctoral degree levels. Dr Rachel Kruft Welton did her master's degree at Imperial College, London and a PhD at University of Birmingham, UK before qualifying as a teacher. She has been a professional biology and science tutor for nearly 20 years, including mentoring undergraduates as part of Birmingham University's alumni scheme.