Atnaujinkite slapukų nuostatas

Insights from Data with R: An Introduction for the Life and Environmental Sciences [Minkštas viršelis]

, (Professor of Integrative Ecology, Department of Evolutionary Biology and Environmental Studies, University of Zürich, Switzerland), , (Professor of Evolutionary Ecology, Department of Animal and Plant Sciences, University of Sheffield, U)
  • Formatas: Paperback / softback, 320 pages, aukštis x plotis x storis: 240x168x15 mm, weight: 608 g, 60 colour line figures and illustrations
  • Išleidimo metai: 25-Feb-2021
  • Leidėjas: Oxford University Press
  • ISBN-10: 0198849826
  • ISBN-13: 9780198849827
Kitos knygos pagal šią temą:
Insights from Data with R: An Introduction for the Life and Environmental SciencesDidesnis paveikslėlis
  • Formatas: Paperback / softback, 320 pages, aukštis x plotis x storis: 240x168x15 mm, weight: 608 g, 60 colour line figures and illustrations
  • Išleidimo metai: 25-Feb-2021
  • Leidėjas: Oxford University Press
  • ISBN-10: 0198849826
  • ISBN-13: 9780198849827
Kitos knygos pagal šią temą:
Pastovi nuoroda: https://www.kriso.lt/db/9780198849827.html
Raktažodžiai:
Experiments, surveys, measurements, and observations all generate data. These data can provide useful insights for solving problems, guiding decisions, and formulating strategy. Progressing from relatively unprocessed data to insight, and doing so efficiently, reliably, and confidently, does not come easily, and yet gaining insights from data is a fundamental skill for science as well as many other fields and often overlooked in most textbooks of statistics and data analysis.

This accessible and engaging book provides readers with the knowledge, experience, and confidence to work with data and unlock essential information (insights) from data summaries and visualisations. Based on a proven and successful undergraduate course structure, it charts the journey from initial question, through data preparation, import, cleaning, tidying, checking, double-checking, manipulation, and final visualization. These basic skills are sufficient to gain useful insights from data without the need for any statistics; there is enough to learn about even before delving into that world!

The book focuses on gaining insights from data via visualisations and summaries. The journey from raw data to insights is clearly illustrated by means of a comprehensive Workflow Demonstration in the book featuring data collected in a real-life study and applicable to many types of question, study, and data. Along the way, readers discover how to efficiently and intuitively use R, RStudio, and tidyverse software, learning from the detailed descriptions of each step in the instructional journey to progress from the raw data to creating elegant and informative visualisations that reveal answers to the initial questions posed. There are an additional three demonstrations online!

Insights from Data with R is suitable for undergraduate students and their instructors in the life and environmental sciences seeking to harness the power of R, RStudio, and tidyverse software to master the valuable and prerequisite skills of working with and gaining insights from data.

Recenzijos

If you want to teach university students to use data to answer scientific questions, you owe it to yourself and them to get this book. * M. Henry H. Stevens, The Quarterly Review of Biology *

Chapter 1 Introduction
1(18)
1.1 What are insights?
1(4)
1.1.1 Dictionary
1(1)
1.1.2 The business perspective
2(1)
1.1.3 Our definition
3(1)
1.1.4 Our ecology example We love fruit
3(2)
1.2 Question, question, question (how are data born?)
5(2)
1.3 But what exactly are data?
7(1)
1.4 Response and predictor variables
8(1)
1.5 Some key features of datasets
9(2)
1.6 Demonstrations of getting insights from data
11(5)
1.7 The general Insights workflow
16(1)
1.8 Summing up and looking forward
17(2)
Chapter 2 Getting acquainted
19(36)
2.1 Getting acquainted with R and RStudio
19(7)
2.1.1 Why R?
20(1)
2.1.2 Why RStudio?
21(1)
2.1.3 Getting and installing R
22(1)
2.1.4 Getting and installing RStudio
23(1)
2.1.5 A brief tour of RStudio
24(2)
2.2 Your first R command!
26(6)
2.2.1 Getting to know R a little better
27(2)
2.2.2 Storing and reusing results
29(2)
2.2.3 What names should I use?
31(1)
2.3 Writing scripts
32(4)
2.3.1 Comments in your scripts
34(1)
2.3.2 Save and keep safe your script file
35(1)
2.3.3 Running your scripts
36(1)
2.4 When things go wrong
36(3)
2.4.1 Errors
37(1)
2.4.2 Warnings
38(1)
2.4.3 The dreaded +
38(1)
2.5 Functions
39(3)
2.5.1 Functions, the sequel
41(1)
2.6 Add-on packages
42(6)
2.6.1 Finding add-on packages
43(1)
2.6.2 Installing (downloading) packages
44(2)
2.6.3 Loading packages
46(1)
2.6.4 An analogy
46(1)
2.6.5 Updating R, RStudio, and your packages
47(1)
2.7 Getting help
48(4)
2.7.1 R help system and files
48(1)
2.7.2 Navigating help files
49(1)
2.7.3 Vignettes
50(1)
2.7.4 Cheatsheets
50(1)
2.7.5 Other sources of help
51(1)
2.7.6 Asking for help from others
51(1)
2.8 Common pitfalls
52(1)
2.9 Summing up and looking forward
52(3)
Chapter 3 Workflow Demonstration part 1: Preparation
55(42)
3.1 What is the question?
57(3)
3.1.1 The three response variables
58(1)
3.1.2 The hypotheses
59(1)
3.2 Design of the study
60(1)
3.3 Preparing your data
61(5)
3.3.1 Acquire the dataset
64(2)
3.4 Preparing your computer
66(6)
3.4.1 Making the project folder for the bat data
67(1)
3.4.2 Projects in RStudio
68(3)
3.4.3 Create a new R script and load packages
71(1)
3.5 Get the data into R
72(6)
3.5.1 View and refine the import
76(2)
3.6 Getting going with data management
78(3)
3.6.1 How the data are stored in R
79(2)
3.7 Clean and tidy the data
81(11)
3.7.1 Tidying the data
82(1)
3.7.2 Cleaning the data
82(1)
3.7.3 Refine the variable names
83(2)
3.7.4 Fix the dates
85(1)
3.7.5 Rename some values in a variable
86(1)
3.7.6 Check for duplicates
87(2)
3.7.7 Check for implausible and invalid values
89(1)
3.7.8 What about those NAs?
90(2)
3.8 Stop that! Don't even think about it!
92(2)
3.8.1 Don't mess with the `working directory'
92(1)
3.8.2 Don't use the data import tool or file choose
93(1)
3.8.3 Don't even think about using the attach function
93(1)
3.8.4 Avoid using square brackets or dollar signs
93(1)
3.9 Summing up and looking forward
94(3)
Chapter 4 Workflow Demonstration part 2: Getting insights
97(44)
4.1 Initial insights 1: Numbers and counting
98(5)
4.1.1 Our first insights: The number, sex, and age of bats
98(5)
4.2 Initial insights 2: Distributions
103(5)
4.2.1 Insights... you've done it!
105(3)
4.3 Transform the data
108(3)
4.4 Insights about our questions
111(14)
4.4.1 Distribution of number of prey
111(2)
4.4.2 Shapes: Mean wingspan
113(1)
4.4.3 Shapes: Proportion migratory
114(2)
4.4.4 Relationships
116(5)
4.4.5 Communication (beautifying the graphs)
121(1)
4.4.6 Beautifying the wingspan, age, sex graph
122(3)
4.5 Another view of the question and data
125(12)
4.5.1 Before you continue
125(1)
4.5.2 A prey-centric view
125(12)
4.6 A caveat
137(1)
4.7 Summing up and looking forward
138(1)
4.8 A small reward, if you like dogs
139(2)
Chapter 5 Dealing with data 1: Digging into dplyr
141(28)
5.1 Introducing dplyr
142(13)
5.1.1 Selecting variables with the select function
143(3)
5.1.2 Renaming variables with select and rename
146(1)
5.1.3 Creating new variables with the mutate function
146(3)
5.1.4 Getting particular observations with filter
149(4)
5.1.5 Ordering observations with arrange
153(2)
5.2 Grouping and summarizing data with dplyr
155(12)
5.2.1 Summarizing data--the nitty-gritty
156(4)
5.2.2 Grouped summaries using group_by magic
160(3)
5.2.3 More than one grouping variable
163(2)
5.2.4 Using group_by with other verbs
165(1)
5.2.5 Removing grouping information
166(1)
5.3 Summing up and looking forward
167(2)
Chapter 6 Dealing with data 2: Expanding your toolkit
169(26)
6.1 Pipes and pipelines
170(5)
6.1.1 Why do we need pipes?
170(4)
6.1.2 On why you shouldn't nest functions
174(1)
6.2 Subduing the pesky string
175(3)
6.3 Elegantly managing dates and times
178(8)
6.3.1 Date/time formats
178(1)
6.3.2 Dates in the bat project data
179(1)
6.3.3 Why parse dates?
180(1)
6.3.4 More about parsing dates/times
181(2)
6.3.5 Calculations with dates/times
183(3)
6.4 Changing between wider and longer data arrangements
186(6)
6.4.1 Going longer
187(3)
6.4.2 Going wider
190(2)
6.5 Summing up and looking forward
192(3)
Chapter 7 Getting to grips with ggplot2
195(16)
7.1 Anatomy of a ggplot
196(5)
7.1.1 Layers
197(3)
7.1.2 Scales
200(1)
7.1.3 Coordinate system
200(1)
7.1.4 Fantastic faceting
201(1)
7.2 Putting it into practice
201(3)
7.2.1 Inheriting data and aesthetics from ggplot
202(2)
7.3 Beautifying plots
204(4)
7.3.1 Working with layer-specific geom properties
205(2)
7.3.2 Adding titles and labels
207(1)
7.3.3 Themes
207(1)
7.4 Summing up and looking forward
208(3)
Chapter 8 Making deeper insights part 1: Working with single variables
211(36)
8.1 Variables and data
212(4)
8.1.1 Numeric versus categorical variables
213(2)
8.1.2 Ratio versus interval scales
215(1)
8.2 Samples and distributions
216(4)
8.2.1 Understanding numerical variables
218(2)
8.3 Graphical summaries of numeric variables
220(14)
8.3.1 Making some insights about wingspan
222(5)
8.3.2 Descriptive statistics for numeric variables
227(1)
8.3.3 Measuring central tendency
228(1)
8.3.4 Measuring dispersion
229(2)
8.3.5 Mapping measures of central tendency and dispersion to a figure
231(2)
8.3.6 Combining histograms and boxplots
233(1)
8.4 A moment with missing values in numeric variables (NAs)
234(2)
8.5 Exploring a categorical variable
236(8)
8.5.1 Understanding categorical variables
236(8)
8.6 Summing up and looking forward
244(1)
8.7 A cat-related reward
245(2)
Chapter 9 Making deeper insights part 2: Relationships among (many) variables
247(24)
9.1 Associations between two numeric variables
248(8)
9.1.1 Descriptive statistics: Correlations
248(3)
9.1.2 Other measures of correlation
251(1)
9.1.3 Graphical summaries between two numeric variables: The scatterplot
252(4)
9.2 Associations between two categorical variables
256(5)
9.2.1 Numerical summaries
256(2)
9.2.2 Graphical summaries
258(2)
9.2.3 An alternative, and perhaps more valuable
260(1)
9.3 Categorical-numerical associations
261(6)
9.3.1 Numerical summaries
262(1)
9.3.2 Graphical summaries for numerical versus categorical data
262(2)
9.3.3 Alternatives to box-and-whisker plots
264(3)
9.4 Building in complexity: Relationships among three or more variables
267(2)
9.5 Summing up and looking forward
269(2)
Chapter 10 Looking back and looking forward
271(12)
10.1 Next learning steps
272(2)
10.2 Reproducibility: What, why, and how?
274(7)
10.2.1 Why should you try and make your work reproducible?
274(1)
10.2.2 How can you make your work more reproducible?
275(6)
10.3 Congratulations!
281(2)
Index 283
Owen L. Petchey is Professor of Integrative Ecology at the Department of Evolutionary Biology and Environmental Studies, University of Zürich, Switzerland. He has used R for nearly 20 years, and has particular expertise in teaching beginners, multivariate statistics, spatial data, programming, maximum likelihood estimation, and visualisation (i.e., nice graphs!). His research focuses on the causes and consequences of extinctions in a changing world. His group performs experiments with microbial communities, models the structure of food webs, analyses variation in biodiversity, and does fieldwork in Iceland, the UK, and Switzerland.

Andrew P. Beckerman is Professor of Evolutionary Ecology at the Department of Animal and Plant Sciences, University of Sheffield, UK. He has used R for nearly 20 years, and has particular expertise in teaching the exploration, visualisation and analysis of simple and complex data. His research focuses on the structure and dynamics of ecological communities facing multiple simultaneous stressors. His group models the structure and dynamics of food webs, analyses trait and population responses to environmental variation, and explores the conservation ecology of endangered species.

Natalie Cooper is a Researcher at the Natural History Museum, London, UK, where her research focuses on understanding the evolution of biodiversity. She works on all kinds of organisms, from parasites to blue whales, and does all of her research in R.

Dylan Z. Childs is a Senior Lecturer at the University of Sheffield, UK. He has used R for over 15 years and has particular expertise in teaching population modelling and advanced statistical tools such as mixed models. His research focuses on data-driven modelling of populations and communities. His group uses demographic methods to model structured population dynamics, analyses trait and population responses to environmental variation, and develops methods for integrating individual- and population-level data into predictive models.