R for conservation and development projects : a primer for practitioners / Nathan Whitmore.

Format:

Book

Author/Creator:

Whitmore, Nathan, author.

Series:

Chapman & Hall the R series

Language:

English

Subjects (All):

R (Computer program language).

Mathematical statistics--Data processing.

Mathematical statistics.

Conservation projects (Natural resources)--Data processing.

Conservation projects (Natural resources).

Physical Description:

1 online resource (391 pages).

Edition:

1st ed.

Place of Publication:

Boca Raton, FL ; London ; New York : CRC Press, Taylor & Francis Group, 2021.

Summary:

"This book is aimed at conservation and development practitioners and who need to learn and use R in a part-time professional context. It gives people with a non-technical background a set of skills to graph, map, and model in R. It also provides background on data integration in project management and covers fundamental statistical concepts. The book aims to demystify R and give practitioners the confidence to use it"-- Provided by publisher.

Contents:

Cover

Half Title

Series Page

Title Page

Copyright Page

Dedication

Contents

Preface

1. Introduction

1.1. What is R?

1.2. Why R?

1.3. Why this book?

1.4. What are development and conservation?

1.5. Science and decision making

1.6. Why data science is important

1.6.1. Monitoring and evaluation

1.6.2. Projects versus programmes

1.6.3. Project delivery versus research projects

1.7. The goal of this book

1.8. How this book is organised

1.9. How code is organised in this book

I: Basics

2. Inference and Evidence

2.1. Inference

2.2. Study design

2.3. Evidence

2.4. What makes good data?

2.5. Recommended resources

2.6. Summary

3. Data integration in project management

3.1. Adaptive management cycles

3.2. The Deming cycle

3.2.1. Plan

3.2.1.1. Development of a project strategy and proposal

3.2.1.2. Proposal submission process

3.2.1.3. What is a logframe?

3.2.1.4. Logframe terminology

3.2.1.5. Pre-implementation planning

3.2.2. Train

3.2.3. Do

3.2.4. Check

3.2.5. Act

3.3. Challenges

3.4. Recommended resources

3.5. Summary

4. Getting started in R

4.1. Installing R

4.2. Installing RStudio

4.3. The R interface

4.3.1. The console

4.3.2. Version information

4.3.3. Writing code in the console

4.3.4. Script editors

4.3.5. Using the default script editor

4.3.6. Using RStudio

4.4. R as a calculator

4.5. How R works

4.5.1. Objects

4.5.2. Functions

4.5.2.1. Getting help on functions

4.5.3. Packages

4.5.3.1. Getting help on packages

4.6. Writing meaningful code

4.7. Reproducibility

4.8. Recommended resources

4.9. Summary

5. Introduction to data frames

5.1. Making data frames

5.2. Importing a data frame

5.3. Saving a data frame

5.4. Investigating a data frame.

5.5. Other functions to examine an R object

5.6. Subsetting using the `[' and `]' operators

5.7. Descriptive statistics

5.8. Viewing data frames

5.9. Making a reproducible example

5.9.1. Reproducible example steps

5.10. Recommended resources

5.11. Summary

6. The Waihi project

6.1. The scenario

6.1.1. Why evidence is important

6.2. The data

6.2.1. Description of condev data sets

6.3. Recommended resources

6.4. Summary

II: First steps

7. ggplot2: graphing with the tidyverse

7.1. Why graph?

7.2. The tidyverse package

7.3. The data

7.4. Graphing in R

7.4.1. Making a ggplot

7.4.2. Scatter plots

7.4.3. Bar plots

7.4.4. Histograms

7.4.5. Box plots

7.4.6. Polygons

7.4.7. Other common geoms

7.5. How to save a ggplot

7.6. Recommended resources

7.7. Summary

8. Customising a ggplot

8.1. Why customise a ggplot?

8.2. The packages

8.3. The data

8.4. Families of layers

8.5. Aesthetics properties

8.5.1. Settings aesthetics

8.5.2. A quick note about colour

8.5.3. Using aesthetics to distinguish groups

8.5.4. Using faceting to distinguish groups

8.6. Improving crowded graphs

8.7. Overlaying

8.8. Labels

8.9. Using the theme() function

8.9.1. The 4 elements

8.9.2. Rotating axis text

8.9.3. Spacing between axis and graph

8.9.4. In-built themes

8.10. Controlling axes

8.10.1. Tick marks

8.10.2. Axis limits

8.10.3. Forcing a common origin

8.10.4. Flipping axes

8.10.5. Forcing a plot to be square

8.10.6. Log scales and large numbers

8.11. Controlling legends

8.12. Recommended resources

8.13. Summary

9. Data wrangling

9.1. What is data wrangling?

9.2. The packages

9.3. The data

9.4. Pipes

9.5. Tibbles versus data frame

9.6. Subsetting

9.6.1. select()

9.6.2. lter().

9.7. Transforming

9.7.1. group_by()

9.7.2. summarise()

9.7.3. mutate()

9.7.4. adorn_totals()

9.8. Tidying

9.8.1. pivot_wider()

9.8.2. pivot_longer()

9.9. Ordering

9.9.1. arrange()

9.9.2. top_n()

9.10. Joining

9.11. Recommended resources

9.12. Summary

10. Data cleaning

10.1. Cleaning is more than correcting mistakes

10.2. The packages

10.3. The data

10.4. Changing names

10.4.1. clean_names()

10.4.2. rename()

10.4.3. fct_recode()

10.4.4. str_replace all()

10.5. Fixing missing values

10.5.1. fct_explicit na()

10.5.2. replace_na()

10.5.3. replace()

10.5.4. drop_na()

10.5.5. Cleaning a whole data set

10.6. Adding and dropping factor levels

10.6.1. fct_drop()

10.6.2. fct_expand()

10.6.3. Keeping empty levels in ggplot

10.7. Fusing duplicate columns

10.7.1. coalesce()

10.8. Organising factor levels

10.8.1. fct_relevel()

10.8.2. fct_reorder()

10.8.3. fct_rev()

10.9. Anonymisation and pseudonymisation

10.9.1. fct_anon()

10.10. Recommended resources

10.11. Summary

11. Working with dates and time

11.1. The two questions

11.2. The packages

11.3. The data

11.4. Formatting dates

11.4.1. Formatting dates with lubridate

11.4.2. Formatting dates with base R

11.4.3. Numerical dates

11.5. Extracting dates

11.6. Time intervals

11.7. Time zones

11.7.1. The importance of time zones

11.7.2. Same times in di erent time zones

11.8. Replacing missing date components

11.9. Graphing: a worked example

11.9.1. Reordering a variable by a date

11.9.2. Summarising date-based data

11.9.3. Date labels with scale_x _date()

11.10. Recommended resources

11.11. Summary

12. Working with spatial data

12.1. The importance of maps

12.2. The packages

12.3. The data

12.4. What is spatial data?.

12.5. Introduction to the sf package

12.5.1. Reading data: st_read()

12.5.2. Converting data: st_as_sf()

12.5.3. Polygon area: st_area()

12.5.4. Plotting maps: geom_sf()

12.5.5. Extracting coordinates st_coordinates()

12.6. Plotting a world map with

12.6.1. Filtering with flter()

12.7. Coordinate reference systems

12.7.1. Finding the CRS of an object with st_crs()

12.7.2. Transform the CRS with st_transform()

12.7.3. Cropping with coord_sf()

12.8. Adding reference information

12.8.1. Adding a scale bar and north arrow

12.8.2. Positioning names with centroids

12.8.3. Adding names with geom_text()

12.9. Making a chloropleth

12.10. Random sampling

12.11. Saving with st_write()

12.12. Rasters with the raster package

12.12.1. Loading rasters

12.12.2. Raster data

12.12.3. Plotting rasters

12.12.4. Basic raster calculations

12.12.5. Sampling

12.12.6. Extracting raster data from points

12.12.7. Turning data frames into rasters

12.12.8. Calculating distances

12.12.9. Masking

12.12.10. Cropping

12.12.11. Saving

12.12.12. Changing to a data frame

12.13. Recommended resources

12.14. Summary

13. Common R code mistakes and quirks

13.1. Making mistakes

13.2. The packages

13.3. The data

13.4. Capitalisation mistakes

13.5. Forgetting brackets

13.6. Forgetting quotation marks

13.7. Forgetting commas

13.8. Forgetting `+' in a ggplot

13.9. Forgetting to call a ggplot object

13.10. Piping but not making an object

13.11. Changing a factor to a number

13.12. Strings automatically read as factors

13.13. Summary

III: Modelling

14. Basic statistical concepts

14.1. Variables and statistics

14.2. The packages

14.3. The data

14.4. Describing things which are variable

14.4.1. Central tendency

14.4.1.1. Mean.

14.4.1.2. Median

14.4.2. Describing variability

14.4.2.1. Range

14.4.2.2. Standard deviation

14.4.2.3. Percentile range

14.4.3. Reporting central tendency and variability

14.4.4. Precision

14.5. Introducing probability

14.6. Probability distributions

14.6.1. Binomial distribution

14.6.1.1. Bernoulli distribution

14.6.2. Poisson distribution

14.6.3. Normal distribution

14.7. Random sampling

14.7.1. Simple random sampling

14.7.2. Strati ed random sampling

14.8. Modelling approaches

14.8.1. Null hypothesis testing

14.8.2. Information-theoretics

14.8.3. Bayesian approaches

14.8.4. Machine learning

14.9. Undertting and overtting

14.10. Recommended resources

14.11. Summary

15. Understanding linear models

15.1. Regression versus classi cation

15.2. The packages

15.3. The data

15.4. Graphing a y variable

15.5. What is a linear model?

15.5.1. How to draw a linear model from an equation

15.6. Predicting the response variable

15.7. Formulating hypotheses

15.8. Goodness-of-fit

15.8.1. Residuals

15.8.2. Correlation

15.9. Making a linear model in R

15.10. Introduction to model selection

15.10.1. Estimating the number of parameters: K

15.10.2. Goodness of t: L

15.11. Doing model selection in R

15.11.1. Interpreting an AIC Table

15.11.1.1. Evidence ratios

15.11.1.2. Keep in mind

15.12. Understanding coe cients

15.13. Model equations and prediction

15.13.1. Dummy variables and a design matrix

15.13.2. Plotting a prediction with geom_abline()

15.13.3. Automatic prediction

15.14. Understanding a model summary

15.15. Standard errors and con dence intervals

15.15.1. Confidence intervals for model predictions

15.16. Model diagnostics

15.16.1. Still problems?

15.17. Log transformations.

15.17.1. What are logarithms?.

Notes:

Description based on print version record.

ISBN:

0-429-26218-3

0-429-55725-6

0-429-55278-5

9780429262180

OCLC:

1200039224