Data Science with R for Psychologists and Healthcare Professionals

313 Pages·2021·13.455 MB·English

by  Christian Ryan

Data Science with R for Psychologists
and Healthcare Professionals
Christian Ryan
Senior Lecturer in Clinical Psychology
and Chartered Clinical Psychologist
University College Cork
Cork, Ireland
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A SCIENCE PUBLISHERS BOOK
A SCIENCE PUBLISHERS BOOK
First edition published 2022
by CRC Press
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Library of Congress Cataloging‑in‑Publication Data
Names: Ryan, Christian, 1968- author.
Title: Data science with R for psychologists and healthcare professionals /
Christian Ryan.
Description: First edition. | Boca Raton : CRC Press, 2021. | Includes
bibliographical references and index. | Summary: “Data science - the
integration of computer technologies with traditional statistical
knowledge - is bringing sweeping changes across industry and academia.
The ability to process, visualise and model data are vital skills for
students of psychology and other health sciences. This book demonstrates
the application of some of these latest approaches to the world of
psychological research. Providing a thorough grounding in the use of R
for data science, with many carefully crafted analyses, using open
datasets, this book will enable beginners and emerging researchers to
learn to harness the power of modern analytic techniques and apply them
to their own research projects”-- Provided by publisher.
Identifiers: LCCN 2021017092 | ISBN 9780367618452 (hardcover)
Subjects: LCSH: Psychology--Statistical methods. |
Psychology--Research--Data processing. | R (Computer program language)
Classification: LCC BF39 .R93 2021 | DDC 150.1/5195--dc23
LC record available at https://lccn.loc.gov/2021017092
ISBN: 978-0-367-61845-2 (hbk)
ISBN: 978-0-367-61856-8 (pbk)
ISBN: 978-1-003-10684-5 (ebk)
DOI: 10.1201/9781003106845
Preface
This book is intended for healthcare professionals and students of healthcare subjects
at university who wish to begin using R (Team, 2017) to configure, analysing and
visualise datasets. Some readers may be acquainted with SPSS and Excel, and the
book draws comparisons and explains differences in working methods with these
programmes.
Though this is an introductory text, primarily aimed at beginners in R, it takes
a contemporary approach, drawing heavily on the functionality of the tidyverse
packages. As much as possible, the strategies employed to import, clean and process
data are done in a way that prioritises readability for a non-technical audience and
scaffolds learning of R in the context of real-world datasets.
No previous experience with R is necessary, as the book begins with the
rudiments of using the programme and offers many suggestions for additional
structured learning. The most productive way to use the book is to follow along,
chapter by chapter, completing the analysis of each dataset on your own computer.
Data analysis is a form of procedural knowledge that is best gained through practical
experience.
I maintain a personal website at https://drchristianryan.com where I occasionally
blog about R. Updates to the companion package for this book called r4psych will be
posted on the website and will be available on my GitHub page at https://github.com/
Christian-Ryan. More details about installing the package are in Chapters 2 and 4.
Acknowledgements
I would like to thank the R community, R developers and package writers for the
unerring generosity in continuing to develop and expand the enormous power and
potential of R software. It is inspiring to be part of a community that values open-
source software, open science and the free exchange of help and ideas. I am grateful
to my colleagues at University College Cork for support and encouragement since
I joined the school. Particular thanks to Shane Galvin and Brendan Palmer; your
teaching kickstarted my journey to becoming an enthusiastic R user. I would like
to thank CRC Press, for the opportunity to add to the extensive literature they have
published on R-related topics. Finally, I want to express my deepest gratitude to my
wife Sandra, daughter Emer, my parents, family and friends, for their unwavering
encouragement and support. Special thanks to my son Fintan Ryan for insightful
comments and feedback on an early draft of this book.
Contents
Preface iii
Acknowledgements iv
1. Introduction 1
1.1 Conventions used in this book 1
1.2 How this book is organised 2
1.3 Why learn R? 2
1.4 FAIR and data repositories 4
1.5 Data science 5
1.6 Avoiding complexity 5
1.7 Learning through real datasets 6
1.8 R as a language 6
1.9 Where to find help 7
1.10 Internal help system 7
1.11 Websites 8
1.12 Blogs 9
1.13 Books 10
1.14 Cheatsheets 10
2. The R Environment 12
2.1 RStudio 12
2.2 Packages 14
2.3 Where to find packages? 15
2.4 How to learn about package functions and datasets? 16
2.5 Installing packages 17
2.6 Examining installed packages 17
2.7 Learn R with swirl and other tools 19
3. The Basics 21
3.1 Overview 21
3.2 Functions and arguments 21
3.3 Creating vectors and dataframes 23
3.4 Adding new variables 27
3.5 Warning: quotation marks come in many styles 28
vi Data Science with R for Psychologists and Healthcare Professionals
3.6 Simple plots 29
3.7 Selecting parts of a dataframe 32
3.8 Saving—write.csv() 32
3.9 Loading data—read.csv() 33
3.10 Data types 35
3.11 Saving objects as Rdata 38
3.12 Installing the tidyverse 39
3.13 Function conflict 40
3.14 Importing datasets 41
3.15 Functions used in this chapter 42
4. Working Practices 43
4.1 Default settings 43
4.2 Projects 44
4.3 Scripts 48
4.4 R Markdown 49
4.5 r4psych and datsets for this book 51
5. Dataset Excel 52
5.1 Downloading data from figshare 52
5.2 Loading dataset from multi-sheet Excel files 52
5.3 Renaming variables 55
5.4 The pipe %>% 56
5.5 Factors—adding labels 57
5.6 Reading new sheets from Excel file 59
5.7 Renaming multiple variables 63
5.8 Joining datasheets 64
5.9 Counting cases, calculating means, sd and proportions 65
5.10 Saving dataframes 66
5.11 Automatically renaming variables 66
5.12 Functions used in this chapter 68
6. Dataset csv 69
6.1 Loading comma-separated value (csv) files 69
6.2 Female psychosis dataset 70
6.3 Checking the data types 71
6.4 Coercion 72
6.5 Counting missing values 74
6.6 Converting multiple variables to numeric types 74
6.7 Factors 75
6.8 Save as Rdata 75
6.9 Functions used in this chapter 75
7. Dataset SPSS 77
7.1 Loading SPSS files—.SAV 77
7.2 Examining the data 78
Contents vii
7.3 The structure of "labelled" variables 79
7.4 Factors, adding levels and labels 80
7.5 Labelled attributes 81
7.6 Removing attributes from multiple variables 82
7.7 Save the file 83
7.8 Functions used in this chapter 84
8. Coding New Variables and Scale Reliability 85
8.1 Principles 85
8.2 Dataset: Branjerdporn et al. (2019) 85
8.3 Adding values with mutate() 87
8.4 Using sum() in mutate() 88
8.5 Numeric scales with reverse scoring—scoreItems() 90
8.6 Psychometric properties—scoreItems() 95
8.7 Converting text responses to numeric values 96
8.8 Factor levels to recode variables 97
8.9 mutate() without naming variables (anonymous functions) 98
8.10 mutate() with function() on real data 100
8.11 Calculate subscale and total scores 102
8.12 Creating categorical variables from continuous scales 102
8.13 Cronbach's alpha 104
8.14 Dropping items 105
8.15 Impact of item deletion on item—whole scale correlation 106
8.16 Inter-Item Correlation Matrix 107
8.17 Functions used in this chapter 108
9. Normality 109
9.1 Introduction 109
9.2 The importance of a normal distribution 109
9.3 Creating a normal distribution 110
9.4 Density plot of a normal distribution 111
9.5 qqplot 111
9.6 Skewness and Kurtosis 113
9.7 Normality tests 114
9.8 Empirical distributions—checking normality 114
9.9 Taking small samples of data 114
9.10 Histogram, qqplot, skewness and kurtosis with real data 115
9.11 Sub-samples and distributions 116
9.12 Sidebar: objects in R 119
9.13 Severe deviations from normality 122
9.14 Summary 123
9.15 Functions used in this chapter 123
1 0. Outliers 125
10.1 Reload data—Larson et al. (2015) 125
10.2 Outliers—Boxplot 125
viii Data Science with R for Psychologists and Healthcare Professionals
10.3 Outlier—numeric methods 127
10.4 Replacing outliers 129
10.5 Functions used in this chapter 132
11. Descriptive Statistics 133
11.1 Summarise by group 133
11.2 Multiple grouping variables 134
11.3 Contingency tables 135
11.4 Chi-Squared test 136
11.5 t-test—using indexing 136
11.6 t-test using formula 138
11.7 Boxplots using formula 139
11.8 Boxplot with two IVs 139
11.9 Functions used in this chapter 140
12. Graphs with ggplot2 141
12.1 Introduction to graphing 141
12.2 Structure of a ggplot() call 142
12.3 Barplot 143
12.4 Axis labels 143
12.5 Colour and fill 144
12.6 Themes 145
12.7 Combining multiple layers 147
12.8 Scatterplot 148
12.9 Saving data objects, then plotting 149
12.10 Facetting 150
12.11 Boxplot 153
12.12 Jitter plot 154
12.13 Density plot 155
12.14 Functions used in this chapter 156
1 3. Correlation—Bivariate 157
13.1 Background 157
13.2 Scatterplot—base R 157
13.3 Scatterplot—ggplot2 159
13.4 Correlation coefficient 161
13.5 APA correlation 161
13.6 Coefficient of determination 162
13.7 Correlation Matrix—association between multiple variables 162
13.8 Plotting multiple correlations—corrplot 163
13.9 Plotting multiple correlations—GGally 164
13.10 Statistical significance in a correlation matrix 165
13.11 Assumptions of correlation 167
13.12 Functions used in this chapter 170
Contents ix
1 4. Correlation—Partial 171
14.1 Spurious correlation 171
14.2 Mediation 172
14.3 Partial correlation with correlation() 177
14.4 Functions used in this chapter 179
1 5. One-Way ANOVA—Model Data 180
15.1 ANOVA overview 180
15.2 ANOVA—organising the data 180
15.3 ANOVA formula—Base R-aov() 183
15.4 Effect size—eta-squared 184
15.5 APA output 185
15.6 Plotting the data 185
15.7 Post hoc tests—Tukey HSD 186
15.8 Planned comparisons 187
15.9 Checking the assumptions 189
15.10 car::Anova() 190
15.11 Functions used in this chapter 191
1 6. One-Way ANOVA—Real Data 192
16.1 Loading data 192
16.2 Visualising group differences 192
16.3 aov() 193
16.4 Post hoc tests: TukeyHSD() 195
16.5 Functions used in this chapter 195
1 7. Factorial ANOVA 197
17.1 Introduction 197
17.2 Dataset—Reilly et al. 2016 197
17.3 Distribution of participants across factor categories 199
17.4 Unbalanced factorial ANOVA 202
17.5 Anova() 204
17.6 Planned comparisons with emmeans() 205
17.7 Functions used in this chapter 207
1 8. ANCOVA 209
18.1 Introduction 209
18.2 Assumptions 210
18.3 Covariates 211
18.4 Preliminary ANOVA 211
18.5 Checking assumptions in van der Velde et al. (2015) 212
18.6 Setting contrast types 215
18.7 ANCOVA calculation 215