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E-raamat: Data Science with R for Psychologists and Healthcare Professionals

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(University College Cork, Ireland)
  • Formaat: 312 pages
  • Ilmumisaeg: 22-Dec-2021
  • Kirjastus: CRC Press
  • ISBN-13: 9781000530582
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Data Science with R for Psychologists and Healthcare ProfessionalsSuurem pilt
  • Formaat: 312 pages
  • Ilmumisaeg: 22-Dec-2021
  • Kirjastus: CRC Press
  • ISBN-13: 9781000530582
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"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"--

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 introduction to R for students of psychology and health sciences aims to fast-track the reader through some of the most difficult aspects of learning to do data analysis and statistics. It demonstrates the benefits for reproducibility and reliability of using a programming language over commercial software packages such as SPSS. The early chapters build at a gentle pace, to give the reader confidence in moving from a point-and-click software environment, to the more robust and reliable world of statistical coding. This is a thoroughly modern and up-to-date approach using RStudio and the tidyverse. A range of R packages relevant to psychological research are discussed in detail. A great deal of research in the health sciences concerns questionnaire data, which may require recoding, aggregation and transformation before quantitative techniques and statistical analysis can be applied. R offers many useful and transparent functions to process data and check psychometric properties. These are illustrated in detail, along with a wide range of tools R affords for data visualisation. Many introductory statistics books for the health sciences rely on toy examples - in contrast, this book benefits from utilising open datasets from published psychological studies, to both motivate and demonstrate the transition from data manipulation and analysis to published report. R Markdown is becoming the preferred method for communicating in the open science community. This book also covers the detail of how to integrate the use of R Markdown documents into the research workflow and how to use these in preparing manuscripts for publication, adhering to the latest APA style guidelines.

Preface iii
Acknowledgements iv
1 Introduction
1(11)
1.1 Conventions used in this book
1(1)
1.2 How this book is organised
2(1)
1.3 Why learn R?
2(2)
1.4 FAIR and data repositories
4(1)
1.5 Data science
5(1)
1.6 Avoiding complexity
5(1)
1.7 Learning through real datasets
6(1)
1.8 Rasa language
6(1)
1.9 Where to find help
7(1)
1.10 Internal help system
7(1)
1.11 Websites
8(1)
1.12 Blogs
9(1)
1.13 Books
10(1)
1.14 Cheatsheets
10(2)
2 The R Environment
12(9)
2.1 RStudio
12(2)
2.2 Packages
14(1)
2.3 Where to find packages?
15(1)
2.4 How to learn about package functions and datasets?
16(1)
2.5 Installing packages
17(1)
2.6 Examining installed packages
17(2)
2.7 Leam R with swirl and other tools
19(2)
3 The Basics
21(22)
3.1 Overview
21(1)
3.2 Functions and arguments
21(2)
3.3 Creating vectors and dataframes
23(4)
3.4 Adding new variables
27(1)
3.5 Warning: quotation marks come in many styles
28(1)
3.6 Simple plots
29(3)
3.7 Selecting parts of a dataframe
32(1)
3.8 Saving--write.csv()
32(1)
3.9 Loading data--read.csv()
33(2)
3.10 Datatypes
35(3)
3.11 Saving objects as Rdata
38(1)
3.12 Instaling the tidy verse
39(1)
3.13 Function conflict
40(1)
3.14 Importing datasets
41(1)
3.15 Functions used in this chapter
42(1)
4 Working Practices
43(9)
4.1 Default settings
43(1)
4.2 Projects
44(4)
4.3 Scripts
48(1)
4.4 R Markdown
49(2)
4.5 r4psych and datsets for this book
51(1)
5 Dataset Excel
52(17)
5.1 Downloading data from figshare
52(1)
5.2 Loading dataset from multi-sheet Excel files
52(3)
5.3 Renaming variables
55(1)
5.4 The pipe %<%
56(1)
5.5 Factors--adding labels
57(2)
5.6 Reading new sheets from Excel file
59(4)
5.7 Renaming multiple variables
63(1)
5.8 Joining datasheets
64(1)
5.9 Counting cases, calculating means, sd and proportions
65(1)
5.10 Saving dataframes
66(1)
5.11 Automatically renaming variables
66(2)
5.12 Functions used in this chapter
68(1)
6 Dataset csv
69(8)
6.1 Loading comma-separated value (csv) files
69(1)
6.2 Female psychosis dataset
70(1)
6.3 Checking the data types
71(1)
6.4 Coercion
72(2)
6.5 Counting missing values
74(1)
6.6 Converting multiple variables to numeric types
74(1)
6.7 Factors
75(1)
6.8 Save as Rdata
75(1)
6.9 Functions used in this chapter
75(2)
7 Dataset SPSS
77(8)
7.1 Loading SPSS files--.SAV
77(1)
7.2 Examining the data
78(1)
7.3 The structure of "labelled" variables
79(1)
7.4 Factors, adding levels and labels
80(1)
7.5 Labelled attributes
81(1)
7.6 Removing attributes from multiple variables
82(1)
7.7 Save the file
83(1)
7.8 Functions used in this chapter
84(1)
8 Coding New Variables and Scale Reliability
85(24)
8.1 Principles
85(1)
8.2 Dataset: Branjerdporn et al. (2019)
85(2)
8.3 Adding values with mutate()
87(1)
8.4 Using sum() in mutate()
88(2)
8.5 Numeric scales with reverse scoring--scoreItems()
90(5)
8.6 Psychometric properties--scoreItems()
95(1)
8.7 Converting text responses to numeric values
96(1)
8.8 Factor levels to recode variables
97(1)
8.9 mutate() without naming variables (anonymous functions)
98(2)
8.10 mutate() with function() on real data
100(2)
8.11 Calculate subscale and total scores
102(1)
8.12 Creating categorical variables from continuous scales
102(2)
8.13 Cronbach's alpha
104(1)
8.14 Dropping items
105(1)
8.15 Impact of item deletion on item--whole scale correlation
106(1)
8.16 Inter-Item Correlation Matrix
107(1)
8.17 Functions used in this chapter
108(1)
9 Normality
109(16)
9.1 Introduction
109(1)
9.2 The importance of a normal distribution
109(1)
9.3 Creating a normal distribution
110(1)
9.4 Density plot of a normal distribution
111(1)
9.5 qqplot
111(2)
9.6 Skewness and Kurtosis
113(1)
9.7 Normality tests
114(1)
9.8 Empirical distributions--checking normality
114(1)
9.9 Taking small samples of data
114(1)
9.10 Histogram, qqplot, skewness and kurtosis with real data
115(1)
9.11 Sub-samples and distributions
116(3)
9.12 Sidebar: objects in R
119(3)
9.13 Severe deviations from normality
122(1)
9.14 Summary
123(1)
9.15 Functions used in this chapter
123(2)
10 Outliers
125(8)
10.1 Reload data--Larson et al. (2015)
125(1)
10.2 Outliers--Boxplot
125(2)
10.3 Outlier--numeric methods
127(2)
10.4 Replacing outliers
129(3)
10.5 Functions used in this chapter
132(1)
11 Descriptive Statistics
133(8)
11.1 Summarise by group
133(1)
11.2 Multiple grouping variables
134(1)
11.3 Contingency tables
135(1)
11.4 Chi-Squared test
136(1)
11.5 t-test--using indexing
136(2)
11.6 t-test using formula
138(1)
11.7 Boxplots using formula
139(1)
11.8 Boxplot with two IVs
139(1)
11.9 Functions used in this chapter
140(1)
12 Graphs with ggplot2
141(16)
12.1 Introduction to graphing
141(1)
12.2 Structure of a ggplot() call
142(1)
12.3 Barplot
143(1)
12.4 Axis labels
143(1)
12.5 Colour and fill
144(1)
12.6 Themes
145(2)
12.7 Combining multiple layers
147(1)
12.8 Scatterplot
148(1)
12.9 Saving data objects, then plotting
149(1)
12.10 Facetting
150(3)
12.11 Boxplot
153(1)
12.12 Jitter plot
154(1)
12.13 Density plot
155(1)
12.14 Functions used in this chapter
156(1)
13 Correlation--Bivariate
157(14)
13.1 Background
157(1)
13.2 Scatterplot--base R
157(2)
13.3 Scatterplot--ggplot2
159(2)
13.4 Correlation coefficient
161(1)
13.5 APA correlation
161(1)
13.6 Coefficient of determination
162(1)
13.7 Correlation Matrix--association between multiple variables
162(1)
13.8 Plotting multiple correlations--corrplot
163(1)
13.9 Plotting multiple correlations--GGally
164(1)
13.10 Statistical significance in a correlation matrix
165(2)
13.11 Assumptions of correlation
167(3)
13.12 Functions used in this chapter
170(1)
14 Correlation--Partial
171(9)
14.1 Spurious correlation
171(1)
14.2 Mediation
172(5)
14.3 Partial correlation with correlation()
177(2)
14.4 Functions used in this chapter
179(1)
15 One-Way ANOVA--Model Data
180(12)
15.1 ANOVA overview
180(1)
15.2 ANOVA--organising the data
180(3)
15.3 ANOVA formula--Base R-aov()
183(1)
15.4 Effect size--eta-squared
184(1)
15.5 APA output
185(1)
15.6 Plotting the data
185(1)
15.7 Post hoc tests--Tukey HSD
186(1)
15.8 Planned comparisons
187(2)
15.9 Checking the assumptions
189(1)
15.10 car::Anova()
190(1)
15.11 Functions used in this chapter
191(1)
16 One-Way ANOVA--Real Data
192(5)
16.1 Loading data
192(1)
16.2 Visualising group differences
192(1)
16.3 aov()
193(2)
16.4 Post hoc tests: TukeyHSD()
195(1)
16.5 Functions used in this chapter
195(2)
17 Factorial ANOVA
197(12)
17.1 Introduction
197(1)
17.2 Dataset--Reillyetal. 2016
197(2)
17.3 Distribution of participants across factor categories
199(3)
17.4 Unbalanced factorial ANOVA
202(2)
17.5 Anova()
204(1)
17.6 Planned comparisons with emmeans()
205(2)
17.7 Functions used in this chapter
207(2)
18 ANCOVA
209(10)
18.1 Introduction
209(1)
18.2 Assumptions
210(1)
18.3 Covariates
211(1)
18.4 Preliminary ANOVA
211(1)
18.5 Checking assumptions in van der Velde et al. (2015)
212(3)
18.6 Setting contrast types
215(1)
18.7 ANCOVA calculation
215(1)
18.8 Adjusted means
216(1)
18.9 Post hoc analysis
216(2)
18.10 Functions used in this chapter
218(1)
19 Repeated Measures ANOVA
219(14)
19.1 Repeated measures study--Edwards et al. (2015)
219(1)
19.2 Dataset
220(1)
19.3 Renaming variables
221(1)
19.4 Reorganising data into long format
222(1)
19.5 Joining the datasets
223(1)
19.6 Split the key
224(2)
19.7 Sphericity and correction
226(1)
19.8 Repeated-measures ANOVA--accuracy
226(1)
19.9 Interpreting the results--accuracy
227(2)
19.10 Repeated-measures ANOVA-reaction time
229(1)
19.11 Interpreting the results--reaction time
230(1)
19.12 Applying the Greenhouse-Geisser correction
231(1)
19.13 Functions used in this chapter
232(1)
20 Regression
233(15)
20.1 Introduction to regression
233(1)
20.2 Simple regression with toy data
233(4)
20.3 Summary on lm()
237(1)
20.4 Residuals
238(1)
20.5 Beta coefficients
239(1)
20.6 Standardizing variables
240(1)
20.7 lm.beta()
241(1)
20.8 Load data for regression
241(1)
20.9 Simple regression from Palseretal. (2018)
242(1)
20.10 Checking regression assumptions
243(3)
20.11 Summary
246(1)
20.12 Functions used in this chapter
246(2)
21 Non-parametric Tests
248(12)
21.1 Using non-parametric tests
248(1)
21.2 Gysin-Maillart et al. (2016) non-parametric data
248(2)
21.3 Correlation
250(2)
21.4 T-test and Wilcoxon rank-sum test
252(2)
21.5 Effect size for Wilcoxon rank-sum test
254(1)
21.6 Kruskal-Wallis rank test
255(3)
21.7 Effect size for Kruskal-Wallis rank test
258(1)
21.8 Post-hoc tests for Kruskal-Wallis
258(1)
21.9 Functions used in this chapter
259(1)
22 Categorical Data Analysis
260(4)
22.1 Categorical data
260(1)
22.2 Chi-squared test--with variables
260(1)
22.3 Chi-squared test--with count values
261(1)
22.4 Visualising categorical data
262(1)
22.5 Functions used in this chapter
263(1)
23 What Else can R Do?
264(4)
23.1 Overview
264(1)
23.2 Bootstrapping and resampling
264(1)
23.3 Bayesian statistics
265(1)
23.4 DAGS
265(1)
23.5 Meta-analysis and systematic review
265(1)
23.6 Missing data and imputation
266(1)
23.7 Robust statistics
266(1)
23.8 Simulation
266(1)
23.9 Text analysis
266(1)
23.10 Twitter
267(1)
24 Functions
268(21)
24.1 annotate()
268(1)
24.2 args()
269(1)
24.3 cbind()
270(1)
24.4 drop_na()
271(1)
24.5 getwd()
272(1)
24.6 gl()
272(1)
24.7 %in%
273(1)
24.8 Indexing
273(2)
24.9 is.na()
275(1)
24.10 kable()
275(1)
24.11 list()
276(1)
24.12 na.rm = TRUE
277(1)
24.13 names()
278(1)
24.14 NROW() and nrow()
279(1)
24.15 pasteO
279(1)
24.16 paste0()
280(1)
24.17 prop.table()
280(1)
24.18 rep()
280(1)
24.19 replace()
281(1)
24.20 replicate()
282(1)
24.21 round()
282(1)
24.22 scale()
283(1)
24.23 str_sub()
283(2)
24.24 summanze()
285(1)
24.25 summary
285(1)
24.26 table()
286(2)
24.27 text()
288(1)
24.28 which()
288(1)
24.29 with()
288(1)
References 289(8)
Index 297
Christian Ryan is Senior Lecturer in clinical psychology in the School of Applied Psychology, University College Cork (UCC). He maintains his clinical practice as a chartered clinical psychologist, working primarily with children and adults with autism spectrum disorder (ASD). He has published a range of peer-reviewed articles in the area of disabilities, adult mental health and psychometrics. The current focus of his research is on the interaction between alexithymia and autism, with a view to improving emotion recognition and regulation interventions.

Christian is the placement coordinator for the doctoral programme in clinical psychology at UCC, and is involved in all aspects of the course, including selection, teaching, research supervision, curriculum development and placement evaluation. He is also the Academic Director of the ASD Studies courses at UCC. He joined the university in 2017 after many years working in front-line services, both as a psychologist and psychology manager.

Christian served as an elected Council Member of the Psychological Society of Ireland (2014-2017); he is a full member of the Division of Clinical Psychology and a former member of Heads of Psychology Services Ireland (HPSI).
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