Dr. Craig P. Hutton

Psychologist | Data Scientist

BC Behavioural Insights Group, Province of British Columbia

Business Intelligence and Data Analytics, University of Victoria Continuing Studies

Data Science Studio, Institute on Aging & Lifelong Health, University of Victoria

I am a Senior Behavioural Scientist with the Province of British Columbia’s Behavioural Insights Group. I also teach an introductory data analysis course at the University of Victoria and occasionally work as a data science consultant for the Winstanley Laboratory at the University of British Columbia. My diverse training in experimental psychology, applied statistics, and data science leads me to apply a multidisciplinary and creative approach to doing research. I deeply enjoy solving complex problems and exploring new ideas.

I’ve worked both as an independent contributor and as a leader of teams of up to 10. In my current role with the Province of British Columbia I primarily work in R.

In my free time I enjoy yoga, hiking, skiing, gaming, coding, reading, travel, live music, and visiting with friends and family.

Learn more about my research interests, skills, and experience, by exploring my website or reading my resume.

Recent Posts

Want to support my blog?

Last updated on May 24, 2021 46 min read R, Reproducible Research, data exploration, data visualization

A Scientist's Guide to R: Step 3.1 - data visualization with ggplot2+

1 TL;DR 2 Introduction 3 Setup 4 ggplot2 basics 5 aesthetic customization 5.1 titles/labels 5.2 fill 5.3 colour/color 5.4 reordering factors 5.5 colour palettes 5.6 colourblind-friendly palettes 5.7 size 5.8 shape 5.9 linetype 5.10 alpha (transparency/opacity) 5.11 jittering to reduce overplotting 5.12 coordinates 5.12.1 coord_flip() 5.12.2 coord_cartesian() 5.13 guides (legends) 5.14 x & y axis modifications 5.14.1 scale_x_continuous()/scale_y_continuous() 5.14.2 scale_x_discrete()/scale_y_discrete() 5.

Last updated on May 24, 2021 63 min read R, Reproducible Research, data exploration, data interrogation, data visualisation, descriptive statistics

A Scientist's Guide to R: Step 3.0 - exploring data with elucidate

1 TL;DR 2 Introduction 3 Installation & Setup 4 Interrogating Data 4.1 checking for row copies() and dupes() 4.2 count()-ing unique values 4.3 describe()-ing missingness & extreme values 5 Descriptives 5.1 describe() a vector with summary statistics 5.2 grouped descriptions 5.3 describe_all() columns in a data frame 5.4 confidence intervals 6 To see, look 6.1 Anscombe’s lesson: numeric descriptions can be misleading 6.2 plot_*-ting data with elucidate 6.

Last updated on May 19, 2021 25 min read R, Reproducible Research, Data Cleaning and Transformation

A Scientist's Guide to R: Step 2.5 - dates & times

1 TL;DR 2 Introduction 2.1 load packages 3 date/time basics 4 which day is it? 5 reading dates 6 time zones 7 month names 8 extracting datetime components 9 days in a month 10 custom date formats 11 date calculations 11.1 correcting excel date-to-numeric conversions 12 planning a behavioural neuroscience experiment 12.1 turning the planning script into a function 13 Navigation 14 Notes 1 TL;DR Dates/times are the last type of data you’ll probably work with on a fairly regular basis.

Last updated on May 19, 2021 13 min read R, Reproducible Research, Data Cleaning and Transformation

A Scientist's Guide to R: Step 2.4 - forcats for factors

1 TL;DR 2 Introduction 2.1 load packages 2.2 import data 3 Factor basics 4 factors and data visualization 5 factors and modelling 6 Navigation 7 Notes 1 TL;DR Factors are one of the two remaining types of data you’ll encounter on a fairly regular basis. This post will show you how to use the forcats tidyverse package in R so you’ll know how to handle factors when you encounter them.

See all posts