R and Data Science · Dr. Steffi LaZerte

R and Data Science

I specialize in managing, preparing and analyzing large and complex data sets used in the biological fields of conservation, ecology, behaviour, and evolution. Data collected from loggers (i.e. telemetry, RFID, weather, etc.) or through extraction from other formats (i.e. sound recordings) can be challenging to manage:

As a biologist specializing in behavioural ecology I also have the training to consider experimental and statistical protocols, biological relevance and other necessary considerations when preparing data for analysis.

The power of R to manage and visualize data, to create reports, and perform reproducible statistics is immense. However, the learning curve is fairly steep to start out. I aim to help reduce this learning curve by explaining R in ways that make sense to non-programmers. I also help explain workflows and general best-practices. There is so much more to R than simply statistics.

I provide services in R instruction, R programming, data management/cleaning and data analysis. If you’re interested in any of these services, please feel free to contact me.

R Packages

R Projects

2015-2017 feedr package to transform and visualize RFID data

Supervisors: Drs. Ken Otter (2015-2016) and David Hill (2016-2017)
Description: Package designed to deal with data collected from RFID feeders (feedr including the design of an online Shiny Web App as a graphical user interface http://animalnexus.ca.

2016-2017 White-throated sparrow song variation

Supervisor: Dr. Ken Otter, University of Northern British Columbia
Description: R lessons on data cleaning, management and exploration. Designed and maintained a website for the White-throated Sparrow Citizen Science Project (http://whitethroatsong.ca) including an R Shiny map of [current data] (http://whitethroatsong.ca/about-project/#map).

2017 Bull trout telemetry

Client: British Columbia Ministry of Forests, Lands and Natural Resource Operations
Description: Exploring migration of bull trout through tagging and detections at telemetry stations. Five years of telemetry data was filtered of background noise and organized into bouts of detection and assessing direction of movements. End product included annotated R scripts designed to return informative HTML reports, a series of clean datasets, and a series of figures for analysis. Tutorials were delivered to discuss, develop and explain R scripts.

2017 SOIL 7240 Module 1: Preparing Data in R

Client: Dr. David Lobb, University of Manitoba, Winnipeg, MB
Description: Remote instructor for 4-week module concerning data management, preparation, and exploration in R.


Volunteer Teaching