Upcoming Courses
Whitehorse, Yukon, Canada
My Whitehorse courses are typically hosted at (co)Space. These are small (max 8 students), highly customized courses. Students are encouraged to bring their own data for the class exercises. To register for Whitehorse courses, use the payment button at the bottom of this page or contact me directly.
Introduction to R and R Studio
A 3-day introduction to working with your data in R. The course is suitable for individuals who have never coded in any language or those wishing to pick up a new programming language. You will learn to read, inspect, manipulate, analyze, visualize, and save your data products. Emphasis is placed on preparing you to become self-sufficient using the learning resources available to help you understand R scripts and continue to build your skills. No prior programming or statistics experience necessary. (Jul 23-25, 2019)
Spatial Data in R
2-days of guided practice working with your spatial data in R. The course will cover the basics of handling both vector (sp and sf packages) and raster data resources. We will also quickly highlight Leaflet as one option for publishing interactive map products. This course assumes basic knowledge of R. (July 30-31, 2019)
Interactive Data Visualization on R Shiny
A 4-day intermediate/advanced course for those who wish to turn their data analytics into interactive web apps with R Shiny. We will cover both Shiny widgets and a select set of htmlwidgets based on student interests. Working with their own data and some class examples, students will learn to design customized user interfaces and to code the associated reactive elements. (Aug 6-9, 2019)
Barcelona, Spain
R Shiny Web App Development
Hosted by: Transmitting Science
Registration and payment via course website
This 5-day course is for individuals considering developing Shiny apps to deliver their research. Thus, the goal will be to teach the skills necessary to translate static products (your current analysis in R) to dynamic products delivered via a simple web-based graphic-user interface. After a brief survey of the available basic tools (widgets such as slider bars, check boxes, and pick lists), we will move quickly to learn more advanced interactive features. Activities interspersed throughout the class will provide hands-on practice with sample biological and ecological data. By the end of the course, students will have built a portfolio of example code and will have designed, constructed, and published at least one example Shiny app. (Feb 18-22, 2019)
Data Manipulation, Analysis, and Visualization in R using the Tidyverse
Hosted by: Transmitting Science
Registration and payment via course website
This 5-day course exposes students to the breadth of resources available in the R tidyverse to build their fluency and confidence when working in R. The tidyverse is a collection of libraries and functions in R sharing an underlying design (philosophy, grammar, and data structures) that aims to help users create efficient, “tidy” code. Many of the libraries address the common data wrangling tasks (read, select, filter, gather, spread, modify, recode, summarize) that are the precursors to both data analysis and visualization. Other libraries address specific tasks (e.g., ggplot2 to create graphs; purrr to create functions; broom to tabulate model results) or specific data types (e.g., sf for spatial data, tibbletime for temporal data). A mix of lectures and exercises will walk students through a series of increasingly complex data manipulation and visualization tasks. These exercises will emphasize the underlying principles and grammar of the tidyverse to help students transfer what they learn in the examples to their own research coding projects. (Nov 5-9, 2019).
Introduction to R
Hosted by: Transmitting Science, Barcelona, Spain
Registration and payment via course website
This 5-day course is for ecologists, biologists, and other natural scientists who have never used R and, possibly, have never done any programming at all. By the end of the course, the participants should be able to import / export data-bases, manage data sets, carry out basic statistic analyses, draw high quality graphs, and program functions. Students are encouraged to bring a dataset with them along with a “previously completed” statistical analysis or graphic. Ideally something fairly introductory and simple from the student’s own field of practice that you’ve worked with in Excel, SAS, or elsewhere. Students will work with these data on the last day to ensure they can load, check, tidy data and then perform the basic statistics or generate the graphs common in their respective disciplines. This time also usually provides opportunity to troubleshoot and learn to navigate web resources to find solutions to errors. Extra datasets will be available for students that prefer not to bring their own work or who want extra practice at specific skills. (TBD 2020)
Additional Courses in Prep for 2020
Project Management Strategies for Research Scientists Using R (in development)
Hosted by: Transmitting Science, Location TBD
Registration and payment information coming soon
Do you find yourself managing increasing complex and/or long-term R coding projects? This course will teach programming project management skills relevant to research scientists who have growing portfolios of increasingly diverse projects in R, become involved in collaborative projects where multiple people contribute to R code, or anticipate supervising projects and assisting others with their R code. In these contexts, establishing data standards, good work flows, and efficient coding practices can make life much easier. Research scientists’ training, needs, and applications differ from most software developers. This course addresses the specific use cases most common in academia: (1) sharing code among collaborators for comment and development, (2) building a library of custom functions for your lab, (3) including code as a digital supplement to publications, and (4) incorporating R assignments into instructional materials. To learn and practice these skills, students will be paired and assigned to collaboratively complete simple exercises, experiencing the roles of both project lead and project contributor. (TBD).
My Whitehorse courses are typically hosted at (co)Space. These are small (max 8 students), highly customized courses. Students are encouraged to bring their own data for the class exercises. To register for Whitehorse courses, use the payment button at the bottom of this page or contact me directly.
Introduction to R and R Studio
A 3-day introduction to working with your data in R. The course is suitable for individuals who have never coded in any language or those wishing to pick up a new programming language. You will learn to read, inspect, manipulate, analyze, visualize, and save your data products. Emphasis is placed on preparing you to become self-sufficient using the learning resources available to help you understand R scripts and continue to build your skills. No prior programming or statistics experience necessary. (Jul 23-25, 2019)
Spatial Data in R
2-days of guided practice working with your spatial data in R. The course will cover the basics of handling both vector (sp and sf packages) and raster data resources. We will also quickly highlight Leaflet as one option for publishing interactive map products. This course assumes basic knowledge of R. (July 30-31, 2019)
Interactive Data Visualization on R Shiny
A 4-day intermediate/advanced course for those who wish to turn their data analytics into interactive web apps with R Shiny. We will cover both Shiny widgets and a select set of htmlwidgets based on student interests. Working with their own data and some class examples, students will learn to design customized user interfaces and to code the associated reactive elements. (Aug 6-9, 2019)
Barcelona, Spain
R Shiny Web App Development
Hosted by: Transmitting Science
Registration and payment via course website
This 5-day course is for individuals considering developing Shiny apps to deliver their research. Thus, the goal will be to teach the skills necessary to translate static products (your current analysis in R) to dynamic products delivered via a simple web-based graphic-user interface. After a brief survey of the available basic tools (widgets such as slider bars, check boxes, and pick lists), we will move quickly to learn more advanced interactive features. Activities interspersed throughout the class will provide hands-on practice with sample biological and ecological data. By the end of the course, students will have built a portfolio of example code and will have designed, constructed, and published at least one example Shiny app. (Feb 18-22, 2019)
Data Manipulation, Analysis, and Visualization in R using the Tidyverse
Hosted by: Transmitting Science
Registration and payment via course website
This 5-day course exposes students to the breadth of resources available in the R tidyverse to build their fluency and confidence when working in R. The tidyverse is a collection of libraries and functions in R sharing an underlying design (philosophy, grammar, and data structures) that aims to help users create efficient, “tidy” code. Many of the libraries address the common data wrangling tasks (read, select, filter, gather, spread, modify, recode, summarize) that are the precursors to both data analysis and visualization. Other libraries address specific tasks (e.g., ggplot2 to create graphs; purrr to create functions; broom to tabulate model results) or specific data types (e.g., sf for spatial data, tibbletime for temporal data). A mix of lectures and exercises will walk students through a series of increasingly complex data manipulation and visualization tasks. These exercises will emphasize the underlying principles and grammar of the tidyverse to help students transfer what they learn in the examples to their own research coding projects. (Nov 5-9, 2019).
Introduction to R
Hosted by: Transmitting Science, Barcelona, Spain
Registration and payment via course website
This 5-day course is for ecologists, biologists, and other natural scientists who have never used R and, possibly, have never done any programming at all. By the end of the course, the participants should be able to import / export data-bases, manage data sets, carry out basic statistic analyses, draw high quality graphs, and program functions. Students are encouraged to bring a dataset with them along with a “previously completed” statistical analysis or graphic. Ideally something fairly introductory and simple from the student’s own field of practice that you’ve worked with in Excel, SAS, or elsewhere. Students will work with these data on the last day to ensure they can load, check, tidy data and then perform the basic statistics or generate the graphs common in their respective disciplines. This time also usually provides opportunity to troubleshoot and learn to navigate web resources to find solutions to errors. Extra datasets will be available for students that prefer not to bring their own work or who want extra practice at specific skills. (TBD 2020)
Additional Courses in Prep for 2020
Project Management Strategies for Research Scientists Using R (in development)
Hosted by: Transmitting Science, Location TBD
Registration and payment information coming soon
Do you find yourself managing increasing complex and/or long-term R coding projects? This course will teach programming project management skills relevant to research scientists who have growing portfolios of increasingly diverse projects in R, become involved in collaborative projects where multiple people contribute to R code, or anticipate supervising projects and assisting others with their R code. In these contexts, establishing data standards, good work flows, and efficient coding practices can make life much easier. Research scientists’ training, needs, and applications differ from most software developers. This course addresses the specific use cases most common in academia: (1) sharing code among collaborators for comment and development, (2) building a library of custom functions for your lab, (3) including code as a digital supplement to publications, and (4) incorporating R assignments into instructional materials. To learn and practice these skills, students will be paired and assigned to collaboratively complete simple exercises, experiencing the roles of both project lead and project contributor. (TBD).