ESR Christchurch Science Centre - Kawakawa Room

November 29-30th, 2018

9:00am - 4:30pm

Instructors: Murray Cadzow, Arindam Basu

Helpers: Pierre-Yves Dupont, David Wood

General Information

Data Carpentry develops and teaches workshops on the fundamental data skills needed to conduct research. Its target audience is researchers who have little to no prior computational experience, and its lessons are domain specific, building on learners' existing knowledge to enable them to quickly apply skills learned to their own research. Participants will be encouraged to help one another and to apply what they have learned to their own research problems.

For more information on what we teach and why, please see our paper "Good Enough Practices for Scientific Computing".

Who: The course is aimed at graduate students and other researchers. You don't need to have any previous knowledge of the tools that will be presented at the workshop.

Where: 27 Creyke Rd, Ilam, Christchurch 8041. Get directions with OpenStreetMap or Google Maps.

When: November 29-30th, 2018. Add to your Google Calendar.

Requirements: Participants will be provided with a laptop with the necessary software on it. They are also required to abide by Data Carpentry's Code of Conduct.

Accessibility: We are committed to making this workshop accessible to everybody. The workshop organizers have checked that:

Materials will be provided in advance of the workshop and large-print handouts are available if needed by notifying the organizers in advance. If we can help making learning easier for you (e.g. sign-language interpreters, lactation facilities) please get in touch (using contact details below) and we will attempt to provide them.

Contact: Please email for more information.


Please be sure to complete these surveys before and after the workshop.

Pre-workshop Survey

Post-workshop Survey


We will use this collaborative document for chatting, taking notes, and sharing URLs and bits of code.


Data Organisation in Spreadsheets

  • Formatting data tables in Spreadsheets
  • Formatting problems
  • Dates as data
  • Quality control
  • Exporting data
  • Reference...

Data Analysis and Visualization in R

  • Introduction to R
  • Starting with data
  • Aggregating and analyzing data with dplyr
  • Data visualization with ggplot2
  • R and Databases

Managing Data with SQL

  • Reading and sorting data
  • Filtering with where
  • Calculating new values on the fly
  • Handling missing values
  • Combining values using aggregation
  • Combining information from multiple tables using join
  • Creating, modifying, and deleting data
  • Programming with databases
  • Reference...


Participants will be provided with a laptop with the necessary software on it