A two-day introduction to modern evidence-based teaching practices, built and maintained by the Carpentry community.

The goal of this lesson is to provide an introduction to R for learners working with geospatial data. It is intended as a pre-requisite for the R for Raster and Vector Data lesson for learners who have no prior experience using R. This lesson can be taught in approximately 4 hours and covers the following topics: Working with R in the RStudio GUI Project management and file organization Importing data into R Introduction to R’s core data types and data structures Manipulation of data frames (tabular data) in R Introduction to visualization Writing data to a file The the R for Raster and Vector Data lesson provides a more in-depth introduction to visualization (focusing on geospatial data), and working with data structures unique to geospatial data.

Library Carpentry lesson: An introduction to Git. What We Will Try to Do Begin to understand and use Git/GitHub. You will not be an expert by the end of the class. You will probably not even feel very comfortable using Git. This is okay. We want to make a start but, as with any skill, using Git takes practice. Be Excellent to Each Other If you spot someone in the class who is struggling with something and you think you know how to help, please give them a hand. Try not to do the task for them: instead explain the steps they need to take and what these steps will achieve. Be Patient With The Instructor and Yourself This is a big group, with different levels of knowledge, different computer systems. This isn’t your instructor’s full-time job (though if someone wants to pay them to play with computers all day they’d probably accept). They will do their best to make this session useful. This is your session. If you feel we are going too fast, then please put up a pink sticky. We can decide as a group what to cover.

This Library Carpentry lesson introduces librarians and others to working with data. This Library Carpentry lesson introduces people with library- and information-related roles to working with data using regular expressions. The lesson provides background on the regular expression language and how it can be used to match and extract text and to clean data.

Library Carpentry lesson: an introduction to OpenRefine for Librarians This Library Carpentry lesson introduces people working in library- and information-related roles to working with data in OpenRefine. At the conclusion of the lesson you will understand what the OpenRefine software does and how to use the OpenRefine software to work with data files.

Library Carpentry, an introduction to SQL for Librarians This Library Carpentry lesson introduces librarians to relational database management system using SQLite. At the conclusion of the lesson you will: understand what SQLite does; use SQLite to summarise and link data.

Library Carpentry lesson to learn how to use the Shell. This Library Carpentry lesson introduces librarians to the Unix Shell. At the conclusion of the lesson you will: understand the basics of the Unix shell; understand why and how to use the command line; use shell commands to work with directories and files; use shell commands to find and manipulate data.

This lesson in part of Software Carpentry workshop and teach novice programmers to write modular code and best practices for using R for data analysis. an introduction to R for non-programmers using gapminder data The goal of this lesson is to teach novice programmers to write modular code and best practices for using R for data analysis. R is commonly used in many scientific disciplines for statistical analysis and its array of third-party packages. We find that many scientists who come to Software Carpentry workshops use R and want to learn more. The emphasis of these materials is to give attendees a strong foundation in the fundamentals of R, and to teach best practices for scientific computing: breaking down analyses into modular units, task automation, and encapsulation. Note that this workshop will focus on teaching the fundamentals of the programming language R, and will not teach statistical analysis. The lesson contains more material than can be taught in a day. The instructor notes page has some suggested lesson plans suitable for a one or half day workshop. A variety of third party packages are used throughout this workshop. These are not necessarily the best, nor are they comprehensive, but they are packages we find useful, and have been chosen primarily for their usability.