This workshop will provide a gentle introduction to open source GIS tools in R and QGIS. We will cover introductory GIS concepts and will explore the functionalities of R and QGIS for manipulating and analyzing vector GIS data. Familiarity with R is required.

Matt Dowle, author of the *data.table* package, describes it as, “provid[ing] a high-performance version of base R’s data.frame with syntax and feature enhancements for ease of use, convenience and programming speed.” In this workshop I will first introduce the *data.table *syntax using generic SQL and the *dplyr *R package as reference points. Topics to be discussed include subsetting, aggregating, and merging data frames. I will then discuss updating by reference and its role in efficiently working with large data sets. Other advanced uses of the powerful *data.table* syntax will be covered as time permits.

*If you have questions about this workshop, please send an email to jbhender@umich.edu*

This is a **two day workshop (March 4 and 5)** in R which is a free and open source environment for data analysis and statistical computing. While R contains many built-in statistical procedures, a powerful feature of R is the facility for users to extend these procedures to suit their own needs. Excellent graphing capability is another reason R is gaining wide popularity.

- How to Obtain R
- Help Tools
- Importing / Exporting Data
- Data Management
- Descriptive and Exploratory Statistics
- Common Statistical Analyses (t-test, Regression Modeling, ANOVA, etc.)
- Graphics
- Creating Functions

This is a **two day workshop (February 4 and 5)** in R which is a free and open source environment for data analysis and statistical computing. While R contains many built-in statistical procedures, a powerful feature of R is the facility for users to extend these procedures to suit their own needs. Excellent graphing capability is another reason R is gaining wide popularity.

- How to Obtain R
- Help Tools
- Importing / Exporting Data
- Data Management
- Descriptive and Exploratory Statistics
- Common Statistical Analyses (t-test, Regression Modeling, ANOVA, etc.)
- Graphics
- Creating Functions

Regular expressions are perfectly suited for people who like puzzles. Regular expressions are a sequence of characters used to define a search pattern. They are commonly used to do “find” and “find and replace” string operations. They are also used to validate strings like phone numbers, passwords, etc. in data entry. Regular expression capabilities can be found in a variety of programming languages and software like ArcGIS, Java, Javascript, Matlab, Perl, PHP, Python, R, Visual Basic, etc. and some text editors. This workshop is part II of a two-part series and will cover more advanced topics like captured groups, backreferences and assertions. The workshop will consist of hands-on example problems. Basic understanding of regular expressions is required. You should be able to understand expressions like “w{3,}-d{1,2}-d{4}“ and “des*ert?s?”. The tutorials will be conducted using Python. A basic programming background is helpful but not required for this workshop.