This course will cover the same topics as the above By Example course, using commands instead of menus. One of the major advantages of R being open source is that experts in many fields not only use R, but also provide their field-specific coding as freely available packages. In fact, the vast majority of R consists of these packages rather than the core or base. As of early August, 2010, there are 2433 packages available on CRAN, with well over 100 on topics related to natural resources, ranging from handling climate data, to non-detects in water quality and toxicology, to mark-recapture, distance sampling, and double sampling approaches to population estimation, to GRTS spatially-balanced sampling (and analysis of data from GRTS samples), to analysis of bird or frog calls, to ordination and powerful extensions of analysis of dissimilarity, and to several approaches to habitat analysis. These packages allow R users to leverage both the topical expertise and the programming of the experts in those fields, but usually require writing simple R code to call the functions. By writing R code and modifying examples, Tom`s version of the Rcourse will provide an entry to using these packages. The course will cover reading data from files, databases, and remote services, common data manipulations such as reshaping monitoring (revisit) data between long and wide formats, simple and customized graphics, and statistical tests relevant to natural resource science and management.
The same Everitt and Hothorn (2010) handbook as in Paul`s course will be used. Tom expects that folks who opt for the non-menu version will have a bit more background and familiarity with some form of computer coding, which would allow examples with participant data as well as the clean textbook examples. This may be overoptimistic. The NPS coding version has a limit of 100 participants, but we anticipate that most participants will opt for Paul`s GUI-based version. If that limit is reached, priority will be given to NPS participants, other DOI participants, Federal, State, Local, Tribal agency participants (including Canadian and Mexican), non-profit conservation organizations, students and academics, and finally for-profit consulting and other businesses. Students and academics receive low priority only because most universities provide courses and workshops on using R, or statistics and data analysis courses that use R for their exercises.