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ARS Home » Plains Area » Las Cruces, New Mexico » Range Management Research » Research » Publications at this Location » Publication #350479

Research Project: Management Technologies for Conservation of Western Rangelands

Location: Range Management Research

Title: DIMA.Tools: An R package for working with the database for inventory, monitoring, and assessment

Author
item Stauffer, Nelson
item Mccord, Sarah

Submitted to: Society for Range Management Meeting Proceedings
Publication Type: Abstract Only
Publication Acceptance Date: 10/23/2017
Publication Date: 1/28/2018
Citation: Stauffer, N.G., Mccord, S.E. 2018. DIMA.Tools: An R package for working with the database for inventory, monitoring, and assessment [abstract]. 2018 Conference of The Society for Range Management. January 28-February 2, 2018. Sparks, Nevada.

Interpretive Summary:

Technical Abstract: The Database for Inventory, Monitoring, and Assessment (DIMA) is a Microsoft Access database used to collect, store and summarize monitoring data. This database is used by both local and national monitoring efforts within the National Park Service, the Forest Service, the Bureau of Land Management, Agricultural Research Service, non-profit organizations, and land management agencies globally, including Mongolia. The Access format permits long-term storage of large datasets and enables electronic data capture in the field, sophisticated error checking procedures, and a graphical user interface, however analysis of large amounts of data in one or more DIMAs is difficult. Therefore, further analysis and the production of quality monitoring information often requires interacting with DIMA via other software interfaces such as R.Here we present an package for R, dima.tools, containing functions which simplify direct user access to raw data tables within DIMAs and combining data from multiple DIMAs. Additionally, common data manipulation functions are available in the package for low-level tasks like tidying data and joining data to metadata as well as higher-level functions for tasks like producing standard indicators and quality assurance and quality control checks. Together, these provide a reproducible framework for users to compute their own custom indicators and to complete complex analyses using other functions and packages in R.