webchem: An R Package to Retrieve Chemical Information from the Web

A wide range of chemical information is freely available online, including identifiers, experimental and predicted chemical properties. However, these data are scattered over various data sources and not easily accessible to researchers. Manual searching and downloading of such data is time-consuming and error-prone. We developed the open-source R package webchem that allows users to automatically query chemical data from currently 14 web sources. These cover a broad spectrum of information. The data are automatically imported into an R object and can directly be used in subsequent analyses. webchem enables easy, structured and reproducible data retrieval and usage from publicly available web sources. In addition, it facilitates data cleaning, identification and reporting of substances. Consequently, it reduces the time researchers need to spend on chemical data compilation

webchem: webchem v0.1.0

webchem v0.1.0 NEW FEATURES added cts_to() and cts_from() to retrieve possible ids that can be queried. cts_(), pp_query(), cir_query(), get_cid(), get_etoxid(), etox_(), pan_query() get_wdid(), aw_query(), get_csid(), cs_prop(), cs_compinfo() and ci_query() can handle multiple inputs. pc_prop() queries properties and pc_synonmy() synonyms from PUG-REST. added extractors for webchem objects: cas(), inchikey() and smiles(). MINOR IMPROVEMENTS rewrite of pubchem functions using PUG-REST chemspider: better use of NA in input (=return NA) more robust matching in get_etoxid BUG FIXES pan_query() did not return numeric values get_cid() failed with multiple results DEPRECATED FUNCTIONS DEFUNCT FUNCTIONS ppdb_query() has been removed due to copyright issues. The new ppdb_parse() parses only a html, but does not interact with the database pan() alanwood() get_cid() cid_compinfo() chemid() physprop(

Data from: Is there an interaction of the effects of salinity and pesticides on the community structure of macroinvertebrates?

Salinization of freshwater ecosystems is a global problem affecting many regions worldwide and can co-occur with pesticides in agricultural regions. Given that both stressors are potent to affect macroinvertebrate communities, their effects could interact. We investigated the effects of salinity and pesticides at 24 sites in an agricultural region of southern Victoria, South-East Australia. We used distance-based redundancy analysis to determine the influence of pesticides, salinity and other environmental variables on the composition of macroinvertebrate communities. Salinity and pesticide toxicity had a statistically significant effect on communities as had the substrate composition and the percentage of pool and riffle sections in the sampled stream reaches. We did not find evidence for interactive effects between salinity and pesticides, i.e. the effect of one of these variables did not depend on the level of the other. Nevertheless, our results show that salinization and exposure to pesticides can be major factors for the structure of macroinvertebrate communities in agricultural regions. Pesticide toxicity acted on a lower taxonomic level compared to salinity, potentially indicating evolutionary adaptation to salinity stress

Preparing GIS data for analysis of stream monitoring data: The R package openSTARS.

Stream monitoring data provides insights into the biological, chemical and physical status of running waters. Additionally, it can be used to identify drivers of chemical or ecological water quality, to inform related management actions, and to forecast future conditions under land use and global change scenarios. Measurements from sites along the same stream may not be statistically independent, and the R package SSN provides a way to describe spatial autocorrelation when modelling relationships between measured variables and potential drivers. However, SSN requires the user to provide the stream network and sampling locations in a certain format. Likewise, other applications require catchment delineation and intersection of different spatial data. We developed the R package openSTARS that provides the functionality to derive stream networks from a digital elevation model, delineate stream catchments and intersect them with land use or other GIS data as potential predictors. Additionally, locations for model predictions can be generated automatically along the stream network. We present an example workflow of all data preparation steps. In a case study using data from water monitoring sites in Southern Germany, the resulting stream network and derived site characteristics matched those constructed using STARS, an ArcGIS custom toolbox. An advantage of openSTARS is that it relies on free and open-source GRASS GIS and R functions, unlike the original STARS toolbox which depends on proprietary ArcGIS. openSTARS also comes without a graphical user interface, to enhance reproducibility and reusability of the workflow, thereby harmonizing and simplifying the data pre-processing prior to statistical modelling. Overall, openSTARS facilitates the use of spatial regression and other applications on stream networks and contributes to reproducible science with applications in hydrology, environmental sciences and ecology

Data from Szöcs, Eduard, Ben J. Kefford, and Ralf B. Schäfer. “Is There an Interaction of the Effects of Salinity and Pesticides on the Community Structure of Macroinvertebrates?” Science of The Total Environment 437 (2012): 121 – 126.

see README.tx

Data from: Effects of anthropogenic salinisation on biological traits and community composition of stream macroinvertebrates.

<p>Szöcs, E., Eckhard Coring, Jürgen Bäthe, Ralf B. Schäfer (2014). Data from: Effects of anthropogenic salinisation on biological traits and community composition of stream macroinvertebrates.  Science of the Total Environment 468–469: 943–949. http://www.sciencedirect.com/science/article/pii/S0048969713009728</p> <p> </p> <p>Data and R-script to reproduce our findings.</p> <p>Note: Only the trait-analysis can be reproduced. Data for trait-frequencies and electric conductivity is provided within the project-folder.<br>Abundance data is propietary and cannot be provided. Nevertheless code for data-cleaning and abundance-analysis is provided here, but commented out.</p> <p> </p> <p> </p> <p>For further details please read the README file.</p

vegandevs/vegan: CRAN 2.4 release

This is a minor release that fixes the following issues orditkplot passes CRAN tests. anova(<cca-object>, by = "axis") ignored partial terms. Function uses now forward testing which is less dangerously biased than the previous marginal tests. summary and inertcomp for RDA, CCA and frieds failed if constraints had zero rank. meandist labels are no longer cropped in plots. Canberra distance in vegdist can now handle negative entries in input