## ----include = FALSE---------------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) ## ----setup-------------------------------------------------------------------- library(specleanr) ## ----Loading datasets from the package---------------------------------------- data("jdsdata") data("efidata") wcd <- terra::rast(system.file('extdata/worldclim.tiff', package = "specleanr")) #match and clean matchd <- match_datasets(datasets = list(jds= jdsdata, efi =efidata), lats = 'lat', lons = 'lon', country = 'JDS4_site_ID', species = c('scientificName', 'speciesname'), date=c('sampling_date','Date')) #matchclean <- check_names(matchd, colsp = 'species', verbose = FALSE, merge = TRUE) db <- sf::read_sf(system.file('extdata/danube.shp.zip', package = "specleanr"), quiet = TRUE) ## ----Extracting environmental data-------------------------------------------- refdata <- pred_extract(data = matchd, raster = wcd, lat = 'decimalLatitude', lon = 'decimalLongitude', bbox = db, colsp = 'species', list = TRUE, verbose = FALSE, minpts = 6, merge = FALSE) ## ----Ecological ranges-------------------------------------------------------- sqcep <- refdata["Squalius cephalus"] optdata <- data.frame(species= c("Squalius cephalus", "Abramis brama"), mintemp = c(6, 1.6),maxtemp = c(8.588, 21), meantemp = c(8.5, 10.4), #ecoparam direction = c('greater', 'greater')) ## ----outlier detection including species ecological ranges-------------------- squalius_outlier <- multidetect(data = sqcep, multiple = TRUE, var = 'bio1', output = 'outlier', exclude = c('x','y'), methods = c('zscore', 'adjbox', 'optimal', 'kmeans', "logboxplot", "hampel"), optpar = list(optdf=optdata, optspcol = 'species', mincol = "mintemp", maxcol = "maxtemp")) ## ----ploting, fig.width = 6, fig.height= 4, fig.align='center'---------------- ggoutliers(squalius_outlier) ## ----clean data extraction and labelling-------------------------------------- squalius_qc_loess <- extract_clean_data(refdata = sqcep, outliers = squalius_outlier, loess = TRUE) #clean dataset nrow(squalius_qc_loess) #reference data nrow(sqcep[[1]]) squalius_qc_labeled <- classify_data(refdata = sqcep, outliers = squalius_outlier) ## ----clean data extraction and labelling_multiple, fig.height=4.1, fig.width=4.5, fig.align='center'---- ggenvironmentalspace(squalius_qc_labeled, type = '1D', ggxangle = 45, scalecolor = 'viridis', xhjust = 1, legend_position = 'blank', ylab = "Number of records", xlab = "Outlier labels")