## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ---- eval=FALSE--------------------------------------------------------------
# ?organiseBirds()
# OB <- organizeBirds(bombusObs, simplifySppName = TRUE)
# OB <- organizeBirds(bombusObs, sppCol = "species", simplifySppName = FALSE,
# taxonRankCol = "taxonRank", taxonRank = c("SPECIES", "SUBSPECIES","VARIETY"))
## ---- eval=FALSE--------------------------------------------------------------
# visitStats <- exploreVisits(OB)
# # open an interactive data explorer
# esquisse::esquisser(visitStats)
#
# # alternativelly, plot the variable you want, e.g.:
# # to see the distribution of distances covered on each visit
# # hist(visitStat$effortDiam)
## ---- eval=FALSE--------------------------------------------------------------
# visits(OB)<-createVisits(x, columns = c("locality", "day", "month", "year", "recordedBy"))
## ---- eval=FALSE--------------------------------------------------------------
# grid <- makeGrid(searchPolygon, gridSize = 10) # grid size in kilometers!
# SB <- summariseBirds(OB, grid=grid)
## ---- eval=FALSE--------------------------------------------------------------
# exportBirds(SB, dimension = "temporal", timeRes = "yearly", variable = "nObs", method = "sum")
# # this is equivalent to
#
# colSums(SB$spatioTemporal[,,"Yearly","nObs"], na.rm = TRUE)
#
#
# exportBirds(SB, dimension = "temporal", timeRes = "month", variable = "nVis", method = "sum")
# # that is wquivalent to
# apply(SB$spatioTemporal[,,1:12,"nVis"], 3, sum, na.rm = TRUE)
#
# exportBirds(SB, dimension = "temporal", timeRes = "monthly", variable = "nVis", method = "sum")
# # that is somehow equivalent to the xts method except the later excludes months without data
# xts::apply.monthly(SB$temporal[,"nVis"], sum)
#
# exportBirds(SB, dimension = "spatial", timeRes = "NULL", variable = "nYears", method = "sum")@data
## ---- eval=FALSE--------------------------------------------------------------
# focalSpSummary(SB, "Bombus humilis")
## ---- eval=FALSE--------------------------------------------------------------
# focalSpReport(SB, "Bombus humilis")
## ---- eval=FALSE--------------------------------------------------------------
# par(mfrow=c(1,2), mar=c(1,1,1,1))
# palBW <- leaflet::colorNumeric(c("white", "navyblue"),
# c(0, max(SB$spatial@data$nVis, na.rm = TRUE)),
# na.color = "transparent")
# seqNVis<-round(seq(0, max(SB$spatial@data$nVis, na.rm = TRUE), length.out = 5))
# plot(SB$spatial, col=palBW(SB$spatial@data$nVis), border = NA)
# plot(gotaland, col=NA, border = "grey", lwd=1, add=TRUE)
# legend("bottomleft", legend=seqNVis, col = palBW(seqNVis),
# title = "Number of \nobservations", pch = 15, bty="n")
#
# ign<-exposeIgnorance(SB$spatial@data$nVis, h = 5)
# palBWR <- leaflet::colorNumeric(c("navyblue", "white","red"), c(0, 1),
# na.color = "transparent")
# plot(gotaland, col="grey90", border = "grey90", lwd=1)
# plot(SB$spatial, col=palBWR(ign), border = NA, add=TRUE)
# plot(gotaland, col=NA, border = "grey", lwd=1, add=TRUE)
# legend("bottomleft", legend=c(seq(0, 1, length.out = 5), "NA"),
# col = c(palBWR(seq(0, 1, length.out = 5)), "grey90"),
# title = "Ignorance \nnVis, \nO0.5=5", pch = 15, bty="n")
## ---- eval=FALSE--------------------------------------------------------------
# ## Community analysis -->
# CM <- communityMatrix(SB, sampleUnit="visit")
# sp1 <- vegan::specaccum(CM, method = "exact")
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