R/meanPropinquity.R
In alarconvv/geocleaMT: A Package to Clean/Test Georeferenced Data
Defines functions
meanPropinquity
Documented in
meanPropinquity
#'@name meanPropinquity
#'
#'@title Mean Propinquity
#'
#'@description Calculate the mean propinquity[1] and some descriptors of the distribution.
#'
#'@param coord.table data frame class. Table with coordinates (decimalLongitude,
#' decimalLatitude). If the process will be calculated by species, the table must
#' include the species column.
#'
#'@param calculatedBy Vector of characters. If the argument is \code{'species'},
#'the mean propinquity will be calculated for each species. If it is \code{'whole'}
#'the propinquity will be calculated as a whole.
#'
#'
#'@param plot.dist Logical vector. If \code{'TRUE'} a density plot for the distance
#'between occurrences will be plotted and saved, by species or as a whole. If
#' \code{'FALSE'} there will be no plot.
#'
#'@param plot.onMap Logical vector. If \code{'TRUE'} a map of occurences will be
#'plotted and saved, by species or as a whole. If \code{'FALSE'} there will be no plot.
#'
#'@param wrt.frmt Vector of characters. Format to save the output file.
#' By default it will be written as a R object using
#' \code{'saveRDS'} argument, but it can be saved as plain text using
#' \code{'saveTXT'} argument. See details.
#'
#'@param save.info.in Vector of characters. Path to the output
#'file with information about Mean propinquity, median, mode, minimal spanning
#'tree; by species or as a whole.
#'
#'@param save.plot.in Vector of characters. Path to the output
#'file for each plot by species or as a whole.
#'
#'
#'@details If the process uses the parameters plot.dist and plot.onMap as \code{'TRUE'}, the process will be slow.
#'The headers of the input file must follow the Darwin Core standard [2].
#'The user can see the guide using data('ID_DarwinCore) command.
#'For more details about the formats to write, see
#'\code{\link{readAndWrite}} function.
#'
#'@return The output file will be saved with all species concatenated.
#'
#'@author R-Alarcon Viviana and Miranda-Esquivel Daniel R.
#'
#'@note See:
#'R-Alarcon V. and Miranda-Esquivel DR. (submitted) geocleaMT: An R package to
#'cleaning geographical data from electronic biodatabases.
#'
#'@seealso \code{\link{readAndWrite}}
#'
#'@references
#'[1] Rapoport. E.H. 1975 Areografia: Estrategias Geograficas de las Especies. Mexico, Fondo de Cultura Economica
#'
#'[2] Wieczorek, J. et al. 2012. Darwin core: An evolving community-developed biodiversity data standard.
#' PloS One 7: e29715.
meanPropinquity <- function(coord.table = NULL,
calculatedBy = 'species',
wrt.frmt = 'saveTXT',
save.info.in = NULL,
plot.dist = FALSE,
plot.onMap = FALSE,
save.plot.in = NULL){
if (any(plot.dist, plot.onMap) == TRUE ) {
if (is.null(save.plot.in)) {
message('If any plot argument is TRUE, please : Assign the \'save.plot.in\' argument ')
}
}
coord.table$decimalLongitude <- as.numeric(as.character(coord.table$decimalLongitude))
coord.table$decimalLatitude <- as.numeric(as.character(coord.table$decimalLatitude))
if (!calculatedBy == 'species') {
uniqueAll <- coord.table[!duplicated(
coord.table[, c('decimalLongitude', 'decimalLatitude')]), ]
coordinates(uniqueAll) <- uniqueAll[, c('decimalLongitude', 'decimalLatitude')]
distanPoints <- dist(coordinates(uniqueAll))
mst <- spantree(d = distanPoints)
meanDist <- mean(mst$dist,na.rm = T)
medianDist <- median(mst$dist,na.rm = T)
sdDist <- sd(mst$dist)
minDist <- mst$dist[which.min(mst$dist)]
maxDist <- mst$dist[which.max(mst$dist)]
skewnessDist <- skewness(x = mst$dist, method = 'bickel', na.rm = T)
modeDist <- mlv(mst$dist,method = 'mfv', na.rm = T)
modeSkewness <- modeDist$skewness
infoTable <- as.data.frame(matrix(data = NA,nrow = 1,ncol = 9))
colnames(infoTable) <- c('Occ','MeanPropinquity','Median','SD','MinDist',
'MaxDist','Skewness','Mode','ModeSkewness')
infoTable$Occ <- nrow(coord.table)
infoTable$MeanPropinquity <- meanDist
infoTable$Median <- medianDist
infoTable$SD <- sdDist
infoTable$MinDist <- minDist
infoTable$MaxDist <- maxDist
infoTable$Skewness <- skewnessDist[1]
infoTable$Mode <- modeDist$M
infoTable$ModeSkewness <- modeSkewness
result <- list('meanPropinquity' = meanDist,
'medianDist' = medianDist,
'sdDist' = sdDist,
'minDist' = minDist,
'maxDist' = maxDist,
'skewnessDist' = skewnessDist[1],
'mode' = modeDist$M,
'modeSkewness' = modeSkewness,
'minimalSpaningTree' = mst)
readAndWrite(action = 'write', frmt = wrt.frmt, path = save.info.in,
name = 'InfoPropinquity_Whole', object = infoTable)
if (plot.dist == TRUE) {
jpeg(paste(save.plot.in, 'plotDistWhole.jpeg', sep = ''), res = 300,
width = 25 ,height = 15 ,units = 'cm')
plotDist <- density(mst$dist)
plot(plotDist, main = 'Distance between Occurrences',
xlab = 'Distance (decimal degrees)', type = 'p',cex = 0.5)
polygon(plotDist, border = 'grey30')
abline(v = meanDist, col = 'red')
abline(v = medianDist, col = 'yellow4')
abline(v = modeDist$M, col = 'blue1')
dev.off()
}
if (plot.onMap == TRUE) {
coordinates(coord.table) <- coord.table[, c('decimalLongitude',
'decimalLatitude')]
data('wrld_simpl')
jpeg(paste(save.plot.in, 'plotOnMapWhole.jpeg', sep = ''), res = 300,
width = 25 ,height = 15 ,units = 'cm')
plot(wrld_simpl,main = 'Distribution Map')
plot(coord.table,add = T, pch = '*', col = 'red' )
dev.off()
}
return(infoTable)
} else {
#by species
spNames <- as.character(unique(coord.table$species))
infoTable <- as.data.frame(matrix(data = NA,nrow = length(spNames),ncol = 10))
colnames(infoTable) <- c('Sp','Occ','MeanPropinquity','Median','SD','MinDist',
'MaxDist','Skewness','Mode','ModeSkewness')
for (i in 1:length(spNames)) {
spOccurrences <- coord.table[which(coord.table$species == spNames[i]),]
uniqueAll <- spOccurrences[!duplicated(spOccurrences[, c('decimalLongitude', 'decimalLatitude')]), ]
coordinates(uniqueAll) <- uniqueAll[, c('decimalLongitude', 'decimalLatitude')]
distanPoints <- dist(coordinates(uniqueAll))
mst <- spantree(d = distanPoints)
meanDist <- mean(mst$dist,na.rm = T)
medianDist <- median(mst$dist,na.rm = T)
sdDist <- sd(mst$dist)
minDist <- mst$dist[which.min(mst$dist)]
maxDist <- mst$dist[which.max(mst$dist)]
skewnessDist <- skewness(x = mst$dist, method = 'bickel', na.rm = T)
modeDist <- mlv(mst$dist,method = 'mfv', na.rm = T)
modeSkewness <- modeDist$skewness
infoTable$Sp[i] <- as.character(spNames[i])
infoTable$Occ[i] <- nrow(spOccurrences)
infoTable$MeanPropinquity[i] <- meanDist
infoTable$Median[i] <- medianDist
infoTable$SD[i] <- sdDist
infoTable$MinDist[i] <- minDist
infoTable$MaxDist[i] <- maxDist
infoTable$Skewness[i] <- skewnessDist[1]
infoTable$Mode[i] <- modeDist$M
infoTable$ModeSkewness[i] <- modeSkewness
if (plot.dist == TRUE) {
jpeg(paste(save.plot.in,'plotDist_',spNames[i],'.jpeg',sep = ''), res = 300,
width = 25 ,height = 15 ,units = 'cm')
plotDist <- density(mst$dist)
plot(plotDist, main = paste('Distance between Occurrences:',spNames[i],sep = ' '),
xlab = 'Distance (decimal degrees)', type = 'p',cex = 0.5)
polygon(plotDist, border = 'grey30')
abline(v = meanDist, col = 'red')
abline(v = medianDist, col = 'yellow4')
abline(v = modeDist$M, col = 'blue1')
dev.off()
}
if (plot.onMap == TRUE) {
coordinates(spOccurrences) <- spOccurrences[, c('decimalLongitude',
'decimalLatitude')]
data('wrld_simpl')
jpeg(paste(save.plot.in, 'plotOnMap_',spNames[i],'.jpeg', sep = ''),
res = 300, width = 25 ,height = 15 ,units = 'cm')
plot(wrld_simpl,main = paste('Distribution Map',spNames[i],sep = ' '))
plot(spOccurrences,add = T, pch = '*', col = 'red' )
dev.off()
}
cat(paste('Saving information:',as.character(spNames[i], sep= ' '),'\n',sep=''))
}
readAndWrite(action = 'write', frmt = wrt.frmt, path = save.info.in,
name = 'InfoPropinquity_species', object = infoTable)
print(paste('See: Information table in: ',save.info.in,'InfoPropinquity',
sep = ''))
cat('DENSITY PLOT LEGEND :::: \n
RED: Mean Propinquity \n
YELLOW: Median Propinquity \n
BLUE: Mode Propinquity')
return(infoTable)
}
}
alarconvv/geocleaMT documentation built on July 10, 2019, 12:50 a.m.
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Defines functions meanPropinquity
Documented in meanPropinquity
#'@name meanPropinquity
#'
#'@title Mean Propinquity
#'
#'@description Calculate the mean propinquity[1] and some descriptors of the distribution.
#'
#'@param coord.table data frame class. Table with coordinates (decimalLongitude,
#' decimalLatitude). If the process will be calculated by species, the table must
#' include the species column.
#'
#'@param calculatedBy Vector of characters. If the argument is \code{'species'},
#'the mean propinquity will be calculated for each species. If it is \code{'whole'}
#'the propinquity will be calculated as a whole.
#'
#'
#'@param plot.dist Logical vector. If \code{'TRUE'} a density plot for the distance
#'between occurrences will be plotted and saved, by species or as a whole. If
#' \code{'FALSE'} there will be no plot.
#'
#'@param plot.onMap Logical vector. If \code{'TRUE'} a map of occurences will be
#'plotted and saved, by species or as a whole. If \code{'FALSE'} there will be no plot.
#'
#'@param wrt.frmt Vector of characters. Format to save the output file.
#' By default it will be written as a R object using
#' \code{'saveRDS'} argument, but it can be saved as plain text using
#' \code{'saveTXT'} argument. See details.
#'
#'@param save.info.in Vector of characters. Path to the output
#'file with information about Mean propinquity, median, mode, minimal spanning
#'tree; by species or as a whole.
#'
#'@param save.plot.in Vector of characters. Path to the output
#'file for each plot by species or as a whole.
#'
#'
#'@details If the process uses the parameters plot.dist and plot.onMap as \code{'TRUE'}, the process will be slow.
#'The headers of the input file must follow the Darwin Core standard [2].
#'The user can see the guide using data('ID_DarwinCore) command.
#'For more details about the formats to write, see
#'\code{\link{readAndWrite}} function.
#'
#'@return The output file will be saved with all species concatenated.
#'
#'@author R-Alarcon Viviana and Miranda-Esquivel Daniel R.
#'
#'@note See:
#'R-Alarcon V. and Miranda-Esquivel DR. (submitted) geocleaMT: An R package to
#'cleaning geographical data from electronic biodatabases.
#'
#'@seealso \code{\link{readAndWrite}}
#'
#'@references
#'[1] Rapoport. E.H. 1975 Areografia: Estrategias Geograficas de las Especies. Mexico, Fondo de Cultura Economica
#'
#'[2] Wieczorek, J. et al. 2012. Darwin core: An evolving community-developed biodiversity data standard.
#' PloS One 7: e29715.
meanPropinquity <- function(coord.table = NULL,
calculatedBy = 'species',
wrt.frmt = 'saveTXT',
save.info.in = NULL,
plot.dist = FALSE,
plot.onMap = FALSE,
save.plot.in = NULL){
if (any(plot.dist, plot.onMap) == TRUE ) {
if (is.null(save.plot.in)) {
message('If any plot argument is TRUE, please : Assign the \'save.plot.in\' argument ')
}
}
coord.table$decimalLongitude <- as.numeric(as.character(coord.table$decimalLongitude))
coord.table$decimalLatitude <- as.numeric(as.character(coord.table$decimalLatitude))
if (!calculatedBy == 'species') {
uniqueAll <- coord.table[!duplicated(
coord.table[, c('decimalLongitude', 'decimalLatitude')]), ]
coordinates(uniqueAll) <- uniqueAll[, c('decimalLongitude', 'decimalLatitude')]
distanPoints <- dist(coordinates(uniqueAll))
mst <- spantree(d = distanPoints)
meanDist <- mean(mst$dist,na.rm = T)
medianDist <- median(mst$dist,na.rm = T)
sdDist <- sd(mst$dist)
minDist <- mst$dist[which.min(mst$dist)]
maxDist <- mst$dist[which.max(mst$dist)]
skewnessDist <- skewness(x = mst$dist, method = 'bickel', na.rm = T)
modeDist <- mlv(mst$dist,method = 'mfv', na.rm = T)
modeSkewness <- modeDist$skewness
infoTable <- as.data.frame(matrix(data = NA,nrow = 1,ncol = 9))
colnames(infoTable) <- c('Occ','MeanPropinquity','Median','SD','MinDist',
'MaxDist','Skewness','Mode','ModeSkewness')
infoTable$Occ <- nrow(coord.table)
infoTable$MeanPropinquity <- meanDist
infoTable$Median <- medianDist
infoTable$SD <- sdDist
infoTable$MinDist <- minDist
infoTable$MaxDist <- maxDist
infoTable$Skewness <- skewnessDist[1]
infoTable$Mode <- modeDist$M
infoTable$ModeSkewness <- modeSkewness
result <- list('meanPropinquity' = meanDist,
'medianDist' = medianDist,
'sdDist' = sdDist,
'minDist' = minDist,
'maxDist' = maxDist,
'skewnessDist' = skewnessDist[1],
'mode' = modeDist$M,
'modeSkewness' = modeSkewness,
'minimalSpaningTree' = mst)
readAndWrite(action = 'write', frmt = wrt.frmt, path = save.info.in,
name = 'InfoPropinquity_Whole', object = infoTable)
if (plot.dist == TRUE) {
jpeg(paste(save.plot.in, 'plotDistWhole.jpeg', sep = ''), res = 300,
width = 25 ,height = 15 ,units = 'cm')
plotDist <- density(mst$dist)
plot(plotDist, main = 'Distance between Occurrences',
xlab = 'Distance (decimal degrees)', type = 'p',cex = 0.5)
polygon(plotDist, border = 'grey30')
abline(v = meanDist, col = 'red')
abline(v = medianDist, col = 'yellow4')
abline(v = modeDist$M, col = 'blue1')
dev.off()
}
if (plot.onMap == TRUE) {
coordinates(coord.table) <- coord.table[, c('decimalLongitude',
'decimalLatitude')]
data('wrld_simpl')
jpeg(paste(save.plot.in, 'plotOnMapWhole.jpeg', sep = ''), res = 300,
width = 25 ,height = 15 ,units = 'cm')
plot(wrld_simpl,main = 'Distribution Map')
plot(coord.table,add = T, pch = '*', col = 'red' )
dev.off()
}
return(infoTable)
} else {
#by species
spNames <- as.character(unique(coord.table$species))
infoTable <- as.data.frame(matrix(data = NA,nrow = length(spNames),ncol = 10))
colnames(infoTable) <- c('Sp','Occ','MeanPropinquity','Median','SD','MinDist',
'MaxDist','Skewness','Mode','ModeSkewness')
for (i in 1:length(spNames)) {
spOccurrences <- coord.table[which(coord.table$species == spNames[i]),]
uniqueAll <- spOccurrences[!duplicated(spOccurrences[, c('decimalLongitude', 'decimalLatitude')]), ]
coordinates(uniqueAll) <- uniqueAll[, c('decimalLongitude', 'decimalLatitude')]
distanPoints <- dist(coordinates(uniqueAll))
mst <- spantree(d = distanPoints)
meanDist <- mean(mst$dist,na.rm = T)
medianDist <- median(mst$dist,na.rm = T)
sdDist <- sd(mst$dist)
minDist <- mst$dist[which.min(mst$dist)]
maxDist <- mst$dist[which.max(mst$dist)]
skewnessDist <- skewness(x = mst$dist, method = 'bickel', na.rm = T)
modeDist <- mlv(mst$dist,method = 'mfv', na.rm = T)
modeSkewness <- modeDist$skewness
infoTable$Sp[i] <- as.character(spNames[i])
infoTable$Occ[i] <- nrow(spOccurrences)
infoTable$MeanPropinquity[i] <- meanDist
infoTable$Median[i] <- medianDist
infoTable$SD[i] <- sdDist
infoTable$MinDist[i] <- minDist
infoTable$MaxDist[i] <- maxDist
infoTable$Skewness[i] <- skewnessDist[1]
infoTable$Mode[i] <- modeDist$M
infoTable$ModeSkewness[i] <- modeSkewness
if (plot.dist == TRUE) {
jpeg(paste(save.plot.in,'plotDist_',spNames[i],'.jpeg',sep = ''), res = 300,
width = 25 ,height = 15 ,units = 'cm')
plotDist <- density(mst$dist)
plot(plotDist, main = paste('Distance between Occurrences:',spNames[i],sep = ' '),
xlab = 'Distance (decimal degrees)', type = 'p',cex = 0.5)
polygon(plotDist, border = 'grey30')
abline(v = meanDist, col = 'red')
abline(v = medianDist, col = 'yellow4')
abline(v = modeDist$M, col = 'blue1')
dev.off()
}
if (plot.onMap == TRUE) {
coordinates(spOccurrences) <- spOccurrences[, c('decimalLongitude',
'decimalLatitude')]
data('wrld_simpl')
jpeg(paste(save.plot.in, 'plotOnMap_',spNames[i],'.jpeg', sep = ''),
res = 300, width = 25 ,height = 15 ,units = 'cm')
plot(wrld_simpl,main = paste('Distribution Map',spNames[i],sep = ' '))
plot(spOccurrences,add = T, pch = '*', col = 'red' )
dev.off()
}
cat(paste('Saving information:',as.character(spNames[i], sep= ' '),'\n',sep=''))
}
readAndWrite(action = 'write', frmt = wrt.frmt, path = save.info.in,
name = 'InfoPropinquity_species', object = infoTable)
print(paste('See: Information table in: ',save.info.in,'InfoPropinquity',
sep = ''))
cat('DENSITY PLOT LEGEND :::: \n
RED: Mean Propinquity \n
YELLOW: Median Propinquity \n
BLUE: Mode Propinquity')
return(infoTable)
}
}
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