Nothing
R/checknames.R
In specleanr: Detecting Environmental Outliers in Data Analysis Pipelines
Defines functions
check_names
Documented in
check_names
#' @title Check species names for inconsistencies
#'
#' @param data \code{dataframe}. Data frame with species names to checked from FishBase.
#' @param colsp \code{string}. A column in the data with the species column names.
#' @param verbose \code{logical}. To indicate the merges during checking of names. The default is FALSE not to show whether the species are in Fish base or not found.
#' @param pct \code{numeric}. The percentage similarity used to assign a relatively similar name from FishBase if the exact match is absent. Default 90%.
#' The higher the values, the higher percentage similarity are considered to replace a species name that is cheeked from Fishbase.
#' @param merge \code{logical}. Default is \strong{FALSE}, not to merge the cleaned species column on to the data frame but rather only two columns
#' returned.
#' @param ecosystem \code{logical} Returns whether the species is freshwater, marine , or brackish or a combination for
#' for estuarine loving species.
#'@param rm_duplicates \code{logical}. If TRUE, removes all duplicate species names especially when a dataframe is
#' is the output from the function.
#' @param sn \code{logical}. Whether to consider synonyms. Default FALSE so accepted names will be considered from
#' FishBase database.
#'
#' @details
#' The function produces a data set with species names corresponding with
#' \href{https://www.fishbase.se/}{Fishase}. If synonym is provided in the data set, the function will by defualt
#' return the accepted name. However, if the synoymn is desired, then set the sn parameter to \strong{TRUE}.
#' The function also check for spellings of species names and returns a name that is closer to the one in FishBase
#' with a particular degree of similarity set with pct parameter. pct of 1 indicates the name must 100% similar.
#' The user can iterate with different pct and decide if the return name is right or wrong. This function is
#' not necessary if the species names are clean and also for other taxa.
#'
#'
#' @importFrom methods is
#'
#' @return Data frame or names of corrected or cleaned species names.
#'
#' @export
#'
#' @examples
#'
#' \dontrun{
#'
#' data(jdsdata)
#'
#' data(efidata)
#'
#' #step 1. match and bind datasets if more than one datasets
#'
#' matchdata <- match_datasets(datasets = list(jds = jdsdata, efi = efidata),
#' lats = 'lat',
#' lons = 'lon',
#' species = c('speciesname','scientificName'),
#' country=c('JDS4_site_ID'),
#' date=c('Date', 'sampling_date'))
#'
#' #clean species names to produce one dataset.
#'
#' datafull <- check_names(data= matchdata, colsp='species', pct = 90, merge = TRUE)
#'
#' data2col <- check_names(data = matchdata, colsp='species', pct = 90) #two columns generated
#'
#' cleansp_name <- check_names(data= 'slamo trutta', pct=90) #wrong names vs FB suggestion
#'
#' clean_sp_epithet <- check_names(data = 'Salmo trutta fario') #Salmo trutta will be returned
#'
#' speciesepithet2 <- check_names(data = 'Salmo trutta lacustris', pct=90)
#'
#' }
#'
#' @seealso \code{\link{match_datasets}} for standardizing and binding datasets.
#'
#'
check_names <- function(data, colsp = NULL, verbose= FALSE, pct = 90, merge=FALSE, sn=FALSE, ecosystem = FALSE, rm_duplicates= FALSE){
if(inherits(data, 'sf') ) {
check_packages(pkgs = c('sf'))
xx <- data |> sf::st_drop_geometry()
}else{
xx <- data
}
if(is(xx, 'data.frame') && is.null(colsp)) stop('Species column names is not provided', call. = FALSE)
if(is(xx, 'data.frame')){
if(length((colnames(xx)[colnames(xx)==colsp]))<1){
stop('Species column name ', colsp, ' is not found in the ', deparse(substitute(data)), ' data provided')
} else{
spls <- unlist(xx[, colsp])
}
}else if(is(xx, 'list')){
spls <- unlist(xx)
}else if(is(xx, 'vector') && length(xx>1)){
spls <- xx
}else{
spls <- xx
}
#standard species list from FishBase
fb_df <- fishbase(tables = 'synonym')
spnames <- unlist(fb_df$synonym)
status <- unlist(fb_df$Status)
spcd <- unlist(fb_df$SpecCode)
acceptednames_list <- spnames[which(status %in%c('accepted name','provisionally accepted name'))]#accepted names
synoynm_list <- spnames[which(status !='accepted name')]#accepted names and others categories
#synoynmtrue <- spnames[which(status=='synonym')]#only synonymns
spna <- replace(spls, grepl("^\\s*$", spls) == TRUE, NA)#if empty space are found in the list or column
unx <- unique(spna)
spl <- c()
speciescheck <- c()
species <- c()
for (iii in seq_along(trimws(unx))) {
species_clean = clean_names(sp = unx[iii])
#1. clear species names
if((species_clean%in%acceptednames_list)==TRUE){
spp = species_clean
}else if((species_clean%in%synoynm_list)==TRUE){ #handle synonyms
if(isTRUE(sn)){ #either output with synonym species or replace with accepted names
if(isTRUE(verbose)) message('The synoynm species ', species_clean, ' maintained in the list.')
spp = species_clean
} else {
s1 <- spcd[which(spnames == species_clean)]
s2 <- spcd[which(status %in% c('accepted name', 'provisionally accepted name'))] #species codes for accepted names
sx <- acceptednames_list[which((s2%in%s1)==TRUE)] #get species name or names that are accepted for a particular synonym
if(length(sx)==0){
spp <- NA
if(isTRUE(verbose)) message("The species ", species_clean, " has no accepted name so it will return NA.")
}else if(length(sx)>1){ # if more than one
s11 <- spcd[which(spnames == species_clean & status=='synonym')]
s21 <- spcd[which(status %in% c('accepted name', 'provisionally accepted name'))] #species codes for accepted names
spaccepted <- acceptednames_list[which((s21%in%s11)==TRUE)] #sometimes the synoymn has more than one accepted name, we select one
if(length(spaccepted)==0){
spp <- NA
if(isTRUE(verbose)) message("The species ", species_clean, " has no accepted name so it will return NA.")
}else if(length(spaccepted)==1){
spp <- spaccepted
if(isTRUE(verbose)) message('The synoynm ', species_clean, ' will be replaced with ',spp,' based on high synonym similarity.')
}else{
#consider for more accepted names, but still check for similarity
dst = utils::adist(species_clean, spaccepted)
sp_prob = spaccepted[which(dst==min(dst))]
if(length(sp_prob)>1) spp <- sp_prob[1] else spp <- sp_prob
if(isTRUE(verbose)) message('The synoynm ', species_clean, ' will be replaced with ', spp,' which is closely similar among the ', length(spaccepted),' accepted names.')
}
}else{
spp <- sx
if(isTRUE(verbose)) message('The synoynm ', species_clean, ' will be replaced with ',spp,'.')
}
}
} else{
#handle morph types by only considering species scientific epithet
txt = scan(text = species_clean, what = ' ', quiet = TRUE)
sp_ex = paste0(txt[1],' ',txt[2])
if((sp_ex %in%acceptednames_list)==TRUE){#check in accepted names
spp = sp_ex
if(isTRUE(verbose)==TRUE) message(species_clean, ' will be replaced with ', spp)
}else if((sp_ex%in%synoynm_list)==TRUE){ #handle synonyms
if(isTRUE(sn)){ #either output with synonym species or replace with accepted names
if(isTRUE(verbose)) message('The synoynm species ', species_clean, ' maintained in the list.')
spp = sp_ex
} else {
s111 <- spcd[which(spnames == sp_ex & status=='synonym')]
s211 <- spcd[which(status %in% c('accepted name', 'provisionally accepted name'))] #species codes for accepted names
sx11 <- acceptednames_list[which((s211%in%s111)==TRUE)] #get species name or names that are accepted for a particular synonym
if(length(sx11)>1){ # if more than one
siv <- spcd[which(spnames == sp_ex & status=='synonym')]
s2v <- spcd[which(status %in% c('accepted name', 'provisionally accepted name'))] #species codes for accepted names
spp <- acceptednames_list[which((s2v%in%siv)==TRUE)]
if(isTRUE(verbose)) message('The synoynm ', sp_ex, ' will be replaced with ',spp,' based on high synonym simialrity.')
}else{
spp <- sx11
if(isTRUE(verbose)) message('The synoynm ', sp_ex, ' will be replaced with ',spp,'.')
}
}
} else if ((sp_ex %in%c(acceptednames_list, synoynm_list))==FALSE) {#spelling mistakes
#use name similarities to infer names from FishBase
#dissimilarity between the names local vs FB
dst = utils::adist(sp_ex, acceptednames_list)
sp_prob = acceptednames_list[which(dst==min(dst))]
if(length(sp_prob)>1){
sp_prob_sel = sp_prob[1]
} else{
sp_prob_sel = sp_prob
}
nc = nchar(sp_prob_sel)
pcts = (100-(min(dst)/nc)*100)
#shortest scientific name is ia io
if(pcts>=pct){
spp = sp_prob_sel
if(isTRUE(verbose)==TRUE) message(species_clean, ' replaced with ', spp, ' from FishBase with name percentage simialirity of ', round(pcts, 1), '%')
} else{
spp = NA
if(isTRUE(verbose)==TRUE) message('No close name in Fish Base for ', species_clean,'.')
}
} else{
spp = NA
if(isTRUE(verbose)==TRUE) message(species_clean, ' not found in Fishbase.')
}
}
if(length(spp)==0) spp <- NA
spl[iii] <- species_clean
species[iii] <- unx[iii]
speciescheck[iii] <- spp
df_sp <- data.frame(species = species, speciescheck = speciescheck)
}
#data output functions
if(!is(data, 'data.frame') && isTRUE(merge)){
stop('the parameter merge is only if the data is a dataframe not list or vector')
}else if(is(data, 'data.frame') && isTRUE(merge)){
#rename standard out df to include the user entered column name in the main dataset
names(df_sp)[1] <- colsp
dfinal <- merge(data, df_sp, by=colsp)
if(isTRUE(ecosystem) && isFALSE(merge)) stop('To get ecosytem types for each species turn merge to TRUE.')
if(isTRUE(ecosystem)){
#get species list
spdf <- fishbase('spnames')#to ecosystem types for the species
#merge based on speccode
mddata <- merge(fb_df, spdf, by='SpecCode')
mdup <- as.data.frame(mddata[!duplicated(mddata[c("synonym")]),])
splistsel <- mdup[,c('synonym', 'Fresh', 'Brack', 'Saltwater')]
#rename the synonym column to speciescheck to match the harmonized column
names(splistsel)[1] <- 'speciescheck'
spnoNA <- dfinal[!is.na(dfinal$speciescheck),]
mddatafinal <- merge(spnoNA, splistsel, by= 'speciescheck')
#remove duplicates by species
if(isTRUE(rm_duplicates)) dfinal <- as.data.frame(mddatafinal[!duplicated(mddatafinal[c('speciescheck')]),]) else dfinal <- mddatafinal
}
return(dfinal)
}else if(is(data, 'data.frame')|| is(data,'list') && isFALSE(merge)){
return(df_sp)
}else if(is(data, 'vector') && length(data)>1 && isFALSE(merge)){
return(df_sp)
}else{
return(spp)
}
}
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Defines functions check_names
Documented in check_names
#' @title Check species names for inconsistencies
#'
#' @param data \code{dataframe}. Data frame with species names to checked from FishBase.
#' @param colsp \code{string}. A column in the data with the species column names.
#' @param verbose \code{logical}. To indicate the merges during checking of names. The default is FALSE not to show whether the species are in Fish base or not found.
#' @param pct \code{numeric}. The percentage similarity used to assign a relatively similar name from FishBase if the exact match is absent. Default 90%.
#' The higher the values, the higher percentage similarity are considered to replace a species name that is cheeked from Fishbase.
#' @param merge \code{logical}. Default is \strong{FALSE}, not to merge the cleaned species column on to the data frame but rather only two columns
#' returned.
#' @param ecosystem \code{logical} Returns whether the species is freshwater, marine , or brackish or a combination for
#' for estuarine loving species.
#'@param rm_duplicates \code{logical}. If TRUE, removes all duplicate species names especially when a dataframe is
#' is the output from the function.
#' @param sn \code{logical}. Whether to consider synonyms. Default FALSE so accepted names will be considered from
#' FishBase database.
#'
#' @details
#' The function produces a data set with species names corresponding with
#' \href{https://www.fishbase.se/}{Fishase}. If synonym is provided in the data set, the function will by defualt
#' return the accepted name. However, if the synoymn is desired, then set the sn parameter to \strong{TRUE}.
#' The function also check for spellings of species names and returns a name that is closer to the one in FishBase
#' with a particular degree of similarity set with pct parameter. pct of 1 indicates the name must 100% similar.
#' The user can iterate with different pct and decide if the return name is right or wrong. This function is
#' not necessary if the species names are clean and also for other taxa.
#'
#'
#' @importFrom methods is
#'
#' @return Data frame or names of corrected or cleaned species names.
#'
#' @export
#'
#' @examples
#'
#' \dontrun{
#'
#' data(jdsdata)
#'
#' data(efidata)
#'
#' #step 1. match and bind datasets if more than one datasets
#'
#' matchdata <- match_datasets(datasets = list(jds = jdsdata, efi = efidata),
#' lats = 'lat',
#' lons = 'lon',
#' species = c('speciesname','scientificName'),
#' country=c('JDS4_site_ID'),
#' date=c('Date', 'sampling_date'))
#'
#' #clean species names to produce one dataset.
#'
#' datafull <- check_names(data= matchdata, colsp='species', pct = 90, merge = TRUE)
#'
#' data2col <- check_names(data = matchdata, colsp='species', pct = 90) #two columns generated
#'
#' cleansp_name <- check_names(data= 'slamo trutta', pct=90) #wrong names vs FB suggestion
#'
#' clean_sp_epithet <- check_names(data = 'Salmo trutta fario') #Salmo trutta will be returned
#'
#' speciesepithet2 <- check_names(data = 'Salmo trutta lacustris', pct=90)
#'
#' }
#'
#' @seealso \code{\link{match_datasets}} for standardizing and binding datasets.
#'
#'
check_names <- function(data, colsp = NULL, verbose= FALSE, pct = 90, merge=FALSE, sn=FALSE, ecosystem = FALSE, rm_duplicates= FALSE){
if(inherits(data, 'sf') ) {
check_packages(pkgs = c('sf'))
xx <- data |> sf::st_drop_geometry()
}else{
xx <- data
}
if(is(xx, 'data.frame') && is.null(colsp)) stop('Species column names is not provided', call. = FALSE)
if(is(xx, 'data.frame')){
if(length((colnames(xx)[colnames(xx)==colsp]))<1){
stop('Species column name ', colsp, ' is not found in the ', deparse(substitute(data)), ' data provided')
} else{
spls <- unlist(xx[, colsp])
}
}else if(is(xx, 'list')){
spls <- unlist(xx)
}else if(is(xx, 'vector') && length(xx>1)){
spls <- xx
}else{
spls <- xx
}
#standard species list from FishBase
fb_df <- fishbase(tables = 'synonym')
spnames <- unlist(fb_df$synonym)
status <- unlist(fb_df$Status)
spcd <- unlist(fb_df$SpecCode)
acceptednames_list <- spnames[which(status %in%c('accepted name','provisionally accepted name'))]#accepted names
synoynm_list <- spnames[which(status !='accepted name')]#accepted names and others categories
#synoynmtrue <- spnames[which(status=='synonym')]#only synonymns
spna <- replace(spls, grepl("^\\s*$", spls) == TRUE, NA)#if empty space are found in the list or column
unx <- unique(spna)
spl <- c()
speciescheck <- c()
species <- c()
for (iii in seq_along(trimws(unx))) {
species_clean = clean_names(sp = unx[iii])
#1. clear species names
if((species_clean%in%acceptednames_list)==TRUE){
spp = species_clean
}else if((species_clean%in%synoynm_list)==TRUE){ #handle synonyms
if(isTRUE(sn)){ #either output with synonym species or replace with accepted names
if(isTRUE(verbose)) message('The synoynm species ', species_clean, ' maintained in the list.')
spp = species_clean
} else {
s1 <- spcd[which(spnames == species_clean)]
s2 <- spcd[which(status %in% c('accepted name', 'provisionally accepted name'))] #species codes for accepted names
sx <- acceptednames_list[which((s2%in%s1)==TRUE)] #get species name or names that are accepted for a particular synonym
if(length(sx)==0){
spp <- NA
if(isTRUE(verbose)) message("The species ", species_clean, " has no accepted name so it will return NA.")
}else if(length(sx)>1){ # if more than one
s11 <- spcd[which(spnames == species_clean & status=='synonym')]
s21 <- spcd[which(status %in% c('accepted name', 'provisionally accepted name'))] #species codes for accepted names
spaccepted <- acceptednames_list[which((s21%in%s11)==TRUE)] #sometimes the synoymn has more than one accepted name, we select one
if(length(spaccepted)==0){
spp <- NA
if(isTRUE(verbose)) message("The species ", species_clean, " has no accepted name so it will return NA.")
}else if(length(spaccepted)==1){
spp <- spaccepted
if(isTRUE(verbose)) message('The synoynm ', species_clean, ' will be replaced with ',spp,' based on high synonym similarity.')
}else{
#consider for more accepted names, but still check for similarity
dst = utils::adist(species_clean, spaccepted)
sp_prob = spaccepted[which(dst==min(dst))]
if(length(sp_prob)>1) spp <- sp_prob[1] else spp <- sp_prob
if(isTRUE(verbose)) message('The synoynm ', species_clean, ' will be replaced with ', spp,' which is closely similar among the ', length(spaccepted),' accepted names.')
}
}else{
spp <- sx
if(isTRUE(verbose)) message('The synoynm ', species_clean, ' will be replaced with ',spp,'.')
}
}
} else{
#handle morph types by only considering species scientific epithet
txt = scan(text = species_clean, what = ' ', quiet = TRUE)
sp_ex = paste0(txt[1],' ',txt[2])
if((sp_ex %in%acceptednames_list)==TRUE){#check in accepted names
spp = sp_ex
if(isTRUE(verbose)==TRUE) message(species_clean, ' will be replaced with ', spp)
}else if((sp_ex%in%synoynm_list)==TRUE){ #handle synonyms
if(isTRUE(sn)){ #either output with synonym species or replace with accepted names
if(isTRUE(verbose)) message('The synoynm species ', species_clean, ' maintained in the list.')
spp = sp_ex
} else {
s111 <- spcd[which(spnames == sp_ex & status=='synonym')]
s211 <- spcd[which(status %in% c('accepted name', 'provisionally accepted name'))] #species codes for accepted names
sx11 <- acceptednames_list[which((s211%in%s111)==TRUE)] #get species name or names that are accepted for a particular synonym
if(length(sx11)>1){ # if more than one
siv <- spcd[which(spnames == sp_ex & status=='synonym')]
s2v <- spcd[which(status %in% c('accepted name', 'provisionally accepted name'))] #species codes for accepted names
spp <- acceptednames_list[which((s2v%in%siv)==TRUE)]
if(isTRUE(verbose)) message('The synoynm ', sp_ex, ' will be replaced with ',spp,' based on high synonym simialrity.')
}else{
spp <- sx11
if(isTRUE(verbose)) message('The synoynm ', sp_ex, ' will be replaced with ',spp,'.')
}
}
} else if ((sp_ex %in%c(acceptednames_list, synoynm_list))==FALSE) {#spelling mistakes
#use name similarities to infer names from FishBase
#dissimilarity between the names local vs FB
dst = utils::adist(sp_ex, acceptednames_list)
sp_prob = acceptednames_list[which(dst==min(dst))]
if(length(sp_prob)>1){
sp_prob_sel = sp_prob[1]
} else{
sp_prob_sel = sp_prob
}
nc = nchar(sp_prob_sel)
pcts = (100-(min(dst)/nc)*100)
#shortest scientific name is ia io
if(pcts>=pct){
spp = sp_prob_sel
if(isTRUE(verbose)==TRUE) message(species_clean, ' replaced with ', spp, ' from FishBase with name percentage simialirity of ', round(pcts, 1), '%')
} else{
spp = NA
if(isTRUE(verbose)==TRUE) message('No close name in Fish Base for ', species_clean,'.')
}
} else{
spp = NA
if(isTRUE(verbose)==TRUE) message(species_clean, ' not found in Fishbase.')
}
}
if(length(spp)==0) spp <- NA
spl[iii] <- species_clean
species[iii] <- unx[iii]
speciescheck[iii] <- spp
df_sp <- data.frame(species = species, speciescheck = speciescheck)
}
#data output functions
if(!is(data, 'data.frame') && isTRUE(merge)){
stop('the parameter merge is only if the data is a dataframe not list or vector')
}else if(is(data, 'data.frame') && isTRUE(merge)){
#rename standard out df to include the user entered column name in the main dataset
names(df_sp)[1] <- colsp
dfinal <- merge(data, df_sp, by=colsp)
if(isTRUE(ecosystem) && isFALSE(merge)) stop('To get ecosytem types for each species turn merge to TRUE.')
if(isTRUE(ecosystem)){
#get species list
spdf <- fishbase('spnames')#to ecosystem types for the species
#merge based on speccode
mddata <- merge(fb_df, spdf, by='SpecCode')
mdup <- as.data.frame(mddata[!duplicated(mddata[c("synonym")]),])
splistsel <- mdup[,c('synonym', 'Fresh', 'Brack', 'Saltwater')]
#rename the synonym column to speciescheck to match the harmonized column
names(splistsel)[1] <- 'speciescheck'
spnoNA <- dfinal[!is.na(dfinal$speciescheck),]
mddatafinal <- merge(spnoNA, splistsel, by= 'speciescheck')
#remove duplicates by species
if(isTRUE(rm_duplicates)) dfinal <- as.data.frame(mddatafinal[!duplicated(mddatafinal[c('speciescheck')]),]) else dfinal <- mddatafinal
}
return(dfinal)
}else if(is(data, 'data.frame')|| is(data,'list') && isFALSE(merge)){
return(df_sp)
}else if(is(data, 'vector') && length(data)>1 && isFALSE(merge)){
return(df_sp)
}else{
return(spp)
}
}
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