| Title: | Taxonomic Monographs Tools |
|---|---|
| Description: | Contains functions intended to facilitate the production of plant taxonomic monographs. The package includes functions to convert tables into taxonomic descriptions, lists of collectors, examined specimens, identification keys (dichotomous and interactive), and can generate a monograph skeleton. Additionally, wrapper functions to batch the production of phenology histograms and distributional and diversity maps are also available. |
| Authors: | Marcelo Reginato |
| Maintainer: | Marcelo Reginato <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 1.3.1 |
| Built: | 2024-11-04 04:49:55 UTC |
| Source: | https://github.com/mreginato/monographar |
This function will generate a MS-Word or html file with a monograph skeleton (draft)
buildMonograph(headings, tableToDescription.data, examinedSpecimens.data = NULL, collectorList.data = NULL, output = "Word", title = "Taxonomic treatment", open = TRUE)buildMonograph(headings, tableToDescription.data, examinedSpecimens.data = NULL, collectorList.data = NULL, output = "Word", title = "Taxonomic treatment", open = TRUE)
headings |
data.frame |
tableToDescription.data |
data.frame |
examinedSpecimens.data |
data.frame (optional) |
collectorList.data |
data.frame (optional) |
output |
"Word" or "html" |
title |
character |
open |
logical |
This function wraps around the functions tableToDescription,
examinedSpecimens and collectorList generating a monograph draft in
MS-Word or html format. The resulting monograph skeleton will include the
taxonomic heading, the description, comments and examined specimens list
for all species found in the input tables, and it will append the collector
list in the end of the file. It requires four tables as input. Three of them
are the same tables used for tableToDescription, collectorList, and
examinedSpecimens functions. The additional input table should have three
columns: species, taxonomic heading and comments. The examinedSpecimens.data and
collectorList.data tables are optional. It uses functions of the rmarkdown
package to export the output file.
Exports a file (MS-Word or html).
Marcelo Reginato
data(monographaR_examples) monographaR_examples$taxonomic_headings -> taxonomic.headings monographaR_examples$collectorList -> col.d monographaR_examples$examinedSpecimens -> exam.d monographaR_examples$tableToDescription -> desc.d desc.d[,-1] -> desc.d ### buildMonograph(headings=taxonomic.headings, ### collectorList.data = col.d, ### examinedSpecimens.data = exam.d, ### tableToDescription.data = desc.d, ### output = "Word", title="Monograph skeleton")data(monographaR_examples) monographaR_examples$taxonomic_headings -> taxonomic.headings monographaR_examples$collectorList -> col.d monographaR_examples$examinedSpecimens -> exam.d monographaR_examples$tableToDescription -> desc.d desc.d[,-1] -> desc.d ### buildMonograph(headings=taxonomic.headings, ### collectorList.data = col.d, ### examinedSpecimens.data = exam.d, ### tableToDescription.data = desc.d, ### output = "Word", title="Monograph skeleton")
This function takes a numeric vector (or a data.frame with two columns including min and max values of a sample) and tries to find breaks in the distribution (gaps), if any gap is found it returns a coded character based on that.
codeGap(x, n = NULL, max.states = NULL, poly.sep = "/", gap.size = NULL)codeGap(x, n = NULL, max.states = NULL, poly.sep = "/", gap.size = NULL)
x |
integer/numeric or a two column data.frame (min and max) |
n |
integer, desired number of states (if NULL the function will try to suggest a number) |
max.states |
integer, the maximum possible number of states |
poly.sep |
character, to indicate polymorphic states (if any) |
gap.size |
numeric, the number that should be considered as a "gap" |
If n = NULL the function will try to find the best scenario of states (n) based on the number of polymophic samples in the resulting classification. In large data sets it will be a good idea to constrain the search using (e.g., max.states=10). This coding tries to replicate the coding traditionally used in taxonomy.
list, including: dat = data.frame including the original value and the coded value (state) polymorphic = the number of polymorphic samples (if n=NULL, it returns for all tested scenarios) dist = a histogram of the data distribution
Marcelo Reginato
c(NA, 1:5, 15:20, 25:42, 49:60, 68:90) -> x data.frame(x,x=x+2) -> x2 codeGap(x, n=3, max.states = 5) -> code1 code1$dat ### check the distribution na.omit(unique(code1$dat$state)) -> b cols <- sort(rep(rainbow(length(b)),2)) as.numeric(unlist(strsplit(b, "-"))) -> b plot(code1$dist) abline(v=b, lty="dashed", col=cols, lwd=2) ### estimate "n" codeGap(x, n=NULL, max.states = NULL) -> code1 code1$dat plot(code1$dist) ### check the distribution na.omit(unique(code1$dat$state)) -> b cols <- sort(rep(rainbow(length(b)),2)) as.numeric(unlist(strsplit(b, "-"))) -> b plot(code1$dist) abline(v=b, lty="dashed", col=cols, lwd=2) ### ranges codeGap(x2, n=NULL, max.states =3 , gap.size=2) -> code1 code1$dat unique(code1$dat$state) ### check the distribution na.omit(unique(code1$dat$state)) -> b cols <- sort(rep(rainbow(length(b)),2)) as.numeric(unlist(strsplit(b, "-"))) -> b plot(code1$dist) abline(v=b, lty="dashed", col=cols, lwd=2)c(NA, 1:5, 15:20, 25:42, 49:60, 68:90) -> x data.frame(x,x=x+2) -> x2 codeGap(x, n=3, max.states = 5) -> code1 code1$dat ### check the distribution na.omit(unique(code1$dat$state)) -> b cols <- sort(rep(rainbow(length(b)),2)) as.numeric(unlist(strsplit(b, "-"))) -> b plot(code1$dist) abline(v=b, lty="dashed", col=cols, lwd=2) ### estimate "n" codeGap(x, n=NULL, max.states = NULL) -> code1 code1$dat plot(code1$dist) ### check the distribution na.omit(unique(code1$dat$state)) -> b cols <- sort(rep(rainbow(length(b)),2)) as.numeric(unlist(strsplit(b, "-"))) -> b plot(code1$dist) abline(v=b, lty="dashed", col=cols, lwd=2) ### ranges codeGap(x2, n=NULL, max.states =3 , gap.size=2) -> code1 code1$dat unique(code1$dat$state) ### check the distribution na.omit(unique(code1$dat$state)) -> b cols <- sort(rep(rainbow(length(b)),2)) as.numeric(unlist(strsplit(b, "-"))) -> b plot(code1$dist) abline(v=b, lty="dashed", col=cols, lwd=2)
This is a wrapper function to codeGap, which accepts a data.frame of characters values including ranges (1-10).
codeMatrix(dat, n = NULL, max.states = NULL, poly.sep = "/", gap.size = NULL)codeMatrix(dat, n = NULL, max.states = NULL, poly.sep = "/", gap.size = NULL)
dat |
data.frame |
n |
integer, see |
max.states |
integer, see |
poly.sep |
character, see |
gap.size |
integer, see |
data.frame
Marcelo Reginato
data(monographaR_examples) monographaR_examples$dichoKey_q -> dat rownames(dat) <- dat[,1] dat[,-1] -> dat codeMatrix(dat, max.states = 3) -> mat head(mat)data(monographaR_examples) monographaR_examples$dichoKey_q -> dat rownames(dat) <- dat[,1] dat[,-1] -> dat codeMatrix(dat, max.states = 3) -> mat head(mat)
This function will generate a txt file with a collector list for all species in data.
collectorList(data = data, filename = "collector_list.txt", paragraphs = TRUE)collectorList(data = data, filename = "collector_list.txt", paragraphs = TRUE)
data |
data.frame |
filename |
character |
paragraphs |
logical |
It requires a data.frame with five columns, ordered as species, collector name, collector number, herbarium acronym and herbarium number. Herbarium columns are only used if any collector number is missing (NA). Thus, if there is no missing values in collector number, then the herbarium columns might be empty.
Exports a txt file.
Marcelo Reginato
## loading the example data data(monographaR_examples) monographaR_examples$collectorList -> data head(data) ## running the function, it will print in the terminal the output. ## To export a txt, place a ## name in the filename argument ## (i.e., filename = "myoutput.txt") collectorList(data, filename = "", paragraphs = TRUE) ## or a second option collectorList(data, filename = "", paragraphs = FALSE)## loading the example data data(monographaR_examples) monographaR_examples$collectorList -> data head(data) ## running the function, it will print in the terminal the output. ## To export a txt, place a ## name in the filename argument ## (i.e., filename = "myoutput.txt") collectorList(data, filename = "", paragraphs = TRUE) ## or a second option collectorList(data, filename = "", paragraphs = FALSE)
This function takes a data.frame with polymorphic and/or missing data (NA) characters and prepares it to use in the function buildKey.
dataKey(dat, poly.sep = "/", return.summary = T)dataKey(dat, poly.sep = "/", return.summary = T)
dat |
data.frame |
poly.sep |
character |
return.summary |
boolean |
This function takes a data.frame with polymorphic characters and prepares it to use in the function buildKey.
data.frame
Marcelo Reginato
data(monographaR_examples) monographaR_examples$dichoKey -> dat colnames(dat)[1] <- "species" ### Prepare matrix (polymorphic) dataKey(dat, poly.sep = "/") -> dat.k dat.k$dat -> dat.pdata(monographaR_examples) monographaR_examples$dichoKey -> dat colnames(dat)[1] <- "species" ### Prepare matrix (polymorphic) dataKey(dat, poly.sep = "/") -> dat.k dat.k$dat -> dat.p
This function generates a dichotomous key
dichoKey(dat, cost = NULL, clean.characters = TRUE, cp = 0)dichoKey(dat, cost = NULL, clean.characters = TRUE, cp = 0)
dat |
data.frame |
cost |
numeric |
clean.characters |
boolean |
cp |
numeric |
This is a wrapper function based on the rpart function of the rpart package. It modifies the output of rpart to a more "taxonomic" dichotomous key.
list
Marcelo Reginato
data(monographaR_examples) monographaR_examples$dichoKey -> dat colnames(dat)[1] <- "species" ### Prepare matrix (polymorphic) dataKey(dat, poly.sep = "/") -> dat.k dat.k$dat -> dat.p ### key dichoKey(dat.p) -> key length(key$unresolved) # Export #cat(key$key, file="Pleiochiton_key.txt") ### key with costs dat.k$summary ((dat.k$summary)+1) -> dat.c colnames(dat.p) dat.c[2] <- max(dat.c) dat.c[11] <- max(dat.c) dichoKey(dat.p, dat.c, cp=0) -> key.c length(key.c$unresolved) # Export #cat(key.c$key, file="Pleiochiton_key_costs.txt")data(monographaR_examples) monographaR_examples$dichoKey -> dat colnames(dat)[1] <- "species" ### Prepare matrix (polymorphic) dataKey(dat, poly.sep = "/") -> dat.k dat.k$dat -> dat.p ### key dichoKey(dat.p) -> key length(key$unresolved) # Export #cat(key$key, file="Pleiochiton_key.txt") ### key with costs dat.k$summary ((dat.k$summary)+1) -> dat.c colnames(dat.p) dat.c[2] <- max(dat.c) dat.c[11] <- max(dat.c) dichoKey(dat.p, dat.c, cp=0) -> key.c length(key.c$unresolved) # Export #cat(key.c$key, file="Pleiochiton_key_costs.txt")
This is a shiny app that generates a dichotomous key
dichoKey.app()dichoKey.app()
A shiny app that runs the function "dichoKey". It allows the user to easily subset the data (taxa and characters), as well as change the cost values of the characters.
NULL
Marcelo Reginato
if (interactive()) { dichoKey.app() }if (interactive()) { dichoKey.app() }
This function will generate a txt file with an examined specimens list.
examinedSpecimens(data, filename = "examined.txt")examinedSpecimens(data, filename = "examined.txt")
data |
data.frame |
filename |
character |
It requires a data.frame with eight columns, ordered as: species, collector name, collector number, herbarium acronym, herbarium number, country, state and municipality.
Exports a txt file.
Marcelo Reginato
## loading the example data data(monographaR_examples) monographaR_examples$examinedSpecimens -> data head(data) ## running the function, it will print in the terminal the output. ## To export a txt, place a name in the filename argument ## (i.e., filename = "myoutput.txt") examinedSpecimens(data, filename = "")## loading the example data data(monographaR_examples) monographaR_examples$examinedSpecimens -> data head(data) ## running the function, it will print in the terminal the output. ## To export a txt, place a name in the filename argument ## (i.e., filename = "myoutput.txt") examinedSpecimens(data, filename = "")
Generates code to build an interactive key (shiny app)
interactiveKey(dat = NULL, txt.labels = NULL, poly.sep = "/", taxa.in.italics = TRUE, theme = "lumen", about.first = FALSE)interactiveKey(dat = NULL, txt.labels = NULL, poly.sep = "/", taxa.in.italics = TRUE, theme = "lumen", about.first = FALSE)
dat |
data.frame |
txt.labels |
data.frame |
poly.sep |
character |
taxa.in.italics |
logical |
theme |
character |
about.first |
logical, whether the tab "about" is the first or the last |
See vignette
It generates 6 files (app.R, server.R, ui.R, about.Rmd, Dat_characters.csv, and Dat_matrix.csv)
Marcelo Reginato
## see vignette data(monographaR_examples) monographaR_examples$interactiveKey## see vignette data(monographaR_examples) monographaR_examples$interactiveKey
It generates a data.frame with txt labels required to use the function interactiveKey
interactiveKeyLabels(taxon = "species", language = "english")interactiveKeyLabels(taxon = "species", language = "english")
taxon |
character, "species", "genus" or "family" |
language |
character, "english" or "portuguese" |
It generates a data.frame with txt labels required to use the function interactiveKey. You can choose what rank will be identified in the key (species, genus or family), and the language (english and portuguese so far). You can further modify the fields prior using it with the interactiveKey function.
data.frame
Marcelo Reginato
### standard labels species in english interactiveKeyLabels(taxon = "species", language = "english") -> labs.spp.eng labs.spp.eng ### standard labels families in portuguese interactiveKeyLabels(taxon = "family", language = "portuguese") -> labs.fam.por labs.fam.por### standard labels species in english interactiveKeyLabels(taxon = "species", language = "english") -> labs.spp.eng labs.spp.eng ### standard labels families in portuguese interactiveKeyLabels(taxon = "family", language = "portuguese") -> labs.fam.por labs.fam.por
This wrapper function will export maps for all species in data.
mapBatch(data, zoom = T, margin = 0.1, axes = T, shape = NULL, export = "pdf", raster = NULL, RGB = NULL, points.col = "black", points.border = "gray50", points.cex = 1, shape.col = "white", shape.border = "black", raster.col = rev(gray.colors(65, start = 0, end = 1)), raster.legend = F, hillshade = F, width = 8, height = 8, image.resolution = 100, figure.number = T, title = T, box = T, add.minimap = F, minimap.shape = NULL, minimap.shape.col = "white", minimap.shape.border = "gray50", minimap.pos = "topleft", minimap.add.points = T, minimap.points.col = "black", minimap.points.border = "gray50", minimap.points.cex = 1, minimap.extent = NULL, minimap.rect.fill = NA, minimap.rect.border = NULL, maxpixels = 1e+05, ...)mapBatch(data, zoom = T, margin = 0.1, axes = T, shape = NULL, export = "pdf", raster = NULL, RGB = NULL, points.col = "black", points.border = "gray50", points.cex = 1, shape.col = "white", shape.border = "black", raster.col = rev(gray.colors(65, start = 0, end = 1)), raster.legend = F, hillshade = F, width = 8, height = 8, image.resolution = 100, figure.number = T, title = T, box = T, add.minimap = F, minimap.shape = NULL, minimap.shape.col = "white", minimap.shape.border = "gray50", minimap.pos = "topleft", minimap.add.points = T, minimap.points.col = "black", minimap.points.border = "gray50", minimap.points.cex = 1, minimap.extent = NULL, minimap.rect.fill = NA, minimap.rect.border = NULL, maxpixels = 1e+05, ...)
data |
data.frame |
zoom |
logical |
margin |
numeric |
axes |
logical |
shape |
a single or a list of spatial shape objects |
export |
"pdf", "jpeg" or "tiff" |
raster |
a raster object |
RGB |
a raster stack object (with three layers) |
points.col |
character |
points.border |
character |
points.cex |
numeric |
shape.col |
character |
shape.border |
character |
raster.col |
character (a vector of colors) |
raster.legend |
logical |
hillshade |
logical |
width |
numeric (in inches) |
height |
numeric (in inches) |
image.resolution |
numeric |
figure.number |
logical |
title |
logical |
box |
logical |
add.minimap |
logical |
minimap.shape |
a spatial shape object |
minimap.shape.col |
character (color) |
minimap.shape.border |
character (color) |
minimap.pos |
"topleft", "topright", "bottomleft" or "bottomright" |
minimap.add.points |
logical |
minimap.points.col |
character (color) |
minimap.points.border |
character (color) |
minimap.points.cex |
numeric |
minimap.extent |
numeric (x1, x2, y1, y2) |
minimap.rect.fill |
character (color) |
minimap.rect.border |
character (color) |
maxpixels |
numeric |
... |
additional arguments for plotting the extra shapes |
The function has three output options: a single pdf with all maps (export =
"pdf") or individual image files for each species (export = "tiff" or "jpeg").
It requires a data.frame with three columns, ordered as: species, longitude
and latitude. If zoom = TRUE, the function will set the limits of the plot
using the distribution of each species plus the margin (relative value). If
zoom = FALSE, the function will use the distribution of the whole data to set
the limits (all maps will have the same limits). Colors can be changed with
the arguments points.col, shape.col, shape.border, while the size of the
points can be changed with points.cex. A raster layer can be provided
(elevation for instance), and the colors of the raster are controlled by
raster.col. The user can provide a single or a list of shape files,
otherwise the rnaturalearth map is used.
Exports a pdf or image files.
Marcelo Reginato
## loading the example data data(monographaR_examples) monographaR_examples$map_data -> data head(data) ## running the function # mapBatch(data , type="simple", zoom=T, margin=0.2, points.col="black", # points.border="white", shape.col="gray90", points.cex=1.5, shape.border # = "gray90", export="pdf")## loading the example data data(monographaR_examples) monographaR_examples$map_data -> data head(data) ## running the function # mapBatch(data , type="simple", zoom=T, margin=0.2, points.col="black", # points.border="white", shape.col="gray90", points.cex=1.5, shape.border # = "gray90", export="pdf")
This function will generate a diversity heatmap using presence/absence of species on grid cells.
mapDiversity(data, resolution = 1, plot = T, plot.with.grid = T, col=rev(terrain.colors(55)), alpha=0.8, export = F, legend = T, filename = "diversity_map")mapDiversity(data, resolution = 1, plot = T, plot.with.grid = T, col=rev(terrain.colors(55)), alpha=0.8, export = F, legend = T, filename = "diversity_map")
data |
data.frame |
resolution |
numeric, size of the grid cells (degrees) |
plot |
logical |
plot.with.grid |
logical, whether or not to add a grid to the plot |
col |
character, a vector of colors |
alpha |
numerical, controls color transparency (0-1) |
export |
logical |
legend |
logical |
filename |
character |
It requires a data.frame with three columns, ordered as: species, longitude and latitude. The function will plot and return a raster object. The resolution of the grid can be changed by the argument "resolution" (in degrees). It uses functions of the package raster.
A raster object.
Marcelo Reginato
## loading the example data data(monographaR_examples) monographaR_examples$map_data -> data head(data) ## running the function mapDiversity(data , resolution=1, plot=TRUE, plot.with.grid=TRUE) ## Without the grid borders mapDiversity(data , resolution=1, plot=TRUE, plot.with.grid=FALSE) ## Changing colors mapDiversity(data , resolution=1, plot=TRUE, col=gray.colors(55)) ## Changing transparency mapDiversity(data , resolution=1, plot=TRUE, alpha=0.5) ## The function returns a raster object mapDiversity(data , resolution=1, plot=TRUE, alpha=0.5, plot.with.grid=FALSE) -> my.div.raster my.div.raster # plot(my.div.raster) # writeRaster(my.div.raster, "mydivraster.tif")## loading the example data data(monographaR_examples) monographaR_examples$map_data -> data head(data) ## running the function mapDiversity(data , resolution=1, plot=TRUE, plot.with.grid=TRUE) ## Without the grid borders mapDiversity(data , resolution=1, plot=TRUE, plot.with.grid=FALSE) ## Changing colors mapDiversity(data , resolution=1, plot=TRUE, col=gray.colors(55)) ## Changing transparency mapDiversity(data , resolution=1, plot=TRUE, alpha=0.5) ## The function returns a raster object mapDiversity(data , resolution=1, plot=TRUE, alpha=0.5, plot.with.grid=FALSE) -> my.div.raster my.div.raster # plot(my.div.raster) # writeRaster(my.div.raster, "mydivraster.tif")
This function will generate phenology maps across time (month, week, etc..).
mapPhenology(data, resolution = 1, time.range = c(1:12), label = "Month", binary = T, by_species = F, plot = T, col = rev(heat.colors(12)), alpha = 0.8, mfrow = c(4, 3), legend = T, pdf = F, height = 11, width = 8.5, filename = "mapPhenology.pdf")mapPhenology(data, resolution = 1, time.range = c(1:12), label = "Month", binary = T, by_species = F, plot = T, col = rev(heat.colors(12)), alpha = 0.8, mfrow = c(4, 3), legend = T, pdf = F, height = 11, width = 8.5, filename = "mapPhenology.pdf")
data |
data.frame |
resolution |
numeric (degrees) |
time.range |
numeric (vector of months, weeks, etc...) |
label |
character ("Month", "Week") |
binary |
logical |
by_species |
logical |
plot |
logical |
col |
character (vector of colors) |
alpha |
numeric (0-1) |
mfrow |
numeric |
legend |
logical |
pdf |
logical |
height |
numerical |
width |
numerical |
filename |
character |
This wrapper function will generate heatmaps of phenology across a time range. The default is to produce 12 heatmaps plotted on a single plate. This can be changed with the argument time.range, where any numerical range can be provided (representing weeks for instance). The argument mfrow controls the plate layout. It requires a data.frame with four columns, ordered as: species, longitude, latitude and phenology. The phenology column should be numeric (i.e., the number of the month, week or day the specimen was collected with flower/fruit). It is possible to change the resolution of the resulting rasters. The function can produce presence/absence heatmaps (if binary = T) or abundance heatmaps (if binary = F). The abundance values are relative (divided by the maximum abundance observed across all rasters). The function returns a RasterStack that can be exported or used in customized plots. To export a pdf, set "pdf=TRUE". The function wraps around functions of the raster package.
RasterStack
Marcelo Reginato
### load the example data data(monographaR_examples) monographaR_examples$mapPhenology -> data head(data) ## check the first rows ### running the function # mapPhenology(data, binary=FALSE, by_species=FALSE, legend=FALSE) ### changing the colors # mapPhenology(data, binary=FALSE, by_species=FALSE, legend=FALSE, col=rev(terrain.colors(55))) ### exporting raster # require(raster) # mapPhenology(data, binary=FALSE, by_species=FALSE, legend=FALSE) -> myphenorasters # plot(myphenorasters[[1]]) ## plot first month # writeRaster(myphenorasters[[2]], "pheno_month2.asc") ## exporting 2nd month ### making an GIF animation # require(animation) # saveGIF( # {mapPhenology(data, binary=F, resolution=0.5, by_species=F, legend=F, mfrow=c(1,1))}, # movie.name="phenology.gif", interval=0.5, ani.width=600, ani.height=600 # )### load the example data data(monographaR_examples) monographaR_examples$mapPhenology -> data head(data) ## check the first rows ### running the function # mapPhenology(data, binary=FALSE, by_species=FALSE, legend=FALSE) ### changing the colors # mapPhenology(data, binary=FALSE, by_species=FALSE, legend=FALSE, col=rev(terrain.colors(55))) ### exporting raster # require(raster) # mapPhenology(data, binary=FALSE, by_species=FALSE, legend=FALSE) -> myphenorasters # plot(myphenorasters[[1]]) ## plot first month # writeRaster(myphenorasters[[2]], "pheno_month2.asc") ## exporting 2nd month ### making an GIF animation # require(animation) # saveGIF( # {mapPhenology(data, binary=F, resolution=0.5, by_species=F, legend=F, mfrow=c(1,1))}, # movie.name="phenology.gif", interval=0.5, ani.width=600, ani.height=600 # )
This function will generate a presence/absence matrix based on a grid (if type="grid") or on countries (if type="countries").
mapTable(data, type = "grid", resolution = 1, pres.abs = TRUE, write.output = FALSE, layer = NULL)mapTable(data, type = "grid", resolution = 1, pres.abs = TRUE, write.output = FALSE, layer = NULL)
data |
data.frame |
type |
"grid", "countries" or "user" |
resolution |
numeric (degrees) |
pres.abs |
logical |
write.output |
logical |
layer |
Spatial DataFrame object |
It requires a data.frame with three columns, ordered as: species, longitude and latitude. The resolution of the grid can be changed by the argument "resolution" (in degrees). If type = "user", a layer to intersect the points and create the matrix should be supplied (a Spatial DataFrame object). It uses functions of the package raster, sf and terra. If pres.abs = F the returned matrix will have "x" instead of 0 and 1.
list, with a matrix and grid (if type="grid), or a matrix (if type="countries").
Marcelo Reginato
## loading the example data data(monographaR_examples) monographaR_examples$map_data -> data head(data) ## running the function with grid map.table <- mapTable(data, type="grid", resolution=3, write.output=FALSE) map.table$table t(map.table$table) map.table$grid -> grid ### load world map library(rnaturalearth) library(sf) library(sp) ne_countries(type="countries", returnclass = "sv") -> wrld_simpl st_as_sf(wrld_simpl) -> wrld_simpl as_Spatial(wrld_simpl) -> wrld_simpl ### plot plot(grid, border="white") plot(wrld_simpl, add=TRUE) plot(grid, add=TRUE) raster::text(grid, grid@data$layer, cex=1)## loading the example data data(monographaR_examples) monographaR_examples$map_data -> data head(data) ## running the function with grid map.table <- mapTable(data, type="grid", resolution=3, write.output=FALSE) map.table$table t(map.table$table) map.table$grid -> grid ### load world map library(rnaturalearth) library(sf) library(sp) ne_countries(type="countries", returnclass = "sv") -> wrld_simpl st_as_sf(wrld_simpl) -> wrld_simpl as_Spatial(wrld_simpl) -> wrld_simpl ### plot plot(grid, border="white") plot(wrld_simpl, add=TRUE) plot(grid, add=TRUE) raster::text(grid, grid@data$layer, cex=1)
monographaR contains functions intended to facilitate the production of plant taxonomic monographs. The package includes functions to convert tables into taxonomic descriptions, lists of collectors, examined specimens, identificatio keys (dichotomous and interactive), and can generate a monograph skeleton. Additionally, wrapper functions to batch the production of phenology histograms and distributional and diversity maps are also available.
| Package: | monographaR |
| Type: | Package |
| Version: | 1.3.1 |
| Date: | 2024-02-01 |
| License: | GPL (>= 2) |
Marcelo Reginato
Maintainer: Marcelo Reginato <[email protected]>
Reginato, M. (2016) monographaR: an R package to facilitate the production of plant taxonomic monographs. Brittonia 68(2): 212-216.
circular
raster
sp
rmarkdown
rpart
Input table examples. Seven data.frames are listed in this example data set. See help files of the functions for details.
data(monographaR_examples) names(monographaR_examples) head(monographaR_examples$colletorList) head(monographaR_examples$examinedSpecimens) head(monographaR_examples$phenoHist) head(monographaR_examples$tableToDescription) head(monographaR_examples$map_data) head(monographaR_examples$mapPhenology) head(monographaR_examples$taxonomic_headings)data(monographaR_examples) names(monographaR_examples) head(monographaR_examples$colletorList) head(monographaR_examples$examinedSpecimens) head(monographaR_examples$phenoHist) head(monographaR_examples$tableToDescription) head(monographaR_examples$map_data) head(monographaR_examples$mapPhenology) head(monographaR_examples$taxonomic_headings)
This wrapper function will generate circular histograms of phenology, using functions of the package circular.
phenoHist(data = data, mfrow = c(1, 1), shrink = 1.2, axis.cex = 1.5, title.cex = 1.5, pdf = F, height=11, width=8.5, filename = "phenology.pdf", flower = "Flower", fruit = "Fruit", both = "Both", flower.col = NULL, flower.border = "black", fruit.col = "darkgray", fruit.border = "darkgray", mar=c(2,2,2,2))phenoHist(data = data, mfrow = c(1, 1), shrink = 1.2, axis.cex = 1.5, title.cex = 1.5, pdf = F, height=11, width=8.5, filename = "phenology.pdf", flower = "Flower", fruit = "Fruit", both = "Both", flower.col = NULL, flower.border = "black", fruit.col = "darkgray", fruit.border = "darkgray", mar=c(2,2,2,2))
data |
data.frame |
mfrow |
numeric, (nrow, ncol) |
shrink |
numeric |
axis.cex |
numeric |
title.cex |
numeric |
pdf |
logical |
height |
numeric |
width |
numeric |
filename |
character |
flower |
character (how is the flower indicated in data, if missing place "missing") |
fruit |
character (how is the fruit indicated in data, if missing place "missing") |
both |
character (how is the both indicated in data, if missing place "missing") |
flower.col |
character (color of flower bars) |
flower.border |
character (color of flower border bars) |
fruit.col |
character (color of fruit bars) |
fruit.border |
character (color of fruit border bars) |
mar |
numeric (plot margins, vector of 4 values) |
It requires a data.frame with three columns, ordered as: species, month and phenology. The month column should be numeric (month number), while the phenology column must have these values: "Flower", "Fruit" and/or "Both". If any of these are missing is possible to indicate in the "flower", "fruit" and "both" arguments (both="missing"). The function will plot the bars indicating flower observations in white, and fruits in gray by default (is possible to change it with the "flower.col", "flower.border", "fruit.col" and "fruit.border" arguments). The size of the bar corresponds to number of observations. The arguments "shrink", "axis.cex" and "title.cex" control sizes, while the "mfrow" changes the number of histograms plotted at the same page (rows, columns).
Exports a pdf file.
Marcelo Reginato
## loading the example data data(monographaR_examples) monographaR_examples$phenoHist -> data head(data) ## running the function phenoHist(data, mfrow=c(2,2), shrink=1.2, axis.cex=1.5, title.cex=1.5, pdf=FALSE) ## changing the color phenoHist(data, mfrow=c(2,2), shrink=1.2, axis.cex=1.5, title.cex=1.5, pdf=FALSE, flower.col=rgb(0.2,1,0.2, 0.5), flower.border=rgb(0.2,1,0.2, 0.5), fruit.col="darkgreen", fruit.border="black") ## plotting only flower (if "fruit" and/or "both" information are ## missing for instance) phenoHist(data, mfrow=c(2,2), shrink=1.2, axis.cex=1.5, title.cex=1.5, pdf=FALSE, fruit="missing", both="missing", flower.col="red", flower.border="darkgray")## loading the example data data(monographaR_examples) monographaR_examples$phenoHist -> data head(data) ## running the function phenoHist(data, mfrow=c(2,2), shrink=1.2, axis.cex=1.5, title.cex=1.5, pdf=FALSE) ## changing the color phenoHist(data, mfrow=c(2,2), shrink=1.2, axis.cex=1.5, title.cex=1.5, pdf=FALSE, flower.col=rgb(0.2,1,0.2, 0.5), flower.border=rgb(0.2,1,0.2, 0.5), fruit.col="darkgreen", fruit.border="black") ## plotting only flower (if "fruit" and/or "both" information are ## missing for instance) phenoHist(data, mfrow=c(2,2), shrink=1.2, axis.cex=1.5, title.cex=1.5, pdf=FALSE, fruit="missing", both="missing", flower.col="red", flower.border="darkgray")
This function will generate a txt file with species descriptions.
tableToDescription(data, filename = "species_descriptions.txt")tableToDescription(data, filename = "species_descriptions.txt")
data |
data.frame |
filename |
character |
It requires a data.frame where the first three columns are the character description, putative complement and the character to use as separator (i.e., words that will remain constant across descriptions). The character description and/or the complement might be empty. The remaining columns are the species with their respective character states, where each row is a character. The function accepts any number of species and/or characters.
Exports a txt file
Marcelo Reginato
## loading the example data data(monographaR_examples) monographaR_examples$tableToDescription -> data head(data) ## the first column is just an identifier for the characters, we need to ## remove it before running the analysis data[,-1] -> data ## running the function, it will print in the terminal the output. ## To export a txt, place a name in the filename argument ## (i.e., filename = "myoutput.txt") tableToDescription(data, filename = "")## loading the example data data(monographaR_examples) monographaR_examples$tableToDescription -> data head(data) ## the first column is just an identifier for the characters, we need to ## remove it before running the analysis data[,-1] -> data ## running the function, it will print in the terminal the output. ## To export a txt, place a name in the filename argument ## (i.e., filename = "myoutput.txt") tableToDescription(data, filename = "")