| Title: | River Network Distance Computation and Applications |
|---|---|
| Description: | Reads river network shape files and computes network distances. Also included are a variety of computation and graphical tools designed for fisheries telemetry research, such as minimum home range, kernel density estimation, and clustering analysis using empirical k-functions with a bootstrap envelope. Tools are also provided for editing the river networks, meaning there is no reliance on external software. |
| Authors: | Matt Tyers [aut, cre] |
| Maintainer: | Matt Tyers <[email protected]> |
| License: | GPL-2 |
| Version: | 0.17.1 |
| Built: | 2024-11-08 10:29:05 UTC |
| Source: | CRAN |
Reads river network shape files and computes network distances. Also included are a variety of computation and graphical tools designed for fisheries telemetry research, such as minimum home range, kernel density estimation, and clustering analysis using empirical k-functions with a bootstrap envelope. Tools are also provided for editing the river networks, meaning there is no reliance on external software.
| Package: | riverdist |
| Type: | Package |
| Version: | 0.17.1 |
| Date: | 2024-11-07 |
| License: | GPL-2 |
The riverdist package provides tools for distance calculation along a river network. The river network is imported from a projected shapefile. Spatial point data may be imported from a shapefile as well, or directly from coordinates.
Some basic formatting of the river shapefile may be necessary. If available, formatting in a geographic information system (GIS) prior to importing into R is recommended (projecting, spatial trimming to the study area, and possibly dissolving river segments), but the riverdist package and its dependencies also include tools for accomplishing the necessary formatting within R.
Matt Tyers
Maintainer: Matt Tyers <[email protected]>
A complex river network object, a subset of the streams in the Absaroka-Beartooth Wilderness.
data(abstreams)data(abstreams)
A river network object, see rivernetwork
An unusably messy river network object, included for the purpose of testing river network editing functions.
data(abstreams0)data(abstreams0)
A river network object, see rivernetwork
Adds a vector of cumulative distances to a river network. Called internally.
addcumuldist(rivers)addcumuldist(rivers)
rivers |
The river network object to use. |
Returns an object of class "rivernetwork" containing all
spatial and topological information. See rivernetwork-class.
Matt Tyers
Gulk1 <- addcumuldist(rivers=Gulk)Gulk1 <- addcumuldist(rivers=Gulk)
In certain cases, such as when there is a lake within a river system, there may be long, straight lines in a river network with vertices only at either end. In these cases, any point data along these stretches would be snapped to the vertices at either end. This function automatically adds equally-spaced vertices along the straight line, according to a specified minimum allowable distance between vertices.
addverts(rivers, mindist = 500)addverts(rivers, mindist = 500)
rivers |
The river network object to use. |
mindist |
The minimum distance to use between vertices. Defaults to 500. |
A new river network object with the specified segments connected (see rivernetwork)
This function is called within cleanup, which is recommended in most cases.
Matt Tyers
line2network
data(Kenai3) Kenai3split <- addverts(Kenai3,mindist=200) zoomtoseg(seg=c(47,74,78), rivers=Kenai3) points(Kenai3$lines[[74]]) # vertices before adding zoomtoseg(seg=c(47,74,78), rivers=Kenai3split) points(Kenai3split$lines[[74]]) # vertices after addingdata(Kenai3) Kenai3split <- addverts(Kenai3,mindist=200) zoomtoseg(seg=c(47,74,78), rivers=Kenai3) points(Kenai3$lines[[74]]) # vertices before adding zoomtoseg(seg=c(47,74,78), rivers=Kenai3split) points(Kenai3split$lines[[74]]) # vertices after adding
Adds lookup tables for distance computation, dramatically reducing computation time. It may take some time to calculate, particularly in a braided network.
buildlookup(rivers)buildlookup(rivers)
rivers |
The river network object to use |
A rivernetwork object, with a new list element, $distlookup, a
list of three matrices. Element [i,j] of each matrix corresponds to
the route between segment i and j. The
distlookup$middist matrix gives the total distance of the "middle"
of each route (between the starting and ending segments"), and the
distlookup$starttop and distlookup$endtop matrices have value
TRUE, FALSE, or NA if the segments at the beginning or
end of the route are connected to the rest of the route at the top of the
coordinate matrix, bottom of the coordinate matrix, or if the route is
contained to just one segment, respectively. (See rivernetwork.)
This will add three n by n matrices to the river network object, which will be very large if the river network has many segments.
This function is called within cleanup, which is recommended in most cases. It is also called within buildsegroutes, and will add lookup tables by default if there are fewer than 400 segments in the river network.
This function can still be called in the presence of a braided network, but all resulting distances used in subsequent analyses will be the shortest route.
If segment routes ($segroutes) are not present, this function may take a very long time to run.
Matt Tyers
data(abstreams) abstreams1 <- buildlookup(abstreams)data(abstreams) abstreams1 <- buildlookup(abstreams)
Adds the travel routes from the mouth (lowest point) of a river network to each segment, and (optionally) distance lookup tables. This greatly reduces the time needed to detect routes, making distance calculation much more efficient, particularly in the case of multiple distance calculations.
buildsegroutes(rivers, lookup = NULL, verbose = FALSE)buildsegroutes(rivers, lookup = NULL, verbose = FALSE)
rivers |
The river network object to use |
lookup |
Whether to build lookup tables as well. This may take
some time, but will result in even faster distance computation in analyses
(see buildlookup). Because of the object size returned, this may
not be advisable in a large river network (more than a few hundred
segments). Accepts |
verbose |
Whether or not to print the segment number the function is currently building a route for (used for error checking). Defaults to FALSE. |
A rivernetwork object, with a new list element, $segroutes,
which gives the route from the mouth to each rivernetwork segment.
Optionally, it may add $distlookup, distance lookup tables for even
faster distance computation. (See rivernetwork.)
In the event of braiding (multiple channels), it is likely that there will be differences in the routes detected. If this is the case, building routes will likely result in a shorter and more efficient route. Regardless, extreme caution is always advised in the event of braiding.
The mouth segment and vertex must be specified (see setmouth).
This function is called within cleanup, which is recommended in most cases.
Matt Tyers
data(abstreams) plot(x=abstreams) abstreams1 <- abstreams abstreams1$segroutes <- NULL #taking out the $segroutes component # before tstart <- Sys.time() detectroute(start=120, end=111, rivers=abstreams1) Sys.time() - tstart # after tstart <- Sys.time() detectroute(start=120, end=111, rivers=abstreams) Sys.time() - tstartdata(abstreams) plot(x=abstreams) abstreams1 <- abstreams abstreams1$segroutes <- NULL #taking out the $segroutes component # before tstart <- Sys.time() detectroute(start=120, end=111, rivers=abstreams1) Sys.time() - tstart # after tstart <- Sys.time() detectroute(start=120, end=111, rivers=abstreams) Sys.time() - tstart
Calculates the connectivity matrix for a river network, during import and editing. Called internally.
calculateconnections(lines, tolerance)calculateconnections(lines, tolerance)
lines |
A list of coordinate matrices, each corresponding to a line segment. |
tolerance |
The spatial tolerance for establishing connectivity. |
A matrix with topological information. See the $connections element of the rivernetwork-class.
Matt Tyers
Gulk_connections <- calculateconnections(lines=Gulk$lines, tolerance=Gulk$tolerance)Gulk_connections <- calculateconnections(lines=Gulk$lines, tolerance=Gulk$tolerance)
Detects braiding (multiple flow channels between two locations) within a river network object. Braiding can either be checked for in the route between two segments, or in the river network as a whole.
checkbraided(rivers, startseg = NULL, endseg = NULL, progress = TRUE)checkbraided(rivers, startseg = NULL, endseg = NULL, progress = TRUE)
rivers |
The river network object to check. |
startseg |
Starting segment of a route to investigate. If this and
|
endseg |
Starting segment of a route to investigate. If this and
|
progress |
Whether to show the progress bar. Defaults to |
This function is called within cleanup, which is recommended in most cases.
Matt Tyers
data(Gulk) plot(x=Gulk) checkbraided(rivers=Gulk) data(KilleyW) plot(x=KilleyW) checkbraided(rivers=KilleyW) Kenai3.subset <- trimriver(trimto=c(22,2,70,30,15,98,96,89,52,3), rivers=Kenai3) plot(x=Kenai3.subset) checkbraided(startseg=1, endseg=7, rivers=Kenai3.subset) checkbraided(startseg=1, endseg=5, rivers=Kenai3.subset)data(Gulk) plot(x=Gulk) checkbraided(rivers=Gulk) data(KilleyW) plot(x=KilleyW) checkbraided(rivers=KilleyW) Kenai3.subset <- trimriver(trimto=c(22,2,70,30,15,98,96,89,52,3), rivers=Kenai3) plot(x=Kenai3.subset) checkbraided(startseg=1, endseg=7, rivers=Kenai3.subset) checkbraided(startseg=1, endseg=5, rivers=Kenai3.subset)
Detects braiding (multiple flow channels between two locations) within a river network object, and returns a logical value for specifying braiding within a river network object.
checkbraidedTF(rivers, toreturn = "rivers", progress = TRUE)checkbraidedTF(rivers, toreturn = "rivers", progress = TRUE)
rivers |
The river network object to check. |
toreturn |
Specifying |
progress |
Whether to show the progress bar. Defaults to |
This function is called within cleanup, which is recommended in most cases.
Matt Tyers
data(Gulk,KilleyW) Gulk <- setmouth(seg=1, vert=1, rivers=Gulk) plot(x=Gulk) checkbraidedTF(rivers=Gulk, toreturn="logical") KilleyW <- setmouth(seg=1, vert=288, rivers=KilleyW) plot(x=KilleyW) checkbraidedTF(rivers=KilleyW, toreturn="logical") checkbraidedTF(rivers=KilleyW, toreturn="routes") KilleyW.1 <- checkbraidedTF(rivers=KilleyW, toreturn="rivers") str(KilleyW.1)data(Gulk,KilleyW) Gulk <- setmouth(seg=1, vert=1, rivers=Gulk) plot(x=Gulk) checkbraidedTF(rivers=Gulk, toreturn="logical") KilleyW <- setmouth(seg=1, vert=288, rivers=KilleyW) plot(x=KilleyW) checkbraidedTF(rivers=KilleyW, toreturn="logical") checkbraidedTF(rivers=KilleyW, toreturn="routes") KilleyW.1 <- checkbraidedTF(rivers=KilleyW, toreturn="rivers") str(KilleyW.1)
This is the recommended function to use for cleanup of a river network. It calls all available river network editing functions in appropriate sequence, detecting which are needed or recommended, and prompts user input wherever necessary.
Currently, it automatically calls removeduplicates, prompts the user whether to run dissolve, automatically runs removemicrosegs and splitsegments if needed, provides user prompts for addverts and setmouth, detects if segments are unconnected and provides user prompts for removeunconnected or connectsegs, automatically runs checkbraidedTF, and prompts the user whether to run buildsegroutes if no braiding is detected.
cleanup(rivers)cleanup(rivers)
rivers |
The river network object to use |
A new river network object, see rivernetwork
Matt Tyers
line2network
data(abstreams0,Koyukuk0,Kenai1) # abstreams_fixed <- cleanup(abstreams0) # Koyukuk <- cleanup(Koyukuk0) # Kenai <- cleanup(Kenai1)data(abstreams0,Koyukuk0,Kenai1) # abstreams_fixed <- cleanup(abstreams0) # Koyukuk <- cleanup(Koyukuk0) # Kenai <- cleanup(Kenai1)
A trial version of a function for deep-cleaning a river network.
Sometimes a shapefile contains errors that are not obvious at an initial check, typically vertices that should not be there.
This function steps through each segment in sequence, and allows the user to interactively remove vertices.
cleanup_verts(rivers, startwith = 1)cleanup_verts(rivers, startwith = 1)
rivers |
The river network object to use |
startwith |
The segment (number) to start with, defaulting to |
A new river network object, see rivernetwork
Stepping through a large and messy river network can be time-consuming.
To resume a cleanup session, use the startwith= argument and the last
returned river network. For example, if rivers1 <- cleanup_verts(rivers)
were initially called and the user selected "save & close" at segment 100,
cleanup can be resumed by calling rivers2 <- cleanup_verts(rivers1, startwith=100).
Matt Tyers
line2network
data(abstreams0,Koyukuk0,Kenai1) # abstreams_fixed1 <- cleanup_verts(abstreams0) # Koyukuk <- cleanup(Koyukuk0) # Kenai <- cleanup(Kenai1)data(abstreams0,Koyukuk0,Kenai1) # abstreams_fixed1 <- cleanup_verts(abstreams0) # Koyukuk <- cleanup(Koyukuk0) # Kenai <- cleanup(Kenai1)
Provides a method to manually connect unconnected segments within a river network. The nearest endpoint (or vertex) of the second segment is added as a new vertex to the first, and the network topology is then updated.
connectsegs( connect, connectto, nearestvert = TRUE, rivers, calcconnections = TRUE )connectsegs( connect, connectto, nearestvert = TRUE, rivers, calcconnections = TRUE )
connect |
The segment(s) to connect to the network. Typically, this is the segment that is disconnected from the rest of the river network. A vector of segments may be used. |
connectto |
The segment(s) to connect it (them) to. Typically, this is a segment
that is connected to the rest of the river network. A vector of segments may be used, corresponding to that used in |
nearestvert |
Whether to connect at the nearest vertex and split the
segment ( |
rivers |
The river network object to use. |
calcconnections |
Whether to recalculate all connections.
Defaults to |
A new river network object with the specified segments connected (see rivernetwork)
This function is called within cleanup, which is recommended in most cases.
Matt Tyers
line2network
data(Koyukuk0) plot(Koyukuk0, ylim=c(1930500,1931500), xlim=c(194900,195100)) topologydots(Koyukuk0, add=TRUE) Koyukuk0.1 <- connectsegs(connect=21, connectto=20, rivers=Koyukuk0) plot(Koyukuk0.1,ylim=c(1930500,1931500), xlim=c(194900,195100)) topologydots(Koyukuk0.1, add=TRUE) # or the vector version zoomtoseg(seg=21:23, rivers=Koyukuk0) Koyukuk0.2 <- connectsegs(connect=c(20,21,22), connectto=c(21,22,23), nearestvert=c(FALSE,FALSE,TRUE), rivers=Koyukuk0) zoomtoseg(seg=21:23, rivers=Koyukuk0.2) topologydots(Koyukuk0.2, add=TRUE)data(Koyukuk0) plot(Koyukuk0, ylim=c(1930500,1931500), xlim=c(194900,195100)) topologydots(Koyukuk0, add=TRUE) Koyukuk0.1 <- connectsegs(connect=21, connectto=20, rivers=Koyukuk0) plot(Koyukuk0.1,ylim=c(1930500,1931500), xlim=c(194900,195100)) topologydots(Koyukuk0.1, add=TRUE) # or the vector version zoomtoseg(seg=21:23, rivers=Koyukuk0) Koyukuk0.2 <- connectsegs(connect=c(20,21,22), connectto=c(21,22,23), nearestvert=c(FALSE,FALSE,TRUE), rivers=Koyukuk0) zoomtoseg(seg=21:23, rivers=Koyukuk0.2) topologydots(Koyukuk0.2, add=TRUE)
Plots kernel density anomaly for each survey, which is defined as the difference between kernel density for each survey and mean kernel density across all surveys. The intent of this function is to highlight areas in which density is higher or lower for specific surveys than it is on average.
The input argument is an object returned from makeriverdensity.
densityanomaly( x, whichplots = NULL, method = c("overlap", "both", "positive", "negative"), negative_ramp = "blue", positive_ramp = "red", parmfrow = NULL, ... )densityanomaly( x, whichplots = NULL, method = c("overlap", "both", "positive", "negative"), negative_ramp = "blue", positive_ramp = "red", parmfrow = NULL, ... )
x |
An object returned from makeriverdensity. |
whichplots |
A vector of plots to produce, if multiple plots are
produced. For example, specifying |
method |
Whether to produce plots for positive and negative anomalies
overlayed ( |
negative_ramp |
Color ramp to use for negative anomaly (see plot.riverdensity
for more details). Defaults to |
positive_ramp |
Color ramp to use for negative anomaly (see plot.riverdensity
for more details). Defaults to |
parmfrow |
Optional argument to |
... |
Additional arguments to plot.riverdensity. |
NULL
Matt Tyers
makeriverdensity, plot.riverdensity, plotriverdensitypoints
data(Gulk, fakefish) Gulk_dens <- makeriverdensity(seg=fakefish$seg, vert=fakefish$vert, rivers=Gulk, survey=fakefish$flight.date) # first, the behavior of plot.riverdensity # # 10 plots will be created, recommend calling par(mfrow=c(2,5)) plot(x=Gulk_dens) # next, showing densityanomaly densityanomaly(x=Gulk_dens, parmfrow=c(2,5)) densityanomaly(x=Gulk_dens, method="negative", parmfrow=c(2,5)) densityanomaly(x=Gulk_dens, method="positive", parmfrow=c(2,5))data(Gulk, fakefish) Gulk_dens <- makeriverdensity(seg=fakefish$seg, vert=fakefish$vert, rivers=Gulk, survey=fakefish$flight.date) # first, the behavior of plot.riverdensity # # 10 plots will be created, recommend calling par(mfrow=c(2,5)) plot(x=Gulk_dens) # next, showing densityanomaly densityanomaly(x=Gulk_dens, parmfrow=c(2,5)) densityanomaly(x=Gulk_dens, method="negative", parmfrow=c(2,5)) densityanomaly(x=Gulk_dens, method="positive", parmfrow=c(2,5))
Called internally within riverdistance. Detects the sequential route from one river network segment to another.
detectroute( start, end, rivers, verbose = FALSE, stopiferror = TRUE, algorithm = NULL )detectroute( start, end, rivers, verbose = FALSE, stopiferror = TRUE, algorithm = NULL )
start |
Segment number of the start of the route |
end |
Segment number of the end of the route |
rivers |
The river network object to use |
verbose |
Whether or not to print all routes being considered (used for error checking). Defaults to FALSE. |
stopiferror |
Whether or not to exit with an error if a route cannot be
found. If this is set to |
algorithm |
Which route detection algorithm to use. If set to
|
A vector of segment numbers corresponding to the ordered route.
Matt Tyers
data(Gulk) plot(x=Gulk, cex=1) detectroute(start=6, end=14, rivers=Gulk) tstart <- Sys.time() detectroute(start=120, end=111, rivers=abstreams, algorithm="sequential") tend <- Sys.time() tend - tstart data(abstreams) tstart <- Sys.time() detectroute(start=120, end=111, rivers=abstreams, algorithm="Dijkstra") tend <- Sys.time() tend - tstart tstart <- Sys.time() detectroute(start=120, end=111, rivers=abstreams, algorithm="segroutes") tend <- Sys.time() tend - tstartdata(Gulk) plot(x=Gulk, cex=1) detectroute(start=6, end=14, rivers=Gulk) tstart <- Sys.time() detectroute(start=120, end=111, rivers=abstreams, algorithm="sequential") tend <- Sys.time() tend - tstart data(abstreams) tstart <- Sys.time() detectroute(start=120, end=111, rivers=abstreams, algorithm="Dijkstra") tend <- Sys.time() tend - tstart tstart <- Sys.time() detectroute(start=120, end=111, rivers=abstreams, algorithm="segroutes") tend <- Sys.time() tend - tstart
Acts like a spatial dissolve within a GIS environment. Simplifies a river network object by combining "runs" of segments with no other connections.
dissolve(rivers)dissolve(rivers)
rivers |
The river network object to use |
A new river network object with segments combined
This function is called within cleanup, which is recommended in most cases.
Matt Tyers
line2network
data(Kenai2) plot(x=Kenai2) Kenai2dissolve <- dissolve(rivers=Kenai2) plot(x=Kenai2dissolve)data(Kenai2) plot(x=Kenai2) Kenai2dissolve <- dissolve(rivers=Kenai2) plot(x=Kenai2dissolve)
A set of observations of Fakefish on the Gulkana River and its tributaries.
data(fakefish)data(fakefish)
A data frame
x. X-coordinate of observation (Alaska Albers Equal Area). Note that the locations do not align with the river network object.
y. Y-coordinate of observation
seg. River segment (with x- and y-coordinates snapped to river network object)
vert. River vertex
fish.id. Numeric identifier for each fish (individual fish were observed more than once)
flight. Numeric identifier for each telemetry flight
flight.date. Date of each telemetry flight
An object created by riverdensity, describing the density of Fakefish points in the Gulkana River during ten surveys.
data(fakefish_density)data(fakefish_density)
A river density object, see riverdensity, plotriverdensity, riverdensity-class
Intended for plotting using plotriverdensity.
A stretch of Gulkana River and tributaries.
data(Gulk)data(Gulk)
A river network object, see rivernetwork
Plots a river network object and displays specified segments in bold, for easy identification.
highlightseg(seg, rivers, cex = 0.8, lwd = 3, add = FALSE, color = FALSE, ...)highlightseg(seg, rivers, cex = 0.8, lwd = 3, add = FALSE, color = FALSE, ...)
seg |
A vector of segments to highlight |
rivers |
The river network object to use |
cex |
The character expansion factor to use for segment labels |
lwd |
The line width to use for highlighted segments |
add |
Whether to add the highlighted segments to an existing plot
( |
color |
Whether to display segment labels as the same color as the
segments. Defaults to |
... |
Additional plotting arguments (see par) |
Matt Tyers
data(Kenai3) plot(Kenai3) highlightseg(seg=c(10,30,68),rivers=Kenai3)data(Kenai3) plot(Kenai3) highlightseg(seg=c(10,30,68),rivers=Kenai3)
Returns the minimum observed home range for multiple observations of each individual fish.
homerange( unique = NULL, survey = NULL, seg, vert, rivers, map = FALSE, algorithm = NULL, main = NULL, ... )homerange( unique = NULL, survey = NULL, seg, vert, rivers, map = FALSE, algorithm = NULL, main = NULL, ... )
unique |
A vector of unique identifiers for each fish. If the default ( |
survey |
A vector of survey identifiers for each fish. This argument is not needed for home range calculation, but can affect plotting (see plot.homerange). |
seg |
A vector of river locations (segment component). |
vert |
A vector of river locations (vertex component). |
rivers |
The river network object to use. |
map |
Deprecated, use plot.homerange for plotting instead. Originally, whether to produce sanity-check maps of observed locations and calculated home range for each fish. |
algorithm |
Which route detection algorithm to use ( |
main |
Deprecated, use plot.homerange for plotting instead. Originally, plot title, if |
... |
Deprecated, use plot.homerange for plotting instead. Originally, additional plotting arguments, if |
An object of the homerange-class. The $ranges element is a data frame with two columns: $ID is a list of unique fish
(as specified by unique=), and $range is calculated minimum
home range, in the units of the coordinate system (this will likely be
meters). The other elements are used for plotting, see homerange-class for more details.
Building routes from the river mouth to each river network segment and/or distance lookup tables will greatly reduce computation time (see buildsegroutes).
Matt Tyers
plot.homerange, homerangeoverlap, plothomerangeoverlap
data(Gulk, fakefish) ranges <- with(fakefish, homerange(unique=fish.id, survey=flight, seg=seg, vert=vert, rivers=Gulk)) ranges # 19 plots will be produced, recommend calling par(mfrow=c(4,5)) plot(ranges) plot(ranges,cumulative=TRUE,label=TRUE) homerangeoverlap(ranges) plothomerangeoverlap(ranges) with(fakefish, riverpoints(seg=seg, vert=vert, rivers=Gulk))data(Gulk, fakefish) ranges <- with(fakefish, homerange(unique=fish.id, survey=flight, seg=seg, vert=vert, rivers=Gulk)) ranges # 19 plots will be produced, recommend calling par(mfrow=c(4,5)) plot(ranges) plot(ranges,cumulative=TRUE,label=TRUE) homerangeoverlap(ranges) plothomerangeoverlap(ranges) with(fakefish, riverpoints(seg=seg, vert=vert, rivers=Gulk))
A class that holds information computed from the homerange function. Contains all information for plotting in plot.homerange.
ranges:Object of class "data.frame". Contains a column of the identifiers for each individual, and a column of the associated home ranges.
subseg_n:List of the number of times each subsegment was traveled. The first level of the list corresponds to individual, the second level to river segment.
subseg_length:List of lengths of each subsegment.
seg, vert, unique, rivers:All inputs from the original homerange call.
Matt Tyers
Returns matrices describing the overlap of the minimum observed home range for multiple observations of each individual fish.
homerangeoverlap(x)homerangeoverlap(x)
x |
An object returned from homerange. |
A list of three matrices, with $either giving the distances represented by the union of home ranges of each pair of individuals, and $both giving the distances represented by the intersection of home ranges of each pair of individuals. Element $prop_both gives the proportion of overlap, defined as intersection/union.
Matt Tyers
homerange, plot.homerange, plothomerangeoverlap
data(Gulk, fakefish) ranges <- with(fakefish, homerange(unique=fish.id, survey=flight, seg=seg, vert=vert, rivers=Gulk)) ranges # 19 plots will be produced, recommend calling par(mfrow=c(4,5)) plot(ranges) plot(ranges,cumulative=TRUE,label=TRUE) homerangeoverlap(ranges) plothomerangeoverlap(ranges) with(fakefish, riverpoints(seg=seg, vert=vert, rivers=Gulk))data(Gulk, fakefish) ranges <- with(fakefish, homerange(unique=fish.id, survey=flight, seg=seg, vert=vert, rivers=Gulk)) ranges # 19 plots will be produced, recommend calling par(mfrow=c(4,5)) plot(ranges) plot(ranges,cumulative=TRUE,label=TRUE) homerangeoverlap(ranges) plothomerangeoverlap(ranges) with(fakefish, riverpoints(seg=seg, vert=vert, rivers=Gulk))
Checks to see if two segments are flow-connected. Called internally within riverdirection and upstream.
isflowconnected(seg1, seg2, rivers, stopiferror = TRUE, algorithm = NULL)isflowconnected(seg1, seg2, rivers, stopiferror = TRUE, algorithm = NULL)
seg1 |
First input segment |
seg2 |
Second input segment |
rivers |
The river network object to use |
stopiferror |
Whether or not to exit with an error if a route cannot be
found. If this is set to |
algorithm |
Which route detection algorithm to use ( |
Logical TRUE if the two segments are flow-connected, FALSE if they are not
The river mouth must be specified (see setmouth).
Matt Tyers
data(Gulk) plot(Gulk) Gulk <- setmouth(seg=1, vert=1, rivers=Gulk) isflowconnected(seg1=13, seg2=14, rivers=Gulk) isflowconnected(seg1=13, seg2=1, rivers=Gulk)data(Gulk) plot(Gulk) Gulk <- setmouth(seg=1, vert=1, rivers=Gulk) isflowconnected(seg1=13, seg2=14, rivers=Gulk) isflowconnected(seg1=13, seg2=1, rivers=Gulk)
A first pass at a messy river network object.
data(Kenai1)data(Kenai1)
A river network object, see rivernetwork
A second pass at a messy river network object. In this iteration of cleanup, several non-connected segments have been removed.
data(Kenai2)data(Kenai2)
A river network object, see rivernetwork
A third pass at a messy river network object. In this iteration of cleanup, several non-connected segments have been removed, and several series of segments have been dissolved into single segments.
data(Kenai3)data(Kenai3)
A river network object, see rivernetwork
Plots K-functions for locations in each of a set of surveys. In this implementation, this can be interpreted as the proportion of additional fish within a given distance. This will increase as a function of distance, and may provide evidence of clustering or dispersion features, particularly if the envelope is used.
kfunc( seg, vert, survey = NULL, rivers, lwd = 2, envelope = TRUE, envreps = 1000, envcol = "grey80", envborder = NA, maxdist = NULL, xlab = "Distance", ylab = "% within", showN = TRUE, whichplots = NULL, returnoutput = FALSE, ... )kfunc( seg, vert, survey = NULL, rivers, lwd = 2, envelope = TRUE, envreps = 1000, envcol = "grey80", envborder = NA, maxdist = NULL, xlab = "Distance", ylab = "% within", showN = TRUE, whichplots = NULL, returnoutput = FALSE, ... )
seg |
A vector of river locations (segment) |
vert |
A vector of river locations (vertex) |
survey |
A vector of survey IDs corresponding to the values of
|
rivers |
The river network object to use |
lwd |
Line width used for plotting. Defaults to 2. |
envelope |
Whether to construct and display a 95 percent confidence
envelope (see note.) Defaults to |
envreps |
Number of bootstrap replicates to use for envelope calculation. Defaults to 1000. |
envcol |
Color to use for envelope plotting. Defaults to
|
envborder |
Border color to use for envelope plotting. Defaults to
|
maxdist |
Maximum distance (x-axis value) for plotting. The default
value ( |
xlab |
X-coordinate label for plotting |
ylab |
Y-coordinate label for plotting |
showN |
Whether to show the sample size for each survey in each plot
title. Defaults to |
whichplots |
A vector of plots to produce, if multiple plots are
produced. For example, specifying |
returnoutput |
Whether to return output instead of producing a plot. Defaults to |
... |
Additional plotting parameters. |
K-function envelopes for each survey are constructed by bootstrapping all within-survey distances, that is, the distances between all individuals within each survey, for all surveys. This results in a confidence envelope under the assumption that spacing is independent of survey; therefore a survey K-function outside the envelope provides evidence of clustering or dispersal in that survey that is outside the typical range. An envelope is not available if only one survey is plotted.
A K-function above the envelope for a given distance range provides evidence of a greater number of individuals than expected at that distance range (clustering); A K-function below the envelope for a given distance range provides evidence of a smaller number of individuals than expected at that distance range (dispersal).
This function is distance-computation intensive, and will be extremely slow-running if a river network is used that does not have segment routes and/or distance lookup tables for fast distance computation. See buildsegroutes and/or buildlookup for more information.
Matt Tyers
data(Gulk, fakefish) # # 10 plots will be created - recommend calling # # par(mfrow=c(3,4)) kfunc(seg=fakefish$seg, vert=fakefish$vert, rivers=Gulk, survey=fakefish$flight, envreps=100, maxdist=200000) # # This shows relatively high amounts of clustering for surveys 1 and 8, # # and relatively high amounts of dispersal in surveys 5 and 6. # # plotting the survey locations that led to this calculation, for comparison # # 10 plots will be created - recommend calling # # par(mfrow=c(3,4)) for(i in 1:10) { plot(x=Gulk, segmentnum=FALSE, color=FALSE, main=i) riverpoints(seg=fakefish$seg[fakefish$flight==i], vert=fakefish$vert[fakefish$flight==i], rivers=Gulk, col=2, pch=15) }data(Gulk, fakefish) # # 10 plots will be created - recommend calling # # par(mfrow=c(3,4)) kfunc(seg=fakefish$seg, vert=fakefish$vert, rivers=Gulk, survey=fakefish$flight, envreps=100, maxdist=200000) # # This shows relatively high amounts of clustering for surveys 1 and 8, # # and relatively high amounts of dispersal in surveys 5 and 6. # # plotting the survey locations that led to this calculation, for comparison # # 10 plots will be created - recommend calling # # par(mfrow=c(3,4)) for(i in 1:10) { plot(x=Gulk, segmentnum=FALSE, color=FALSE, main=i) riverpoints(seg=fakefish$seg[fakefish$flight==i], vert=fakefish$vert[fakefish$flight==i], rivers=Gulk, col=2, pch=15) }
A messy and braided section of the Kenai River network - actually a subset of Kenai3.
data(KilleyW)data(KilleyW)
A river network object, see rivernetwork
An unusably messy river network object, included for the purpose of testing river network editing functions.
data(Koyukuk0)data(Koyukuk0)
A river network object, see rivernetwork
A first pass at a messy river network object. The way it was dissolved in ArcGIS makes the endpoints appear disconnected to line2network and the topologies do not work.
data(Koyukuk1)data(Koyukuk1)
A river network object, see rivernetwork
A second pass at a messy river network object, with topologies fixed from Koyukuk1.
data(Koyukuk2)data(Koyukuk2)
A river network object, see rivernetwork
Uses read_sf in package 'sf' to read a river shapefile, and establishes connectivity of segment endpoints based on spatial proximity.
line2network( sf = NULL, path = ".", layer = NA, tolerance = 100, reproject = NULL, autofix = TRUE )line2network( sf = NULL, path = ".", layer = NA, tolerance = 100, reproject = NULL, autofix = TRUE )
sf |
Optional input as an sf object, if shapefile has already been read into the R environment. |
path |
File path, default is the current working directory. |
layer |
Name of the shapefile, without the .shp extension. |
tolerance |
Snapping tolerance of segment endpoints to determine connectivity. Default is 100, therefore care should be exercised when working with larger units of distance, such as km. |
reproject |
A valid projection, if the shapefile is to be re-projected. Re-projection is done using st_transform in package 'sf'. |
autofix |
Whether to automatically apply two corrections: removal of duplicate segments, and segments with lengths shorter than the connectivity tolerance. Defaults to 'TRUE'. |
Returns an object of class "rivernetwork" containing all
spatial and topological information. See rivernetwork-class.
Since distance can only be calculated using projected coordinates,
line2network() will generate an error if a non-projected input
shapefile is detected. To resolve this, the shapefile can be re-projected
in a GIS environment, or using reproject=, shown in the second
example below.
Matt Tyers, Jemma Stachelek
filepath <- system.file("extdata", package="riverdist") Gulk_UTM5 <- line2network(path=filepath, layer="Gulk_UTM5") plot(Gulk_UTM5) ## Reading directly from an sf object sf <- sf::read_sf(dsn = filepath, layer = "Gulk_UTM5") Gulk_UTM5 <- line2network(sf=sf) plot(Gulk_UTM5) ## Re-projecting in Alaska Albers Equal Area projection: AKalbers <- "+proj=aea +lat_1=55 +lat_2=65 +lat_0=50 +lon_0=-154 +x_0=0 +y_0=0 +datum=NAD83 +units=m +no_defs +ellps=GRS80" Gulk_AKalbers <- line2network(path=filepath, layer="Gulk_UTM5", reproject=AKalbers) plot(Gulk_AKalbers)filepath <- system.file("extdata", package="riverdist") Gulk_UTM5 <- line2network(path=filepath, layer="Gulk_UTM5") plot(Gulk_UTM5) ## Reading directly from an sf object sf <- sf::read_sf(dsn = filepath, layer = "Gulk_UTM5") Gulk_UTM5 <- line2network(sf=sf) plot(Gulk_UTM5) ## Re-projecting in Alaska Albers Equal Area projection: AKalbers <- "+proj=aea +lat_1=55 +lat_2=65 +lat_0=50 +lon_0=-154 +x_0=0 +y_0=0 +datum=NAD83 +units=m +no_defs +ellps=GRS80" Gulk_AKalbers <- line2network(path=filepath, layer="Gulk_UTM5", reproject=AKalbers) plot(Gulk_AKalbers)
A matrix of coordinates in longitude-latitude, used to illustrate coordinate transformation. Coordinates come from arbitrary line number 98 in the Kenai River 1 shapefile, rendered in long-lat.
data(line98)data(line98)
A matrix of values
Uses spatial point data (segment and vertex) to calculate a kernel density object to use in the output class plotting method,plot.riverdensity. Scaled kernel density is calculated at approximately regularly-spaced locations, with spacing specified by the user.
If an argument is used in the survey field, kernel densities will be
calculated for each unique value of survey, resulting in a separate
plot for each.
The purpose of this function is to generate a kernel density object to plot using plot(), see plot.riverdensity.
makeriverdensity( seg, vert, rivers, survey = NULL, kernel = "gaussian", bw = NULL, resolution = NULL )makeriverdensity( seg, vert, rivers, survey = NULL, kernel = "gaussian", bw = NULL, resolution = NULL )
seg |
A vector of river locations (segment) |
vert |
A vector of river locations (vertex) |
rivers |
The river network object to use |
survey |
A vector of survey IDs corresponding to the values of
|
kernel |
The type of density kernel to use. Allowed types are
|
bw |
The kernel bandwidth to use. If |
resolution |
The approximate spacing of the river locations used for
kernel density calculation. Accepting the default ( |
A river density object, see riverdensity-class.
It is likely that calculation will be very slow. Use of this function with a river network for which segment routes has not yet been calculated is not recommended.
This function is distance-computation intensive, and may be slow-running if a river network is used that does not have segment routes and/or distance lookup tables for fast distance computation. See buildsegroutes and/or buildlookup for more information.
Matt Tyers
plot.riverdensity, densityanomaly, plotriverdensitypoints
data(Gulk, fakefish) Gulk_dens <- makeriverdensity(seg=fakefish$seg, vert=fakefish$vert, rivers=Gulk, survey=fakefish$flight.date) # # 10 plots will be created, recommend calling par(mfrow=c(2,5)) plot(x=Gulk_dens)data(Gulk, fakefish) Gulk_dens <- makeriverdensity(seg=fakefish$seg, vert=fakefish$vert, rivers=Gulk, survey=fakefish$flight.date) # # 10 plots will be created, recommend calling par(mfrow=c(2,5)) plot(x=Gulk_dens)
Provides a check that river network segments were appropriately named.
mapbyname(rivers, scale = TRUE, cex = 0.6, ...)mapbyname(rivers, scale = TRUE, cex = 0.6, ...)
rivers |
The river network object to use. Function checks segment names contained in the river network object. |
scale |
Whether or not to give x- and y-axes the same scale |
cex |
Global character expansion factor for plotting |
... |
Additional plotting arguments (see par) |
Matt Tyers
data(Gulk) str(Gulk) Gulk$names <- c("Gulkana River","Trib 1","West Fork","Gulkana River","Trib 1", "West Fork","Trib 2","West Fork","Twelvemile Creek","Gulkana River", "Middle Fork","Gulkana River","Middle Fork","Hungry Hollow") str(Gulk) mapbyname(rivers=Gulk)data(Gulk) str(Gulk) Gulk$names <- c("Gulkana River","Trib 1","West Fork","Gulkana River","Trib 1", "West Fork","Trib 2","West Fork","Twelvemile Creek","Gulkana River", "Middle Fork","Gulkana River","Middle Fork","Hungry Hollow") str(Gulk) mapbyname(rivers=Gulk)
Returns a list of matrices, each giving the river distance, direction, or upstream travel distance between all observations of one unique fish. This function is principally intended for producing an object to plot in plotmatbysurveylist.
matbysurveylist( unique, survey, seg, vert, rivers, indiv = NULL, method = "upstream", flowconnected = FALSE, net = FALSE, stopiferror = TRUE, algorithm = NULL )matbysurveylist( unique, survey, seg, vert, rivers, indiv = NULL, method = "upstream", flowconnected = FALSE, net = FALSE, stopiferror = TRUE, algorithm = NULL )
unique |
A vector of unique identifiers for each fish. |
survey |
A vector of identifiers for each survey. It is recommended to use a numeric or date format (see as.Date) to preserve survey order. |
seg |
A vector of river locations (segment component). |
vert |
A vector of river locations (vertex component). |
rivers |
The river network object to use. |
indiv |
A vector of unique individuals to use. Accepting the default ( |
method |
Which general method to use. Setting |
flowconnected |
Optional parameter to pass to the distance or direction calculation. Defaults to |
net |
Optional parameter to pass to the distance or direction calculation. Defaults to |
stopiferror |
Optional parameter to pass to the distance or direction calculation. Defaults to |
algorithm |
Optional parameter to pass to the distance or direction calculation. Defaults to |
A list with each element corresponding to a unique fish. Each list element is the output from either riverdistancematbysurvey, riverdirectionmatbysurvey, or upstreammatbysurvey.
Building routes from the river mouth to each river network segment and/or distance lookup tables will greatly reduce computation time (see buildsegroutes).
Matt Tyers
riverdistance, riverdirection, upstream, riverdistancematbysurvey, riverdirectionmatbysurvey, upstreammatbysurvey, plotmatbysurveylist
data(Gulk, smallset) matbysurveylist <- matbysurveylist(unique=smallset$id, survey=smallset$flight, seg=smallset$seg, vert=smallset$vert, rivers=Gulk) plotmatbysurveylist(matbysurveylist) plotmatbysurveylist(matbysurveylist,type="confint") plotmatbysurveylist(matbysurveylist,type="dotplot") data(fakefish) # matbysurveylist <- matbysurveylist(unique=fakefish$fish.id, survey=fakefish$flight, # seg=fakefish$seg, vert=fakefish$vert, rivers=Gulk) # plotmatbysurveylist(matbysurveylist)data(Gulk, smallset) matbysurveylist <- matbysurveylist(unique=smallset$id, survey=smallset$flight, seg=smallset$seg, vert=smallset$vert, rivers=Gulk) plotmatbysurveylist(matbysurveylist) plotmatbysurveylist(matbysurveylist,type="confint") plotmatbysurveylist(matbysurveylist,type="dotplot") data(fakefish) # matbysurveylist <- matbysurveylist(unique=fakefish$fish.id, survey=fakefish$flight, # seg=fakefish$seg, vert=fakefish$vert, rivers=Gulk) # plotmatbysurveylist(matbysurveylist)
Calculates distance from river locations (given as vectors of segment and vertex) and the specified mouth of the river network. The mouth must first be specified (see setmouth).
mouthdist(seg, vert, rivers, stopiferror = TRUE, algorithm = NULL)mouthdist(seg, vert, rivers, stopiferror = TRUE, algorithm = NULL)
seg |
Vector of segments |
vert |
Vector of vertices |
rivers |
The river network object to use |
stopiferror |
Whether or not to exit with an error if a route cannot be
found. If this is set to |
algorithm |
Which route detection algorithm to use ( |
Distance (numeric)
Building routes from the river mouth to each river network segment and/or distance lookup tables will greatly reduce computation time (see buildsegroutes).
Matt Tyers
data(Gulk) # Mouth must be specified Gulk$mouth$mouth.seg <- 1 Gulk$mouth$mouth.vert <- 1 mouthdist(seg=4, vert=40, rivers=Gulk) mouthdist(seg=c(4,5), vert=c(40,20), rivers=Gulk)data(Gulk) # Mouth must be specified Gulk$mouth$mouth.seg <- 1 Gulk$mouth$mouth.vert <- 1 mouthdist(seg=4, vert=40, rivers=Gulk) mouthdist(seg=c(4,5), vert=c(40,20), rivers=Gulk)
Calculates distance from the mouth of a river network to all observations of each individual (given as segment and vertex). and the specified mouth of the river network. The mouth must first be specified (see setmouth). Returns a matrix of distances, with a row for each unique individual and a column for each survey.
A plotting method is provided for the output; see plotseq.
mouthdistbysurvey( unique, survey, seg, vert, rivers, logical = NULL, stopiferror = TRUE, algorithm = NULL )mouthdistbysurvey( unique, survey, seg, vert, rivers, logical = NULL, stopiferror = TRUE, algorithm = NULL )
unique |
A vector of identifiers for each fish. |
survey |
A vector of identifiers for each survey. It is recommended to use a numeric or date format (see as.Date) to preserve survey order. |
seg |
A vector of river locations (segment) |
vert |
A vector pf rover coordinates (vertex) |
rivers |
The river network object to use |
logical |
A boolean vector that can be used for subsetting - if used,
|
stopiferror |
Whether or not to exit with an error if a route cannot be
found. If this is set to |
algorithm |
Which route detection algorithm to use ( |
A vector of river network distances (numeric), with each row
corresponding to a unique fish and each column corresponding to a unique
survey. Values of NA indicate the individual not being located
during the survey in question.
Building routes from the river mouth to each river network segment and/or distance lookup tables will greatly reduce computation time (see buildsegroutes).
Matt Tyers
data(Gulk, fakefish) seqbysurvey <- mouthdistbysurvey(unique=fakefish$fish.id, survey=fakefish$flight.date, seg=fakefish$seg, vert=fakefish$vert, rivers=Gulk) seqbysurvey plotseq(seqbysurvey)data(Gulk, fakefish) seqbysurvey <- mouthdistbysurvey(unique=fakefish$fish.id, survey=fakefish$flight.date, seg=fakefish$seg, vert=fakefish$vert, rivers=Gulk) seqbysurvey plotseq(seqbysurvey)
Pythagorean distance between two points. Called internally.
pdist(p1, p2)pdist(p1, p2)
p1 |
X-Y coordinates of point 1 |
p2 |
X-Y coordinates of point 2 |
Distance (numeric)
Matt Tyers
point1 <- c(1,3) point2 <- c(4,7) pdist(point1,point2)point1 <- c(1,3) point2 <- c(4,7) pdist(point1,point2)
Total Pythagorean distance of a sequence of points. Called internally.
pdisttot(xy)pdisttot(xy)
xy |
A matrix of X-Y coordinates of the sequence of points. |
Distance (numeric)
Matt Tyers
points <- matrix(c(1:10), nrow=5, ncol=2, byrow=FALSE) pdisttot(xy=points)points <- matrix(c(1:10), nrow=5, ncol=2, byrow=FALSE) pdisttot(xy=points)
Plotting method for home range, the minimum observed home range for multiple observations of each individual fish.
## S3 method for class 'homerange' plot( x, cumulative = FALSE, lwd = 3, maxlwd = 10, col = 4, pch = 21, label = FALSE, main = NULL, ... )## S3 method for class 'homerange' plot( x, cumulative = FALSE, lwd = 3, maxlwd = 10, col = 4, pch = 21, label = FALSE, main = NULL, ... )
x |
An object returned from homerange. |
cumulative |
Whether to plot travel as cumulative, with line thickness depending on the number of times a given region was traveled by a given individual. Defaults to |
lwd |
The line width for plotting homerange, or minimum line width if |
maxlwd |
The maximum line width if |
col |
The line color to use. Defaults to |
pch |
The point character to use for individual points. Defaults to open circles, the color of lines. |
label |
Whether to add survey labels to individual points, if used in homerange. Defaults to |
main |
Plot title. If the default |
... |
Additional plotting parameters, see plot.rivernetwork. |
Matt Tyers, bug fix by Jordy Bernard
homerange, homerangeoverlap, plothomerangeoverlap
data(Gulk, fakefish) ranges <- with(fakefish, homerange(unique=fish.id, survey=flight, seg=seg, vert=vert, rivers=Gulk)) ranges # 19 plots will be produced, recommend calling par(mfrow=c(4,5)) plot(ranges) plot(ranges,cumulative=TRUE,label=TRUE) homerangeoverlap(ranges) plothomerangeoverlap(ranges) with(fakefish, riverpoints(seg=seg, vert=vert, rivers=Gulk))data(Gulk, fakefish) ranges <- with(fakefish, homerange(unique=fish.id, survey=flight, seg=seg, vert=vert, rivers=Gulk)) ranges # 19 plots will be produced, recommend calling par(mfrow=c(4,5)) plot(ranges) plot(ranges,cumulative=TRUE,label=TRUE) homerangeoverlap(ranges) plothomerangeoverlap(ranges) with(fakefish, riverpoints(seg=seg, vert=vert, rivers=Gulk))
Produces a kernel density plot from a kernel density object created by makeriverdensity.
If the kernel density object includes densities from multiple surveys, a new plot will be created for each survey.
Densities can be displayed using either line widths, color, or both.
The relative densities that are displayed in the plot are calculated
according to the form (density/maxdensity)^pwr, with the value of pwr set
by the pwr argument. Setting pwr to a value less than 1
allows smaller values to be more visible on the plot.
## S3 method for class 'riverdensity' plot( x, whichplots = NULL, points = TRUE, bycol = TRUE, bylwd = TRUE, maxlwd = 10, pwr = 0.7, scalebyN = TRUE, ramp = c("grey", "gray", "red", "green", "blue", "heat", "stoplight", "rainbow"), lwd = 1, linecol = NULL, denscol = NULL, alpha = 1, dark = 1, showN = TRUE, main = NULL, xlab = "", ylab = "", add = FALSE, scalebar = TRUE, ... )## S3 method for class 'riverdensity' plot( x, whichplots = NULL, points = TRUE, bycol = TRUE, bylwd = TRUE, maxlwd = 10, pwr = 0.7, scalebyN = TRUE, ramp = c("grey", "gray", "red", "green", "blue", "heat", "stoplight", "rainbow"), lwd = 1, linecol = NULL, denscol = NULL, alpha = 1, dark = 1, showN = TRUE, main = NULL, xlab = "", ylab = "", add = FALSE, scalebar = TRUE, ... )
x |
A river density object created by makeriverdensity. |
whichplots |
A vector of plots to produce, if multiple plots are
produced. For example, specifying |
points |
Whether to add the points used for density calculation.
Defaults to |
bycol |
Whether to use a color ramp to show densities. Defaults to
|
bylwd |
Whether to use line thickness to show densities. Defaults to
|
maxlwd |
The maximum line width to use if |
pwr |
The power to use in the nonlinear transformation calculating the relative density values to be displayed (see above.) Defaults to 0.7. |
scalebyN |
Whether to display relative density values scaled by sample
size. Specifying |
ramp |
The color ramp used to display densities if |
lwd |
The line width to use for background lines if |
linecol |
The line color to use for background lines if |
denscol |
The line color to use for showing density if |
alpha |
The opacity value for lines. This could potentially allow multiple density plots to be overlayed with different colors. |
dark |
A color-saturation adjustment, with values in [0,1]. A value of 1 uses the true colors, and a value less than 1 will render the colors as slightly darker (less saturated), which may be appear better. Defaults to 1. |
showN |
Whether to automatically include the number of points used as part of the plot title(s). |
main |
Plot title(s), either given as a single text string which is
repeated if multiple plots are produced, or a vector of text strings (one
for each plot produced). If multiple plots are produced (resulting from
multiple surveys), accepting the default ( |
xlab |
X-axis label |
ylab |
Y-axis label |
add |
Whether to produce a new plot ( |
scalebar |
Whether to add a scale bar to plot(s). Defaults to |
... |
Additional plotting parameters. |
Matt Tyers
makeriverdensity, densityanomaly, plotriverdensitypoints
data(Gulk, fakefish) Gulk_dens <- makeriverdensity(seg=fakefish$seg, vert=fakefish$vert, rivers=Gulk, survey=fakefish$flight.date) # # 10 plots will be created, recommend calling par(mfrow=c(2,5)) plot(x=Gulk_dens)data(Gulk, fakefish) Gulk_dens <- makeriverdensity(seg=fakefish$seg, vert=fakefish$vert, rivers=Gulk, survey=fakefish$flight.date) # # 10 plots will be created, recommend calling par(mfrow=c(2,5)) plot(x=Gulk_dens)
S3 plotting method for the rivernetwork-class. Produces a map of all river segments of a river network object.
## S3 method for class 'rivernetwork' plot( x, segmentnum = TRUE, offset = TRUE, lwd = 1, cex = 0.6, scale = TRUE, color = TRUE, empty = FALSE, linecol = 1, xlab = "", ylab = "", ... )## S3 method for class 'rivernetwork' plot( x, segmentnum = TRUE, offset = TRUE, lwd = 1, cex = 0.6, scale = TRUE, color = TRUE, empty = FALSE, linecol = 1, xlab = "", ylab = "", ... )
x |
The river network object to plot |
segmentnum |
Whether or not to plot segment numbers (defaults to TRUE) |
offset |
Whether to offset segment numbers from lines (defaults to TRUE) |
lwd |
Line width |
cex |
Global character expansion factor for plotting |
scale |
Whether or not to give x- and y-axes the same scale |
color |
How to differentiate segments. If |
empty |
Creates an empty plot if set to |
linecol |
Line color to use if |
xlab |
Label for X-axis (defaults to "") |
ylab |
Label for Y-axis (defaults to "") |
... |
Additional plotting arguments (see par) |
This function is intended to provide basic visual checks for the user, not for any real mapping.
Matt Tyers
data(Gulk) plot(x=Gulk)data(Gulk) plot(x=Gulk)
Produces a plot of the overlap of the minimum observed home range for multiple observations of each individual fish, with line thickness illustrating the respective number of individuals' homeranges represented.
plothomerangeoverlap(x, lwd = 3, maxlwd = 10, col = 4, ...)plothomerangeoverlap(x, lwd = 3, maxlwd = 10, col = 4, ...)
x |
An object returned from homerange. |
lwd |
Minimum line width to use, defaults to 3. |
maxlwd |
Maximum line width to use, defaults to 10. |
col |
Line color to use, defaults to |
... |
Additional plotting parameters, see plot.rivernetwork. |
Matt Tyers
homerange, plot.homerange, homerangeoverlap
data(Gulk, fakefish) ranges <- with(fakefish, homerange(unique=fish.id, survey=flight, seg=seg, vert=vert, rivers=Gulk)) ranges # 19 plots will be produced, recommend calling par(mfrow=c(4,5)) plot(ranges) plot(ranges,cumulative=TRUE,label=TRUE) homerangeoverlap(ranges) plothomerangeoverlap(ranges) with(fakefish, riverpoints(seg=seg, vert=vert, rivers=Gulk))data(Gulk, fakefish) ranges <- with(fakefish, homerange(unique=fish.id, survey=flight, seg=seg, vert=vert, rivers=Gulk)) ranges # 19 plots will be produced, recommend calling par(mfrow=c(4,5)) plot(ranges) plot(ranges,cumulative=TRUE,label=TRUE) homerangeoverlap(ranges) plothomerangeoverlap(ranges) with(fakefish, riverpoints(seg=seg, vert=vert, rivers=Gulk))
Produces a matrix of plots (boxplots are default), with plot [i,j] giving the
distribution of upstream distances from observation i to observation
j, for all individuals.
plotmatbysurveylist(matbysurveylist, type = "boxplot", showN = TRUE, ...)plotmatbysurveylist(matbysurveylist, type = "boxplot", showN = TRUE, ...)
matbysurveylist |
A list of distance matrices returned from matbysurveylist. |
type |
If |
showN |
Whether to display the sample size for each cell. Defaults to TRUE. |
... |
Additional plotting arguments. |
Building routes from the river mouth to each river network segment and/or distance lookup tables will greatly reduce computation time (see buildsegroutes).
Matt Tyers
data(Gulk, smallset) matbysurveylist <- matbysurveylist(unique=smallset$id, survey=smallset$flight, seg=smallset$seg, vert=smallset$vert, rivers=Gulk) plotmatbysurveylist(matbysurveylist) plotmatbysurveylist(matbysurveylist,type="confint") plotmatbysurveylist(matbysurveylist,type="dotplot") data(fakefish) # matbysurveylist <- matbysurveylist(unique=fakefish$fish.id, survey=fakefish$flight, # seg=fakefish$seg, vert=fakefish$vert, rivers=Gulk) # plotmatbysurveylist(matbysurveylist)data(Gulk, smallset) matbysurveylist <- matbysurveylist(unique=smallset$id, survey=smallset$flight, seg=smallset$seg, vert=smallset$vert, rivers=Gulk) plotmatbysurveylist(matbysurveylist) plotmatbysurveylist(matbysurveylist,type="confint") plotmatbysurveylist(matbysurveylist,type="dotplot") data(fakefish) # matbysurveylist <- matbysurveylist(unique=fakefish$fish.id, survey=fakefish$flight, # seg=fakefish$seg, vert=fakefish$vert, rivers=Gulk) # plotmatbysurveylist(matbysurveylist)
Plots the points used to calculate a kernel density object in makeriverdensity.
This function is intended as a visual check that a sufficient resolution was used.
plotriverdensitypoints(riverdensity)plotriverdensitypoints(riverdensity)
riverdensity |
A river density object created by makeriverdensity. |
Matt Tyers
makeriverdensity, plot.riverdensity
data(Gulk, fakefish) Gulk_dens <- makeriverdensity(seg=fakefish$seg, vert=fakefish$vert, rivers=Gulk) plotriverdensitypoints(riverdensity=Gulk_dens)data(Gulk, fakefish) Gulk_dens <- makeriverdensity(seg=fakefish$seg, vert=fakefish$vert, rivers=Gulk) plotriverdensitypoints(riverdensity=Gulk_dens)
Plots the sequence of observations or movements of each individual (given as segment and vertex). This function is primarily intended for use with mouthdistbysurvey, but will also work with riverdistanceseq and upstreamseq.
plotseq( seqbysurvey, type = "boxplot", xlab = "", ylab = "", main = "", cex.axisX = 0.8, lowerbound = NULL, upperbound = NULL, boundtype = "negative", surveysareDates = F, ... )plotseq( seqbysurvey, type = "boxplot", xlab = "", ylab = "", main = "", cex.axisX = 0.8, lowerbound = NULL, upperbound = NULL, boundtype = "negative", surveysareDates = F, ... )
seqbysurvey |
A matrix returned from mouthdistbysurvey, riverdistanceseq, or upstreamseq. |
type |
The type of plot to generate. Options are
|
xlab |
X-axis label |
ylab |
Y-axis label |
main |
Plot title |
cex.axisX |
Character expansion factor for X-axis labels |
lowerbound |
An optional vector of lower survey bounds |
upperbound |
An optional vector of upper survey bounds |
boundtype |
Method of plotting survey bounds. Options are
|
surveysareDates |
If surveys are in Date format (see as.Date), a
value of |
... |
Additional plotting parameters |
Plots are intended as descriptive only. Any ANOVA-like inference that is suggested from these plots is strongly discouraged. The user is instead advised to use a mixed-effects model or some other inferential tool that accounts for repeated-measures and/or temporal autocorrelation.
Matt Tyers
data(Gulk, fakefish) x <- mouthdistbysurvey(unique=fakefish$fish.id, survey=fakefish$flight.date, seg=fakefish$seg, vert=fakefish$vert, rivers=Gulk) plotseq(seqbysurvey=x) plotseq(seqbysurvey=x, type="boxline") plotseq(seqbysurvey=x, type="dotplot") plotseq(seqbysurvey=x, type="dotline") plotseq(seqbysurvey=x, type="dotline", surveysareDates=TRUE) from_upstreamseq <- upstreamseq(unique=fakefish$fish.id, survey=fakefish$flight, seg=fakefish$seg, vert=fakefish$vert, rivers=Gulk) plotseq(seqbysurvey=from_upstreamseq)data(Gulk, fakefish) x <- mouthdistbysurvey(unique=fakefish$fish.id, survey=fakefish$flight.date, seg=fakefish$seg, vert=fakefish$vert, rivers=Gulk) plotseq(seqbysurvey=x) plotseq(seqbysurvey=x, type="boxline") plotseq(seqbysurvey=x, type="dotplot") plotseq(seqbysurvey=x, type="dotline") plotseq(seqbysurvey=x, type="dotline", surveysareDates=TRUE) from_upstreamseq <- upstreamseq(unique=fakefish$fish.id, survey=fakefish$flight, seg=fakefish$seg, vert=fakefish$vert, rivers=Gulk) plotseq(seqbysurvey=from_upstreamseq)
This function reads a point shapefile and determines the closest vertex in the river network to each point of XY data, returning a data frame with river locations, defined as segment numbers and vertex numbers, along with the data table read from the input shapefile.
pointshp2segvert(path = ".", layer, rivers)pointshp2segvert(path = ".", layer, rivers)
path |
File path, default is the current working directory. |
layer |
Name of the shapefile, without the .shp extension. |
rivers |
The river network object to use. |
A data frame of river locations, with segment numbers in
$seg, vertex numbers in $vert, snapping distances in
$snapdist, snapped x- and y-coordinates in $snap_x and $snap_y,
and the remaining columns
corresponding to the data table in the input point shapefile.
If the input shapefile is detected to be in a different projection than the river network, the input shapefile will be re-projected before conversion to river locations.
Matt Tyers
filepath <- system.file("extdata", package="riverdist") fakefish_UTM5 <- pointshp2segvert(path=filepath, layer="fakefish_UTM5", rivers=Gulk) head(fakefish_UTM5) plot(x=Gulk) points(fakefish_UTM5$x, fakefish_UTM5$y) riverpoints(seg=fakefish_UTM5$seg, vert=fakefish_UTM5$vert, rivers=Gulk, pch=16, col=2)filepath <- system.file("extdata", package="riverdist") fakefish_UTM5 <- pointshp2segvert(path=filepath, layer="fakefish_UTM5", rivers=Gulk) head(fakefish_UTM5) plot(x=Gulk) points(fakefish_UTM5$x, fakefish_UTM5$y) riverpoints(seg=fakefish_UTM5$seg, vert=fakefish_UTM5$vert, rivers=Gulk, pch=16, col=2)
Removes duplicated line segments, which can sometimes exist within a shapefile.
removeduplicates(rivers)removeduplicates(rivers)
rivers |
The river network object to use |
A new river network object with duplicated segments removed, see rivernetwork
Matt Tyers
line2network
data(abstreams0) zoomtoseg(seg=c(170,171,157),rivers=abstreams0) abstreams1 <- removeduplicates(rivers=abstreams0) zoomtoseg(seg=c(166,167,154),rivers=abstreams1)data(abstreams0) zoomtoseg(seg=c(170,171,157),rivers=abstreams0) abstreams1 <- removeduplicates(rivers=abstreams0) zoomtoseg(seg=c(166,167,154),rivers=abstreams1)
Automatically detects and removes segments with total displacement (straight-line distance between endpoints) less than the connectivity tolerance. These segments do not serve any real purpose, are bypassed in routes, and cannot be dissolved.
removemicrosegs(rivers)removemicrosegs(rivers)
rivers |
The river network object to use. |
A new river network object with the specified segments connected (see rivernetwork)
This function is called within cleanup, which is recommended in most cases.
Matt Tyers
line2network
data(abstreams0) abstreams1 <- removemicrosegs(abstreams0)data(abstreams0) abstreams1 <- removemicrosegs(abstreams0)
Detects and removes segments that are not connected to the river mouth.
removeunconnected(rivers)removeunconnected(rivers)
rivers |
The river network object to use. |
This function is called within cleanup, which is recommended in most cases.
Matt Tyers
data(Koyukuk2) Koy_subset <- trimriver(trimto=c(30,28,29,3,19,27,4),rivers=Koyukuk2) Koy_subset <- setmouth(seg=1,vert=427,rivers=Koy_subset) plot(Koy_subset) Koy_subset_trim <- removeunconnected(Koy_subset) plot(Koy_subset_trim)data(Koyukuk2) Koy_subset <- trimriver(trimto=c(30,28,29,3,19,27,4),rivers=Koyukuk2) Koy_subset <- setmouth(seg=1,vert=427,rivers=Koy_subset) plot(Koy_subset) Koy_subset_trim <- removeunconnected(Koy_subset) plot(Koy_subset_trim)
A class that holds density information computed from point data along a river network.
Created by makeriverdensity from point data and a river network. Contains all information for plotting in plot.riverdensity.
densities:Object of class "list". Each list element corresponds to a unique value of survey. Each element is itself of class "list", with each element corresponding to a segment from the associated river network. Each element is a vector of class "numeric", with values equal to the scaled densities calculated at the river network vertices stored in $densverts of the associated river network segment.
endptverts:List of vectors of class "numeric". Each list element is a vector of the vertices of the endpoints of the subsegments considered for density calculation. Each list element corresponds to a river segment from the associated river network.
densverts:List of vectors of class "numeric". Each element is a vector of the vertices of the points of the subsegments considered for density calculation, that were used for density calculation. Each list element corresponds to a river segment from the associated river network.
pointsegs:Vector of class "numeric". Defined as the segment numbers of the point data used for density calculation.
pointverts:Vector of class "numeric". Defined as the vertex numbers of the point data used for density calculation.
survey:Vector of class "numeric" or class "character". Defined as the survey identifiers associated with the point data used for density calculation.
rivers:Object of class "rivernetwork" ; see rivernetwork-class
.
Matt Tyers
Calculates direction of travel between two points. Only works if river mouth (lowest point) has been specified (see setmouth).
riverdirection( startseg, endseg, startvert, endvert, rivers, flowconnected = FALSE, stopiferror = TRUE, algorithm = NULL )riverdirection( startseg, endseg, startvert, endvert, rivers, flowconnected = FALSE, stopiferror = TRUE, algorithm = NULL )
startseg |
Segment number of the start of the route |
endseg |
Segment number of the end of the route |
startvert |
Vertex number of the start of the route |
endvert |
Vertex number of the end of the route |
rivers |
The river network object to use |
flowconnected |
If |
stopiferror |
Whether or not to exit with an error if a route cannot be
found. If this is set to |
algorithm |
Which route detection algorithm to use ( |
Direction: "up", "down", or "0" (character). Returns NA if flowconnected==TRUE and the two segments are not flow-connected.
Building routes from the river mouth to each river network segment and/or distance lookup tables will greatly reduce computation time (see buildsegroutes).
Matt Tyers
data(Gulk) # Mouth must be specified Gulk$mouth$mouth.seg <- 1 Gulk$mouth$mouth.vert <- 1 plot(x=Gulk) riverdirection(startseg=6, endseg=3, startvert=40, endvert=40, rivers=Gulk)data(Gulk) # Mouth must be specified Gulk$mouth$mouth.seg <- 1 Gulk$mouth$mouth.vert <- 1 plot(x=Gulk) riverdirection(startseg=6, endseg=3, startvert=40, endvert=40, rivers=Gulk)
Returns a matrix of calculated travel direction between every point and every other point of given river locations (segment and vertex), or of a subset. The mouth (lowest point) segment and vertex must be specified (see setmouth).
riverdirectionmat( seg, vert, rivers, logical = NULL, ID = NULL, flowconnected = FALSE, stopiferror = TRUE, algorithm = NULL )riverdirectionmat( seg, vert, rivers, logical = NULL, ID = NULL, flowconnected = FALSE, stopiferror = TRUE, algorithm = NULL )
seg |
A vector of river locations (segment component). |
vert |
A vector of river locations (vertex component). |
rivers |
The river network object to use |
logical |
A boolean vector that can be used for subsetting - if used,
|
ID |
a vector of observation IDs for aid in interpreting the output table |
flowconnected |
If |
stopiferror |
Whether or not to exit with an error if a route cannot be
found. If this is set to |
algorithm |
Which route detection algorithm to use ( |
A matrix of directions (character) with rows and columns labeled by
corresponding values of ID. See riverdirection for additional information.
Building routes from the river mouth to each river network segment and/or distance lookup tables will greatly reduce computation time (see buildsegroutes).
Matt Tyers
data(Gulk, fakefish) # Mouth must be specified Gulk$mouth$mouth.seg <- 1 Gulk$mouth$mouth.vert <- 1 logi1 <- (fakefish$flight.date==as.Date("2015-11-25")) riverdirectionmat(seg=fakefish$seg, vert=fakefish$vert, rivers=Gulk, logical=logi1)data(Gulk, fakefish) # Mouth must be specified Gulk$mouth$mouth.seg <- 1 Gulk$mouth$mouth.vert <- 1 logi1 <- (fakefish$flight.date==as.Date("2015-11-25")) riverdirectionmat(seg=fakefish$seg, vert=fakefish$vert, rivers=Gulk, logical=logi1)
Returns a matrix of travel direction between all observations of one unique fish.
riverdirectionmatbysurvey( indiv, unique, survey, seg, vert, rivers, full = TRUE, flowconnected = FALSE, stopiferror = TRUE, algorithm = NULL )riverdirectionmatbysurvey( indiv, unique, survey, seg, vert, rivers, full = TRUE, flowconnected = FALSE, stopiferror = TRUE, algorithm = NULL )
indiv |
The unique identifier of the fish in question. |
unique |
A vector of identifiers for each fish. |
survey |
A vector of identifiers for each survey. It is recommended to use a numeric or date format (see as.Date) to preserve survey order. |
seg |
A vector of river locations (segment component). |
vert |
A vector of river locations (vertex component). |
rivers |
The river network object to use. |
full |
Whether to return the full matrix, with |
flowconnected |
If |
stopiferror |
Whether or not to exit with an error if a route cannot be
found. If this is set to |
algorithm |
Which route detection algorithm to use ( |
A matrix of directions (character), with rows and columns defined by
survey. In the resulting matrix, the element with the row identified as
A and column identified as B is defined as the direction
traveled from survey A to survey B. Therefore, it is likely that only the
upper triangle of the matrix will be of interest.
Building routes from the river mouth to each river network segment and/or distance lookup tables will greatly reduce computation time (see buildsegroutes).
Matt Tyers
data(Gulk, fakefish) riverdirectionmatbysurvey(indiv=1, unique=fakefish$fish.id, survey=fakefish$flight, seg=fakefish$seg, vert=fakefish$vert, rivers=Gulk) riverdirectionmatbysurvey(indiv=1, unique=fakefish$fish.id, survey=fakefish$flight, seg=fakefish$seg, vert=fakefish$vert, rivers=Gulk, full=FALSE)data(Gulk, fakefish) riverdirectionmatbysurvey(indiv=1, unique=fakefish$fish.id, survey=fakefish$flight, seg=fakefish$seg, vert=fakefish$vert, rivers=Gulk) riverdirectionmatbysurvey(indiv=1, unique=fakefish$fish.id, survey=fakefish$flight, seg=fakefish$seg, vert=fakefish$vert, rivers=Gulk, full=FALSE)
Returns a matrix of directions traveled by unique fish between sequential surveys. The mouth (lowest point) segment and vertex must be specified (see setmouth).
riverdirectionseq( unique, survey, seg, vert, rivers, logical = NULL, flowconnected = FALSE, stopiferror = TRUE, algorithm = NULL )riverdirectionseq( unique, survey, seg, vert, rivers, logical = NULL, flowconnected = FALSE, stopiferror = TRUE, algorithm = NULL )
unique |
A vector of identifiers for each fish. |
survey |
A vector of identifiers for each survey. It is recommended to use a numeric or date format (see as.Date) to preserve survey order. |
seg |
A vector of river locations (segment component). |
vert |
A vector of river locations (vertex component). |
rivers |
The river network object to use. |
logical |
A boolean vector that can be used for subsetting - if used,
|
flowconnected |
If |
stopiferror |
Whether or not to exit with an error if a route cannot be
found. If this is set to |
algorithm |
Which route detection algorithm to use ( |
A data frame of directions (character), with rows defined by unique fish and columns defined by observation increment (1 to 2, 2 to 3, etc.) See riverdirection for additional information.
Building routes from the river mouth to each river network segment and/or distance lookup tables will greatly reduce computation time (see buildsegroutes).
Matt Tyers
data(Gulk, fakefish) # Mouth must be specified Gulk$mouth$mouth.seg <- 1 Gulk$mouth$mouth.vert <- 1 riverdirectionseq(unique=fakefish$fish.id, survey=fakefish$flight, seg=fakefish$seg, vert=fakefish$vert, rivers=Gulk) riverdirectionseq(unique=fakefish$fish.id, survey=fakefish$flight.date, seg=fakefish$seg, vert=fakefish$vert, rivers=Gulk)data(Gulk, fakefish) # Mouth must be specified Gulk$mouth$mouth.seg <- 1 Gulk$mouth$mouth.vert <- 1 riverdirectionseq(unique=fakefish$fish.id, survey=fakefish$flight, seg=fakefish$seg, vert=fakefish$vert, rivers=Gulk) riverdirectionseq(unique=fakefish$fish.id, survey=fakefish$flight.date, seg=fakefish$seg, vert=fakefish$vert, rivers=Gulk)
Returns a matrix of directions between each river location in two datasets, with one expressed as rows and the other expressed as columns.
riverdirectiontofrom( seg1, vert1, seg2, vert2, rivers, logical1 = NULL, logical2 = NULL, ID1 = NULL, ID2 = NULL, flowconnected = FALSE, stopiferror = TRUE, algorithm = NULL )riverdirectiontofrom( seg1, vert1, seg2, vert2, rivers, logical1 = NULL, logical2 = NULL, ID1 = NULL, ID2 = NULL, flowconnected = FALSE, stopiferror = TRUE, algorithm = NULL )
seg1 |
First vector of river locations (segment component). These are expressed as rows in the output matrix. |
vert1 |
First vector of river locations (vertex component). These are expressed as rows in the output matrix. |
seg2 |
Second vector of river locations (segment component). These are expressed as columns in the output matrix. |
vert2 |
Second vector of river locations (vertex component). These are expressed as columns in the output matrix. |
rivers |
The river network object to use. |
logical1 |
A boolean vector that can be used for subsetting. If used,
|
logical2 |
A boolean vector that can be used for subsetting. If used,
|
ID1 |
a vector of observation IDs for the first dataset that will be used as row names in the output matrix. |
ID2 |
a vector of observation IDs for the second dataset that will be used as column names in the output matrix. |
flowconnected |
If |
stopiferror |
Whether or not to exit with an error if a route cannot be
found. If this is set to |
algorithm |
Which route detection algorithm to use ( |
A matrix of directions (character) with rows and columns labeled by corresponding values of ID. See riverdirection for additional information.
Building routes from the river mouth to each river network segment and/or distance lookup tables will greatly reduce computation time (see buildsegroutes).
Matt Tyers
data(Gulk) streamlocs.seg <- c(1,8,11) streamlocs.vert <- c(50,70,90) streamlocs.ID <- c("A","B","C") fish.seg <- c(1,4,9,12,14) fish.vert <- c(10,11,12,13,14) fish.ID <- c("fish1","fish2","fish3","fish4","fish5") Gulk <- setmouth(seg=1, vert=1, rivers=Gulk) riverdirectiontofrom(seg1=streamlocs.seg, vert1=streamlocs.vert, seg2=fish.seg, vert2=fish.vert, rivers=Gulk, ID1=streamlocs.ID, ID2=fish.ID) logi1 <- streamlocs.ID=="B" | streamlocs.ID=="C" logi2 <- fish.ID!="fish3" riverdirectiontofrom(seg1=streamlocs.seg, vert1=streamlocs.vert, seg2=fish.seg, vert2=fish.vert, rivers=Gulk, logical1=logi1, logical2=logi2, ID1=streamlocs.ID, ID2=fish.ID)data(Gulk) streamlocs.seg <- c(1,8,11) streamlocs.vert <- c(50,70,90) streamlocs.ID <- c("A","B","C") fish.seg <- c(1,4,9,12,14) fish.vert <- c(10,11,12,13,14) fish.ID <- c("fish1","fish2","fish3","fish4","fish5") Gulk <- setmouth(seg=1, vert=1, rivers=Gulk) riverdirectiontofrom(seg1=streamlocs.seg, vert1=streamlocs.vert, seg2=fish.seg, vert2=fish.vert, rivers=Gulk, ID1=streamlocs.ID, ID2=fish.ID) logi1 <- streamlocs.ID=="B" | streamlocs.ID=="C" logi2 <- fish.ID!="fish3" riverdirectiontofrom(seg1=streamlocs.seg, vert1=streamlocs.vert, seg2=fish.seg, vert2=fish.vert, rivers=Gulk, logical1=logi1, logical2=logi2, ID1=streamlocs.ID, ID2=fish.ID)
Calculates the total river network distance between two points on the river network, given in river locations (segment and vertex).
riverdistance( startseg = NULL, endseg = NULL, startvert, endvert, rivers, path = NULL, map = FALSE, add = FALSE, stopiferror = TRUE, algorithm = NULL )riverdistance( startseg = NULL, endseg = NULL, startvert, endvert, rivers, path = NULL, map = FALSE, add = FALSE, stopiferror = TRUE, algorithm = NULL )
startseg |
Segment number of the start of the route |
endseg |
Segment number of the end of the route |
startvert |
Vertex number of the start of the route |
endvert |
Vertex number of the end of the route |
rivers |
The river network object to use |
path |
(optional) The vector-format route of segment numbers can also be supplied instead of the starting and ending segments. |
map |
Whether or not to draw a sanity-check map, showing the calculated route in entirety. Defaults to FALSE. |
add |
If |
stopiferror |
Whether or not to exit with an error if a route cannot be
found. If this is set to |
algorithm |
Which route detection algorithm to use ( |
Total route distance, in the units of the coordinate system used (this will likely be meters).
If a distance lookup table ($distlookup) is present in the river network object, accepting NULL will bypass route detection and return distance automatically, the fastest algorithm of all. This is done automatically in buildsegroutes, but can be called directly using buildlookup.
Building routes from the river mouth to each river network segment and/or distance lookup tables will greatly reduce computation time (see buildsegroutes).
Matt Tyers
data(Gulk) riverdistance(startseg=6, endseg=14, startvert=100, endvert=200, rivers=Gulk) riverdistance(startvert=100, endvert=200, path=c(6,3,4,10,11,14), rivers=Gulk) riverdistance(startseg=6, endseg=14, startvert=100, endvert=200, rivers=Gulk, map=TRUE) # speed comparison: data(abstreams) tstart <- Sys.time() riverdistance(startseg=120, startvert=10, endseg=131, endvert=10, rivers=abstreams, algorithm="sequential") Sys.time()- tstart tstart <- Sys.time() riverdistance(startseg=120, startvert=10, endseg=131, endvert=10, rivers=abstreams, algorithm="Dijkstra") Sys.time()- tstart tstart <- Sys.time() riverdistance(startseg=120, startvert=10, endseg=131, endvert=10, rivers=abstreams) # Note: it is not necessary to specify the algorithm here: the distance function # will automatically select the fastest algorithm unless otherwise specified. Sys.time()- tstartdata(Gulk) riverdistance(startseg=6, endseg=14, startvert=100, endvert=200, rivers=Gulk) riverdistance(startvert=100, endvert=200, path=c(6,3,4,10,11,14), rivers=Gulk) riverdistance(startseg=6, endseg=14, startvert=100, endvert=200, rivers=Gulk, map=TRUE) # speed comparison: data(abstreams) tstart <- Sys.time() riverdistance(startseg=120, startvert=10, endseg=131, endvert=10, rivers=abstreams, algorithm="sequential") Sys.time()- tstart tstart <- Sys.time() riverdistance(startseg=120, startvert=10, endseg=131, endvert=10, rivers=abstreams, algorithm="Dijkstra") Sys.time()- tstart tstart <- Sys.time() riverdistance(startseg=120, startvert=10, endseg=131, endvert=10, rivers=abstreams) # Note: it is not necessary to specify the algorithm here: the distance function # will automatically select the fastest algorithm unless otherwise specified. Sys.time()- tstart
Used to calculate a list of possible river distances, in the event of braiding. Calls routelist to detect a list of routes from one river location to another, and uses riverdistance to calculate the distances along those routes. Different routes are detected by randomly reordering the segment numbers of the input river network object, thus changing the internal hierarchy of segment selection.
riverdistancelist(startseg, endseg, startvert, endvert, rivers, reps = 100)riverdistancelist(startseg, endseg, startvert, endvert, rivers, reps = 100)
startseg |
Segment number of the start of the route |
endseg |
Segment number of the end of the route |
startvert |
Vertex number of the start of the route |
endvert |
Vertex number of the end of the route |
rivers |
The river network object to use |
reps |
Deprecated. Was the number of randomized reorderings to try. |
A list with two objects, $routes being a list of detected routes in
ascending order by distance, and $distances being the respective distances
along the routes detected.
Since this function uses randomization, there is no guarantee that the list of routes will be comprehensive. Larger numbers of reps can be tried, but computation can be slow, particularly in the presence of a complex river network. It may be advantageous to use trimriver to create a smaller, more specific river network object to work with.
Matt Tyers
data(KilleyW) plot(x=KilleyW) Killey.dists <- riverdistancelist(startseg=1, endseg=16, startvert=100, endvert=25, rivers=KilleyW) Killey.dists # 18 routes are detected. # mapping the shortest route detected... riverdistance(startvert=100, endvert=25, path=Killey.dists$routes[[1]], rivers=KilleyW, map=TRUE) # mapping the shortest longest detected... riverdistance(startvert=100, endvert=25, path=Killey.dists$routes[[18]], rivers=KilleyW, map=TRUE)data(KilleyW) plot(x=KilleyW) Killey.dists <- riverdistancelist(startseg=1, endseg=16, startvert=100, endvert=25, rivers=KilleyW) Killey.dists # 18 routes are detected. # mapping the shortest route detected... riverdistance(startvert=100, endvert=25, path=Killey.dists$routes[[1]], rivers=KilleyW, map=TRUE) # mapping the shortest longest detected... riverdistance(startvert=100, endvert=25, path=Killey.dists$routes[[18]], rivers=KilleyW, map=TRUE)
Returns a matrix of distances between every point and every other point of given river locations (segment and vertex), or of a subset.
riverdistancemat( seg, vert, rivers, logical = NULL, ID = NULL, stopiferror = TRUE, algorithm = NULL )riverdistancemat( seg, vert, rivers, logical = NULL, ID = NULL, stopiferror = TRUE, algorithm = NULL )
seg |
A vector of river locations (segment component). |
vert |
A vector of river locations (vertex component). |
rivers |
The river network object to use. |
logical |
A boolean vector that can be used for subsetting. If used,
|
ID |
a vector of observation IDs for aid in interpreting the output table |
stopiferror |
Whether or not to exit with an error if a route cannot be
found. If this is set to |
algorithm |
Which route detection algorithm to use ( |
A matrix of distances (numeric) with rows and columns labeled by corresponding values of ID.
Building routes from the river mouth to each river network segment and/or distance lookup tables will greatly reduce computation time (see buildsegroutes).
Matt Tyers
data(Gulk, fakefish) logi1 <- (fakefish$flight.date==as.Date("2015-11-25")) riverdistancemat(seg=fakefish$seg, vert=fakefish$vert, rivers=Gulk, logical=logi1)data(Gulk, fakefish) logi1 <- (fakefish$flight.date==as.Date("2015-11-25")) riverdistancemat(seg=fakefish$seg, vert=fakefish$vert, rivers=Gulk, logical=logi1)
Returns a matrix of network distances between all observations of one unique fish.
riverdistancematbysurvey( indiv, unique, survey, seg, vert, rivers, full = TRUE, stopiferror = TRUE, algorithm = NULL )riverdistancematbysurvey( indiv, unique, survey, seg, vert, rivers, full = TRUE, stopiferror = TRUE, algorithm = NULL )
indiv |
The unique identifier of the fish in question. |
unique |
A vector of identifiers for each fish. |
survey |
A vector of identifiers for each survey. It is recommended to use a numeric or date format (see as.Date) to preserve survey order. |
seg |
A vector of river locations (segment component). |
vert |
A vector of river locations (vertex component). |
rivers |
The river network object to use. |
full |
Whether to return the full matrix, with |
stopiferror |
Whether or not to exit with an error if a route cannot be
found. If this is set to |
algorithm |
Which route detection algorithm to use ( |
A matrix of distances (numeric), with rows and columns defined by survey.
Building routes from the river mouth to each river network segment and/or distance lookup tables will greatly reduce computation time (see buildsegroutes).
Matt Tyers
data(Gulk, fakefish) riverdistancematbysurvey(indiv=1, unique=fakefish$fish.id, survey=fakefish$flight, seg=fakefish$seg, vert=fakefish$vert, rivers=Gulk) riverdistancematbysurvey(indiv=1, unique=fakefish$fish.id, survey=fakefish$flight, seg=fakefish$seg, vert=fakefish$vert, rivers=Gulk, full=FALSE)data(Gulk, fakefish) riverdistancematbysurvey(indiv=1, unique=fakefish$fish.id, survey=fakefish$flight, seg=fakefish$seg, vert=fakefish$vert, rivers=Gulk) riverdistancematbysurvey(indiv=1, unique=fakefish$fish.id, survey=fakefish$flight, seg=fakefish$seg, vert=fakefish$vert, rivers=Gulk, full=FALSE)
Returns a matrix of distances traveled by unique fish, between sequential surveys. A plotting method is also provided for the output; see plotseq
riverdistanceseq( unique, survey, seg, vert, rivers, logical = NULL, stopiferror = TRUE, algorithm = NULL )riverdistanceseq( unique, survey, seg, vert, rivers, logical = NULL, stopiferror = TRUE, algorithm = NULL )
unique |
A vector of identifiers for each fish. |
survey |
A vector of identifiers for each survey. It is recommended to use a numeric or date format (see as.Date) to preserve survey order. |
seg |
A vector of river locations (segment component). |
vert |
A vector of river locations (vertex component). |
rivers |
The river network object to use. |
logical |
A boolean vector that can be used for subsetting. If used,
|
stopiferror |
Whether or not to exit with an error if a route cannot be
found. If this is set to |
algorithm |
Which route detection algorithm to use ( |
A data frame of distances (numeric), with rows defined by unique fish and columns defined by observation increment (1 to 2, 2 to 3, etc.)
Building routes from the river mouth to each river network segment and/or distance lookup tables will greatly reduce computation time (see buildsegroutes).
Matt Tyers
data(Gulk, fakefish) riverdistanceseq(unique=fakefish$fish.id, survey=fakefish$flight, seg=fakefish$seg, vert=fakefish$vert, rivers=Gulk) seqbysurvey <- riverdistanceseq(unique=fakefish$fish.id, survey=fakefish$flight.date, seg=fakefish$seg, vert=fakefish$vert, rivers=Gulk) seqbysurvey plotseq(seqbysurvey)data(Gulk, fakefish) riverdistanceseq(unique=fakefish$fish.id, survey=fakefish$flight, seg=fakefish$seg, vert=fakefish$vert, rivers=Gulk) seqbysurvey <- riverdistanceseq(unique=fakefish$fish.id, survey=fakefish$flight.date, seg=fakefish$seg, vert=fakefish$vert, rivers=Gulk) seqbysurvey plotseq(seqbysurvey)
Returns a matrix of distances between each river location in two datasets, with one expressed as rows and the other expressed as columns.
riverdistancetofrom( seg1, vert1, seg2, vert2, rivers, logical1 = NULL, logical2 = NULL, ID1 = NULL, ID2 = NULL, stopiferror = TRUE, algorithm = NULL )riverdistancetofrom( seg1, vert1, seg2, vert2, rivers, logical1 = NULL, logical2 = NULL, ID1 = NULL, ID2 = NULL, stopiferror = TRUE, algorithm = NULL )
seg1 |
First vector of river locations (segment component). These are expressed as rows in the output matrix. |
vert1 |
First vector of river locations (vertex component). These are expressed as rows in the output matrix. |
seg2 |
Second vector of river locations (segment component). These are expressed as columns in the output matrix. |
vert2 |
Second vector of river locations (vertex component). These are expressed as columns in the output matrix. |
rivers |
The river network object to use. |
logical1 |
A boolean vector that can be used for subsetting. If used,
|
logical2 |
A boolean vector that can be used for subsetting. If used,
|
ID1 |
a vector of observation IDs for the first dataset that will be used as row names in the output matrix. |
ID2 |
a vector of observation IDs for the second dataset that will be used as column names in the output matrix. |
stopiferror |
Whether or not to exit with an error if a route cannot be
found. If this is set to |
algorithm |
Which route detection algorithm to use ( |
A matrix of distances (numeric) with rows and columns labeled by corresponding values of ID.
Building routes from the river mouth to each river network segment and/or distance lookup tables will greatly reduce computation time (see buildsegroutes).
Matt Tyers
data(Gulk) streamlocs.seg <- c(1,8,11) streamlocs.vert <- c(50,70,90) streamlocs.ID <- c("A","B","C") fish.seg <- c(1,4,9,12,14) fish.vert <- c(10,11,12,13,14) fish.ID <- c("fish1","fish2","fish3","fish4","fish5") riverdistancetofrom(seg1=streamlocs.seg, vert1=streamlocs.vert, seg2=fish.seg, vert2=fish.vert, rivers=Gulk, ID1=streamlocs.ID, ID2=fish.ID) logi1 <- streamlocs.ID=="B" | streamlocs.ID=="C" logi2 <- fish.ID!="fish3" riverdistancetofrom(seg1=streamlocs.seg, vert1=streamlocs.vert, seg2=fish.seg, vert2=fish.vert, rivers=Gulk, logical1=logi1, logical2=logi2, ID1=streamlocs.ID, ID2=fish.ID)data(Gulk) streamlocs.seg <- c(1,8,11) streamlocs.vert <- c(50,70,90) streamlocs.ID <- c("A","B","C") fish.seg <- c(1,4,9,12,14) fish.vert <- c(10,11,12,13,14) fish.ID <- c("fish1","fish2","fish3","fish4","fish5") riverdistancetofrom(seg1=streamlocs.seg, vert1=streamlocs.vert, seg2=fish.seg, vert2=fish.vert, rivers=Gulk, ID1=streamlocs.ID, ID2=fish.ID) logi1 <- streamlocs.ID=="B" | streamlocs.ID=="C" logi2 <- fish.ID!="fish3" riverdistancetofrom(seg1=streamlocs.seg, vert1=streamlocs.vert, seg2=fish.seg, vert2=fish.vert, rivers=Gulk, logical1=logi1, logical2=logi2, ID1=streamlocs.ID, ID2=fish.ID)
A class that holds spatial coordinates for river networks, as well as network topology and attributes.
Created by line2network from an input line shapefile. Contains all information for network distance calculation, plotting, etc. in the 'riverdist' package.
Plotting methods are described in plot.rivernetwork.
sf:Object of class "sf" from package 'sf'; see sf. This is the original object as read by read_sf, and is preserved to maintain plotting capability.
sf_current:Object of class "sf" from package 'sf'; see sf. This is an updated sf object generated from the coordinates in the 'lines' element, incorporating any changes to geometry. Any corresponding data will be dropped.
lines:Object of class "list". Each list element is a matrix of XY coordinates of the vertices of a single river segment.
connections:Object of class "matrix", with "numeric" elements. Defined as a square matrix, with elements describing the type of connection detected between line segments.
A value of 1 in element [i,j] indicates that the beginning of segment i is connected to the beginning of segment j.
A value of 2 in element [i,j] indicates that the beginning of segment i is connected to the end of segment j.
A value of 3 in element [i,j] indicates that the end of segment i is connected to the beginning of segment j.
A value of 4 in element [i,j] indicates that the end of segment i is connected to the end of segment j.
A value of 5 in element [i,j] indicates that segments i and j are connected at both beginning and end.
A value of 6 in element [i,j] indicates that the beginning of segment i is connected to the end of segment j, and the end of segment i is connected to the beginning of segment j.
A value of NA in element [i,j] indicates that segments i and j are not connected.
lengths:Vector of class "numeric". Defined as the calculated total lengths of each river segment.
names:Vector of class "character". Defined as the names of each river segment.
mouth:Object of class "list", with two elements. Element mouth.seg gives the segment number of the mouth (lowest point) of the river network, and mouth.vert gives the vertex number.
sequenced:"logical": has value of TRUE if line vertices have been stored in upstream sequence using sequenceverts.
tolerance:"numeric": the spatial tolerance that was used in determining river segment endpoint connectivity; see line2network, splitsegments.
units:"character": the spatial units detected from the input shapefile.
braided:"logical": Has value of TRUE if checkbraidedTF has detected braiding, FALSE if no braiding has been detected, and NA if braiding has not yet been checked.
cumuldist:List of class "numeric": Each element is a vector of cumulative distances along each river segment, beginning with 0.
segroutes:Object of class "list", with each element defined as a vector of class "numeric", describing the route from the mouth segment to the specific segment. This element only exists if buildsegroutes has been run, and can greatly speed up route and distance calculation.
distlookup:List of three matrices, of class "numeric" or "logical". Element [i,j] of each matrix corresponds to
the route between segment i and j. The
distlookup$middist matrix gives the total distance of the "middle"
of each route (between the starting and ending segments"), and the
distlookup$starttop and distlookup$endtop matrices have value
TRUE, FALSE, or NA if the segments at the beginning or
end of the route are connected to the rest of the route at the top of the
coordinate matrix, bottom of the coordinate matrix, or if the route is
contained to just one segment, respectively.
Matt Tyers
Adds points to an active plot. Works like points but with river locations (segments and vertices) rather than xy coordinates.
riverpoints(seg, vert, rivers, pch = 1, col = 1, jitter = 0, ...)riverpoints(seg, vert, rivers, pch = 1, col = 1, jitter = 0, ...)
seg |
A vector of segments |
vert |
A vector of vertices |
rivers |
The river network object to use |
pch |
Point character, as a vector or single value |
col |
Point color, as a vector or single value |
jitter |
Maximum amount of random noise to add to "jitter" points if desired, so points do not overlap one another |
... |
Additional arguments for points |
Matt Tyers
data(fakefish,Gulk) plot(x=Gulk, xlim=c(862000,882000), ylim=c(6978000,6993000)) points(x=fakefish$x, y=fakefish$y, pch=16, col=2) riverpoints(seg=fakefish$seg, vert=fakefish$vert, rivers=Gulk, pch=15, col=4) plot(x=Gulk, empty=TRUE) with(fakefish, riverpoints(seg=seg, vert=vert, rivers=Gulk, pch=16, col=flight, jitter=1000))data(fakefish,Gulk) plot(x=Gulk, xlim=c(862000,882000), ylim=c(6978000,6993000)) points(x=fakefish$x, y=fakefish$y, pch=16, col=2) riverpoints(seg=fakefish$seg, vert=fakefish$vert, rivers=Gulk, pch=15, col=4) plot(x=Gulk, empty=TRUE) with(fakefish, riverpoints(seg=seg, vert=vert, rivers=Gulk, pch=16, col=flight, jitter=1000))
Called internally within riverdistancelist. Detects all possible routes from one river network segment to another, in the event of braiding.
routelist(startseg, endseg, rivers, reps = 100)routelist(startseg, endseg, rivers, reps = 100)
startseg |
Segment number of the start of the route |
endseg |
Segment number of the end of the route |
rivers |
The river network object to use |
reps |
Deprecated. Was used in a previous version using randomization. |
A list of vectors, each describing a route in segment numbers.
The previous version of this function returned many possible routes using randomization - this algorithm now computes all possible routes.
Matt Tyers
data(KilleyW) plot(x=KilleyW) routelist(startseg=1, endseg=16, rivers=KilleyW)data(KilleyW) plot(x=KilleyW) routelist(startseg=1, endseg=16, rivers=KilleyW)
This function is almost the reverse of xy2segvert, and returns a data frame of the XY spatial coordinates corresponding to vectors of segment and vertex. It should be noted that this only returns the spatial coordinates from the river network itself, and will not necessarily correspond to the original set of point data.
segvert2xy(seg, vert, rivers)segvert2xy(seg, vert, rivers)
seg |
A vector of river segments to transform |
vert |
A vector of river vertices to transform |
rivers |
The river network object to use |
A data frame of XY coordinates, given as $snap_x and $snap_y..
Matt Tyers
data(Gulk,fakefish) head(fakefish) xy_from_segvert <- segvert2xy(seg=fakefish$seg, vert=fakefish$vert, rivers=Gulk) head(xy_from_segvert) plot(x=Gulk, xlim=c(862000,882000), ylim=c(6978000,6993000)) points(x=fakefish$x, y=fakefish$y, pch=16, col=2) points(x=xy_from_segvert$snap_x, y=xy_from_segvert$snap_y, pch=15, col=4)data(Gulk,fakefish) head(fakefish) xy_from_segvert <- segvert2xy(seg=fakefish$seg, vert=fakefish$vert, rivers=Gulk) head(xy_from_segvert) plot(x=Gulk, xlim=c(862000,882000), ylim=c(6978000,6993000)) points(x=fakefish$x, y=fakefish$y, pch=16, col=2) points(x=xy_from_segvert$snap_x, y=xy_from_segvert$snap_y, pch=15, col=4)
Rearranges the vertices of a river network object so that vertices are stored sequentially moving up river for all segments (coordinates [1,] are the bottom of each segment).
sequenceverts(rivers)sequenceverts(rivers)
rivers |
The river network object to use |
A new river network object (see rivernetwork)
Even without calling sequenceverts, the vertices will be stored
sequentially - either moving up river or down for a given segment. What
sequenceverts() adds is a standardized direction.
Currently, no function in package 'riverdist' requires the vertices to be stored sequentially.
Matt Tyers
data(Gulk) Gulk <- setmouth(seg=1, vert=1, rivers=Gulk) str(Gulk) Gulk.dir <- sequenceverts(rivers=Gulk) str(Gulk.dir)data(Gulk) Gulk <- setmouth(seg=1, vert=1, rivers=Gulk) str(Gulk) Gulk.dir <- sequenceverts(rivers=Gulk) str(Gulk.dir)
Provides a user-friendly way of specifying the segment and vertex of the mouth (lowest point) of a river network object.
setmouth(seg, vert, rivers)setmouth(seg, vert, rivers)
seg |
The segment number to store for the mouth |
vert |
The vertex number to store for the mouth |
rivers |
The river network object to use |
A new river network object (see rivernetwork)
The mouth segment and vertex can also be specified using direct
assignment to the $mouth$seg and $mouth$vert components of the river network
object.
This function is called within cleanup, which is recommended in most cases.
Matt Tyers
data(Gulk) # say we know that segment 1 is the lowest segment in this river network, but we don't know # which end is the mouth. showends(seg=1, rivers=Gulk) # this means that the mouth is row 1, so we can specify this: Gulk <- setmouth(seg=1, vert=1, rivers=Gulk)data(Gulk) # say we know that segment 1 is the lowest segment in this river network, but we don't know # which end is the mouth. showends(seg=1, rivers=Gulk) # this means that the mouth is row 1, so we can specify this: Gulk <- setmouth(seg=1, vert=1, rivers=Gulk)
Identifies the vertex coordinates (row numbers) of the endpoints of a given segment. The main purpose is determining which of the endpoints is the mouth (or lowest point) of the river system.
showends(seg, rivers)showends(seg, rivers)
seg |
The segment (number) to check |
rivers |
The river network object to use |
This function is called within cleanup, which is recommended in most cases.
Matt Tyers
data(Gulk) # say we know that segment 1 is the lowest segment in this river network, but we don't know # which end is the mouth. showends(seg=1, rivers=Gulk) # this means that the mouth is row 1, so we can specify this: Gulk <- setmouth(seg=1, vert=1, rivers=Gulk)data(Gulk) # say we know that segment 1 is the lowest segment in this river network, but we don't know # which end is the mouth. showends(seg=1, rivers=Gulk) # this means that the mouth is row 1, so we can specify this: Gulk <- setmouth(seg=1, vert=1, rivers=Gulk)
A small set of observations of fakefish on the Gulkana River and its tributaries.
data(smallset)data(smallset)
A data frame
x. X-coordinate of observation (Alaska Albers Equal Area). Note that the locations do not align with the river network object.
y. Y-coordinate of observation
seg. River segment
vert. River vertex
fish.id. Numeric identifier for each fish (individual fish were observed more than once)
flight. Numeric identifier for each telemetry flight
Splits a segment at a specified vertex, creating two new segments.
splitsegmentat(seg, vert, rivers)splitsegmentat(seg, vert, rivers)
seg |
The segment to split |
vert |
The vertex to split it at |
rivers |
The river network object to use |
A new, updated river network object
Matt Tyers
data(Gulk) plot(x=Gulk) Gulk2 <- splitsegmentat(seg=1, vert=400, rivers=Gulk) plot(x=Gulk2)data(Gulk) plot(x=Gulk) Gulk2 <- splitsegmentat(seg=1, vert=400, rivers=Gulk) plot(x=Gulk2)
Detects cases in which segments should be split to establish appropriate topology, and splits them. Specifically, it looks for segment endpoints intersecting (or within a tolerance of) another segment. It then splits the intersected segment at the point where the endpoint of the other segment breaks it.
splitsegments( rivers, tolerance = NULL, splitthese = NULL, splitthemat = NULL, one2one = FALSE, append = FALSE )splitsegments( rivers, tolerance = NULL, splitthese = NULL, splitthemat = NULL, one2one = FALSE, append = FALSE )
rivers |
The river network object to use |
tolerance |
The spatial snapping tolerance to use for detecting intersection. If a NULL value is used (default), it will default to the tolerance that was used in river network creation in line2network. |
splitthese |
An optional vector of target segments to split. If this argument is used, only these segments will be split. If the default ( |
splitthemat |
An optional vector of segments (endpoints) to use for splitting. If this argument is used, segments will only be split at the endpoints of these segments. If the default ( |
one2one |
Logical, indicating a one-to-one correspondence between arguments |
append |
Logical, indicating how to organize the output river network. If |
A new, updated river network object
This function is called within cleanup, which is recommended in most cases.
Matt Tyers
data(Koyukuk1) topologydots(rivers=Koyukuk1) # Segments 7, 8, 13, and 16 need to be split so topologies will work. # Since endpoints are not in the same place, they are not detected as # being connected. plot(x=Koyukuk1) Koyukuk1split <- splitsegments(rivers=Koyukuk1) topologydots(rivers=Koyukuk1split) plot(x=Koyukuk1split) # if only segment 17 were to be split in three places plot(x=splitsegments(rivers=Koyukuk1, splitthese=c(7,7,7), splitthemat=c(14,5,12))) # if only segment 16 were to be split, showing behavior of append= plot(x=splitsegments(rivers=Koyukuk1, splitthese=c(7,7,7), splitthemat=c(14,5,12), append=TRUE))data(Koyukuk1) topologydots(rivers=Koyukuk1) # Segments 7, 8, 13, and 16 need to be split so topologies will work. # Since endpoints are not in the same place, they are not detected as # being connected. plot(x=Koyukuk1) Koyukuk1split <- splitsegments(rivers=Koyukuk1) topologydots(rivers=Koyukuk1split) plot(x=Koyukuk1split) # if only segment 17 were to be split in three places plot(x=splitsegments(rivers=Koyukuk1, splitthese=c(7,7,7), splitthemat=c(14,5,12))) # if only segment 16 were to be split, showing behavior of append= plot(x=splitsegments(rivers=Koyukuk1, splitthese=c(7,7,7), splitthemat=c(14,5,12), append=TRUE))
Produces a graphical check of the connectivity of a river network object. It produces a plot of the river network object, and overlays red dots at non-connected endpoints and green dots at connected endpoints.
topologydots(rivers, add = FALSE, ...)topologydots(rivers, add = FALSE, ...)
rivers |
The river network object to check |
add |
Whether call a new plot ( |
... |
Additional plotting arguments (see par) |
Matt Tyers
data(Gulk) topologydots(rivers=Gulk)data(Gulk) topologydots(rivers=Gulk)
Removes line segments from a river network object. User can
specify which segments to remove (trim) or which segments to
keep (trimto).
trimriver(trim = NULL, trimto = NULL, rivers)trimriver(trim = NULL, trimto = NULL, rivers)
trim |
Vector of line segments to remove |
trimto |
Vector of line segments to keep |
rivers |
The river network object |
A new river network object
Specifying segments in both trim and trimto arguments will result in an error.
Matt Tyers
data(Kenai1) plot(x=Kenai1) Kenai1.trim <- trimriver(trim=c(46,32,115,174,169,114,124,142,80), rivers=Kenai1) plot(x=Kenai1.trim) Kenai1.trim.2 <- trimriver(trimto=c(20,57,118,183,45,162,39,98,19), rivers=Kenai1) plot(x=Kenai1.trim.2)data(Kenai1) plot(x=Kenai1) Kenai1.trim <- trimriver(trim=c(46,32,115,174,169,114,124,142,80), rivers=Kenai1) plot(x=Kenai1.trim) Kenai1.trim.2 <- trimriver(trimto=c(20,57,118,183,45,162,39,98,19), rivers=Kenai1) plot(x=Kenai1.trim.2)
Removes line segments from a river network object that are not adjacent to a set of point data, given in X-Y coordinates.
trimtopoints(x, y, rivers, method = "snap", dist = NULL)trimtopoints(x, y, rivers, method = "snap", dist = NULL)
x |
Vector of x-coordinates of point data to buffer around |
y |
Vector of y-coordinates of point data to buffer around |
rivers |
The river network object to use |
method |
Three methods are available. If |
dist |
Distance to use for buffering, if |
A new river network object (see rivernetwork)
If method=="buffer", only distances to segment endpoints
and midpoints are checked, and still only whole segments are removed.
Matt Tyers
data(Koyukuk2) x <- c(139241.0, 139416.1, 124600.1, 122226.8) y <- c(1917577, 1913864, 1898723, 1898792) plot(x=Koyukuk2) points(x, y, pch=15, col=4) legend(par("usr")[1], par("usr")[4], legend="points to buffer around", pch=15, col=4, cex=.6) Koyukuk2.buf1 <- trimtopoints(x, y, rivers=Koyukuk2, method="snap") plot(x=Koyukuk2.buf1) points(x, y, pch=15, col=4) Koyukuk2.buf2 <- trimtopoints(x, y, rivers=Koyukuk2, method="snaproute") plot(x=Koyukuk2.buf2) points(x, y, pch=15, col=4) Koyukuk2.buf3 <- trimtopoints(x, y, rivers=Koyukuk2, method="buffer", dist=1000) plot(x=Koyukuk2.buf3) points(x, y, pch=15, col=4)data(Koyukuk2) x <- c(139241.0, 139416.1, 124600.1, 122226.8) y <- c(1917577, 1913864, 1898723, 1898792) plot(x=Koyukuk2) points(x, y, pch=15, col=4) legend(par("usr")[1], par("usr")[4], legend="points to buffer around", pch=15, col=4, cex=.6) Koyukuk2.buf1 <- trimtopoints(x, y, rivers=Koyukuk2, method="snap") plot(x=Koyukuk2.buf1) points(x, y, pch=15, col=4) Koyukuk2.buf2 <- trimtopoints(x, y, rivers=Koyukuk2, method="snaproute") plot(x=Koyukuk2.buf2) points(x, y, pch=15, col=4) Koyukuk2.buf3 <- trimtopoints(x, y, rivers=Koyukuk2, method="buffer", dist=1000) plot(x=Koyukuk2.buf3) points(x, y, pch=15, col=4)
Calculates river network distances as +/-, defined as upriver/downriver.
Specifying net=TRUE will compute net upriver distance (3 river km
down a tributary and then 15 river km up the mainstem will mean 12 rkm net.
Otherwise the function will return 18 rkm upriver travel.)
The mouth (lowest point) segment and vertex must be specified (see setmouth).
upstream( startseg, endseg, startvert, endvert, rivers, flowconnected = FALSE, net = FALSE, stopiferror = TRUE, algorithm = NULL )upstream( startseg, endseg, startvert, endvert, rivers, flowconnected = FALSE, net = FALSE, stopiferror = TRUE, algorithm = NULL )
startseg |
Segment number of the start of the route |
endseg |
Segment number of the end of the route |
startvert |
Vertex number of the start of the route |
endvert |
Vertex number of the end of the route |
rivers |
The river network object to use |
flowconnected |
If |
net |
Whether to calculate net distance ( |
stopiferror |
Whether or not to exit with an error if a route cannot be
found. If this is set to |
algorithm |
Which route detection algorithm to use ( |
Upstream distance (numeric). Returns NA if flowconnected has value TRUE and the two segments are not flow-connected.
Building routes from the river mouth to each river network segment and/or distance lookup tables will greatly reduce computation time (see buildsegroutes).
Matt Tyers
data(Gulk) # Mouth must be specified Gulk$mouth$mouth.seg <- 1 Gulk$mouth$mouth.vert <- 1 plot(x=Gulk) riverpoints(seg=c(6,4), vert=c(140,140), pch=16, col=2, rivers=Gulk) upstream(startseg=6, endseg=4, startvert=140, endvert=40, rivers=Gulk, net=TRUE) upstream(startseg=6, endseg=4, startvert=140, endvert=40, rivers=Gulk, net=FALSE) upstream(startseg=6, endseg=4, startvert=140, endvert=40, rivers=Gulk, flowconnected=TRUE)data(Gulk) # Mouth must be specified Gulk$mouth$mouth.seg <- 1 Gulk$mouth$mouth.vert <- 1 plot(x=Gulk) riverpoints(seg=c(6,4), vert=c(140,140), pch=16, col=2, rivers=Gulk) upstream(startseg=6, endseg=4, startvert=140, endvert=40, rivers=Gulk, net=TRUE) upstream(startseg=6, endseg=4, startvert=140, endvert=40, rivers=Gulk, net=FALSE) upstream(startseg=6, endseg=4, startvert=140, endvert=40, rivers=Gulk, flowconnected=TRUE)
Returns a matrix of upstream distance between every point and every other point of given river locations (segment and vertex), or of a subset. The mouth (lowest point) segment and vertex must be specified (see setmouth).
upstreammat( seg, vert, rivers, logical = NULL, ID = NULL, flowconnected = FALSE, net = FALSE, stopiferror = TRUE, algorithm = NULL )upstreammat( seg, vert, rivers, logical = NULL, ID = NULL, flowconnected = FALSE, net = FALSE, stopiferror = TRUE, algorithm = NULL )
seg |
A vector of river locations (segment component). |
vert |
A vector of river locations (vertex component). |
rivers |
The river network object to use. |
logical |
A boolean vector that can be used for subsetting - if used, riverdirectionseq() will only return pairwise distances in which a specified condition is met. |
ID |
a vector of observation IDs for aid in interpreting the output table |
flowconnected |
If |
net |
Whether to calculate net upstream distance (net=TRUE) or total distance (net=FALSE, default). See upstream. |
stopiferror |
Whether or not to exit with an error if a route cannot be
found. If this is set to |
algorithm |
Which route detection algorithm to use ( |
A matrix of upstream distances (numeric) with rows and columns
labeled by corresponding values of ID. See upstream for additional information.
Building routes from the river mouth to each river network segment and/or distance lookup tables will greatly reduce computation time (see buildsegroutes).
Matt Tyers
data(Gulk, fakefish) # Mouth must be specified Gulk$mouth$mouth.seg <- 1 Gulk$mouth$mouth.vert <- 1 logi1 <- (fakefish$flight.date==as.Date("2015-11-25")) upstreammat(seg=fakefish$seg, vert=fakefish$vert, rivers=Gulk, logical=logi1)data(Gulk, fakefish) # Mouth must be specified Gulk$mouth$mouth.seg <- 1 Gulk$mouth$mouth.vert <- 1 logi1 <- (fakefish$flight.date==as.Date("2015-11-25")) upstreammat(seg=fakefish$seg, vert=fakefish$vert, rivers=Gulk, logical=logi1)
Returns a matrix of upstream travel distance between all observations of one unique fish.
upstreammatbysurvey( indiv, unique, survey, seg, vert, rivers, full = TRUE, flowconnected = FALSE, net = FALSE, stopiferror = TRUE, algorithm = NULL )upstreammatbysurvey( indiv, unique, survey, seg, vert, rivers, full = TRUE, flowconnected = FALSE, net = FALSE, stopiferror = TRUE, algorithm = NULL )
indiv |
The unique identifier of the fish in question. |
unique |
A vector of identifiers for each fish. |
survey |
A vector of identifiers for each survey. It is recommended to use a numeric or date format (see as.Date) to preserve survey order. |
seg |
A vector of river locations (segment component). |
vert |
A vector of river locations (vertex component). |
rivers |
The river network object to use. |
full |
Whether to return the full matrix, with |
flowconnected |
If |
net |
Whether to calculate net upstream distance (net=TRUE) or total distance (net=FALSE, default). |
stopiferror |
Whether or not to exit with an error if a route cannot be
found. If this is set to |
algorithm |
Which route detection algorithm to use ( |
A matrix of upstream distances (numeric), with rows and columns defined by
survey. In the resulting matrix, the element with the row identified as
A and column identified as B is defined as the upstream distance
traveled from survey A to survey B. Therefore, it is likely that only the
upper triangle of the matrix will be of interest.
Building routes from the river mouth to each river network segment and/or distance lookup tables will greatly reduce computation time (see buildsegroutes).
Matt Tyers
data(Gulk, fakefish) upstreammatbysurvey(indiv=1, unique=fakefish$fish.id, survey=fakefish$flight, seg=fakefish$seg, vert=fakefish$vert, rivers=Gulk) upstreammatbysurvey(indiv=1, unique=fakefish$fish.id, survey=fakefish$flight, seg=fakefish$seg, vert=fakefish$vert, rivers=Gulk, full=FALSE)data(Gulk, fakefish) upstreammatbysurvey(indiv=1, unique=fakefish$fish.id, survey=fakefish$flight, seg=fakefish$seg, vert=fakefish$vert, rivers=Gulk) upstreammatbysurvey(indiv=1, unique=fakefish$fish.id, survey=fakefish$flight, seg=fakefish$seg, vert=fakefish$vert, rivers=Gulk, full=FALSE)
Returns a matrix of distance with direction by unique fish between sequential surveys. The mouth (lowest point) segment and vertex must be specified (see setmouth). A plotting method is provided for the output; see plotseq.
upstreamseq( unique, survey, seg, vert, rivers, logical = NULL, flowconnected = FALSE, net = FALSE, stopiferror = TRUE, algorithm = NULL )upstreamseq( unique, survey, seg, vert, rivers, logical = NULL, flowconnected = FALSE, net = FALSE, stopiferror = TRUE, algorithm = NULL )
unique |
A vector of identifiers for each fish. |
survey |
A vector of identifiers for each survey. It is recommended to use a numeric or date format (see as.Date) to preserve survey order. |
seg |
A vector of river locations (segment component). |
vert |
A vector of river locations (vertex component). |
rivers |
The river network object to use. |
logical |
A boolean vector that can be used for subsetting - if used,
|
flowconnected |
If |
net |
Whether to calculate net upstream distance (net=TRUE) or total distance (net=FALSE, default). |
stopiferror |
Whether or not to exit with an error if a route cannot be
found. If this is set to |
algorithm |
Which route detection algorithm to use ( |
A data frame of upstream distances (numeric), with rows defined by unique fish and columns defined by observation increment (1 to 2, 2 to 3, etc.) See upstream for additional information.
Returns either net upstream distance (net=TRUE) or total distance (net=FALSE, default). See upstream.
Building routes from the river mouth to each river network segment and/or distance lookup tables will greatly reduce computation time (see buildsegroutes).
Matt Tyers
data(Gulk, fakefish) # Mouth must be specified Gulk$mouth$mouth.seg <- 1 Gulk$mouth$mouth.vert <- 1 upstreamseq(unique=fakefish$fish.id, survey=fakefish$flight, seg=fakefish$seg, vert=fakefish$vert, rivers=Gulk) seqbysurvey <- upstreamseq(unique=fakefish$fish.id, survey=fakefish$flight.date, seg=fakefish$seg, vert=fakefish$vert, rivers=Gulk) seqbysurvey plotseq(seqbysurvey)data(Gulk, fakefish) # Mouth must be specified Gulk$mouth$mouth.seg <- 1 Gulk$mouth$mouth.vert <- 1 upstreamseq(unique=fakefish$fish.id, survey=fakefish$flight, seg=fakefish$seg, vert=fakefish$vert, rivers=Gulk) seqbysurvey <- upstreamseq(unique=fakefish$fish.id, survey=fakefish$flight.date, seg=fakefish$seg, vert=fakefish$vert, rivers=Gulk) seqbysurvey plotseq(seqbysurvey)
Returns a matrix of upstream distances between each river location in two datasets, with one expressed as rows and the other expressed as columns.
upstreamtofrom( seg1, vert1, seg2, vert2, rivers, logical1 = NULL, logical2 = NULL, ID1 = NULL, ID2 = NULL, net = FALSE, flowconnected = FALSE, stopiferror = TRUE, algorithm = NULL )upstreamtofrom( seg1, vert1, seg2, vert2, rivers, logical1 = NULL, logical2 = NULL, ID1 = NULL, ID2 = NULL, net = FALSE, flowconnected = FALSE, stopiferror = TRUE, algorithm = NULL )
seg1 |
First vector of river locations (segment component). These are expressed as rows in the output matrix. |
vert1 |
First vector of river locations (vertex component). These are expressed as rows in the output matrix. |
seg2 |
Second vector of river locations (segment component). These are expressed as columns in the output matrix. |
vert2 |
Second vector of river locations (vertex component). These are expressed as columns in the output matrix. |
rivers |
The river network object to use. |
logical1 |
A boolean vector that can be used for subsetting. If used,
|
logical2 |
A boolean vector that can be used for subsetting. If used,
|
ID1 |
a vector of observation IDs for the first dataset that will be used as row names in the output matrix. |
ID2 |
a vector of observation IDs for the second dataset that will be used as column names in the output matrix. |
net |
Whether to calculate net upstream distance ( |
flowconnected |
If |
stopiferror |
Whether or not to exit with an error if a route cannot be
found. If this is set to |
algorithm |
Which route detection algorithm to use ( |
A matrix of upstream distances (numeric) with rows and columns labeled by corresponding values of ID. See upstream for additional information.
Building routes from the river mouth to each river network segment and/or distance lookup tables will greatly reduce computation time (see buildsegroutes).
Matt Tyers
data(Gulk) streamlocs.seg <- c(1,8,11) streamlocs.vert <- c(50,70,90) streamlocs.ID <- c("A","B","C") fish.seg <- c(1,4,9,12,14) fish.vert <- c(10,11,12,13,14) fish.ID <- c("fish1","fish2","fish3","fish4","fish5") Gulk <- setmouth(seg=1, vert=1, rivers=Gulk) upstreamtofrom(seg1=streamlocs.seg, vert1=streamlocs.vert, seg2=fish.seg, vert2=fish.vert, rivers=Gulk, ID1=streamlocs.ID, ID2=fish.ID) logi1 <- streamlocs.ID=="B" | streamlocs.ID=="C" logi2 <- fish.ID!="fish3" upstreamtofrom(seg1=streamlocs.seg, vert1=streamlocs.vert, seg2=fish.seg, vert2=fish.vert, rivers=Gulk, logical1=logi1, logical2=logi2, ID1=streamlocs.ID, ID2=fish.ID)data(Gulk) streamlocs.seg <- c(1,8,11) streamlocs.vert <- c(50,70,90) streamlocs.ID <- c("A","B","C") fish.seg <- c(1,4,9,12,14) fish.vert <- c(10,11,12,13,14) fish.ID <- c("fish1","fish2","fish3","fish4","fish5") Gulk <- setmouth(seg=1, vert=1, rivers=Gulk) upstreamtofrom(seg1=streamlocs.seg, vert1=streamlocs.vert, seg2=fish.seg, vert2=fish.vert, rivers=Gulk, ID1=streamlocs.ID, ID2=fish.ID) logi1 <- streamlocs.ID=="B" | streamlocs.ID=="C" logi2 <- fish.ID!="fish3" upstreamtofrom(seg1=streamlocs.seg, vert1=streamlocs.vert, seg2=fish.seg, vert2=fish.vert, rivers=Gulk, logical1=logi1, logical2=logi2, ID1=streamlocs.ID, ID2=fish.ID)
Returns which segments are connected to a specified segment within a river network. It may be useful for error checking.
whoconnected(seg, rivers)whoconnected(seg, rivers)
seg |
The segment to check |
rivers |
The river network object it belongs to |
A vector of segment numbers
Matt Tyers
data(Gulk) plot(Gulk) whoconnected(seg=4, rivers=Gulk)data(Gulk) plot(Gulk) whoconnected(seg=4, rivers=Gulk)
This function determines the closest vertex in the river network to each point of XY data and returns a list of river locations, defined as segment numbers and vertex numbers.
xy2segvert(x, y, rivers)xy2segvert(x, y, rivers)
x |
A vector of x-coordinates to transform |
y |
A vector of y-coordinates to transform |
rivers |
The river network object to use |
A data frame of river locations, with segment numbers in $seg,
vertex numbers in $vert, and the snapping distance for each point in
$snapdist. Two additional columns are $snap_x and $snap_y,
which give the x- and y-coordinates snapped to the river network.
Conversion to river locations is only valid if the input XY coordinates and river network are in the same projected coordinate system. Point data in geographic coordinates can be projected using sf_project in package 'sf', and an example is shown below.
Matt Tyers
data(Gulk,fakefish) head(fakefish) fakefish.riv <- xy2segvert(x=fakefish$x, y=fakefish$y, rivers=Gulk) head(fakefish.riv) plot(x=Gulk, xlim=c(862000,882000), ylim=c(6978000,6993000)) points(fakefish$x, fakefish$y, pch=16, col=2) riverpoints(seg=fakefish.riv$seg, vert=fakefish.riv$vert, rivers=Gulk, pch=15, col=4) ## converting a matrix of points stored in long-lat to Alaska Albers Equal Area: data(line98, Kenai1) head(line98) # note that coordinates are stored in long-lat, NOT lat-long line98albers <- sf::sf_project(pts=line98, to="+proj=aea +lat_1=55 +lat_2=65 +lat_0=50 +lon_0=-154 +x_0=0 +y_0=0 +datum=NAD83 +units=m +no_defs +ellps=GRS80") head(line98albers) zoomtoseg(seg=c(162,19), rivers=Kenai1) points(line98albers)data(Gulk,fakefish) head(fakefish) fakefish.riv <- xy2segvert(x=fakefish$x, y=fakefish$y, rivers=Gulk) head(fakefish.riv) plot(x=Gulk, xlim=c(862000,882000), ylim=c(6978000,6993000)) points(fakefish$x, fakefish$y, pch=16, col=2) riverpoints(seg=fakefish.riv$seg, vert=fakefish.riv$vert, rivers=Gulk, pch=15, col=4) ## converting a matrix of points stored in long-lat to Alaska Albers Equal Area: data(line98, Kenai1) head(line98) # note that coordinates are stored in long-lat, NOT lat-long line98albers <- sf::sf_project(pts=line98, to="+proj=aea +lat_1=55 +lat_2=65 +lat_0=50 +lon_0=-154 +x_0=0 +y_0=0 +datum=NAD83 +units=m +no_defs +ellps=GRS80") head(line98albers) zoomtoseg(seg=c(162,19), rivers=Kenai1) points(line98albers)
Calls plot.rivernetwork and automatically zooms to a specified segment or vector of segments. Not intended for any real mapping - just investigating and error checking.
zoomtoseg(seg, rivers, ...)zoomtoseg(seg, rivers, ...)
seg |
A segment or vector of segments to zoom to |
rivers |
The river network object to use |
... |
Additional plotting arguments (see par) |
Matt Tyers
data(Kenai3) plot(x=Kenai3) # checking out a particularly messy region... zoomtoseg(c(110,63), rivers=Kenai3)data(Kenai3) plot(x=Kenai3) # checking out a particularly messy region... zoomtoseg(c(110,63), rivers=Kenai3)