Arcs of a directed graph

Description

This function and its documentation have been lifted from the igraph function E with arguments according to DAG conventions. An arc sequence is a vector containing numeric arc ids, with a special class attribute that allows custom operations: selecting subsets of arcs based on attributes, or graph structure, creating the intersection, union of arcs, etc.

Usage

A(G, P, path)

Arguments

Details

Arc sequences are usually used as function arguments that refer to arcs of a graph.

An arc sequence is tied to the graph it refers to: it really denoted the specific arcs of that graph, and cannot be used together with another graph.

An arc sequence is most often created by the A() function. The result includes arcs in increasing arc id order by default (if none of the P and path arguments are used). An arc sequence can be indexed by a numeric vector, just like a regular R vector.

Value

An arc sequence of the graph.

Author(s)

Gabor Csardi

See Also

See E

Examples

 G <- graph_from_literal(a --+ b, c --+ d, d --+ e, b --+ e, e --+ j, j --+ m, f --+ g, g --+ i, 
 h --+ i, i --+ k, k --+ l, l --+ m, m --+ n,  n --+ o)
 
A(G)

Raise an adjacency matrix to some power

Description

When applying the definition of matrix multiplication to an adjacency matrix \mathbf{A}, the i,j entry in \mathbf{A}^k will give the number of paths in the graph from node i to node j of length k.

Usage

A.mult(G, power, text.summary = TRUE)

Arguments

Value

Returns either a character vector of paths of a specified length or, if text.summary = TRUE, the adjacency matrix raised to a specified power.

Author(s)

Ken Aho

Examples

kon_full <- streamDAGs("konza_full")
A.mult(kon_full, power = 6)

Nodal coordinates for graphs in the AIMS project

Description

Contains spatial coordinates for graph nodes for stream networks in the Aquatic Intermittency effects on Microbiomes in Streams (AIMS) project

Usage

data("AIMS.node.coords")

Format

A data frame with 307 observations on the following 7 variables.

Object.ID

Nodal identifier

lat

Latitude

long

Longitude

site

Stream network name, currently includes: "KZ" = Konza Prairie, "TD" = Talladega, "WH" = Weyerhauser, "PR" = Painted Rock, "JD" = Johnson Draw, "DC" = Dry Creek, and "GJ" = Johnson Draw.

piezo

Logical, indicating whether the location contains a peizometer.

microbial_seasonal_network

Logical, whether the location was sampled as part of AIMS seasonal microbial sampling.

STIC_inferred_PA

Logical, whether surface water presence/absence data were obtained from STIC (Stream Temperature, Intermittency, and Conductivity) sensors at the location.

New_in_2023

Logical, referring to sites at Johnson Draw added in 2023.

Generalized DAG indices

Description

Calculates global generalized topopological indices for a digraph

Usage

I.D(G, mode = "gen.rand", alpha = -1/2, mult = FALSE, degrees = "out.in")

Arguments

Details

For an arc a={\overrightarrow{uv}}, a \in A, we denote the out degree of u as d_u^+, and the in degree of v as d_v^-. Now let I(D) represent a generalized topopological index for a digraph, D (cf. Deng et al., 2021) that depends on d_u^+ and d_v^-:

I(D)=1/2 \sum_{uv \in A}\omega(d_u^+,d_v^-)

Six basic configurations for I(D) can be recognized:

1. If \omega(x,y)=(xy)^\alpha, for \alpha \neq 0, then I(D) is the general directed Randic index (Kincaid et al., 2016) for D. Specific variants include the Randic index (\alpha =-1/2), the second Zagreb index (\alpha =1) and the second modified Zagreb index (\alpha =-1) (Anthony & Marr, 2021).

2. If \omega(x,y)=(x+y)^\alpha, then I(D) is the general sum-connectivity index for D (Deng et al., 2021). Further, if \omega(x,y)=2(x+y)^\alpha, then I(D) is the sum connectivity (Zhou & Trinajstic, 2009), and the directed first Zagreb index (Anthony & Marr, 2021) for \alpha =-1/2 and \alpha=1, respectively .

3. If \omega(x,y)=\sqrt{((x+y-2)/xy)}, then I(D) is the atom bond connectivity of D (Estrada et al., 1998).

4. If \omega(x,y)=\sqrt{xy}/(1/2(x+y)), then I(D) is the geometric-arithmetic index for D (Vukicevic & Furtula, 2009).

5. If \omega(x,y)=2/(x+y), then I(D) is the harmonic index of D (Favaron et al., 1993).

6. If \omega(x,y)=\left(\frac{xy}{x+y-2}\right)^3, then I(D) is the augmented Randic index of D (Furtula et al. 2010). This index is not reccomended for stream DAGs as it will contained undefined terms for any network with unbranched paths.

More options are possible under the generalization of Kincaid (1996). Specifically, for an arc a=\vec{uv},a\in A, let \gamma,\tau\in-,+ index the degree type: -= in, +=out. Then, four combinations of d_u^\gamma, d_v^\tau can occur, resulting in four different versions of each I(D) metric described above. These combinations are: d_u^+,d_v^- (as shown above), d_u^+,d_v^+, d_u^-,d_v^-, and d_u^-,d_v^+. The default d_u^+,d_v^- is strongly reccomended for stream DAGs over other variants.

Value

Index values for a DAG

Author(s)

Ken Aho, Gabor Csardi wrote degree

References

Anthony, B. M., & Marr, A. M. (2021). Directed zagreb indices. Graphs and Combinatorial Optimization: From Theory to Applications: CTW 2020 Proceedings, 181-193.

Deng, H., Yang, J., Tang, Z., Yang, J., & You, M. (2021). On the vertex-degree based invariants of digraphs. arXiv Preprint arXiv:2104.14742.

Estrada, E., Torres, L., Rodriguez, L., & Gutman, I. (1998). An atom-bond connectivity index: Modelling the enthalpy of formation of alkanes. NISCAIR-CSIR, India.

Favaron, O., Maheo, M., & Sacle, J.-F. (1993). Some eigenvalue properties in graphs (conjectures of graffitii). Discrete Mathematics, 111(1-3), 197-220.

Furtula, B., Graovac, A., & Vukicevic, D. (2010). Augmented Zagreb index. Journal of Mathematical Chemistry, 48(2), 370-380.

Kincaid, R. K., Kunkler, S. J., Lamar, M. D., & Phillips, D. J. (2016). Algorithms and complexity results for finding graphs with extremal Randic index. Networks, 67(4), 338-347.

Vukicevic, D., & Furtula, B. (2009). Topological index based on the ratios of geometrical and arithmetical means of end-vertex degrees of edges. Journal of Mathematical Chemistry, 46(4), 1369-1376.

Zhou, B., & Trinajstic, N. (2009). On a novel connectivity index. Journal of Mathematical Chemistry, 46(4), 1252-1270.

See Also

degree

Examples

network_a <- graph_from_literal(a --+ b, c --+ d, d --+ e, b --+ e, 
e --+ j, j --+ m, f --+ g, g --+ i, h --+ i, i --+ k, k --+ l, 
l --+ m, m --+ n,  n --+ o)
I.D(network_a)

Integral connectivity scale length (ICSL)

Description

Integral connectivity scale lengths (ICSL, Western et al. 2013) is the average distance between wet locations using either (1) Euclidean distance or (2) topographically-defined hydrologic distance, e.g., instream hydrologic distance, subsurface distance (Ali and Roy 2009) and outlet distance, in which connected saturated paths must reach the catchment outlet.

Usage

ICSL(G, coords = NULL, names = NULL, lengths = NULL, 
dist.matrix = NULL, show.dist = FALSE)

Arguments

Details

Computes either: 1) the average Euclidean distance of connected nodal locations as defined in G, if coords are provided, 2) if dist.matrix is provided, the average nodal distances of a distance matrix provided in dist.matrix for nodes that remain in G, or 3) the instream distances of connected nodal locations if stream lenghts are provided in lengths. For 3), the length vector will need to be trimmed as arcs disappear within intermittent streams (see Examples).

Value

Returns a global distance scalar. See Details.

Author(s)

Ken Aho, Gabor Csardi wrote underlying functions distances and E

References

Ali, G. A., & Roy, A. G. (2010). Shopping for hydrologically representative connectivity metrics in a humid temperate forested catchment. Water Resources Research, 46(12).

Western, A. W., Bloschl, G., & Grayson, R. B. (2001). Toward capturing hydrologically significant connectivity in spatial patterns. Water Resources Research, 37(1), 83-97.

See Also

distances

Examples

murphy_spring <- graph_from_literal(IN_N --+ M1984 --+ M1909, IN_S --+ M1993,  
M1993 --+ M1951 --+ M1909 --+ M1799 --+ M1719 --+ M1653 --+ M1572 --+ M1452, 
M1452 --+ M1377 --+ M1254 --+ M1166 --+ M1121 --+ M1036 --+ M918 --+ M823, 
M823 --+ M759 --+ M716 --+ M624 --+ M523 --+ M454 --+ M380 --+ M233 --+ M153, 
M153 --+ M91 --+ OUT)

#---- ICSL based on nodal Euclidean distances ----#
data(mur_coords)
ICSL(murphy_spring, coords = mur_coords[,2:3], names = mur_coords[,1])

#---- ICSL based on in-stream length data ----#
data(mur_lengths)
ICSL(murphy_spring, lengths = mur_lengths[,2], names = mur_coords[,1])

# or, simply
ms <- murphy_spring
E(ms)$weight <- mur_lengths[,2]
ICSL(ms)

# Arcs 1 and 3 dry
B <- graph_from_literal(IN_N, M1984, IN_S --+ M1993 --+ M1951 --+ M1909, 
M1909 --+ M1799 --+ M1719 --+ M1653 --+ M1572 --+ M1452 --+ M1377 --+ M1254,
M1254 --+ M1166 --+ M1121 --+ M1036 --+ M918 --+ M823 --+ M759 --+ M716, 
M716 --+ M624 --+ M523 --+ M454 --+ M380 --+ M233 --+ M153 --+ M91 --+ OUT)
ICSL(B, lengths = mur_lengths[,2][-c(1,3)], show.dist = TRUE)

Bounds for the correlation of two (or more) Benrnoulli random variables

Description

Replaces impossible correlations (values too small or too large) with minimum and maximum correlations, respectively.

Usage

min_r(p1, p2)
max_r(p1, p2)
R.bounds(p, R, pad = 0.001)

Arguments

Details

The functions r.min and r.max define minimum and maximimum possible correlations. The function R.bounds replaces impossibly large or small values with maximally large or small values repectively.

Value

Functions return a scalar defining minimum or maximimum possible correlations. See Aho et al. (2023).

Author(s)

Ken Aho

References

Aho, K., Derryberry, D., Godsey, S. E., Ramos, R., Warix, S., Zipper, S. (2023) The communication distance of non-perennial streams. EarthArXiv https://eartharxiv.org/repository/view/4907/

Examples

min_r(0.6, 0.9)
max_r(0.1, 0.2)

x1 <- rep(c(1,0),5)
x2 <- c(rep(1,7), rep(0,3))
x3 <- c(rep(1,3), rep(0,7))
R <- cor(cbind(x1, x2, x3))
R.bounds(c(0.5, 0.7, 1), R)

A wrapper for missForest for random forest STIC imputation

Description

A simple wrapper for the missForest random forest imputation algorithm. STIC.RFimpute first converts STIC (Stream Temperature, Intermittency, and Conductivity) presence/absence data to categorical outcomes to avoid regression fitting. One should consult missForest for specifics on the underlying algorithm.

Usage

STIC.RFimpute(p.a, ...)

Arguments

Value

Provides the conventional unaltered missForest output.

Author(s)

Daniel J. Stekhoven, <stekhoven@stat.math.ethz.ch>

References

Stekhoven, D.J. and Buehlmann, P. (2012), 'MissForest - nonparametric missing value imputation for mixed-type data', Bioinformatics, 28(1) 2012, 112-118, doi: 10.1093/bioinformatics/btr597

Examples

arc.pa <- data.frame(matrix(ncol = 3, data = c(1,1,1, 0,1,1, 1,1,1, 0,NA,1), byrow = TRUE))
names(arc.pa) <- c("n1 --> n2", "n2 --> n3", "n3 --> n4")

STIC.RFimpute(arc.pa)

Obtain arc stream activity outcomes based on bounding nodes

Description

Given nodal water presence absence data \in \{0,1\} for a graph, G, the function calculates arc water presence probabilities using particular rules (see approaches in Details).

Usage

arc.pa.from.nodes(G, node.pa, approach = "aho", na.rm = TRUE)

Arguments

Details

The approach argument currently supports three alternatives "aho", "dstream" and "ustream". Let x_{k} represent the kth arc with bounding nodes u and v.

Under approach = "aho" there are three possibilities: x_{k} = 1.0 if both u and v are wet, x_{k} = 0 if both u and v are dry, and x_{k} = 0.5 if only one of u or v is wet.

Under approach = "dstream", x_{k} = 1.0 if v is wet, and x_{k} = 0 if v is dry.

Conversely, if approach = "ustream", x_{k} = 1.0 if u is wet, and x_{k} = 0 if u is dry.

Value

Returns a matrix whose entries are estimated probabilities of success (e.g. surface water presence) based on the rules given in Aho et al. (2023). Matrix columns specify arcs and rows typically represent time series observations.

Author(s)

Ken Aho

References

Aho, K., Derryberry, D., Godsey, S. E., Ramos, R., Warix, S., Zipper, S. (2023) The communication distance of non-perennial streams. EarthArXiv https://eartharxiv.org/repository/view/4907/

Examples

murphy_spring <- graph_from_literal(IN_N --+ M1984 --+ M1909, IN_S --+ M1993,  
M1993 --+ M1951 --+ M1909 --+ M1799 --+ M1719 --+ M1653 --+ M1572 --+ M1452, 
M1452 --+ M1377 --+ M1254 --+ M1166 --+ M1121 --+ M1036 --+ M918 --+ M823, 
M823 --+ M759 --+ M716 --+ M624 --+ M523 --+ M454 --+ M380 --+ M233 --+ M153, 
M153 --+ M91 --+ OUT)

data(mur_node_pres_abs)
pa <- mur_node_pres_abs[400:405,][,-1]
arc.pa.from.nodes(murphy_spring, pa)
arc.pa.from.nodes(murphy_spring, pa, "dstream")

Assortativity

Description

Calculates graph assortativity

Usage

assort(G, mode = "in.out")

Arguments

Details

The definitive measure of graph assortativity is the Pearson correlation coefficient of the degree of pairs of adjacent nodes (Newman, 2002). Let \overrightarrow{u_iv_i} define nodes and directionality of the ith arc, i=1,2,3,\ldots,m, let \gamma,\tau\in{-,+} index the degree type: -=in, +=out, and let \left(u_i^\gamma,v_i^\tau\right), be the \gamma- and \tau-degree of the ith arc. Then, the general form of assortativity index is:

r\left(\gamma,\tau\right)=m^{-1}\frac{\sum_{i= 1}^m (u_i^\gamma-\bar{u}^\gamma)(v^\tau_i-\bar{v}^\tau)}{s^\gamma s^\tau}

where \bar{u}^\gamma and \bar{v}^\gamma are the arithmetic means of the u_i^\gammas and v_i^\taus, and s^\gamma and s^\tau are the population standard deviations of the u_i^\gammas and v_i^\taus. Under this framework, there are four possible forms to r\left(\gamma,\tau\right) (Foster et al., 2010). These are: r\left(+,-\right), r\left(-,+\right), r\left(-,-\right), and r\left(+,+\right).

Value

Assortativity coefficeint outcome(s)

Author(s)

Ken Aho, Gabor Csardi wrote degree

References

Newman, M. E. (2002). Assortative mixing in networks. Physical Review Letters, 89(20), 208701.

Foster, J. G., Foster, D. V., Grassberger, P., & Paczuski, M. (2010). Edge direction and the structure of networks. Proceedings of the National Academy of Sciences, 107(24), 10815-10820.

Examples

network_a <- graph_from_literal(a --+ b, c --+ d, d --+ e, b --+ e, e --+ j, 
j --+ m, f --+ g, g --+ i, h --+ i, i --+ k, k --+ l, l --+ m, m --+ n,  
n --+ o)
assort(network_a)

Botter and Durighetto Bernoulli stream length

Description

A simple function for calculating the dot product of a vector of stream arc lengths and a corresponding vector of either binary (stream presence or absence) outcomes, probabilities of stream presence or inverse probabilites of stream presence.

Usage

bern.length(lengths, pa, mode = "local")

Arguments

Value

When pa is a vector of binary (stream presence or absence) data, the function provides a measure of instantaneous stream length (in the units used in lengths). When pa is a vector of probabilities of stream presence, the function provides average stream length (in units used in lengths). When pa is a vector of inverse probabilites of stream presence, the function provides average communication distance (in units used in lengths).

Author(s)

Ken Aho

References

Botter, G., & Durighetto, N. (2020). The stream length duration curve: A tool for characterizing the time variability of the flowing stream length. Water Resources Research, 56(8), e2020WR027282.

Examples

lengths <- rexp(10, 10)
pa <- rbinom(10, 11, 0.4)
bern.length(lengths, pa)

Posterior Beta and Inverse-beta summaries

Description

Calculates summaries for beta and inverse-beta posteriors given prior probabilities for success, binary data and prior weight specification. Summaries include beta and inverse beta posterior means and variances and stream length and communication distance summaries given that stream length is provided for intermittent stream segments.

Usage

beta.posterior(p.prior, dat, length = NULL, w = 0.5)

Arguments

Details

As our Bayesian framework we assume a conjugate beta prior \theta_k \sim BETA(\alpha, \beta) and binomial likelihood \boldsymbol{x}_k \mid \theta_k \sim BIN(n, \theta_k) resulting in the posterior \theta_k \mid \boldsymbol{x}_k \sim BETA(\alpha + \sum \boldsymbol{x}_k, \beta + n - \sum \boldsymbol{x}_k).

Value

Returns a list with components:

Author(s)

Ken Aho

See Also

dinvbeta.

Bivariate Bernoulli Distribution

Description

Densities (probabilities) of a bivariate Bernoulli distribution, Y_1,Y_2.

Usage

biv.bern(p11, p10, p01, p00, y1, y2)

Arguments

Value

Densities (probability) of the joint Bernoulli distribution.

Author(s)

Ken Aho

Examples

biv.bern(0.25,0.25,0.25,0.25,1,0)
biv.bern(0.1,0.4,0.3,0.2,1,0)

Stream segment presence absence data for Dry Cr. Idaho

Description

Stream segment presence absence data for Dry Cr. Idaho (outlet coordinates: 43.71839^\circN, 116.13747^\circW). Arc outcomes determined from STIC (Stream Temperature, Intermittency, and Conductivity) sensors at bounding nodes.

Usage

data("dc_arc_pres_abs")

Format

A data frame with 46187 observations on the following 29 variables.

datetime

a POSIXlt

‘⁠DC10-->C1⁠’

a numeric vector

‘⁠C1-->DC12⁠’

a numeric vector

‘⁠DC11-->C1⁠’

a numeric vector

‘⁠DC12-->C2⁠’

a numeric vector

‘⁠C2-->DC15⁠’

a numeric vector

‘⁠DC13-->C2⁠’

a numeric vector

‘⁠DC15-->C3⁠’

a numeric vector

‘⁠C3-->DC16⁠’

a numeric vector

‘⁠DC14-->C3⁠’

a numeric vector

‘⁠DC16-->C4⁠’

a numeric vector

‘⁠C4-->DC19⁠’

a numeric vector

‘⁠DC17-->C5⁠’

a numeric vector

‘⁠C5-->C4⁠’

a numeric vector

‘⁠DC18-->C5⁠’

a numeric vector

‘⁠DC19-->C6⁠’

a numeric vector

‘⁠C6-->DC4⁠’

a numeric vector

‘⁠DC20-->C6⁠’

a numeric vector

‘⁠DC4-->C7⁠’

a numeric vector

‘⁠C7-->DC5⁠’

a numeric vector

‘⁠DC1-->DC2⁠’

a numeric vector

‘⁠DC2-->DC3⁠’

a numeric vector

‘⁠DC3-->C7⁠’

a numeric vector

‘⁠DC5-->C8⁠’

a numeric vector

‘⁠C8-->DC6⁠’

a numeric vector

‘⁠DC9-->C9⁠’

a numeric vector

‘⁠C9-->DC7⁠’

a numeric vector

‘⁠DC8-->C9⁠’

a numeric vector

‘⁠DC7-->C8⁠’

a numeric vector

Source

Maggie Kraft

Lengths of Dry Creek stream (arc) segments

Description

Lengths of stream (arc) segments from Dry Creek Idaho (outlet coordinates: 43.71839^\circN, 116.13747^\circW).

Usage

data("dc_lengths")

Format

A data frame with 28 observations on the following 2 variables.

Arcs

Arc names, arrows directionally connect nodes.

Lengths

Stream segment (arc) length in kilometers.

Source

Maggie Kraft

Stream node presence absence data for Dry Cr. Idaho

Description

Stream node surface water presence absence at Dry Creek ID (outlet coordinates: 43.71839^\circN, 116.13747^\circW). Outcomes based on STIC (Stream Temperature, Intermittency, and Conductivity) sensor and piezometer responses, resulting in binary observations for 36 nodes at 15 minutes intervals, over four years.

Usage

data("dc_node_pres_abs")

Format

A data frame with 86958 observations on the following 37 variables.

datetime

a POSIXlt object

DC10

a numeric vector

C1

a numeric vector

DC11

a numeric vector

DPZ07

a numeric vector

DPZ06

a numeric vector

DC12

a numeric vector

C2

a numeric vector

DC13

a numeric vector

DC15

a numeric vector

C3

a numeric vector

DC14

a numeric vector

DC16

a numeric vector

DPZ05

a numeric vector

C4

a numeric vector

DC17

a numeric vector

C5

a numeric vector

DC18

a numeric vector

DC19

a numeric vector

C6

a numeric vector

DC20

a numeric vector

DC4

a numeric vector

C7

a numeric vector

DC1

a numeric vector

DC2

a numeric vector

DC3

a numeric vector

DC5

a numeric vector

DPZ02

a numeric vector

C8

a numeric vector

DPZ04

a numeric vector

DPZ03

a numeric vector

DC9

a numeric vector

C9

a numeric vector

DC8

a numeric vector

DC7

a numeric vector

DC6

a numeric vector

DSS01

a numeric vector

Source

Maggie Kraft

Potential degree distributions

Description

Calculates degree distribution probability density. By default calculates an uncorrelated (random) density for a given degree.

Usage

degree.dists(d, exp.lambda = 3/2, normalize = TRUE)

Arguments

Details

In general f(d) = \exp(-\lambda d) where d is the degree. For random degree distributions, \lambda = \log(3/2).

Value

Returns a density plot for a degree.

Author(s)

Ken Aho

See Also

degree.distribution, plot_degree.dist.

Delete arcs based on presence absence data

Description

Create a new graph after deleting stream graph arcs based on presence/absence data, e.g., data based on outcomes from STIC (Stream Temperature, Intermittency, and Conductivity) loggers.

Usage

delete.arcs.pa(G, pa)

Arguments

Value

Returns a igraph graph object missing the arcs indicated with 0 in pa.

Author(s)

Ken Aho, Gabor Csardi wrote delete.edges

Examples

G <- graph_from_literal(a--+b--+c--+d--+e)
delete.arcs.pa(G, c(0,0,1,1)) 

Delete nodes based on presence absence data

Description

Create a new graph after deleting stream graph nodes based on presence/absence data, e.g., data based on outcomes from STIC (Stream Temperature, Intermittency, and Conductivity) loggers.

Usage

delete.nodes.pa(G, pa, na.response = "none")

Arguments

Details

A perennial problem with STIC (Stream Temperature, Intermittency, and Conductivity) sensors is the presence of missing data. If na.response = "none" and NAs exist then the waring message "NAs in data need to be addressed. NAs converted 0." is printed. One can also choose na.response = "treat.as.0" or na.response = "treat.as.1" which converts NAs to zeroes or ones. Clearly, none of these draconian approaches is optimal. Thus, if NAs occur, an attribute is added to the output graph object returned by the function, which lists the nodes with missing data. This attribute can be obtained with out$NA.vertices where out <- delete.nodes.pa(...), see Examples below. An alternative is to use a classification algorithm for imputation e.g., STIC.RFimpute, which uses missForest::missForest.

Value

Returns a igraph graph object, missing the nodes indicated with 0 in pa.

Author(s)

Ken Aho, Gabor Csardi wrote delete.vertices

Examples

G <- graph_from_literal(a--+b--+c--+d--+e)
delete.nodes.pa(G, c(0,0,1,1,1)) 
# delete.nodes.pa(G, c(0,0,NA,1,1)) # gives warning and converts NA to 0 
d <- delete.nodes.pa(G, c(0,0,NA,1,1), "treat.as.0")
d
d$NA.vertices

Inverse Beta Distribution

Description

Calculates density (dinvbeta), lower-tailed probability (pinvbeta) and obtains random outcomes (rinvbeta) for an inverse beta distribution

Usage

dinvbeta(x, alpha, beta)
pinvbeta(x, alpha, beta)
rinvbeta(n, alpha, beta)

Arguments

Value

Returns density, probability, and random outcomes for an inverse beta distribution.

Author(s)

Ken Aho and Dwayne Derryberry

See Also

See Also dbeta.

Examples

dinvbeta(1,1,1)
pinvbeta(1,1,1)
rinvbeta(1,1,1)

Local and global efficiency

Description

Efficiency is the reciprocal of internodal distance. Thus, the efficiency beween nodes i and j is defined as e_{i,j} = \frac{1}{d_{i,j}} where d_{i,j} denotes the distance between nodes i and j for all i \neq j.

Usage

efficiency.matrix(G, mode = "in")

avg.efficiency(G, mode = "in")

global.efficiency(G, mode = "in")

Arguments

Details

The function efficiency.matrix calculates an efficiency matrix whose elements correspond to elements in the graph distance matrix. The function avg.efficiency calculates average efficiencies of nodes to all other nodes, thus providing a local measure of graph connectedness. The function global.efficiency calculates the mean of the of all pairwise efficiencies, thus providing a global measure of graph connectedness. For all three functions, reciprocals of infinite distances are taken to be zero.

Value

The function efficiency.matrix returns a reciprocal distance matrix for nodes in G. The function avg.efficiency treats efficiency as a local measure, and thus returns a vector whose entries are average efficiencies for each node. The function global.efficiency returns a scalar (the mean of the reciprocal distance matrix).

Author(s)

Ken Aho. Gabor Csardi wrote the function distances in igraph.

References

Ek, B., VerSchneider, C., & Narayan, D. A. (2015). Global efficiency of graphs. AKCE International Journal of Graphs and Combinatorics, 12(1), 1-13.

Examples

kon_full <- streamDAGs("konza_full")
efficiency.matrix(kon_full)
avg.efficiency(kon_full)
global.efficiency(kon_full)

Loads AIMS dataset associated with a particular AIMS graph

Description

The function creates a list of associated dataframes for particular AIMS graph objects. Currently these include one of more of $coords $arc.length $node.pa.

Usage

get.AIMS.data(graph = "mur_full", supress.message = FALSE)

Arguments

Details

The function radically simplifies code gymnastics required to obtain datasets associated AIMS graphs (see, for instance, Detaails in streamDAGs).

Value

Returns a list containg up to three dataframes:

Author(s)

Ken Aho

See Also

streamDAGs

Examples

jd <- get.AIMS.data("jd_full", TRUE)
head(jd$coords)
head(jd$arc.length)
head(jd$node.pa)

Coordinates of nodes at Gibson Jack Creek, Idaho for a 2016 survey

Description

Latitudes and Longitudes of nodes established at Gibson Jack in 2016. Datum: WGS 84.

Usage

data("gj_coords16")

Format

A data frame with 124 observations on the following 3 variables.

Object.ID

Node name

lat

Latitude

long

Longitude

Lengths of Gibson Jack stream (arc) segments

Description

Lengths of stream (arc) segments from the Gibson Jack watershed in southeast Idaho (outlet coordinates: 42.767180^\circN, 112.480240^\circW). The dataframe gj_lengths contains arc lengths for a network including STICs, but excluding piezometers. The dataframe gj_lengths_piezo_full contains arc lengths for a network that includes both STICs and piezometers.

Usage

data("gj_lengths")

Format

A data frame with 28 observations jd_lengths or 35 observations jd_lengths_piezo_full on the following 2 variables.

Arcs

Arc names, arrows directionally connect nodes.

Lengths

Stream segment (arc) length in kilometers.

Source

Maggie Kraft

Stream node presence absence data for Gibson Jack Idaho

Description

Stream node surface water presence absence data from Gibson Jack, Idaho (outlet coordinates: 42.767180^\circN, 112.480240^\circW). Outcomes based on STIC (Stream Temperature, Intermittency, and Conductivity) sensors, resulting in binary observations for 29 nodes at 15 minutes intervals over three years.

Usage

data("gj_node_pres_abs")

Format

A data frame with 55109 observations on the following 36 variables.

datetime

a POSIXlt vector

GJ16

a numeric vector

GJ14

a numeric vector

C2

a numeric vector

C3

a numeric vector

C4

a numeric vector

GJ11

a numeric vector

GJ13

a numeric vector

GJ12

a numeric vector

GJ19

a numeric vector

GJ20

a numeric vector

GJ18

a numeric vector

GJ17

a numeric vector

C5

a numeric vector

GJ10

a numeric vector

GJ9

a numeric vector

C6

a numeric vector

GJ23

a numeric vector

GJ22

a numeric vector

C1

a numeric vector

C7

a numeric vector

GJ25

a numeric vector

GJ21

a numeric vector

GJ8

a numeric vector

GJ7

a numeric vector

GJ3

a numeric vector

C8

a numeric vector

GJ6

a numeric vector

GJ5

a numeric vector

GJ4

a numeric vector

GPZ02

a numeric vector

GPZ03

a numeric vector

GPZ04

a numeric vector

GPZ05

a numeric vector

GPZ06

a numeric vector

GPZ07

a numeric vector

GSS01

a numeric vector

Source

Maggie Kraft

Stream node presence absence data for Gibson Jack Cr. Idaho, for a 2016 survey

Description

Streamflow presence and absence data for each node location collected by manual observation November 6, 2016, May 6, 2017, and August 14, 2017. Note, a longer dataset 2021-2023, gathered by the AIMS team at fewer points, is available for Gibson Jack under the name gj_node_pres_abs.

Usage

data("gj_node_pres_abs16")

Format

A data frame with 3 observations on the following 125 variables.

Date

a character vector

GJ_ST1_0600

a numeric vector

GJ_ST1_0400

a numeric vector

GJ_ST1_0200

a numeric vector

GJ_ST1_0000

a numeric vector

GJ_SF_2800

a numeric vector

GJ_SF_2600

a numeric vector

GJ_SF_2400

a numeric vector

GJ_SF_2200

a numeric vector

GJ_SF_2000

a numeric vector

GJ_SF_1800

a numeric vector

GJ_SF_1600

a numeric vector

GJ_SF_1400

a numeric vector

GJ_SF_1200

a numeric vector

GJ_SF_1000

a numeric vector

GJ_SF_0800

a numeric vector

GJ_SF_0600

a numeric vector

GJ_SF_0400

a numeric vector

GJ_SF_0200

a numeric vector

GJ_SF_0000

a numeric vector

GJ_NT1_WF_FH

a numeric vector

GJ_NT1_WF_000

a numeric vector

GJ_NT1_0800

a numeric vector

GJ_NT1_0600

a numeric vector

GJ_NT1_0400

a numeric vector

GJ_NT1_0200

a numeric vector

GJ_NT1_0000

a numeric vector

GJ_NT2_1600

a numeric vector

GJ_NT2_1400

a numeric vector

GJ_NT2_1200

a numeric vector

GJ_NT2_1000

a numeric vector

GJ_NT2_0800

a numeric vector

GJ_NT2_0600

a numeric vector

GJ_NT2_0400

a numeric vector

GJ_NT2_0200

a numeric vector

GJ_NT2_0000

a numeric vector

GJ_NT3_0200

a numeric vector

GJ_NT3_0000

a numeric vector

GJ_NT4_0600

a numeric vector

GJ_NT4_0400

a numeric vector

GJ_NT4_0200

a numeric vector

GJ_NT4_0000

a numeric vector

GJ_NF_3800

a numeric vector

GJ_NF_3750

a numeric vector

GJ_NF_3600

a numeric vector

GJ_NF_3400

a numeric vector

GJ_NF_3200

a numeric vector

GJ_NF_3000_CU

a numeric vector

GJ_NF_3000_CD

a numeric vector

GJ_NF_2800_CU

a numeric vector

GJ_NF_2800_CD

a numeric vector

GJ_NF_2600

a numeric vector

GJ_NF_2400_CU

a numeric vector

GJ_NF_2400_CD

a numeric vector

GJ_NF_2200

a numeric vector

GJ_NF_2000

a numeric vector

GJ_NF_1800

a numeric vector

GJ_NF_1600

a numeric vector

GJ_NF_1400

a numeric vector

GJ_NF_1200

a numeric vector

GJ_NF_1060_CU

a numeric vector

GJ_NF_1060_CD

a numeric vector

GJ_NF_1000

a numeric vector

GJ_NF_0800

a numeric vector

GJ_NF_0600

a numeric vector

GJ_NF_0400

a numeric vector

GJ_NF_0200

a numeric vector

GJ_NF_0000

a numeric vector

GJ_MT2_0900

a numeric vector

GJ_MT2_0800

a numeric vector

GJ_MT2_0600

a numeric vector

GJ_MT2_0400

a numeric vector

GJ_MT2_0200

a numeric vector

GJ_MT2_0000

a numeric vector

GJ_MT1_1650

a numeric vector

GJ_MT1_1600

a numeric vector

GJ_MT1_1550

a numeric vector

GJ_MT1_1500

a numeric vector

GJ_MT1_1450

a numeric vector

GJ_MT1_1400

a numeric vector

GJ_MT1_1350

a numeric vector

GJ_MT1_1300

a numeric vector

GJ_MT1_1250

a numeric vector

GJ_MT1_1200

a numeric vector

GJ_MT1_1150

a numeric vector

GJ_MT1_1100

a numeric vector

GJ_MT1_1050

a numeric vector

GJ_MT1_1000

a numeric vector

GJ_MT1_0950

a numeric vector

GJ_MT1_0900

a numeric vector

GJ_MT1_0850

a numeric vector

GJ_MT1_0800

a numeric vector

GJ_MT1_0750

a numeric vector

GJ_MT1_0700

a numeric vector

GJ_MT1_0650

a numeric vector

GJ_MT1_0600

a numeric vector

GJ_MT1_0550

a numeric vector

GJ_MT1_0500

a numeric vector

GJ_MT1_0450

a numeric vector

GJ_MT1_0400

a numeric vector

GJ_MT1_0350

a numeric vector

GJ_MT1_0300

a numeric vector

GJ_MT1_0250

a numeric vector

GJ_MT1_0200

a numeric vector

GJ_MT1_0150

a numeric vector

GJ_MT1_0100

a numeric vector

GJ_MT1_0050

a numeric vector

GJ_MT1_0000

a numeric vector

GJ_MS_3000

a numeric vector

GJ_MS_2800

a numeric vector

GJ_MS_2600

a numeric vector

GJ_MS_2400

a numeric vector

GJ_MS_2200

a numeric vector

GJ_MS_2000

a numeric vector

GJ_MS_1800_CU

a numeric vector

GJ_MS_1800_CD

a numeric vector

GJ_MS_1600

a numeric vector

GJ_MS_1400

a numeric vector

GJ_MS_1200

a numeric vector

GJ_MS_1000

a numeric vector

GJ_MS_0800

a numeric vector

GJ_MS_0600

a numeric vector

GJ_MS_0400

a numeric vector

GJ_MS_0200

a numeric vector

GJ_MS_0000

a numeric vector

Global Summary

Description

This function calculates useful DAG global summaries including size, diameter, number of paths to sink, mean path length, mean alpha centrality, mean PageRank centrality, graph centralization, Strahler order, Shreve order, the Randic index, the first Zagreb Index, the second Zagreb index, atom-bond connectivity, the geometric-arithmatic index, the harmonic index, the Harary index, global efficiency, the assortativity correlation (+, -), and the assortativity correlation (+, +).

Usage

global.summary(G, which = "all", sink, mode = "in", inf.paths = FALSE)

Arguments

Details

Simple global graph measures of complexivity and/or connectivity of a stream DAG include size, diameter, and number of paths to a sink. The size is equal to the number of arcs in the stream network. The diameter equals the length of the longest path, i.e., the height of the sink, and in eccentricity of the sink. The number of paths to the sink is equivalent to the number of nodes from which the sink node is reachable, which will be n-1 for a fully active stream. For more information on I(D) metrics see I.D. Links describing other metrics are provided below.

Value

Returns a vector of global graph measures for G.

Author(s)

Ken Aho, Gabor Csardi wrote alpha_centrality and other underlying functions.

References

Kunkler, S. J., LaMar, M. D., Kincaid, R. K., & Phillips, D. (2013). Algorithm and complexity for a network assortativity measure. arXiv Preprint arXiv:1307.0905.

Das, K. C., Gutman, I., & Furtula, B. (2011). On atom-bond connectivity index. Chemical Physics Letters, 511(4-6), 452-454.

Li, X., & Shi, Y. (2008). A survey on the randic index. MATCH Commun. Math. Comput. Chem, 59(1), 127-156.

See Also

alpha_centrality, I.D, spath.lengths, n.sources, stream.order, harary

Examples

network_a <- graph_from_literal(a --+ b, c --+ d, d --+ e, b --+ e, 
e --+ j, j --+ m, f --+ g, g --+ i, h --+ i, i --+ k, k --+ l, 
l --+ m, m --+ n,  n --+ o)

global.summary(network_a, sink ="o")

Harary Index

Description

Computes the Harary global metric for a stream DAG.

Usage

harary(G)

Arguments

Details

The Harary index is computed as:

\frac{1}{2}\sum_i^m\sum_j^m (RD)_{ij}

where (RD)_{ij} is the reciprocal of the ijth element of the graph distance matrix. Reciprocals of infinite values in the distance matrix are taken to be zero. Users should be aware that the graph object G is assumed to be DAG, and that distances are based on in-paths.

Value

Returns a scalar: the global Harary index.

Author(s)

Ken Aho, Gabor Csardi wote distances

References

Plavsic, D., Nikolic, S., Trinajstic, N., & Mihalic, Z. (1993). On the Harary index for the characterization of chemical graphs. Journal of Mathematical Chemistry, 12(1), 235-250.

Examples

harary(streamDAGs("konza_full"))

Improved Closeness Centrality

Description

Calculates improved closeness centrality of individual nodes in a DAG.

Usage

imp.closeness(G)

Arguments

Details

Improved closeness centrality (Beauchamp, 1965) was developed for weakly connected or disconnected digraphs. The measure is based on the reciprocal of nodal shortest path distances from the jth node to the kth node, 1/\delta_{j,k}. For the jth node this is:

H_j=(n-1) \sum_{j \neq k} 1/\delta_{j,k}

where, for disconnected nodes, the reciprocal distance 1/\infty is taken to be zero.

Value

Improved closeness centrality of a node

Author(s)

Ken Aho, Gabor Csardi wrote distances

References

Beauchamp, M. A. (1965). An improved index of centrality. Behavioral Science, 10(2), 161-163.

See Also

distances

Examples

network_a <- graph_from_literal(a --+ b, c --+ d, d --+ e, b --+ e, 
e --+ j, j --+ m, f --+ g, g --+ i, h --+ i, i --+ k, k --+ l, 
l --+ m, m --+ n,  n --+ o)
imp.closeness(network_a)

Detects and defines islands in a streamDAG

Description

The function was written primarilly to recognize DAG islands to allow correct implementation of the function stream.order and is still early in its development.

Usage

isle(G)

Arguments

Details

The function currently allows detection of simple island structures (those that don't contain sub-islands). One of the output objects from the function is a new graph object with island nodes into a single node(s).

Value

Output consists of the following:

Author(s)

Ken Aho

See Also

stream.order, delete.vertices, add.vertices, add.edges

Examples

G <- graph_from_literal(a --+ c --+ e, b --+ d --+ e --+ f --+ p, g --+ i --+ j --+ m, 
i --+ k --+ m, m --+ n --+ o --+ p, h --+ l --+ n, p --+ q --+ r)
plot(G)
isle(G)

Lengths of Johnson Draw stream (arc) segments

Description

Lengths of stream (arc) segments from Johnson Draw in southwest Idaho (outlet coordinates: 43.12256^\circN, 116.77630^\circW). The dataframe jd_lengths contains segment lengths in the absence of piezos [nodes are currently defined by STIC (Stream Temperature, Intermittency, and Conductivity) locations only] and thus correspond to the network in streamDAGs("jd_full"). The dataframe jd_lengths_full contains segment lengths for node designated by both STICs and piezos. A corresponding network that includes piezos is depicted by streamDAGs("jd_piezo_full"). The dataframe jd_lengths_2023 contains segment lengths for node designated by both STICs, piezos and additional STC sites established in 2023. A correspding network can be obtained with streamDAGs("jd_piezo_full_2023").

Usage

data("jd_lengths")

Format

A data frame with observations on the following 2 variables.

Arcs

Arc names, arrows directionally connect nodes.

Lengths

Lengths in in km.

Source

Arya Legg, Maggie Kraft

Stream node presence absence data for Johnson Draw Idaho

Description

Stream node surface water presence absence data from 2022-2023 for the Johnson Draw watershed in southwest Idaho (outlet coordinates: 43.12256^\circN, 116.77630^\circW). Outcomes are based on STIC sensors and piezometers, resulting in binary observations for 35 nodes (28 STICs and 7 piezometers) at 15 minutes intervals over three years. JD21, JD22, JD23, JD25, JD26, JD27, JD28 were added in 2023 for the AIMS experiment.

Usage

data("jd_node_pres_abs")

Format

A data frame with 51653 observations on the following 36 variables.

datetime

a POSIXlt

JD5

a numeric vector

JD6

a numeric vector

JD7

a numeric vector

C1

a numeric vector

JD10

a numeric vector

JD9

a numeric vector

JD8

a numeric vector

JD11

a numeric vector

JD12

a numeric vector

JD13

a numeric vector

C2

a numeric vector

JD16

a numeric vector

JD17

a numeric vector

JD18

a numeric vector

JD19

a numeric vector

JD20

a numeric vector

JD4

a numeric vector

JD3

a numeric vector

JD2

a numeric vector

JD1

a numeric vector

JD15

a numeric vector

JD14

a numeric vector

JSS01

a numeric vector

JPZ02

a numeric vector

JPZ03

a numeric vector

JPZ04

a numeric vector

JPZ05

a numeric vector

JPZ06

a numeric vector

JPZ07

a numeric vector

JD21

a numeric vector

JD22

a numeric vector

JD23

a numeric vector

JD25

a numeric vector

JD26

a numeric vector

JD27

a numeric vector

JD28

a numeric vector

Source

Maggie Kraft

Coordinates of nodes in the Konza Praire dataset

Description

Coordinates (in Lat/Long) of nodes established at the Konza Prairie stream network.

Usage

data("kon_coords")

Format

A data frame with 46 observations on the following 3 variables.

Object.ID

Node name

lat

Latitude

long

Longitude

Lengths of Murphy Cr. stream (arc) segments

Description

Lengths of Murphy Cr. stream (arc) segments

Usage

data("kon_lengths")

Format

A data frame with 45 observations on the following 2 variables.

Arcs

Arc names, arrows directionally connect nodes.

Lengths

Lengths in meters

Source

Rob Ramos

local (nodal) summaries of a DAG

Description

Obtains local (nodal) summaries from a DAG

Usage

local.summary(G, metric = "all", mode = "in")

Arguments

Value

Nodes are returned with values measuring the indegree, alpha centrality, PageRank centrality, improved closeness centrality, betweenness centrality, upstream network length, and upstream in-path length mean, variance, max (i.e., in-eccentricity), skew, kurtosis, and mean efficiency.

Author(s)

Ken Aho, Gabor Csardi wrote degree, page_rank and alpha_centrality functions.

See Also

degree, alpha_centrality, page_rank, betweenness, imp.closeness, skew, kurt

Examples

network_a <- graph_from_literal(a --+ b, c --+ d, d --+ e, b --+ e, 
e --+ j, j --+ m, f --+ g, g --+ i, h --+ i, i --+ k, k --+ l, 
l --+ m, m --+ n,  n --+ o)
local.summary(network_a)

Stream segment presence absence data for Murphy Cr. Idaho

Description

Simulated multivariate Benroulli outcomes for 27 stream segments, based on their observed marginal probabilities for steam presence and covariance structures. "M"-labelling for nodes indicates "meters above outlet".

Usage

data("mur_arc_pres_abs")

Format

A data frame with 1000 observations on the following 27 variables.

‘⁠IN_N-->M1984⁠’

a numeric vector

‘⁠M1984-->M1909⁠’

a numeric vector

‘⁠M1909-->M1799⁠’

a numeric vector

‘⁠IN_S-->M1993⁠’

a numeric vector

‘⁠M1993-->M1951⁠’

a numeric vector

‘⁠M1951-->M1909⁠’

a numeric vector

‘⁠M1799-->M1719⁠’

a numeric vector

‘⁠M1719-->M1653⁠’

a numeric vector

‘⁠M1653-->M1572⁠’

a numeric vector

‘⁠M1572-->M1452⁠’

a numeric vector

‘⁠M1452-->M1377⁠’

a numeric vector

‘⁠M1377-->M1254⁠’

a numeric vector

‘⁠M1254-->M1166⁠’

a numeric vector

‘⁠M1166-->M1121⁠’

a numeric vector

‘⁠M1121-->M1036⁠’

a numeric vector

‘⁠M1036-->M918⁠’

a numeric vector

‘⁠M918-->M823⁠’

a numeric vector

‘⁠M823-->M759⁠’

a numeric vector

‘⁠M759-->M716⁠’

a numeric vector

‘⁠M716-->M624⁠’

a numeric vector

‘⁠M624-->M523⁠’

a numeric vector

‘⁠M523-->M454⁠’

a numeric vector

‘⁠M454-->M380⁠’

a numeric vector

‘⁠M380-->M233⁠’

a numeric vector

‘⁠M233-->M153⁠’

a numeric vector

‘⁠M153-->M91⁠’

a numeric vector

‘⁠M91-->OUT⁠’

a numeric vector

Coordinates of nodes at Murphy Ck. Idaho

Description

UTM coordinates (Zone 11T) and Latitudes and Longitudes of nodes established at Murphy Cr. Idaho. Datum: WGS 84.

Usage

data("mur_coords")

Format

A data frame with 28 observations on the following 5 variables.

Object.ID

Node name

E

UTM Easting

N

UTM Northing

lat

Latitude

long

Longitude

Lengths of Murphy Cr. stream (arc) segments

Description

Lengths of Murphy Cr. stream (arc) segments

Usage

data("mur_lengths")

Format

A data frame with 27 observations on the following 2 variables.

Arcs

Arc names, arrows directionally connect nodes.

Lengths

Stream segment (arc) length in meters.

Source

Warix, S. R., Godsey, S. E., Lohse, K. A., & Hale, R. L. (2021). Influence of groundwater and topography on stream drying in semi-arid headwater streams. Hydrological Processes, 35(5), e14185.

Stream node presence absence data for Murphy Cr. Idaho

Description

A subset of stream node presence absence data from Warix et al. (2019) resulting in binary observations for 28 nodes at 2.5 hr intervals.

Usage

data("mur_node_pres_abs")

Format

A data frame with 1163 observations on the following 29 variables.

Datetime

a character vector

IN_N

a numeric vector

M1984

a numeric vector

M1909

a numeric vector

IN_S

a numeric vector

M1993

a numeric vector

M1951

a numeric vector

M1799

a numeric vector

M1719

a numeric vector

M1653

a numeric vector

M1572

a numeric vector

M1452

a numeric vector

M1377

a numeric vector

M1254

a numeric vector

M1166

a numeric vector

M1121

a numeric vector

M1036

a numeric vector

M918

a numeric vector

M823

a numeric vector

M759

a numeric vector

M716

a numeric vector

M624

a numeric vector

M523

a numeric vector

M454

a numeric vector

M380

a numeric vector

M233

a numeric vector

M153

a numeric vector

M91

a numeric vector

OUT

a numeric vector

References

Warix, S. R., Godsey, S. E., Lohse, K. A., & Hale, R. L. (2021). Influence of groundwater and topography on stream drying in semi-arid headwater streams. Hydrological Processes, 35(5), e14185.

Simulated seasonal arc presence absence data for Murphy Cr

Description

A data frame with one hundred multivariate Bernoulli simulated outcomes representing spring, summer and fall.

Usage

data("mur_seasons_arc_pa")

Format

A data frame with 300 observations on the following 28 variables.

‘⁠IN_N -> M1984⁠’

a numeric vector

‘⁠M1984 -> M1909⁠’

a numeric vector

‘⁠M1909 -> M1799⁠’

a numeric vector

‘⁠IN_S -> M1993⁠’

a numeric vector

‘⁠M1993 -> M1951⁠’

a numeric vector

‘⁠M1951 -> M1909⁠’

a numeric vector

‘⁠M1799 -> M1719⁠’

a numeric vector

‘⁠M1719 -> M1653⁠’

a numeric vector

‘⁠M1653 -> M1572⁠’

a numeric vector

‘⁠M1572 -> M1452⁠’

a numeric vector

‘⁠M1452 -> M1377⁠’

a numeric vector

‘⁠M1377 -> M1254⁠’

a numeric vector

‘⁠M1254 -> M1166⁠’

a numeric vector

‘⁠M1166 -> M1121⁠’

a numeric vector

‘⁠M1121 -> M1036⁠’

a numeric vector

‘⁠M1036 -> M918⁠’

a numeric vector

‘⁠M918 -> M823⁠’

a numeric vector

‘⁠M823 -> M759⁠’

a numeric vector

‘⁠M759 -> M716⁠’

a numeric vector

‘⁠M716 -> M624⁠’

a numeric vector

‘⁠M624 -> M523⁠’

a numeric vector

‘⁠M523 -> M454⁠’

a numeric vector

‘⁠M454 -> M380⁠’

a numeric vector

‘⁠M380 -> M233⁠’

a numeric vector

‘⁠M233 -> M153⁠’

a numeric vector

‘⁠M153 -> M91⁠’

a numeric vector

‘⁠M91 -> OUT⁠’

a numeric vector

Season

A categorical variable with three levels: "spring" (6/3/2019 - 7/13/2019), "summer" (7/13/2019 - 8/23/2019) and "fall" (8/23/2019 - 10/2/2019)

Identify source and sink nodes

Description

Identify the number of sources and the source nodes. Sources are assumed to be linked to the sink.

Usage

n.sources(G, sink = NULL)
sources(G, sink = NULL)

Arguments

Value

Returns a character vector listing streamDAG source nodes (those linked to the sink with indegree 0).

Author(s)

Ken Aho, Gabor Csardi wrote degree

Examples

sources(streamDAGs("konza_full"), sink = "SFM01_1")

Path Lengths

Description

Obtains all shortest in paths to a sink

Usage

path.lengths.sink(G, sink = NULL, inf.paths = TRUE)

Arguments

Value

Length of path to a sink

Author(s)

Ken Aho, Gabor Csardi wrote distances

Examples

murphy_spring <- graph_from_literal(IN_N --+ M1984 --+ M1909, IN_S --+ M1993, 
M1993 --+ M1951 --+ M1909 --+ M1799 --+ M1719 --+ M1653 --+ M1572 --+ M1452,
M1452--+ M1377 --+ M1254 --+ M1166 --+ M1121 --+ M1036 --+ M918 --+ M823, 
M823 --+ M759 --+ M716 --+ M624 --+ M523 --+ M454 --+ M380 --+ M233 --+ M153,
M153 --+ M91 --+ OUT)

path.lengths.sink(murphy_spring, sink = "OUT")

# with stream lengths as weights
data(mur_lengths)

E(murphy_spring)$weights <- mur_lengths[,2]
path.lengths.sink(murphy_spring, "OUT")

Path Visibilities

Description

Functions detect and summarize visibilities of path nodes from one or several source nodes to an sink. Specifically, the function The function path.visibility determines path visibilities from single source node to a single sink. multi.path.visibility Generates tables of path visibilities and visibility summaries for multiple source nodes to a single sink.

Ordering of nodes, vitally important to the calculation of visibility is currently obtained by identifying paths from each source node to the sink. The sum of node distances in each path are then sorted decreasingly to define an initial order for calculating visibilities. It is currently assumed that the user will manually handle disconnected paths via the source argument of visibility functions. Use of source nodes disconnected to the sink will result in the message: "only use source nodes connected to sink". Because of this situation disconnected graphs will be handled by a function in development single.node.visibility.

Usage

path.visibility(G, degree = "in", source = NULL, sink = NULL, weights = NULL)

multi.path.visibility(G, degree = "in", source = NULL, sink = NULL, 
weights = NULL, autoprint = TRUE)

Arguments

Details

Following Lacasa et al. (2008), let t_a represent the occurrance number of the ath node in a time series or stream path, and let y_a represent a data outcome from the ath node. Nodes a and b will have visibility if all other data, y_c, between a and b fufill:

y_c < y_b + (y_a - y_b)\frac{t_b - t_c}{t_b - t_a}.

Value

The function path.visibility returns a symmetric matrix whose upper triangle denotes nodal co-visibilities. The lower triangle is left empty for efficiency. Reading down a column in the upper triangle shows upstream visibilites to and from a node, while reading across rows shows downstream visibilities.

The function multi.path.visibility returns a list containing the three objects. The first is printed and the latter two are invisible by default.

Output is summarized based on a deduced ordering of nodes from sources to sin. The ordering is based on nodal path lengths.

Author(s)

Ken Aho, Gabor Csardi wrote degree and shortest_paths.

References

Lacasa, L., Luque, B., Ballesteros, F., Luque, J., & Nuno, J. C. (2008). From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences, 105(13), 4972-4975.

See Also

degree, shortest_paths

Examples

A <- graph_from_literal(a --+ b, c --+ d, d --+ e, b --+ e, 
e --+ j, j --+ m, f --+ g, g --+ i, h --+ i, i --+ k, k --+ l, 
l --+ m, m --+ n,  n --+ o)

path.visibility(A, source = "a", sink = "o")
  
multi.path.visibility(A, source = c("a","c","f","h"), 
sink = "o")

# From Lacasa et al. (2008)

B <- graph_from_literal(a --+ b --+ c --+ d --+ e --+ f --+ g)
weights <- data.frame(matrix(nrow = 1, data = c(0.87, 0.49, 0.36, 0.83, 0.87, 0.49, 0.36))) 
names(weights) = letters[1:7]
path.visibility(B, source = "a", sink = "g", weights = weights) 

Plot degree distributions

Description

Plots bserved degree distribution against models for uncorrelated random, chaotic and correlated random processes.

Usage

plot_degree.dist(G, mode = "all", exp.lambda = c(1.1, 3/2, 2), leg.loc = "topright")

Arguments

Value

Plots processes for observed versus distributions under random or chaotic degrees.

Author(s)

Ken Aho

See Also

degree.dists, degree.distribution

Examples

network_a <- graph_from_literal(a --+ b, c --+ d, d --+ e, b --+ e, 
e --+ j, j --+ m, f --+ g, g --+ i, h --+ i, i --+ k, k --+ l, 
l --+ m, m --+ n,  n --+ o)
plot_degree.dist(network_a)

Size of the intact network above an arc

Description

The function measures the “size” of the intact network or sub-network (either number of upstream nodes, or user defined defined length, e.g., m, km) with respect to network arcs.

Usage

size.intact.to.arc(G, arc.node = "in")

Arguments

Details

For an unweighted graph, the upstream network “size” equates to the number of nodes in the intact network or sub-network upstream of an arc. For a graph whose arcs are weighted with actual stream segment lengths (see Examples), this will be the length (in measured units of length given in the weights) of the intact network or sub-network upstream of the arc. The argument arc.node allows upstream network size to be calculated with respect to either the upstream ("in") nodes of arcs or the downstream ("out") nodes of arcs. This designation will be applied to define the end (outlet) of the network or sub-network. Thus, option "out" may produce unexpectedly large results when these downstream "out" nodes of arcs occur at confluences.

Value

Output is a numeric vector whose length will be equal to the number of arcs in G.

Author(s)

Ken Aho, Gabor Csardi wrote distances

See Also

local.summary

Examples

mur <- streamDAGs("mur_full")
data(mur_lengths)
E(mur)$weight <- mur_lengths[,2]
size.intact.to.arc(mur) # upstream network sizes are in meters

Size of intact network that feeds into the sink or a particular node

Description

The length of the subgraph network that ends (feeds into) a particular node, e.g., the sink. For a weighted graph, the sum of the weights of the subgraph are given. Thus, if weights are stream lengths the function will give the stream length of the portion of the intact stream network that feeds into a particular node.

Usage

size.intact.to.sink(G, sink = NULL)

size.intact.to.node(G, node = NULL)

Arguments

Value

Returns the size of the graph or subgraph whose downstream end (outlet) is a node of interest.

Author(s)

Ken Aho, Gabor Csardi wrote several important function components including subgraph.

Examples

# Murphy Cr. disconnected network, no arc from M1799 to M1719!

G <- graph_from_literal(IN_N --+ M1984 --+ M1909, IN_S --+ M1993 --+ M1951, 
M1951 --+ M1909 --+ M1799, M1719 --+ M1653 --+ M1572 --+ M1452 --+ M1377, 
M1377 --+ M1254 --+ M1166 --+ M1121 --+ M1036 --+ M918 --+ M823 --+ M759, 
M759 --+ M716 --+ M624 --+ M523 --+ M454 --+ M380 --+ M233 --+ M153 --+ M91, 
M91 --+ OUT)

data(mur_coords) # coordinate data
spatial.plot(G, mur_coords[,2], mur_coords[,3], names =  mur_coords[,1])

data(mur_lengths) # segment length data 

lengths_new <- mur_lengths[-7,] # Drop M1799 -> M1719 arc length
E(G)$weight <- lengths_new[,2] # units are in meters
size.intact.to.node(G, node = "all") 
size.intact.to.sink(G, sink = "OUT") # same as output below:
size.intact.to.node(G, node = "OUT")

Shortest path lengths and number of paths

Description

The function spath.lengths calculates path lengths from all possible nodes to or from a designated node, i.e., the shortest in-paths and out-paths repsectively. Weighted path length are possible, including weighted path lengths based on field-observed instream arc lengths (see Examples). This results in "actual" path lengths in observed units. The function n.tot.paths calculates the total number of paths beginning or ending at all nodes in a graph, based on exponention of the the adjacency matrix.

Usage

spath.lengths(G, node = NULL, mode = "in", ignore.inf = TRUE)

n.tot.paths(G, mode = "in", sink = NULL)

Arguments

Value

Lengths of paths to a node of interest.

Author(s)

Ken Aho , Gabor Csardi wrote distances

Examples

data(mur_lengths)
mur <- streamDAGs("mur_full")
n.tot.paths(mur)

spath.lengths(mur, "M1653")
E(mur)$weight <- mur_lengths[,2] # weighted (actual in-stream lengths in meters)
spath.lengths(mur, "M1653")

Spatial plot of an igraph object or stream shapefile

Description

Makes a spatial plot of a igraph object or stream shapefile, given nodal coordinates and node IDs.

Usage

spatial.plot(G, x, y, names = NULL, 
                         plot = TRUE,
                         col = "lightblue", 
                         cex.text = .4, cex = 1,
                         arrow.col = "lightblue", arrow.lwd = 1, 
                         plot.bg = "white", pch = 21, 
                         pt.bg = "orange", grid.lwd = 2, 
                         plot.dry = FALSE,
                         col.dry = gray(.7),
                         cex.dry = 1, pch.dry = 19, 
                         arrow.col.dry = gray(.7), arrow.lwd.dry = 1,
                         cnw = NULL, xlim = NULL, ylim = NULL, 
                         arrow.warn = TRUE, ...)


spatial.plot.sf(x, y, names, shapefile = NULL, cex = 1, arrow.col = "lightblue", 
                arrow.lwd = 1, pch = 21, pt.bg = "orange")

Arguments

Details

The function spatial.plot makes a plot of a stream DAG, showing arc flow directions to and from spatial node locations. The function can also be used to identify node and arc arrow coordinates for plotting (see Examples). The function spatial.plot.sf can create a spatially explicit graph from a stream shapefile with the stream outlay under a ggplot framework (see Examples). The function spatial.plot can be used to distinguish dry and wet nodes and arcs) (see Examples).

Value

A plot and an invisible list containing the x and y coordinates of nodes: the objects $x and $y, respectively, and the x and y coordinates of start and end points of arc arrows:the objects $x0, $y0, $x1, and $y1, respectively.

Author(s)

Ken Aho

Examples

G <- graph_from_literal(IN_N --+ M1984 --+ M1909, IN_S --+ M1993, 
M1993 --+ M1951 --+ M1909 --+ M1799 --+ M1719 --+ M1653 --+ M1572 --+ M1452,
M1452--+ M1377 --+ M1254 --+ M1166 --+ M1121 --+ M1036 --+ M918 --+ M823, 
M823 --+ M759 --+ M716 --+ M624 --+ M523 --+ M454 --+ M380 --+ M233 --+ M153,
M153 --+ M91 --+ OUT)

data(mur_coords)

x <- mur_coords[,2]
y <- mur_coords[,3]
names <- mur_coords[,1]
spatial.plot(G, x, y, names)

# using shapefiles


library(ggplot2); library(sf); library(ggrepel)
mur_sf <- st_read(system.file("shape/Murphy_Creek.shp", package="streamDAG"))
g1 <- spatial.plot.sf(x, y, names, shapefile = mur_sf)

# modify ggplot
g1 + theme_classic()


#-- Distinguishing wet and dry arcs and nodes --#

data(mur_node_pres_abs) # STIC H2O presence/absence
npa <- mur_node_pres_abs[650,][,-1] # STC data from 8/9/2019 22:30 
G1 <- delete.nodes.pa(G, npa) # delete nodes based STIC data

# Example 1 (only show wet nodes and arcs with associated wet nodes)
spatial.plot(G1, x, y, names)

# Example 2 (show wet nodes and arcs with associated wet nodes, and dry nodes)
spatial.plot(G1, x, y, names, plot.dry = TRUE)

# Example 3 (show wet nodes and arcs wet node arcs, and underlying network)
entire <- spatial.plot(G, x, y, names, plot = FALSE)
spatial.plot(G1, x, y, names, plot.dry = TRUE, cnw = entire)


#-- Animation: drying of Johnson Draw drainage --#

jd_graph <- streamDAGs("jd_full")
data(AIMS.node.coords)
jd_coords <- AIMS.node.coords[AIMS.node.coords$site == "JD",]
jd_coords <- jd_coords[jd_coords$STIC_inferred_PA,] 
data(jd_node_pres_abs)

# Drop 2023 sites 
jd_pa <- jd_node_pres_abs[,-c( 22, 23, 24, 25, 26, 27, 
28, 31, 32, 33, 34, 35, 36, 37)]

pb = txtProgressBar(min = 1, max = 250, initial = 1, style = 3) 
times <- round(seq(1,50322, length = 250),0)

for(i in 1:250){
  dev.flush()
  jd_sub <- delete.nodes.pa(jd_graph, 
                            jd_pa[times[i],][-1],
                            na.response = "treat.as.1")
  spatial.plot(jd_sub, 
               x = jd_coords[,3], 
               y = jd_coords[,2], 
               names = jd_coords[,1], 
               ylim = c(43.122, 43.129), 
               xlim = c(-116.8, -116.775), 
               plot.dry = TRUE, main = jd_node_pres_abs[,1][times[i]], 
               xlab = "Longitude", ylab = "Latitude")
  dev.hold()
  Sys.sleep(.05)
  setTxtProgressBar(pb, i)
  }

Strahler or Shreve stream order of a stream DAG

Description

The function stream.order calculates Strahler or Shreve number for each each in a stream DAG. The function sink.G is a utility algorithm that subsets the graph if the sink node is part of a sub-graph that is disconnected from other nodes.

Usage

sink.G(G, sink = NULL)

stream.order(G, sink = NULL, method = "strahler")

Arguments

Details

Strahler stream order (Strahler 1957) is a "top down" system in which first order stream sections occur at the outermost tributaries. A stream section resulting from the merging of tributaries of the same order will have a Strahler number one unit greater than the order of those tributaries. A stream section resulting from the merging of tributaries of different order will have the Strahler stream order of the tributary with the larger Strahler number. Under Shreve stream order, (Shreve 1966) a stream section resulting from the merging of tributaries will have an order that is the sum of the order of those tributaries.

The function can currently only handle graphs with confluences (which, as noted above, serve to define the stream order) and simple islands (those without sub-islands and those whose downstream endpoint does not occur at a join). Under the current version, islands will not change the order of a reach.

Value

Returns Stahler or Shreve numbers for each stream DAG node.

Note

May be slow for extremely large and complex streams due to a reliance on loops.

Author(s)

Ken Aho

References

Shreve, R. L. (1966). Statistical law of stream numbers. The Journal of Geology, 74(1), 17-37.

Strahler, A. N. (1952). Hypsometric (area-altitude) analysis of erosional topology. Geological Society of America Bulletin, 63 (11): 1117-1142

Examples

stream.order(G = streamDAGs("konza_full"), sink = "SFM01_1", method = "strahler")

stream.order(G = streamDAGs("konza_full"), sink = "SFM01_1", method = "shreve")

Stream DAG datasets

Description

The function contains a number of stream direct acyclic graph datasets written in igraph format. See: graph_from_literal. Many of the graphs were based on sampling regimes for the National Science Foundation Aquatic Intermittency Effects on Microbiomes in Streams (AIMS) project.

Usage

streamDAGs(graph = c("dc_piezo_full", "dc_full", "gj_full16", "gj_synoptic_2023", 
"gj_full", "gj_piezo_full", "jd_piezo_full", "jd_piezo_full_2023","jd_full", 
"konza_full", "KD0521", "KD0528", "KD0604", "mur_full", "td_full", "wh_full", 
"pr_full"))

Arguments

Details

Currently, the following graph options exist. Note that many of the graphs have associated datasets. Obtaining these datasets is now greatly simplified through the use of get.AIMS.data (code steps shown below are unnecessary).

1. "dc_piezo_full" codifies the Dry Creek stream network in southwestern Idaho for STIC (Stream Temperature, Intermittency, and Conductivity) sensors, confluences, and piezometer locations (outlet coordinates: 43.71839^\circN, 116.13747^\circW).

   • Network spatial coordinates for this graph can be subset from AIMS.node.coords using: AIMS.node.coords$site == "DC".

   • Nodal surface water presence/absence data for this graph can be obtained directly from dc_node_pres_abs.

   • Arc lengths for this graph can be obtained directly from dc_lengths.

2. "dc_full" codifies the Dry Creek stream network in southwestern Idaho but only for STICs and confluences, not piezometer locations (outlet coordinates: 43.71839^\circN, 116.13747^\circW).

   • Network spatial coordinates for this graph can be subset from AIMS.node.coords using: AIMS.node.coords$site == "DC" & AIMS.node.coords$STIC_inferred_PA.

   • Nodal surface water presence/absence data for this graph can be obtained using dc <- streamDAGs("dc_full") followed by dc_node_pres_abs[attributes(V(dc))$names].

3. "gj_full16" codifies nodes established at the Gibson Jack drainage in southeast Idaho, as defined in 2016 (outlet coordinates: 42.767180^\circN, 112.480240^\circW).

4. "gj_full" codifies nodes established at the Gibson Jack drainage in southeast Idaho for STIC sensors in 2022-2023, along with confluence locations. Piezometer locations not included (outlet coordinates: 42.767180^\circN, 112.480240^\circW).

   • Network spatial coordinates for this graph can be subset from AIMS.node.coords using: AIMS.node.coords$site == "GJ" & AIMS.node.coords$STIC_inferred_PA.

   • Nodal surface water presence/absence data for this graph can be obtained from: gj_node_pres_abs using gj <- streamDAGs("gj_full"); vnames <- attributes(V(gj))$names; w <- which(names(gj_node_pres_abs)[-1] %in% vnames) + 1; node.pa <- gj_node_pres_abs[,c(1,w)].

   • Arc lengths for this graph can be obtained from gj_lengths_piezo_full using gj <- streamDAGs("gj_full"); anames <- attributes(E(gj))$vnames; enames <- gsub("\|", " -> ", anames); m <- match(enames, gj_lengths_piezo_full[,1]) gj_lengths_piezo_full[m,].

5. "gj_piezo_full" codifies nodes established at the Gibson Jack drainage in southeast Idaho, by the the AIMS team which include longterm STICs and piezometers, along with confluence locations (outlet coordinates: 42.767180^\circN, 112.480240^\circW).

   • Network spatial coordinates for this graph can be subset from AIMS.node.coords using: AIMS.node.coords$site == "GJ" & (AIMS.node.coords$STIC_inferred_PA | AIMS.node.coords$piezo).

   • Nodal surface water presence/absence data for this graph can be obtained using gj <- streamDAGs("gj_piezo_full") followed by gj_node_pres_abs[attributes(V(gj))$names].

   • Arc lengths for this graph can be obtained from gj_lengths_piezo_full using gj <- streamDAGs("gj_piezo_full"); anames <- attributes(E(gj))$vnames; enames <- gsub("\|", " -> ", anames); m <- match(enames, gj_lengths_piezo_full[,1]); gj_lengths_piezo_full[m,].

6. "gj_synoptic_2023" codifies nodes established at the Gibson Jack drainage in southeast Idaho by the AIMS team during synoptic sampling in 2023, includes piezometers and additional sites to those sampled in "gj_full" (outlet coordinates: 42.767180^\circN, 112.480240^\circW).

7. "jd_piezo_full" codifies the Johnson Draw stream network in southwestern Idaho for both STIC and and piezometer locations (outlet coordinates: 43.12256^\circN, 116.77630^\circW).

   • Network spatial coordinates for this graph can be subset from AIMS.node.coords using: AIMS.node.coords$site == "JD" & AIMS.node.coords$New_in_2023 == FALSE.

   • Nodal surface water presence/absence data for this graph can be obtained from jd_node_pres_abs using jd <- streamDAGs("jd_piezo_full") followed by jd_node_pres_abs[attributes(V(jd))$names].

   • Arc lengths can be subset from jd_lengths_2023 using something like: jd <- streamDAGs("jd_piezo_full"); anames <- attributes(E(jd))$vnames; nnames <- gsub("\\|", " -> ", anames); jd_lengths_2023[which(jd_lengths_2023[,1] %in% nnames ),].

8. "jd_piezo_full_2023" codifies the Johnson Draw stream network in southwestern Idaho for both STIC and and piezometer locations, along with new STIC locations added in 2023 (outlet coordinates: 43.12256^\circN, 116.77630^\circW).

   • Network spatial coordinates for this graph can be subset from AIMS.node.coords using: AIMS.node.coords$site == "JD".

   • Nodal surface water presence/absence data for this graph can be obtained from jd_node_pres_abs using jd <- streamDAGs("jd_piezo_full_2023") followed by jd_node_pres_abs[attributes(V(jd))$names].

   • Arc lengths can be subset from jd_lengths_2023 using something like: jd <- streamDAGs("jd_piezo_full_2023"); anames <- attributes(E(jd))$vnames; nnames <- gsub("\\|", " -> ", anames); jd_lengths_2023[which(jd_lengths_2023[,1] %in% nnames ),].

9. "jd_full" codifies the Johnson Draw stream network in southwestern Idaho, but only for STICs, not piezometers (outlet coordinates: 43.12256^\circN, 116.77630^\circW).

   • Network spatial coordinates for this graph can be subset from AIMS.node.coords using: AIMS.node.coords$site == "JD" & AIMS.node.coords$STIC_inferred_PA.

   • Nodal surface water presence/absence data for this graph can be obtained using jd <- streamDAGs("jd_full") followed by jd_node_pres_abs[attributes(V(jd))$names].

   • Arc lengths can be obtained from jd_lengths.

10. "konza_full" provides codification of a complete intermittent stream network of Konza Prairie in the northern Flint Hills region of Kansas (outlet coordinates: 39.11394^\circN, 96.61153^\circW).

    • Network spatial coordinates for this graph can be obtained directly from kon_coords

    • Arc lengths can be obtained from kon_lengths.

11. Options "KD0521","KD0528", and "KD0604" provide networks for Konza Prairie at 05/21/2021 (before spring snow melt), 05/28/2021 (during spring snow melt) and 06/04/2021 (drying following snow melt), respectively.

12. "mur_full" is an igraph codification of the complete Murphy Creek dataset from the Owyhee Mountains in SW Idaho (outlet coordinates: 43.256^\circN, 116.817^\circW) established in 2019 by Warix et al. (2021), also see Aho et al. (2023).

    • Network spatial coordinates for the graph can be obtained directly from mur_coords

    • Nodal surface water presence/absence data for this graph can be obtained from mur_node_pres_abs

    • Arc lengths can be obrtained from mur_lengths.

13. "pr_full" codifies the Painted Rock stream network in northern Alabama (outlet coordinates: 34.96867^\circN, 86.16544^\circW).

    • Network spatial coordinates for this graph can be subset from AIMS.node.coords using: AIMS.node.coords$site == "PR".

14. "td_full" codifies the Talladega stream network in central Alabama (outlet coordinates: 33.76218^\circN, 85.59552^\circW).

    • Network spatial coordinates for this graph can be subset from AIMS.node.coords using: AIMS.node.coords$site == "TD".

15. "wh_full" codifies the Weyerhauser stream network in western Alabama (outlet coordinates: 32.98463^\circN, 88.01227^\circW).

    • Network spatial coordinates for this graph can be obtained from AIMS.node.coords using: AIMS.node.coords$site == "WH".

Value

Returns a graph object of class igraph.

Author(s)

Ken Aho, Maggie Kraft, Rob Ramos, Rebecca L. Hale, Charles T. Bond, Arya Legg

References

Aho, K., Derryberry, D., Godsey, S. E., Ramos, R., Warix, S., & Zipper, S. (2023). The communication distance of non-perennial streams. EarthArvix doi: 10.31223/X5Q367

Warix, S. R., Godsey, S. E., Lohse, K. A., & Hale, R. L. (2021), Influence of groundwater and topography on stream drying in semi-arid headwater streams. Hydrological Processes, 35(5), e14185.

Examples

streamDAGs("mur_full")

Functions for overlaying networks on shapefiles

Description

The function vector_segments and assign_pa_to_segments were written to facilitate the generation of plots (including ggplots) that overlay user defined digraphs (based on arc designations) on GIS shapefiles or other tightly packed cartesian coordinate structures.

Usage

vector_segments(sf.coords, node.coords, realign = TRUE, arcs, arc.symbol = " --> ", 
nneighbors = 40, remove.duplicates = FALSE)

assign_pa_to_segments(input, n, arc.pa, datetime = NULL)

Arguments

Details

The function vector_segments assigns network arc designations (from the argument arcs) to shape file coordinates. The function assign_pa_to_segments presence/absence surface water designations to these arcs based on information from arc.pa.

Value

The function vector_segments creates an object of class network_to_sf. It also returns a list with two components, with only the first being visible.

The function assign_pa_to_segments returns a dataframe that adds a stream/presence absence column to the to the df dataframe output from vector_segments, based on the argument arc.pa

Note

The assign_pa_to_segments function will return a warning (but will try to run anyway) if input is not the output from vector_segments.

Author(s)

Ken Aho

See Also

spatial.plot

Examples

# Data

sfx <- c(-3,0,1.5,2,2.9,4,5,6)
sfy <- c(5,2,1.7,1.6,1.5,1.4,1.2,1)
sf.coords <- data.frame(x = sfx, y = sfy) 
node.coords <- data.frame(x = c(-2.1,2,4,6), y = c(3.75,1.6,1.4,1))
row.names(node.coords) <- c("n1","n2","n3","n4") # must be consistent with arc names
arc.pa <- data.frame(matrix(ncol = 3, data = c(1,1,1, 0,1,1, 1,1,1, 0,0,1), byrow = TRUE))
names(arc.pa) <- c("n1 --> n2", "n2 --> n3", "n3 --> n4")

# Use of vector_segments
vs <- vector_segments(sf.coords, node.coords, realign = TRUE, names(arc.pa))
vs

# Plotting example
plot(sf.coords, pch = 19, col = c(rep(1,4),rep(2,2),rep(3,2)))

vsd <- vs$df
fal <- as.factor(vsd$arc.label)
lvls <- levels(fal)

for(i in 1:nlevels(fal)){
  temp <- vsd[fal == lvls[i],]
  lines(temp$x, temp$y, col = i) 
}

vs4 <- assign_pa_to_segments(vs, 4, arc.pa)
head(vs4)