,
David Cunillera-Montcusi [ctb]
,
Andrea Funk [ctb] | Title: | Fragmentation and Connectivity Indices for Riverscapes |
|---|---|
| Description: | Indices for assessing riverscape fragmentation, including the Dendritic Connectivity Index, the Population Connectivity Index, the River Fragmentation Index, the Probability of Connectivity, and the Integral Index of connectivity. For a review, see Jumani et al. (2020) <doi:10.1088/1748-9326/abcb37> and Baldan et al. (2022) <doi:10.1016/j.envsoft.2022.105470> Functions to calculate temporal indices improvement when fragmentation due to barriers is reduced are also included. |
| Authors: | Damiano Baldan [aut, cre] ,
David Cunillera-Montcusi [ctb]
,
Andrea Funk [ctb] |
| Maintainer: | Damiano Baldan <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 0.3.33 |
| Built: | 2024-10-29 05:17:44 UTC |
| Source: | https://github.com/damianobaldan/riverconn |
Calculates B_ij: the functional contribution to dispersal probability I_ij
B_ij_fun( graph, field_B = "length", dir_distance_type = "symmetric", disp_type = "exponential", param_u, param_d, param, param_l )B_ij_fun( graph, field_B = "length", dir_distance_type = "symmetric", disp_type = "exponential", param_u, param_d, param, param_l )
graph |
an object of class igraph. Can be both directed or undirected. |
field_B |
the 'graph' edge attribute to be used to calculate the distance. Default is |
dir_distance_type |
how directionality in B_ij calculations is dealt with:
|
disp_type |
the formula used to calculate the probabilities in the B_ij matrix.
Use |
param_u |
the upstream dispersal parameter. Must be a numeric value. Only used if |
param_d |
the downstream dispersal parameter. Must be a numeric value. Only used if |
param |
the dispersal parameter. Must be a numeric value. Only used if |
param_l |
the parameters for the leptokurtic dispersal mode. Must be a numeric vector of the
type |
dir_distance_type = "symmetric" is to be used when the directionality of the river network is not relevant.
The distance between reaches midpoints is calculated for each couple of reaches.
dir_distance_type = "asymmetric" is to be used when the directionality is relevant.
The distance between reaches midpoints is calculated for each couple of reaches and splitted
between 'upstream travelled' distance and 'downstream travelled' distance.
When disp_type ="leptokurtic" is selected, symmetric dispersal is assumed.
The 'param_u', 'param_d', and 'param' values are interpreted differently based on the formula used to relate distance (d_ij) and probability (B_ij).
When disp_type ="exponential", those values are used as the base of the exponential dispersal kernel: B_ij = param^d_ij.
When disp_type ="threshold", those values are used to define the maximum dispersal length: B_ij = ifelse(d_ij < param, 1, 0).
When disp_type ="leptokurtic" is selected, a leptokurtic dispersal kernel is used to calculate B_ij.
A leptokurtic dispersal kernel is a mixture of two zero-centered gaussian distributions with standard deviations
sigma_stat (static part of the population), and sigma_mob (mobile part of the population).
The probability of dispersal is calculated as: B_ij = p F(0, sigma_stat, d_ij) + (1-p) F(0, sigma_mob, d_ij)
where F is the upper tail of the gaussian cumulative density function.
a square matrix of size length(V(graph)) containing B_ij values. The matrix is organized with "from" nodes on the columns and "to" nodes on the rows
library(igraph) g <- igraph::graph_from_literal(1-+2, 2-+5, 3-+4, 4-+5, 6-+7, 7-+10, 8-+9, 9-+10, 5-+11, 11-+12, 10-+13, 13-+12, 12-+14, 14-+15, 15-+16) E(g)$id_dam <- c("1", NA, "2", "3", NA, "4", NA, "5", "6", NA, NA, NA, NA, "7", NA) E(g)$type <- ifelse(is.na(E(g)$id_dam), "joint", "dam") V(g)$length <- c(1, 1, 2, 3, 4, 1, 5, 1, 7, 7, 3, 2, 4, 5, 6, 9) V(g)$HSI <- c(0.2, 0.1, 0.3, 0.4, 0.5, 0.5, 0.5, 0.6, 0.7, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8) V(g)$Id <- V(g)$name E(g)$pass_u <- E(g)$pass_d <- ifelse(!is.na(E(g)$id_dam),0.1,NA) dist_mat <- B_ij_fun(g, param = 0.9)library(igraph) g <- igraph::graph_from_literal(1-+2, 2-+5, 3-+4, 4-+5, 6-+7, 7-+10, 8-+9, 9-+10, 5-+11, 11-+12, 10-+13, 13-+12, 12-+14, 14-+15, 15-+16) E(g)$id_dam <- c("1", NA, "2", "3", NA, "4", NA, "5", "6", NA, NA, NA, NA, "7", NA) E(g)$type <- ifelse(is.na(E(g)$id_dam), "joint", "dam") V(g)$length <- c(1, 1, 2, 3, 4, 1, 5, 1, 7, 7, 3, 2, 4, 5, 6, 9) V(g)$HSI <- c(0.2, 0.1, 0.3, 0.4, 0.5, 0.5, 0.5, 0.6, 0.7, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8) V(g)$Id <- V(g)$name E(g)$pass_u <- E(g)$pass_d <- ifelse(!is.na(E(g)$id_dam),0.1,NA) dist_mat <- B_ij_fun(g, param = 0.9)
Calculates c_ij: the structural contribution to the dispersal probability I_ij
c_ij_fun( graph, dir_fragmentation_type = "symmetric", pass_confluence = 1, pass_u = "pass_u", pass_d = "pass_d" )c_ij_fun( graph, dir_fragmentation_type = "symmetric", pass_confluence = 1, pass_u = "pass_u", pass_d = "pass_d" )
graph |
an object of class igraph. Can be both directed or undirected. |
dir_fragmentation_type |
how directionality in c_ij calculations is dealt with:
|
pass_confluence |
a value in the range [0,1] that defines the passability of confluences (default is 1). |
pass_u |
the 'graph' edge attribute to be used as upstream passability. Default is "pass_u". |
pass_d |
the 'graph' edge attribute to be used as downstream passability. Default is "pass_d". |
dir_fragmentation_type = "symmetric" is to be used when the directionality of the river network is not relevant.
The equivalent passability for each barrier is calculated as the product of upstream and downstream passabilities.
dir_fragmentation_type = "asymmetric" is to be used when the directionality is relevant.
The equivalent passability of each barrier is calculated as a function of the path connecting each couple of reaches
and depends on the direction of the path. Check the package vignette for more details.
a square matrix of size length(V(graph)) containing c_ij values. The matrix is organized with "from" nodes on the columns and "to" nodes on the rows
library(igraph) g <- igraph::graph_from_literal(1-+2, 2-+5, 3-+4, 4-+5, 6-+7, 7-+10, 8-+9, 9-+10, 5-+11, 11-+12, 10-+13, 13-+12, 12-+14, 14-+15, 15-+16) E(g)$id_dam <- c("1", NA, "2", "3", NA, "4", NA, "5", "6", NA, NA, NA, NA, "7", NA) E(g)$type <- ifelse(is.na(E(g)$id_dam), "joint", "dam") V(g)$length <- c(1, 1, 2, 3, 4, 1, 5, 1, 7, 7, 3, 2, 4, 5, 6, 9) V(g)$HSI <- c(0.2, 0.1, 0.3, 0.4, 0.5, 0.5, 0.5, 0.6, 0.7, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8) V(g)$Id <- V(g)$name E(g)$pass_u <- E(g)$pass_d <- ifelse(!is.na(E(g)$id_dam),0.1,NA) dist_mat <- c_ij_fun(g)library(igraph) g <- igraph::graph_from_literal(1-+2, 2-+5, 3-+4, 4-+5, 6-+7, 7-+10, 8-+9, 9-+10, 5-+11, 11-+12, 10-+13, 13-+12, 12-+14, 14-+15, 15-+16) E(g)$id_dam <- c("1", NA, "2", "3", NA, "4", NA, "5", "6", NA, NA, NA, NA, "7", NA) E(g)$type <- ifelse(is.na(E(g)$id_dam), "joint", "dam") V(g)$length <- c(1, 1, 2, 3, 4, 1, 5, 1, 7, 7, 3, 2, 4, 5, 6, 9) V(g)$HSI <- c(0.2, 0.1, 0.3, 0.4, 0.5, 0.5, 0.5, 0.6, 0.7, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8) V(g)$Id <- V(g)$name E(g)$pass_u <- E(g)$pass_d <- ifelse(!is.na(E(g)$id_dam),0.1,NA) dist_mat <- c_ij_fun(g)
Calculate Reach- and Catchment-scale index improvement for scenarios of barriers removal
d_index_calculation( graph, ..., barriers_metadata, id_barrier = "id_barrier", pass_u_updated = "pass_u_updated", pass_d_updated = "pass_d_updated", mode = "leave_one_out", parallel = TRUE, ncores )d_index_calculation( graph, ..., barriers_metadata, id_barrier = "id_barrier", pass_u_updated = "pass_u_updated", pass_d_updated = "pass_d_updated", mode = "leave_one_out", parallel = TRUE, ncores )
graph |
an object of class 'igraph'. Can be both directed or undirected. |
... |
other arguments passed to the function 'index_calculation' |
barriers_metadata |
data.frame that must contain a column having the same name as the 'id_barrier' attribute of the graph, and two columns with the corresponding upstream and downstream improved passabilities (see 'pass_u_updated' and 'pass_d_updated' parameters). |
id_barrier |
graph edges attribute used to label barriers. Default is |
pass_u_updated |
field in barrier_metadata where updated value for upstream passability is stored (recommended values higher than the original passability). |
pass_d_updated |
field in barrier_metadata where updated value for downstream passability is stored (recommended values higher than the original passability). |
mode |
currentlym only |
parallel |
logical value to flag if parallel option is to be used. |
ncores |
define how many cores are used in parallel processing. Active only when |
Setting c_ij_flag = FALSE (see index_calculation arguments) removes from the calculations the effect of barriers, i.e. the c_ij contribution
is not used in the calculation of the index.
Setting B_ij_flag = FALSE (see index_calculation arguments) removes from the calculations the effect of movement/dispersal,
i.e. the B_ij contribution is not used in the calculation of the index.
Note that it is not possible to set both c_ij_flag = FALSE and B_ij_flag = FALSE.
The setting dir_distance_type = "symmetric" (see index_calculation arguments) is to be used when the directionality of the river network is not relevant.
The distance between reaches midpoints is calculated for each couple of reaches.
The setting dir_distance_type = "asymmetric" (see index_calculation arguments) is to be used when the directionality is relevant.
The distance between reaches midpoints is calculated for each couple of reaches and splitted
between 'upstream travelled' distance and 'downstream travelled' distance
The 'param_u', 'param_d', and 'param' values are interpreted differently based on the formula used to relate distance and probability.
When disp_type ="exponential" (see index_calculation arguments), those values are used as the base of the exponential dispersal kernel: B_ij = param^d_ij.
When disp_type ="threshold" (see index_calculation arguments), those values are used to define the maximum dispersal length: B_ij = ifelse(d_ij < param, 1, 0).
returns a data.frame containing the percent improvement of the index for
each barrier present in the 'barriers_metadata' variable.
If index_type = "full" (see index_calculation arguments), the data.frame is organized by 'id_barrier'.
If index_type = "reach" (see index_calculation arguments), the data.frame is organized by 'id_barrier' and 'name'.
In both cases, both numerator and denominator used in the index calculations are reported in the columns 'num' and 'den'.
The column 'd_index' contains the relative index improvement when each barrier is removed.
Baldan, D., Cunillera-Montcusí, D., Funk, A., & Hein, T. (2022). Introducing ‘riverconn’: an R package to assess river connectivity indices. Environmental Modelling & Software, 156, 105470.
library(igraph) library(igraph) g <- igraph::graph_from_literal(1-+2, 2-+4, 3-+2, 4-+6, 6-+7, 5-+6, 7-+8, 9-+5, 10-+5 ) E(g)$id_dam <- c(NA, NA, "1", NA, NA, "2", NA, NA, NA) E(g)$type <- ifelse(is.na(E(g)$id_dam), "joint", "dam") V(g)$length <- c(1, 1, 2, 3, 4, 1, 5, 1, 2, 1) V(g)$Id <- V(g)$name E(g)$pass_u <- E(g)$pass_d <- ifelse(!is.na(E(g)$id_dam),0.1,NA) dams_metadata <- data.frame("id_dam" = c("1", "2"), "pass_u_updated" = c(1, 1), "pass_d_updated" = c(1, 1)) d_index <- d_index_calculation(g, barriers_metadata = dams_metadata, id_barrier = "id_dam", parallel = FALSE, param = 0.6)library(igraph) library(igraph) g <- igraph::graph_from_literal(1-+2, 2-+4, 3-+2, 4-+6, 6-+7, 5-+6, 7-+8, 9-+5, 10-+5 ) E(g)$id_dam <- c(NA, NA, "1", NA, NA, "2", NA, NA, NA) E(g)$type <- ifelse(is.na(E(g)$id_dam), "joint", "dam") V(g)$length <- c(1, 1, 2, 3, 4, 1, 5, 1, 2, 1) V(g)$Id <- V(g)$name E(g)$pass_u <- E(g)$pass_d <- ifelse(!is.na(E(g)$id_dam),0.1,NA) dams_metadata <- data.frame("id_dam" = c("1", "2"), "pass_u_updated" = c(1, 1), "pass_d_updated" = c(1, 1)) d_index <- d_index_calculation(g, barriers_metadata = dams_metadata, id_barrier = "id_dam", parallel = FALSE, param = 0.6)
Reach- and Catchment-scale indices of connectivity
index_calculation( graph, weight = "length", nodes_id = "name", index_type = "full", index_mode = "to", c_ij_flag = TRUE, B_ij_flag = TRUE, dir_fragmentation_type = "symmetric", pass_confluence = 1, pass_u = "pass_u", pass_d = "pass_d", field_B = "length", dir_distance_type = "symmetric", disp_type = "exponential", param_u, param_d, param, param_l )index_calculation( graph, weight = "length", nodes_id = "name", index_type = "full", index_mode = "to", c_ij_flag = TRUE, B_ij_flag = TRUE, dir_fragmentation_type = "symmetric", pass_confluence = 1, pass_u = "pass_u", pass_d = "pass_d", field_B = "length", dir_distance_type = "symmetric", disp_type = "exponential", param_u, param_d, param, param_l )
graph |
an object of class igraph. Can be both directed or undirected. |
weight |
graph vertex attribute used to assign weights to the reaches (nodes/vertices). Should not be also an edge attribute.
Default is |
nodes_id |
graph vertex attribute used to univoquely label reaches (nodes/vertices). Should not be also an edge attribute.
Default is |
index_type |
indicates if the index should be calculated for the whole catchment ( |
index_mode |
indicates if reach index should be calculated based on inbound links ("to") or outbound links ("from").
Only active when |
c_ij_flag |
include the presence of barriers in the calculations (c_ij term). |
B_ij_flag |
include dispersal/movement among reaches in the calculations (B_ij term). |
dir_fragmentation_type |
how directionality in c_ij calculations is dealt with:
|
pass_confluence |
a value in the range [0,1] that defines the passability of confluences (default is 1). |
pass_u |
the 'graph' edge attribute to be used as upstream passability. Default is "pass_u". |
pass_d |
the 'graph' edge attribute to be used as downstream passability. Default is "pass_d". |
field_B |
the 'graph' vertex attribute to be used to calculate the distance. Should not be also an edge attribute.
Default is |
dir_distance_type |
how directionality in B_ij calculations is dealt with:
|
disp_type |
the formula used to calculate the probabilities in the B_ij matrix.
Use |
param_u |
upstream dispersal parameter. Must be a numeric value.
Only used if |
param_d |
downstream dispersal parameter.
Must be a numeric value. Only used if |
param |
dispersal parameter. Must be a numeric value.
Only used if |
param_l |
the parameters for the leptokurtic dispersal mode. Must be a numeric vector of the
type |
Setting c_ij_flag = FALSE removes from the calculations the effect of barriers, i.e. the c_ij contribution
is not used in the calculation of the index.
Setting B_ij_flag = FALSE removes from the calculations the effect of movement/dispersal,
i.e. the B_ij contribution is not used in the calculation of the index.
Note that it is not possible to set both c_ij_flag = FALSE and B_ij_flag = FALSE.
The setting dir_distance_type = "symmetric" is to be used when the directionality of the river network is not relevant.
The distance between reaches midpoints is calculated for each couple of reaches.
The setting dir_distance_type = "asymmetric" is to be used when the directionality is relevant.
The distance between reaches midpoints is calculated for each couple of reaches and splitted
between 'upstream travelled' distance and 'downstream travelled' distance.
When disp_type ="leptokurtic" is selected, symmetric dispersal is assumed.
The 'param_u', 'param_d', and 'param' values are interpreted differently based on the formula used to relate distance (d_ij) and probability (B_ij).
When disp_type ="exponential", those values are used as the base of the exponential dispersal kernel: B_ij = param^d_ij.
When disp_type ="threshold", those values are used to define the maximum dispersal length: B_ij = ifelse(d_ij < param, 1, 0).
When disp_type ="leptokurtic" is selected, a leptokurtic dispersal kernel is used to calculate B_ij.
A leptokurtic dispersal kernel is a mixture of two zero-centered gaussian distributions with standard deviations
sigma_stat (static part of the population), and sigma_mob (mobile part of the population).
The probability of dispersal is calculated as: B_ij = p F(0, sigma_stat, d_ij) + (1-p) F(0, sigma_mob, d_ij)
where F is the upper tail of the gaussian cumulative density function.
If index_type = "full", returns a numeric value with the index value (column 'index').
if index_type = c("reach", "sum"), returns a data frame with the index value (column 'index') for each reach
(the field specified in 'nodes_id' is used for reach identification in the data frame).
In both cases, both numerator and denominator used in the index calculations are reported in the columns 'num' and 'den'.
Baldan, D., Cunillera-Montcusí, D., Funk, A., & Hein, T. (2022). Introducing ‘riverconn’: an R package to assess river connectivity indices. Environmental Modelling & Software, 156, 105470.
Jumani, S., Deitch, M. J., Kaplan, D., Anderson, E. P., Krishnaswamy, J., Lecours, V., & Whiles, M. R. (2020). River fragmentation and flow alteration metrics: a review of methods and directions for future research. Environmental Research Letters, 15(12), 123009.
Radinger, J., & Wolter, C. (2014). Patterns and predictors of fish dispersal in rivers. Fish and fisheries, 15(3), 456-473.
library(igraph) g <- igraph::graph_from_literal(1-+2, 2-+5, 3-+4, 4-+5, 6-+7, 7-+10, 8-+9, 9-+10, 5-+11, 11-+12, 10-+13, 13-+12, 12-+14, 14-+15, 15-+16) E(g)$id_dam <- c("1", NA, "2", "3", NA, "4", NA, "5", "6", NA, NA, NA, NA, "7", NA) E(g)$type <- ifelse(is.na(E(g)$id_dam), "joint", "dam") V(g)$length <- c(1, 1, 2, 3, 4, 1, 5, 1, 7, 7, 3, 2, 4, 5, 6, 9) V(g)$HSI <- c(0.2, 0.1, 0.3, 0.4, 0.5, 0.5, 0.5, 0.6, 0.7, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8) V(g)$Id <- V(g)$name E(g)$pass_u <- E(g)$pass_d <- ifelse(!is.na(E(g)$id_dam),0.1,NA) index <- index_calculation(g, param = 0.9)library(igraph) g <- igraph::graph_from_literal(1-+2, 2-+5, 3-+4, 4-+5, 6-+7, 7-+10, 8-+9, 9-+10, 5-+11, 11-+12, 10-+13, 13-+12, 12-+14, 14-+15, 15-+16) E(g)$id_dam <- c("1", NA, "2", "3", NA, "4", NA, "5", "6", NA, NA, NA, NA, "7", NA) E(g)$type <- ifelse(is.na(E(g)$id_dam), "joint", "dam") V(g)$length <- c(1, 1, 2, 3, 4, 1, 5, 1, 7, 7, 3, 2, 4, 5, 6, 9) V(g)$HSI <- c(0.2, 0.1, 0.3, 0.4, 0.5, 0.5, 0.5, 0.6, 0.7, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8) V(g)$Id <- V(g)$name E(g)$pass_u <- E(g)$pass_d <- ifelse(!is.na(E(g)$id_dam),0.1,NA) index <- index_calculation(g, param = 0.9)
The input graph can be either directed or undirected. If directed, then it is made undirected before directionality is assigned.
set_graph_directionality(graph, field_name = "name", outlet_name)set_graph_directionality(graph, field_name = "name", outlet_name)
graph |
an 'igraph' object representing a river structure where reaches are nodes and confluences (or fragmentation items) are links. |
field_name |
a character value that flags the vertices attribute used to designate the outlet. Each vertex must have an unique value for this field. |
outlet_name |
a character value corresponding to the 'field_name' attribute |
an object of class 'igraph' containing a directed graph.
library(igraph) g <- igraph::graph_from_literal(1-2, 2-4, 3-2, 4-6, 6-7, 5-6, 7-8, 9-5, 10-5 ) g1 <- set_graph_directionality(g, field_name = "name", "8")library(igraph) g <- igraph::graph_from_literal(1-2, 2-4, 3-2, 4-6, 6-7, 5-6, 7-8, 9-5, 10-5 ) g1 <- set_graph_directionality(g, field_name = "name", "8")
Calculates time-dependent index when nodes weights or barriers passability are changing
t_index_calculation( graph = graph, ..., barriers_metadata, id_barrier = "id_barrier", year = "year", pass_u = "pass_u", pass_d = "pass_d", weights_metadata, weight = "length", nodes_id = "name", parallel = TRUE, ncores )t_index_calculation( graph = graph, ..., barriers_metadata, id_barrier = "id_barrier", year = "year", pass_u = "pass_u", pass_d = "pass_d", weights_metadata, weight = "length", nodes_id = "name", parallel = TRUE, ncores )
graph |
an object of class igraph. Can be both directed or undirected. |
... |
other arguments passed to the function index_calculation |
barriers_metadata |
data.frame that must contain a column having the same name as the 'id_barrier' attribute of the graph,
and two columns with the corresponding upstream and downstream improved passabilities (see pass_u and pass_d), and a column with the year
passability was changed. This data frame can be obtained from easily-formatted data with the function |
id_barrier |
graph edges attribute used to label barriers. Default is |
year |
field of the 'barriers metadata' where temporal information on the changes in passability is stored. |
pass_u |
field of the 'barriers metadata' where temporal-dependent upstream passability is stored. |
pass_d |
field of the 'barriers metadata' where temporal-dependent downstream passability is stored. |
weights_metadata |
data.frame that must contain a column having the same name as the 'nodes_id' attribute of the graph,
a column with he corresponding weight information (see 'weight' parameter), and a column with the year
weight was changed. This data frame can be obtained from easily-formatted data with the function |
weight |
param weight graph vertex attribute used to assign weights to the reaches (nodes). Default is |
nodes_id |
graph vertex attribute used to uniquely label reaches (nodes). Default is |
parallel |
logical value to flag if parallel option is to be used. |
ncores |
define how many cores are used in parallel processing. Active only when |
a data.frame with a 'year' field and related connectivity index.
If index_type = "reach", the data.frame is organized by 'year' and 'name'.
Baldan, D., Cunillera-Montcusí, D., Funk, A., & Hein, T. (2022). Introducing ‘riverconn’: an R package to assess river connectivity indices. Environmental Modelling & Software, 156, 105470.
library(igraph) g <- igraph::graph_from_literal(1-+2, 2-+4, 3-+2, 4-+6, 6-+5) E(g)$id_barrier <- c(NA, NA, "1", NA, NA) E(g)$type <- ifelse(is.na(E(g)$id_barrier), "joint", "dam") V(g)$length <- c(1, 1, 2, 3, 4,5) V(g)$Id <- V(g)$name E(g)$pass_u <- E(g)$pass_d <- ifelse(!is.na(E(g)$id_barrier),0.1,NA) barriers_data <- data.frame("id_barrier" = c("1"), "year_c" = 2000, "pass_c_u" = 0.1, "pass_c_d" = 0.4) seq_ops <- c("c") barriers_metadata <- t_passability_sequencer(barriers_data, seq_ops) weights_dataframe <- data.frame("name" = seq(1,6) %>% as.character, "length_1999" = c(1, 1, 2, 3, 4,5)) weights_metadata <- t_weights_sequencer(weights_dataframe, weight = "length") t_index <- t_index_calculation(g, barriers_metadata = barriers_metadata, weights_metadata = weights_metadata, weight = "length", parallel = FALSE, B_ij_flag = FALSE)library(igraph) g <- igraph::graph_from_literal(1-+2, 2-+4, 3-+2, 4-+6, 6-+5) E(g)$id_barrier <- c(NA, NA, "1", NA, NA) E(g)$type <- ifelse(is.na(E(g)$id_barrier), "joint", "dam") V(g)$length <- c(1, 1, 2, 3, 4,5) V(g)$Id <- V(g)$name E(g)$pass_u <- E(g)$pass_d <- ifelse(!is.na(E(g)$id_barrier),0.1,NA) barriers_data <- data.frame("id_barrier" = c("1"), "year_c" = 2000, "pass_c_u" = 0.1, "pass_c_d" = 0.4) seq_ops <- c("c") barriers_metadata <- t_passability_sequencer(barriers_data, seq_ops) weights_dataframe <- data.frame("name" = seq(1,6) %>% as.character, "length_1999" = c(1, 1, 2, 3, 4,5)) weights_metadata <- t_weights_sequencer(weights_dataframe, weight = "length") t_index <- t_index_calculation(g, barriers_metadata = barriers_metadata, weights_metadata = weights_metadata, weight = "length", parallel = FALSE, B_ij_flag = FALSE)
Create the time-dependent metadata for barriers
t_passability_sequencer(passability_information, seq_ops)t_passability_sequencer(passability_information, seq_ops)
passability_information |
a data frame in wide format. Must contain an 'id_barrier' column. Each change in passability is listed in a group of 3 columns: 'year_op', 'pass_op_u', and 'pass_op_d', listing the year the operation (op) took place, and the related upstream and downstream passabilities. In case the passability did not change, a NA value should be used. See details. |
seq_ops |
A charachter vector with the temporal sequence of operations. It should contain all the operation strings in the 'passability_information' data frame. |
This function is meant to help processing data the way they can be obtained from a database, or the way they are stored in a spreadsheet.
The substring 'op' in the fields 'year_op', 'pass_op_u', and 'pass_op_d' is used to identify each operation and to relate it to the
relative passability parameters. For example, c can be used for construction, and fp for the implementation of a fish pass.
In this case, passability_information will have the fields 'year_c', 'pass_c_u', and 'pass_c_d', 'year_fp', 'pass_fp_u', and 'pass_fp_d'.
Then, the input seq_ops = c("c", "fp"), meaning that first the operation named 'c' occurred, and then the operation named 'fp' occurred.
a dataframe in a long format that can be used as input to the tDCI function.
barriers_data <- data.frame("id_barrier" = c("1", "2"), "year_c" = c(1950, 1990), "pass_c_u" = c(0.1, 0.1), "pass_c_d" = c(0.4, 0.4), "year_fp" = c(2000, 2010), "pass_fp_u" = c(0.5, 0.5), "pass_fp_d" = c(0.8, 0.8)) seq_ops <- c("c", "fp") t_metadata <- t_passability_sequencer(barriers_data, seq_ops)barriers_data <- data.frame("id_barrier" = c("1", "2"), "year_c" = c(1950, 1990), "pass_c_u" = c(0.1, 0.1), "pass_c_d" = c(0.4, 0.4), "year_fp" = c(2000, 2010), "pass_fp_u" = c(0.5, 0.5), "pass_fp_d" = c(0.8, 0.8)) seq_ops <- c("c", "fp") t_metadata <- t_passability_sequencer(barriers_data, seq_ops)
Create the time-dependent weights data
t_weights_sequencer(weights_information, weight = "length", nodes_id = "name")t_weights_sequencer(weights_information, weight = "length", nodes_id = "name")
weights_information |
a data.frame that must contain a 'nodes_id' column and several 'weight' columns.
Weight columns are named with the string contained in the 'weight' input and the relative year (4 digits format),
separated by an underscore (e.g. when |
weight |
a character object containing the label of the columns whose weight change with time |
nodes_id |
a character object containing the label of the columns that uniquely identify reaches. |
a data frame with columns 'name', 'year', and 'weight' to be used in the function t_index_calculation
weights_dataframe <- data.frame("id" = c("1", "2", "3", "4", "5"), "weight_1900" = c(10, 15, 100, 50, 40), "weight_1950"= c(11, 16, 90, 55, 45), "weight_2000"= c(13, 19, 80, 49, 44)) weights_metadata <- t_weights_sequencer(weights_dataframe, weight = "weight", nodes_id = "id")weights_dataframe <- data.frame("id" = c("1", "2", "3", "4", "5"), "weight_1900" = c(10, 15, 100, 50, 40), "weight_1950"= c(11, 16, 90, 55, 45), "weight_2000"= c(13, 19, 80, 49, 44)) weights_metadata <- t_weights_sequencer(weights_dataframe, weight = "weight", nodes_id = "id")