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################ PLOT GRAPH ###############
#' plotGraph
#'
#' @description Graphical interactive representation of the network.
#' @param graph The output of prepGraph.
#'
#' @return Creates an interactive plot, a D3 JavaScript network graph.
#' @import networkD3
#' @export
#'
#' @examples
#' my_file <- system.file("example/football.gml", package="robin")
#' graph <- prepGraph(file=my_file, file.format="gml")
#' plotGraph (graph)
plotGraph <- function(graph)
{
graph_d3 <- networkD3::igraph_to_networkD3(g=graph)
plot <- networkD3::simpleNetwork(graph_d3$links, opacity=0.8, zoom=TRUE,
nodeColour = "#2E66AC",
fontSize=12)
return(plot)
}
######################## PLOT COMMUNITIES ##############
#' plotComm
#'
#' @description Graphical interactive representation of the network and its
#' communities.
#'
#' @param graph The output of prepGraph.
#' @param members A membership vector of the community structure, the output of
#' membershipCommunities.
#'
#' @return Creates an interactive plot with colorful communities, a D3
#' JavaScript network graph.
#' @import networkD3
#' @importFrom methods is
#' @export
#'
#' @examples
#' my_file <- system.file("example/football.gml", package="robin")
#' graph <- prepGraph(file=my_file, file.format="gml")
#' members <- membershipCommunities (graph=graph, method="louvain")
#' plotComm(graph, members)
plotComm <- function(graph, members)
{
stopifnot(methods::is(graph, "igraph"))
stopifnot(methods::is(members, "membership"))
graph_d3 <- networkD3::igraph_to_networkD3(g=graph, group=members)
# Create network plot
plot <- networkD3::forceNetwork(Links=graph_d3$links, Nodes=graph_d3$nodes,
Source='source', Target='target',
NodeID='name', Group='group', opacity=0.8,
fontSize=12, legend=TRUE)
return(plot)
}
############PLOT##############
#' plot.robin
#'
#' @description This function plots two curves: the measure of the null model and the measure
#' of the real graph or the measure of the two community detection algorithms.
#' @param x A robin class object. The output of the functions:
#' \code{\link{robinRobust}} and \code{\link{robinCompare}}.
#' @param title The title for the graph. The default is "Robin plot".
#' @param ... other parameter
#'
#' @return A ggplot object.
#' @import ggplot2 igraph
#' @export
#'
#' @examples
#' \dontrun{my_file <- system.file("example/football.gml", package="robin")
#' graph <- prepGraph(file=my_file, file.format="gml")
#' comp <- robinCompare(graph=graph, method1="fastGreedy",method2="louvain")
#' plot(comp)}
#'
plot.robin <- function(x, title="Robin plot", ...)
{
legend <- c(x$model1,x$model2)
if (length(x$Mean1)==0)
{
model1 <- x$Mean
model2 <- x$MeanRandom
}else{
model1 <- x$Mean1
model2 <- x$Mean2
}
mvimodel1 <- as.vector((apply(model1, 2, mean)))
mvimodel2 <- as.vector((apply(model2, 2, mean)))
percPert <- rep((seq(0,60,5)/100), 2)
mvi <- c(mvimodel1, mvimodel2)
model <-c(rep("model1",13),rep("model2",13))
dataFrame <- data.frame(percPert, mvi, model)
plotModel <- ggplot2::ggplot(dataFrame, aes(x = percPert,
y = as.numeric(as.character(mvi)),
colour = model,
group = factor(model))) +
geom_line() +
geom_point() +
xlab("Percentage of perturbation") +
ylab("Measure") +
ggplot2::ylim(0,1)+
ggtitle(title)
cols <- c("model1" = "#00BFC4", "model2" = "#F8766D")
plot <- plotModel+ggplot2::scale_colour_manual(values = cols,
breaks = c("model1", "model2"),
labels=c(legend[1], legend[2]))
return(plot)
}
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