I posted last week a simple function to plot networks using ggplot2 package. Here is version 2. I still need to work on figuring out efficient vertex placement.
Changes in version 2:
-You have one of three options: use an igraph object, a matrix, or a dataframe (matrices will be converted to data frames within the function)
-If you have data on food webs similar to that provided in the Takapoto dataset provided in the NetIndices package, you can set trophic = "TRUE", and gggraph will use the function TrophInd to assign trophic levels (the y axis value) to each vertex/node. You have to provide additional information along with this option such as what the imports and exports are, see NetIndices documentation.
-I added some simple error checking.
-if using method="df" and trophic="FALSE", x axis placement of vertices is now done using the function degreex (see inside the fxn), which sorts vertices according to their degree (so the least connected species are on the left of the graph; note that species with the same degree are not stacked on the y-axis because e.g., two vertices of degree=5 would get x=3 then x=4).
######### ggraph version 2
require(bipartite)
require(igraph)
require(ggplot2)
require(NetIndices)
# gggraph, version 2
# g = an igraph graph object, a matrix, or data frame
# vplace = type of vertex placement assignment, one of rnorm, runif, etc.
# method = one of 'df' for data frame, "mat' for matrix or "igraph" for an igraph graph object
# trophic = TRUE or FALSE for using Netindices function TrophInd to determine trophic level (y value in graph)
# trophinames = columns in matrix or dataframe to use for calculating trophic level
# import = named or refereced by col# columns of matrix or dataframe to use for import argument of TrophInd
# export = named or refereced by col# columns of matrix or dataframe to use for export argument of TrophInd
# dead = named or refereced by col# columns of matrix or dataframe to use for dead argument of TrophInd
gggraph <- function(g, vplace = rnorm, method, trophic = "FALSE",
trophinames, import, export)
{
degreex <- function(x) {
degreecol <- apply(x, 2, function(y) length(y[y>0]))
degreerow <- apply(x, 1, function(y) length(y[y>0]))
degrees <- sort(c(degreecol, degreerow))
df <- data.frame(degrees, x = seq(1, length(degrees), 1))
df$value <- rownames(df)
df
}
# require igraph
if(!require(igraph)) stop("must first install 'igraph' package.")
# require ggplot2
if(!require(ggplot2)) stop("must first install 'ggplot2' package.")
if(method=="df"){
if(class(g)=="matrix"){ g <- as.data.frame(g) }
if(class(g)!="data.frame") stop("object must be of class 'data.frame.'")
if(trophic=="FALSE"){
# data preparation from adjacency matrix
temp <- data.frame(expand.grid(dimnames(g))[1:2], as.vector(as.matrix(g)))
temp <- temp[(temp[, 3] > 0) & !is.na(temp[, 3]), ]
temp <- temp[sort.list(temp[, 1]), ]
g_df <- data.frame(rows = temp[, 1], cols = temp[, 2], freqint = temp[, 3])
g_df$id <- 1:length(g_df[,1])
g_df <- data.frame(id=g_df[,4], rows=g_df[,1], cols=g_df[,2], freqint=g_df[,3])
g_df <- melt(g_df, id=c(1,4))
xy_s <- data.frame(degreex(g), y = rnorm(length(unique(g_df$value))))
g_df_2 <- merge(g_df, xy_s, by = "value")
} else if(trophic=="TRUE") {
# require NetIndices
if(!require(NetIndices)) stop("must first install 'NetIndices' package.")
# data preparation from adjacency matrix
temp <- data.frame(expand.grid(dimnames(g[-trophinames, -trophinames]))[1:2],
as.vector(as.matrix(g[-trophinames, -trophinames])))
temp <- temp[(temp[, 3] > 0) & !is.na(temp[, 3]), ]
temp <- temp[sort.list(temp[, 1]), ]
g_df <- data.frame(rows = temp[, 1], cols = temp[, 2], freqint = temp[, 3])
g_df$id <- 1:length(g_df[,1])
g_df <- data.frame(id=g_df[,4], rows=g_df[,1],
cols=g_df[,2], freqint=g_df[,3])
g_df <- melt(g_df, id=c(1,4))
xy_s <- data.frame(value = unique(g_df$value),
x = rnorm(length(unique(g_df$value))),
y = TrophInd(g, Import=import, Export=export)[,1])
g_df_2 <- merge(g_df, xy_s, by = "value")
}
# plotting
p <- ggplot(g_df_2, aes(x, y)) +
geom_point(size = 5) +
geom_line(aes(size = freqint, group = id)) +
geom_text(size = 3, hjust = 1.5, aes(label = value)) +
theme_bw() +
opts(panel.grid.major=theme_blank(),
panel.grid.minor=theme_blank(),
axis.text.x=theme_blank(),
axis.text.y=theme_blank(),
axis.title.x=theme_blank(),
axis.title.y=theme_blank(),
axis.ticks=theme_blank(),
panel.border=theme_blank(),
legend.position="none")
p # return graph
} else if(method=="igraph") {
if(class(g)!="igraph") stop("object must be of class 'igraph.'")
# data preparation from igraph object
g <- get.edgelist(g)
g_df <- as.data.frame(g_)
g_df$id <- 1:length(g_df[,1])
g_df <- melt(g_df, id=3)
xy_s <- data.frame(value = unique(g_df$value),
x = vplace(length(unique(g_df$value))),
y = vplace(length(unique(g_df$value))))
g_df2 <- merge(g_df, xy_s, by = "value")
# plotting
p <- ggplot(g_df2, aes(x, y)) +
geom_point(size = 2) +
geom_line(size = 0.3, aes(group = id, linetype = id)) +
geom_text(size = 3, hjust = 1.5, aes(label = value)) +
theme_bw() +
opts(panel.grid.major=theme_blank(),
panel.grid.minor=theme_blank(),
axis.text.x=theme_blank(),
axis.text.y=theme_blank(),
axis.title.x=theme_blank(),
axis.title.y=theme_blank(),
axis.ticks=theme_blank(),
panel.border=theme_blank(),
legend.position="none")
p # return graph
} else
stop(paste("do not recognize method = \"",method,"\";
methods are \"df\" and \"igraph\"",sep=""))
}
plants <- round(rlnorm(n=5, meanlog=2, sdlog=1))
animals <- round(rlnorm(n=5, meanlog=2, sdlog=1))
plants <- plants(100/sum(plants))
animals <- animals(100/sum(animals))
z <- r2dtable(1,animals,plants) # if you get errors on this step just rerun again until no error
z <- as.data.frame(z[[1]])
rownames(z) <- c("a","b","c","d","e")
gggraph(z, vplace = rnorm, method = "df", trophic = "FALSE")
g <- grg.game(20, 0.45, torus=FALSE)
gggraph(g, vplace = rnorm, method = "igraph", trophic = "FALSE")
Posted in ggplot2 bipartite Networks igraph R
23 Mar 2011
