Nothing
#
# ggplot versions of of vizualization functions
#
viz.scenarios <- function(ssc)
{
if (!is.null(req(ssc@analysis.results)))
length(ssc@analysis.results)
else
NULL
}
global.stats <- function(ssc)
{
if (!is.null(ssc@analysis.results))
unique(sort(as.character(globalDF(ssc)$statistic)))
}
gg.global <- function(ssc,stats=global.stats(ssc),scenario=1) #ssc is an ssClass (skelesim class) object
{
gdf <- globalDF(ssc)
gdf <- gdf[gdf$scenario%in%scenario,]
gdf <- gdf[gdf$statistic%in%stats,]
gdf <- gdf[gdf$Locus!="Overall",]
p <- ggplot(data=gdf,aes(x=Locus,y=value)) +
facet_wrap(~statistic, scales="free") +
geom_violin()+geom_jitter(width=0.1)
print(p)
}
gg.global.scmp <- function(ssc,stats=global.stats(ssc)) #ssc is an ssClass (skelesim class) object
{
gdf <- globalDF(ssc)
gdf <- gdf[gdf$Locus!="Overall",]
gdf <- filter(gdf,statistic%in%stats) %>% filter(Locus!="Overall") %>%
group_by(statistic,scenario,rep) %>% summarise(value=mean(value,na.rm=T))
gdf$scenario <- as.factor(gdf$scenario)
p <- ggplot(data=gdf,aes(x=scenario,y=value)) +
facet_wrap(~statistic, scales="free") +
geom_violin()+geom_jitter(width=0.1)
print(p)
}
df.global <- function(ssc,stats=global.stats(ssc)) #ssc is an ssClass (skelesim class) object
{
gdf <- globalDF(ssc)
gdf[gdf$statistic%in%stats,]
}
locus.stats <- function(ssc)
{
if (!is.null(ssc@analysis.results))
unique(sort(as.character(locusDF(ssc)$statistic)))
}
gg.locus <- function(ssc,stats=locus.stats(ssc),scenario=1)
{
ldf <- locusDF(ssc)
ldf <- ldf[ldf$statistic%in%stats,]
ldf <- ldf[ldf$scenario%in%scenario,]
ldf <- ldf[ldf$pop!="overall",]
l <- ggplot(data=ldf,aes(x=locus,y=value)) + geom_violin()+
geom_jitter(width=0.15)+facet_wrap(~statistic,scales="free")
p <- ggplot(data=ldf,aes(x=pop,y=value)) + geom_violin()+
geom_jitter(width=0.15)+facet_wrap(~statistic,scales="free")
multiplot(plotlist=list(l,p))
}
gg.locus.scmp <- function(ssc,stats=locus.stats(ssc))
{
ldf <- locusDF(ssc)
ldf <- ldf[ldf$statistic%in%stats,]
ldf <- ldf[ldf$pop!="overall",]
ldf$scenario <- as.factor(ldf$scenario)
ldf <- group_by(ldf,pop,locus,scenario,statistic)%>%summarise(value=mean(value,na.rm=T))
l <- ggplot(data=ldf,aes(x=scenario,y=value,group=locus,col=locus)) + geom_violin(aes(group=scenario))+
geom_jitter(width=0.15)+facet_wrap(~statistic,scales="free")+stat_summary(fun.y="mean",geom="line")
p <- ggplot(data=ldf,aes(x=scenario,y=value,group=pop,col=pop)) + geom_violin(aes(group=scenario))+
geom_jitter(width=0.15)+facet_wrap(~statistic,scales="free")+stat_summary(fun.y="mean",geom="line")
p
multiplot(plotlist=list(l,p))
}
df.locus <- function(ssc,stats=locus.stats(ssc))
{
ldf <- locusDF(ssc)
ldf <- ldf[ldf$statistic%in%stats,]
}
pairwise.stats <- function(ssc)
{
if (!is.null(ssc@analysis.results))
unique(sort(as.character(pairwiseDF(ssc)$statistic)))
}
gg.pairwise <- function(ssc,stats=pairwise.stats(ssc),scenario=1)
{
pwdf <- pairwiseDF(ssc)
pwdf <- pwdf[pwdf$statistic%in%stats,]
pwdf <- pwdf[pwdf$scenario%in%scenario,]
pwdf.mn <- pwdf %>% group_by(pop1,pop2,rep,statistic) %>% summarise(value=mean(value))
plts <- list()
for (s in unique(pwdf$statistic))
{
df <- pwdf.mn[pwdf.mn$statistic==s,]
p <- ggplot(data=df,aes(pop1,pop2))
p <- p + ggtitle(s)
p <- p + geom_tile(aes(fill=value), colour="white")
p <- p + scale_fill_gradient(low="white",high="steelblue")
plts[[length(plts)+1]] <- p
}
multiplot(plotlist=plts,cols=4)
}
gg.pairwise.scmp <- function(ssc,stats=pairwise.stats(ssc))
{
pwdf <- pairwiseDF(ssc)
pwdf <- pwdf[pwdf$statistic%in%stats,]
pwdf$scenario <- as.factor(pwdf$scenario)
pwdf.mn <- pwdf %>% group_by(pop1,pop2,statistic,scenario) %>% summarise(value=mean(value))
plts <- list()
for (s in unique(pwdf$statistic))
# for (scen in unique(pwdf.mn$scenario))
{
df <- pwdf.mn[pwdf.mn$statistic==s,]
p <- ggplot(data=df,aes(pop1,pop2))
p <- p + ggtitle(s)
p <- p + geom_tile(aes(fill=value), colour="white")
p <- p + scale_fill_gradient(low="white",high="steelblue")
p <- p + facet_wrap(~scenario,scales="free")
plts[[length(plts)+1]] <- p
}
multiplot(plotlist=plts,cols=ifelse(length(plts)>=4,4,length(plts)))
}
df.pairwise <- function(ssc,stats=pairwise.stats(ssc))
{
pwdf <- pairwiseDF(ssc)
pwdf <- pwdf[pwdf$statistic%in%stats,]
}
# Multiple plot function
#
# ggplot objects can be passed in ..., or to plotlist (as a list of ggplot objects)
# - cols: Number of columns in layout
# - layout: A matrix specifying the layout. If present, 'cols' is ignored.
#
# If the layout is something like matrix(c(1,2,3,3), nrow=2, byrow=TRUE),
# then plot 1 will go in the upper left, 2 will go in the upper right, and
# 3 will go all the way across the bottom.
#
multiplot <- function(..., plotlist=NULL, file, cols=1, layout=NULL) {
library(grid)
# Make a list from the ... arguments and plotlist
plots <- c(list(...), plotlist)
numPlots = length(plots)
# If layout is NULL, then use 'cols' to determine layout
if (is.null(layout)) {
# Make the panel
# ncol: Number of columns of plots
# nrow: Number of rows needed, calculated from # of cols
layout <- matrix(seq(1, cols * ceiling(numPlots/cols)),
ncol = cols, nrow = ceiling(numPlots/cols))
}
if (numPlots==1) {
print(plots[[1]])
} else {
# Set up the page
grid.newpage()
pushViewport(viewport(layout = grid.layout(nrow(layout), ncol(layout))))
# Make each plot, in the correct location
for (i in 1:numPlots) {
# Get the i,j matrix positions of the regions that contain this subplot
matchidx <- as.data.frame(which(layout == i, arr.ind = TRUE))
print(plots[[i]], vp = viewport(layout.pos.row = matchidx$row,
layout.pos.col = matchidx$col))
}
}
}
vizMegaPlot <- function(ssc)
{
for (s in 1:length(ssc@scenarios)) gg.global(ssc=ssc,scenario=s)
gg.global.scmp(ssc=ssc)
for (s in 1:length(ssc@scenarios)) gg.locus(ssc=ssc,scenario=s)
gg.locus.scmp(ssc=ssc)
for (s in 1:length(ssc@scenarios)) gg.pairwise(ssc=ssc,scenario=s)
gg.pairwise.scmp(ssc=ssc)
}
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