Does this help?
To make it not look super ugly, I made all the random mean values positive, and then just made the example standard deviations negative. The way of plotting the values on the same graph is to feed in the datasets separately to each geom, rather than defining in initial ggplot() function.
Let me know if this isn't what you were thinking:
library("ggplot2")
library("dplyr")
means <- as.data.frame(abs(cbind(rnorm(16),rnorm(16), rnorm(16), rnorm(16))))
means <- mutate(means, id = rownames(means))
colnames(means)<-c("1", "2", "3", "4", "Symptoms")
means_long <- reshape2::melt(means, id="Symptoms")
means_long$Symptoms <- as.numeric(means_long$Symptoms)
names(means_long)[2] <- "Datasets"
sds_long <- means_long
sds_long$value <- -sds_long$value
ggplot() +
geom_line(aes(x=Symptoms, y=value, colour=Datasets), lty=1, data=means_long) +
geom_point(aes(x=Symptoms, y=value, colour=Datasets), data=means_long, shape = 21, fill = "white", size = 1.5, stroke = 1) +
geom_line(aes(x=Symptoms, y=value, colour=Datasets), lty=2, data=sds_long) +
geom_point( aes(x=Symptoms, y=value, colour=Datasets), data=sds_long, shape = 21, fill = "white", size = 1.5, stroke = 1) +
xlab("Symptoms") + ylab("Means") +
scale_y_continuous() +
scale_x_continuous(breaks=c(1:16)) +
theme_bw() +
theme(panel.grid.minor=element_blank()) +
coord_flip()
To answer your legend query. In short, I think this is very hard because the same mapping aesthetic is being used with both datasets.
However, using the code from this answer - I did the following. The idea is to get the legend from two plots only plotting means/sds and then adding those legends to a version of the plot with no legend. It could be adapted so you position the legends more manually...
### Step 1
# Draw a plot with the colour legend
p1 <- ggplot() +
geom_line(aes(x=Symptoms, y=value, colour=Datasets), lty=1, data=means_long) +
geom_point(aes(x=Symptoms, y=value, colour=Datasets), data=means_long, shape = 21, fill = "white", size = 1.5, stroke = 1) +
scale_color_manual(name = "Means",values=c("red","blue", "green","pink")) +
coord_flip()+
theme_bw() +
theme(panel.grid.minor=element_blank()) +
theme(legend.position = "top")
# Extract the colour legend - leg1
library(gtable)
leg1 <- gtable_filter(ggplot_gtable(ggplot_build(p1)), "guide-box")
### Step 2
# Draw a plot with the size legend
p2 <- ggplot() +
geom_line(aes(x=Symptoms, y=value, color=Datasets), lty=2, data=sds_long) +
geom_point( aes(x=Symptoms, y=value, color=Datasets), data=sds_long, shape = 21, fill = "white", size = 1.5, stroke = 1) +
coord_flip()+
theme_bw() +
theme(panel.grid.minor=element_blank()) +
scale_color_manual(name = "SDs",values=c("red","blue", "green","pink"))
# Extract the size legend - leg2
leg2 <- gtable_filter(ggplot_gtable(ggplot_build(p2)), "guide-box")
# Step 3
# Draw a plot with no legends - plot
p3<-ggplot() +
geom_line(aes(x=Symptoms, y=value, colour=Datasets), lty=1, data=means_long) +
geom_point(aes(x=Symptoms, y=value, colour=Datasets), data=means_long, shape = 21, fill = "white", size = 1.5, stroke = 1) +
geom_line(aes(x=Symptoms, y=value, color=Datasets), lty=2, data=sds_long) +
geom_point( aes(x=Symptoms, y=value, color=Datasets), data=sds_long, shape = 21, fill = "white", size = 1.5, stroke = 1) +
xlab("Symptoms") + ylab("Means") +
scale_y_continuous() +
scale_x_continuous(breaks=c(1:16)) +
theme_bw() +
theme(panel.grid.minor=element_blank()) +
coord_flip()+
scale_color_manual(values=c("red","blue", "green","pink")) +
theme(legend.position = "none")
### Step 4
# Arrange the three components (plot, leg1, leg2)
# The two legends are positioned outside the plot:
# one at the top and the other to the side.
library(grid)
plotNew <- arrangeGrob(leg1, p3,
heights = unit.c(leg1$height, unit(1, "npc") - leg1$height), ncol = 1)
plotNew <- arrangeGrob(plotNew, leg2,
widths = unit.c(unit(1, "npc") - leg2$width, leg2$width), nrow = 1)
grid.newpage()
grid.draw(plotNew)
