I am trying to plot the parameter estimates and levels of hierarchy from a stan model output. For the legend, I am hoping to remove all labels except for the "Overall Effects" label but I can't figure out how to remove all of the species successfully.
Here is the code:
ggplot(dfwide, aes(x=Estimate, y=var, color=factor(sp), size=factor(rndm),
alpha=factor(rndm))) +
geom_point(position =pd) +
geom_errorbarh(aes(xmin=(`2.5%`), xmax=(`95%`)), position=pd,
size=.5, height = 0, width=0) +
geom_vline(xintercept=0) +
scale_colour_manual(values=c("blue", "red", "orangered1","orangered3", "sienna4",
"sienna2", "green4", "green3", "purple2", "magenta2"),
labels=c("Overall Effects", expression(italic("A. pensylvanicum"),
italic("A. rubrum"), italic("A. saccharum"),
italic("B. alleghaniensis"), italic("B. papyrifera"),
italic("F. grandifolia"), italic("I. mucronata"),
italic("P. grandidentata"), italic("Q. rubra")))) +
scale_size_manual(values=c(3, 1, 1, 1, 1, 1, 1, 1, 1, 1)) +
scale_shape_manual(labels="", values=c("1"=16,"2"=16)) +
scale_alpha_manual(values=c(1, 0.4)) + guides(size=FALSE, alpha=FALSE) +
ggtitle(label = "A.") +
scale_y_discrete(limits = rev(unique(sort(dfwide$var))), labels=estimates) +
ylab("") +
labs(col="Effects") + theme(legend.title=element_blank())
The key points you need to notice is that remove part of the labels in legend can't be achieved by the function in ggplot2, what you need to do is interact with grid, which more underlying since both lattice and ggplot2 are based grid，to do some more underlying work, we need some functions in the grid.
To remove part of the labels in legend, there are three functions need to be used, they are grid.force(), grid.ls() and grid.remove() . After draw the picture by ggplot2, then using grid.force() and grid.ls(), we can find all the elements in the picture, they all are point, line, text, etc. Then we may need to find the elements we are interested, this process is interactive, since names of the element in ggplot2 are made by some numbers and text, they are not always meanful, after we identify the names of the element we are interested, we can use the grid.remove() function to remove the elements, blew is the sample code I made.
library(grid)
library(ggplot2)
set.seed(1)
data <- data.frame(x = rep(1:10, 2), y = sample(1:100, 20),
type = sample(c("A", "B"), 20, replace = TRUE))
ggplot(data, aes(x = x, y =y,color = type))+
geom_point()+
geom_line()+
scale_color_manual(values = c("blue", "darkred"))+
theme_bw()
until now, we have finished draw the whole picture, then we need to do some works remove some elements in the picture.
grid.force()
grid.ls()
grid.ls() list all the element names
grid.remove("key-4-1-1.5-2-5-2")
grid.remove("key-4-1-2.5-2-5-2")
grid.remove("label-4-3.5-4-5-4")
It's not perfect, but my solution would be to actually make two plots and combine them together. See this post where I lifted the extraction code from.
I don't have your data, but I think you will get the idea below:
library(ggplot2)
library(gridExtra)
library(grid)
#g_table credit goes to https://stackoverflow.com/a/11886071/2060081
g_legend<-function(a.gplot){
tmp <- ggplot_gtable(ggplot_build(a.gplot))
leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
legend <- tmp$grobs[[leg]]
return(legend)}
p_legend = ggplot(dfwide[sp=='Overall Effects'], aes(x=Estimate, y=var, color=factor(sp),
size=factor(rndm),
alpha=factor(rndm))) +
geom_point(position =pd) +
geom_errorbarh(aes(xmin=(`2.5%`), xmax=(`95%`)), position=pd,
size=.5, height = 0, width=0) +
geom_vline(xintercept=0) +
scale_colour_manual(values=c("blue"),
labels=c("Overall Effects"))) +
scale_size_manual(values=c(3)) +
scale_shape_manual(labels="", values=c("1"=16,"2"=16)) +
scale_alpha_manual(values=c(1, 0.4)) + guides(size=FALSE, alpha=FALSE) +
ggtitle(label = "A.") +
scale_y_discrete(limits = rev(unique(sort(dfwide$var))), labels=estimates) +
ylab("") +
labs(col="Effects") + theme(legend.title=element_blank())
p_legend = g_legend(p_legend)
One of your plots will just be the legend. Subset your data based on the Overall Effects and then plot the two plots together as a grid.
Related
I am running a several ggplot barplots in a loop, including added text on top of each bar. I have defined plot scale via coord_fixed and expand_limits. Unfortunately, the y-axis differs from plot to plot, so that scale settings will not fit in all cases, i.e. the text gets cut off and/or the axes get compressed. Let me illustrate:
period <- c(rep("A",4),rep("B",4))
group <- rep(c("C","C","D","D"),2)
size <- rep(c("E","F"),4)
value <- c(23,29,77,62,18,30,54,81)
df <- data.frame(period,group,size,value)
library(ggplot2)
for (i in levels(df$group))
{
p <- ggplot(subset(df, group==i), aes(x=size, y=value, fill = period)) +
geom_bar(position="dodge", stat="identity", show.legend=F) +
geom_text(data=subset(df, group==i), aes(x=size, y=value,label=value),
size=10, fontface="bold", position = position_dodge(width=1),vjust = -0.5) +
expand_limits(y = max(df$value)*0.6) +
coord_fixed(ratio = 0.01)
ggsave(paste0("yourfilepath",i,".png"), width=7.72, height=4.5, units="in", p)
}
I would like the settings of coord_fixed and expand_limits to be case sensitive, dependening on value. I have experimented with using e.g. expand_limits(y = max(df$value * ifelse(df$value <= 50, 0.6, 1))), but that doesn't work in the way I had hoped. Any suggestions will be greatly appreciated!
Based on #Z.Lin's comment, I have added the df$value[df$group==i] argument to my ifelse function: expand_limits(y = max(df$value[df$group==i] * ifelse(df$value[df$group==i] <= 50, 5, 8))).
This is a continuation of a question I recently asked (Manually assigning colors with scale_fill_manual only works for certain hexagon sizes).
I was unable to plot geom_hex() so that all hexagons were the same size. Someone solved the problem. However, their solution removed the legend key. Now, I am unable to keep all the hexagons the same size while also retaining the legend.
To be specific, I really want to keep the legend labels sensical. In the example below, the legend has values (0,2,4,6,8,20), rather than hexadecimal labels (#08306B, #08519C, etc).
Below is MWE illustrating the problem. At the end, as per the 3 comments, you can see that I am able to 1) Create a plot with consistent hexagon sizes but no legend, 2) Create a plot with legend, but inconsistent hexagon sizes, 3) Attempt to create a plot with consistent hexagon sizes and legend but fail:
library(ggplot2)
library(hexbin)
library(RColorBrewer)
library(reshape)
set.seed(1)
xbins <- 10
x <- abs(rnorm(10000))
y <- abs(rnorm(10000))
minVal <- min(x, y)
maxVal <- max(x, y)
maxRange <- c(minVal, maxVal)
buffer <- (maxRange[2] - maxRange[1]) / (xbins / 2)
bindata = data.frame(x=x,y=y,factor=as.factor(1))
h <- hexbin(bindata, xbins = xbins, IDs = TRUE, xbnds = maxRange, ybnds = maxRange)
counts <- hexTapply (h, bindata$factor, table)
counts <- t (simplify2array (counts))
counts <- melt (counts)
colnames (counts) <- c ("factor", "ID", "counts")
counts$factor =as.factor(counts$factor)
hexdf <- data.frame (hcell2xy (h), ID = h#cell)
hexdf <- merge (counts, hexdf)
my_breaks <- c(2, 4, 6, 8, 20, 1000)
clrs <- brewer.pal(length(my_breaks) + 3, "Blues")
clrs <- clrs[3:length(clrs)]
hexdf$countColor <- cut(hexdf$counts, breaks = c(0, my_breaks, Inf), labels = rev(clrs))
# Has consistent hexagon sizes, but no legend
ggplot(hexdf, aes(x=x, y=y, hexID=ID, counts=counts, fill=countColor)) + geom_hex(stat="identity", fill=hexdf$countColor) + scale_fill_manual(labels = as.character(c(0, my_breaks)), values = rev(clrs), name = "Count") + geom_abline(intercept = 0, color = "red", size = 0.25) + labs(x = "A", y = "C") + coord_fixed(xlim = c(-0.5, (maxRange[2]+buffer)), ylim = c(-0.5, (maxRange[2]+buffer))) + theme(aspect.ratio=1)
# Has legend, but inconsistent hexagon sizes
ggplot(hexdf, aes(x=x, y=y, hexID=ID, counts=counts, fill=countColor)) + geom_hex(data=hexdf, stat="identity", aes(fill=countColor)) + scale_fill_manual(labels = as.character(c(0, my_breaks)), values = rev(clrs), name = "Count") + geom_abline(intercept = 0, color = "red", size = 0.25) + labs(x = "A", y = "C") + coord_fixed(xlim = c(-0.5, (maxRange[2]+buffer)), ylim = c(-0.5, (maxRange[2]+buffer))) + theme(aspect.ratio=1)
# One attempt to create consistent hexagon sizes and retain legend
ggplot(hexdf, aes(x=x, y=y, hexID=ID, counts=counts, fill=countColor)) + geom_hex(data=hexdf, aes(fill=countColor)) + geom_hex(stat="identity", fill=hexdf$countColor) + scale_fill_manual(labels = as.character(c(0, my_breaks)), values = rev(clrs), name = "Count") + geom_abline(intercept = 0, color = "red", size = 0.25) + labs(x = "A", y = "C") + coord_fixed(xlim = c(-0.5, (maxRange[2]+buffer)), ylim = c(-0.5, (maxRange[2]+buffer))) + theme(aspect.ratio=1)
Any suggestions on how to keep the hexagon sizes consistent while retaining the legend would be very helpful!
Wow, this is an interesting one -- geom_hex seems to really dislike mapping color/fill onto categorical variables. I assume that's because it is designed to be a two-dimensional histogram and visualize continuous summary statistics, but if anyone has any insight into what's going on behind the scenes, I would love to know.
For your specific problem, that really throws a wrench in the works, because you're attempting to have categorical colorization that assigns non-linear groups to the individual hexagons. Conceptually, you might consider why you're doing that. There may be a good reason, but you're essentially taking a linear color gradient and mapping it non-linearly onto your data, which can end up being visually misleading.
However, if that is what you want to do, the best approach I could come up with was to create a new continuous variable that mapped linearly onto your chosen colors and then use those to create a color gradient. Let me try to walk you through my thought process.
You essentially have a continuous variable (counts) that you want to map onto colors. That's easy enough with a simple color gradient, which is the default in ggplot2 for continuous variables. Using your data:
ggplot(hexdf, aes(x=x, y=y)) +
geom_hex(stat="identity", aes(fill=counts))
yields something close.
However, the bins with really high counts wash out the gradient for points with much lower counts, so we need to change the way the gradient maps colors onto values. You've already declared the colors you want to use in the clrs variable; we just need to add a column to your data frame to use in conjunction with these colors to create a smooth gradient. I did that as follows:
all_breaks <- c(0, my_breaks)
breaks_n <- 1:length(all_breaks)
get_break_n <- function(n) {
break_idx <- max(which((all_breaks - n) < 0))
breaks_n[break_idx]
}
hexdf$bin <- sapply(hexdf$counts, get_break_n)
We create the bin variable as the index of the break that is nearest the count variable without exceeding it. Now, you'll notice that:
ggplot(hexdf, aes(x=x, y=y)) +
geom_hex(stat="identity", aes(fill=bin))
is getting much closer to the goal.
The next step is to change how the color gradient maps onto that bin variable, which we can do by adding a call to scale_fill_gradientn:
ggplot(hexdf, aes(x=x, y=y)) +
geom_hex(stat="identity", aes(fill=bin)) +
scale_fill_gradientn(colors=rev(clrs[-1])) # odd color reversal to
# match OP's color mapping
This takes a vector of colors between which you want to interpolate a gradient. The way we've set it up, the points along the interpolation will perfectly match up with the unique values of the bin variable, meaning each value will get one of the colors specified.
Now we're cooking with gas, and the only thing left to do is add the various bells and whistles from the original graph. Most importantly, we need to make the legend look the way we want. This requires three things: (1) changing it from the default color bar to a discretized legend, (2) specifying our own custom labels, and (3) giving it an informative title.
# create the custom labels for the legend
all_break_labs <- as.character(all_breaks[1:(length(allb)-1)])
ggplot(hexdf, aes(x=x, y=y)) +
geom_hex(stat="identity", aes(fill=bin)) +
scale_fill_gradientn(colors=rev(clrs[-1]),
guide="legend", # (1) make legend discrete
labels=all_break_labs, # (2) specify labels
name="Count") + # (3) legend title
# All the other prettification from the OP
geom_abline(intercept = 0, color = "red", size = 0.25) +
labs(x = "A", y = "C") +
coord_fixed(xlim = c(-0.5, (maxRange[2]+buffer)),
ylim = c(-0.5, (maxRange[2]+buffer))) +
theme(aspect.ratio=1)
All of this leaves us with the following graph:
Hopefully that helps you out. For completeness, here's the new code in full:
# ... the rest of your code before the plots
clrs <- clrs[3:length(clrs)]
hexdf$countColor <- cut(hexdf$counts,
breaks = c(0, my_breaks, Inf),
labels = rev(clrs))
### START OF NEW CODE ###
# create new bin variable
all_breaks <- c(0, my_breaks)
breaks_n <- 1:length(all_breaks)
get_break_n <- function(n) {
break_idx <- max(which((all_breaks - n) < 0))
breaks_n[break_idx]
}
hexdf$bin <- sapply(hexdf$counts, get_break_n)
# create legend labels
all_break_labs <- as.character(all_breaks[1:(length(all_breaks)-1)])
# create final plot
ggplot(hexdf, aes(x=x, y=y)) +
geom_hex(stat="identity", aes(fill=bin)) +
scale_fill_gradientn(colors=rev(clrs[-1]),
guide="legend",
labels=all_break_labs,
name="Count") +
geom_abline(intercept = 0, color = "red", size = 0.25) +
labs(x = "A", y = "C") +
coord_fixed(xlim = c(-0.5, (maxRange[2]+buffer)),
ylim = c(-0.5, (maxRange[2]+buffer))) +
theme(aspect.ratio=1)
Example code and figure:
data <- data.frame( ID = c(LETTERS[1:26], paste0("A",LETTERS[1:26])),
Group = rep(c("Control","Treatment"),26),
x = rnorm(52,50,20),
y = rnorm(52,50,10))
ggplot(data, aes(y=y,x=x, label=ID, color=Group)) +
geom_text(size=8) +
scale_color_manual(values=c("blue","red")) +
theme_classic() +
theme(legend.text = element_text(color=c("blue","red")))
What I'm trying to solve is removing the legend symbols (the "a") and coloring the Group labels (Control and Treatment) as they appear in the plot (Blue and Red respectively).
I've tried:
geom_text(show_guide = F)
But that just removes the legend entirely.
To keep it simple I could just use annotate...but wondering if there's a legend specific solution.
ggplot(data, aes(y=y,x=x, label=ID, color=Group)) +
geom_text(size=8, show_guide=F) +
scale_color_manual(values=c("blue","red")) +
theme_classic() +
annotate("text",label="Control", color="blue",x=20,y=80,size=8) +
annotate("text",label="Treatment", color="Red",x=23,y=77,size=8)
Another option is to use point markers (instead of the letter "a") as the legend symbols, which you can do with the following workaround:
Remove the geom_text legend.
Add a "dummy" point geom and set the point marker size to NA, so no points are actually plotted, but a legend will be generated.
Override the size of the point markers in the legend, so that point markers will appear in the legend key to distinguish each group.
ggplot(data, aes(y=y,x=x, label=ID, color=Group)) +
geom_text(size=8, show.legend=FALSE) +
geom_point(size=NA) +
scale_color_manual(values=c("blue","red")) +
theme_classic() +
labs(colour="") +
guides(colour=guide_legend(override.aes=list(size=4)))
Beginning with ggplot2 2.3.2, you can specify the glyph used in the legend using the argument key_glyph:
ggplot(data, aes(x=x, y=y, label=ID, color=Group)) +
geom_text(size=8, key_glyph="point") +
scale_color_manual(values=c("blue", "red")) +
labs(color=NULL) +
theme_classic()
For a full list of glyphs, refer to the ggplot2 documentation for draw_key. Credit to R Data Berlin for alerting me to this simple solution. Emil Hvitfeldt also has a nice blog post showcasing the options.
As a quick fix you can tweak the legend key, by hard coding the info you want, although around the other way - keep the key and remove the label.
library(grid)
GeomText$draw_key <- function (data, params, size) {
txt <- ifelse(data$colour=="blue", "Control", "Treatment")
# change x=0 and left justify
textGrob(txt, 0, 0.5,
just="left",
gp = gpar(col = alpha(data$colour, data$alpha),
fontfamily = data$family,
fontface = data$fontface,
# also added 0.5 to reduce size
fontsize = data$size * .pt* 0.5))
}
And when you plot you suppress the legend labels, and make legend key a bit wider to fit text.
ggplot(data, aes(y=y,x=x, label=ID, color=Group)) +
geom_text(size=8) +
scale_color_manual(values=c("blue","red")) +
theme_classic() +
theme(legend.text = element_blank(),
legend.key.width = unit(1.5, "cm"))
R 3.1.2/ggplot2_1.0.0/Windows7
How can one add 2 vertical lines with legends in a faceted graph without them changing the linetype? In the following example i can't get the legends to appear as I would imagine them to (two solid lines and a adecuate legend) from the code I'm writing. A reproducible example:
library(ggplot2)
library(plyr)
library(e1071)
set.seed(89)
pm <- data.frame(pm10=rnorm(400, 150, 50), estacion=gl(4,100, labels = c('sur', 'norte', 'este', 'oeste'))) # data
curtosis <- ddply(pm, .(estacion), function(val) sprintf("curtosis==%.2f", kurtosis(val$pm10)))
asimetria <- ddply(pm, .(estacion), function(val) sprintf("asimetría==%.2f", skewness(val$pm10)))
p1 <- ggplot(data=pm, aes(x=pm10, y=..density..)) +
geom_histogram(bin=15, fill='#deebf7', colour='#bdbdbd')+
geom_density(size=1, colour='#cccccc')+
geom_vline(data=aggregate(pm[1], pm[2], quantile, .8), mapping=aes(xintercept=pm10, linetype='percentil .8'), size=1, colour='#dfc27d', show_guide = T)+
geom_vline(data=aggregate(pm[1], pm[2], median), mapping=aes(xintercept=pm10, linetype='mediana'), size=1, colour='#80cdc1', show_guide = T)+
geom_text(data=curtosis, aes(x=350, y=.010, label=V1), size=3, parse=T)+
geom_text(data=asimetria, aes(x=350, y=.008, label=V1), size=3, parse=T)+
guides(linetype=guide_legend(override.aes=list(colour = c("#dfc27d","#80cdc1"))))+
xlim(0,500)+
facet_wrap(~ estacion, ncol=2)
print(p1)
I want the lines to be solid (color is ok) and the legend's title to say: "Medida de tendencia".
The main idea for rather compicated ggplots like yours is to separate the data preparation from actual plotting. Within the data preparation step you can organize your data according to the plotting concept you have in mind.
In your case this implies precomputing the statistic in question:
df_vline <- rbind(
aggregate(pm[1], pm[2], quantile, .8),
aggregate(pm[1], pm[2], median)
)
df_vline$stat <- rep(c("percentil .8", "mediana"), each = nrow(df_vline) / 2)
and then the mapping scheme is quite straightforward, so you don't have to think about manual overriding of the legend.
ggplot(data=pm, aes(x=pm10, y=..density..)) +
geom_histogram(bin=15, fill='#deebf7', colour='#bdbdbd')+
geom_density(size=1, colour='#cccccc')+
geom_vline(data=df_vline, mapping=aes(xintercept=pm10, colour = stat),
linetype = 1, size=1, show_guide = T)+
geom_text(data=curtosis, aes(x=350, y=.010, label=V1), size=3, parse=T)+
geom_text(data=asimetria, aes(x=350, y=.008, label=V1), size=3, parse=T)+
scale_colour_manual(values = c("#dfc27d","#80cdc1"), name = "Medida de tendencia")+
xlim(0,500)+
facet_wrap(~ estacion, ncol=2)
(Nice plot, by the way.)
How to overlay one plot on top of the other in ggplot2 as explained in the following sentences? I want to draw the grey time series on top of the red one using ggplot2 in R (now the red one is above the grey one and I want my graph to be the other way around). Here is my code (I generate some data in order to show you my problem, the real dataset is much more complex):
install.packages("ggplot2")
library(ggplot2)
time <- rep(1:100,2)
timeseries <- c(rep(0.5,100),rep(c(0,1),50))
upper <- c(rep(0.7,100),rep(0,100))
lower <- c(rep(0.3,100),rep(0,100))
legend <- c(rep("red should be under",100),rep("grey should be above",100))
dataset <- data.frame(timeseries,upper,lower,time,legend)
ggplot(dataset, aes(x=time, y=timeseries)) +
geom_line(aes(colour=legend, size=legend)) +
geom_ribbon(aes(ymax=upper, ymin=lower, fill=legend), alpha = 0.2) +
scale_colour_manual(limits=c("grey should be above","red should be under"),values = c("grey50","red")) +
scale_fill_manual(values = c(NA, "red")) +
scale_size_manual(values=c(0.5, 1.5)) +
theme(legend.position="top", legend.direction="horizontal",legend.title = element_blank())
Convert the data you are grouping on into a factor and explicitly set the order of the levels. ggplot draws the layers according to this order. Also, it is a good idea to group the scale_manual codes to the geom it is being applied to for readability.
legend <- factor(legend, levels = c("red should be under","grey should be above"))
c <- data.frame(timeseries,upper,lower,time,legend)
ggplot(c, aes(x=time, y=timeseries)) +
geom_ribbon(aes(ymax=upper, ymin=lower, fill=legend), alpha = 0.2) +
scale_fill_manual(values = c("red", NA)) +
geom_line(aes(colour=legend, size=legend)) +
scale_colour_manual(values = c("red","grey50")) +
scale_size_manual(values=c(1.5,0.5)) +
theme(legend.position="top", legend.direction="horizontal",legend.title = element_blank())
Note that the ordering of the values in the scale_manual now maps to "grey" and "red"