Related
I'm new to R. Legends and plotting seem to be more difficult than in Python. How can I change the graph to display each node as a different color in the legend? Now I have something like the picture.
Thank you for your help.
library(ggplot2)
library(MASS)
library(car)
library(robustbase)
data("airquality")
# Select only Ozone and Temp variables
air = airquality[c("Ozone" , "Temp")]
# We need to remove NA from data set
air = na.omit(air)
air.center = colMeans(air)
air.cov = cov(air)
rad = sqrt(qchisq(p = 0.95 , df = ncol(air)))
ellipse <- ellipse(center = air.center , shape = air.cov , radius = rad ,
segments = 150 , draw = FALSE)
ellipse <- as.data.frame(ellipse)
colnames(ellipse) <- colnames(air)
# Finding distances
distances <- mahalanobis(x = air , center = air.center , cov = air.cov)
# Cutoff value for ditances from Chi-Sqaure Dist.
# with p = 0.95 df = 2 which in ncol(air)
cutoff <- qchisq(p = 0.95 , df = ncol(air))
### Minimum Covariance Determinant (MCD)
Y_mcd <- covMcd(air)
# Robust estimate of location
Y_mcd$center
# Robust estimate of scatter
Y_mcd$cov
# Make elilipse
ellipse_mcd <- data.frame(ellipse(center = Y_mcd$center,
shape = Y_mcd$cov,
radius= rad,
segments=100,draw=FALSE))
#the same names as in previous plot
colnames(ellipse_mcd) <- colnames(air)
plot_fig <- ggplot(air , aes(x = Ozone , y = Temp)) +
geom_point(size = 2) +
geom_polygon(data = ellipse , fill = "blue" , color = "blue" , alpha = 0.5,show.legend =T)+
geom_point(aes(air.center[1] , air.center[2],fill='Mahalanobis') , size = 5 , color = "blue") +
geom_text(data=subset(air, distances > cutoff),
aes(Ozone,Temp,label=row.names(air[distances > cutoff ,])), hjust = 1 , vjust = -1.5 ,size = 3.5)+
ylab("Temperature Values") + xlab("Ozone Values")+ggtitle("Mahalanobis distance")+ theme(
legend.position = c(0.95, 0.15),
legend.justification = c("right", "top")
) + geom_polygon(data=ellipse_mcd,aes(x = Ozone,y = Temp, colour='LINE2'), color="red", fill="red",
alpha=0.3, inherit.aes = FALSE) +
geom_point(aes(x = Y_mcd$center[1], y = Y_mcd$center[2],fill='MCD'),
color = "red", size = 6)
plot_fig
The issue is that you mapped on the fill aesthetic and set the color of the points as arguments. Instead map the labels on the color aes and set the color values via scale_color_manual:
plot_fig <- ggplot(air , aes(x = Ozone , y = Temp)) +
geom_point(size = 2) +
geom_polygon(data = ellipse , fill = "blue" , color = "blue" , alpha = 0.5, show.legend =T)+
geom_point(aes(air.center[1] , air.center[2], color = 'Mahalanobis'), size = 5) +
geom_text(data=subset(air, distances > cutoff),
aes(Ozone,Temp,label=row.names(air[distances > cutoff ,])), hjust = 1 , vjust = -1.5 ,size = 3.5)+
ylab("Temperature Values") +
xlab("Ozone Values")+
ggtitle("Mahalanobis distance")+
theme(
legend.position = c(0.95, 0.15),
legend.justification = c("right", "top")
) +
geom_polygon(data=ellipse_mcd,aes(x = Ozone,y = Temp, colour='LINE2'), color="red", fill="red",
alpha=0.3, inherit.aes = FALSE) +
geom_point(aes(x = Y_mcd$center[1], y = Y_mcd$center[2], color='MCD'), size = 6) +
scale_color_manual(values = c(MCD = "red", Mahalanobis = "blue"))
plot_fig
If you map the Mahalanobis/MCD categorical variables to colour, then let fill be dependent on the mapped colour, the legend should sort itself out naturally and you can set the colours with scale_colour_manual().
ggplot(air, aes(Ozone, Temp)) +
geom_point(size = 2) +
geom_polygon(
data = ellipse,
aes(fill = after_scale(alpha(colour, 0.5)), colour = "Mahalanobis")
) +
geom_polygon(data = ellipse_mcd,
aes(fill = after_scale(alpha(colour, 0.3)), colour = "MCD")) +
geom_point(aes(air.center[1], air.center[2], colour = "Mahalanobis"),
size = 5) +
geom_point(aes(Y_mcd$center[1], Y_mcd$center[2], colour = "MCD"), size = 5) +
geom_text(data = subset(air, distances > cutoff),
aes(label = row.names(air[distances > cutoff, ])),
hjust = 1, vjust = -1.5, size = 3.5) +
scale_colour_manual(values = c("blue", "red")) +
labs(x = "Ozone Values", y = "Temperature values",
title = "Mahalanobis distance") +
theme(legend.position = c(0.95, 0.15),
legend.justification = c("right", "top"))
I am trying to reproduce this kind of Figure, with two densities, a first one pointing upwards and a second one pointing downwards. I would also like to have some blank space between the two densities.
Here is the code I am currently using.
library(hrbrthemes)
library(tidyverse)
library(RWiener)
# generating data
df <- rwiener(n = 1e2, alpha = 2, tau = 0.3, beta = 0.5, delta = 0.5)
df %>%
ggplot(aes(x = q) ) +
geom_density(
data = . %>% filter(resp == "upper"),
aes(y = ..density..),
colour = "steelblue", fill = "steelblue",
outline.type = "upper", alpha = 0.8, adjust = 1, trim = TRUE
) +
geom_density(
data = . %>% filter(resp == "lower"),
aes(y = -..density..), colour = "orangered", fill = "orangered",
outline.type = "upper", alpha = 0.8, adjust = 1, trim = TRUE
) +
# stimulus onset
geom_vline(xintercept = 0, lty = 1, col = "grey") +
annotate(
geom = "text",
x = 0, y = 0,
# hjust = 0,
vjust = -1,
size = 3, angle = 90,
label = "stimulus onset"
) +
# aesthetics
theme_ipsum_rc(base_size = 12) +
theme(axis.text.y = element_blank() ) +
labs(x = "Reaction time (in seconds)", y = "") +
xlim(0, NA)
Which results in something like...
How could I add some vertical space between the two densities to reproduce the above Figure?
If you want to try without faceting, you're probably best to just plot the densities as polygons with adjusted y values according to your desired spacing:
s <- 0.25 # set to change size of the space
ud <- density(df$q[df$resp == "upper"])
ld <- density(df$q[df$resp == "lower"])
x <- c(ud$x[1], ud$x, ud$x[length(ud$x)],
ld$x[1], ld$x, ld$x[length(ld$x)])
y <- c(s, ud$y + s, s, -s, -ld$y - s, -s)
df2 <- data.frame(x = x, y = y,
resp = rep(c("upper", "lower"), each = length(ud$x) + 2))
df2 %>%
ggplot(aes(x = x, y = y, fill = resp, color = resp) ) +
geom_polygon(alpha = 0.8) +
scale_fill_manual(values = c("steelblue", "orangered")) +
scale_color_manual(values = c("steelblue", "orangered"), guide = guide_none()) +
geom_vline(xintercept = 0, lty = 1, col = "grey") +
annotate(
geom = "text",
x = 0, y = 0,
# hjust = 0,
vjust = -1,
size = 3, angle = 90,
label = "stimulus onset"
) +
# aesthetics
theme_ipsum_rc(base_size = 12) +
theme(axis.text.y = element_blank() ) +
labs(x = "Reaction time (in seconds)", y = "")
you can try facetting
set.seed(123)
q=rbeta(100, 0.25, 1)
df_dens =data.frame(gr=1,
x=density(df$q)$x,
y=density(df$q)$y)
df_dens <- rbind(df_dens,
data.frame(gr=2,
x=density(df$q)$x,
y=-density(df$q)$y))
ggplot(df_dens, aes(x, y, fill = factor(gr))) +
scale_x_continuous(limits = c(0,1)) +
geom_area(show.legend = F) +
facet_wrap(~gr, nrow = 2, scales = "free_y") +
theme_minimal() +
theme(strip.background = element_blank(),
strip.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.y = element_blank())
The space between both plots can be increased using panel.spacing = unit(20, "mm"). Instead of facet_grid you can also try facet_grid(gr~., scales = "free_y")
I would like to produce a graphic combining four facets of a graph with insets in each facet showing a detail of the respective plot. This is one of the things I tried:
#create data frame
n_replicates <- c(rep(1:10,15),rep(seq(10,100,10),15),rep(seq(100,1000,100),15),rep(seq(1000,10000,1000),15))
sim_years <- rep(sort(rep((1:15),10)),4)
sd_data <- rep (NA,600)
for (i in 1:600) {
sd_data[i]<-rnorm(1,mean=exp(0.1 * sim_years[i]), sd= 1/n_replicates[i])
}
max_rep <- sort(rep(c(10,100,1000,10000),150))
data_frame <- cbind.data.frame(n_replicates,sim_years,sd_data,max_rep)
#do first basic plot
library(ggplot2)
plot1<-ggplot(data=data_frame, aes(x=sim_years,y=sd_data,group =n_replicates, col=n_replicates)) +
geom_line() + theme_bw() +
labs(title ="", x = "year", y = "sd")
plot1
#make four facets
my_breaks = c(2, 10, 100, 1000, 10000)
facet_names <- c(
`10` = "2, 3, ..., 10 replicates",
`100` = "10, 20, ..., 100 replicates",
`1000` = "100, 200, ..., 1000 replicates",
`10000` = "1000, 2000, ..., 10000 replicates"
)
plot2 <- plot1 +
facet_wrap( ~ max_rep, ncol=2, labeller = as_labeller(facet_names)) +
scale_colour_gradientn(name = "number of replicates", trans = "log",
breaks = my_breaks, labels = my_breaks, colours = rainbow(20))
plot2
#extract inlays (this is where it goes wrong I think)
library(ggpmisc)
library(tibble)
library(dplyr)
inset <- tibble(x = 0.01, y = 10.01,
plot = list(plot2 +
facet_wrap( ~ max_rep, ncol=2, labeller = as_labeller(facet_names)) +
coord_cartesian(xlim = c(13, 15),
ylim = c(3, 5)) +
labs(x = NULL, y = NULL, color = NULL) +
scale_colour_gradient(guide = FALSE) +
theme_bw(10)))
plot3 <- plot2 +
expand_limits(x = 0, y = 0) +
geom_plot_npc(data = inset, aes(npcx = x, npcy = y, label = plot)) +
annotate(geom = "rect",
xmin = 13, xmax = 15, ymin = 3, ymax = 5,
linetype = "dotted", fill = NA, colour = "black")
plot3
That leads to the following graphic:
As you can see, the colours in the insets are wrong, and all four of them appear in each of the facets even though I only want the corresponding inset of course. I read through a lot of questions here (to even get me this far) and also some examples in the ggpmisc user guide but unfortunately I am still a bit lost on how to achieve what I want. Except maybe to do it by hand extracting four insets and then combining them with plot2. But I hope there will be a better way to do this. Thank you for your help!
Edit: better graphic now thanks to this answer, but problem remains partially unsolved:
The following code does good insets, but unfortunately the colours are not preserved. As in the above version each inset does its own rainbow colours anew instead of inheriting the partial rainbow scale from the facet it belongs to. Does anyone know why and how I could change this? In comments I put another (bad) attempt at solving this, it preserves the colors but has the problem of putting all four insets in each facet.
library(ggpmisc)
library(tibble)
library(dplyr)
# #extract inlays: good colours, but produces four insets.
# fourinsets <- tibble(#x = 0.01, y = 10.01,
# x = c(rep(0.01, 4)),
# y = c(rep(10.01, 4)),
# plot = list(plot2 +
# facet_wrap( ~ max_rep, ncol=2) +
# coord_cartesian(xlim = c(13, 15),
# ylim = c(3, 5)) +
# labs(x = NULL, y = NULL, color = NULL) +
# scale_colour_gradientn(name = "number of replicates", trans = "log", guide = FALSE,
# colours = rainbow(20)) +
# theme(
# strip.background = element_blank(),
# strip.text.x = element_blank()
# )
# ))
# fourinsets$plot
library(purrr)
pp <- map(unique(data_frame$max_rep), function(x) {
plot2$data <- plot2$data %>% filter(max_rep == x)
plot2 +
coord_cartesian(xlim = c(12, 14),
ylim = c(3, 4)) +
labs(x = NULL, y = NULL) +
theme(
strip.background = element_blank(),
strip.text.x = element_blank(),
legend.position = "none",
axis.text=element_blank(),
axis.ticks=element_blank()
)
})
#pp[[2]]
inset_new <- tibble(x = c(rep(0.01, 4)),
y = c(rep(10.01, 4)),
plot = pp,
max_rep = unique(data_frame$max_rep))
final_plot <- plot2 +
geom_plot_npc(data = inset_new, aes(npcx = x, npcy = y, label = plot, vp.width = 0.3, vp.height =0.6)) +
annotate(geom = "rect",
xmin = 12, xmax = 14, ymin = 3, ymax = 4,
linetype = "dotted", fill = NA, colour = "black")
#final_plot
final_plot then looks like this:
I hope this clarifies the problem a bit. Any ideas are very welcome :)
Modifying off #user63230's excellent answer:
pp <- map(unique(data_frame$max_rep), function(x) {
plot2 +
aes(alpha = ifelse(max_rep == x, 1, 0)) +
coord_cartesian(xlim = c(12, 14),
ylim = c(3, 4)) +
labs(x = NULL, y = NULL) +
scale_alpha_identity() +
facet_null() +
theme(
strip.background = element_blank(),
strip.text.x = element_blank(),
legend.position = "none",
axis.text=element_blank(),
axis.ticks=element_blank()
)
})
Explanation:
Instead of filtering the data passed into plot2 (which affects the mapping of colours), we impose a new aesthetic alpha, where lines belonging to the other replicate numbers are assigned 0 for transparency;
Use scale_alpha_identity() to tell ggplot that the alpha mapping is to be used as-is: i.e. 1 for 100%, 0 for 0%.
Add facet_null() to override plot2's existing facet_wrap, which removes the facet for the inset.
Everything else is unchanged from the code in the question.
I think this will get you started although its tricky to get the size of the inset plot right (when you include a legend).
#set up data
library(ggpmisc)
library(tibble)
library(dplyr)
library(ggplot2)
# create data frame
n_replicates <- c(rep(1:10, 15), rep(seq(10, 100, 10), 15), rep(seq(100,
1000, 100), 15), rep(seq(1000, 10000, 1000), 15))
sim_years <- rep(sort(rep((1:15), 10)), 4)
sd_data <- rep(NA, 600)
for (i in 1:600) {
sd_data[i] <- rnorm(1, mean = exp(0.1 * sim_years[i]), sd = 1/n_replicates[i])
}
max_rep <- sort(rep(c(10, 100, 1000, 10000), 150))
data_frame <- cbind.data.frame(n_replicates, sim_years, sd_data, max_rep)
# make four facets
my_breaks = c(2, 10, 100, 1000, 10000)
facet_names <- c(`10` = "2, 3, ..., 10 replicates", `100` = "10, 20, ..., 100 replicates",
`1000` = "100, 200, ..., 1000 replicates", `10000` = "1000, 2000, ..., 10000 replicates")
Get overall plot:
# overall facet plot
overall_plot <- ggplot(data = data_frame, aes(x = sim_years, y = sd_data, group = n_replicates, col = n_replicates)) +
geom_line() +
theme_bw() +
labs(title = "", x = "year", y = "sd") +
facet_wrap(~max_rep, ncol = 2, labeller = as_labeller(facet_names)) +
scale_colour_gradientn(name = "number of replicates", trans = "log", breaks = my_breaks, labels = my_breaks, colours = rainbow(20))
#plot
overall_plot
which gives:
Then from the overall plot you want to extract each plot, see here. We can map over the list to extract one at a time:
pp <- map(unique(data_frame$max_rep), function(x) {
overall_plot$data <- overall_plot$data %>% filter(max_rep == x)
overall_plot + # coord_cartesian(xlim = c(13, 15), ylim = c(3, 5)) +
labs(x = NULL, y = NULL) +
theme_bw(10) +
theme(legend.position = "none")
})
If we look at one of these (I've removed the legend) e.g.
pp[[1]]
#pp[[2]]
#pp[[3]]
#pp[[4]]
Gives:
Then we want to add these inset plots into a dataframe so that each plot has its own row:
inset <- tibble(x = c(rep(0.01, 4)),
y = c(rep(10.01, 4)),
plot = pp,
max_rep = unique(data_frame$max_rep))
Then merge this into the overall plot:
overall_plot +
expand_limits(x = 0, y = 0) +
geom_plot_npc(data = inset, aes(npcx = x, npcy = y, label = plot, vp.width = 0.8, vp.height = 0.8))
Gives:
Here is a solution based on Z. Lin's answer, but using ggforce::facet_wrap_paginate() to do the filtering and keeping colourscales consistent.
First, we can make the 'root' plot containing all the data with no facetting.
library(ggpmisc)
library(tibble)
library(dplyr)
n_replicates <- c(rep(1:10,15),rep(seq(10,100,10),15),rep(seq(100,1000,100),15),rep(seq(1000,10000,1000),15))
sim_years <- rep(sort(rep((1:15),10)),4)
sd_data <- rep (NA,600)
for (i in 1:600) {
sd_data[i]<-rnorm(1,mean=exp(0.1 * sim_years[i]), sd= 1/n_replicates[i])
}
max_rep <- sort(rep(c(10,100,1000,10000),150))
data_frame <- cbind.data.frame(n_replicates,sim_years,sd_data,max_rep)
my_breaks = c(2, 10, 100, 1000, 10000)
facet_names <- c(
`10` = "2, 3, ..., 10 replicates",
`100` = "10, 20, ..., 100 replicates",
`1000` = "100, 200, ..., 1000 replicates",
`10000` = "1000, 2000, ..., 10000 replicates"
)
base <- ggplot(data=data_frame,
aes(x=sim_years,y=sd_data,group =n_replicates, col=n_replicates)) +
geom_line() +
theme_bw() +
scale_colour_gradientn(
name = "number of replicates",
trans = "log10", breaks = my_breaks,
labels = my_breaks, colours = rainbow(20)
) +
labs(title ="", x = "year", y = "sd")
Next, the main plot will be just the root plot with facet_wrap().
main <- base + facet_wrap(~ max_rep, ncol = 2, labeller = as_labeller(facet_names))
Then the new part is to use facet_wrap_paginate with nrow = 1 and ncol = 1 for every max_rep, which we'll use as insets. The nice thing is that this does the filtering and it keeps colour scales consistent with the root plot.
nmax_rep <- length(unique(data_frame$max_rep))
insets <- lapply(seq_len(nmax_rep), function(i) {
base + ggforce::facet_wrap_paginate(~ max_rep, nrow = 1, ncol = 1, page = i) +
coord_cartesian(xlim = c(12, 14), ylim = c(3, 4)) +
guides(colour = "none", x = "none", y = "none") +
theme(strip.background = element_blank(),
strip.text = element_blank(),
axis.title = element_blank(),
plot.background = element_blank())
})
insets <- tibble(x = rep(0.01, nmax_rep),
y = rep(10.01, nmax_rep),
plot = insets,
max_rep = unique(data_frame$max_rep))
main +
geom_plot_npc(data = insets,
aes(npcx = x, npcy = y, label = plot,
vp.width = 0.3, vp.height = 0.6)) +
annotate(geom = "rect",
xmin = 12, xmax = 14, ymin = 3, ymax = 4,
linetype = "dotted", fill = NA, colour = "black")
Created on 2020-12-15 by the reprex package (v0.3.0)
I have the code below, and it works fine. The problem is, I would like to add "k" and plot a straight line similar to "z", but "k" is a vector of different numbers. Each element in "k" should be plotted as a line on the 3 facets created. If k was a singular value, I would just repeat the geom_segment() command with different y limits. Is there an easy way to do this? The final output should look like attached, assuming I could draw straight lines.
x <- iris[-1:-3]
bw <- 1
nbin <- 100
y <- head(iris, 50)[2]
z <- 1
k <- c(2, 3, 4)
ggplot(x, aes(x = Petal.Width)) +
geom_density(aes(y = bw *..count.., fill = Species), size = 1, alpha = 0.4) +
geom_segment(aes(x = 5, y = 250, xend = z, yend = 250, color = "red")) +
facet_wrap(~Species)+
scale_x_continuous(labels = scales::math_format(10^.x), limits = c(0, 5), expand = c(0,0)) +
scale_y_continuous(expand = c(0,0), limits = c(0, NA)) +
annotation_logticks(sides = "b", short=unit(-1,"mm"), mid=unit(-2,"mm"), long=unit(-3,"mm")) +
coord_cartesian(clip='off') + theme(panel.background = element_blank(),
panel.border = element_rect(colour = "black", fill=NA))
you can try this. Assuming that your plot is saved as p1.
k_data = data.frame(k, Species = levels(x$Species))
p1 + geom_segment(data = k_data, aes(x =5, y = 200, xend = k, yend = 200),
color = "blue", inherit.aes = F)
The idea is to create a dataframe with the columns k and Species and use this data exclusivley in a geom by setting inherit.aes = F
In this solution, the value of k is made part of the data set being plotted through a pipe. It is a temporary modification of the data set, since it is not assigned back to it nor to any other data set.
library(ggplot2)
library(dplyr)
x <- iris[-1:-3]
str(x)
bw <- 1
nbin <- 100
y <- head(iris, 50)[2]
z <- 1
k <- c(2, 3, 4)
x %>%
mutate(k = rep(k, each = 50)) %>%
ggplot(aes(x = Petal.Width)) +
geom_density(aes(y = bw *..count.., fill = Species), size = 1, alpha = 0.4) +
geom_segment(aes(x = 5, y = 250, xend = z, yend = 250), color = "red") +
geom_segment(aes(x = 5, y = 200, xend = k, yend = 200), color = "blue") +
facet_wrap(~Species)+
scale_x_continuous(labels = scales::math_format(10^.x), limits = c(0, 5), expand = c(0,0)) +
scale_y_continuous(expand = c(0,0), limits = c(0, NA)) +
annotation_logticks(sides = "b", short=unit(-1,"mm"), mid=unit(-2,"mm"), long=unit(-3,"mm")) +
coord_cartesian(clip='off') +
theme(panel.background = element_blank(),
panel.border = element_rect(colour = "black", fill=NA))
So I want to annotate outside my plot to create a new x-axis, as is shown here by Henrik: Multi-row x-axis labels in ggplot line chart
But everything breaks when I try to add a log y axis.
Here's my example data:
# Data
Type = rep(c("type1", "type2"), each = 6)
Gen = rep(rep(c("-", "G+"), each = 3), 2)
A = c(4.98E+05, 5.09E+05, 1.03E+05, 3.08E+05, 5.07E+03, 4.22E+04, 6.52E+05, 2.51E+04, 8.66E+05, 8.10E+04, 6.50E+06, 1.64E+06)
B = c(6.76E+07, 3.25E+07, 1.11E+07, 2.34E+06, 4.10E+04, 1.20E+06, 7.50E+07, 1.65E+05, 9.52E+06, 5.92E+06, 3.11E+08, 1.93E+08)
df = melt(data.frame(Type, Gen, A, B))
Here's my code showing what I want the x axis to look like, but without a log y axis:
# main graph without log y axis
g1 <- ggplot(data = df, aes(x = interaction(Type, Gen, lex.order = TRUE), y = value, group = 1)) +
stat_summary(fun.y = "mean", geom = "bar") +
scale_x_discrete(limits = c("type1.-", "type2.-", "type1.G+", "type2.G+")) +
coord_cartesian(ylim = c(1, 10^9), expand = FALSE) +
annotate(geom = "text", x = (1:4), y = -1*10^8, label = c("type1", "type2", "type1", "type2"), size = 4) +
annotate(geom = "text", x = 3.5, y = -2*10^8, label = "G+", size = 4) +
geom_segment(aes(x = 3, y = -1.5*10^8, xend = 4, yend = -1.5*10^8))+
theme(plot.margin = unit(c(1, 1, 4, 1), "lines"),
axis.title.x = element_blank(),
axis.text.x = element_blank(),
panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank())
# turning off clipping
g2 <- ggplot_gtable(ggplot_build(g1))
g2$layout$clip[g2$layout$name == "panel"] <- "off"
grid::grid.draw(g2)
This is all fine, but as soon as I toss in
scale_y_log10()
To yield:
# main graph with log y axis
g1 <- ggplot(data = df, aes(x = interaction(Type, Gen, lex.order = TRUE), y = value, group = 1)) +
scale_y_log10() +
stat_summary(fun.y = "mean", geom = "bar") +
scale_x_discrete(limits=c("type1.-", "type2.-", "type1.G+", "type2.G+")) +
coord_cartesian(ylim = c(1, 10^9), expand = FALSE) +
annotate(geom = "text", x = (1:4), y = -1*10^2, label = c("type1","type2","type1","type2"), size = 4) +
annotate(geom = "text", x = 3.5, y = -2*10^2, label = "G+", size = 4) +
geom_segment(aes(x = 3, y = -1.5*10^2, xend = 4, yend = -1.5*10^2)) +
theme(plot.margin = unit(c(1, 1, 4, 1), "lines"),
axis.title.x = element_blank(),
axis.text.x = element_blank(),
panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank())
# turning off clipping
g2 <- ggplot_gtable(ggplot_build(g1))
g2$layout$clip[g2$layout$name == "panel"] <- "off"
grid::grid.draw(g2)
it all stops working and I get errors:
1: In self$trans$transform(x) : NaNs produced
2: Transformation introduced infinite values in continuous y-axis
3: Removed 4 rows containing missing values (geom_text).
(You actually get more errors, but they're just repeats for each annotation you add)
Can anyone offer some suggestions?