I would like to plot rectangles between specific values listed in a data frame, such as:
Region <- c("A","B","A","B","A","C","B","C","A"),
Lon <- c(31.03547, 37.25443, 65.97450, 69.90290, 101.77630,
105.32550, 148.86270, 147.72010, 146.10420)
var1 <- rnorm(n = 9, mean = 15, sd = 100)
regions <- data.frame(Region, Lon, var1)
This is an example where I show the region limits using geom_vline:
ggplot(NULL)+
geom_vline(data = regions, aes(xintercept=Lon,
linetype=region,
color = region),
size=0.6)+
geom_point(data = regions, aes(x=Lon, y=var1, color=Region))+
theme_bw()
I want to plot background rectangles that would be limited by those verticle lines.
I tried to look at this previous question:
How to find the start and the end of sequences automatically in R for rectangles in ggplot
However, it does not satisfy completely my needs, because I would like to plot rectangles for every region.
# Convert to runlength encoding
rle <- rle(regions$Region == "B")
# Determine starts and ends
starts <- {ends <- cumsum(rle$lengths)} - rle$lengths + 1
# Build a data.frame from the rle
dfrect <- data.frame(
xmin = regions$Lon[starts],
# We have to +1 the ends, because the linepieces end at the next datapoint
# Though we should not index out-of-bounds, so we need to cap at the last end
xmax = regions$Lon[pmin(ends + 1, max(ends))],
fill = rle$values
)
ggplot(NULL)+
geom_vline(data = regions, aes(xintercept=Lon,
linetype=region,
color = region),
size=0.6)+
geom_rect(data = dfrect,
aes(xmin = xmin, xmax = xmax, ymin = -Inf, ymax = Inf,
fill = fill),
alpha = 0.4) +
geom_point(data = regions, aes(x=Lon, y=var1, color=Region))+
theme_bw()
How can I define the rectangles for A and C too? consider that I have multiple regions, not only 3.
sample data
Region <- c("A","B","A","B","A","C","B","C","A")
Lon <- c(31.03547, 37.25443, 65.97450, 69.90290, 101.77630,
105.32550, 148.86270, 147.72010, 146.10420)
var1 <- rnorm(n = 9, mean = 15, sd = 100)
regions <- data.frame(Region, Lon, var1)
code
library(data.table)
# Make regions a data.table
setDT(regions)
# first sort by lon, to avoind overlap in rectangles
setkey(regions, Lon)
# create boundaries of rectangles
regions[, Lon_end := data.table::shift(Lon, type = "lead", fill = Inf)][]
# plot
ggplot(data = regions) +
geom_vline(aes(xintercept = Lon, linetype = Region, color = Region), size = 0.6) +
geom_rect(mapping = aes(xmin = Lon, xmax = Lon_end, ymin = 0, ymax = 1, fill = Region), alpha = 0.1)
output
Related
I am attempting to plot the blood test results for a patient in a time series. I have managed to do this and included a reference range between two shaded y-intercepts. My problem is that the annotate() or geom_segment() calls want me to specify, in the units of my independent variable, which is, unhelpfully, a date (YYYY-MM-DD).
Is it possible to get R to ignore the units of the x- and y-axis and specify the arrow co-ordinates as if they were on a grid?
result <- runif(25, min = 2.0, max = 3.5)
start_date <- ymd("2021-08-16")
end_date <- ymd("2022-10-29")
date <- sample(seq(start_date, end_date, by = "days"), 25, replace = TRUE)
q <- data.table(numbers, date)
ggplot(q, aes(x = date, y = result)) +
geom_line() +
geom_point(aes(x = date, y = result), shape = 21, size = 3) +
scale_x_date(limits = c(min(q$date), max(q$date)),
breaks = date_breaks("1 month"),
labels = date_format("%b %Y")) +
ylab("Corrected calcium (mmol/L")+
xlab("Date of blood test") +
ylim(1,4)+
geom_ribbon(aes(ymin=2.1, ymax=2.6), fill="grey", alpha=0.2, colour="grey")+
geom_vline(xintercept=as.numeric(q$date[c(3, 2)]),
linetype=4, colour="black") +
theme(axis.text.x = element_text(angle = 45)) + theme_prism(base_size = 10) +
annotate("segment", x = 1, y = 2, xend = 3, yend = 4, arrow = arrow(length = unit(0.15, "cm")))
The error produced is Error: Invalid input: date_trans works with objects of class Date only.
I can confirm that:
> class(q$date)
[1] "Date"
I've just gone with test co-ordinates (1,2,3,4) for the annotate("segment"...), ideally I want to be able to get the arrow to point to a specific data point on the plot to indicate when the patient went on treatment.
Many thanks,
Sandro
You don't need to convert to points or coordinates. Just use the actual values from your data frame. I am just subsetting within annotate using a hard coded index (you can also automate this of course), but you will need to "remind" R that you are dealing with dates - thus the added lubridate::as_date call.
library(ggplot2)
library(lubridate)
result <- runif(25, min = 2.0, max = 3.5)
start_date <- ymd("2021-08-16")
end_date <- ymd("2022-10-29")
date <- sample(seq(start_date, end_date, by = "days"), 25, replace = TRUE)
q <- data.frame(result, date)
## I am arranging the data frame by date
q <- dplyr::arrange(q, date)
ggplot(q, aes(x = date, y = result)) +
geom_line() +
## for start use a random x and y so it starts whereever you want it to start
## for end, use the same row from your data frame, in this case row 20
annotate(geom = "segment",
x = as_date(q$date[2]), xend = as_date(q$date[20]),
y = min(q$result), yend = q$result[20],
arrow = arrow(),
size = 2, color = "red")
Objective:
Create the XY scatterplot of variables (xx,yy). Color the corresponding Cartesian quadrants according to a third variable's (return) median.
I've created the color vector using colorRampPalette. The issue is that it is being read as continuous (though the vector is discrete).
Have the scatter points be blue (not labeled "blue")
Include a label on each quadrant according to dt.data[, quadrants] so that it is easy to identify what the area corresponds to. So the mark A or the top right, B on bottom right, etc.
This is the code I've written.
library(data.table)
set.seed(42)
dt <- data.table(
xx = rnorm(40, 0, 2),
yy = rnorm(40, 0, 2),
return = rnorm(40, 1, 3))
## compute the range we're going to want to plot over
## in this case 50% more than the max value
RANGE <- 1.5 * dt[, max(abs(c(xx, yy)))]
## compute the medians per quadrant
dtMedians <- dt[,
.(med = median(return)),
.(sign_x = sign(xx), sign_y = sign(yy))]
## set up some fake labels
dtMedians[, quadrant := letters[1:4]]
## compute a color scale for the medians and assign it
fcol <- colorRampPalette(c("#FC4445", "#3FEEE6", "#5CDB95"))
dtMedians[, col := fcol(4)[rank(med)]]
Mycol <- dt.Medians[, .(col)]
dt.rects2<- data.table(
quadrant = letters[1:4],
xmin= c(0,0,-RANGE, -RANGE),
xmax= c(RANGE,RANGE,0,0),
ymin= c(0,-RANGE,-RANGE,0),
ymax= c(RANGE,0,0,RANGE))
dt.data <- merge(dtMedians, dt.rects2, by ="quadrant")
gg<- ggplot() +
geom_rect(data = dt.data,
aes(xmin = xmin, xmax = xmax, ymin = ymin, ymax = ymax, fill = med ))
gg+
scale_fill_manual(values = Mycol ) +
labs(x="xx", y="yy", title='US. Growth Quadrant') +
geom_point(data = dt,
aes(x = xx,
y = yy,
color = 'blue'))
While I think the code could be much cleaner, I left it unchanged to the extent possible - there were a few mistakes (e.g., with the variables x and y) that I had to correct to be able to run the code. Now as to your questions:
You can tell R to treat a variable as a factor with fill = as.factor(med). In addition, I had to adjust scale_fill_manual(values = Mycol$col) to select the colors defined in variable col of df Mycol.
To make the scatters blue, I took the color = 'blue' outside of the aes() in the geom_point().
I used annotate() to label the corners of the plot, which relies on manually defining the x and y coordinates. I am sure there are other, potentially better (and automated) solutions out there.
Full code for the plot (taking your data):
ggplot() +
geom_rect(data = dt.data,
aes(xmin = xmin, xmax = xmax, ymin = ymin, ymax = ymax, fill = as.factor(med))) +
scale_fill_manual(values = Mycol$col) +
labs(x="xx", y="yy", title='US. Growth Quadrant') +
geom_point(data = dt,
aes(x = x,
y = y),
color = 'blue') +
annotate(geom = 'text', label = 'A', x = 5, y = 5, size = 8) +
annotate(geom = 'text', label = 'B', x = 5, y = -5, size = 8) +
annotate(geom = 'text', label = 'C', x = -5, y = -5, size = 8) +
annotate(geom = 'text', label = 'D', x = -5, y = 5, size = 8)
Output:
I'm trying to map different ranges (lines) into different regions in the plot (see below) using geom_segment but some of the ranges overlap and can't be shown at all.
This is a minimal example for a dataframes:
start = c(1, 5,8, 14)
end =c(3, 6,12, 16)
regions = c(1,2,3, 4)
regions = data_frame(regions, start, end)
from = c(1,2, 5.5, 13.5)
to = c(3,2.5,6, 15)
lines = data_frame(from, to)
I plotted the regions with geom_rect and then plot the ranges (lines) with geom_segment.
This is the plot:
plot_splice <- ggplot() +
scale_x_continuous(breaks = seq(1,16)) +
scale_y_continuous() +
geom_hline(yintercept = 1.6,
size = 20,
alpha = 0.1) +
geom_rect(
data = regions,
mapping = aes(
xmin = start,
xmax = end,
ymin = 1.5,
ymax = 1.8,
)) +
geom_segment(
data = lines,
x = (lines$from),
xend = (lines$to),
y = 1.48,
yend = 1.48,
colour = "red",
size = 3
) +
ylim(1.0, 2.2) +
xlab("") +
theme_minimal()
The first plot is the one generated with the code whereas the second one is the desired plot.
As you can see, the second line overlaps with the first one, so you can't see the second line at all.
How can I change the code to produce the second plot?
I'm trying to use ifelse statement but not sure what is test argument should be:
I want it to check for each range (line) if it is overlapped with any previous range (line) to change the y position by around .05, so it doesn't overlap.
lines <- lines %>%
dplyr::arrange(desc(from))
new_y$lines = ifelse(from[1] < to[0], 1.48, 1.3)
geom_segment(
data = lines,
x = (lines$from),
xend = (lines$to),
y = new_y,
yend = new_y,
colour = "red",
size = 3
)
Your geom_segment call isn't using any aesthetic mapping, which is how you normally get ggplot elements to change position based on a particular variable (or set of variables).
The stacking of the geom_segment based on the number of overlapping regions is best calculated ahead of the call to ggplot. This allows you to pass the x and y values into an aesthetic mapping:
# First ensure that the data feame is ordered by the start time
lines <- lines[order(lines$from),]
# Now iterate through each row, calculating how many previous rows have
# earlier starts but haven't yet finished when the current row starts.
# Multiply this number by a small negative offset and add the 1.48 baseline value
lines$offset <- 1.48 - 0.03 * sapply(seq(nrow(lines)), function(i) {
with(lines[seq(i),], length(which(from < from[i] & to > from[i])))
})
Now do the same plot but using aesthetic mapping inside geom_segment:
ggplot() +
scale_x_continuous(breaks = seq(1,16), name = "") +
scale_y_continuous(limits = c(1, 2.2), name = "") +
geom_hline(yintercept = 1.6,
size = 20,
alpha = 0.1) +
geom_rect(
data = regions,
mapping = aes(
xmin = start,
xmax = end,
ymin = 1.5,
ymax = 1.8,
)) +
geom_segment(
data = lines,
mapping = aes(
x = from,
xend = to,
y = offset,
yend = offset),
colour = "red",
size = 3
) +
theme_minimal()
I need to reproduce plots generated in InDesign in ggplot for reproducibility.
In this particular example, I have two plots that are combined into one composite plot (I have used the package {patchwork} for this).
I then need to overlay lines joining key points on one plot with the corresponding points on the bottom plot.
The two plots are generated from the same data, have the same x-axis values, but different y-axis values.
I have seen these examples on Stack Overflow, but these deal with drawing lines across facets, which doesn't work here as I'm attempting to draw lines across separate plots:
ggplot, drawing multiple lines across facets
ggplot, drawing line between points across facets
I've tried several approaches, and my closest so far has been to:
Add the lines with grobs using {grid} package
Convert the second plot to a gtable using {gtable} and set the clip of the panel to off so that I can extend the lines upwards beyond the panel of the plot.
Combine the plots again into a single image with {patchwork}.
The problem comes in the last step as the x-axes now do not line up anymore as they did before adding the lines and setting the clip to off (see example in code).
I have also tried combining the plots with ggarrange, {cowplot} and {egg} and {patchwork} comes the closest.
Following is my attempt at the best minimal reprex I can create, but still capturing the nuances of what it is I want to achieve.
library(ggplot2)
library(dplyr)
library(tidyr)
library(patchwork)
library(gtable)
library(grid)
# DATA
x <- 1:20
data <- data.frame(
quantity = x,
curve1 = 10 + 50*exp(-0.2 * x),
curve2 = 5 + 50*exp(-0.5 * x),
profit = c(seq(10, 100, by = 10),
seq(120, -240, by = -40))
)
data_long <- data %>%
gather(key = "variable", value = "value", -quantity)
# POINTS AND LINES
POINTS <- data.frame(
label = c("B", "C"),
quantity = c(5, 10),
value = c(28.39397, 16.76676),
profit = c(50, 100)
)
GROB <- linesGrob()
# Set maximum y-value to extend lines to outside of plot area
GROB_MAX <- 200
# BASE PLOTS
# Plot 1
p1 <- data_long %>%
filter(variable != "profit") %>%
ggplot(aes(x = quantity, y = value)) +
geom_line(aes(color = variable)) +
labs(x = "") +
coord_cartesian(xlim = c(0, 20), ylim = c(0, 30), expand = FALSE) +
theme(legend.justification = "top")
p1
# Plot 2
p2 <- data_long %>%
filter(variable == "profit") %>%
ggplot(aes(x = quantity, y = value)) +
geom_line(color = "darkgreen") +
coord_cartesian(xlim = c(0, 20), ylim = c(-100, 120), expand = FALSE) +
theme(legend.position = "none")
p2
# PANEL A
panel_A <- p1 + p2 + plot_layout(ncol = 1)
panel_A
# PANEL B
# ATTEMPT - adding grobs to plot 1 that end at x-axis of p1
p1 <- p1 +
annotation_custom(GROB,
xmin = 0,
xmax = POINTS$quantity[POINTS$label == "B"],
ymin = POINTS$value[POINTS$label == "B"],
ymax = POINTS$value[POINTS$label == "B"]) +
annotation_custom(GROB,
xmin = POINTS$quantity[POINTS$label == "B"],
xmax = POINTS$quantity[POINTS$label == "B"],
ymin = 0,
ymax = POINTS$value[POINTS$label == "B"]) +
geom_point(data = POINTS %>% filter(label == "B"), size = 1)
# ATTEMPT - adding grobs to plot 2 that extend up to meet plot 1
p2 <- p2 + annotation_custom(GROB,
xmin = POINTS$quantity[POINTS$label == "B"],
xmax = POINTS$quantity[POINTS$label == "B"],
ymin = POINTS$profit[POINTS$label == "B"],
ymax = GROB_MAX)
# Create gtable from ggplot
g2 <- ggplotGrob(p2)
# Turn clip off for panel so that line can extend above
g2$layout$clip[g2$layout$name == "panel"] <- "off"
panel_B <- p1 + g2 + plot_layout(ncol = 1)
panel_B
# Problems:
# 1. Note the shift in axes when turning the clip off so now they do not line up anymore.
# 2. Turning the clip off mean plot 2 extends below the axis. Tried experimenting with various clips.
The expectation is that the plots in panel_B should still appear as they do in panel_A but have the joining lines linking points between the plots.
I am looking for help with solving the above, or else, alternative approaches to try out.
As a reference without running the code above - links to images as I can't post them.
Panel A
Panel B: What it currently looks like
Panel B: What I want it to look like!
My solution is a little ad hoc, but it seems to work. I based it on the following previous answer Left align two graph edges (ggplot).
I will break the solution in three parts to address some of the issues you were facing separately.
The solution that matches what you want is the third one!
First trial
Here I get the axis aligned using the same approach as this answer Left align two graph edges (ggplot).
# first trial
# plots are aligned but line in bottom plot extends to the bottom
#
p1_1 <- p1 +
annotation_custom(GROB,
xmin = 0,
xmax = POINTS$quantity[POINTS$label == "B"],
ymin = POINTS$value[POINTS$label == "B"],
ymax = POINTS$value[POINTS$label == "B"]) +
annotation_custom(GROB,
xmin = POINTS$quantity[POINTS$label == "B"],
xmax = POINTS$quantity[POINTS$label == "B"],
ymin = 0,
ymax = POINTS$value[POINTS$label == "B"]) +
geom_point(data = POINTS %>% filter(label == "B"), size = 1)
p2_1 <- p2 + annotation_custom(GROB,
xmin = POINTS$quantity[POINTS$label == "B"],
xmax = POINTS$quantity[POINTS$label == "B"],
ymin = POINTS$profit[POINTS$label == "B"],
ymax = GROB_MAX)
# Create gtable from ggplot
gA <- ggplotGrob(p1_1)
gB <- ggplotGrob(p2_1)
# Turn clip off for panel so that line can extend above
gB$layout$clip[gB$layout$name == "panel"] <- "off"
# get max width of left axis between both plots
maxWidth = grid::unit.pmax(gA$widths[2:5], gB$widths[2:5])
# set maxWidth to both plots (to align left axis)
gA$widths[2:5] <- as.list(maxWidth)
gB$widths[2:5] <- as.list(maxWidth)
# now apply all widths from plot A to plot B
# (this is specific to your case because we know plot A is the one with the legend)
gB$widths <- gA$widths
grid.arrange(gA, gB, ncol=1)
Second trial
The problem now is that the line in the bottom plot extends beyond the plot area. One way to deal with this is to change coord_cartesian() to scale_y_continuous() and scale_x_continuous() because this will remove data that falls out of the plot area.
# second trial
# using scale_y_continuous and scale_x_continuous to remove data out of plot limits
# (this could resolve the problem of the bottom plot, but creates another problem)
#
p1_2 <- p1_1
p2_2 <- data_long %>%
filter(variable == "profit") %>%
ggplot(aes(x = quantity, y = value)) +
geom_line(color = "darkgreen") +
scale_x_continuous(limits = c(0, 20), expand = c(0, 0)) +
scale_y_continuous(limits=c(-100, 120), expand=c(0,0)) +
theme(legend.position = "none") +
annotation_custom(GROB,
xmin = POINTS$quantity[POINTS$label == "B"],
xmax = POINTS$quantity[POINTS$label == "B"],
ymin = POINTS$profit[POINTS$label == "B"],
ymax = GROB_MAX)
# Create gtable from ggplot
gA <- ggplotGrob(p1_2)
gB <- ggplotGrob(p2_2)
# Turn clip off for panel so that line can extend above
gB$layout$clip[gB$layout$name == "panel"] <- "off"
# get max width of left axis between both plots
maxWidth = grid::unit.pmax(gA$widths[2:5], gB$widths[2:5])
# set maxWidth to both plots (to align left axis)
gA$widths[2:5] <- as.list(maxWidth)
gB$widths[2:5] <- as.list(maxWidth)
# now apply all widths from plot A to plot B
# (this is specific to your case because we know plot A is the one with the legend)
gB$widths <- gA$widths
# but now the line does not go all the way to the bottom y axis
grid.arrange(gA, gB, ncol=1)
Third trial
The problem now is that the line does not extend all the way to the bottom of the y-axis (because the point below y=-100 was removed). The way I solved this (very ad hoc) was to interpolate the point at y=-100 and add this to the data frame.
# third trial
# modify the data set so value data stops at bottom of plot
#
p1_3 <- p1_1
# use approx() function to interpolate value of x when y value == -100
xvalue <- approx(x=data_long$value, y=data_long$quantity, xout=-100)$y
p2_3 <- data_long %>%
filter(variable == "profit") %>%
# add row with interpolated point!
rbind(data.frame(quantity=xvalue, variable = "profit", value=-100)) %>%
ggplot(aes(x = quantity, y = value)) +
geom_line(color = "darkgreen") +
scale_x_continuous(limits = c(0, 20), expand = c(0, 0)) +
scale_y_continuous(limits=c(-100, 120), expand=c(0,0)) +
theme(legend.position = "none") +
annotation_custom(GROB,
xmin = POINTS$quantity[POINTS$label == "B"],
xmax = POINTS$quantity[POINTS$label == "B"],
ymin = POINTS$profit[POINTS$label == "B"],
ymax = GROB_MAX)
# Create gtable from ggplot
gA <- ggplotGrob(p1_3)
gB <- ggplotGrob(p2_3)
# Turn clip off for panel so that line can extend above
gB$layout$clip[gB$layout$name == "panel"] <- "off"
# get max width of left axis between both plots
maxWidth = grid::unit.pmax(gA$widths[2:5], gB$widths[2:5])
# set maxWidth to both plots (to align left axis)
gA$widths[2:5] <- as.list(maxWidth)
gB$widths[2:5] <- as.list(maxWidth)
# now apply all widths from plot A to plot B
# (this is specific to your case because we know plot A is the one with the legend)
gB$widths <- gA$widths
# Now line goes all the way to the bottom y axis
grid.arrange(gA, gB, ncol=1)
This makes use of facet_grid to force the x-axis to match.
grobbing_lines <- tribble(
~facet, ~x, ~xend, ~y, ~yend,
'profit', 5, 5, 50, Inf,
# 'curve', 5, 5, -Inf, 28.39397
'curve', -Inf, 5, 28.39397, 28.39397
)
grobbing_points <- tribble(
~facet, ~x, ~y,
'curve', 5, 28.39397
)
data_long_facet <- data_long%>%
mutate(facet = if_else(variable == 'profit', 'profit', 'curve'))
p <- ggplot(data_long_facet, aes(x = quantity, y = value)) +
geom_line(aes(color = variable))+
facet_grid(rows = vars(facet), scales = 'free_y')+
geom_segment(data = grobbing_lines, aes(x = x, xend = xend, y = y, yend = yend),inherit.aes = F)+
geom_point(data = grobbing_points, aes(x = x, y = y), size = 3, inherit.aes = F)
pb <- ggplot_build(p)
pg <- ggplot_gtable(pb)
#formulas to determine points in x and y locations
data2npc <- function(x, panel = 1L, axis = "x") {
range <- pb$layout$panel_params[[panel]][[paste0(axis,".range")]]
scales::rescale(c(range, x), c(0,1))[-c(1,2)]
}
data_y_2npc <- function(y, panel, axis = 'y') {
range <- pb$layout$panel_params[[panel]][[paste0(axis,".range")]]
scales::rescale(c(range, y), c(0,1))[-c(1,2)]
}
# add the new grob
pg <- gtable_add_grob(pg,
segmentsGrob(x0 = data2npc(5),
x1 = data2npc(5),
y0=data_y_2npc(50, panel = 2)/2,
y1 = data_y_2npc(28.39397, panel = 1L)+ 0.25) ,
t = 7, b = 9, l = 5)
#print to page
grid.newpage()
grid.draw(pg)
The legend and the scales are what do not match your intended output.
In the below example i have a simple plot of mean values with standard deviation error bars for both X and Y axis. I would like to control the error bar width so both axis always plot the same size.
Ideally I would like the bar width/height to be the same size as the symbols (i.e. in this case cex = 3) irrelevant of the final plot dimensions. Is there a way to do this?
# Load required packages:
library(ggplot2)
library(plyr)
# Create dataset:
DF <- data.frame(
group = rep(c("a", "b", "c", "d"),each=10),
Ydata = c(seq(1,10,1),seq(5,50,5),seq(20,11,-1),seq(0.3,3,0.3)),
Xdata = c(seq(1,10,1),seq(5,50,5),seq(20,11,-1),seq(0.3,3,0.3)))
# Summarise data:
subDF <- ddply(DF, .(group), summarise,
X = mean(Xdata), Y = mean(Ydata),
X_sd = sd(Xdata, na.rm = T), Y_sd = sd(Ydata))
# Plot data with error bars:
ggplot(subDF, aes(x = X, y = Y)) +
geom_errorbar(aes(x = X,
ymin = (Y-Y_sd),
ymax = (Y+Y_sd)),
width = 1, size = 0.5) +
geom_errorbarh(aes(x = X,
xmin = (X-X_sd),
xmax = (X+X_sd)),
height = 1, size = 0.5) +
geom_point(cex = 3)
Looks fine when plotted at 1:1 ratio (500x500):
but the errorbar width/heigh look different when plotted at 600x200
I'd use a size-variable so you can control all of the 3 plot elements at the same time
geom_size <- 3
# Plot data with error bars:
ggplot(subDF, aes(x = X, y = Y)) +
geom_errorbar(aes(x = X,
ymin = (Y-Y_sd),
ymax = (Y+Y_sd)),
width = 1, size = geom_size) +
geom_errorbarh(aes(x = X,
xmin = (X-X_sd),
xmax = (X+X_sd)),
height = 1, size = geom_size) +
geom_point(cex = geom_size)
This is just building on brettljausn earlier answer. You can control the ratio of your plot with a variable as well. This will only work when you actually save the file with ggsave() not in any preview. I also used size to control the size of the point. It scaled nicer with the error bar ends.
plotheight = 100
plotratio = 3
geomsize = 3
plot = ggplot(subDF, aes(x = X, y = Y)) +
geom_errorbar(aes(x = X,
ymin = (Y-Y_sd),
ymax = (Y+Y_sd)),
width = .5 * geomsize / plotratio, size = 0.5) +
geom_errorbarh(aes(x = X,
xmin = (X-X_sd),
xmax = (X+X_sd)),
height = .5 * geomsize, size = 0.5) +
geom_point(size = geomsize)
ggsave(filename = "~/Desktop/plot.png", plot = plot,
width = plotheight * plotratio, height = plotheight, units = "mm")
Change the plotheight, plotratio, and geomsize to whatever you need it to be to look good. You will have to change the filename in the one but last line to get the file in the folder of your choice.