I am trying to create some multiplots with facet_wrap. However I am not sure if is the right approach for my graph. Here is a short reproducible example:
ggplot(airquality, aes(x = Day, y = Temp)) +
facet_wrap(~Month) +
geom_line()
This produces this plot here:
Is it possible to "center" the two plots in the 2nd row with the facet_wrap approach ?
Note, that I don't want to reorder the plots, I just want to Center the 2nd row
#Tjebo's suggestion of using cowplot will work:
p <- ggplot(mapping = aes(x = Day, y = Temp)) +
facet_wrap(~Month) +
geom_line()
cowplot::plot_grid(
p %+% subset(airquality, Month < 8),
p %+% subset(airquality, Month > 7),
nrow = 2
)
You might also consider the function set_panel_size from the egg package which lets you set the panel size (width, height) of multiple plots to an absolute measurement, see this vignette for more details.
Using Axeman's code to create the plots
library(egg)
library(gridExtra)
p <- ggplot(mapping = aes(x = Day, y = Temp)) +
facet_wrap(~Month) +
geom_line()
p1 <- p %+% subset(airquality, Month < 8) + labs(x = NULL)
p2 <- p %+% subset(airquality, Month > 7)
Now arrange the plots using grid.arrange after specifying the panel sizes
grid.arrange(grobs = lapply(
list(p1, p2),
set_panel_size,
width = unit(5, "cm"),
height = unit(4, "cm")
))
And just in case anyone wants to know how to do it from scratch in grid...
library(ggplot2)
library(dplyr)
library(grid)
vps <- c("top_left", "top_mid", "top_right", "bottom_left", "bottom_right")
main_vp <- vpTree(viewport(name = "main"), vpList(
viewport(x = 1/6, y = 0.75, width = 1/3, height = 0.5, name = vps[1]),
viewport(x = 3/6, y = 0.75, width = 1/3, height = 0.5, name = vps[2]),
viewport(x = 5/6, y = 0.75, width = 1/3, height = 0.5, name = vps[3]),
viewport(x = 1/3, y = 0.25, width = 1/3, height = 0.5, name = vps[4]),
viewport(x = 2/3, y = 0.25, width = 1/3, height = 0.5, name = vps[5])))
grid.newpage()
pushViewport(main_vp)
plots <- lapply(1:5, function(i){
seekViewport(vps[i])
invisible(grid.draw(ggplotGrob(
ggplot(filter(airquality, Month == levels(as.factor(airquality$Month))[i]),
mapping = aes(Day, Temp)) +
geom_line() +
facet_grid(.~Month)
)))})
Created on 2020-07-03 by the reprex package (v0.3.0)
And yet another option to achieve the desired result would the ggh4x package which via ggh4x::facet_manual adds some flexibility to position the facets via the design argument:
library(ggplot2)
library(ggh4x)
base <- ggplot(airquality, aes(x = Day, y = Temp)) +
geom_line()
design <- c(
"
AABBCC
#DDEE#
"
)
base + ggh4x::facet_manual(~Month, design = design)
design <- c(
"
AABBCC
DDDEEE
"
)
base + ggh4x::facet_manual(~Month, design = design)
Related
I am trying to add captions as it appears in this post.
For that reason, I need the real scale of the plot (x and y axis) when I am using facet_grid. I know that I can use layer_data, since it saves everything from the plot... However, it is not really accurate, because when I try to establish the limits using min and max from that output, the plot changes.
Here you have an example:
library(ggplot2)
library(dplyr)
val1 <- c(2.1490626,2.2035281,1.5927854,3.1399245,2.3967338,3.7915825,4.6691277,3.0727319,2.9230937,2.6239759,3.7664386,4.0160378,1.2500835,4.7648343,0.0000000,5.6740227,2.7510256,3.0709322,2.7998003,4.0809085,2.5178086,5.9713330,2.7779843,3.6724801,4.2648527,3.6841084,2.5597235,3.8477471,2.6587736,2.2742209,4.5862788,6.1989269,4.1167091,3.1769325,4.2404515,5.3627032,4.1576810,4.3387921,1.4024381,0.0000000,4.3999099,3.4381837,4.8269218,2.6308474,5.3481382,4.9549753,4.5389650,1.3002293,2.8648220,2.4015338,2.0962332,2.6774765,3.0581759,2.5786137,5.0539080,3.8545796,4.3429043,4.2233248,2.0434363,4.5980727)
val2 <- c(3.7691229,3.6478055,0.5435826,1.9665861,3.0802654,1.2248374,1.7311236,2.2492826,2.2365337,1.5726119,2.0147144,2.3550348,1.9527204,3.3689502,1.7847986,3.5901329,1.6833872,3.4240479,1.8372175,0.0000000,2.5701453,3.6551315,4.0327091,3.8781182)
df1 <- data.frame(value = val1)
df2 <- data.frame(value = val2)
data <- bind_rows(lst(df1, df2), .id = 'id')
data$Sex <- rep(c("Male", "Female"), times=84/2)
p <- data %>%
ggplot(aes(value)) +
geom_density(lwd = 1.2, colour="red", show.legend = FALSE) +
geom_histogram(aes(y=..density.., fill = id), bins=10, col="black", alpha=0.2) +
facet_grid(id ~ Sex ) +
xlab("type_data") +
ylab("Density") +
ggtitle("title") +
guides(fill=guide_legend(title="legend_title")) +
theme(strip.text.y = element_blank())
p
plot_info <- layer_data(p)
> min(plot_info$density)
[1] 7.166349e-09
> max(plot_info$density)
[1] 0.5738021
As you can see in the plot, the y-axis starts at 0 and if finishes around 0.7 more less. However, the maximum density is 0.57.
If I try to use the info from layer_data:
p + coord_cartesian(clip="off", ylim=c(min(plot_info$density), max(plot_info$density)),
xlim = c(min(plot_info$x), max(plot_info$x)))
The plot changes completely.
Does anyone know how can I get the scales that ggplot2 and facet_grid are using? I need the information of the density (y_axis) and the info from the x_axis.
Yes, to get the scales directly, use layer_scales(p), which gives you the range of the axes rather than just the range of the data, which is what you get from layer_data(p)
p + coord_cartesian(clip = "off",
ylim = layer_scales(p)$y$range$range,
xlim = layer_scales(p)$x$range$range)
Or, to combine this question with your last, where you add the text labels outside of the plotting panels, your result might be something like:
p + coord_cartesian(clip = "off",
ylim = layer_scales(p)$y$range$range,
xlim = layer_scales(p)$x$range$range) +
geom_text(data = data.frame(value = c(0, 6), id = c("df2", "df2"),
Sex = c('Female', 'Male')),
aes(y = -0.15, label = c('Female', 'Male')))
Does this help?
?layer_data
summary(layer_data(p, i = 2))
i is the layer you want to return
Can min the xmin and max the xmax etc
Having a tibble and a simple scatterplot:
p <- tibble(
x = rnorm(50, 1),
y = rnorm(50, 10)
)
ggplot(p, aes(x, y)) + geom_point()
I get something like this:
I would like to align (center, left, right, as the case may be) the title of the x-axis - here rather blandly x - with a specific value on the axis, say the off-center 0 in this case. Is there a way to do that declaratively, without having to resort to the dumb (as in "free of context") trial-and-error element_text(hjust=??). The ?? are rather appropriate here because every value is a result of experimentation (my screen and PDF export in RStudio never agree on quite some plot elements). Any change in the data or the dimensions of the rendering may (or may not) invalidate the hjust value and I am looking for a solution that graciously repositions itself, much like the axes do.
Following the suggestions in the comments by #tjebo I dug a little deeper into the coordinate spaces. hjust = 0.0 and hjust = 1.0 clearly align the label with the Cartesian coordinate system extent (but magically left-aligned and right-aligned, respectively) so when I set specific limits, calculation of the exact value of hjust is straightforward (aiming for 0 and hjust = (0 - -1.5) / (3.5 - -1.5) = 0.3):
ggplot(p, aes(x, y)) +
geom_point() +
coord_cartesian(ylim = c(8, 12.5), xlim = c(-1.5, 3.5), expand=FALSE) +
theme(axis.title.x = element_text(hjust = 0.3))
This gives an acceptable result for a label like x, but for longer labels the alignment is off again:
ggplot(p %>% mutate(`Longer X label` = x), aes(x = `Longer X label`, y = y)) +
geom_point() +
coord_cartesian(ylim = c(8, 12.5), xlim = c(-1.5, 3.5), expand=FALSE) +
theme(axis.title.x = element_text(hjust = 0.3))
Any further suggestions much appreciated.
Another option (different enough hopefully to justify the second answer) is as already mentioned to create the annotation as a separate plot. This removes the range problem. I like {patchwork} for this.
library(tidyverse)
library(patchwork)
p <- tibble( x = rnorm(50, 1), y = rnorm(50, 10))
p1 <- tibble( x = rnorm(50, 1), y = 100*rnorm(50, 10))
## I like to define constants outside my ggplot call
mylab <- "longer_label"
x_demo <- c(-1, 2)
demo_fct <- function(p){
p1 <- ggplot(p, aes(x, y)) +
geom_point() +
labs(x = NULL) +
theme(plot.margin = margin())
p2 <- ggplot(p, aes(x, y)) +
## you need that for your correct alignment with the first plot
geom_blank() +
annotate(geom = "text", x = x_demo, y = 1,
label = mylab, hjust = 0) +
theme_void() +
# you need that for those annoying margin reasons
coord_cartesian(clip = "off")
p1 / p2 + plot_layout(heights = c(1, .05))
}
demo_fct(p) + plot_annotation(title = "demo1 with x at -1 and 2")
demo_fct(p1) + plot_annotation(title = "demo2 with larger data range")
Created on 2021-12-04 by the reprex package (v2.0.1)
I still think you will fair better and easier with custom annotation. There are typically two ways to do that. Either direct labelling with a text layer (for single labels I prefer annotate(geom = "text"), or you create a separate plot and stitch both together, e.g. with patchwork.
The biggest challenge is the positioning in y dimension. For this I typically take a semi-automatic approach where I only need to define one constant, and set the coordinates relative to the data range, so changes in range should in theory not matter much. (they still do a bit, because the panel dimensions also change). Below showing examples of exact label positioning for two different data ranges (using the same constant for both)
library(tidyverse)
# I only need patchwork for demo purpose, it is not required for the answer
library(patchwork)
p <- tibble( x = rnorm(50, 1), y = rnorm(50, 10))
p1 <- tibble( x = rnorm(50, 1), y = 100*rnorm(50, 10))
## I like to define constants outside my ggplot call
y_fac <- .1
mylab <- "longer_label"
x_demo <- c(-1, 2)
demo_fct <- function(df, x) {map(x_demo,~{
## I like to define constants outside my ggplot call
ylims <- range(df$y)
ggplot(df, aes(x, y)) +
geom_point() +
## set hjust = 0 for full positioning control
annotate(geom = "text", x = ., y = min(ylims) - y_fac*mean(ylims),
label = mylab, hjust = 0) +
coord_cartesian(ylim = ylims, clip = "off") +
theme(plot.margin = margin(b = .5, unit = "in")) +
labs(x = NULL)
})
}
demo_fct(p, x_demo) %>% wrap_plots() + plot_annotation(title = "demo 1, label at x = -1 and x = 2")
demo_fct(p1, x_demo) %>% wrap_plots() + plot_annotation(title = "demo 2 - different data range")
Created on 2021-12-04 by the reprex package (v2.0.1)
I have, e.g., the following plot:
library(ggplot2)
dat = data.frame(x = rnorm(100), y = rexp(100), grp = factor(sample(1:2, 100, replace = TRUE)))
ggplot(dat, aes(x = x, y = y, color = grp)) +
geom_point() +
facet_wrap(~grp) +
theme(panel.spacing = unit(2, "lines"))
and want to add a vertical line between the two plots - that is, in the middle of the panel spacing. My problem is, I am not sure of how to get the coordinates of the inner plot edges / the panel spacing in native units.
Both panels have unit 0.5 npc -- and I am not sure how I would convert this. I tried using viewports, but that did not work. Is there a way other than arranging plot 1 - plot of vertical line - plot 2 ?
Is this what you had in mind? You can tweak around with the parameter to change the position where the line will appear.
# loading the libraries
library(ggplot2)
library(grid)
library(cowplot)
# preparing the data
dat = data.frame(x = rnorm(100),
y = rexp(100),
grp = factor(sample(1:2, 100, replace = TRUE)))
# preparing the plot
plot <- ggplot(dat, aes(x = x, y = y, color = grp)) +
geom_point() +
facet_wrap( ~ grp) +
theme(panel.spacing = unit(2, "lines"))
# preparing the line
gline <- grid::linesGrob(x = 0.5)
# plotting both the plot and the line
cowplot::ggdraw() +
cowplot::draw_plot(plot) +
cowplot::draw_plot(gline)
Created on 2018-01-24 by the reprex
package (v0.1.1.9000).
library(grid)
library(gtable)
library(magrittr)
ggplotGrob(p) %>%
gtable_add_grob(segmentsGrob(0.5, 0, 0.5, 1),
t = 4, b = 8, l = 7, r = 7) %>%
grid.draw()
enter image description here
I usually save the plots from ggplot2 using the the png device. The width and the height of the output are set by the arguments of the function. Blank zones are drawn when the "natural proportions" of the graph dont't suit the proportions of the device. In order to avoid this and use the whole defined canvas, the proportions of the plot must be known. ¿Is there a way to find out this value without trial and error?
This code can be used as an example:
x <- seq(from = 0, to = 1, by = 0.1)
y <- seq(from = 1, to = 2, by = 0.1)
df <- expand.grid(x = x, y = y)
df <- cbind(df, z = rnorm(ncol(df), 0, 1))
p <- ggplot(df, aes(x,y, fill = z)) + geom_raster() + coord_fixed()
ppi <- 300
#Value 0.4 is used to change inches into milimeters
png("plot.png", width = 16*0.4*ppi, height = 20*0.4*ppi, res = ppi)
print(p)
dev.off()
It can be seen that some blank space is added at the top and at the bottom to fill the png file. This could be easily corrected by using a proportion different from 20/16, which is not optimal.
You can modify the ratio arg inside coord_fixed():
p <- ggplot(df, aes(x,y, fill = z)) +
geom_raster() +
coord_fixed(ratio = 20/16)
Alteratively you can specify the aspect.ratio inside the theme():
p <- ggplot(df, aes(x,y, fill = z)) +
geom_raster() +
theme(aspect.ratio = 20/16)
The result is the same:
Length of x-axis is important for my plot because it allows one to compare between facets, therefore I want facets to have different x-axis sizes. Here is my example data:
group1 <- seq(1, 10, 2)
group2 <- seq(1, 20, 3)
x = c(group1, group2)
mydf <- data.frame (X =x , Y = rnorm (length (x),5,1),
groups = c(rep(1, length (group1)), rep(2, length(group2))))
And my code:
p1 = ggplot(data=mydf,aes(x=X,y=Y,color=factor(groups)) )+
geom_point(size=2)+
scale_x_continuous(labels=comma)+
theme_bw()
p1+facet_grid(groups ~ .,scales = "fixed",space="free_x")
And the resulting figure:
Panel-1 has x-axis values less then 10 whereas panel-2 has x-axis value extending to 20. Still both panels and have same size on x-axis. Is there any way to make x-axis panel size different for different panels, so that they correspond to their (x-axis) values?
I found an example from some different package that shows what I am trying to do, here is the figure:
Maybe something like this can get you started. There's still some formatting to do, though.
library(grid)
library(gridExtra)
library(dplyr)
library(ggplot2)
p1 <- ggplot(data=mydf[mydf$groups==1,],aes(x=X,y=Y))+
geom_point(size=2)+
theme_bw()
p2 <- ggplot(data=mydf[mydf$groups==2,],aes(x=X,y=Y))+
geom_point(size=2)+
theme_bw()
summ <- mydf %>% group_by(groups) %>% summarize(len=diff(range(X)))
summ$p <- summ$len/max(summ$len)
summ$q <- 1-summ$p
ng <- nullGrob()
grid.arrange(arrangeGrob(p1,ng,widths=summ[1,3:4]),
arrangeGrob(p2,ng,widths=summ[2,3:4]))
I'm sure there's a way to make this more general, and the axes don't line up perfectly yet, but it's a beginning.
Here is a solution following OP's clarifying comment ("I guess axis will be same but the boxes will be of variable size. Is it possible by plotting them separately and aligning in grid?").
library(plyr); library(ggplot2)
buffer <- 0.5 # Extra space around the box
#Calculate box parameters
mydf.box <- ddply(mydf, .(groups), summarise,
max.X = max(X) + buffer,
min.X = 0,
max.Y = max(Y) + buffer,
min.Y = 0,
X = mean(X), Y = mean(Y)) #Dummy values for X and Y needed for geom_rect
p2 <- ggplot(data=mydf,aes(x=X, y=Y) )+
geom_rect(data = mydf.box, aes( xmax = max.X, xmin = min.X,
ymax = max.Y, ymin = min.Y),
fill = "white", colour = "black", fill = NA) +
geom_point(size=2) + facet_grid(groups ~ .,scales = "free_y") +
theme_classic() +
#Extra formatting to make your plot like the example
theme(panel.background = element_rect(fill = "grey85"),
strip.text.y = element_text(angle = 0),
strip.background = element_rect(colour = NA, fill = "grey65"))