Related
I have a line chart built using ggplot2. It looks following:
Lines are close to each other and data labels are overlapping. It is not convenient. It would be better if light red labels were below the line and green labels where there is room for them. Something of the sort:
This post is helpful. However, I do not know in advance for which line it would be better to put labels above and for which it would be better to keep them below. Therefore I am looking for a generic solution.
ggrepel does a great job in organizing labels. But cannot figure out how to make it work in my case. I tried different parameters. Here is one of the simplest variants (not the best looking):
Questions:
Is there any way to make in R the chart look like on the 2nd picture?
I think ggrepel computes the best label position taking into account the size of the chart. If I export the chart to PowerPoint, for example, the size of the PowerPoint chart might be different from the size used to get optimal data label positions. Is there any way to pass the size of the chart to ggrepel?
Here is a code I used to generate data and charts:
library(ggplot2)
library(ggrepel)
set.seed(1)
x = rep(1:20, 3)
y = c(runif(20, 10, 11),
runif(20, 11, 12),
runif(20, 12, 13))
z = rep(c("a", "b", "c"), each = 20)
df = data.frame(x = x, y = y, z = z)
ggplot(data = df, aes(x = x, y = y, group = z, color = z)) +
geom_line() +
geom_text(aes(label = round(y, 1)), nudge_y = 1) +
ylim(c(0, 20))
ggplot(data = df, aes(x = x, y = y, group = z, color = z)) +
geom_line() +
geom_text_repel(aes(label = round(y, 1)), nudge_y = 1) +
ylim(c(0, 20))
Changing the theme to theme_bw() and removing gridlines from {ggExtra}'s removeGridX() gets the plot closer your second image. I also increased the size of the lines, limited the axes, and changed geom_text_repel to geom_label_repel to improve readability.
library(ggplot2)
library(ggrepel)
library(ggExtra)
set.seed(1)
x = rep(1:20, 3)
y = c(runif(20, 10, 11),
runif(20, 11, 12),
runif(20, 12, 13))
z = rep(c("a", "b", "c"), each = 20)
df = data.frame(x = x, y = y, z = z)
ggplot(data = df, aes(x = x, y = y, group = z, color = z)) +
theme_bw() + removeGridX() +
geom_line(size = 2) +
geom_label_repel(aes(label = round(y, 1)),
nudge_y = 0.5,
point.size = NA,
segment.color = NA,
min.segment.length = 0.1,
key_glyph = draw_key_path) +
scale_x_continuous(breaks=seq(0,20,by=1)) +
scale_y_continuous(breaks = seq(0, 14, 2), limits = c(0, 14))
I am helping someone translate hand-drawn economics supply and demand functions into image files that can be included in a Word document. These have been going well using Hmisc::bezier and geom_path modeled after Andrew Heiss's recon plots and using his curve_intersect function. That is, until the author asked that one of the supply curves should pass through a specified set of coordinates. The Hmisc::bezier function only uses the first and last control point as absolute, and bends toward intermediate points so the specified intersection point does not match the curve. I tried creating a spline of 2 bezier curves with the bezier function from the bezier package (v1.1.2, https://cran.r-project.org/web/packages/bezier/bezier.pdf), but this fails with "Error in FUN(X[[i]], ...) : object 'x' not found", which I do not understand or know how to fix.
Please let me know where I am going wrong or if there is a better method! I will include the commented out attempts using various functions. Please excuse the amateurish code, as I am a relative newb at R and ggplot2.
This section not directly relevant to my question
# Graph figures for physical economics, negative oil prices paper
library(reconPlots)
library(dplyr)
library(ggplot2)
library(patchwork)
library(ggrepel)
library(bezier)
library(ggforce)
options(ggrepel.max.time = 1)
options(ggrepel.max.iter = 20000)
#Set seed value for ggrepel
set.seed(52)
# panel (a)
#Set values of curves using the bezier function, each pair of c() values
# is an xy coordinate, and the sets of coordinates control the shape of the
# curve
supply <- Hmisc::bezier(c(1, 5, 6), c(3, 4, 9)) %>%
as_data_frame()
demand <- Hmisc::bezier(c(0, 9, 9), c(6, 6, 6)) %>%
as_data_frame()
label_height <- Hmisc::bezier(c(0, 9, 9), c(8, 8, 8)) %>%
as_data_frame()
# Calculate the intersections of the two curves
intersections <- bind_rows(curve_intersect(supply, demand))
# Calculate point where the curve label(s) intersect a specified height
supply_label <- bind_rows(curve_intersect(supply, label_height))
labels <- data_frame(label = expression("PS"[CR]^DRL),
x = supply_label$x,
y = supply_label$y)
production <- ggplot(mapping = aes(x = x, y = y)) +
#Draw the supply curve. Demand is not drawn in this figure, but the
# intersections of an imaginary demand curve are used to illustrate P0
# and Q0, the intersection point, and the dotted lines
geom_path(data = supply, color = "#0073D9", size = 1) +
geom_segment(data = intersections,
aes(x = x, y = 0, xend = x, yend = y), lty = "dotted") +
geom_segment(data = intersections,
aes(x = 0, y = y, xend = x, yend = y), lty = "dotted") +
#Draw the supply curve label using the intersection calculated above, using
# GGrepel so that the labels do not overlap the curve line
geom_text_repel(data = labels
,aes(x = x, y = y, label = label)
,parse = TRUE
,direction = "x"
,force = 3
,force_pull = 0.1
,hjust = 0
,min.segment.length = 0
) +
#Draw the intersection point based on intersection function between supply
# and the phantom flat demand curve at height y=6
geom_point(data = intersections, size = 3) +
#Use scale functions to set y-axis label, axis intersection point labels,
# and limits of the viewing area
scale_x_continuous(expand = c(0, 0), breaks = intersections$x
,labels = expression(Q[CR]^{DRL-PS})
,limits=c(0,9)
) +
scale_y_continuous(expand = c(0, 0), breaks = c(intersections$y, 9)
,labels = c(expression(P[CR]==frac("$",brl))
,expression(P[CR]))
,limits=c(0,9)
) +
#Use labs function to set x-axis title and title of each graph using the
# caption function so that it displays on the bottom
labs(x = expression(frac(Barrels,Week)),
caption = expression(atop("(a) Driller Production Supply", "of Crude Oil"))
) +
#Set classic theme, x-axis title on right-hand side using larger font of
# relative size 1.2, graph title on left-hand side using same larger font
theme_classic() +
theme(axis.title.y = element_blank(),
axis.title.x = element_text(hjust = 1),
axis.text = element_text(size=rel(1.2)),
plot.caption = element_text(hjust = 0.5, size=rel(1.2))
) +
coord_equal()
# Save the intersections so we can set the same quantity, price for panel (c)
specified_intersections = intersections
# Panel (b)
supply <- Hmisc::bezier(c(3.99, 4), c(0, 9)) %>%
as_data_frame()
demand <- Hmisc::bezier(c(2, 3, 4, 5), c(9, 6.5, 6, 5.5)) %>%
as_data_frame()
demand_capacity <- Hmisc::bezier(c(5, 5), c(0, 5.5)) %>%
as_data_frame()
supply_capacity <- Hmisc::bezier(c(4.999, 5), c(0, 9)) %>%
as_data_frame()
supply_label_height <- Hmisc::bezier(c(0, 9), c(9, 9)) %>%
as_data_frame()
demand_label_height <- Hmisc::bezier(c(0, 9), c(8, 8)) %>%
as_data_frame()
capacity_label_height <- Hmisc::bezier(c(0, 9), c(9, 9)) %>%
as_data_frame()
# Calculate the intersections of the two curves
intersections <- bind_rows(curve_intersect(supply,
demand))
supply_label <- bind_rows(curve_intersect(supply
,supply_label_height))
demand_label <- bind_rows(curve_intersect(demand
,demand_label_height))
capacity_label <- bind_rows(curve_intersect(supply_capacity
,capacity_label_height))
labels <- data_frame(label = c(expression("OD"[CR]^DRL),expression("OS"[CR]^DRL)
,expression("Q"[CR]^CAP)
),
x = c(demand_label$x, supply_label$x
, capacity_label$x
),
y = c(demand_label$y, supply_label$y
, capacity_label$y
)
)
inventory <- ggplot(mapping = aes(x = x, y = y)) +
geom_path(data = supply, color = "#0073D9", size = 1) +
geom_path(data = demand, color = "#FF4036", size = 1) +
geom_path(data = demand_capacity, color = "#FF4036", size = 1) +
geom_path(data = supply_capacity, color = "#0073D9", size = 1, lty = "dashed") +
geom_segment(data = intersections,
aes(x = 0, y = y, xend = x, yend = y), lty = "dotted") +
geom_text_repel(data = labels
,aes(x = x, y = y, label = label)
,parse = TRUE
,direction = "x"
,force = 3
,force_pull = 0.1
,hjust = c(0, 0, 1)
,min.segment.length = 0
) +
geom_point(data = intersections, size = 3) +
scale_x_continuous(expand = c(0, 0), breaks = c(intersections$x
, 5),
labels = c(expression(paste(Q[CR]^{DRL-OS},phantom(12345)))
,expression(Q[CR]^CAP)
)
, limits=c(0,9)) +
scale_y_continuous(expand = c(0, 0), breaks = c(intersections$y, 9),
labels = c(expression(P[CR]),expression(P[CR]))
, limits=c(0,9)) +
labs(x = "Barrels",
caption = expression(atop("(b) Driller Storage / Ownership", "of Crude Oil"))
) +
theme_classic() +
theme(axis.title.y = element_blank(),
axis.title.x = element_text(hjust = 1),
axis.text = element_text(size=rel(1.2)),
plot.caption = element_text(hjust = 0.5, size=rel(1.2))
) +
coord_equal()
Relevant section
# panel (c)
# ggforce package method
#supply <- list(c(1, 4, specified_intersections$x, 5, 7),
# c(3, 4, specified_intersections$y, 7, 9)) %>%
# as_data_frame()
# bezier package method: Fails with "Error in FUN(X[[i]], ...) : object 'x' not found"
t <- seq(0, 2, length=10)
p <- list(c(1, 4, specified_intersections$x, 7, 8),
c(3, 4, specified_intersections$y, 6, 9))
#p <- matrix(c(1,3, 4,4, specified_intersections$x,specified_intersections$y,
# 7,6, 8,9), nrow=5, ncol=2, byrow=TRUE)
supply <- bezier(t=t, p=p) %>%
as_data_frame()
# Original: Fails because it does not pass through the specified intersection
#supply <- Hmisc::bezier(c(1, specified_intersections$x, 8),
# c(3, specified_intersections$y, 9)) %>%
# as_data_frame()
# Hmisc method: Fails because there is no way to get the two curves to appear
# contiguous
#supply1 <- Hmisc::bezier(c(1, 4, specified_intersections$x),
# c(3, 4, specified_intersections$y)) %>%
# as_data_frame()
#supply2 <- Hmisc::bezier(c(specified_intersections$x, 6, 7),
# c(specified_intersections$y, 8, 9)) %>%
# as_data_frame()
#demand <- Hmisc::bezier(c(0, 9), c(specified_intersections$y, specified_intersections$y)) %>%
# as_data_frame()
label_height <- Hmisc::bezier(c(0, 9), c(8, 8)) %>%
as_data_frame()
# Calculate the intersections of the two curves
#intersections <- bind_rows(curve_intersect(supply, demand))
#supply_label <- bind_rows(curve_intersect(supply,
# label_height))
#labels <- data_frame(label = expression("SS"[CR]^DRL),
# x = supply_label$x,
# y = supply_label$y)
sales <- ggplot(mapping = aes(x = x, y = y)) +
# ggforce package method
# geom_bspline(data = supply, color = "#0073D9", size = 1) +
# Original geom_path method
geom_path(data = supply, color = "#0073D9", size = 1) +
# Supply 1 and 2 for Hmisc method
# geom_path(data = supply1, color = "#0073D9", size = 1) +
# geom_path(data = supply2, color = "#0073D9", size = 1) +
geom_segment(data = specified_intersections,
aes(x = x, y = 0, xend = x, yend = y), lty = "dotted") +
geom_segment(data = specified_intersections,
aes(x = 0, y = y, xend = x, yend = y), lty = "dotted") +
# geom_text_repel(data = labels
# ,aes(x = x, y = y, label = label)
# ,parse = TRUE
# ,direction = "x"
# ,force = 3
# ,force_pull = 0.1
# ,hjust = 0
# ,min.segment.length = 0
# ) +
geom_point(data = specified_intersections, size = 3) +
scale_x_continuous(expand = c(0, 0), breaks = specified_intersections$x,
labels = expression(Q[CR]^{DRL-SS}), limits=c(0,9)) +
scale_y_continuous(expand = c(0, 0), breaks = c(specified_intersections$y, 9),
labels = c(expression(P[CR]),expression(P[CR]))) +
labs(x = expression(frac(Barrels,Week)),
caption = expression(atop("(c) Driller Sales Supply", "of Crude Oil"))
) +
theme_classic() +
theme(axis.title.y = element_blank(),
axis.title.x = element_text(hjust = 1),
axis.text = element_text(size=rel(1.2)),
plot.caption = element_text(hjust = 0.5, size=rel(1.2))
) +
coord_equal()
patchwork <- (production | inventory | sales)
patchwork
Graphs before implementation of fixed coordinates. Need to move panel (c) intersection point to match panel (a)
I solved the "Error in FUN(X[[i]], ...) : object 'x' not found" by printing the supply variable and noticing that the bezier function names its rows V1,V2 and not x,y. I needed to set the aesthetics of the geom_path to the correct mapping.
Relevant Section, trimmed to only the bezier method
# panel (c)
# bezier package method
t <- seq(0, 2, length = 100)
p <- matrix(c(1,3, 4,4, specified_intersections$x,specified_intersections$y,
7,6, 8,9), nrow=5, ncol=2, byrow=TRUE)
supply <- bezier::bezier(t=t, p=p, deg=2) %>%
as_data_frame()
sales <- ggplot(mapping = aes(x = x, y = y)) +
# Original geom_path method
geom_path(data = supply, mapping = aes(x = V1, y = V2),
color = "#0073D9", size = 1, inherit.aes = FALSE) +
geom_segment(data = specified_intersections,
aes(x = x, y = 0, xend = x, yend = y), lty = "dotted") +
geom_segment(data = specified_intersections,
aes(x = 0, y = y, xend = x, yend = y), lty = "dotted") +
geom_point(data = specified_intersections, size = 3) +
scale_x_continuous(expand = c(0, 0), breaks = specified_intersections$x,
labels = expression(Q[CR]^{DRL-SS}), limits=c(0,9)) +
scale_y_continuous(expand = c(0, 0), breaks = c(specified_intersections$y, 9),
labels = c(expression(P[CR]),expression(P[CR]))) +
labs(x = expression(frac(Barrels,Week)),
caption = expression(atop("(c) Driller Sales Supply", "of Crude Oil"))
) +
theme_classic() +
theme(axis.title.y = element_blank(),
axis.title.x = element_text(hjust = 1),
axis.text = element_text(size=rel(1.2)),
plot.caption = element_text(hjust = 0.5, size=rel(1.2))
) +
coord_equal()
patchwork <- (production | inventory | sales)
patchwork
This does not solve my larger problem of needing a smooth curve that passes through a specified set of coordinates, as it produces two bezier curves that do not match.
I will do some research on using functions to specify bezier curves and find out if there is some mathematical or programmatic way to specify a bezier curve that passes through a set of fixed coordinates. If I find one, I'll edit this answer.
If anyone knows how to accomplish this, I would appreciate any help!
Kinked bezier curves
I have the following data structure:
y <- rep(1:10, 2)
group <- rep(c('a', 'b'), each = 10)
dens <- c(c(seq(from = 0, to = 0.8, by = 0.1), 0),
c(seq(from = -0, to = -0.8, by = -0.1), 0))
my_dat <- data.frame(group, dens, y, stringsAsFactors = FALSE )
These are calculated density disributions, in order to make a grouped violin plot, such as in
Split violin plot with ggplot2
# Plot 1:
require(ggplot2)
ggplot(my_dat, aes(x = dens, y = y, fill = group)) +
geom_polygon(color = 'black', show.legend = FALSE)
Now this is simplified, because my data contains hundreds of rows for a smooth outline. (However, there is the central vertical line in my case.) I would now like to remove exactly this vertical central line.
(I guess the problem is removing any specified part of the polygon.)
An idea in my example was to overplot this with a vertical line:
#Plot 2
ggplot(my_dat, aes(x = dens, y = y, fill = group)) +
geom_polygon(color = 'black', show.legend = FALSE) +
geom_segment(x = 0,
xend = 0,
y = min(y) + 0.2,
yend = max(y) - 0.2,
color = '#00BFC4')
But to get the end of the over plotting segment line correct is tricky. (I have purposefully left the line a bit too short for demonstration)
edit
the groups are not distributed in a symmetrical fashion, although my example strongly suggests so.
You can always just plot another polygon on top
x <- with(my_dat, chull(dens, y))
my_dat2 <- my_dat[c(x, x[1L]), ]
ggplot(my_dat, aes(x = dens, y = y, fill = group)) +
geom_polygon(show.legend = FALSE) +
geom_polygon(data = my_dat2, aes(group = 1), size = 1,
fill = 'transparent',
# fill = NA, ## or this
color = 'black')
I think the simpler solution is to first draw all the outlines and then all the filled areas. This should work for any arbitrary polygon shapes.
y <- rep(1:10, 2)
group <- rep(c('a', 'b'), each = 10)
dens <- c(c(seq(from = 0, to = 0.8, by = 0.1), 0),
c(seq(from = -0, to = -0.8, by = -0.1), 0))
my_dat <- data.frame(group, dens, y, stringsAsFactors = FALSE )
require(ggplot2)
ggplot(my_dat, aes(x = dens, y = y)) +
geom_polygon(color = 'black', fill = NA, size = 2) +
geom_polygon(aes(fill = group), color = NA)
How can I make a plot like this with two different-sized half circles (or other shapes such as triangles etc.)?
I've looked into a few options: Another post suggested using some unicode symbol, that didn't work for me. And if I use a vector image, how can I properly adjust the size parameter so the 2 circles touch each other?
Sample data (I would like to make the size of the two half-circles equal to circle1size and circle2size):
df = data.frame(circle1size = c(1, 3, 2),
circle2size = c(3, 6, 5),
middlepointposition = c(1, 2, 3))
And ultimately is there a way to position the half-circles at different y-values too, to encode a 3rd dimension, like so?
Any advice is much appreciated.
What you're asking for is a bar plot in polar coordinates. This can be done easily in ggplot2. Note that we need to map y = sqrt(count) to get the area of the half circle proportional to the count.
df <- data.frame(x = c(1, 2),
type = c("Investors", "Assignees"),
count = c(19419, 1132))
ggplot(df, aes(x = x, y = sqrt(count), fill = type)) + geom_col(width = 1) +
scale_x_discrete(expand = c(0,0), limits = c(0.5, 2.5)) +
coord_polar(theta = "x", direction = -1)
Further styling would have to be applied to remove the gray background, remove the axes, change the color, etc., but that's all standard ggplot2.
Update 1: Improved version with multiple countries.
df <- data.frame(x = rep(c(1, 2), 3),
type = rep(c("Investors", "Assignees"), 3),
country = rep(c("Japan", "Germany", "Korea"), each = 2),
count = c(19419, 1132, 8138, 947, 8349, 436))
df$country <- factor(df$country, levels = c("Japan", "Germany", "Korea"))
ggplot(df, aes(x=x, y=sqrt(count), fill=type)) + geom_col(width =1) +
scale_x_continuous(expand = c(0, 0), limits = c(0.5, 2.5)) +
scale_y_continuous(expand = c(0, 0)) +
coord_polar(theta = "x", direction = -1) +
facet_wrap(~country) +
theme_void()
Update 2: Drawing the individual plots at different locations.
We can do some trickery to take the individual plots and plot them at different locations in an enclosing plot. This works, and is a generic method that can be done with any sort of plot, but it's probably overkill here. Anyways, here is the solution.
library(tidyverse) # for map
library(cowplot) # for draw_text, draw_plot, get_legend, insert_yaxis_grob
# data frame of country data
df <- data.frame(x = rep(c(1, 2), 3),
type = rep(c("Investors", "Assignees"), 3),
country = rep(c("Japan", "Germany", "Korea"), each = 2),
count = c(19419, 1132, 8138, 947, 8349, 436))
# list of coordinates
coord_list = list(Japan = c(1, 3), Germany = c(2, 1), Korea = c(3, 2))
# make list of individual plots
split(df, df$country) %>%
map( ~ ggplot(., aes(x=x, y=sqrt(count), fill=type)) + geom_col(width =1) +
scale_x_continuous(expand = c(0, 0), limits = c(0.5, 2.5)) +
scale_y_continuous(expand = c(0, 0), limits = c(0, 160)) +
draw_text(.$country[1], 1, 160, vjust = 0) +
coord_polar(theta = "x", start = 3*pi/2) +
guides(fill = guide_legend(title = "Type", reverse = T)) +
theme_void() + theme(legend.position = "none") ) -> plotlist
# extract the legend
legend <- get_legend(plotlist[[1]] + theme(legend.position = "right"))
# now plot the plots where we want them
width = 1.3
height = 1.3
p <- ggplot() + scale_x_continuous(limits = c(0.5, 3.5)) + scale_y_continuous(limits = c(0.5, 3.5))
for (country in names(coord_list)) {
p <- p + draw_plot(plotlist[[country]], x = coord_list[[country]][1]-width/2,
y = coord_list[[country]][2]-height/2,
width = width, height = height)
}
# plot without legend
p
# plot with legend
ggdraw(insert_yaxis_grob(p, legend))
Update 3: Completely different approach, using geom_arc_bar() from the ggforce package.
library(ggforce)
df <- data.frame(start = rep(c(-pi/2, pi/2), 3),
type = rep(c("Investors", "Assignees"), 3),
country = rep(c("Japan", "Germany", "Korea"), each = 2),
x = rep(c(1, 2, 3), each = 2),
y = rep(c(3, 1, 2), each = 2),
count = c(19419, 1132, 8138, 947, 8349, 436))
r <- 0.5
scale <- r/max(sqrt(df$count))
ggplot(df) +
geom_arc_bar(aes(x0 = x, y0 = y, r0 = 0, r = sqrt(count)*scale,
start = start, end = start + pi, fill = type),
color = "white") +
geom_text(data = df[c(1, 3, 5), ],
aes(label = country, x = x, y = y + scale*sqrt(count) + .05),
size =11/.pt, vjust = 0)+
guides(fill = guide_legend(title = "Type", reverse = T)) +
xlab("x axis") + ylab("y axis") +
coord_fixed() +
theme_bw()
If you don't need to have ggplot2 map aesthetics other than x and y you could try egg::geom_custom,
# devtools::install_github("baptiste/egg")
library(egg)
library(grid)
library(ggplot2)
d = data.frame(r1= c(1,3,2), r2=c(3,6,5), x=1:3, y=1:3)
gl <- Map(mushroomGrob, r1=d$r1, r2=d$r2, gp=list(gpar(fill=c("bisque","maroon"), col="white")))
d$grobs <- I(gl)
ggplot(d, aes(x,y)) +
geom_custom(aes(data=grobs), grob_fun=I) +
theme_minimal()
with the following grob,
mushroomGrob <- function(x=0.5, y=0.5, r1=0.2, r2=0.1, scale = 0.01, angle=0, gp=gpar()){
grob(x=x,y=y,r1=r1,r2=r2, scale=scale, angle=angle, gp=gp , cl="mushroom")
}
preDrawDetails.mushroom <- function(x){
pushViewport(viewport(x=x$x,y=x$y))
}
postDrawDetails.mushroom<- function(x){
upViewport()
}
drawDetails.mushroom <- function(x, recording=FALSE, ...){
th2 <- seq(0,pi, length=180)
th1 <- th2 + pi
d1 <- x$r1*x$scale*cbind(cos(th1+x$angle*pi/180),sin(th1+x$angle*pi/180))
d2 <- x$r2*x$scale*cbind(cos(th2+x$angle*pi/180),sin(th2+x$angle*pi/180))
grid.polygon(unit(c(d1[,1],d2[,1]), "snpc")+unit(0.5,"npc"),
unit(c(d1[,2],d2[,2]), "snpc")+unit(0.5,"npc"),
id=rep(1:2, each=length(th1)), gp=x$gp)
}
# grid.newpage()
# grid.draw(mushroomGrob(gp=gpar(fill=c("bisque","maroon"), col=NA)))
Sorry for the possibly simple question. I'm a programmer, though I rarely deal with graphics, and after tearing my hair out for hours with this problem, it's time to get some help. I'm creating a multi-panel plot in r using ggplot, but I cannot find a way to display figure labels, outside of the figure, when using ggplot.
Here is what I want my code to do:
par(mfrow = c(1, 2), pty = "s", las = 1, mgp = c(2, 0.4, 0), tcl = -0.3)
qqnorm(rnorm(100), main = "")
mtext("a", side = 3, line = 1, adj = 0, cex = 1.1)
qqnorm(rnorm(100), main = "")
mtext("b", side = 3, line = 1, adj = 0, cex = 1.1)
How would I get those "a" and "b" labels, in the location that they are in for the figure created by the above code, into this type of code:
df = data.frame(gp = factor(rep(letters[1:3], each = 10)), y = rnorm(30))
p = ggplot(df) + geom_point(aes(x = gp, y = y))
p2 = ggplot(df) + geom_point(aes(x = y, y = gp))
grid.arrange(p, p2, ncol = 2)
Thank you in advance for your help!
You could use ggtitle and theme:
df = data.frame(gp = factor(rep(letters[1:3], each = 10)), y = rnorm(30))
p = ggplot(df) + geom_point(aes(x = gp, y = y)) + ggtitle('a') + theme(plot.title=element_text(hjust=0))
p2 = ggplot(df) + geom_point(aes(x = y, y = gp)) + ggtitle('b') + theme(plot.title=element_text(hjust=0))
grid.arrange(p, p2, ncol = 2)
Two (less than ideal) options:
#Use faceting, similar to Matthew's ggtitle option
df = data.frame(gp = factor(rep(letters[1:3], each = 10)), y = rnorm(30))
df$lab1 <- 'a'
df$lab2 <- 'b'
p = ggplot(df) + geom_point(aes(x = gp, y = y)) + facet_wrap(~lab1)
p2 = ggplot(df) + geom_point(aes(x = y, y = gp)) + facet_wrap(~lab2)
j <- theme(strip.text = element_text(hjust = 0.05))
grid.arrange(p + j, p2 + j, ncol = 2)
#Use grid.text
grid.text(letters[1:2],x = c(0.09,0.59),y = 0.99)
For the grid.text option, if you delve into the ggplot object you can probably avoid having to tinker to get those values right manually.