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I am trying to make a figure in ggplot where color, shape and size are mapped to a variable as follows: 0 values are shown as red crosses. Values > 0 are shown as circles with the circle size and color scaled to the variable (i.e. the larger the circle, the higher the value). I want to use a binned viridis scale for the color. The values mapped to color vary randomly, so the scaling should not be hardcoded. Here is the figure:
library(tidyverse)
x <- tibble(x = sample(1:100, 10), y = sample(1:100, 10), z = c(0, sample(1:1e6, 9)))
color_breaks <- sort(unique(c(0, 1, pretty(x$z, n = 5), ceiling(max(x$z)))))
ggplot(x, aes(x = x, y = y, color = z, shape = z == 0, size = z)) +
geom_point(stroke = 1.5) +
scale_shape_manual(values = c(`TRUE` = 3, `FALSE` = 21), guide = "none") +
scale_size(range = c(1, 8),
breaks = color_breaks,
limits = c(0, ceiling(max(x$z)))
) +
binned_scale(aesthetics = "color",
scale_name = "stepsn",
palette = function(x) c("red", viridis::viridis(length(color_breaks) - 3)),
limits = c(0, ceiling(max(x$z))),
breaks = color_breaks,
show.limits = TRUE
) +
guides(color = guide_legend(), size = guide_legend()) +
theme_bw()
Created on 2022-03-31 by the reprex package (v2.0.1)
How do I combine the variables to a single legend, which should look like this (edited in Illustrator)?
You can override the aesthetics inside guides:
x <- tibble(x = sample(1:100, 10), y = sample(1:100, 10), z = c(0, sample(1:1e6, 9)))
color_breaks <- sort(unique(c(0, pretty(x$z, n = 5)[-6], ceiling(max(x$z)) + 1)))
ggplot(x, aes(x = x, y = y, color = z, shape = z == 0, size = z)) +
geom_point(stroke = 1.5) +
scale_shape_manual(values = c(`TRUE` = 3, `FALSE` = 21), guide = "none") +
scale_size(range = c(1, 8),
breaks = color_breaks,
limits = c(-1, ceiling(max(x$z)) + 2)
) +
binned_scale(aesthetics = "color",
scale_name = "stepsn",
palette = function(x) c("red", viridis::viridis(length(color_breaks) - 1)),
limits = c(-1, ceiling(max(x$z)) + 2),
breaks = color_breaks,
show.limits = FALSE
) +
guides(color = guide_legend(),
size = guide_legend(override.aes = list(shape = c(3, rep(16, 5))))) +
theme_bw()
I recently asked this question. However, I am asking a separate question now as the scope of my new question falls outside the range of the last question.
I am trying to create a heatmap in ggplot... however, outside of the axis I am trying to plot geom_tile. The issue is I cannot find a consistent way to get it to work. For example, the code I am using to plot is:
library(colorspace)
library(ggplot2)
library(ggnewscale)
library(tidyverse)
asd <- expand_grid(paste0("a", 1:9), paste0("b", 1:9))
df <- data.frame(
a = asd$`paste0("a", 1:9)`,
b = asd$`paste0("b", 1:9)`,
c = sample(20, 81, replace = T)
)
# From discrete to continuous
df$a <- match(df$a, sort(unique(df$a)))
df$b <- match(df$b, sort(unique(df$b)))
z <- sample(10, 18, T)
# set color palettes
pal <- rev(diverging_hcl(palette = "Blue-Red", n = 11))
palEdge <- rev(sequential_hcl(palette = "Plasma", n = 11))
# plot
ggplot(df, aes(a, b)) +
geom_tile(aes(fill = c)) +
scale_fill_gradientn(
colors = pal,
guide = guide_colorbar(
frame.colour = "black",
ticks.colour = "black"
),
name = "C"
) +
theme_classic() +
labs(x = "A axis", y = "B axis") +
new_scale_fill() +
geom_tile(data = tibble(a = 1:9,
z = z[1:9]),
aes(x = a, y = 0, fill = z, height = 0.3)) +
geom_tile(data = tibble(b = 1:9,
z = z[10:18]),
aes(x = 0, y = b, fill = z, width = 0.3)) +
scale_fill_gradientn(
colors = palEdge,
guide = guide_colorbar(
frame.colour = "black",
ticks.colour = "black"
),
name = "Z"
)+
coord_cartesian(clip = "off", xlim = c(0.5, NA), ylim = c(0.5, NA)) +
theme(aspect.ratio = 1,
plot.margin = margin(10, 15.5, 25, 25, "pt")
)
This produces something like this:
However, I am trying to find a consistent way to plot something more like this (which I quickly made in photoshop):
The main issue im having is being able to manipulate the coordinates of the new scale 'outside' of the plotting area. Is there a way to move the tiles that are outside so I can position them in an area that makes sense?
There are always the two classic options when plotting outside the plot area:
annotate/ plot with coord_...(clip = "off")
make different plots and combine them.
The latter option usually gives much more flexibility and way less headaches, in my humble opinion.
library(colorspace)
library(tidyverse)
library(patchwork)
asd <- expand_grid(paste0("a", 1:9), paste0("b", 1:9))
df <- data.frame(
a = asd$`paste0("a", 1:9)`,
b = asd$`paste0("b", 1:9)`,
c = sample(20, 81, replace = T)
)
# From discrete to continuous
df$a <- match(df$a, sort(unique(df$a)))
df$b <- match(df$b, sort(unique(df$b)))
z <- sample(10, 18, T)
# set color palettes
pal <- rev(diverging_hcl(palette = "Blue-Red", n = 11))
palEdge <- rev(sequential_hcl(palette = "Plasma", n = 11))
# plot
p_main <- ggplot(df, aes(a, b)) +
geom_tile(aes(fill = c)) +
scale_fill_gradientn("C",colors = pal,
guide = guide_colorbar(frame.colour = "black",
ticks.colour = "black")) +
theme_classic() +
labs(x = "A axis", y = "B axis")
p_bottom <- ggplot() +
geom_tile(data = tibble(a = 1:9, z = z[1:9]),
aes(x = a, y = 0, fill = z, height = 0.3)) +
theme_void() +
scale_fill_gradientn("Z",limits = c(0,10),
colors = palEdge,
guide = guide_colorbar(
frame.colour = "black", ticks.colour = "black"))
p_left <- ggplot() +
theme_void()+
geom_tile(data = tibble(b = 1:9, z = z[10:18]),
aes(x = 0, y = b, fill = z, width = 0.3)) +
scale_fill_gradientn("Z",limits = c(0,10),
colors = palEdge,
guide = guide_colorbar( frame.colour = "black", ticks.colour = "black"))
p_left + p_main +plot_spacer()+ p_bottom +
plot_layout(guides = "collect",
heights = c(1, .1),
widths = c(.1, 1))
Created on 2021-02-21 by the reprex package (v1.0.0)
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$max_rep))
main +
geom_plot_npc(data = insets,
aes(npcx = x, npcy = y, label = plot,
vp.width = 0.3, vp.height = 0.6)) +
annotate(geom = "rect",
xmin = 12, xmax = 14, ymin = 3, ymax = 4,
linetype = "dotted", fill = NA, colour = "black")
Created on 2020-12-15 by the reprex package (v0.3.0)
I mean, I'd want to paint only the square area P1 X (Q1-Q2).
Not the trapezoid (P2+P1) X (Q1-Q2/2).
Here's code that I used. I used ggplot and dplyr. How can I solve this problem?
How can I paint the only square area not the trapezoied area!!!!
library(ggplot2)
library(dplyr)
supply <- Hmisc::bezier(x = c(1, 8, 9),
y = c(1, 5, 9)) %>%
as_data_frame()
demand <- Hmisc::bezier(c(1, 3, 9),
c(9, 3, 1)) %>%
as_data_frame()
fun_supply <- approxfun(supply$x, supply$y, rule = 2)
fun_supply(c(2, 6, 8))
fun_demand <- approxfun(demand$x, demand$y, rule = 2)
intersection_funs <- uniroot(function(x) fun_supply(x) - fun_demand(x), c(1, 9))
intersection_funs
y_root <- fun_demand(intersection_funs$root)
curve_intersect <- function(curve1, curve2) {
# Approximate the functional form of both curves
curve1_f <- approxfun(curve1$x, curve1$y, rule = 2)
curve2_f <- approxfun(curve2$x, curve2$y, rule = 2)
# Calculate the intersection of curve 1 and curve 2 along the x-axis
point_x <- uniroot(function(x) curve1_f(x) - curve2_f(x),
c(min(curve1$x), max(curve1$x)))$root
# Find where point_x is in curve 2
point_y <- curve2_f(point_x)
# Finish
return(list(x = point_x, y = point_y))
}
intersection_xy <- curve_intersect(supply, demand)
intersection_xy
intersection_xy_df <- intersection_xy %>% as_data_frame()
demand2 <- Hmisc::bezier(c(1.5, 3.5, 9.5),
c(9.5, 3.5, 1.5)) %>%
as_data_frame()
supply2 <- Hmisc::bezier(c(1,7,8),
c(3,7,11)) %>%
as_data_frame()
#Make a data frame of the intersections of the supply curve and both demand curves
intersections <- bind_rows(curve_intersect(supply, demand),
curve_intersect(supply2, demand2))
plot_labels <- data_frame(label = c("S", "D","S[1]","D[1]"),
x = c(9, 1, 6.5, 3),
y = c(8, 8, 8, 8))
ggplot(mapping = aes(x = x, y = y)) +
geom_path(data = supply, color = "#0073D9", size = 1, linetype = "dashed") +
geom_path(data = demand, color = "#FF4036", size = 1, linetype = "dashed") +
geom_path(data = demand2, color = "#FF4036", size = 1) +
geom_path(data = supply2, color = "#0073D9", size = 1) +
geom_segment(data = intersections,
aes(x = x, y = 0, xend = x, yend = y), lty = "dotted") +
geom_segment(data = intersections,
aes(x = 0, y = y, xend = x, yend = y), lty = "dotted") +
geom_segment(data = intersections,
aes(x = x, y = y, xend = x, yend= y), lty = "dotted") +
geom_point(data = intersections, size = 3) +
geom_text(data = plot_labels,
aes(x = x, y = y, label = label), parse = TRUE) +
scale_x_continuous(expand = c(0, 0), breaks = intersections$x,
labels = expression(Q[1], Q[2])) +
scale_y_continuous(expand = c(0, 0), breaks = intersections$y,
labels = expression(P[1], P[2]))+
labs(x = "Quantity", y = "Price") +
geom_area(data =intersections, fill="#9999FF", alpha=0.5) +
theme_classic() +
coord_equal()
Could you help me to paint the area that I mentioned.
You might try adding geom_rect(data=intersections[1,], aes(xmin=0, xmax=x, ymin=0, ymax=y),fill='green', alpha=0.5) to your plot call.
So we have:
ggplot(mapping = aes(x = x, y = y)) +
geom_path(data = supply, color = "#0073D9", size = 1, linetype = "dashed") +
geom_path(data = demand, color = "#FF4036", size = 1, linetype = "dashed") +
geom_path(data = demand2, color = "#FF4036", size = 1) +
geom_path(data = supply2, color = "#0073D9", size = 1) +
geom_segment(data = intersections,
aes(x = x, y = 0, xend = x, yend = y), lty = "dotted") +
geom_segment(data = intersections,
aes(x = 0, y = y, xend = x, yend = y), lty = "dotted") +
geom_segment(data = intersections,
aes(x = x, y = y, xend = x, yend= y), lty = "dotted") +
geom_point(data = intersections, size = 3) +
geom_text(data = plot_labels,
aes(x = x, y = y, label = label), parse = TRUE) +
scale_x_continuous(expand = c(0, 0), breaks = intersections$x,
labels = expression(Q[1], Q[2])) +
scale_y_continuous(expand = c(0, 0), breaks = intersections$y,
labels = expression(P[1], P[2]))+
labs(x = "Quantity", y = "Price") +
geom_area(data =intersections, fill="#9999FF", alpha=0.5) +
theme_classic() +
coord_equal()+
geom_rect(data=intersections[1,], aes(xmin=0, xmax=x, ymin=0, ymax=y),fill='green', alpha=0.5)
Edit based on comment:
geom_rect(data=intersections, aes(xmin=x[2], xmax=x[1], ymin=0, ymax=y[1]),fill='green', alpha=0.5)
Though the answer from J Con is in depth and does provide a solution, a cleaner approach in ggplot2 may be to use the annotate function, with geom and other arguments set appropriately. (See link for help page.)
This is because using something like geom_rect involves passing positions and so on as a data.frame, which is a bit more of a hack as, conceptually, from a grammar of graphics perspective, the data layer and the annotation layer are distinct: the act of mapping data variables to graphical aesthetics in a systematic and objective way, and of marking up features within the dataset in a piecemeal and subjective way, are separate activities, and using annotate explicitly for the latter purpose makes this divide clearer in terms of the code and concepts.
Edit
To be more specific, the annotate equivalent of the following:
geom_rect(data=intersections, aes(xmin=x[2], xmax=x[1], ymin=0, ymax=y[1]),fill='green', alpha=0.5)
Would likely be as follows
annotate(
geom = "rect",
xmin = intersections$x[2], x = intersections$x[1],
ymin = 0, ymax = intersections$y[1],
fill = 'green', alpha = 0.5
)
Functionally this is exactly the same, but conceptually it makes the separation between the data layer and the annotation layer much clearer in the code expressed.
Note: Annotate could also be used for the points and text.