Related
library(terra)
library(raster)
library(rasterVis)
f <- system.file("external/test.grd", package="raster")
r <- rast(f)
r
rr <- project(x=r,y='epsg:4326')
rr
gplot(rr)+
geom_raster(aes(fill = value),na.rm=T)+
scale_fill_distiller(na.value = NA)+
ggtitle(label = 'Test')+
coord_sf(crs=4326)+
theme(axis.title = element_blank(),
axis.line = element_line(colour='black', linewidth = 3,
linetype=1),
axis.line.x.top = element_line(colour='black', linewidth = 1,
linetype=1),
axis.line.y.right = element_line(colour='black', linewidth = 1,
linetype=1),
plot.title = element_text(hjust = 0.5,size = 20,
face = 'bold',family = 'serif'),
axis.text = element_text(size=20,face='bold',
family = 'serif',color = 'black'),
legend.title=element_text(face='bold',family = 'serif',
size = 15),
legend.text = element_text(size = 15,
face='bold',
family='serif'),
panel.background = element_rect(fill=NA,color = 'black')
)
I met this situation. I checked these codes many times, but the problem seemed still occured.
And I have tried tidyterra package as well. The same problem.
Ref:https://github.com/oscarperpinan/rastervis/issues/96
Aim: add the linetypes of the segments to the legend, as well as the colour.
Problem: only the colour is showing.
Data:
m = as.data.frame(matrix(c(1:10), ncol = 2, nrow = 10))
Plot:
ggplot(m, aes(v1,v2)) + geom_segment(aes(x = 0, xend = 9.75, y = 10, yend = 10, colour = "PEL"), linetype = "dotted") + geom_segment(aes(x = 0, xend = 9.75, y = 5, yend = 5, colour = "AL1"), linetype = "longdash") + geom_segment(aes(x = 0, xend = 9.75, y = 2, yend = 2, colour = "ISQG"), linetype = "solid") + scale_colour_manual("legend", values = c("PEL" = "black", "AL1" = "blue", "ISQG" = "purple"), guide = guide_legend(override.aes = list(alpha = 1))) + theme(legend.position = "bottom")
I've tried adding scale_linetype_manual(values = c("PEL" = "dotted", "AL1" = "longdash", "ISQG" = "solid") but nothing changes.
This answer is similar, Legend linetype in ggplot but I couldn't figure out how to make it work with geom_segment
Thank you in advance
The most ggplot-esque way of doing this, is to include a linetype variable as part of mapping in the aes() functions. You must then ensure that both the linetype and colour scales have the same titles, breaks, limits, labels etc.
Alternatively, you can also include the linetype in the override.aes part of guide_legend().
library(ggplot2)
ggplot() +
geom_segment(
aes(x = 0, xend = 9.75, y = 10, yend = 10, colour = "PEL", linetype ="PEL"),
) +
geom_segment(
aes(x = 0, xend = 9.75, y = 5, yend = 5, colour = "AL1", linetype ="AL1"),
) +
geom_segment(
aes(x = 0, xend = 9.75, y = 2, yend = 2, colour = "ISQG", linetype = "ISQG"),
) +
scale_colour_manual(
"legend",
values = c("PEL" = "black", "AL1" = "blue", "ISQG" = "purple"),
) +
scale_linetype_manual(
"legend",
values = c("PEL" = "dotted", "AL1" = "longdash", "ISQG" = "solid"),
) +
theme(legend.position = "bottom")
Created on 2022-05-19 by the reprex package (v2.0.1)
I have created a ggplot graph with three lines. Each line represents a different column in a data frame and colored in a different color. For some reason, the colors in the final graph are not coordinated to the code.
The data frame:
Scenario 1 Scenario 2 Scenario 3 Years
0.0260 0.0340 0.0366 1
0.0424 0.0562 0.0696 2
0.0638 0.0878 0.1150 3
0.0848 0.1280 0.1578 4
0.1096 0.1680 0.2074 5
0.1336 0.2106 0.2568 6
This is the code:
ggplot(ext2, aes(x = Years))+
geom_line(aes(y = `Scenario 1`, color = "darkblue"))+
geom_line(aes(y = `Scenario 2`, color = "darkred"))+
geom_line(aes(y = `Scenario 3`, color = "darkgreen"))+
xlab("Years")+
ylab("Quasi - extinction probability")+
ggtitle("2 mature individuals")+
geom_segment(aes(x = 45,y = 0.5, xend = 45, yend = 1.1),linetype = "longdash")+
geom_segment(aes(x = 75,y = 0.2, xend = 75, yend = 0.5),linetype = "longdash")+
geom_segment(aes(x = 0,y = 0.5, xend = 100, yend = 0.5),linetype = "longdash")+
geom_segment(aes(x = 0,y = 0.2, xend = 100, yend = 0.2),linetype = "longdash")+
geom_text(x = 20, y = 0.80, label = "CE")+
geom_text(x = 40, y = 0.35, label = "EN")+
scale_colour_manual(values = c("darkblue", "darkred","darkgreen"), labels = c("Scenario 1","Scenario 2","Scenario 3"))+
theme(legend.title = element_blank())+
theme_minimal()
and this is the graph:
Click here to see graph
The problem is that what I defined as 'scenario 3' in the code is actually a representation of 'scenario 2' in the data frame. You can see it according to the values under scenario 2 in the data frame.
For ggplot, the data needs to be in long format before you plot. Then, you can make "Scenarios" (i.e., name) the group, so that you can manually color the individual lines (i.e., with scale_colour_manual).
library(tidyverse)
ext_long <- ext2 %>%
pivot_longer(!Years)
ggplot(ext_long, aes(x = Years, color = name)) +
geom_line(aes(y = value)) +
xlab("Years") +
ylab("Quasi - extinction probability") +
ggtitle("2 mature individuals") +
geom_segment(aes(
x = 45,
y = 0.5,
xend = 45,
yend = 1.1
), linetype = "longdash") +
geom_segment(aes(
x = 75,
y = 0.2,
xend = 75,
yend = 0.5
), linetype = "longdash") +
geom_segment(aes(
x = 0,
y = 0.5,
xend = 100,
yend = 0.5
), linetype = "longdash") +
geom_segment(aes(
x = 0,
y = 0.2,
xend = 100,
yend = 0.2
), linetype = "longdash") +
geom_text(x = 20, y = 0.80, label = "CE") +
geom_text(x = 40, y = 0.35, label = "EN") +
scale_colour_manual(
values = c("darkblue", "darkred", "darkgreen"),
labels = c("Scenario 1", "Scenario 2", "Scenario 3")
) +
theme(legend.title = element_blank()) +
theme_minimal()
Output (only have a small part of the data, which is the reason the lines do not extend across the graph)
Data
ext2 <- structure(
list(
Scenario.1 = c(0.026, 0.0424, 0.0638, 0.0848,
0.1096, 0.1336),
Scenario.2 = c(0.034, 0.0562, 0.0878, 0.128,
0.168, 0.2106),
Scenario.3 = c(0.0366, 0.0696, 0.115, 0.1578,
0.2074, 0.2568),
Years = 1:6
),
class = "data.frame",
row.names = c(NA,-6L)
)
I have total 7 plots.
Six of them are line charts which are to be aligned and arranged one below each other such that there is no space between them - to make one composite plot.
Here is the data and ggplot2 code and I am using the same line chart 6 times just to explain my problem
x<- 1:10
y<- rnorm(10)
data <- data.frame(x,y)
library(ggplot2)
k<- ggplot(data, aes(x= x, y= y)) + geom_line() + theme(panel.background=element_blank()) + theme(aspect.ratio = 0.15) + theme(panel.grid.minor = element_blank(), panel.grid.major = element_blank(), axis.line.x = element_line(colour = "black", size= 0.5), axis.line.y = element_line(colour = "black", size= 0.5), axis.ticks.y = element_line(colour = "black", size= 0.5), axis.ticks.x = element_blank()) + xlab(label = "")+ ylab(label = "") + scale_x_continuous(label= NULL) +theme(plot.margin = unit(c(-0.25, 2, -0.25, 2), "cm"))
k
Seventh is scatter plot with regression line
a<- 1:10
a
b<- 11:20
b
data1 <- data.frame(a,b)
data1
library(ggplot2)
k3<-ggplot(data1, aes(x=a, y=b))+ geom_point(shape=1, fill ="black", alpha= 1, color= "black", size=3) + geom_smooth(method = lm, size = 0.5, linetype ="dotted", fill ="black", color= "black", alpha = 0.3)
k3
k3<- k3 + expand_limits(x = c(0.5, 10.5), y = c(10.5,20.5)) + scale_x_continuous(expand = c(0, 0), breaks = c(2,4, 6, 8, 10)) + scale_y_continuous(expand = c(0, 0),breaks = c(10, 12, 14, 16, 18, 20))
k3
k3 <- k3 + theme(panel.background=element_blank())+ theme(aspect.ratio = 1) + theme(panel.grid.minor = element_blank(), panel.grid.major = element_blank(), axis.line.x = element_line(colour = "black"), axis.line.y = element_line(colour = "black", size= 0.5), axis.ticks.y = element_line(colour = "black", size= 0.5), axis.ticks.x = element_line(colour = "black", size= 0.5))
k3
k3<- k3 + scale_x_reverse(expand = c(0, 0))
k3
#Flip axes
k3<- k3 + coord_flip()
k3<- k3 + theme(plot.margin = unit(c(0, 0, 0, 0), "cm"))
k3
I want to arrange (1) composite plot (on left) and (2) scatter plot (on right)side by side. So I tried arranging that way using (1) ggarrange() [in ggpubr] and (2) plot_grid()[in cowplot], but I couldn't.
Could anybody help? Thankyou!
I want the layout to look like this
I really hope, that I've understand you correctly. To be honest your code is a mess, so I was using a default iris dataset.
The hint is to use plot_grid twice:
library(ggplot2)
library(cowplot)
k <- iris %>%
ggplot(aes(x = Sepal.Length, y = Petal.Length)) +
geom_line() +
labs(x = "", y = "") +
theme_classic() +
theme(axis.ticks.x = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.ticks.y = element_blank())
k3 <- iris %>%
ggplot(aes(x = Sepal.Length, y = Sepal.Width)) +
geom_point() +
labs(x = "", y = "") +
theme_classic()
grid1 <- cowplot::plot_grid(
k, k, k, k, k, k,
ncol = 1,
align = "hv"
)
cowplot::plot_grid(grid1, k3,
align = "hv",
rel_widths = c(1.5, 1), # you can control the relative width and height
nrow = 1)
Found solution! Got desired result with package 'patchwork' in combination with some changes in plot margins.
Here is the code and result
library(ggplot2)
iris
# Line charts
k <- ggplot(iris, aes(x = Sepal.Length, y = Petal.Length)) +
geom_line() +
labs(x = "", y = "") +
theme_classic() +
theme(axis.ticks.x = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.ticks.y = element_blank()) +
theme(plot.margin = unit(c(-0.25,-3,-0.25,0), "cm")) +
theme(aspect.ratio = 0.15)
# Line chart (k4) with y-axis label
k4 <- ggplot(iris, aes(x = Sepal.Length, y = Petal.Length)) +
geom_line() +
labs(x = "", y = "") +
theme_classic() +
theme(axis.ticks.x = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.ticks.y = element_blank()) +
theme(axis.title.y = element_text(
vjust = 2,
color = "black",
size = 10,
face = "bold"
))+
theme(plot.margin = unit(c(-0.25,-3,-0.25,0), "cm"))+
theme(aspect.ratio = 0.15)
# scatter plot
sc <-ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width)) +
geom_point() +
labs(x = "", y = "") +
theme_classic()+
theme(plot.margin = unit(c(0,0,0,-0.5), "cm"))+
theme(aspect.ratio = 0.7)
sc
library(patchwork)
p<- (k/k/k/k4/k/k)| sc
p
I am trying to create a Kaplan-Meier plot with 95% confidence bands plus having the censored data in a table beneath it. I can create the plot, but not the table. I get the error message: Error in grid.draw(both) : object 'both' not found.
library(survival)
library(ggplot2)
library(GGally)
library(gtable)
data(lung)
sf.sex <- survfit(Surv(time, status) ~ sex, data = lung)
pl.sex <- ggsurv(sf.sex) +
geom_ribbon(aes(ymin=low,ymax=up,fill=group),alpha=0.3) +
guides(fill=guide_legend("sex"))
pl.sex
tbl <- ggplot(df_nums, aes(x = Time, y = factor(variable), colour = variable,+
label=value)) +
geom_text() +
theme_bw() +
theme(panel.grid.major = element_blank(),+
legend.position = "none",+
plot.background = element_blank(), +
panel.grid.major = element_blank(),+
panel.grid.minor = element_blank(),+
panel.border = element_blank(),+
legend.position="none",+
axis.line = element_blank(),+
axis.text.x = element_blank(),+
axis.text.y = element_text(size=15, face="bold", color = 'black'),+
axis.ticks=element_blank(),+
axis.title.x = element_blank(),+
axis.title.y = element_blank(),+
plot.title = element_blank()) +
scale_y_discrete(breaks=c("Group.A", "Group.B"), labels=c("Group A", "Group B"))
both = rbind(ggplotGrob(g), ggplotGrob(tbl), size="last")
panels <- both$layout$t[grep("panel", both$layout$name)]
both$heights[panels] <- list(unit(1,"null"), unit(2, "lines"))
both <- gtable_add_rows(both, heights = unit(1,"line"), 8)
both <- gtable_add_grob(both, textGrob("Number at risk", hjust=0, x=0), t=9, l=2, r=4)
grid.newpage()
grid.draw(both)
I solved the problem by using the Rcmdrplugin KMggplot2 The code is generated by the plugin after selecting the data and variables.
library(survival, pos=18)
data(lung, package="survival")
lung <- within(lung, {
sex <- factor(sex, labels=c('male','female'))
})
ggthemes_data <- ggthemes::ggthemes_data
require("ggplot2")
.df <- na.omit(data.frame(x = lung$time, y = lung$status, z = lung$sex))
.df <- .df[do.call(order, .df[, c("z", "x"), drop = FALSE]), , drop = FALSE]
.fit <- survival::survfit(survival::Surv(time = x, event = y, type = "right") ~ z,
.df)
.pval <- plyr::ddply(.df, plyr::.(),
function(x) {
data.frame(
x = 0, y = 0, df = 1,
chisq = survival::survdiff(
survival::Surv(time = x, event = y, type = "right") ~ z, x
)$chisq
)})
.pval$label <- paste0(
"paste(italic(p), \" = ",
signif(1 - pchisq(.pval$chisq, .pval$df), 3),
"\")"
)
.fit <- data.frame(x = .fit$time, y = .fit$surv, nrisk = .fit$n.risk, nevent =
.fit$n.event, ncensor= .fit$n.censor, upper = .fit$upper, lower = .fit$lower)
.df <- .df[!duplicated(.df[,c("x", "z")]), ]
.df <- .fit <- data.frame(.fit, .df[, c("z"), drop = FALSE])
.med <- plyr::ddply(.fit, plyr::.(z), function(x) {
data.frame(
median = min(subset(x, y < (0.5 + .Machine$double.eps^0.5))$x)
)})
.df <- .fit <- rbind(unique(data.frame(x = 0, y = 1, nrisk = NA, nevent = NA,
ncensor = NA, upper = 1, lower = 1, .df[, c("z"), drop = FALSE])), .fit)
.cens <- subset(.fit, ncensor == 1)
.tmp1 <- data.frame(as.table(by(.df, .df[, c("z"), drop = FALSE], function(d)
max(d$nrisk, na.rm = TRUE))))
.tmp1$x <- 0
.nrisk <- .tmp1
for (i in 1:9) {.df <- subset(.fit, x < 100 * i); .tmp2 <-
data.frame(as.table(by(.df, .df[, c("z"), drop = FALSE], function(d) if
(all(is.na(d$nrisk))) NA else min(d$nrisk - d$nevent - d$ncensor, na.rm = TRUE))));
.tmp2$x <- 100 * i; .tmp2$Freq[is.na(.tmp2$Freq)] <- .tmp1$Freq[is.na(.tmp2$Freq)];
.tmp1 <- .tmp2; .nrisk <- rbind(.nrisk, .tmp2)}
.nrisk$y <- rep(seq(0.075, 0.025, -0.05), 10)
.plot <- ggplot(data = .fit, aes(x = x, y = y, colour = z)) +
RcmdrPlugin.KMggplot2::geom_stepribbon(data = .fit, aes(x = x, ymin = lower, ymax =
upper, fill = z), alpha = 0.25, colour = "transparent", show.legend = FALSE, kmplot
= TRUE) + geom_step(size = 1.5) +
geom_linerange(data = .cens, aes(x = x, ymin = y,
ymax = y + 0.02), size = 1.5) +
geom_text(data = .pval, aes(y = y, x = x, label =
label), colour = "black", hjust = 0, vjust = -0.5, parse = TRUE, show.legend =
FALSE, size = 14 * 0.282, family = "sans") +
geom_vline(data = .med, aes(xintercept
= median), colour = "black", lty = 2) + scale_x_continuous(breaks = seq(0, 900, by
= 100), limits = c(0, 900)) +
scale_y_continuous(limits = c(0, 1), expand = c(0.01,0)) + scale_colour_brewer(palette = "Set1") + scale_fill_brewer(palette = "Set1") +
xlab("Time from entry") + ylab("Proportion of survival") + labs(colour = "sex") +
ggthemes::theme_calc(base_size = 14, base_family = "sans") + theme(legend.position
= c(1, 1), legend.justification = c(1, 1))
.nrisk$y <- ((.nrisk$y - 0.025) / (max(.nrisk$y) - 0.025) + 0.5) * 0.5
.plot2 <- ggplot(data = .nrisk, aes(x = x, y = y, label = Freq, colour = z)) +
geom_text(size = 14 * 0.282, family = "sans") + scale_x_continuous(breaks = seq(0,900, by = 100), limits = c(0, 900)) +
scale_y_continuous(limits = c(0, 1)) +
scale_colour_brewer(palette = "Set1") + ylab("Proportion of survival") +
RcmdrPlugin.KMggplot2::theme_natrisk(ggthemes::theme_calc, 14, "sans")
.plot3 <- ggplot(data = subset(.nrisk, x == 0), aes(x = x, y = y, label = z, colour = z)) +
geom_text(hjust = 0, size = 14 * 0.282, family = "sans") +
scale_x_continuous(limits = c(-5, 5)) + scale_y_continuous(limits = c(0, 1)) +
scale_colour_brewer(palette = "Set1") +
RcmdrPlugin.KMggplot2::theme_natrisk21(ggthemes::theme_calc, 14, "sans")
.plotb <- ggplot(.df, aes(x = x, y = y)) + geom_blank() +
RcmdrPlugin.KMggplot2::theme_natriskbg(ggthemes::theme_calc, 14, "sans")
grid::grid.newpage(); grid::pushViewport(grid::viewport(layout =
grid::grid.layout(2, 2, heights = unit(c(1, 3), c("null", "lines")), widths =
unit(c(4, 1), c("lines", "null")))));
print(.plotb, vp =
grid::viewport(layout.pos.row = 1:2, layout.pos.col = 1:2));
print(.plot , vp =
grid::viewport(layout.pos.row = 1 , layout.pos.col = 1:2));
print(.plot2, vp =
grid::viewport(layout.pos.row = 2 , layout.pos.col = 1:2));
print(.plot3, vp =
grid::viewport(layout.pos.row = 2 , layout.pos.col = 1 ));
.plot <- recordPlot()
print(.plot)
Here's a start (code below)
I guess you can create the table need and replace it by the random.table
# install.packages("ggplot2", dependencies = TRUE)
# install.packages("RGraphics", dependencies = TRUE)
# install.packages("gridExtra", dependencies = TRUE)
# install.packages("survival", dependencies = TRUE)
require(ggplot2)
library(RGraphics)
library(gridExtra)
library(survival)
# Plot
data(lung)
sf.sex <- survfit(Surv(time, status) ~ sex, data = lung)
pl.sex <- ggsurv(sf.sex) +
geom_ribbon(aes(ymin=low,ymax=up,fill=group),alpha=0.3) +
guides(fill=guide_legend("sex"))
# Table
random.table <- data.frame("CL 95"=rnorm(5),n=runif(5,1,3))
pl.table <- tableGrob(random.table)
# Arrange the plots on the same page
grid.arrange(pl.sex, pl.table, ncol=1)