在线的末端绘制标签

Not sure if it is the best way, but you could try the following (play a bit with xlim for correctly setting the limits):

library(dplyr)
lab <- tapply(temp.dat$Capex, temp.dat$State, last)
ggplot(temp.dat) +
geom_line(aes(x = Year, y = Capex, group = State, colour = State)) +
scale_color_discrete(guide = FALSE) +
geom_text(aes(label = names(lab), x = 12, colour = names(lab), y = c(lab), hjust = -.02))

You didn't emulate @Baptiste's solution 100%. You need to use annotation_custom and loop through all your Capex's:

library(ggplot2)
library(dplyr)
library(grid)


temp.dat <- structure(list(Year = c("2003", "2004", "2005", "2006", "2007",
"2008", "2009", "2010", "2011", "2012", "2013", "2014", "2003",
"2004", "2005", "2006", "2007", "2008", "2009", "2010", "2011",
"2012", "2013", "2014", "2003", "2004", "2005", "2006", "2007",
"2008", "2009", "2010", "2011", "2012", "2013", "2014", "2003",
"2004", "2005", "2006", "2007", "2008", "2009", "2010", "2011",
"2012", "2013", "2014"), State = structure(c(1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L), .Label = c("VIC",
"NSW", "QLD", "WA"), class = "factor"), Capex = c(5.35641472365348,
5.76523240652641, 5.24727577535625, 5.57988239709746, 5.14246402568366,
4.96786288162828, 5.493190785287, 6.08500616799372, 6.5092228474591,
7.03813541623157, 8.34736513875897, 9.04992300432169, 7.15830329914056,
7.21247045701994, 7.81373928617117, 7.76610217197542, 7.9744994967006,
7.93734452080786, 8.29289899132255, 7.85222269563982, 8.12683746325074,
8.61903784301649, 9.7904327253813, 9.75021175267288, 8.2950673974226,
6.6272705639724, 6.50170524635367, 6.15609626379471, 6.43799637295979,
6.9869551384028, 8.36305663640294, 8.31382617231745, 8.65409824343971,
9.70529678167458, 11.3102788081848, 11.8696420977237, 6.77937303542605,
5.51242844820827, 5.35789621712839, 4.38699327451101, 4.4925792218211,
4.29934654081527, 4.54639175257732, 4.70040615159951, 5.04056109514957,
5.49921208937735, 5.96590909090909, 6.18700407463007)), class = "data.frame", row.names = c(NA,
-48L), .Names = c("Year", "State", "Capex"))


temp.dat$Year <- factor(temp.dat$Year)


color <- c("#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072")


gg <- ggplot(temp.dat)
gg <- gg + geom_line(aes(x=Year, y=Capex, group=State, colour=State))
gg <- gg + scale_color_manual(values=color)
gg <- gg + labs(x=NULL)
gg <- gg + theme_bw()
gg <- gg + theme(legend.position="none")


states <- temp.dat %>% filter(Year==2014)


for (i in 1:nrow(states))  {
print(states$Capex[i])
print(states$Year[i])
gg <- gg + annotation_custom(
grob=textGrob(label=states$State[i],
hjust=0, gp=gpar(cex=0.75, col=color[i])),
ymin=states$Capex[i],
ymax=states$Capex[i],
xmin=states$Year[i],
xmax=states$Year[i])
}


gt <- ggplot_gtable(ggplot_build(gg))
gt$layout$clip[gt$layout$name == "panel"] <- "off"
grid.newpage()
grid.draw(gt)

(You'll want to change the yellow if you keep the white background.)

To use Baptiste's idea, you need to turn off clipping. But when you do, you get garbage. In addition, you need to suppress the legend, and, for geom_text, select Capex for 2014, and increase the margin to give room for the labels. (Or you can adjust the hjust parameter to move the labels inside the plot panel.) Something like this:

library(ggplot2)
library(grid)


p = ggplot(temp.dat) +
geom_line(aes(x = Year, y = Capex, group = State, colour = State)) +
geom_text(data = subset(temp.dat, Year == "2014"), aes(label = State, colour = State, x = Inf, y = Capex), hjust = -.1) +
scale_colour_discrete(guide = 'none')  +
theme(plot.margin = unit(c(1,3,1,1), "lines"))


# Code to turn off clipping
gt <- ggplotGrob(p)
gt$layout$clip[gt$layout$name == "panel"] <- "off"
grid.draw(gt)

But, this is the sort of plot that is perfect for directlabels.

library(ggplot2)
library(directlabels)


ggplot(temp.dat, aes(x = Year, y = Capex, group = State, colour = State)) +
geom_line() +
scale_colour_discrete(guide = 'none') +
scale_x_discrete(expand=c(0, 1)) +
geom_dl(aes(label = State), method = list(dl.combine("first.points", "last.points")), cex = 0.8)

Edit To increase the space between the end point and the labels:

ggplot(temp.dat, aes(x = Year, y = Capex, group = State, colour = State)) +
geom_line() +
scale_colour_discrete(guide = 'none') +
scale_x_discrete(expand=c(0, 1)) +
geom_dl(aes(label = State), method = list(dl.trans(x = x + 0.2), "last.points", cex = 0.8)) +
geom_dl(aes(label = State), method = list(dl.trans(x = x - 0.2), "first.points", cex = 0.8))

A newer solution is to use ggrepel:

library(ggplot2)
library(ggrepel)
library(dplyr)


temp.dat %>%
mutate(label = if_else(Year == max(Year), as.character(State), NA_character_)) %>%
ggplot(aes(x = Year, y = Capex, group = State, colour = State)) +
geom_line() +
geom_label_repel(aes(label = label),
nudge_x = 1,
na.rm = TRUE)

I provide another answer for weary ggplot folk.

This solution's principle can be applied quite generally.

Plot_df <-
temp.dat %>% mutate_if(is.factor, as.character) %>%  # Who has time for factors..
mutate(Year = as.numeric(Year))

And now, we can subset our data

ggplot() +
geom_line(data = Plot_df, aes(Year, Capex, color = State)) +
geom_text(data = Plot_df %>% filter(Year == last(Year)), aes(label = State,
x = Year + 0.5,
y = Capex,
color = State)) +
guides(color = FALSE) + theme_bw() +
scale_x_continuous(breaks = scales::pretty_breaks(10))

The last pretty_breaks part is just to fix the axis below.

I'd like to add a solution for cases when you have longer label names. In all of the solutions provided, the labels are within the plot canvas, but if you have longer names, they'll get cut off. Here's how I solved that issue:

library(tidyverse)


# Make the "State" variable have longer levels
temp.dat <- temp.dat %>%
mutate(State = paste0(State, '-a-long-string'))


ggplot(temp.dat, aes(x = Year, y = Capex, color = State, group = State)) +
geom_line() +
# Add labels at the end of the line
geom_text(data = filter(temp.dat, Year == max(Year)),
aes(label = State),
hjust = 0, nudge_x = 0.1) +
# Allow labels to bleed past the canvas boundaries
coord_cartesian(clip = 'off') +
# Remove legend & adjust margins to give more space for labels
# Remember, the margins are t-r-b-l
theme(legend.position = 'none',
plot.margin = margin(0.1, 2.6, 0.1, 0.1, "cm"))

I came to this question looking to direct label a fitted line (e.g. loess()) at the last fitted point, not the last data point. I eventually worked out an approach to do this, largely based on tidyverse It should also work for linear regression with a few mods, so I leave it here for posterity.

library(tidyverse)


temp.dat$Year <- as.numeric(temp.dat$Year)
temp.dat$State <- as.character(temp.dat$State)


#example of loess for multiple models
#https://stackoverflow.com/a/55127487/4927395


models <- temp.dat %>%
tidyr::nest(-State) %>%
dplyr::mutate(
# Perform loess calculation on each CpG group
m = purrr::map(data, loess,
formula = Capex ~ Year, span = .75),
# Retrieve the fitted values from each model
fitted = purrr::map(m, `[[`, "fitted")
)


# Apply fitted y's as a new column
results <- models %>%
dplyr::select(-m) %>%
tidyr::unnest()


#find final x values for each group
my_last_points <- results %>% group_by(State) %>% summarise(Year = max(Year, na.rm=TRUE))


#Join dataframe of predictions to group labels
my_last_points$pred_y <- left_join(my_last_points, results)


# Plot with loess line for each group
ggplot(results, aes(x = Year, y = Capex, group = State, colour = State)) +
geom_line(alpha = I(7/10), color="grey", show.legend=F) +
#stat_smooth(size=2, span=0.3, se=F, show_guide=F)
geom_point(size=1) +
geom_smooth(se=FALSE)+
geom_text(data = my_last_points, aes(x=Year+0.5, y=pred_y$fitted, label = State))

There is a new package to address this very popular problem. {geomtextpath} gives some very flexible options for direct labelling, more than "only" labelling at the end...

Moreover, the labels will follow the curves! This might not be to everyone's taste, but I find this an extremely neat look.

library(geomtextpath)


## end of line
ggplot(temp.dat) +
geom_textline(aes(
x = Year, y = Capex, group = State, colour = State, label = State
),
hjust = 1
) +
theme(legend.position = "none")

## somewhere in the middle
ggplot(temp.dat) +
geom_textline(aes(
x = Year, y = Capex, group = State, colour = State, label = State
),
hjust = .7
) +
theme(legend.position = "none")

There are plenty of geoms and also one for prediction curves based on geom_smooth. (answering to user Mark Neal)

ggplot(temp.dat, aes(x = Year, y = Capex, group = State, colour = State)) +
geom_line() +
## note this is using the current dev version. you currently have to specify method argument, otherwise the disambiguation of some function fails.
## see also https://github.com/AllanCameron/geomtextpath/issues/79) +
geom_textsmooth(aes(label = State),
lty = 2,
hjust = 1) +
theme(legend.position = "none")
#> `geom_smooth()` using method = 'loess' and formula 'y ~ x'

^{Created on 2022-07-12 by the reprex package (v2.0.1)}