I want to show the x-axis labels and the form of the line clearly on this plot. It is a point plot with a lot of categories along the x-axis which makes the plot very wide and very hard to read the x-axis.
Would it be possible to fold the plot in half and display it on two panels, one above the other? How would I do that? I thought about hacking around with facet_wrap but this got ugly with the ordered points (as I wish to maintain the order of the x-axis based on the value).
Or are there better ways of showing this data? The position of the categories along the x-axis is of interest, as is the shape of the line formed by the points.
I generated the example plot using this code:
library(stringi)
example <- data.frame(
cat = do.call(paste0, Map(stri_rand_strings, n=150, length=c(25, 14, 13), pattern = c('[A-Z]', '[0-9]', '[A-Z]'))),
val = rnorm(150, mean = 20)
)
cat_ordered_by_val <- example$cat[order(example$val)]
example$cat = factor(example$cat, levels=cat_ordered_by_val)
ggplot(example, aes(y = val, x = cat)) +
geom_point() +
ylab("Value") + xlab("Category") +
theme_bw() +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1, size=5))
ggsave("~/Desktop/what_a_plot.jpg")
This puts points in one of the two facets in alternating ways. You can also do mutate(facet = row_number() < nrow(example) / 2) to put the first half of the points in one facet and the other half in the other facet:
library(tidyverse)
example <- data.frame(
cat = do.call(paste0, Map(stri_rand_strings, n = 150, length = c(25, 14, 13), pattern = c("[A-Z]", "[0-9]", "[A-Z]"))),
val = rnorm(150, mean = 20)
)
cat_ordered_by_val <- example$cat[order(example$val)]
example$cat <- factor(example$cat, levels = cat_ordered_by_val)
example %>%
arrange(cat) %>%
mutate(facet = row_number() %% 2) %>%
ggplot(aes(y = val, x = cat)) +
geom_point() +
ylab("Value") +
xlab("Category") +
theme_bw() +
facet_wrap(~facet, ncol = 1, scales = "free") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1, size = 5))
Related
With this data:
df <- data.frame(value =c(20, 50, 90),
group = c(1, 2,3))
I can get a bar chart:
df %>% ggplot(aes(x = group, y = value, fill = value)) +
geom_col() +
coord_flip()+
scale_fill_viridis_c(option = "C") +
theme(legend.position = "none")
But I would like to have the colors of those bars to vary according to their corresponding values in value.
I have managed to change them using geom_raster:
ggplot() +
geom_raster(aes(x = c(0:20), y = .9, fill = c(0:20)),
interpolate = TRUE) +
geom_raster(aes(x = c(0:50), y = 2, fill = c(0:50)),
interpolate = TRUE) +
geom_raster(aes(x = c(0:90), y = 3.1, fill = c(0:90)),
interpolate = TRUE) +
scale_fill_viridis_c(option = "C") +
theme(legend.position = "none")
This approach is not efficient when I have many groups in real data. Any suggestions to get it done more efficiently would be appreciated.
I found the accepted answer to a previous similar question, but "These numbers needs to be adjusted depending on the number of x values and range of y". I was looking for an approach that I do not have to adjust numbers based on data. David Gibson's answer fits my purpose.
It does not look like this is supported natively in ggplot. I was able to get something close by adding additional rows, ranging from 0 to value) to the data. Then use geom_tile and separating the tiles by specifying width.
library(tidyverse)
df <- data.frame(value = c(20, 50, 90),
group = c(1, 2, 3))
df_expanded <- df %>%
rowwise() %>%
summarise(group = group,
value = list(0:value)) %>%
unnest(cols = value)
df_expanded %>%
ggplot() +
geom_tile(aes(
x = group,
y = value,
fill = value,
width = 0.9
)) +
coord_flip() +
scale_fill_viridis_c(option = "C") +
theme(legend.position = "none")
If this is too pixilated you can increase the number of rows generated by replacing list(0:value) with seq(0, value, by = 0.1).
This is a real hack using ggforce. This package has a geom that can take color gradients but it is for a line segment. I've just increased the size to make the line segment look like a bar. I made all the bars the same length to get the correct gradient, then covered a portion of each bar over with the same color as the background color to make them appear to be the correct length. Had to hide the grid lines, however. :-)
df %>%
ggplot() +
geom_link(aes(x = 0, xend = max(value), y = group, yend = group, color = stat(index)), size = 30) +
geom_link(aes(x = value, xend = max(value), y = group, yend = group), color = "grey", size = 31) +
scale_color_viridis_c(option = "C") +
theme(legend.position = "none", panel.background = element_rect(fill = "grey"),
panel.grid = element_blank()) +
ylim(0.5, max(df$group)+0.5 )
I have created one monthly plot with facet_wrap
.
So in the plot I have 3 rows and 4 columns. Now I want to set my common y axis for each rows e.g 1st row should have one common y values, same goes with the 2nd and 3rd rows.
I tried but not able to do it.
I used
ggplot(data = PB,
aes(x = new_date, y = Mean, group = 1)) +
geom_line(aes(color = experiment)) +
theme(legend.title = element_blank()) +
facet_wrap( ~MonthAbb, ncol = 4, scales = "free")
The issue is the scales = "free". Remove this and it will set a common scale across rows and columns (or use "free_y" or "free_x" to adjust accordingly).
If what you're looking for is a separate scale for each row, it will require a bit more work. Check this solution at R: How do I use coord_cartesian on facet_grid with free-ranging axis which layers invisible points on the plot to force the look you want. Otherwise a simple solution might to look at using gridExtra and plot each row separately, then merge into a grid.
Edit: a gridExtra solution would look something like:
library(gridExtra)
g1 <- ggplot(data = PB1, aes(x=new_date, y = Mean, group = 1)) +
geom_line(aes(color = experiment)) +
theme(legend.title = element_blank())
g2 <- ggplot(data = PB2, aes(x=new_date, y = Mean, group = 1)) +
geom_line(aes(color = experiment)) +
theme(legend.title = element_blank())
grid.arrange(g1, g2, nrow=2)
Here is an option to set these on a per-panel basis. It is based on a function I've put in a github package. I'm using some dummy data as example.
library(ggplot2)
library(ggh4x)
df <- data.frame(
x = rep(1:20, 9),
y = c(cumsum(rnorm(60)) + 90,
cumsum(rnorm(60)) - 90,
cumsum(rnorm(60))),
row = rep(LETTERS[1:3], each = 60),
col = rep(LETTERS[1:3], each = 20)
)
ggplot(df, aes(x, y)) +
geom_line() +
facet_wrap(row ~ col, scales = "free_y") +
facetted_pos_scales(
y = rep(list(
scale_y_continuous(limits = c(90, 100)),
scale_y_continuous(limits = c(-100, -80)),
scale_y_continuous(limits = c(0, 20))
), each = 3)
)
I have a time-series of data, where I'm plotting diagnosis rates for a disease on the y-axis DIAG_RATE_65_PLUS, and geographical groups for comparison on the x-axis NAME as a simple bar graph. My time variable is ACH_DATEyearmon, which the animation is cycling through as seen in the title.
df %>% ggplot(aes(reorder(NAME, DIAG_RATE_65_PLUS), DIAG_RATE_65_PLUS)) +
geom_bar(stat = "identity", alpha = 0.66) +
labs(title='{closest_state}') +
theme(plot.title = element_text(hjust = 1, size = 22),
axis.text.x=element_blank()) +
transition_states(ACH_DATEyearmon, transition_length = 1, state_length = 1) +
ease_aes('linear')
I've reordered NAME so it gets ranked by DIAG_RATE_65_PLUS.
What gganimate produces:
I now have two questions:
1) How exactly does gganimate reorder the data? There is some overall general reordering, but each month has no frame where the groups are perfectly ordered by DIAG_RATE_65_PLUS from smallest to biggest. Ideally, I would like the final month "Aug 2018" to be ordered perfectly. All of the previous months can have their x-axis based on the ordered NAME for "Aug 2018`.
2) Is there an option in gganimate where the groups "shift" to their correct rank for each month in the bar chart?
Plots for my comment queries:
https://i.stack.imgur.com/s2UPw.gif
https://i.stack.imgur.com/Z1wfd.gif
#JonSpring
df %>%
ggplot(aes(ordering, group = NAME)) +
geom_tile(aes(y = DIAG_RATE_65_PLUS/2,
height = DIAG_RATE_65_PLUS,
width = 0.9), alpha = 0.9, fill = "gray60") +
geom_hline(yintercept = (2/3)*25, linetype="dotdash") +
# text in x-axis (requires clip = "off" in coord_cartesian)
geom_text(aes(y = 0, label = NAME), hjust = 2) + ## trying different hjust values
theme(plot.title = element_text(hjust = 1, size = 22),
axis.ticks.y = element_blank(), ## axis.ticks.y shows the ticks on the flipped x-axis (the now metric), and hides the ticks from the geog layer
axis.text.y = element_blank()) + ## axis.text.y shows the scale on the flipped x-axis (the now metric), and hides the placeholder "ordered" numbers from the geog layer
coord_cartesian(clip = "off", expand = FALSE) +
coord_flip() +
labs(title='{closest_state}', x = "") +
transition_states(ACH_DATEyearmon,
transition_length = 2, state_length = 1) +
ease_aes('cubic-in-out')
With hjust=2, labels are not aligned and move around.
Changing the above code with hjust=1
#eipi10
df %>%
ggplot(aes(y=NAME, x=DIAG_RATE_65_PLUS)) +
geom_barh(stat = "identity", alpha = 0.66) +
geom_hline(yintercept=(2/3)*25, linetype = "dotdash") + #geom_vline(xintercept=(2/3)*25) is incompatible, but geom_hline works, but it's not useful for the plot
labs(title='{closest_state}') +
theme(plot.title = element_text(hjust = 1, size = 22)) +
transition_states(ACH_DATEyearmon, transition_length = 1, state_length = 50) +
view_follow(fixed_x=TRUE) +
ease_aes('linear')
To add on to #eipi10's great answer, I think this is a case where it's worth replacing geom_bar for more flexibility. geom_bar is normally quite convenient for discrete categories, but it doesn't let us take full advantage of gganimate's silky-smooth animation glory.
For instance, with geom_tile, we can recreate the same appearance as geom_bar, but with fluid movement on the x-axis. This helps to keep visual track of each bar and to see which bars are shifting order the most. I think this addresses the 2nd part of your question nicely.
To make this work, we can add to the data a new column showing the ordering that should be used at each month. We save this order as a double, not an integer (by using* 1.0). This will allow gganimate to place a bar at position 1.25 when it's animating between position 1 and 2.
df2 <- df %>%
group_by(ACH_DATEyearmon) %>%
mutate(ordering = min_rank(DIAG_RATE_65_PLUS) * 1.0) %>%
ungroup()
Now we can plot in similar fashion, but using geom_tile instead of geom_bar. I wanted to show the NAME both on top and at the axis, so I used two geom_text calls with different y values, one at zero and one at the height of the bar. vjust lets us align each vertically using text line units.
The other trick here is to turn off clipping in coord_cartesian, which lets the bottom text go below the plot area, into where the x-axis text would usually go.
p <- df2 %>%
ggplot(aes(ordering, group = NAME)) +
geom_tile(aes(y = DIAG_RATE_65_PLUS/2,
height = DIAG_RATE_65_PLUS,
width = 0.9), alpha = 0.9, fill = "gray60") +
# text on top of bars
geom_text(aes(y = DIAG_RATE_65_PLUS, label = NAME), vjust = -0.5) +
# text in x-axis (requires clip = "off" in coord_cartesian)
geom_text(aes(y = 0, label = NAME), vjust = 2) +
coord_cartesian(clip = "off", expand = FALSE) +
labs(title='{closest_state}', x = "") +
theme(plot.title = element_text(hjust = 1, size = 22),
axis.ticks.x = element_blank(),
axis.text.x = element_blank()) +
transition_states(ACH_DATEyearmon,
transition_length = 2, state_length = 1) +
ease_aes('cubic-in-out')
animate(p, nframes = 300, fps = 20, width = 400, height = 300)
Back to your first question, here's a color version that I made by removing fill = "gray60" from the geom_tile call. I sorted the NAME categories in order of Aug 2017, so they will look sequential for that one, as you described.
There's probably a better way to do that sorting, but I did it by joining df2 to a table with just the Aug 2017 ordering.
Aug_order <- df %>%
filter(ACH_DATEyearmon == "Aug 2017") %>%
mutate(Aug_order = min_rank(DIAG_RATE_65_PLUS) * 1.0) %>%
select(NAME, Aug_order)
df2 <- df %>%
group_by(ACH_DATEyearmon) %>%
mutate(ordering = min_rank(DIAG_RATE_65_PLUS) * 1.0) %>%
ungroup() %>%
left_join(Aug_order) %>%
mutate(NAME = fct_reorder(NAME, -Aug_order))
The bar ordering is done by ggplot and is not affected by gganimate. The bars are being ordered based on the sum of DIAG_RATE_65_PLUS within each ACH_DATEyearmon. Below I'll show how the bars are ordered and then provide code for creating the animated plot with the desired sorting from low to high in each frame.
To see how the bars are ordered, first let's create some fake data:
library(tidyverse)
library(gganimate)
theme_set(theme_classic())
# Fake data
dates = paste(rep(month.abb, each=10), 2017)
set.seed(2)
df = data.frame(NAME=c(replicate(12, sample(LETTERS[1:10]))),
ACH_DATEyearmon=factor(dates, levels=unique(dates)),
DIAG_RATE_65_PLUS=c(replicate(12, rnorm(10, 30, 5))))
Now let's make a single bar plot. The bars are the sum of DIAG_RATE_65_PLUS for each NAME. Note the order of the x-axis NAME values:
df %>%
ggplot(aes(reorder(NAME, DIAG_RATE_65_PLUS), DIAG_RATE_65_PLUS)) +
geom_bar(stat = "identity", alpha = 0.66) +
labs(title='{closest_state}') +
theme(plot.title = element_text(hjust = 1, size = 22))
You can see below that the ordering is the same when we explicitly sum DIAG_RATE_65_PLUS by NAME and sort by the sum:
df %>% group_by(NAME) %>%
summarise(DIAG_RATE_65_PLUS = sum(DIAG_RATE_65_PLUS)) %>%
arrange(DIAG_RATE_65_PLUS)
NAME DIAG_RATE_65_PLUS
1 A 336.1271
2 H 345.2369
3 B 346.7151
4 I 350.1480
5 E 356.4333
6 C 367.4768
7 D 368.2225
8 F 368.3765
9 J 368.9655
10 G 387.1523
Now we want to create an animation that sorts NAME by DIAG_RATE_65_PLUS separately for each ACH_DATEyearmon. To do this, let's first generate a new column called order that sets the ordering we want:
df = df %>%
arrange(ACH_DATEyearmon, DIAG_RATE_65_PLUS) %>%
mutate(order = 1:n())
Now we create the animation. transition_states generates the frames for each ACH_DATEyearmon. view_follow(fixed_y=TRUE)shows x-values only for the current ACH_DATEyearmon and maintains the same y-axis range for all frames.
Note that we use order as the x variable, but then we run scale_x_continuous to change the x-labels to be the NAME values. I've included these labels in the plot so you can see that they change with each ACH_DATEyearmon, but you can of course remove them in your actual plot as you did in your example.
p = df %>%
ggplot(aes(order, DIAG_RATE_65_PLUS)) +
geom_bar(stat = "identity", alpha = 0.66) +
labs(title='{closest_state}') +
theme(plot.title = element_text(hjust = 1, size = 22)) +
scale_x_continuous(breaks=df$order, labels=df$NAME) +
transition_states(ACH_DATEyearmon, transition_length = 1, state_length = 50) +
view_follow(fixed_y=TRUE) +
ease_aes('linear')
animate(p, nframes=60)
anim_save("test.gif")
If you turn off view_follow(), you can see what the "whole" plot looks like (and you can, of course, see the full, non-animated plot by stopping the code before the transition_states line).
p = df %>%
ggplot(aes(order, DIAG_RATE_65_PLUS)) +
geom_bar(stat = "identity", alpha = 0.66) +
labs(title='{closest_state}') +
theme(plot.title = element_text(hjust = 1, size = 22)) +
scale_x_continuous(breaks=df$order, labels=df$NAME) +
transition_states(ACH_DATEyearmon, transition_length = 1, state_length = 50) +
#view_follow(fixed_y=TRUE) +
ease_aes('linear')
UPDATE: To answer your questions...
To order by a given month's values, turn the data into a factor with the levels ordered by that month. To plot a rotated graph, instead of coord_flip, we'll use geom_barh (horizontal bar plot) from the ggstance package. Note that we have to switch the y's and x's in aes and view_follow() and that the order of the y-axis NAME values is now constant:
library(ggstance)
# Set NAME order based on August 2017 values
df = df %>%
arrange(DIAG_RATE_65_PLUS) %>%
mutate(NAME = factor(NAME, levels=unique(NAME[ACH_DATEyearmon=="Aug 2017"])))
p = df %>%
ggplot(aes(y=NAME, x=DIAG_RATE_65_PLUS)) +
geom_barh(stat = "identity", alpha = 0.66) +
labs(title='{closest_state}') +
theme(plot.title = element_text(hjust = 1, size = 22)) +
transition_states(ACH_DATEyearmon, transition_length = 1, state_length = 50) +
view_follow(fixed_x=TRUE) +
ease_aes('linear')
animate(p, nframes=60)
anim_save("test3.gif")
For smooth transitions, it seems like #JonSpring's answer handles that well.
I have a test dataset like this:
df_test <- data.frame(
proj_manager = c('Emma','Emma','Emma','Emma','Emma','Alice','Alice'),
proj_ID = c(1, 2, 3, 4, 5, 6, 7),
stage = c('B','B','B','A','C','A','C'),
value = c(15,15,20,20,20,70,5)
)
Preparation for viz:
input <- select(df_test, proj_manager, proj_ID, stage, value) %>%
filter(proj_manager=='Emma') %>%
do({
proj_value_by_manager = sum(distinct(., proj_ID, value)$value);
mutate(., proj_value_by_manager = proj_value_by_manager)
}) %>%
group_by(stage) %>%
do({
sum_value_byStage = sum(distinct(.,proj_ID,value)$value);
mutate(.,sum_value_byStage= sum_value_byStage)
}) %>%
mutate(count_proj = length(unique(proj_ID)))
commapos <- function(x, ...) {
format(abs(x), big.mark = ",", trim = TRUE,
scientific = FALSE, ...) }
Visualization:
ggplot (input, aes(x=stage, y = count_proj)) +
geom_bar(stat = 'identity')+
geom_bar(aes(y=-proj_value_by_manager),
stat = "identity", fill = "Blue") +
scale_y_continuous(labels = commapos)+
coord_flip() +
ylab('') +
geom_text(aes(label= sum_value_byStage), hjust = 5) +
geom_text(aes(label= count_proj), hjust = -1) +
labs(title = "Emma: 4 projects| $90M Values \n \n Commitment|Projects") +
theme(plot.title = element_text(hjust = 0.5)) +
geom_hline(yintercept = 0, linetype =1)
My questions are:
Why is the y-values not showing up right? e.g. C is labeled 20, but nearing hitting 100 on the scale.
How to adjust the position of labels so that it sits on the top of its bar？
How to re-scale the y axis so that both the very short bar of 'count of project' and long bar of 'Project value' can be well displayed?
Thank you all for the help!
I think your issues are coming from the fact that:
(1) Your dataset has duplicated values. This causes geom_bar to add all of them together. For example there are 3 obs for B where proj_value_by_manager = 90 which is why the blue bar extends to 270 for that group (they all get added).
(2) in your second geom_bar you use y = -proj_value_by_manager but in the geom_text to label this you use sum_value_byStage. That's why the blue bar for A is extending to 90 (since proj_value_by_manager is 90) but the label reads 20.
To get you what I believe the chart you want is you could do:
#Q1: No dupe dataset so it doesnt erroneous add columns
input2 <- input[!duplicated(input[,-c(2,4)]),]
ggplot (input2, aes(x=stage, y = count_proj)) +
geom_bar(stat = 'identity')+
geom_bar(aes(y=-sum_value_byStage), #Q1: changed so this y-value matches your label
stat = "identity", fill = "Blue") +
scale_y_continuous(labels = commapos)+
coord_flip() +
ylab('') +
geom_text(aes(label= sum_value_byStage, y = -sum_value_byStage), hjust = 1) + #Q2: Added in y-value for label and hjust so it will be on top
geom_text(aes(label= count_proj), hjust = -1) +
labs(title = "Emma: 4 projects| $90M Values \n \n Commitment|Projects") +
theme(plot.title = element_text(hjust = 0.5)) +
geom_hline(yintercept = 0, linetype =1)
For your last question, there is no good way to display both of these. One option would be to rescale the small data and still label it with a 1 or 3. However, I didn't do this because once you scale down the blue bars the other bars look OK to me.
I want to explore the directlabels package with ggplot. I am trying to plot labels at the endpoint of a simple line chart; however, the labels are clipped by the plot panel. (I intend to plot about 10 financial time series in one plot and I thought directlabels would be the best solution.)
I would imagine there may be another solution using annotate or some other geoms. But I would like to solve the problem using directlabels. Please see code and image below. Thanks.
library(ggplot2)
library(directlabels)
library(tidyr)
#generate data frame with random data, for illustration and plot:
x <- seq(1:100)
y <- cumsum(rnorm(n = 100, mean = 6, sd = 15))
y2 <- cumsum(rnorm(n = 100, mean = 2, sd = 4))
data <- as.data.frame(cbind(x, y, y2))
names(data) <- c("month", "stocks", "bonds")
tidy_data <- gather(data, month)
names(tidy_data) <- c("month", "asset", "value")
p <- ggplot(tidy_data, aes(x = month, y = value, colour = asset)) +
geom_line() +
geom_dl(aes(colour = asset, label = asset), method = "last.points") +
theme_bw()
On data visualization principles, I would like to avoid extending the x-axis to make the labels fit--this would mean having data space with no data. Rather, I would like the labels to extend toward the white space beyond the chart box/panel (if that makes sense).
In my opinion, direct labels is the way to go. Indeed, I would position labels at the beginning and at the end of the lines, creating space for the labels using expand(). Also note that with the labels, there is no need for the legend.
This is similar to answers here and here.
library(ggplot2)
library(directlabels)
library(grid)
library(tidyr)
x <- seq(1:100)
y <- cumsum(rnorm(n = 100, mean = 6, sd = 15))
y2 <- cumsum(rnorm(n = 100, mean = 2, sd = 4))
data <- as.data.frame(cbind(x, y, y2))
names(data) <- c("month", "stocks", "bonds")
tidy_data <- gather(data, month)
names(tidy_data) <- c("month", "asset", "value")
ggplot(tidy_data, aes(x = month, y = value, colour = asset, group = asset)) +
geom_line() +
scale_colour_discrete(guide = 'none') +
scale_x_continuous(expand = c(0.15, 0)) +
geom_dl(aes(label = asset), method = list(dl.trans(x = x + .3), "last.bumpup")) +
geom_dl(aes(label = asset), method = list(dl.trans(x = x - .3), "first.bumpup")) +
theme_bw()
If you prefer to push the labels into the plot margin, direct labels will do that. But because the labels are positioned outside the plot panel, clipping needs to be turned off.
p1 <- ggplot(tidy_data, aes(x = month, y = value, colour = asset, group = asset)) +
geom_line() +
scale_colour_discrete(guide = 'none') +
scale_x_continuous(expand = c(0, 0)) +
geom_dl(aes(label = asset), method = list(dl.trans(x = x + .3), "last.bumpup")) +
theme_bw() +
theme(plot.margin = unit(c(1,4,1,1), "lines"))
# Code to turn off clipping
gt1 <- ggplotGrob(p1)
gt1$layout$clip[gt1$layout$name == "panel"] <- "off"
grid.draw(gt1)
This effect can also be achieved using geom_text (and probably also annotate), that is, without the need for direct labels.
p2 = ggplot(tidy_data, aes(x = month, y = value, group = asset, colour = asset)) +
geom_line() +
geom_text(data = subset(tidy_data, month == 100),
aes(label = asset, colour = asset, x = Inf, y = value), hjust = -.2) +
scale_x_continuous(expand = c(0, 0)) +
scale_colour_discrete(guide = 'none') +
theme_bw() +
theme(plot.margin = unit(c(1,3,1,1), "lines"))
# Code to turn off clipping
gt2 <- ggplotGrob(p2)
gt2$layout$clip[gt2$layout$name == "panel"] <- "off"
grid.draw(gt2)
Since you didn't provide a reproducible example, it's hard to say what the best solution is. However, I would suggest trying to manually adjust the x-scale. Use a "buffer" increase the plot area.
#generate data frame with random data, for illustration and plot:
p <- ggplot(tidy_data, aes(x = month, y = value, colour = asset)) +
geom_line() +
geom_dl(aes(colour = asset, label = asset), method = "last.points") +
theme_bw() +
xlim(minimum_value, maximum_value + buffer)
Using scale_x_discrete() or scale_x_continuous() would likely also work well here if you want to use the direct labels package. Alternatively, annotate or a simple geom_text would also work well.