ggplot2: Add points to geom_line

Question

I have a line-plot in ggplot2 and I want to add points (=shapes) for each data row to clearly identify it. I do not(!) need a shape/point at every data-point but instead some values would be sufficient. See the following example:

library(ggplot2)
library(data.table)
d=data.table(x=seq(0, 100, by=0.1), y=seq(0,1000)))
ggplot(d, aes(x=x, y=y))+geom_line()
ggplot(d, aes(x=x, y=y))+geom_line()+geom_point()

Line Only With added points

Due to the huge number of samples, the shapes are not visible anymore but overdraw each other. I only need some of them, perhaps a equidistant spacing would look the best, but I'm open to any other solution.

emhart · Accepted Answer · 2012-09-10 04:05:42Z

13

You can also add some points, just thin the data with an index.

library(ggplot2)
library(data.table)
d=data.table(x=seq(0, 100, by=0.1), y=seq(0,1000))
ggplot(d, aes(x=x, y=y))+geom_line()
#Change the length parameter for fewer or more points
thinned <- floor(seq(from=1,to=dim(d)[1],length=70))
ggplot(d, aes(x=x, y=y))+geom_line()+geom_point(data=d[thinned,],aes(x=x,y=y))

enter image description here

edited Sep 10, 2012 at 4:05

answered Sep 9, 2012 at 23:34

emhart

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Sven Hohenstein · Accepted Answer · 2017-04-10 16:57:46Z

6

You can plot points at certain quantiles with quantile. For example, the following sequence generates deciles.

quantile(rnorm(100), probs = seq(0, 1, .1))
#         0%         10%         20%         30%         40%         50%         60%         70%         80%         90%        100% 
#-2.43934306 -1.17208001 -0.91497203 -0.69489868 -0.46306926 -0.24133438 -0.03434118  0.39989589  0.72331902  1.06402664  2.02892420 

library(ggplot2)
library(data.table)
d <- data.table(x = seq(0, 100, by=0.1), y = seq(0,1000))

ggplot(d, aes(x=x, y=y))+
geom_line()+
geom_point(aes(x = quantile(x, probs = seq(0, 1, .1)),
               y = quantile(y, probs = seq(0, 1, .1))))

Plot with points at deciles

edited Apr 10, 2017 at 16:57

answered Sep 8, 2012 at 16:18

Sven Hohenstein

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Comments

Bar · Accepted Answer · 2016-12-01 11:10:18Z

Just wanted to add a data.table solution that can work with grouped data as well:

library(ggplot2)
library(data.table)

# Creates data from the Weibull distribution
weib_dt <- function(x = seq(0, 4.0, 0.01), w_shape = 1, w_scale = 1) {
  y = dweibull(x, shape = w_shape, scale = w_scale)
  data.table("shape" = as.factor(w_shape), "scale" = as.factor(w_scale), "x" = x, "y" = y)
}

dt_a <- weib_dt(w_shape = 0.5)
dt_b <- weib_dt(w_shape = 1.0)
dt_c <- weib_dt(w_shape = 2.0)
# Bind multiple Weibull samples together, created from different parametrizations
dt_merged <- rbindlist(list(dt_a, dt_b, dt_c))

# Create the plot, using all the points for the lines, and only 9 points per group for the points.
ggplot(dt_merged, aes(x, y, group=shape, color=shape)) + 
  coord_cartesian(ylim = c(0, 1.5)) +
  geom_line() +
  geom_point(data=dt_merged[, .SD[floor(seq(1, .N, length=9))], by=shape], 
             aes(x, y, group = shape,  color = shape, shape = shape))

The trick here is the use of seq as with the suggested solutions above, but this time it's done within the group (using .SD). Note that currently .SD can have bad performance, you can use the more verbose dt[dt[, ..., by =shape]$V1] if this is slow.

This will create the following output:

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