I'd like to put legend and more values in x limits.
test <- data.frame(x = runif(10), y = runif(10), mean_y = 0.1)
ggplot(test, aes(x = x, y = y)) + geom_point(color = 'red') +
geom_line(aes(x, mean_y))
Your question needs a bit more explanation and a reproducible example, but to get "more values in x limits" you can try adding
+ expand_limits(x = c(1, 1000))
Just replace the values in the vector by the ones that actually fit your needs.
Related
I have made the graph below with ggplot. I would like to reduce the distance between the y axis and the first category (a). Which function should I use? Thanks! :)
library(ggplot2)
library(reshape2)
data <- data.frame(a = rnorm(10), b = rnorm(10), c = rnorm(10), group = 1:10)
data <- melt(data, id = "group")
ggplot(data, aes(x = variable, y = value, group = group, color = as.factor(group))) + geom_point() + geom_line() + theme_minimal() + theme(axis.line = element_line(), panel.grid = element_blank())
Suppose we have the following plot:
library(ggplot2)
df <- data.frame(x = rep(LETTERS[1:3], 3),
y = rnorm(9),
z = rep(letters[1:3], each = 3))
ggplot(df, aes(x, y, colour = z, group = z)) +
geom_line() +
geom_point()
We can reduce the space between the extreme points and the panel edges by adjusting the expand argument in a scale function:
ggplot(df, aes(x, y, colour = z, group = z)) +
geom_line() +
geom_point() +
scale_x_discrete(expand = c(0,0.1))
Setting expand = c(0,0) completely removes the space. The first argument is a relative number, the second an absolute; so in the example above we set the expand to 0.1 x-axis units.
I am creating several plots in order to create frames for a gif. It is supposed to show growing points over time. (see plot 1 and 2 - the values increase). Using size aesthetic is problematic, because the scaling is done for each plot individually.
I tried to set breaks with scale_size_area() to provide a sequence of absolute values, in order to scale on 'all values' rather than only the values present in each plot. (no success).
Plot 3 shows how the points should be scaled, but this scaling should be achieved in each plot.
library(tidyverse)
df1 <- data.frame(x = letters[1:5], y = 1:5, size2 = 21:25)
ggplot(df1, aes(x, y, size = y)) +
geom_point() +
scale_size_area(breaks = seq(0,25,1))
ggplot(df1, aes(x, y, size = size2)) +
geom_point() +
scale_size_area(breaks = seq(0,25,1))
df2 <- data.frame(x = letters[1:5], y = 1:5, size2 = 21:25) %>% gather(key, value, y:size2)
ggplot(df2, aes(x, value, size = value)) +
geom_point() +
scale_size_area(breaks = seq(0,25,1))
Created on 2019-05-12 by the reprex package (v0.2.1)
Pass lower and upper bound to limits argument in scale_size_area function:
ggplot(df1, aes(x, y, size = y)) +
geom_point() +
labs(
title = "Y on y-axis",
size = NULL
) +
scale_size_area(limits = c(0, 25))
ggplot(df1, aes(x, y, size = size2 )) +
geom_point() +
labs(
title = "size2 on y-axis",
size = NULL
) +
scale_size_area(limits = c(0, 25))
How about this?
library("ggplot2")
df1 <- data.frame(x = letters[1:5],
y = 1:5)
ggplot(data = df1,
aes(x = x,
y = y,
size = y)) +
geom_point() +
scale_size_area(breaks = seq(1,25,1),
limits = c(1, 25))
I would like to make a plot where the breaks along the x-axis are the negative of the actual values I plot. Something like this
df <- tibble(x = seq(-1000, 0, length.out = 100),
y = 2 * x + 3)
ggplot(df) +
geom_line(aes(x = x, y = y)) +
scale_x_continuous(breaks = df$x, labels = -df$x)
except that this puts a break at every x value and I would like the breaks to be what I would get using waiver(). I recall seeing a solution for this, but for the life of me I cannot remember what it is.
ggplot(df) +
geom_line(aes(x = x, y = y)) +
scale_x_continuous(breaks = pretty(df$x), labels = -pretty(df$x))
pretty is all you need.
Alternatively, just plot directly and use a reversed scale:
ggplot(df) +
geom_line(aes(x = -x, y = y)) +
scale_x_reverse()
I am trying to plot a point histogram (a histogram that shows the values with a point instead of bars) that is log-scaled. The result should look like this:
MWE:
Lets simulate some Data:
set.seed(123)
d <- data.frame(x = rnorm(1000))
To get the point histogram I need to calculate the histogram data (hdata) first
hdata <- hist(d$x, plot = FALSE)
tmp <- data.frame(mids = hdata$mids,
density = hdata$density,
counts = hdata$counts)
which we can plot like this
p <- ggplot(tmp, aes(x = mids, y = density)) + geom_point() +
stat_function(fun = dnorm, col = "red")
p
to get this graph:
In theory we should be able to apply the log scales (and set the y-limits to be above 0) and we should have a similar picture to the target graph.
However, if I apply it I get the following graph:
p + scale_y_log10(limits = c(0.001, 10))
The stat_function clearly shows non-scaled values instead of producing a figure closer to the solid line in the first picture.
Any ideas?
Bonus
Are there any ways to graph the histogram with dots without using the hist(..., plot = FALSE) function?
EDIT Workaround
One possible solution is to calculate the dnorm-data outside of ggplot and then insert it as a line. For example
tmp2 <- data.frame(mids = seq(from = min(tmp$mids), to = max(tmp$mids),
by = (max(tmp$mids) - min(tmp$mids))/10000))
tmp2$dnorm <- dnorm(tmp2$mids)
# Plot it
ggplot() +
geom_point(data = tmp, aes(x = mids, y = density)) +
geom_line(data = tmp2, aes(x = mids, y = dnorm), col = "red") +
scale_y_log10()
This returns a graph like the following. This is basically the graph, but it doesn't resolve the stat_function issue.
library(ggplot2)
set.seed(123)
d <- data.frame(x = rnorm(1000))
ggplot(d, aes(x)) +
stat_bin(geom = "point",
aes(y = ..density..),
#same breaks as function hist's default:
breaks = pretty(range(d$x), n = nclass.Sturges(d$x), min.n = 1),
position = "identity") +
stat_function(fun = dnorm, col = "red") +
scale_y_log10(limits = c(0.001, 10))
Another possible solution that I found while revisiting this issue is to apply the log10 to the stat_function-call.
library(ggplot2)
set.seed(123)
d <- data.frame(x = rnorm(1000))
hdata <- hist(d$x, plot = FALSE)
tmp <- data.frame(mids = hdata$mids,
density = hdata$density,
counts = hdata$counts)
ggplot(tmp, aes(x = mids, y = density)) + geom_point() +
stat_function(fun = function(x) log10(dnorm(x)), col = "red") +
scale_y_log10()
Created on 2018-07-25 by the reprex package (v0.2.0).
If you look at this
ggplot(mtcars,aes(x=disp,y=mpg,colour=mpg))+geom_line()
you will see that the line colour varies according to the corresponding y value, which is what I want, but only section-by-section. I would like the colour to vary continuously according to the y value. Any easy way?
One possibility which comes to mind would be to use interpolation to create more x- and y-values, and thereby make the colours more continuous. I use approx to " linearly interpolate given data points". Here's an example on a simpler data set:
# original data and corresponding plot
df <- data.frame(x = 1:3, y = c(3, 1, 4))
library(ggplot2)
ggplot(data = df, aes(x = x, y = y, colour = y)) +
geom_line(size = 3)
# interpolation to make 'more values' and a smoother colour gradient
vals <- approx(x = df$x, y = df$y)
df2 <- data.frame(x = vals$x, y = vals$y)
ggplot(data = df2, aes(x = x, y = y, colour = y)) +
geom_line(size = 3)
If you wish the gradient to be even smoother, you may use the n argument in approx to adjust the number of points to be created ("interpolation takes place at n equally spaced points spanning the interval [min(x), max(x)]"). With a larger number of values, perhaps geom_point gives a smoother appearance:
vals <- approx(x = df$x, y = df$y, n = 500)
df2 <- data.frame(x = vals$x, y = vals$y)
ggplot(data = df2, aes(x = x, y = y, colour = y)) +
geom_point(size = 3)
Since ggplot2 v0.8.5 one can use geom_line or geom_path with different lineend options (right now there are three options: round, butt and square). Selection depends on the nature of the data.
round would work on sharp edges (like in given OPs data):
library(ggplot2)
ggplot(mtcars, aes(disp, mpg, color = mpg)) +
geom_line(size = 3, lineend = "round")
square would work on a more continuous variable:
df <- data.frame(x = seq(0, 100, 10), y = seq(0, 100, 10) ^ 2)
ggplot(data = df, aes(x = x, y = y, colour = y)) +
geom_path(size = 3, lineend = "square")
Maybe this will work for you:
library(dplyr)
library(ggplot2)
my_mtcars <-
mtcars %>%
mutate(my_colors = cut(disp, breaks = c(0, 130, 200, 400, Inf)))
ggplot(my_mtcars, aes(x = disp, y = mpg, col = mpg)) +
geom_line() + facet_wrap(~ my_colors, scales = 'free_x')