I would like to add decile breaks to the following plot.
ggplot(data = data.frame(x = c(-0, 1)), aes(x)) +
stat_function(fun = dexp, n = 101,
args = list(rate=3)
)
As an example of what I would like to achieve, here is another plot but I would like to find a way to stick to stat_function since I can easily change the underlying distribution and parameters and also the plot is always more smooth.
vals <-rexp(1001,rate=3)
dens <- density(vals)
plot(dens)
df <- data.frame(x=dens$x, y=dens$y)
probs <-seq(0,1,0.1)
quantiles <- quantile(vals, prob=probs)
df$quant <- factor(findInterval(df$x,quantiles))
ggplot(df, aes(x,y)) + geom_line() + geom_ribbon(aes(ymin=0, ymax=y, fill=quant)) +
scale_x_continuous(breaks=quantiles) + scale_fill_brewer(guide="none")
This is a bit hacky, uses pexp with some rounding to calculate which decile each x location is in. It doesn't use stat_function but has the same advantages as you mention.
# Set up range and number of x points
x=seq(0,1,l=1e4)
# Create x and y positions with dexp
df <- data.frame(x=x, y=dexp(x,rate=3))
# Identify quantiles using pexp
df$quant <- scales::number(floor(10*pexp(df$x,rate=3))*10, suffix="%")
# Find x positions for breaks
xpos <- aggregate(df, x~quant, min)
# Plot
ggplot(df, aes(x,y)) +
geom_line() +
geom_ribbon(aes(ymin=0, ymax=y, fill=quant)) +
scale_x_continuous(breaks=xpos$x, labels=xpos$quant)
Related
How would I ignore outliers in ggplot2 boxplot? I don't simply want them to disappear (i.e. outlier.size=0), but I want them to be ignored such that the y axis scales to show 1st/3rd percentile. My outliers are causing the "box" to shrink so small its practically a line. Are there some techniques to deal with this?
Edit
Here's an example:
y = c(.01, .02, .03, .04, .05, .06, .07, .08, .09, .5, -.6)
qplot(1, y, geom="boxplot")
Use geom_boxplot(outlier.shape = NA) to not display the outliers and scale_y_continuous(limits = c(lower, upper)) to change the axis limits.
An example.
n <- 1e4L
dfr <- data.frame(
y = exp(rlnorm(n)), #really right-skewed variable
f = gl(2, n / 2)
)
p <- ggplot(dfr, aes(f, y)) +
geom_boxplot()
p # big outlier causes quartiles to look too slim
p2 <- ggplot(dfr, aes(f, y)) +
geom_boxplot(outlier.shape = NA) +
scale_y_continuous(limits = quantile(dfr$y, c(0.1, 0.9)))
p2 # no outliers plotted, range shifted
Actually, as Ramnath showed in his answer (and Andrie too in the comments), it makes more sense to crop the scales after you calculate the statistic, via coord_cartesian.
coord_cartesian(ylim = quantile(dfr$y, c(0.1, 0.9)))
(You'll probably still need to use scale_y_continuous to fix the axis breaks.)
Here is a solution using boxplot.stats
# create a dummy data frame with outliers
df = data.frame(y = c(-100, rnorm(100), 100))
# create boxplot that includes outliers
p0 = ggplot(df, aes(y = y)) + geom_boxplot(aes(x = factor(1)))
# compute lower and upper whiskers
ylim1 = boxplot.stats(df$y)$stats[c(1, 5)]
# scale y limits based on ylim1
p1 = p0 + coord_cartesian(ylim = ylim1*1.05)
I had the same problem and precomputed the values for Q1, Q2, median, ymin, ymax using boxplot.stats:
# Load package and generate data
library(ggplot2)
data <- rnorm(100)
# Compute boxplot statistics
stats <- boxplot.stats(data)$stats
df <- data.frame(x="label1", ymin=stats[1], lower=stats[2], middle=stats[3],
upper=stats[4], ymax=stats[5])
# Create plot
p <- ggplot(df, aes(x=x, lower=lower, upper=upper, middle=middle, ymin=ymin,
ymax=ymax)) +
geom_boxplot(stat="identity")
p
The result is a boxplot without outliers.
One idea would be to winsorize the data in a two-pass procedure:
run a first pass, learn what the bounds are, e.g. cut of at given percentile, or N standard deviation above the mean, or ...
in a second pass, set the values beyond the given bound to the value of that bound
I should stress that this is an old-fashioned method which ought to be dominated by more modern robust techniques but you still come across it a lot.
gg.layers::geom_boxplot2 is just what you want.
# remotes::install_github('rpkgs/gg.layers')
library(gg.layers)
library(ggplot2)
p <- ggplot(mpg, aes(class, hwy))
p + geom_boxplot2(width = 0.8, width.errorbar = 0.5)
https://rpkgs.github.io/gg.layers/reference/geom_boxplot2.html
If you want to force the whiskers to extend to the max and min values, you can tweak the coef argument. Default value for coef is 1.5 (i.e. default length of the whiskers is 1.5 times the IQR).
# Load package and create a dummy data frame with outliers
#(using example from Ramnath's answer above)
library(ggplot2)
df = data.frame(y = c(-100, rnorm(100), 100))
# create boxplot that includes outliers
p0 = ggplot(df, aes(y = y)) + geom_boxplot(aes(x = factor(1)))
# create boxplot where whiskers extend to max and min values
p1 = ggplot(df, aes(y = y)) + geom_boxplot(aes(x = factor(1)), coef = 500)
Simple, dirty and effective.
geom_boxplot(outlier.alpha = 0)
The "coef" option of the geom_boxplot function allows to change the outlier cutoff in terms of interquartile ranges. This option is documented for the function stat_boxplot. To deactivate outliers (in other words they are treated as regular data), one can instead of using the default value of 1.5 specify a very high cutoff value:
library(ggplot2)
# generate data with outliers:
df = data.frame(x=1, y = c(-10, rnorm(100), 10))
# generate plot with increased cutoff for outliers:
ggplot(df, aes(x, y)) + geom_boxplot(coef=1e30)
I'm trying to plot something like this, where alpha is intended to be a mean of dat$c per bin:
library(ggplot2)
set.seed(1)
dat <- data.frame(a = rnorm(1000), b = rnorm(1000), c = 1/rnorm(1000),
d = as.factor(sample(c(0, 1), 1000, replace=TRUE)))
# plot
p <- ggplot(dat, environment = environment()) +
geom_bin2d(aes(x=a, y=b, alpha=c, fill=d),
binwidth = c(1.0/10, 1.0/10))
but it doesn't look like alpha is correct. Please help
I'm not sure what you're expecting to see, but this will calculate mean(dat$c) in each bin and plot the result.
library(ggplot2)
brks <- seq(-5,5,0.1)
lbls <- brks[-1]-0.05
gg.df <- aggregate(c~cut(a, brks, lbls)+cut(b,brks, lbls)+d,dat,FUN=mean)
names(gg.df)[1:2] <- c("a","b")
gg.df$a <- as.numeric(as.character(gg.df$a))
gg.df$b <- as.numeric(as.character(gg.df$b))
ggplot(gg.df, aes(x=a, y=b, alpha=c, fill=d)) + geom_raster() + coord_fixed()
Edit: Response to OP's comment.
You could try:
dat$c <- with(dat,1/(a^2+b^2))
This makes dat$c inversely proportional to the radius (distance from (0,0) to the point). Now running the same code as above:
gg.df <- aggregate(c~cut(a, brks, lbls)+cut(b,brks, lbls)+d,dat,FUN=mean)
names(gg.df)[1:2] <- c("a","b")
gg.df$a <- as.numeric(as.character(gg.df$a))
gg.df$b <- as.numeric(as.character(gg.df$b))
ggplot(gg.df, aes(x=a, y=b, alpha=c, fill=d)) + geom_raster() + coord_fixed() +
scale_alpha_continuous(trans="log",breaks=10^(0:3))
Produces this, as expected: a plot having tiles with higher alpha (less transparent) near the center.
I needed to use a log scale for alpha because the values range over several orders of magnitude.
I'd like to create a faceted plot using ggplot2 in which the minimum limit of the y axis will be fixed (say at 0) and the maximum limit will be determined by the data in the facet (as it is when scales="free_y". I was hoping that something like the following would work, but no such luck:
library(plyr)
library(ggplot2)
#Create the underlying data
l <- gl(2, 10, 20, labels=letters[1:2])
x <- rep(1:10, 2)
y <- c(runif(10), runif(10)*100)
df <- data.frame(l=l, x=x, y=y)
#Create a separate data frame to define axis limits
dfLim <- ddply(df, .(l), function(y) max(y$y))
names(dfLim)[2] <- "yMax"
dfLim$yMin <- 0
#Create a plot that works, but has totally free scales
p <- ggplot(df, aes(x=x, y=y)) + geom_point() + facet_wrap(~l, scales="free_y")
#Add y limits defined by the limits dataframe
p + ylim(dfLim$yMin, dfLim$yMax)
It's not too surprising to me that this throws an error (length(lims) == 2 is not TRUE) but I can't think of a strategy to get started on this problem.
In your case, either of the following will work:
p + expand_limits(y=0)
p + aes(ymin=0)
I have two graphs with the same x axis - the range of x is 0-5 in both of them.
I would like to combine both of them to one graph and I didn't find a previous example.
Here is what I got:
c <- ggplot(survey, aes(often_post,often_privacy)) + stat_smooth(method="loess")
c <- ggplot(survey, aes(frequent_read,often_privacy)) + stat_smooth(method="loess")
How can I combine them?
The y axis is "often privacy" and in each graph the x axis is "often post" or "frequent read".
I thought I can combine them easily (somehow) because the range is 0-5 in both of them.
Many thanks!
Example code for Ben's solution.
#Sample data
survey <- data.frame(
often_post = runif(10, 0, 5),
frequent_read = 5 * rbeta(10, 1, 1),
often_privacy = sample(10, replace = TRUE)
)
#Reshape the data frame
survey2 <- melt(survey, measure.vars = c("often_post", "frequent_read"))
#Plot using colour as an aesthetic to distinguish lines
(p <- ggplot(survey2, aes(value, often_privacy, colour = variable)) +
geom_point() +
geom_smooth()
)
You can use + to combine other plots on the same ggplot object. For example, to plot points and smoothed lines for both pairs of columns:
ggplot(survey, aes(often_post,often_privacy)) +
geom_point() +
geom_smooth() +
geom_point(aes(frequent_read,often_privacy)) +
geom_smooth(aes(frequent_read,often_privacy))
Try this:
df <- data.frame(x=x_var, y=y1_var, type='y1')
df <- rbind(df, data.frame(x=x_var, y=y2_var, type='y2'))
ggplot(df, aes(x, y, group=type, col=type)) + geom_line()
How would I ignore outliers in ggplot2 boxplot? I don't simply want them to disappear (i.e. outlier.size=0), but I want them to be ignored such that the y axis scales to show 1st/3rd percentile. My outliers are causing the "box" to shrink so small its practically a line. Are there some techniques to deal with this?
Edit
Here's an example:
y = c(.01, .02, .03, .04, .05, .06, .07, .08, .09, .5, -.6)
qplot(1, y, geom="boxplot")
Use geom_boxplot(outlier.shape = NA) to not display the outliers and scale_y_continuous(limits = c(lower, upper)) to change the axis limits.
An example.
n <- 1e4L
dfr <- data.frame(
y = exp(rlnorm(n)), #really right-skewed variable
f = gl(2, n / 2)
)
p <- ggplot(dfr, aes(f, y)) +
geom_boxplot()
p # big outlier causes quartiles to look too slim
p2 <- ggplot(dfr, aes(f, y)) +
geom_boxplot(outlier.shape = NA) +
scale_y_continuous(limits = quantile(dfr$y, c(0.1, 0.9)))
p2 # no outliers plotted, range shifted
Actually, as Ramnath showed in his answer (and Andrie too in the comments), it makes more sense to crop the scales after you calculate the statistic, via coord_cartesian.
coord_cartesian(ylim = quantile(dfr$y, c(0.1, 0.9)))
(You'll probably still need to use scale_y_continuous to fix the axis breaks.)
Here is a solution using boxplot.stats
# create a dummy data frame with outliers
df = data.frame(y = c(-100, rnorm(100), 100))
# create boxplot that includes outliers
p0 = ggplot(df, aes(y = y)) + geom_boxplot(aes(x = factor(1)))
# compute lower and upper whiskers
ylim1 = boxplot.stats(df$y)$stats[c(1, 5)]
# scale y limits based on ylim1
p1 = p0 + coord_cartesian(ylim = ylim1*1.05)
I had the same problem and precomputed the values for Q1, Q2, median, ymin, ymax using boxplot.stats:
# Load package and generate data
library(ggplot2)
data <- rnorm(100)
# Compute boxplot statistics
stats <- boxplot.stats(data)$stats
df <- data.frame(x="label1", ymin=stats[1], lower=stats[2], middle=stats[3],
upper=stats[4], ymax=stats[5])
# Create plot
p <- ggplot(df, aes(x=x, lower=lower, upper=upper, middle=middle, ymin=ymin,
ymax=ymax)) +
geom_boxplot(stat="identity")
p
The result is a boxplot without outliers.
One idea would be to winsorize the data in a two-pass procedure:
run a first pass, learn what the bounds are, e.g. cut of at given percentile, or N standard deviation above the mean, or ...
in a second pass, set the values beyond the given bound to the value of that bound
I should stress that this is an old-fashioned method which ought to be dominated by more modern robust techniques but you still come across it a lot.
gg.layers::geom_boxplot2 is just what you want.
# remotes::install_github('rpkgs/gg.layers')
library(gg.layers)
library(ggplot2)
p <- ggplot(mpg, aes(class, hwy))
p + geom_boxplot2(width = 0.8, width.errorbar = 0.5)
https://rpkgs.github.io/gg.layers/reference/geom_boxplot2.html
If you want to force the whiskers to extend to the max and min values, you can tweak the coef argument. Default value for coef is 1.5 (i.e. default length of the whiskers is 1.5 times the IQR).
# Load package and create a dummy data frame with outliers
#(using example from Ramnath's answer above)
library(ggplot2)
df = data.frame(y = c(-100, rnorm(100), 100))
# create boxplot that includes outliers
p0 = ggplot(df, aes(y = y)) + geom_boxplot(aes(x = factor(1)))
# create boxplot where whiskers extend to max and min values
p1 = ggplot(df, aes(y = y)) + geom_boxplot(aes(x = factor(1)), coef = 500)
Simple, dirty and effective.
geom_boxplot(outlier.alpha = 0)
The "coef" option of the geom_boxplot function allows to change the outlier cutoff in terms of interquartile ranges. This option is documented for the function stat_boxplot. To deactivate outliers (in other words they are treated as regular data), one can instead of using the default value of 1.5 specify a very high cutoff value:
library(ggplot2)
# generate data with outliers:
df = data.frame(x=1, y = c(-10, rnorm(100), 10))
# generate plot with increased cutoff for outliers:
ggplot(df, aes(x, y)) + geom_boxplot(coef=1e30)