I'm trying to generate a ggplot only C.D.F. plot for some of my data. I am also looking to be able to plot an arbitrary number of percentiles as points on top. I have a solution that works for adding a single point to my curve but fails for multiple values.
This works for plotting one percentile value
TestDf <- as.data.frame(rnorm(1000))
names(TestDf) <- c("Values")
percentiles <- c(0.5)
ggplot(data = TestDf, aes(x = Values)) +
stat_ecdf() +
geom_point(aes(x = quantile(TestDf$Values, percentiles),
y = percentiles))
However this fails
TestDf <- as.data.frame(rnorm(1000))
names(TestDf) <- c("Values")
percentiles <- c(0.25,0.5,0.75)
ggplot(data = TestDf, aes(x = Values)) +
stat_ecdf() +
geom_point(aes(x = quantile(TestDf$Values, percentiles),
y = percentiles))
With error
Error: Aesthetics must be either length 1 or the same as the data (1000): x, y
How can I add an arbitrary number of points to a stat_ecdf() plot?
You need to define a new dataset, outside of the aesthetics. aes refers to the original dataframe that you used for making the CDF (in the original ggplot argument).
ggplot(data = TestDf, aes(x = Values)) +
stat_ecdf() +
geom_point(data = data.frame(x=quantile(TestDf$Values, percentiles),
y=percentiles), aes(x=x, y=y))
Related
I have "long" format data frame which contains two columns: first col - values, second col- sex [Male - 1/Female - 2]. I wrote some code to make a histogram of entire dataset (code below).
ggplot(kz6, aes(x = values)) +
geom_histogram()
However, I want also add a density over histogram to emphasize the difference between sexes i.e. I want to combine 3 plots: histogram for entire dataset, and 2 density plots for each sex. I tried to use some examples (one, two, three, four), but it still does not work. Code for density only works, while the combinations of hist + density does not.
density <- ggplot(kz6, aes(x = x, fill = factor(sex))) +
geom_density()
both <- ggplot(kz6, aes(x = values)) +
geom_histogram() +
geom_density()
both_2 <- ggplot(kz6, aes(x = values)) +
geom_histogram() +
geom_density(aes(x = kz6[kz6$sex == 1,]))
P.S. some examples contains y=..density.. what does it mean? How to interpret this?
To plot a histogram and superimpose two densities, defined by a categorical variable, use appropriate aesthetics in the call to geom_density, like group or colour.
ggplot(kz6, aes(x = values)) +
geom_histogram(aes(y = ..density..), bins = 20) +
geom_density(aes(group = sex, colour = sex), adjust = 2)
Data creation code.
I will create a test data set from built-in data set iris.
kz6 <- iris[iris$Species != "virginica", 4:5]
kz6$sex <- "M"
kz6$sex[kz6$Species == "versicolor"] <- "F"
kz6$Species <- NULL
names(kz6)[1] <- "values"
head(kz6)
I have discreet data that looks like this:
height <- c(1,2,3,4,5,6,7,8)
weight <- c(100,200,300,400,500,600,700,800)
person <- c("Jack","Jim","Jill","Tess","Jack","Jim","Jill","Tess")
set <- c(1,1,1,1,2,2,2,2)
dat <- data.frame(set,person,height,weight)
I'm trying to plot a graph with same x-axis(person), and 2 different y-axis (weight and height). All the examples, I find is trying to plot the secondary axis (sec_axis), or discreet data using base plots.
Is there an easy way to use sec_axis for discreet data on ggplot2?
Edit: Someone in the comments suggested I try the suggested reply. However, I run into this error now
Here is my current code:
p1 <- ggplot(data = dat, aes(x = person, y = weight)) +
geom_point(color = "red") + facet_wrap(~set, scales="free")
p2 <- p1 + scale_y_continuous("height",sec_axis(~.*1.2, name="height"))
p2
I get the error: Error in x < range[1] :
comparison (3) is possible only for atomic and list types
Alternately, now I have modified the example to match this example posted.
p <- ggplot(dat, aes(x = person))
p <- p + geom_line(aes(y = height, colour = "Height"))
# adding the relative weight data, transformed to match roughly the range of the height
p <- p + geom_line(aes(y = weight/100, colour = "Weight"))
# now adding the secondary axis, following the example in the help file ?scale_y_continuous
# and, very important, reverting the above transformation
p <- p + scale_y_continuous(sec.axis = sec_axis(~.*100, name = "Relative weight [%]"))
# modifying colours and theme options
p <- p + scale_colour_manual(values = c("blue", "red"))
p <- p + labs(y = "Height [inches]",
x = "Person",
colour = "Parameter")
p <- p + theme(legend.position = c(0.8, 0.9))+ facet_wrap(~set, scales="free")
p
I get an error that says
"geom_path: Each group consists of only one observation. Do you need to
adjust the group aesthetic?"
I get the template, but no points get plotted
R function arguments are fed in by position if argument names are not specified explicitly. As mentioned by #Z.Lin in the comments, you need sec.axis= before your sec_axis function to indicate that you are feeding this function into the sec.axis argument of scale_y_continuous. If you don't do that, it will be fed into the second argument of scale_y_continuous, which by default, is breaks=. The error message is thus related to you not feeding in an acceptable data type for the breaks argument:
p1 <- ggplot(data = dat, aes(x = person, y = weight)) +
geom_point(color = "red") + facet_wrap(~set, scales="free")
p2 <- p1 + scale_y_continuous("weight", sec.axis = sec_axis(~.*1.2, name="height"))
p2
The first argument (name=) of scale_y_continuous is for the first y scale, where as the sec.axis= argument is for the second y scale. I changed your first y scale name to correct that.
My data set has a response variable and a 2-level factor explanatory variable. Is there a function for creating a scatter plot with no x axis variable? I'd like the variables to be randomly spread out along the x axis to make them easier to see and differentiate the 2 groups by color. I'm able to create a plot by creating an "ID" variable, but I'm wondering if it's possible to do it without it? The "ID" variable is causing problems when I try to add + facet_grid(. ~ other.var) to view the same plot broken out by another factor variable.
#Create dummy data set
response <- runif(500)
group <- c(rep('group1',250), rep('group2',250))
ID <- c(seq(from=1, to=499, by=2), seq(from=2, to=500, by=2))
data <- data.frame(ID, group, response)
#plot results
ggplot() +
geom_point(data=data, aes(x=ID, y=response, color=group))
How about using geom_jitter, setting the x axis to some fixed value?
ggplot() +
geom_jitter(data=data, aes(x=1, y=response, color=group))
You could plot x as the row number?
ggplot() +
geom_point(data=data, aes(x=1:nrow(data), y=response, color=group))
Or randomly order it first?
RandomOrder <- sample(1:nrow(data), nrow(data))
ggplot() +
geom_point(data=data, aes(x= RandomOrder, y=response, color=group))
Here's how you can scatter plot a variable against row index without intermediate variable:
ggplot(data = data, aes(y = response, x = seq_along(response), color = group)) +
geom_point()
To shuffle row index just add a sample function, like this:
ggplot(data = data, aes(y = response, x = sample(seq_along(response)), color = group)) +
geom_point()
Consider this minimum working example:
library(ggplot2)
x <- c(1,2,3,4,5,6)
y <- c(3,2,5,1,3,1)
data <- data.frame(x,y)
pClass <- c(0,1,1,2,2,0)
plottedGraph <- ggplot(data, aes(x = x, y = y, colour = factor(pClass))) + geom_line()
print(plottedGraph)
I have a time series y = f(x) where x is a timestep. Each timestep should have a color which depends on the category of the timestep, recorded in pClass.
This is the result it gives:
It doesn't make any kind of sense to me why ggplot would connect points with the same color together and not points that follow each other (which is what geom_line should do according to the documentation).
How do I make it plot the following:
You should use group = 1 inside the aes() to tell ggplot that the different colours in fact belong to the same line (ie. group).
ggplot(data, aes(x = x, y = y, colour = factor(pClass), group = 1)) +
geom_line()
So, I have a fairly large dataset (Dropbox: csv file) that I'm trying to plot using geom_boxplot. The following produces what appears to be a reasonable plot:
require(reshape2)
require(ggplot2)
require(scales)
require(grid)
require(gridExtra)
df <- read.csv("\\Downloads\\boxplot.csv", na.strings = "*")
df$year <- factor(df$year, levels = c(2010,2011,2012,2013,2014), labels = c(2010,2011,2012,2013,2014))
d <- ggplot(data = df, aes(x = year, y = value)) +
geom_boxplot(aes(fill = station)) +
facet_grid(station~.) +
scale_y_continuous(limits = c(0, 15)) +
theme(legend.position = "none"))
d
However, when you dig a little deeper, problems creep in that freak me out. When I labeled the boxplot medians with their values, the following plot results.
df.m <- aggregate(value~year+station, data = df, FUN = function(x) median(x))
d <- d + geom_text(data = df.m, aes(x = year, y = value, label = value))
d
The medians plotted by geom_boxplot aren't at the medians at all. The labels are plotted at the correct y-axis value, but the middle hinge of the boxplots are definitely not at the medians. I've been stumped by this for a few days now.
What is the reason for this? How can this type of display be produced with correct medians? How can this plot be debugged or diagnosed?
The solution to this question is in the application of scale_y_continuous. ggplot2 will perform operations in the following order:
Scale Transformations
Statistical Computations
Coordinate Transformations
In this case, because a scale transformation is invoked, ggplot2 excludes data outside the scale limits for the statistical computation of the boxplot hinges. The medians calculated by the aggregate function and used in the geom_text instruction will use the entire dataset, however. This can result in different median hinges and text labels.
The solution is to omit the scale_y_continuous instruction and instead use:
d <- ggplot(data = df, aes(x = year, y = value)) +
geom_boxplot(aes(fill = station)) +
facet_grid(station~.) +
theme(legend.position = "none")) +
coord_cartesian(y = c(0,15))
This allows ggplot2 to calculate the boxplot hinge stats using the entire dataset, while limiting the plot size of the figure.