I often find myself doing this:
# Original data
df.test <- data.frame(value=floor(rexp(10000, 1/2)))
# Compute the frequency of every value
# or the probability
freqs <- tabulate(df.test$value)
probs <- freqs / sum(freqs)
# Create a new dataframe with the frequencies (or probabilities)
df.freqs <- data.frame(n=1:length(freqs), freq=freqs, probs=probs)
# Plot them, usually in log-log
g <- ggplot(df.freqs, aes(x=n, y = freq)) + geom_point() +
scale_y_log10() + scale_x_log10()
plot(g)
Can it be done just using ggplot without creating an intermediate dataset?
For frequency count, you can specify the stat parameter in geom_point as count:
ggplot(df.test, aes(x = value)) + geom_point(stat = "count") +
scale_x_log10() + scale_y_log10()
Related
I'm trying to generate a ggplot only C.D.F. plot for some of my data. I am also looking to be able to plot an arbitrary number of percentiles as points on top. I have a solution that works for adding a single point to my curve but fails for multiple values.
This works for plotting one percentile value
TestDf <- as.data.frame(rnorm(1000))
names(TestDf) <- c("Values")
percentiles <- c(0.5)
ggplot(data = TestDf, aes(x = Values)) +
stat_ecdf() +
geom_point(aes(x = quantile(TestDf$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))
Suppose I make a violin plot, with say 10 violins, using the following code:
library(ggplot2)
library(reshape2)
df <- melt(data.frame(matrix(rnorm(500),ncol=10)))
p <- ggplot(df, aes(x = variable, y = value)) +
geom_violin()
p
I can add a dot representing the mean of each variable as follows:
p + stat_summary(fun.y=mean, geom="point", size=2, color="red")
How can I do something similar but for arbitrary points?
For example, if I generate 10 new points, one drawn from each distribution, how could I plot those as dots on the violins?
You can give any function to stat_summary provided it just returns a single value. So one can use the function sample. Put extra arguments such as size, in the fun.args
p + stat_summary(fun.y = "sample", geom = "point", fun.args = list(size = 1))
Assuming your points are qualified using the same group names (i.e., variable), you should be able to define them manually with:
newdf <- group_by(df, variable) %>% sample_n(10)
p + geom_point(data=newdf)
The points can be anything, including static numbers:
newdf <- data.frame(variable = unique(df$variable), value = seq(-2, 2, len=10))
p + geom_point(data=newdf)
I had a similar problem. Code below exemplifies the toy problem - How does one add arbitrary points to a violin plot? - and solution.
## Visualize data set that comes in base R
head(ToothGrowth)
## Make a violin plot with dose variable on x-axis, len variable on y-axis
# Convert dose variable to factor - Important!
ToothGrowth$dose <- as.factor(ToothGrowth$dose)
# Plot
p <- ggplot(ToothGrowth, aes(x=dose, y=len)) +
geom_violin(trim = FALSE) +
geom_boxplot(width=0.1)
# Suppose you want to add 3 blue points
# [0.5, 10], [1,20], [2, 30] to the plot.
# Make a new data frame with these points
# and add them to the plot with geom_point().
TrueVals <- ToothGrowth[1:3,]
TrueVals$len <- c(10,20,30)
# Make dose variable a factor - Important for positioning points correctly!
TrueVals$dose <- as.factor(c(0.5, 1, 2))
# Plot with 3 added blue points
p <- ggplot(ToothGrowth, aes(x=dose, y=len)) +
geom_violin(trim = FALSE) +
geom_boxplot(width=0.1) +
geom_point(data = TrueVals, color = "blue")
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()
In the code below I build a 40x1000 data frame where in each column I have the cumulative means for successive random draws from an exponential distribution with parameter lambda = 0.2.
I add an additional column to host the specific number of the "draw".
I also calculate the rowmeans as df_means.
How do I add df_means (as a black line) on top of all my simulated RVs? I don't understand ggplot well enough to do this.
df <- data.frame(replicate(1000,cumsum(rexp(40,lambda))/(1:40)))
df$draw <- seq(1,40)
df_means <- rowMeans(df)
Molten <- melt(df, id.vars="draw")
ggplot(Molten, aes(x = draw, y = value, colour = variable)) + geom_line() + theme(legend.position = "none") + geom_line(df_means)
How would I add plot(df_means, type="l") to my ggplot, below?
Thank you,
You can make another data.frame with the means and ids and use that to draw the line,
df_means <- rowMeans(df)
means <- data.frame(id=1:40, mu=df_means)
ggplot(Molten, aes(x=draw, y=value, colour=variable)) +
geom_line() +
theme(legend.position = "none") +
geom_line(data=means, aes(x=id, y=mu), color="black")
As described here
stat_sum_df <- function(fun, geom="crossbar", ...) {
stat_summary(fun.data=fun, colour="red", geom=geom, width=0.2, ...)
}
k<-ggplot(Molten, aes(x = draw, y = value, colour = variable)) + geom_line() + theme(legend.position = "none")
k+stat_sum_single(mean) #gives you the required plot
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()