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)
Related
** Edited with Repeatable Data **
I have a data.frame with plots of growth over time for 50 experimental treatments. I have plotted them as a faceted 5x10 plot grid. I also ordered them in a way that makes sense considering my experimental treatments.
I ran a regression function to find growth rate in each treatment, and saved the slope values in another data frame. I have plotted the data, the regression line, and the value of growth rate, but I want to color the backgrounds of the individual faceted plots according to that regression slope value, but I can't figure out how to set color to call to a continuous variable, and especially one from a different df with a different number of rows (original df has 300 rows, df I want to call has 50 - one for each treatment).
My code is as follows:
Df:
df <- data.frame(matrix(ncol = 3,nrow=300))
colnames(df) <- c("Trt", "Day", "Size")
df$Trt <- rep(1:50, each=6)
df$Day <- rep_len(1:6, length.out=300)
df$Size <- rep_len(c(3,5,8,9,12,12,3,7,10,16,17,20),length.out = 300)
Regression function and output dataframe:
regression=function(df){
reg_fun<-lm(formula=df$Size~df$Day)
slope<-round(coef(reg_fun)[2],3)
intercept<-round(coef(reg_fun)[1],3)
R2<-round(as.numeric(summary(reg_fun)[8]),3)
R2.Adj<-round(as.numeric(summary(reg_fun)[9]),3)
c(slope,intercept,R2,R2.Adj)
}
library(plyr)
slopevalues<-ddply(df,"Trt",regression)
colnames(slopevalues)<-c ("Trt","slope","intercept","R2","R2.Adj")
Plot:
ggplot(data=df, aes(x=Day, y=Size))+
geom_line() +
geom_point() +
xlab("Day") + ylab("Size (μm)")+
geom_smooth(method="lm",size=.5,se=FALSE)+
geom_text(data=slopevalues,
inherit.aes=FALSE,
aes(x =1, y = 16,hjust=0,
label=paste(slope)))+
facet_wrap(~ Trt, nrow=5)
What I want to do is color the backgrounds of the individual graphs according to the slope value (slopevalues$slope) on a gradient. My real data are not just 2 values repeated, so I want to do this on a gradient of colors according to that value.
Any advice welcome.
enter image description here
You can use geom_rect with infinite coordinates to do this:
ggplot(data=df, aes(x=Day, y=Size))+
## This is the only new bit
geom_rect(
aes(fill = slope, xmin = -Inf, xmax = Inf, ymin = -Inf, ymax = Inf),
slopevalues,
inherit.aes = FALSE
) +
## New bit ends here
geom_line() +
geom_point() +
xlab("Day") + ylab("Size (μm)")+
geom_smooth(method="lm",size=.5,se=FALSE)+
geom_text(data=slopevalues,
inherit.aes=FALSE,
aes(x =1, y = 16,hjust=0,
label=paste(slope)))+
facet_wrap(~ Trt, nrow=5)
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()
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.