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.
Related
I have a set of pvalues i.e 0<=pval<=1
I want to plot qqplot using ggplot2
As in the documentation the following code will plot a q_q plot, however if my data are pvalues I want the therotical values to be also probabilites ie. 0<=therotical v<=1
df <- data.frame(y = rt(200, df = 5))
p <- ggplot(df, aes(sample = y))
p + stat_qq() + stat_qq_line()
I am aware of the qqplot.pvalues from gaston package it does the job but the plot is not as customizable as the ggplot version.
In gaston package the theoretical data are plotted as -log10((n:1)/(n + 1)) where n is number of pvalues. How to pass these values to ggplot as theoritical data?
Assuming you have some p-values, say from a normal distribution you could create it manually
library(ggplot2)
data <- data.frame(outcome = rnorm(150))
data$pval <- pnorm(data$outcome)
data <- data[order(data$pval),]
ggplot(data = data, aes(y = pval, x = pnorm(qnorm(ppoints(nrow(data)))))) +
geom_point() +
geom_abline(slope = 1) +
labs(x = 'theoraetical p-val', y = 'observed p-val', title = 'qqplot (pval-scale)')
Although I am not sure this plot is sensible to use for conclusions.
The grouping variable for creating a geom_violin() plot in ggplot2 is expected to be discrete for obvious reasons. However my discrete values are numbers, and I would like to show them on a continuous scale so that I can overlay a continuous function of those numbers on top of the violins. Toy example:
library(tidyverse)
df <- tibble(x = sample(c(1,2,5), size = 1000, replace = T),
y = rnorm(1000, mean = x))
ggplot(df) + geom_violin(aes(x=factor(x), y=y))
This works as you'd imagine: violins with their x axis values (equally spaced) labelled 1, 2, and 5, with their means at y=1,2,5 respectively. I want to overlay a continuous function such as y=x, passing through the means. Is that possible? Adding + scale_x_continuous() predictably gives Error: Discrete value supplied to continuous scale. A solution would presumably spread the violins horizontally by the numeric x values, i.e. three times the spacing between 2 and 5 as between 1 and 2, but that is not the only thing I'm trying to achieve - overlaying a continuous function is the key issue.
If this isn't possible, alternative visualisation suggestions are welcome. I know I could replace violins with a simple scatter plot to give a rough sense of density as a function of y for a given x.
The functionality to plot violin plots on a continuous scale is directly built into ggplot.
The key is to keep the original continuous variable (instead of transforming it into a factor variable) and specify how to group it within the aesthetic mapping of the geom_violin() object. The width of the groups can be modified with the cut_width argument, depending on the data at hand.
library(tidyverse)
df <- tibble(x = sample(c(1,2,5), size = 1000, replace = T),
y = rnorm(1000, mean = x))
ggplot(df, aes(x=x, y=y)) +
geom_violin(aes(group = cut_width(x, 1)), scale = "width") +
geom_smooth(method = 'lm')
By using this approach, all geoms for continuous data and their varying functionalities can be combined with the violin plots, e.g. we could easily replace the line with a loess curve and add a scatter plot of the points.
ggplot(df, aes(x=x, y=y)) +
geom_violin(aes(group = cut_width(x, 1)), scale = "width") +
geom_smooth(method = 'loess') +
geom_point()
More examples can be found in the ggplot helpfile for violin plots.
Try this. As you already guessed, spreading the violins by numeric values is the key to the solution. To this end I expand the df to include all x values in the interval min(x) to max(x) and use scale_x_discrete(drop = FALSE) so that all values are displayed.
Note: Thanks #ChrisW for the more general example of my approach.
library(tidyverse)
set.seed(42)
df <- tibble(x = sample(c(1,2,5), size = 1000, replace = T), y = rnorm(1000, mean = x^2))
# y = x^2
# add missing x values
x.range <- seq(from=min(df$x), to=max(df$x))
df <- df %>% right_join(tibble(x = x.range))
#> Joining, by = "x"
# Whatever the desired continuous function is:
df.fit <- tibble(x = x.range, y=x^2) %>%
mutate(x = factor(x))
ggplot() +
geom_violin(data=df, aes(x = factor(x, levels = 1:5), y=y)) +
geom_line(data=df.fit, aes(x, y, group=1), color = "red") +
scale_x_discrete(drop = FALSE)
#> Warning: Removed 2 rows containing non-finite values (stat_ydensity).
Created on 2020-06-11 by the reprex package (v0.3.0)
I have "long" format data frame which contains two columns: first col - values, second col- sex [Male - 1/Female - 2]. I wrote some code to make a histogram of entire dataset (code below).
ggplot(kz6, aes(x = values)) +
geom_histogram()
However, I want also add a density over histogram to emphasize the difference between sexes i.e. I want to combine 3 plots: histogram for entire dataset, and 2 density plots for each sex. I tried to use some examples (one, two, three, four), but it still does not work. Code for density only works, while the combinations of hist + density does not.
density <- ggplot(kz6, aes(x = x, fill = factor(sex))) +
geom_density()
both <- ggplot(kz6, aes(x = values)) +
geom_histogram() +
geom_density()
both_2 <- ggplot(kz6, aes(x = values)) +
geom_histogram() +
geom_density(aes(x = kz6[kz6$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$Species <- NULL
names(kz6)[1] <- "values"
head(kz6)
I'm trying to generate a ggplot only C.D.F. plot for some of my data. I am also looking to be able to plot an arbitrary number of percentiles as points on top. I have a solution that works for adding a single point to my curve but fails for multiple values.
This works for plotting one percentile value
TestDf <- as.data.frame(rnorm(1000))
names(TestDf) <- c("Values")
percentiles <- c(0.5)
ggplot(data = TestDf, aes(x = Values)) +
stat_ecdf() +
geom_point(aes(x = quantile(TestDf$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))
I'm using stat_summary to display the mean and, based off my calculations, "type1, G-" should have a mean of ~10^7.3. And that's the value I get from plotting it without a log10 axis. But when I add in the log10 axis, suddenly "type1, G-" shows a value of 10^6.5.
What's going on?
#Data
Type = rep(c("type1", "type2"), each = 6)
Gen = rep(rep(c("G-", "G+"), each = 3), 2)
A = c(4.98E+05, 5.09E+05, 1.03E+05, 3.08E+05, 5.07E+03, 4.22E+04, 6.52E+05, 2.51E+04, 8.66E+05, 8.10E+04, 6.50E+06, 1.64E+06)
B = c(6.76E+07, 3.25E+07, 1.11E+07, 2.34E+06, 4.10E+04, 1.20E+06, 7.50E+07, 1.65E+05, 9.52E+06, 5.92E+06, 3.11E+08, 1.93E+08)
df = melt(data.frame(Type, Gen, A, B))
#Correct, non-log10 version ("type1 G-" has a value over 1e+07)
ggplot(data = df, aes(x =Type,y = value)) +
stat_summary(fun.y="mean",geom="bar",position="dodge",aes(fill=Gen))+
scale_x_discrete(limits=c("type1"))+
coord_cartesian(ylim=c(10^7,10^7.5))
#Incorrect, log10 version ("type1 G-" has a value under 1e+07)
ggplot(data = df, aes(x =Type,y = value)) +
stat_summary(fun.y="mean",geom="bar",position="dodge",aes(fill=Gen))+
scale_y_log10()
You want coord_trans. As its documentation says:
# The difference between transforming the scales and
# transforming the coordinate system is that scale
# transformation occurs BEFORE statistics, and coordinate
# transformation afterwards.
However, you cannot make a barplot with this, since bars start at 0 and log10(0) is not defined. But barplots are usually not a good visualization anyway.
ggplot(data = df, aes(x =Type,y = value)) +
stat_summary(fun.y="mean",geom="point",position="identity",aes(color=Gen))+
coord_trans(y = "log10", limy = c(1e5, 1e8)) +
scale_y_continuous(breaks = 10^(5:8))
Obviously you should plot some kind of uncertainty information. I'd recommend a boxplot.