I'd like to plot histogram and density on the same plot. What I would like to add to the following is custom y-axis label which would be something like sprintf("[%s] %s", ..density.., ..count..) - two numbers at one tick value. Is it possible to obtain this with scale_y_continuous or do I need to work this around somehow?
Below current progress using scales::trans_new and sec_axis. sec_axis is kind of acceptable but the most desirable output is as on the image below.
set.seed(1)
var <- rnorm(4000)
binwidth <- 2 * IQR(var) / length(var) ^ (1 / 3)
count_and_proportion_label <- function(x) {
sprintf("%s [%.2f%%]", x, x/sum(x) * 100)
}
ggplot(data = data.frame(var = var), aes(x = var, y = ..count..)) +
geom_histogram(binwidth = binwidth) +
geom_density(aes(y = ..count.. * binwidth)) +
scale_y_continuous(
# this way
trans = trans_new(name = "count_and_proportion",
format = count_and_proportion_label,
transform = function(x) x,
inverse = function(x) x),
# or this way
sec.axis = sec_axis(trans = ~./sum(.),
labels = percent,
name = "proportion (in %)")
)
I've tried to create object with breaks before basing on the graphics::hist output - but these two histogram differs.
bins <- (max(var) - min(var))/binwidth
hdata <- hist(var, breaks = bins, right = FALSE)
# hist generates different bins than `ggplot2`
At the end I would like to get something like this:
Would it be acceptable to add percentage as a secondary axis? E.g.
your_plot + scale_y_continuous(sec.axis = sec_axis(~.*2, name = "[%]"))
Perhaps it would be possible to overlay the secondary axis on the primary one, but I'm not sure how you would go about doing that.
You can achieve your desired output by creating a custom set of labels, and adding it to the plot:
library(tidyverse)
library(ggplot2)
set.seed(1)
var <- rnorm(400)
bins <- .1
df <- data.frame(yvals = seq(0, 20, 5), labels = c("[0%]", "[10%]", "[20%]", "[30%]", "[40%]"))
df <- df %>% tidyr::unite("custom_labels", labels, yvals, sep = " ", remove = TRUE)
ggplot(data = data.frame(var = var), aes(x = var, y = ..count..)) +
geom_histogram(aes(y = ..count..), binwidth = bins) +
geom_density(aes(y = ..count.. * bins), color = "black", alpha = 0.7) +
ylab("[density] count") +
scale_y_continuous(breaks = seq(0, 20, 5), labels = df$custom_labels)
Related
I would like to keep the default labels ggplot2 provides for Y-axis below, but always have a Y-axis tick and/or label at y = 100 to highlight the horizontal line intercept.
library(ggplot2)
maxValue <- 1000
df <- data.frame(
var1 = seq(1, maxValue, by = 25),
var2 = seq(1, maxValue, by = 50)
)
ggplot(df, aes(x = var1, y = var2)) +
geom_point() +
geom_hline(yintercept = 100, color = "red")
Created on 2022-04-09 by the reprex package (v2.0.1.9000)
Expected output:
Note that maxValue can be anything. So the solution to just increase in steps of 100 doesn't work. For example:
plot <- plot +
scale_y_continuous(
breaks = seq(0, max(df$y) + 100, 100),
labels = as.character(seq(0, max(df$y) + 100, 100))
)
This is because if the max value is 10000 or a similar big number like that, the number of labels will be overwhelming. This is why I would like to stay with the default Y-axis labels that ggplot2 provides and only add a single additional label at y = 100.
By default ggplot2 will compute the default axis breaks in the following manner (Refer to this answer for more details):
labeling::extended(min(df$var1), max(df$var1), m = 5))
We can just add your custom value 100 to this vector and pass it to scale_y_continous
def_breaks <- labeling::extended(min(df$var1), max(df$var1), m = 5)
ggplot(df, aes(x = var1, y = var2)) +
geom_point() +
geom_hline(yintercept = 100, color = "red") +
scale_y_continuous(breaks = c(100, def_breaks),
# pass to minor breaks so that they are not messed up
minor_breaks = def_breaks)
I was trying to recreate this plot:
using the following code -
library(tidyverse)
set.seed(0); r <- rnorm(10000);
df <- as.data.frame(r)
avg <- round(mean(r),2)
SD <- round(sd(r),2)
x.scale <- seq(from = avg - 3*SD, to = avg + 3*SD, by = SD)
x.lab <- c("-3SD", "-2SD", "-1SD", "Mean", "1SD", "2SD", "3SD")
df %>% ggplot(aes(r)) +
geom_histogram(aes(y=..density..), bins = 20,
colour="black", fill="lightblue") +
geom_density(alpha=.2, fill="darkblue") +
scale_x_continuous(breaks = x.scale, labels = x.lab) +
labs(x = "")
Using the code I plotted this:
,
but this isn't near to the plot that I am trying to create. How do I make an additional axis with the X axis? How do I add the lines to automatically show the percentage of observations? Is there any way, that I can create the plot as nearly identical as possible using ggplot2?
Welcome to SO. Excellent first question!
It's actually quite tricky. You'd need to create a second plot (the second x axis) but it's not the most straight forward to align both perfectly.
I will be using Z.lin's amazing modification of the cowplot package.
I am not using the reprex package, because I think I'd need to define every single function (and I don't know how to use trace within reprex.)
library(tidyverse)
library(cowplot)
set.seed(0); r <- rnorm(10000);
foodf <- as.data.frame(r)
avg <- round(mean(r),2)
SD <- round(sd(r),2)
x.scale <- round(seq(from = avg - 3*SD, to = avg + 3*SD, by = SD), 1)
x.lab <- c("-3SD", "-2SD", "-1SD", "Mean", "1SD", "2SD", "3SD")
x2lab <- -3:3
# calculate the density manually
dens_r <- density(r)
# for each x value, calculate the closest x value in the density object and get the respective y values
y_dens <- dens_r$y[sapply(x.scale, function(x) which.min(abs(dens_r$x - x)))]
# added annotation for segments and labels.
# Arrow segments can be added in a similar way.
p1 <-
ggplot(foodf, aes(r)) +
geom_histogram(aes(y=..density..), bins = 20,
colour="black", fill="lightblue") +
geom_density(alpha=.2, fill="darkblue") +
scale_x_continuous(breaks = x.scale, labels = x.lab) +
labs(x = NULL) +# use NULL here
annotate(geom = "segment", x = x.scale, xend = x.scale,
yend = 1.1 * max(dens_r$y), y = y_dens, lty = 2 ) +
annotate(geom = "text", label = x.lab,
x = x.scale, y = 1.2 * max(dens_r$y))
p2 <-
ggplot(foodf, aes(r)) +
scale_x_continuous(breaks = x.scale, labels = x2lab) +
labs(x = NULL) +
theme_classic() +
theme(axis.line.y = element_blank())
# This is with the modified plot_grid() / align_plot() function!!!
plot_grid(p1, p2, ncol = 1, align = "v", rel_heights = c(1, 0.1))
I am creating animated plotly graph for my assignment in r, where I am comparing several models with various number of observations. I would like to add annotation showing what is the RMSE of the current model - this means I would like to have text that changes together with slider. Is there any easy way how to do that?
Here is my dataset stored on GitHub. There already is created variable with RMSE: data
The base ggplot graphic is as follows:
library(tidyverse)
library(plotly)
p <- ggplot(values_predictions, aes(x = x)) +
geom_line(aes(y = preds_BLR, frame = n, colour = "BLR")) +
geom_line(aes(y = preds_RLS, frame = n, colour = "RLS")) +
geom_point(aes(x = x, y = target, frame = n, colour = "target"), alpha = 0.3) +
geom_line(aes(x = x, y = sin(2 * pi * x), colour = "sin(2*pi*x)"), alpha = 0.3) +
ggtitle("Comparison of performance) +
labs(y = "predictions and targets", colour = "colours")
This is converted to plotly, and I have added an animation to the Plotly graph:
plot <- ggplotly(p) %>%
animation_opts(easing = "linear",redraw = FALSE)
plot
Thanks!
You can add annotations to a ggplot graph using the annotate function: http://ggplot2.tidyverse.org/reference/annotate.html
df <- data.frame(x = rnorm(100, mean = 10), y = rnorm(100, mean = 10))
# Build model
fit <- lm(x ~ y, data = df)
# function finds RMSE
RMSE <- function(error) { sqrt(mean(error^2)) }
library(ggplot2)
ggplot(df, aes(x, y)) +
geom_point() +
annotate("text", x = Inf, y = Inf, hjust = 1.1, vjust = 2,
label = paste("RMSE", RMSE(fit$residuals)) )
There seems to be a bit of a problem converting between ggplot and plotly. However this workaround here shows a workaround which can be used:
ggplotly(plot) %>%
layout(annotations = list(x = 12, y = 13, text = paste("RMSE",
RMSE(fit$residuals)), showarrow = F))
Here's an example of adding data dependent text using the built in iris dataset with correlation as text to ggplotly.
library(plotly)
library(ggplot2)
library(dplyr)
mydata = iris %>% rename(variable1=Sepal.Length, variable2= Sepal.Width)
shift_right = 0.1 # number from 0-1 where higher = more right
shift_down = 0.02 # number from 0-1 where higher = more down
p = ggplot(mydata, aes(variable1,variable2))+
annotate(geom = "text",
label = paste0("Cor = ",as.character(round(cor.test(mydata$variable1,mydata$variable2)$estimate,2))),
x = min(mydata$variable1)+abs(shift_right*(min(mydata$variable1)-max(mydata$variable1))),
y = max(mydata$variable2)-abs(shift_down*(min(mydata$variable2)-max(mydata$variable2))), size=4)+
geom_point()
ggplotly(p) %>% style(hoverinfo = "none", traces = 1) # remove hover on text
I want to plot a line graph, with multiple lines, coloured depending on a grouping variable. Now I want to set the legend labels via scale-command:
scale_color_manual(values = colors_values, labels = ...)
The legend labels are as following: "x^2", "x^3", "x^4" etc., where the range is dynamically created. I would now like to dynamically create the expression as label text, i.e.
"x^2" should become x2
"x^3" should become x3
etc.
The amount of legend labels varies, so I thought about something like as.expression(sprintf("x^%i", number)), which does of course not work as label parameter for the scale function.
I have searched google and stack overflow, however, I haven't found a working solution yet, so I hope someone can help me here.
Here's a reproducible example:
poly.term <- runif(100, 1, 60)
resp <- rnorm(100, 40, 5)
poly.degree <- 2:4
geom.colors <- scales::brewer_pal(palette = "Set1")(length(poly.degree))
plot.df <- data.frame()
for (i in poly.degree) {
mydat <- na.omit(data.frame(x = poly.term, y = resp))
fit <- lm(mydat$y ~ poly(mydat$x, i, raw = TRUE))
plot.df <- rbind(plot.df, cbind(mydat, predict(fit), sprintf("x^%i", i)))
}
colnames(plot.df) <- c("x","y", "pred", "grp")
ggplot(plot.df, aes(x, y, colour = grp)) +
stat_smooth(method = "loess", se = F) +
geom_line(aes(y = pred))
scale_color_manual(values = geom.colors
# here I want to change legend labels
# lables = expresion???
)
I would like to have the legend labels to be x2, x3 and x4.
ggplot(plot.df, aes(x, y, colour = grp)) +
stat_smooth(method = "loess", se = F) +
geom_line(aes(y = pred)) +
scale_color_manual(values = setNames(geom.colors,
paste0("x^",poly.degree)),
labels = setNames(lapply(poly.degree, function(i) bquote(x^.(i))),
paste0("x^",poly.degree)))
It's important to ensure correct mapping if you change values or labels in the scale. Thus, you should always use named vectors.
this is my first stack overflow post and I am a relatively new R user, so please go gently!
I have a data frame with three columns, a participant identifier, a condition (factor with 2 levels either Placebo or Experimental), and an outcome score.
set.seed(1)
dat <- data.frame(Condition = c(rep("Placebo",10),rep("Experimental",10)),
Outcome = rnorm(20,15,2),
ID = factor(rep(1:10,2)))
I would like to construct a bar plot with two bars with the mean outcome score for each condition and the standard deviation as an error bar. I would like to then overlay lines connecting points for each participant's score in each condition. So the plot displays the individual response as well as the group mean.If it is also possible I would like to include an axis break.
I don't seem to be able to find any advice in other threads, apologies if I am repeating a question.
Many Thanks.
p.s. I realise that presenting data in this way will not be to everyones tastes. It is for a specific requirement!
This ought to work:
library(ggplot2)
library(dplyr)
dat.summ <- dat %>% group_by(Condition) %>%
summarize(mean.outcome = mean(Outcome),
sd.outcome = sd(Outcome))
ggplot(dat.summ, aes(x = Condition, y = mean.outcome)) +
geom_bar(stat = "identity") +
geom_errorbar(aes(ymin = mean.outcome - sd.outcome,
ymax = mean.outcome + sd.outcome),
color = "dodgerblue", width = 0.3) +
geom_point(data = dat, aes(x = Condition, y = Outcome),
color = "firebrick", size = 1.2) +
geom_line(data = dat, aes(x = Condition, y = Outcome, group = ID),
color = "firebrick", size = 1.2, alpha = 0.5) +
scale_y_continuous(limits = c(0, max(dat$Outcome)))
Some people are better with ggplot's stat functions and arguments than I am and might do it differently. I prefer to just transform my data first.
set.seed(1)
dat <- data.frame(Condition = c(rep("Placebo",10),rep("Experimental",10)),
Outcome = rnorm(20,15,2),
ID = factor(rep(1:10,2)))
dat.w <- reshape(dat, direction = 'wide', idvar = 'ID', timevar = 'Condition')
means <- colMeans(dat.w[, 2:3])
sds <- apply(dat.w[, 2:3], 2, sd)
ci.l <- means - sds
ci.u <- means + sds
ci.width <- .25
bp <- barplot(means, ylim = c(0,20))
segments(bp, ci.l, bp, ci.u)
segments(bp - ci.width, ci.u, bp + ci.width, ci.u)
segments(bp - ci.width, ci.l, bp + ci.width, ci.l)
segments(x0 = bp[1], x1 = bp[2], y0 = dat.w[, 2], y1 = dat.w[, 3], col = 1:10)
points(c(rep(bp[1], 10), rep(bp[2], 10)), dat$Outcome, col = 1:10, pch = 19)
Here is a method using the transfomations inside ggplot2
ggplot(dat) +
stat_summary(aes(x=Condition, y=Outcome, group=Condition), fun.y="mean", geom="bar") +
stat_summary(aes(x=Condition, y=Outcome, group=Condition), fun.data="mean_se", geom="errorbar", col="green", width=.8, size=2) +
geom_line(aes(x=Condition, y=Outcome, group=ID), col="red")