Two-Way Density Plot Combined with One Way Density Plot with Selected Regions in R

two-way density plot combined with one way density plot with selected regions in r

Here is the example for combining multiple plots with alignment:

library(ggplot2)
library(grid)

set.seed (123)
xvar <- c(rnorm (100, 50, 30), rnorm (100, 40, 10), rnorm (100, 70, 10))
yvar <-   xvar + rnorm (length (xvar), 0, 20)
myd <- data.frame (xvar, yvar)

p1 <- ggplot(myd,aes(x=xvar,y=yvar))+
  stat_density2d(aes(fill=..level..), geom="polygon") +
  coord_cartesian(c(0, 150), c(0, 150)) +
  opts(legend.position = "none")

p2 <- ggplot(myd, aes(x = xvar)) + stat_density() +
  coord_cartesian(c(0, 150))
p3 <- ggplot(myd, aes(x = yvar)) + stat_density() + 
  coord_flip(c(0, 150))

gt <- ggplot_gtable(ggplot_build(p1))
gt2 <- ggplot_gtable(ggplot_build(p2))
gt3 <- ggplot_gtable(ggplot_build(p3))

gt1 <- ggplot2:::gtable_add_cols(gt, unit(0.3, "null"), pos = -1)
gt1 <- ggplot2:::gtable_add_rows(gt1, unit(0.3, "null"), pos = 0)

gt1 <- ggplot2:::gtable_add_grob(gt1, gt2$grobs[[which(gt2$layout$name == "panel")]],
                                  1, 4, 1, 4)
gt1 <- ggplot2:::gtable_add_grob(gt1, gt2$grobs[[which(gt2$layout$name == "axis-l")]],
                                 1, 3, 1, 3, clip = "off")

gt1 <- ggplot2:::gtable_add_grob(gt1, gt3$grobs[[which(gt3$layout$name == "panel")]],
                                 4, 6, 4, 6)
gt1 <- ggplot2:::gtable_add_grob(gt1, gt3$grobs[[which(gt3$layout$name == "axis-b")]],
                                 5, 6, 5, 6, clip = "off")
grid.newpage()
grid.draw(gt1)

note that this works with gglot2 0.9.1, and in the future release you may do it more easily.

And finally

you can do that by:

library(ggplot2)
library(grid)

set.seed (123)
xvar <- c(rnorm (100, 50, 30), rnorm (100, 40, 10), rnorm (100, 70, 10))
yvar <-   xvar + rnorm (length (xvar), 0, 20)
myd <- data.frame (xvar, yvar)

p1 <- ggplot(myd,aes(x=xvar,y=yvar))+
  stat_density2d(aes(fill=..level..), geom="polygon") +
  geom_polygon(aes(x, y), 
               data.frame(x = c(-Inf, -Inf, 30, 30), y = c(-Inf, 30, 30, -Inf)),
               alpha = 0.5, colour = NA, fill = "red") +
  geom_polygon(aes(x, y), 
               data.frame(x = c(Inf, Inf, 80, 80), y = c(Inf, 80, 80, Inf)),
               alpha = 0.5, colour = NA, fill = "green") +
  coord_cartesian(c(0, 120), c(0, 120)) +
  opts(legend.position = "none")

xd <- data.frame(density(myd$xvar)[c("x", "y")])
p2 <- ggplot(xd, aes(x, y)) + 
  geom_area(data = subset(xd, x < 30), fill = "red") +
  geom_area(data = subset(xd, x > 80), fill = "green") +
  geom_line() +
  coord_cartesian(c(0, 120))

yd <- data.frame(density(myd$yvar)[c("x", "y")])
p3 <- ggplot(yd, aes(x, y)) + 
  geom_area(data = subset(yd, x < 30), fill = "red") +
  geom_area(data = subset(yd, x > 80), fill = "green") +
  geom_line() +
  coord_flip(c(0, 120))

gt <- ggplot_gtable(ggplot_build(p1))
gt2 <- ggplot_gtable(ggplot_build(p2))
gt3 <- ggplot_gtable(ggplot_build(p3))

gt1 <- ggplot2:::gtable_add_cols(gt, unit(0.3, "null"), pos = -1)
gt1 <- ggplot2:::gtable_add_rows(gt1, unit(0.3, "null"), pos = 0)

gt1 <- ggplot2:::gtable_add_grob(gt1, gt2$grobs[[which(gt2$layout$name == "panel")]],
                                  1, 4, 1, 4)
gt1 <- ggplot2:::gtable_add_grob(gt1, gt2$grobs[[which(gt2$layout$name == "axis-l")]],
                                 1, 3, 1, 3, clip = "off")

gt1 <- ggplot2:::gtable_add_grob(gt1, gt3$grobs[[which(gt3$layout$name == "panel")]],
                                 4, 6, 4, 6)
gt1 <- ggplot2:::gtable_add_grob(gt1, gt3$grobs[[which(gt3$layout$name == "axis-b")]],
                                 5, 6, 5, 6, clip = "off")
grid.newpage()
grid.draw(gt1)

Combine two density plots in R into one plot

# create density plot for total sales and costs
dens_plot_sales <- final_data %>%
drop_na(tot_sales, firm_size) %>%
ggplot()+
geom_density(aes(x = tot_sales, colour = firm_size)) +
geom_density(aes(x = tot_costs, colour = firm_size)) + # It's that simple
labs(title = "Density plot of total sales and costs across firm size levels", 
x = "Total sales/costs ($)", y = "Density", col= "Firm size") +
theme_classic()

I can't test it fully without knowing what final_data is, but this should work

R: Combining Two Contour Plots Together

Do you mean sth. like this? Here, a contour plot is used to overlap multiple 2d kernel densities:

library(ggplot2)
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union

set.seed(1337)
data_1 = data.frame(a = rnorm(100,10,10), b = rnorm(100,10,10), c = as.factor("one"))
data_2 = data.frame(a = rnorm(100,10,10), b = rnorm(100,10,10), c = as.factor("two"))

bind_rows(
  data_1,
  data_2
) %>%
  ggplot(aes(a, b, color = c)) +
    geom_density2d()

^{Created on 2021-12-07 by the reprex package (v2.0.1)}

R: Overlay density plots by condition and by average plot

Is this what you're going for? I use after_stat here to scale down the conditional density plots to be comfortably lower than the total density. (which, being by definition less spiky, will tend to have lower peak densities than the conditional densities.)

ggplot(mtcars) +
  geom_density(aes(mpg)) +
  geom_density(aes(mpg, after_stat(count) * 0.01, 
                   group = cyl, fill = as.character(cyl)), alpha = 0.2)

If you want to convert this to a function, you could use something like the following. The {{ }} or "embrace" operator works to forward the variable names into the environment of the function. More at https://rlang.r-lib.org/reference/topic-data-mask-programming.html#embrace-with-

plot_densities <- function(df, var, group) {
  ggplot(df) +
    geom_density(aes( {{ var }} )) +
    geom_density(aes( {{ var }}, after_stat(count) * 0.01, 
                     group = {{ group }}, 
                     fill = as.character( {{ group }} )), alpha = 0.2)
}

plot_densities(mtcars, mpg, cyl)

Is there a way in R to overlay 3 density plots, with time as the x axis, and count as the y axis?

the step you are missing is that you need to change your dataframe into long format

let's assume your data frame looks as follows

library(tidyverse)
library(scales)

df <- data.frame(fb= lubridate::ymd(c("2020-01-01","2020-01-02","2020-01-03", "2020-01-03")),
                      twi = lubridate::ymd(c("2020-01-05","2020-01-05","2020-01-6", "2020-01-09")),
                      insta = lubridate::ymd(c("2020-01-01","2020-01-02","2020-01-05", "2020-01-05"))
                      )

now change the data frame into long format:

df_long <- df %>% pivot_longer(everything())

and this can be plotted

df %>% ggplot( aes(x =value, color=name, fill= name)) +
  geom_density( alpha=0.8)+
  theme_bw()+
  scale_x_date(labels = date_format("%Y-%m"), 
               breaks = date_breaks("3 months")) +
  labs(title = "Posts over time")+
  xlab("month")+
  ylab("density")

How to plot a density map between two lines with ggplot2?

Of course. Maybe:

library(tidyverse)

expand.grid(x = seq(0, 1, .001),    # make a grid of x and y values
            y = seq(0, 1, .001)) %>% 
    filter(y < x, y > x ^ 2) %>%    # filter to rows between curves
    ggplot(aes(x, y, fill = x + y)) + 
    geom_raster() + 
    stat_function(fun = identity, color = 'red') +    # add y = x
    stat_function(fun = function(x){x^2}, color = 'red', linetype = 'dashed') + 
    scale_fill_gradient(low = 'black', high = 'white') + 
    coord_equal()

Density plots of four groups in the same plot

Your gr field is numeric. Converting it to factor within the aes() argument was the solution:

ggplot(df, aes(x = LENGTH,y=..count..,fill=factor(gr))) +
  geom_density(alpha=0.6) +
  scale_color_manual(values = c("red", "green","blue","yellow")) +
  theme_bw()

How to create a density plot of all data overlaid with a density plot of a subset of the data in ggplot

The help page for ?geom_density states that the first argument is mapping, not data. So the correct usage will be

ggplot(hrql.scores, aes(x=PCS, y=..density..)) +
    geom_histogram(fill="cornsilk", colour="grey35", binwidth=5) +
    geom_density() + 
    geom_density(data = apdj) + 
    geom_density(data = health.studio)

Note that there is no need for additional aes mapping, since it is inherited from the top ggplot call.

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