How to Define Fill Colours in Ggplot Histogram

how to define fill colours in ggplot histogram?

Hopefully this should get you started:

ggplot(data, aes(status, fill = ..x..))+
  geom_histogram(binwidth = 1) + 
  scale_fill_gradient(low = "black", high = "white")

ggplot(data, aes(status, fill = ..x.. > 9))+
  geom_histogram(binwidth = 1) + 
  scale_fill_grey()

Filling up histograms with ggplot - Changing colours

You could use scale_fill_gradient:

df4 <- data.frame(rnorm(10000,100,10))
colnames(df4) <- c("Value")

library(ggplot2)

ggplot(df4, aes(x=Value)) + 
  geom_histogram(binwidth = 1, alpha=0.8, aes(fill=..count..)) + 
  scale_fill_gradient(low = "red", high = "green") +
  labs(title="My first Histogram")

Filling bar colours with the mean of another continuous variable in ggplot2 histograms

If you want a genuine histogram you need to transform your data to do this by summarizing it first, and plot with geom_col rather than geom_histogram. The base R function hist will help you here to generate the breaks and midpoints:

library(ggplot2)
library(dplyr)

mtcars %>% 
  mutate(mpg = cut(x      = mpg, 
                   breaks = hist(mpg, breaks = 0:4 * 10, plot = FALSE)$breaks,
                   labels = hist(mpg, breaks = 0:4 * 10, plot = FALSE)$mids)) %>%
  group_by(mpg) %>%
  summarize(n = n(), wt = mean(wt)) %>%
  ggplot(aes(x = as.numeric(as.character(mpg)), y = n, fill = wt)) +
  scale_x_continuous(limits = c(0, 40), name = "mpg") +
  geom_col(width = 10) +
  theme_bw()

How to color a ggplot histogram differently based on precise cut off points?

Maybe this'll work for you. You can specify the bin-breaks in geom_histogram. So we first create an evenly spaced bin-vector and add some cutoff points to it:

n.bins <- 5 # number of bins
additional.cutoffs <- c(3.9, 2.9) # additional bins

bins <- seq(min(mtcars$wt), max(mtcars$wt), length.out = n.bins)    
bins <- c(bins, additional.cutoffs) %>% sort()

mtcars %>% 
  mutate(wt_color = ifelse(wt < cutoff_point, "red", "blue")) %>% 
  select(wt, wt_color) %>% 
  ggplot(aes(x=wt, fill = wt_color)) +
  geom_histogram(breaks = bins) +
  geom_vline(xintercept=additional.cutoffs, colour="black")

r ggplot colored histogram is wrong colour

You can do this

x<-rnorm(1000)

df <- data.frame(x = x)

p1<-ggplot() +
  geom_histogram(data=df, aes(x=x),fill="blue")
p1

The fill argument can't understand the value of one column.

If you have different colors, this will do:

p4 <- ggplot(df, aes(x=x, fill=colors)) +
  geom_histogram() +
  scale_fill_manual(values=c("blue","green","orange","red"))
p4

Please note the colors in alphabetical order.

ggplot histogram colour by group not working

Another way of doing it, a need to be factor

    library(ggplot2)
    a <- c(1,2,2,3)
    b <- c(4,5,6,7)
    ab <- data.frame(a, b)
    ab 
    
    ggplot(ab, aes(x=a, fill=as.factor(a))) +
  geom_histogram()

ggplot2 and R - Applying custom colors to a multi group histogram in long format

Delete the scale_color_viridis and scale_fill_viridis lines - these are applying the Viridis color scale. Replace with scale_fill_manual(values = c(lightgreen, lightred, darkpurple)). And in your aesthetic mapping replace color = variable with fill = variable. For a histogram, color refers to the color of the lines outlining each bar, and fill refers to the color each bar is filled in.

This should leave you with:

p <- density2 %>%
  ggplot(aes(x = value, fill = variable)) +
  geom_histogram(binwidth = 1, alpha = 0.5, position = "identity") +
  scale_fill_manual(values = c(lightgreen, lightred, darkpurple)) +
  theme_bw() +
  labs(fill = "") +
  theme(panel.grid = element_blank())

p + scale_y_sqrt() + 
  theme(legend.position = "none") +
  labs(y = "data pts", x = "elevation (m)")

I've also done some other clean-up. show.legend = FALSE does not belong inside aes() - and your theme(legend.position = "none") should take care of it.

I did not download your data, save it in my working directory, import it into R, and test this code on it. If you need more help, please post a small subset of your data in a copy/pasteable format (e.g., dput(density2[1:20, ]) for the first 20 rows---choose a suitable subset) and I'll be happy to test and adjust.

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