I was trying to do a ggplot scatter plot with var_x, var_y, and var_group as user-defined parameter. The code is as below
var_x='Expression'
var_y='Purity'
var_group = c('Type', 'Stage')
ggplot(data=d, mapping = aes(x=get(var_x), y=get(var_y), color=get(var_group[2]))) +
geom_point(size=2)+
xlab(var_x) +
ylim(0,1) + ylab(var_y)+
theme(legend.position = "top",
legend.text=element_text(size=40),
axis.text = element_text(size=30))+
ggtitle(paste0("Gene expression vs tumor purity in TCGA"))+
stat_poly_line() + stat_poly_eq()+
facet_wrap(.~get(var_group[1]), ncol=wNum, scale='fixed')
It's working but the legend is showing as "get(var_group[2]", while I'd like it to be displayed as the real value ("Type" in my test case). How can I do that?
Related
So here is my code:
plotbmi <- function(variable) {
plot <- ggplot(data = data, aes(variable, bmi))+
geom_boxplot()+
labs(title = paste("BMI boxplot split up by", variable))
ylab('BMI')+
theme_bw()+
theme(plot.title = element_text(size=14, hjust = 0.5, face = 'bold'))
return(plot)
}
plotbmi(data$region)
and my issue is that using this paste() in the title to concat the text with the variable is not working as I would like it to work. The plot title is not "BMI boxplot split up by regions" but it's "BMI boxplot split up by northeast" (northeast is a possible value in the regions variable.
How could I fix this?
In your implementation you're passing a character vector as the argument, which means it passes that vector itself to the xlab call. You can change this around to use tidyselect parts to both choose the correct column by column name and deparse it to paste in:
library(ggplot2)
plotbmi <- function(variable) {
plot <- ggplot(data = mtcars, aes({{variable}}, group = factor(gear)))+
geom_boxplot()+
labs(title = paste("cars boxplot split up by", rlang::get_expr(rlang::enquo(variable))))
ylab('Gears')+
theme_bw()+
theme(plot.title = element_text(size=14, hjust = 0.5, face = 'bold'))
return(plot)
}
plotbmi(cyl)
I've created a plot (below) and basically want the left and right ends of each facet to state "Ov" and "Cx." I've tried using scale_x_continuous but the issue is that the x-axis for each facet is different.
What I have right now (image):
What I'd like to get ideally:
all_prm %>% ggplot(aes(y_coord, prominence)) +
geom_point() +
facet_wrap(~interaction(ms, sample), scales="free_x") +
scale_x_continuous(breaks=c(10000), labels=c("Ov")) +
theme(
axis.title.y=element_text(margin=margin(r=7)),
axis.title.x=element_text(margin=margin(t=7)),
panel.background = element_rect(fill='white', color='grey10')) +
xlab("Oviduct-Cervical Axis") +
ylab("Prominence")
You can use a custom function to set the break points, in this case using the range of the x limit values with an adjustment argument to move the labels away from the axis limits relative to their scale.
Using the iris dataset:
break_range <- function(x, adjust = .025) {
rng <- range(x)
rng + diff(rng) * c(adjust, -adjust)
}
ggplot(iris, aes(Petal.Length, Sepal.Length )) +
geom_point() +
facet_wrap(. ~ Species, nrow = 3, scales="free_x") +
scale_x_continuous(breaks = break_range, labels = c("Ov", "Cx")) +
theme(
axis.title.y=element_text(margin=margin(r=7)),
axis.title.x=element_text(margin=margin(t=7)),
panel.background = element_rect(fill='white', color='grey10'))
I've made a histogram graph that shows the distribution of lidar returns per elevation for three lidar scans I have done.
I've converted my data to long format, with:
one column called 'value', describing the z position of each point
one column called 'variable', containing the name of each
scan group
In the attached image you can see the histograms of my three scan groups. I am currently using viridis to color the histogram by scan group (ie. the name of the scan in the variable column). However, I want to match the colours in the graph with colours I already have.
How might I do this?
The hexcols I'd like to like color each of my three histograms with are:
lightgreen = "#62FE96"
lightred = "#FE206B"
darkpurple = "#62278E"
A link to my data - 'density2'
My current code:
library(tidyverse)
library(viridisLite)
library(viridis)
# histogram
p <- density2 %>%
ggplot( aes(x=value,color = variable, show.legend = FALSE)) +
geom_histogram(binwidth = 1, alpha = 0.5, position="identity") +
scale_color_viridis(discrete =TRUE) +
scale_fill_viridis(discrete=TRUE) +
theme_bw() +
labs(fill="") +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())
p + scale_y_sqrt() + theme(legend.position="none") + labs(y = "data pts", x = "elevation (m)")
Any help would be most appreciated!
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.
I would like to plot densities of two variables ("red_variable", "green_variable") from two independent dataframes on one density plot, using red and green color for the two variables.
This is my attempt at coding:
library(ggplot2)
### Create dataframes
red_dataframe <- data.frame(red_variable = c(10,11,12,13,14))
green_dataframe <- data.frame(green_variable = c(6,7,8,9,10))
mean(red_dataframe$red_variable) # mean is 12
mean(green_dataframe$green_variable) # mean is 8
### Set colors
red_color= "#FF0000"
green_color= "#008000"
### Trying to plot densities with correct colors and correct legend entries
ggplot() +
geom_density(aes(x=red_variable, fill = red_color, alpha=0.5), data=red_dataframe) +
geom_density(aes(x=green_variable, fill = green_color, alpha=0.5), data=green_dataframe) +
scale_fill_manual(labels = c("Density of red_variable", "Density of green_variable"), values = c(red_color, green_color)) +
xlab("X value") +
ylab("Density") +
labs(fill = "Legend") +
guides(alpha=FALSE)
Result: The legend shows correct colors, but the colors on the plot are wrong: The "red" variable is plotted with green color, the "green" variable with red color. The "green" density (mean=8) should appear left and the "red" density (mean=12) on the right on the x-axis. This behavior of the plot doesn't make any sense to me.
I can in fact get the desired result by switching red and green in the code:
### load ggplot2
library(ggplot2)
### Create dataframes
red_dataframe <- data.frame(red_variable = c(10,11,12,13,14))
green_dataframe <- data.frame(green_variable = c(6,7,8,9,10))
mean(red_dataframe$red_variable) # mean is 12
mean(green_dataframe$green_variable) # mean is 8
### Set colors
red_color= "#FF0000"
green_color= "#008000"
### Trying to plot densities with correct colors and correct legend entries
ggplot() +
geom_density(aes(x=red_variable, fill = green_color, alpha=0.5), data=red_dataframe) +
geom_density(aes(x=green_variable, fill = red_color, alpha=0.5), data=green_dataframe) +
scale_fill_manual(labels = c("Density of red_variable", "Density of green_variable"), values = c(red_color, green_color)) +
xlab("X value") +
ylab("Density") +
labs(fill = "Legend") +
guides(alpha=FALSE)
... While the plot makes sense now, the code doesn't. I cannot really trust code doing the opposite of what I would expect it to do. What's the problem here? Am I color blind?
On your code, in order to have color at the right position, you need to specify fill = red_color or fill = green_color (as well as alpha as it is a constant - as pointed out by #Gregor) outside of the aes such as:
...+
geom_density(aes(x=red_variable), alpha=0.5, fill = red_color, data=red_dataframe) +
geom_density(aes(x=green_variable), alpha=0.5, fill = green_color, data=green_dataframe) + ...
Alternatively, you can bind your dataframes together, reshape them into a longer format (much more appropriate to ggplot) and then add color column that you can use with scale_fill_identity function (https://ggplot2.tidyverse.org/reference/scale_identity.html):
df <- cbind(red_dataframe,green_dataframe)
library(tidyr)
library(ggplot2)
library(dplyr)
df <- df %>% pivot_longer(.,cols = c(red_variable,green_variable), names_to = "var",values_to = "val") %>%
mutate(Color = ifelse(grepl("red",var),red_color,green_color))
ggplot(df, aes(val, fill = Color))+
geom_density(alpha = 0.5)+
scale_fill_identity(guide = "legend", name = "Legend", labels = levels(as.factor(df$var)))+
xlab("X value") +
ylab("Density")
Does it answer your question ?
You're trying to use ggplot as if it's base graphics... the mindset shift can take a little while to get used to. dc37's answer shows how you should do it. I'll try to explain what goes wrong in your attempt:
When you put fill = green_color inside aes(), because it's inside aes() ggplot essentially creates a new column of data filled with the green_color values in your green_data_frame, i.e., "#008000", "#008000", "#008000", .... Ditto for the red color values in the red data frame. We can see this if we modify your plot by simply deleting your scale:
ggplot() +
geom_density(aes(x = red_variable, fill = green_color, alpha = 0.5), data =
red_dataframe) +
geom_density(aes(x = green_variable, fill = red_color, alpha = 0.5), data =
green_dataframe) +
xlab("X value") +
ylab("Density") +
labs(fill = "Legend") +
guides(alpha = FALSE)
We can actually get what you want by putting the identity scale, which is designed for the (common in base, rare in ggplot2) case where you actually put color values in the data.
ggplot() +
geom_density(aes(x = red_variable, fill = green_color, alpha = 0.5), data =
red_dataframe) +
geom_density(aes(x = green_variable, fill = red_color, alpha = 0.5), data =
green_dataframe) +
scale_fill_identity() +
xlab("X value") +
ylab("Density") +
labs(fill = "Legend") +
guides(alpha = FALSE)
When you added your scale_fill_manual, ggplot was like "okay, cool, you want to specify colors and labels". But you were thinking in the order that you added the layers to the plot (much like base graphics), whereas ggplot was thinking of these newly created variables "#FF0000" and "#008000", which it ordered alphabetically by default (just as if they were factor or character columns in a data frame). And since you happened to add the layers in reverse alphabetical order, it was switched.
dc37's answer shows a couple better methods. With ggplot you should (a) work with a single, long-format data frame whenever possible (b) don't put constants inside aes() (constant color, constant alpha, etc.), (c) set colors in a scale_fill_* or scale_color_* function when they're not constant.
I have a empirical PDF + CDF combo I'd like to plot on the same panel. distro.df has columns pdf, cdf, and day. I'd like the pdf values to be plotted as bars, and the cdf as lines. This does the trick for making the plot:
p <- ggplot(distro.df, aes(x=day))
p <- p + geom_bar(aes(y=pdf/max(pdf)), stat="identity", width=0.95, fill=fillCol)
p <- p + geom_line(aes(y=cdf))
p <- p + xlab("Day") + ylab("")
p <- p + theme_bw() + theme_update(panel.background = element_blank(), panel.border=element_blank())
However, I'm having trouble getting a legend to appear. I'd like a line for the cdf and a filled block for the pdf. I've tried various contortions with guides, but can't seem to get anything to appear.
Suggestions?
EDIT:
Per #Henrik's request: to make a suitable distro.df object:
df <- data.frame(day=0:10)
df$pdf <- runif(length(df$day))
df$pdf <- df$pdf / sum(df$pdf)
df$cdf <- cumsum(df$pdf)
Then the above to make the plot, then invoke p to see the plot.
This generally involves moving fill into aes and using it in both the geom_bar and geom_line layers. In this case, you also need to add show_guide = TRUE to geom_line.
Once you have that, you just need to set the fill colors in scale_fill_manual so CDF doesn't have a fill color and use override.aes to do the same thing for the lines. I didn't know what your fill color was, so I just used red.
ggplot(df, aes(x=day)) +
geom_bar(aes(y=pdf/max(pdf), fill = "PDF"), stat="identity", width=0.95) +
geom_line(aes(y=cdf, fill = "CDF"), show_guide = TRUE) +
xlab("Day") + ylab("") +
theme_bw() +
theme_update(panel.background = element_blank(),
panel.border=element_blank()) +
scale_fill_manual(values = c(NA, "red"),
breaks = c("PDF", "CDF"),
name = element_blank(),
guide = guide_legend(override.aes = list(linetype = c(0,1))))
I'd still like a solution to the above (and will checkout #aosmith's answer), but I am currently going with a slightly different approach to eliminate the need to solve the problem:
p <- ggplot(distro.df, aes(x=days, color=pdf, fill=pdf))
p <- p + geom_bar(aes(y=pdf/max(pdf)), stat="identity", width=0.95)
p <- p + geom_line(aes(y=cdf), color="black")
p <- p + xlab("Day") + ylab("CDF")
p <- p + theme_bw() + theme_update(panel.background = element_blank(), panel.border=element_blank())
p
This also has the advantage of displaying some of the previously missing information, namely the PDF values.