I want to plot of Gaussian distributions that are included in samp, but the size of samp may varies. Here there are 4 components but it may there are less or more components. How could I adjust the code two plot as many components as there are in samp?
ggplot(samp, aes(x=seq1, y=seq2)) +
geom_contour(mapping = aes(z=prob.1), color = "tomato") +
geom_contour(mapping = aes(z=prob.2), color = "darkblue") +
geom_contour(mapping = aes(z=prob.3), color = "green4") +
geom_contour(mapping = aes(z=prob.4), color = "purple") +
labs(x = "PC1", y = "PC2", title="Posterior probability") +
theme_bw()
The best way to do this is probably to pivot all columns whose names include the string "prob" into long format. That way, it's also easy to map the name of each probability to the color aesthetic, which gives you the option of a legend:
library(tidyverse)
ggplot(pivot_longer(samp, contains("prob")), aes(seq1, seq2, colour = name)) +
geom_contour(aes(z = value)) +
labs(x = "PC1", y = "PC2", title="Posterior probability") +
theme_bw()
Sample data used
set.seed(1)
samp <- setNames(as.data.frame(replicate(4, rnorm(100))), paste0("prob.", 1:4))
samp$seq1 <- rep(1:10, 10)
samp$seq2 <- rep(1:10, each = 10)
Related
I would like to change the y axis label (or main title would also be fine) of a ggplot to reflect the column name being iterated over within an apply function.
Here is some sample data and my working apply function:
trial_df <- data.frame("Patient" = c(1,1,2,2,3,3,4,4),
"Outcome" = c("NED", "NED", "NED", "NED", "Relapse","Relapse","Relapse","Relapse"),
"Time_Point" = c("Baseline", "Week3", "Baseline", "Week3","Baseline", "Week3","Baseline", "Week3"),
"CD4_Param" = c(50.8,53.1,20.3,18.1,30.8,24.5,35.2,31.0),
"CD8_Param" = c(5.3,9.7,4.4,4.3,3.1,3.2,5.6,5.3),
"CD3_Param" = c(11.6,16.6,5.0,5.1,14.3,7.1,5.9,8.1))
apply(trial_df[,4:length(trial_df)], 2, function(i) ggplot(data = trial_df, aes_string(x = "Time_Point", y = i )) +
facet_wrap(~Outcome) +
geom_boxplot(alpha = 0.1) +
geom_point(aes(color = `Outcome`, fill = `Outcome`)) +
geom_path(aes(group = `Patient`, color = `Outcome`)) +
theme_minimal() +
ggpubr::stat_compare_means( method = "wilcox.test") +
scale_fill_manual(values=c("blue", "red")) +
scale_color_manual(values=c("blue", "red")))
Example plot output
This creates 3 graphs as expected, however the y axis just says "y". I would like this to display the column name for the column in that iteration. It would also be fine to add a main title with this information, as I just need to know which graph corresponds to which column.
Here are things I have already tried adding to the ggplot code above based on some similar questions I found, but all of them give me the error "non-numeric argument to binary operator":
ggtitle(paste(i))
labs(y = i)
labs(y = as.character(i))
Any help or resources I may have missed would be greatly appreciated, thanks!
So.....for the strangest of reasons I cannot figure out why. This gives what you want but for only one graph!!!
apply(trial_df[,4:length(trial_df)], 2, function(i) ggplot(data = trial_df, aes_string(x = "Time_Point", y = i )) +
facet_wrap(~Outcome) +
geom_boxplot(alpha = 0.1) +
geom_point(aes(color = `Outcome`, fill = `Outcome`)) +
geom_path(aes(group = `Patient`, color = `Outcome`)) +
theme_minimal() +
stat_compare_means( method = "wilcox.test") +
scale_fill_manual(values=c("blue", "red")) +
scale_color_manual(values=c("blue", "red"))+
labs(y=colnames(trial_df)[i]))
Gives these:
I need to make 5 plots of bacteria species. Each plot has a different number of species present in a range of 30-90. I want each bacteria to always have the same color in all plots, therefore I need to set an assigned color to each name.
I tried to use scale_colour_manual to create a color set but, the environment created has only 16 colors. How can I increase the number of colors present in the environment created?
the code I am using can be replicated as follow:
colour_genus <- stringi::stri_rand_strings(90, 5) #to be random names
nb.cols = nrow(colour_genus) #to set the length of my string
MyPalette = colorRampPalette(brewer.pal(12,"Set1"))(nb.cols) # the palette of choice
colGenus <- scale_color_manual(name = colour_genus, values = MyPalette)
The output formed contains only 16 values, so when I try to apply it to a figure with 90 factors, it complains I have only 16 values
abundance <- runif(90, min = 10, max = 100)
my_data <- data.frame(colour_genus, abundance)
p <- ggplot(my_data, aes(x = colour_genus, y= abundance)) +
geom_bar(aes(color = colour_genus, fill = colour_genus), stat = "identity", position = "stack") +
labs(x = "", y = "Relative Abundance\n") +
theme(panel.background = element_blank())
p + theme(legend.text= element_text(size=7, face="bold"), axis.text.x = element_text(angle = 90)) + guides(fill=guide_legend(ncol=2)) + scale_fill_manual(values=colGenus)
The following error shows:
Error: Insufficient values in manual scale. 90 needed but only 16 provided.
Thank you very much for your help.
When you know all your 90 bacci names in front of plotting, you can try.
set.seed(123)
colour_genus <- sort(stringi::stri_rand_strings(90, 5))#to be random names. I sorted the vector to illustrate the output better (optional).
MyPalette <- sample(colors(), length(colour_genus))
# named vector for scale_fill
names(MyPalette) <- colour_genus
# data
abundance <- runif(90, min = 10, max = 100)
my_data <- data.frame(colour_genus, abundance)
# two sets to show results
set1 <- my_data[20:30,]
set2 <- my_data[25:35,]
ggplot(set1, aes(x = colour_genus, y= abundance)) +
geom_col(aes(fill = colour_genus)) +
scale_fill_manual(values = MyPalette)
ggplot(set2, aes(x = colour_genus, y= abundance)) +
geom_col(aes(fill = colour_genus)) +
scale_fill_manual(values = MyPalette)
I'm trying to plot box plots with normal distribution of the underlying data next to the plots in a vertical format like this:
This is what I currently have graphed from an excel sheet uploaded to R:
And the code associated with them:
set.seed(12345)
library(ggplot2)
library(ggthemes)
library(ggbeeswarm)
#graphing boxplot and quasirandom scatterplot together
ggplot(X8_17_20_R_20_60, aes(Type, Diameter)) +
geom_quasirandom(shape=20, fill="gray", color = "gray") +
geom_boxplot(fill="NA", color = c("red4", "orchid4", "dark green", "blue"),
outlier.color = "NA") +
theme_hc()
Is this possible in ggplot2 or R in general? Or is the only way this would be feasible is through something like OrignLab (where the first picture came from)?
You can do something similar to your example plot with the gghalves package:
library(gghalves)
n=0.02
ggplot(iris, aes(Species, Sepal.Length)) +
geom_half_boxplot(center=TRUE, errorbar.draw=FALSE,
width=0.5, nudge=n) +
geom_half_violin(side="r", nudge=n) +
geom_half_dotplot(dotsize=0.5, alpha=0.3, fill="red",
position=position_nudge(x=n, y=0)) +
theme_hc()
There are a few ways to do this. To gain full control over the look of the plot, I would just calculate the curves and plot them. Here's some sample data that's close to your own and shares the same names, so it should be directly applicable:
set.seed(12345)
X8_17_20_R_20_60 <- data.frame(
Diameter = rnorm(4000, rep(c(41, 40, 42, 40), each = 1000), sd = 6),
Type = rep(c("AvgFeret", "CalcDiameter", "Feret", "MinFeret"), each = 1000))
Now we create a little data frame of normal distributions based on the parameters taken from each group:
df <- do.call(rbind, mapply( function(d, n) {
y <- seq(min(d), max(d), length.out = 1000)
data.frame(x = n - 5 * dnorm(y, mean(d), sd(d)) - 0.15, y = y, z = n)
}, with(X8_17_20_R_20_60, split(Diameter, Type)), 1:4, SIMPLIFY = FALSE))
Finally, we draw your plot and add a geom_path with the new data.
library(ggplot2)
library(ggthemes)
library(ggbeeswarm)
ggplot(X8_17_20_R_20_60, aes(Type, Diameter)) +
geom_quasirandom(shape = 20, fill = "gray", color = "gray") +
geom_boxplot(fill="NA", aes(color = Type), outlier.color = "NA") +
scale_color_manual(values = c("red4", "orchid4", "dark green", "blue")) +
geom_path(data = df, aes(x = x, y = y, group = z), size = 1) +
theme_hc()
Created on 2020-08-21 by the reprex package (v0.3.0)
I'm working with stock prices and trying to plot the price difference.
I created one using autoplot.zoo(), my question is, how can I manage to change the point shapes to triangles when they are above the upper threshold and to circles when they are below the lower threshold. I understand that when using the basic plot() function you can do these by calling the points() function, wondering how I can do this but with ggplot2.
Here is the code for the plot:
p<-autoplot.zoo(data, geom = "line")+
geom_hline(yintercept = threshold, color="red")+
geom_hline(yintercept = -threshold, color="red")+
ggtitle("AAPL vs. SPY out of sample")
p+geom_point()
We can't fully replicate without your data, but here's an attempt with some sample generated data that should be similar enough that you can adapt for your purposes.
# Sample data
data = data.frame(date = c(2001:2020),
spread = runif(20, -10,10))
# Upper and lower threshold
thresh <- 4
You can create an additional variable that determines the shape, based on the relationship in the data itself, and pass that as an argument into ggplot.
# Create conditional data
data$outlier[data$spread > thresh] <- "Above"
data$outlier[data$spread < -thresh] <- "Below"
data$outlier[is.na(data$outlier)] <- "In Range"
library(ggplot2)
ggplot(data, aes(x = date, y = spread, shape = outlier, group = 1)) +
geom_line() +
geom_point() +
geom_hline(yintercept = c(thresh, -thresh), color = "red") +
scale_shape_manual(values = c(17,16,15))
# If you want points just above and below# Sample data
data = data.frame(date = c(2001:2020),
spread = runif(20, -10,10))
thresh <- 4
data$outlier[data$spread > thresh] <- "Above"
data$outlier[data$spread < -thresh] <- "Below"
ggplot(data, aes(x = date, y = spread, shape = outlier, group = 1)) +
geom_line() +
geom_point() +
geom_hline(yintercept = c(thresh, -thresh), color = "red") +
scale_shape_manual(values = c(17,16))
Alternatively, you can just add the points above and below the threshold as individual layers with manually specified shapes, like this. The pch argument points to shape type.
# Another way of doing this
data = data.frame(date = c(2001:2020),
spread = runif(20, -10,10))
# Upper and lower threshold
thresh <- 4
ggplot(data, aes(x = date, y = spread, group = 1)) +
geom_line() +
geom_point(data = data[data$spread>thresh,], pch = 17) +
geom_point(data = data[data$spread< (-thresh),], pch = 16) +
geom_hline(yintercept = c(thresh, -thresh), color = "red") +
scale_shape_manual(values = c(17,16))
I have temporal data of gas emissions from two species of plant, both of which have been subjected to the same treatments. With some previous help to get this code together [edit]:
soilflux = read.csv("soil_fluxes.csv")
library(ggplot2)
soilflux$Treatment <- factor(soilflux$Treatment,levels=c("L-","C","L+"))
soilplot = ggplot(soilflux, aes(factor(Week), Flux, fill=Species, alpha=Treatment)) + stat_boxplot(geom ='errorbar') + geom_boxplot()
soilplot = soilplot + labs(x = "Week", y = "Flux (mg m-2 d-1)") + theme_bw(base_size = 12, base_family = "Helvetica")
soilplot
Producing this which works well but has its flaws.
Whilst it conveys all the information I need it to, despite Google trawls and looking through here I just couldn't get the 'Treatment' part of the legend to show that L- is light and L+ darkest. I've also been told that a monochrome colour scheme is easier to differentiate hence I'm trying to get something like this where the legend is clear.
(source: biomedcentral.com)
As a workaround you could create a combined factor from species and treatment and assign the fill colors manually:
library(ggplot2)
library(RColorBrewer)
d <- expand.grid(week = factor(1:4), species = factor(c("Heisteria", "Simarouba")),
trt = factor(c("C", "L-", "L+"), levels = c("L-", "C", "L+")))
d <- d[rep(1:24, each = 30), ]
d$flux <- runif(NROW(d))
# Create a combined factor for coding the color
d$spec.trt <- interaction(d$species, d$trt, lex.order = TRUE, sep = " - ")
ggplot(d, aes(x = week, y = flux, fill = spec.trt)) +
stat_boxplot(geom ='errorbar') + geom_boxplot() +
scale_fill_manual(values = c(brewer.pal(3, "Greens"), brewer.pal(3, "Reds")))