I've been trying to generate a Manhattan plot using ggplot, which I finally got to work. However, I cannot get the points to be colored by chromosome, despite having tried several different examples I've seen online. I'm attaching my code and the resulting plot below. Can anyone see why the code is failing to color points by chromosome?
library(tidyverse)
library(vroom)
# threshold to drop really small -log10 p values so I don't have to plot millions of uninformative points. Just setting to 0 since I'm running for a small subset
min_p <- 0.0
# reading in data to brassica_df2, converting to data frame, removing characters from AvsDD p value column, converting to numeric, filtering by AvsDD (p value)
brassica_df2 <- vroom("manhattan_practice_data.txt", col_names = c("chromosome", "position", "num_SNPs", "prop_SNPs_coverage", "min_coverage", "AvsDD", "AvsWD", "DDvsWD"))
brassica_df2 <- as.data.frame(brassica_df2)
brassica_df2$AvsDD <- gsub("1:2=","",as.character(brassica_df2$AvsDD))
brassica_df2$AvsDD <- as.numeric(brassica_df2$AvsDD)
brassica_df2 <- filter(brassica_df2, AvsDD > min_p)
# setting significance threshhold
sig_cut <- -log10(1)
# settin ylim for graph
ylim <- (max(brassica_df2$AvsDD) + 2)
# setting up labels for x axis
axisdf <- as.data.frame(brassica_df2 %>% group_by(chromosome) %>% summarize(center=( max(position) + min(position) ) / 2 ))
# making manhattan plot of statistically significant SNP shifts
manhplot <- ggplot(data = filter(brassica_df2, AvsDD > sig_cut), aes(x=position, y=AvsDD), color=as.factor(chromosome)) +
geom_point(alpha = 0.8) +
scale_x_continuous(label = axisdf$chromosome, breaks= axisdf$center) +
scale_color_manual(values = rep(c("#276FBF", "#183059"), unique(length(axisdf$chromosome)))) +
geom_hline(yintercept = sig_cut, lty = 2) +
ylab("-log10 p value") +
ylim(c(0,ylim)) +
theme_classic() +
theme(legend.position = "n")
print(manhplot)
I think you just need to move your color=... argument inside the call to aes():
ggplot(
data = filter(brassica_df2, AvsDD > sig_cut),
aes(x=position, y=AvsDD),
color=as.factor(chromosome))
becomes...
ggplot(
data = filter(brassica_df2, AvsDD > sig_cut),
aes(x=position, y=AvsDD, color=as.factor(chromosome)))
Related
The following sample or R code displays contour levels and the data points used in generating the contours.
n <- 10
x <- c(rnorm(n,-1,0.5), rnorm(n,1,0.5))
y <- c(rnorm(n,-1,1), rnorm(n,1,0.5))
df <- data.frame(x,y)
# categorise the points
df$cat <- sample(c(1,2), n, replace=T)
library(ggplot2)
p <- ggplot(df)
# for manual colouring of points, but not showing contours due to error
#p <- p + geom_point(aes(x=x,y=y,col=factor(cat)))
#cols <- c("1"="red", "2"="blue")
#p <- p + scale_color_manual(values=cols)
# this works fine except I am not controlling the colours
p <- p + geom_point(aes(x=x,y=y,col=cat))
p <- p + geom_density2d(aes(x=x,y=y,color=..level..))
print(p)
I am able to colour the points according to their binary category (see commented out code above) manually if I do not display the contours, but adding the contours results in a "Continuous value supplied to discrete scale" error.
Various attempts have failed.
The question: Is it possible to colour the points (according to category) and independently colour the contour levels (according to height)?
You can try
library(tidyverse)
df %>%
ggplot(aes(x=x,y=y)) +
stat_density_2d(aes(fill = ..level..), geom = "polygon") +
geom_point(aes(color=factor(cat)), size=5) +
theme_bw()
Or switch to points where fill is working like shape=21
df %>%
ggplot(aes(x=x,y=y)) +
geom_density2d(aes(color=..level..))+
geom_point(aes(fill=factor(cat)),color="black",shape=21, size=5) +
theme_bw() +
scale_fill_manual(values = c(2,4)) +
scale_color_continuous(low = "green", high = "orange")
or try to add scale_color_gradientn(colours = rainbow(10)) instead.
I would like to create a colour blind test, similar to that below, using ggplot.
The basic idea is to use geom_hex (or perhaps a voronoi diagram, or possibly even circles as in the figure above) as the starting point, and define a dataframe that, when plotted in ggplot, produces the image.
We would start by creating a dataset, such as:
df <- data.frame(x = rnorm(10000), y = rnorm(10000))
then plot this:
ggplot(df, aes(x, y)) +
geom_hex() +
coord_equal() +
scale_fill_gradient(low = "red", high = "green", guide = FALSE) +
theme_void()
which gives the image below:
The main missing step is to create a dataset that actually plots a meaningful symbol (letter or number), and I'm not sure how best to go about this without painstakingly mapping the coordinates. Ideally one would be able to read in the coordinates perhaps from an image file.
Finally, a bit of tidying up could round the plot edges by removing the outlying points.
All suggestions are very welcome!
EDIT
Getting a little closer to what I'm after, we can use the image below of the letter 'e':
Using the imager package, we can read this in and convert it to a dataframe:
img <- imager::load.image("e.png")
df <- as.data.frame(img)
then plot that dataframe using geom_raster:
ggplot(df, aes(x, y)) +
geom_raster(aes(fill = value)) +
coord_equal() +
scale_y_continuous(trans = scales::reverse_trans()) +
scale_fill_gradient(low = "red", high = "green", guide = FALSE) +
theme_void()
If we use geom_hex instead of geom_raster, we can get the following plot:
ggplot(df %>% filter(value %in% 1), aes(x, y)) +
geom_hex() +
coord_equal() +
scale_y_continuous(trans = scales::reverse_trans()) +
scale_fill_gradient(low = "red", high = "green", guide = FALSE) +
theme_void()
so, getting there but clearly still a long way off...
Here's an approach for creating this plot:
Packages you need:
library(tidyverse)
library(packcircles)
Get image into a 2D matrix (x and y coordinates) of values. To do this, I downloaded the .png file of the e as "e.png" and saved in my working directory. Then some processing:
img <- png::readPNG("e.png")
# From http://stackoverflow.com/questions/16496210/rotate-a-matrix-in-r
rotate <- function(x) t(apply(x, 2, rev))
# Convert to one colour layer and rotate it to be in right direction
img <- rotate(img[,,1])
# Check that matrix makes sense:
image(img)
Next, create a whole lot of circles! I did this based on this post.
# Create random "circles"
# *** THESE VALUES WAY NEED ADJUSTING
ncircles <- 1200
offset <- 100
rmax <- 80
x_limits <- c(-offset, ncol(img) + offset)
y_limits <- c(-offset, nrow(img) + offset)
xyr <- data.frame(
x = runif(ncircles, min(x_limits), max(x_limits)),
y = runif(ncircles, min(y_limits), max(y_limits)),
r = rbeta(ncircles, 1, 10) * rmax)
# Find non-overlapping arrangement
res <- circleLayout(xyr, x_limits, y_limits, maxiter = 1000)
cat(res$niter, "iterations performed")
#> 1000 iterations performed
# Convert to data for plotting (just circles for now)
plot_d <- circlePlotData(res$layout)
# Check circle arrangement
ggplot(plot_d) +
geom_polygon(aes(x, y, group=id), colour = "white", fill = "skyblue") +
coord_fixed() +
theme_minimal()
Finally, interpolate the image pixel values for the centre of each circle. This will indicate whether a circle is centered over the shape or not. Add some noise to get variance in colour and plot.
# Get x,y positions of centre of each circle
circle_positions <- plot_d %>%
group_by(id) %>%
summarise(x = min(x) + (diff(range(x)) / 2),
y = min(y) + (diff(range(y)) / 2))
# Interpolate on original image to get z value for each circle
circle_positions <- circle_positions %>%
mutate(
z = fields::interp.surface(
list(x = seq(nrow(img)), y = seq(ncol(img)), z = img),
as.matrix(.[, c("x", "y")])),
z = ifelse(is.na(z), 1, round(z)) # 1 is the "empty" area shown earlier
)
# Add a little noise to the z values
set.seed(070516)
circle_positions <- circle_positions %>%
mutate(z = z + rnorm(n(), sd = .1))
# Bind z value to data for plotting and use as fill
plot_d %>%
left_join(select(circle_positions, id, z)) %>%
ggplot(aes(x, y, group = id, fill = z)) +
geom_polygon(colour = "white", show.legend = FALSE) +
scale_fill_gradient(low = "#008000", high = "#ff4040") +
coord_fixed() +
theme_void()
#> Joining, by = "id"
To get colours right, tweak them in scale_fill_gradient
I have two data frames, one larger (10 people) and one smaller (two people). I have generated a gantt chart for each data frame. How do I get it so the distance between lines is the same for each plot (i.e. not scaled based on number of entries).
# Generate vectors:
name <- paste("person", seq(10), sep = '_')
start <- sample(seq(5), size = 10, replace = T)
end <- sample(seq(6,10), size = 10, replace = T)
# Generate data frames:
big_chart <- data.frame(name = c(name,name), value = c(start,end))
small_chart <- big_chart[c(1:2,11:12),]
# big plot
library(ggplot)
ggplot(big_chart, aes(value, name)) +
geom_line()
# small plot
ggplot(small_chart, aes(value, name)) +
geom_line()
Below is my solution for you, hopefully it is what you were looking for. I made use of the coord_fixed function to control the overall scaling. In addition, I also fixed your x-axis range using the xlim function.
library(ggplot2)
ggplot(big_chart, aes(value, name)) +
geom_line() +
xlim(0, 10) + #optional
coord_fixed(ratio = 0.5)
ggplot(small_chart, aes(value, name)) +
geom_line() +
xlim(0, 10) + #optional
coord_fixed(ratio = 0.5)
Suppose I make a violin plot, with say 10 violins, using the following code:
library(ggplot2)
library(reshape2)
df <- melt(data.frame(matrix(rnorm(500),ncol=10)))
p <- ggplot(df, aes(x = variable, y = value)) +
geom_violin()
p
I can add a dot representing the mean of each variable as follows:
p + stat_summary(fun.y=mean, geom="point", size=2, color="red")
How can I do something similar but for arbitrary points?
For example, if I generate 10 new points, one drawn from each distribution, how could I plot those as dots on the violins?
You can give any function to stat_summary provided it just returns a single value. So one can use the function sample. Put extra arguments such as size, in the fun.args
p + stat_summary(fun.y = "sample", geom = "point", fun.args = list(size = 1))
Assuming your points are qualified using the same group names (i.e., variable), you should be able to define them manually with:
newdf <- group_by(df, variable) %>% sample_n(10)
p + geom_point(data=newdf)
The points can be anything, including static numbers:
newdf <- data.frame(variable = unique(df$variable), value = seq(-2, 2, len=10))
p + geom_point(data=newdf)
I had a similar problem. Code below exemplifies the toy problem - How does one add arbitrary points to a violin plot? - and solution.
## Visualize data set that comes in base R
head(ToothGrowth)
## Make a violin plot with dose variable on x-axis, len variable on y-axis
# Convert dose variable to factor - Important!
ToothGrowth$dose <- as.factor(ToothGrowth$dose)
# Plot
p <- ggplot(ToothGrowth, aes(x=dose, y=len)) +
geom_violin(trim = FALSE) +
geom_boxplot(width=0.1)
# Suppose you want to add 3 blue points
# [0.5, 10], [1,20], [2, 30] to the plot.
# Make a new data frame with these points
# and add them to the plot with geom_point().
TrueVals <- ToothGrowth[1:3,]
TrueVals$len <- c(10,20,30)
# Make dose variable a factor - Important for positioning points correctly!
TrueVals$dose <- as.factor(c(0.5, 1, 2))
# Plot with 3 added blue points
p <- ggplot(ToothGrowth, aes(x=dose, y=len)) +
geom_violin(trim = FALSE) +
geom_boxplot(width=0.1) +
geom_point(data = TrueVals, color = "blue")
I have two graphs with the same x axis - the range of x is 0-5 in both of them.
I would like to combine both of them to one graph and I didn't find a previous example.
Here is what I got:
c <- ggplot(survey, aes(often_post,often_privacy)) + stat_smooth(method="loess")
c <- ggplot(survey, aes(frequent_read,often_privacy)) + stat_smooth(method="loess")
How can I combine them?
The y axis is "often privacy" and in each graph the x axis is "often post" or "frequent read".
I thought I can combine them easily (somehow) because the range is 0-5 in both of them.
Many thanks!
Example code for Ben's solution.
#Sample data
survey <- data.frame(
often_post = runif(10, 0, 5),
frequent_read = 5 * rbeta(10, 1, 1),
often_privacy = sample(10, replace = TRUE)
)
#Reshape the data frame
survey2 <- melt(survey, measure.vars = c("often_post", "frequent_read"))
#Plot using colour as an aesthetic to distinguish lines
(p <- ggplot(survey2, aes(value, often_privacy, colour = variable)) +
geom_point() +
geom_smooth()
)
You can use + to combine other plots on the same ggplot object. For example, to plot points and smoothed lines for both pairs of columns:
ggplot(survey, aes(often_post,often_privacy)) +
geom_point() +
geom_smooth() +
geom_point(aes(frequent_read,often_privacy)) +
geom_smooth(aes(frequent_read,often_privacy))
Try this:
df <- data.frame(x=x_var, y=y1_var, type='y1')
df <- rbind(df, data.frame(x=x_var, y=y2_var, type='y2'))
ggplot(df, aes(x, y, group=type, col=type)) + geom_line()