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I would like to make a PCA score plot using ggplot2, and then convert the plot into interactive plot using plotly.
What I want to do is to add a frame (not ellipse using stat_ellipse, I know it worked).
My problem is that when I try to use sample name as tooltip in ggplotly, the frame will disappear. I don't know how to fix it.
Below is my code
library(ggplot2)
library(plotly)
library(dplyr)
## Demo data
dat <- iris[1:4]
Group <- iris$Species
## Calculate PCA
df_pca <- prcomp(dat, center = T, scale. = FALSE)
df_pcs <- data.frame(df_pca$x, Group = Group)
percentage <-round(df_pca$sdev^2 / sum(df_pca$sdev^2) * 100, 2)
percentage <-paste(colnames(df_pcs),"(", paste(as.character(percentage), "%", ")", sep = ""))
## Visualization
Sample_Name <- rownames(df_pcs)
p <- ggplot(df_pcs, aes(x = PC1, y = PC2, color = Group, label = Sample_Name)) +
xlab(percentage[1]) +
ylab(percentage[2]) +
geom_point(size = 3)
ggplotly(p, tooltip = "label")
Until here it works! You can see that sample names can be properly shown in the ggplotly plot.
Next I tried to add a frame
## add frame
hull_group <- df_pcs %>%
dplyr::mutate(Sample_Name = Sample_Name) %>%
dplyr::group_by(Group) %>%
dplyr::slice(chull(PC1, PC2))
p2 <- p +
ggplot2::geom_polygon(data = hull_group, aes(fill = Group), alpha = 0.1)
You can see that the static plot still worked! The frame is properly added.
However, when I tried to convert it to plotly interactive plot. The frame disappeared.
ggplotly(p2, tooltip = "label")
Thanks a lot for your help.
It works if you move the data and mapping from the ggplot() call to the geom_point() call:
p2 <- ggplot() +
geom_point(data = df_pcs, mapping = aes(x = PC1, y = PC2, color = Group, label = Sample_Name), size = 3) +
ggplot2::geom_polygon(data = hull_group, aes(x = PC1, y = PC2, fill = Group, group = Group), alpha = 0.2)
ggplotly(p2, tooltip = "label")
You might want to change the order of the geom_point and geom_polygon to make sure that the points are on top of the polygon (this also affects the tooltip location).
I have a boxplot with multiple groups in R.
When i add the dots within the boxplots, they are not in the center.
Since each week has a different number of boxplots, the dots are not centered within the box.
The problem is in the geom_point part.
I uploaded my data of df.m in a text file and a figure of what i get.
I am using ggplot, and here is my code:
setwd("/home/usuario")
dput("df.m")
df.m = read.table("df.m.txt")
df.m$variable <- as.factor(df.m$variable)
give.n = function(elita){
return(c(y = median(elita)*-0.1, label = length(elita)))
}
p = ggplot(data = df.m, aes(x=variable, y=value))
p = p + geom_boxplot(aes(fill = Label))
p = p + geom_point(aes(fill = Label), shape = 21,
position = position_jitterdodge(jitter.width = 0))
p = p + stat_summary(fun.data = give.n, geom = "text", fun.y = median)
p
Here is my data in a text file:
https://drive.google.com/file/d/1kpMx7Ao01bAol5eUC6BZUiulLBKV_rtH/view?usp=sharing
Only in variable 12 is in the center, because there are 3 groups (the maximum of possibilities!
I would also like to show the counting of observations. If I use the code shown, I can only get the number of observations for all the groups. I would like to add the counting for EACH GROUP.
Thank you in advance
enter image description here
Here's a solution using boxplot and dotplot and an example dataset:
library(tidyverse)
# example data
dt <- data.frame(week = c(1,1,1,1,1,1,1,1,1,
2,2,2,2,2,2,2,2,2),
value = c(6.40,6.75,6.11,6.33,5.50,5.40,5.83,4.57,5.80,
6.00,6.11,6.40,7.00,3,5.44,6.00,5,6.00),
donor_type = c("A","A","A","A","CB","CB","CB","CB","CB",
"CB","CB","CB","CB","CB","A","A","A","A"))
# create the plot
ggplot(dt, aes(x = factor(week), y = value, fill = donor_type)) +
geom_boxplot() +
geom_dotplot(binaxis='y', stackdir='center', position = position_dodge(0.75))
You should be able to adjust my code to your real dataset easily.
Edited answer with OP's dataset:
Using some generated data and geom_point():
library(tidyverse)
df.m <- df.m %>%
mutate(variable = as.factor(variable)) %>%
filter(!is.na(value))
ggplot(df.m, aes(x = variable, y = value, fill = Label)) +
geom_boxplot() +
geom_point(shape = 21, position = position_jitterdodge(jitter.width = 0)) +
scale_x_discrete("variable", drop = FALSE)
I have created the following plot which gives the shape of the plot I desire. But when I facet wrap it, the shapes no longer remain triangular and become almost cellular. How can I keep the triangular shape after faceting?
Sample data:
lvls <- c("a","b","c","d","e","1","2","3","4","5","6","7","8","9","10","11","12","13","14","15")
df <- data.frame(Product = factor(rep(lvls, 3)),
variable = c(rep("Ingredients", 20),
rep("Defence", 20),
rep("Benefit", 20)),
value = rnorm(60, mean = 5))
Now when I use this code, I get the shapes I desire.
ggplot(df,
aes(x = variable,
y = value,
color = Product,
group = Product)) +
geom_polygon(fill = NA) +
coord_polar()
However, the products are all on top of one another so ideally I would like to facet wrap.
ggplot(df,
aes(x = variable,
y = value,
color = Product,
group = Product)) +
geom_polygon(fill = NA) +
coord_polar() +
facet_wrap(~Product)
But when I facet wrap, the shapes become oddly cellular and not triangular (straight lines from point to point). Any ideas on how to alter this output?
Thanks.
I have got a data frame with several 1000 rows in the form of
group = c("gr1","gr1","gr1","gr1","gr1","gr1","gr1","gr1","gr1","gr1","gr2","gr2","gr2","gr2","gr2","gr2","gr2","gr2","gr2","gr2","gr3","gr3","gr3","gr3","gr3","gr3","gr3","gr3","gr3","gr3")
pos = c(1,2,3,4,5,6,7,8,9,10,1,2,3,4,5,6,7,8,9,10,1,2,3,4,5,6,7,8,9,10)
color = c(2,2,2,2,3,3,2,2,3,2,1,2,2,2,1,1,1,1,1,1,2,2,2,2,2,2,1,1,2,2)
df = data.frame(group, pos, color)
and would like to make a kind of heatmap in which one axes has a continuous scale (position). The color column is categorical. However due to the large amount of data points I want to use binning, i.e. use it as a continuous variable.
This is more or less how the plot should look like:
I can't think of a way to create such a plot using ggplot2/R. I have tried several geometries, e.g. geom_point()
ggplot(data=df, aes(x=strain, y=pos, color=color)) +
geom_point() +
scale_colour_gradientn(colors=c("yellow", "black", "orange"))
Thanks for your help in advance.
Does this help you?
library(ggplot2)
group = c("gr1","gr1","gr1","gr1","gr1","gr1","gr1","gr1","gr1","gr1","gr2","gr2","gr2","gr2","gr2","gr2","gr2","gr2","gr2","gr2","gr3","gr3","gr3","gr3","gr3","gr3","gr3","gr3","gr3","gr3")
pos = c(1,2,3,4,5,6,7,8,9,10,1,2,3,4,5,6,7,8,9,10,1,2,3,4,5,6,7,8,9,10)
color = c(2,2,2,2,3,3,2,2,3,2,1,2,2,2,1,1,1,1,1,1,2,2,2,2,2,2,1,1,2,2)
df = data.frame(group, pos, color)
ggplot(data = df, aes(x = group, y = pos)) + geom_tile(aes(fill = color))
Looks like this
Improved version with 3 color gradient if you like
library(scales)
ggplot(data = df, aes(x = group, y = pos)) + geom_tile(aes(fill = color))+ scale_fill_gradientn(colours=c("orange","black","yellow"),values=rescale(c(1, 2, 3)),guide="colorbar")
I'm hoping to use ggplot2 to generate a set of stacked bars in pairs, much like this:
With the following example data:
df <- expand.grid(name = c("oak","birch","cedar"),
sample = c("one","two"),
type = c("sapling","adult","dead"))
df$count <- sample(5:200, size = nrow(df), replace = T)
I would want the x-axis to represent the name of the tree, with two bars per tree species: one bar for sample one and one bar for sample two. Then the colors of each bar should be determined by type.
The following code generates the stacked bar with colors by type:
ggplot(df, aes(x = name, y = count, fill = type)) + geom_bar(stat = "identity")
And the following code generates the dodged bars by sample:
ggplot(df, aes(x = name, y = count, group = sample)) + geom_bar(stat = "identity", position = "dodge")
But I can't get it to dodge one of the groupings (sample) and stack the other grouping (type):
ggplot(df, aes(x = name, y = count, fill = type, group = sample)) + geom_bar(stat = "identity", position = "dodge")
One workaround would be to put interaction of sample and name on x axis and then adjust the labels for the x axis. Problem is that bars are not put close to each other.
ggplot(df, aes(x = as.numeric(interaction(sample,name)), y = count, fill = type)) +
geom_bar(stat = "identity",color="white") +
scale_x_continuous(breaks=c(1.5,3.5,5.5),labels=c("oak","birch","cedar"))
Another solution is to use facets for name and sample as x values.
ggplot(df,aes(x=sample,y=count,fill=type))+
geom_bar(stat = "identity",color="white")+
facet_wrap(~name,nrow=1)