I am dealing with a function that takes a big data frame (36 rows and 194 columns) which performs a Principal Component Analysis and then generates a list of plots where I have the combination of 26 Principal Components which are 325 in total, using 'expand.grid'.
My problem is that when I am using ggarrange(), from ggpubr, to merge all the plots in only one pdf file, this file is empty.
My code:
a = 26
row.pairs = 325
PC.Graph <- function(df, col1, col2, tag, id){
df1 <- df[,-c(col1:col2)]
pca <- prcomp(df1, scale. = T)
pc.summ <- summary(pca)
a <- sum(pc.summ$importance[3,] < 0.975)
b <- c(1:a)
pc.grid <- expand.grid(b, b)
pc.pairs <- pc.grid[pc.grid$Var1 < pc.grid$Var2,]
row.pairs <- nrow(pc.pairs)
components <- c(1:row.pairs)
S.apply.FUN <- function(x){
c <- sapply(pc.pairs, "[", x, simplify = F)
pcx <- c$Var1
pcy <- c$Var2
df2 <- df
row.names(df2) <- df[, tag]
name = paste("PCA_", pcx, "_vs_", pcy)
autoplot(pca, data = df2, colour = id, label = T, label.repel = T, main = name,
x = pcx, y = pcy)
}
all.plots <- Map(S.apply.FUN, components)
pdf(file = "All_PC.pdf", width = 50, height = 70)
print(ggarrange(all.plots))
dev.off()
}
PC.Graph(Final_DF, col1 = 1, col2 = 5, tag = "Sample", id = "Maturation")
You would have to pass a plotlist to ggarrange, but I am not sure you would get any useful plot out of that plot area in the PDF file, so I would advise you to split the plotlist into chunks (e.g. of 20) and plot these to multiple pages.
Specifically, I would export all.plots from your PC.Graph function (and remove the code to write to PDF there).
I would also change the expand.grid(b, b) to t(combn(b, 2)), since you don't need to plot the PC combinations twice.
Then I would do something like this:
# export the full list of plots
plots <- PC.Graph(Final_DF, col1 = 1, col2 = 5, tag = "Sample", id = "Maturation")
# split the plotlist
splitPlots <- split(plots, ceiling(seq_along(plots)/20))
plotPlots <- function(x){
out <- cowplot::plot_grid(plotlist = x, ncol = 5, nrow = 4)
plot(out)
}
pdf(file = "All_PC.pdf", width = 50, height = 45)
lapply(splitPlots, plotPlots)
dev.off()
Related
What I would like to do is creating several boxplots (all displayed in a single boxplot) only from certain values of my original data frame.
My data frame looks as follows:
enter image description here
So now I want R to visualise Parameter ~ Station (Parameter are all variables coloured green and Station is the "station id")
Is there a way to tell R that I want all my Parameters on the x-axis ONLY for BB0028 for example, which would mean that I only take the first 6 values of mean_area, mean_area_exc, esd, feret, min and max into account in the boxplot?
That would look like this:
enter image description here
I tried it in very complicated way to add single boxplots one by one but I am sure there must be a more simple way.
This is what I tried:
bb28 <- df[c(1:6),]
bb28area <- boxplot(bb28$mean_area ~ bb28$BBnr)
bb28area_exc <- boxplot(bb28$mean_area_exc ~ bb28$BBnr)
bb28esd <- boxplot(bb28$mean_esd ~ bb28$BBnr)
bb28feret <- boxplot(bb28$mean_feret ~ bb28$BBnr)
bb28min <- boxplot(bb28$mean_min ~ bb28$BBnr)
bb28max <- boxplot(bb28$mean_max ~ bb28$BBnr)
boxplot(bb28$mean_area ~ bb28$BBnr)
boxplot(bb28$mean_area_exc ~ bb28$BBnr, add=TRUE, at = 1:1+0.45)
Also it doesn't look very nice because in the plot the x-axis does not adjust to the new boxplot which is cut off then:
enter image description here
I hope you can help me with simple a proper code to get my plot.
Thank you!
Cheers, Merle
Maybe the function multi.boxplot below is what you are looking for. It uses base R only.
Data.
First, make up a dataset, since you have not provided us with one in a copy&paste friendly format.
set.seed(1234)
n <- 50
BBnr <- sort(sprintf("BB%04d", sample(28:30, n, TRUE)))
bb28 <- data.frame(col1 = 1:n, col2 = n:1, BBnr = BBnr)
tmp <- matrix(runif(3*n), ncol = 3)
colnames(tmp) <- paste("mean", c("this", "that", "other"), sep = "_")
bb28 <- cbind(bb28, tmp)
rm(BBnr, tmp)
Code.
multi.boxplot <- function(x, by, col=0, ...){
x <- as.data.frame(x)
uniq.by <- unique(by)
len <- length(uniq.by) - 1
n <- ncol(x)
n1 <- n + 1
col <- rep(col, n)[seq_len(n)]
boxplot(x[[ 1 ]] ~ by, at = 0:len*n1 + 1,
xlim = c(0, (len + 1)*n1), ylim = range(unlist(x)), xaxt = "n", col=col[1], ...)
for(i in seq_len(n)[-1])
boxplot(x[[i]] ~ by, at = 0:len*n1 + i, xaxt = "n", add = TRUE, col=col[i], ...)
axis(1, at = 0:len*n1 + n1/2, labels = uniq.by, tick = TRUE)
}
inx <- grep("mean", names(bb28))
multi.boxplot(bb28[inx], by = bb28$BBnr, col = rainbow(length(inx)))
I have multiple plots to save as .pdf files and they are created in R by using par(mfrow=c(1,2)), i.e. per each figure (to be saved) there are 2 plots disposed by 1 row and 2 columns.
Since my total number of plots is quite high I am creating the plots with a for loop.
How can I save the figures (with 2 plots each one) as pdf files in the for loop?
Here's same funky code:
## create data.frames
df_1 = data.frame(x = c(1:100), y = rnorm(100))
df_2 = data.frame(x = c(1:100), y = rnorm(100))
df_3 = data.frame(x = c(1:100), y = rnorm(100))
df_4 = data.frame(x = c(1:100), y = rnorm(100))
## create list of data.frames
df_lst = list(df_1, df_2, df_3, df_4)
## plot in for loop by 1 row and 2 cols
par(mar=c(3,3,1,0), mfrow=c(1,2))
for (i in 1:length(df_lst)) {
barplot(df_lst[[i]]$y)
}
Let's say I want to save the plots with the pdf function. Here's what I tried:
for (i in 1:length(df_lst)) {
pdf(paste('my/directory/file_Name_', i, '.pdf', sep = ''), height = 6, width = 12)
barplot(df_lst[[i]]$y)
dev.off()
}
My solution is clearly wrong because the pdf function saves a figure at each loop (i.e. 4 instead of 2).
Any suggestion?
Thanks
Sounds like you could use a nested loop here: an outer loop for each file you create, and an inner loop for each multi-panel figure you create. Since all the data frames are stored in a 1-d list, you'll then need to keep track of the index of the list that you are plotting.
Here's one way to do that:
nrow <- 1
ncol <- 2
n_panels <- nrow * ncol
n_files <- length(df_lst) / n_panels
for (i in seq_len(n_files)) {
file <- paste0("file_", i, ".pdf")
pdf(file, height = 6, width = 12)
# plot params need to be set for each device
par(mar = c(3, 3, 1, 0), mfrow = c(nrow, ncol))
for (j in seq_len(n_panels)) {
idx <- (i - 1) * n_panels + j
barplot(df_lst[[idx]]$y)
}
# updated to also add a legend
legend("bottom", legend = "Bar", fill = "grey")
dev.off()
}
If you just want one file with multiple pages, all you need to do is move the pdf() call outside your original loop, and move the parameter setting after the pdf():
pdf('my/directory/file_Name.pdf', height = 6, width = 12)
par(mar=c(3,3,1,0), mfrow=c(1,2))
for (i in 1:length(df_lst)) {
barplot(df_lst[[i]]$y)
}
dev.off()
I wish to visualize how well a clustering algorithm is doing (with certain distance metric). I have samples and their corresponding classes.
To visualize, I cluster and I wish to color the branches of a dendrogram by the items in the cluster. The color will be the color most items in the hierarchical cluster correspond to (given by the data\classes).
Example: If my clustering algorithm chose indexes 1,21,24 to be a certain cluster (at a certain level) and I have a csv file containing a class number in each row corresponding to lets say 1,2,1. I want this edge to be coloured 1.
Example Code:
require(cluster)
suppressPackageStartupMessages(library(dendextend))
dir <- 'distance_metrics/'
filename <- 'aligned.csv'
my.data <- read.csv(paste(dir, filename, sep=""), header = T, row.names = 1)
my.dist <- as.dist(my.data)
real.clusters <-read.csv("clusters", header = T, row.names = 1)
clustered <- diana(my.dist)
# dend <- colour_branches(???dend, max(real.clusters)???)
plot(dend)
EDIT:
another example partial code
dir <- 'distance_metrics/' # csv in here contains a symmetric matrix
clust.dir <- "clusters/" #csv in here contains a column vector with classes
my.data <- read.csv(paste(dir, filename, sep=""), header = T, row.names = 1)
filename <- 'table.csv'
my.dist <- as.dist(my.data)
real.clusters <-read.csv(paste(clust.dir, filename, sep=""), header = T, row.names = 1)
clustered <- diana(my.dist)
dnd <- as.dendrogram(clustered)
Both node and edge color attributes can be set recursively on "dendrogram" objects (which are just deeply nested lists) using dendrapply. The cluster package also features an as.dendrogram method for "diana" class objects, so conversion between the object types is seamless. Using your diana clustering and borrowing some code from #Edvardoss iris example, you can create the colored dendrogram as follows:
library(cluster)
set.seed(999)
iris2 <- iris[sample(x = 1:150,size = 50,replace = F),]
clust <- diana(iris2)
dnd <- as.dendrogram(clust)
## Duplicate rownames aren't allowed, so we need to set the "labels"
## attributes recursively. We also label inner nodes here.
rectify_labels <- function(node, df){
newlab <- df$Species[unlist(node, use.names = FALSE)]
attr(node, "label") <- (newlab)
return(node)
}
dnd <- dendrapply(dnd, rectify_labels, df = iris2)
## Create a color palette as a data.frame with one row for each spp
uniqspp <- as.character(unique(iris$Species))
colormap <- data.frame(Species = uniqspp, color = rainbow(n = length(uniqspp)))
colormap[, 2] <- c("red", "blue", "green")
colormap
## Now color the inner dendrogram edges
color_dendro <- function(node, colormap){
if(is.leaf(node)){
nodecol <- colormap$color[match(attr(node, "label"), colormap$Species)]
attr(node, "nodePar") <- list(pch = NA, lab.col = nodecol)
attr(node, "edgePar") <- list(col = nodecol)
}else{
spp <- attr(node, "label")
dominantspp <- levels(spp)[which.max(tabulate(spp))]
edgecol <- colormap$color[match(dominantspp, colormap$Species)]
attr(node, "edgePar") <- list(col = edgecol)
}
return(node)
}
dnd <- dendrapply(dnd, color_dendro, colormap = colormap)
## Plot the dendrogram
plot(dnd)
The function you are looking for is color_brances from the dendextend R package, using the arguments clusters and col. Here is an example (based on Shaun Wilkinson's example):
library(cluster)
set.seed(999)
iris2 <- iris[sample(x = 1:150,size = 50,replace = F),]
clust <- diana(iris2)
dend <- as.dendrogram(clust)
temp_col <- c("red", "blue", "green")[as.numeric(iris2$Species)]
temp_col <- temp_col[order.dendrogram(dend)]
temp_col <- factor(temp_col, unique(temp_col))
library(dendextend)
dend %>% color_branches(clusters = as.numeric(temp_col), col = levels(temp_col)) %>%
set("labels_colors", as.character(temp_col)) %>%
plot
there are suspicions that misunderstood the question however I'll try to answer:
from my previous objectives were rewritten by the example of iris
clrs <- rainbow(n = 3) # create palette
clrs <- clrs[iris$Species] # assign colors
plot(x = iris$Sepal.Length,y = iris$Sepal.Width,col=clrs) # simple test colors
# cluster
dt <- cbind(iris,clrs)
dt <- dt[sample(x = 1:150,size = 50,replace = F),] # create short dataset for visualization convenience
empty.labl <- gsub("."," ",dt$Species) # create a space vector with length of names intended for reserve place to future text labels
dst <- dist(x = scale(dt[,1:4]),method = "manhattan")
hcl <- hclust(d = dst,method = "complete")
plot(hcl,hang=-1,cex=1,labels = empty.labl, xlab = NA,sub=NA)
dt <- dt[hcl$order,] # sort rows for order objects in dendrogramm
text(x = seq(nrow(dt)), y=-.5,labels = dt$Species,srt=90,cex=.8,xpd=NA,adj=c(1,0.7),col=as.character(dt$clrs))
This question already has answers here:
Save ggplot within a function
(2 answers)
Closed 5 years ago.
I have list of data frames and i want to export one bar chart per data frame... I am trying use lapply but it does not work... Does anyone know how to do this?
my_data <- lapply(X = seq(from = 1, to = length(in_files_path), by = 1), FUN = function(x){
data_tables <- read.table(file = in_files_path[[x]], header = TRUE)
})
lapply(X = seq(from = 1, to = length(in_files_path), by = 1), FUN = function(x){
setwd(dir = ou_graph_path)
png(filename = in_files_name[[x]],
units = "in",
width = 15,
height = 10,
res = 300)
ggplot(data = my_data[[x]], aes(x = my_data[[x]]$A, y = my_data[[x]]$B)) +
geom_bar()
dev.off()
})
I would advice using the following approach
I would advice using the following approach
# Get list of files
# Start loop -
# read files
# make plot
# store plots in list
# - end loop
#
# Start loop -
# perform plot operation
# save plots
# - end loop
setwd(your_location_of_the_files)
list_files = list.files(pattern = ".csv")
for(i_file in list_files){
dummy = fread(i_file,header = TRUE)
png(filename = paste(your_location_for_the_plots,in_files_name[[x]],sep="/"),
units = "in",
width = 15,
height = 10,
res = 300)
# You can just say A here, not dummy$A
plot(ggplot(data = dummy, aes(x = A, y = B)) + geom_bar())
dev.off()
}
This is the code I'm currently running:
n <- 7
N <- 52
r <- 13
reps <- 1000000
deck <- rep(c('h','d','c','s'), each = r)
diamonds <- rep(NA, length.out = reps)
pos <- sample(x = 1:52, size = 7, replace = FALSE)
for(i in 1:reps) {
hand <- sample(x = deck, replace = FALSE)[pos]
diamonds[i] <- sum(ifelse(hand == 'd', 1, 0))
}
barplot(table(diamonds), col = 'red', xlab = '# of diamonds',
ylab = paste('frequency out of',reps,'trials'),
main = paste('Positions:',pos[1],pos[2],pos[3],pos[4],
pos[5],pos[6],pos[7]))
What I'd really like is to be able to give a title to the barplot with something like the following
barplot(..., main = paste('Positions:',pos))
and have the title say "Positions: p1 p2 p3 p4 p5 p6 p7", where p1,p2,...,p7 are the elements of pos.
For anyone that's interested, this code randomly chooses 7 positions from 52 and then counts the number of diamonds ('d') within those 7 positions after each shuffle of the deck for 1000000 shuffles. Then the empirical distribution of the number of diamonds within those 7 cards is plotted.
Use collapse in paste to collapse the multiple elements in a vector containing the base test and pos,
paste(c('Positions:', pos), collapse=" ")
Otherwise, when you paste "Positions:" to pos you get the former recycled to the length of pos.