Drawing board with non-empty cells in R - r

I try to draw something like this (but very simplified):
So the thing is that I set height, width and nob - number of bombs and I want to draw table with height*width cells, where there will by nob bombs ramdomly set (it could be for example text 'bomb',. it is not important). Furthermore, for every empty cell I want to count number of bombs in neighborhood and put that number in the middle of that cell (when zero - nothing). But I really have no idea for some "algorithm" for this. I draw board with proper size and that's all I can do. Any ideas, help?
w <- 7
h <- 5
nob <- 5
plot.new()
plot.window(xlim=c(0,w), ylim=c(0,h))
rect(0, 0, w, h)
for (i in 1:h-1){
lines(x=c(0,w), y=c(i,i))
}
for (j in 1:w-1){
lines(x=c(j,j), y=c(0, h))
}
sample(w*h, nob)

Some nice fun for Xmas time:
w <- 7
h <- 5
nob <- 5
nwal <- 7
set.seed(42) #for reproducibility
m <- matrix(0, ncol=w, nrow=h)
#place the walls
m[sample(length(m), nwal)] <- 1
o <- matrix("", ncol=w, nrow=h)
#place the bombs
o[sample(which(m == 0), nob)] <- "o"
#http://stackoverflow.com/a/22573306/1412059
#there is probably an alternative using igraph
sumNeighbors <- function(z) {
rbind(z[-1,],0) +
rbind(0,z[-nrow(z),]) +
cbind(z[,-1],0) +
cbind(0,z[,-ncol(z)]) +
cbind(rbind(z[-1,-1],0),0) +
cbind(0,rbind(z[-1,-ncol(z)],0)) +
cbind(rbind(0,z[-nrow(z),-1]),0) +
cbind(0,rbind(0,z[-nrow(z),-ncol(z)]))
}
library(reshape2)
DF <- melt(m, varnames = c("x", "y"), value.name = "z")
DF <- merge(DF, melt(o, varnames = c("x", "y"), value.name = "b"))
DF <- merge(DF, melt(sumNeighbors(o == "o"), varnames = c("x", "y"), value.name = "n"))
DF$n[DF$n == 0 | DF$b == "o" | DF$z == 1] <- ""
DF$t <- paste0(DF$n, DF$b)
library(ggplot2)
ggplot(DF, aes(x=x, y=y, fill=factor(z))) +
geom_tile(color="dark grey") +
geom_text(aes(label=t)) +
theme(axis.title = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank(),
panel.grid = element_blank(),
legend.position = "none")

Related

Is it possible to create a R function for my code?

Is there a way to create a function for my code below? I have multiple csv files that I'm running this exact code on, but it has been tiring using this same code over and over to do the same thing (and has made my script very long). Here is my code-
#define a function "add_peaks" to transpose peak values into summary table
add_peaks <- function(df, vec, colname) {
if(colname %in% names(df)) vec <- c(df[[colname]], vec)
new_row <- max(nrow(df), length(vec))
new_df <- df[1:new_row, ,drop = FALSE]
new_df[colname] <- c(vec, rep(NA, new_row - length(vec)))
new_df[is.na(new_df)] <- 0
rownames(new_df) <- NULL
new_df
}
#####INPUT#####
#Read in appropriate experiment number
#Remove first 2 columns
#Rename first column to "mintime" and convert to minutes
#Normalize raw fluorescence values
flowdata <- read_csv("####.csv") ####CHANGE EXP NUMBER
title <- "CONDITION HERE" ####CHANGE CONDITION"
flowdata <- flowdata[, -c(1:2)] %>%
rename(mintime = 1) %>%
transform(mintime = mintime / 60)
flowdata[,-1] <- data.frame(lapply(flowdata[,-1], function(X) X/X[1]))
#Exclude values up to 5 minutes
#Determine number of peaks per cell
#Add number of peaks per cell to summary table
flowdata_cut <- flowdata[which(flowdata$mintime>=5),]
peak_info <- lapply(flowdata_cut[,-1], findpeaks, threshold=2)
numberpeak <- unlist(lapply(peak_info, nrow))
summarypeaks <- add_peaks(summarypeaks, numberpeak, title)
#Prepare data for line graph
melted <- melt(flowdata, id.vars="mintime")
#####CREATE GRAPH#####
#Plot graph
ggplot(data=melted, aes(x=mintime, y=value, group=variable)) +
geom_line(show.legend = FALSE) +
scale_x_continuous(limits = c(3, 12), breaks = seq(3, 12, by = 3)) +
labs(y="Fluo-4 fluorescence (F/F0)", x = "Time (min)") +
ggtitle(title) +
theme_bw() +
# remove elements we don't need
theme(panel.grid = element_blank(),
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black"),
panel.background = element_blank())
#####SAVE GRAPH#####
#Save line graph as .png file
ggsave(filename = "####_Line_Graph.png", ####CHANGE EXP NUMBER
width = 8, height = 4)
Please let me know if it is possible (code is appreciated), even if it is part of it only. Obviously I am new to coding. Thank you!
Below is your code, extracted into a function that takes 2 arguments - the experiment number (exp) and the condition (cond).
my_function <- function(exp, cond) {
#Read in appropriate experiment number
#Remove first 2 columns
#Rename first column to "mintime" and convert to minutes
#Normalize raw fluorescence values
flowdata <- read_csv(paste0(exp, ".csv"))
title <- cond
flowdata <- flowdata[, -c(1:2)] %>%
rename(mintime = 1) %>%
transform(mintime = mintime / 60)
flowdata[,-1] <- data.frame(lapply(flowdata[,-1], function(X) X/X[1]))
#Exclude values up to 5 minutes
#Determine number of peaks per cell
#Add number of peaks per cell to summary table
flowdata_cut <- flowdata[which(flowdata$mintime>=5),]
peak_info <- lapply(flowdata_cut[,-1], findpeaks, threshold=2)
numberpeak <- unlist(lapply(peak_info, nrow))
summarypeaks <- add_peaks(summarypeaks, numberpeak, title)
#Prepare data for line graph
melted <- melt(flowdata, id.vars="mintime")
#####CREATE GRAPH#####
#Plot graph
ggplot(data=melted, aes(x=mintime, y=value, group=variable)) +
geom_line(show.legend = FALSE) +
scale_x_continuous(limits = c(3, 12), breaks = seq(3, 12, by = 3)) +
labs(y="Fluo-4 fluorescence (F/F0)", x = "Time (min)") +
ggtitle(title) +
theme_bw() +
# remove elements we don't need
theme(panel.grid = element_blank(),
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black"),
panel.background = element_blank())
#####SAVE GRAPH#####
#Save line graph as .png file
ggsave(filename = paste0(exp, "_Line_Graph.png"), width = 8, height = 4)
# Return
return(summarypeaks)
}
If you have experiment number 005 and condition "test", call the function like so, assigning the result to replace the old value of summarypeaks:
summarypeaks <- my_function(005, "test")

Creating a timeline in ggplot

Edited below with complete and functioning code:
I am trying to create a timeline similar to the one this code creates from the Timeline package, however, the options are not very flexible. For example, I would like to create space between each bar so they are not touching. Also, I am wondering if there is a way to add the "End_Status" column to the graph so that it it is obvious that the data stops there because the animal died. Any help is greatly appreciated.
Example dataset:
df <- data.frame(id = c(rep(1201, 10), rep(1202, 14), rep(1203, 6), rep(1204, 22)),
date = c(seq(1,5,1), seq(5,7,1), seq(7,8,1), seq(2,5,1), seq(7,9,1), seq(11,17,1), seq(1,8,1), seq(8,12, 1), seq(12,26,1 )),
schedule = as.factor(c(rep(1, 5,), rep(2, 3), rep(3, 6),
rep (1, 3), rep (2, 2), rep(3, 5),
rep(1,8), rep(2, 5), rep(1,3), rep(3, 12))),
status = c(rep("", 9), "Mort", rep("", 41), "Mort"))
Code to get the output table I am interested in:
library("data.table")
library(plyr)
library(dplyr)
df<-as.data.table(df)
z <- df[, unique(id)]
################
value_change_first <- function(x,a) { for(i in 1:length(x[,schedule])) {
ifelse(x[a,schedule] == x[a+1,schedule],
x <- x[-(a+1)],
a <- a+1)}
return(x)
}
value_change_second <- function(x,a) {
for(i in 1:length(x[,schedule])) {
ifelse(x[a,schedule] == x[a+1,schedule],
x <- x[-(a)],
a <- a+1)}
return(x)
}
#################
output_1 <- c()
for(i in 1:length(z)){
ids <- df[df$id==z[i],]
out<-value_change_first(ids,1)
output_1<-as.data.frame(rbind(output_1, out))}
#################
output_2 <- c()
for(i in 1:length(z)){
ids <- df[df$id==z[i],]
out<-value_change_second(ids,1)
output_2<-as.data.frame(rbind(output_2, out))}
################
output_1$End_Date <- output_2$date
output_1$End_Status <- output_2$status
names(output_1)[names(output_1)=="date"] <- "Start_Date"
output <- output_1[c(1:2, 5, 3, 6)]
From here I can use the Timeline Package to get something close to what I want:
require(timeline)
tl <- timeline(output,
label.col=names(output)[4],
text.color= NA,
group.col=names(output)[1],
start.col=names(output)[2],
end.col = names(output)[3])
tl + theme_bw() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())
My question is how to build something similar in ggplot. Furthermore, I want to specifically add the "Mort" message at a given date by individual from the output dataframe.
I ended up getting this to work like I want:
### creating a end status column that includes mortalities and failures
output$End_Status_Date<-NA
for(i in 1:nrow(output)){
if(output$End_Status[i] == "Mort"){
output$End_Status_Date[i]=as.character(output$End_Date)[i]
}
}
for(i in 1:nrow(output)){
if(output$End_Status[i] == "Failure"){
output$End_Status_Date[i]=as.character(output$End_Date)[i]
}
}
### data structuring
output$id<-as.factor(output$id)
output$End_Status_Date<-as.numeric(output$End_Status_Date)
output$End_Status[output$End_Status == ""] <- NA
output$End_Status<-as.character(output$End_Status)
output$End_Status<-as.factor(output$End_Status)
library(ggplot2)
g2 <- ggplot() +
geom_segment(data=output, aes(x=Start_Date, xend=End_Date, y=id, yend=id, color=schedule), linetype=1, size=2) +
geom_point(data=subset(output, is.na(End_Status)==FALSE),
mapping=aes(x=End_Status_Date, y=id, shape=End_Status, fill=End_Status), size=4)+
scale_colour_manual(values=c("blue4", "chartreuse4", "darkmagenta"))+
scale_fill_manual(values=c("white", "red"))+
scale_shape_manual(values=c(21,24))+
xlab("Time")+
ylab("Individuals")+
theme_bw() + theme(panel.grid.minor = element_blank(), panel.grid.major = element_blank())
g2
You did most of the work already. The key here is just to use geom_rect and take advantage of your y-axis labels to set the ymin and ymax values.
ggplot(output, aes(xmin = Start_Date, xmax = End_Date,
ymin = as.numeric(id) - 1, ymax = as.numeric(id),
fill = schedule)) +
geom_rect() +
geom_text(aes(x = End_Date, y = id, label = End_Status))

A shared legend for z-scores and corresponding p-values in a heatmap

I have a z-scores matrix:
set.seed(1)
z.score.mat <- matrix(rnorm(1000),nrow=100,ncol=10)
which are the result of some biological experimental data, and a corresponding p-value matrix:
p.val.mat <- pnorm(abs(z.score.mat),lower.tail = F)
Both have identical dimnames:
rownames(z.score.mat) <- paste("p",1:100,sep="")
colnames(z.score.mat) <- paste("c",1:10,sep="")
rownames(p.val.mat) <- paste("p",1:100,sep="")
colnames(p.val.mat) <- paste("c",1:10,sep="")
I'm plotting a hierarchically clustered heatmap of the z-scores like this:
hc.col <- hclust(dist(z.score.mat))
dd.col <- as.dendrogram(hc.col)
col.ord <- order.dendrogram(dd.col)
hc.row <- hclust(dist(t(z.score.mat)))
dd.row <- as.dendrogram(hc.row)
row.ord <- order.dendrogram(dd.row)
clustered.mat <- z.score.mat[col.ord,row.ord]
clustered.mat.names <- attr(clustered.mat,"dimnames")
clustered.mat.df <- as.data.frame(clustered.mat)
colnames(clustered.mat.df) <- clustered.mat.names[[2]]
clustered.mat.df[,"process"] <- clustered.mat.names[[1]]
clustered.mat.df[,"process"] <- with(clustered.mat.df,factor(clustered.mat.df[,"process"],levels=clustered.mat.df[,"process"],ordered=TRUE))
require(reshape2)
clustered.mat.df <- reshape2::melt(clustered.mat.df,id.vars="process")
colnames(clustered.mat.df)[2:3] <- c("condition","z.score")
clustered.mat.df$p.value <- sapply(1:nrow(clustered.mat.df),function(x) p.val.mat[which(rownames(p.val.mat) == clustered.mat.df$process[x]),which(colnames(p.val.mat) == clustered.mat.df$condition[x])])
lab.legend <- colnames(clustered.mat.df)[3]
lab.row <- colnames(clustered.mat.df)[1]
lab.col <- colnames(clustered.mat.df)[2]
require(ggplot2)
ggplot(clustered.mat.df,aes(x=condition,y=process))+
geom_tile(aes(fill=z.score))+
scale_fill_gradient2(lab.legend,high="darkred",low="darkblue")+
theme_bw()+
theme(legend.key=element_blank(),
legend.position="right",
panel.border=element_blank(),
strip.background=element_blank(),
axis.text.x=element_text(angle=45,vjust=0.5)
)
My question is if it is possible, and how, to have on one side of the legend bar the z-score range (which is currently on the right hand) and on the other side the corresponding p-value range?
This is quite fiddly when the plot dimensions change, but you do get the required result:
br <- seq(-3, 3, 1)
lab <- round(pnorm(abs(br),lower.tail = F), 3)
p <- ggplot(clustered.mat.df,aes(x=condition,y=process))+
geom_tile(aes(fill=z.score), show.legend = FALSE)+
scale_fill_gradient2(lab.legend, high="darkred", low="darkblue", breaks = br)
p1 <- ggplot(clustered.mat.df,aes(x=condition,y=process))+
geom_tile(aes(fill=z.score))+
scale_fill_gradient2(lab.legend, high="darkred", low="darkblue", breaks = br) +
guides(fill = guide_colorbar(title = '', label.position = 'right', barheight = 10))
p2 <- ggplot(clustered.mat.df,aes(x=condition,y=process))+
geom_tile(aes(fill=z.score))+
scale_fill_gradient2(lab.legend, high="darkred", low="darkblue", breaks = br, labels = lab) +
guides(fill = guide_colorbar('', label.position = 'left', barheight = 10))
library(cowplot)
l1 <- get_legend(p1)
l2 <- get_legend(p2)
ggdraw() +
draw_plot(p, width = 0.85) +
draw_grob(l1, 0.89, 0, 0.1, 1) +
draw_grob(l2, 0.85, 0, 0.1, 1) +
draw_label('p z', 0.88, 0.675, hjust = 0)
This approach uses gtable and grid functions. It takes the legend from your plot, edits the legend so that the p values appear on the left side, then puts the edited legend back into the plot.
# Your data
set.seed(1)
z.score.mat <- matrix(rnorm(1000),nrow=100,ncol=10)
# which are the result of some biological experimental data, and a corresponding p-value matrix:
p.val.mat <- pnorm(abs(z.score.mat),lower.tail = F)
rownames(z.score.mat) <- paste("p",1:100,sep="")
colnames(z.score.mat) <- paste("c",1:10,sep="")
rownames(p.val.mat) <- paste("p",1:100,sep="")
colnames(p.val.mat) <- paste("c",1:10,sep="")
hc.col <- hclust(dist(z.score.mat))
dd.col <- as.dendrogram(hc.col)
col.ord <- order.dendrogram(dd.col)
hc.row <- hclust(dist(t(z.score.mat)))
dd.row <- as.dendrogram(hc.row)
row.ord <- order.dendrogram(dd.row)
clustered.mat <- z.score.mat[col.ord,row.ord]
clustered.mat.names <- attr(clustered.mat,"dimnames")
clustered.mat.df <- as.data.frame(clustered.mat)
colnames(clustered.mat.df) <- clustered.mat.names[[2]]
clustered.mat.df[,"process"] <- clustered.mat.names[[1]]
clustered.mat.df[,"process"] <- with(clustered.mat.df,factor(clustered.mat.df[,"process"],levels=clustered.mat.df[,"process"],ordered=TRUE))
require(reshape2)
clustered.mat.df <- reshape2::melt(clustered.mat.df,id.vars="process")
colnames(clustered.mat.df)[2:3] <- c("condition","z.score")
clustered.mat.df$p.value <- sapply(1:nrow(clustered.mat.df),function(x) p.val.mat[which(rownames(p.val.mat) == clustered.mat.df$process[x]),which(colnames(p.val.mat) == clustered.mat.df$condition[x])])
lab.legend <- colnames(clustered.mat.df)[3]
lab.row <- colnames(clustered.mat.df)[1]
lab.col <- colnames(clustered.mat.df)[2]
# Your plot
require(ggplot2)
p = ggplot(clustered.mat.df,aes(x=condition,y=process))+
geom_tile(aes(fill=z.score))+
scale_fill_gradient2(lab.legend,high="darkred",low="darkblue") +
theme_bw()+
theme(legend.key=element_blank(),
legend.position="right",
panel.border=element_blank(),
strip.background=element_blank(),
axis.text.x=element_text(angle=45,vjust=0.5))
library(gtable)
library(grid)
# Get the ggplot grob
g = ggplotGrob(p)
# Get the legend
index = which(g$layout$name == "guide-box")
leg = g$grobs[[index]]
# Get the legend labels
# and calculate corresponding p values
z.breaks = as.numeric(leg$grobs[[1]]$grobs[[3]]$label)
p.breaks = as.character(round(pnorm(abs(z.breaks), lower.tail = F), 3))
# Get the width of the longest p.break string, taking account of font and font size
w = lapply(na.omit(p.breaks), function(x) grobWidth(textGrob(x,
gp = gpar(fontsize = leg$grobs[[1]]$grobs[[3]]$gp$fontsize,
fontfamily = leg$grobs[[1]]$grobs[[3]]$gp$fontfamily))))
w = do.call(unit.pmax, w)
w = convertX(w, "mm")
# Add columns to the legend gtable to take p.breaks,
# setting the width of relevant column to w
leg$grobs[[1]] = gtable_add_cols(leg$grobs[[1]], leg$grobs[[1]]$widths[3], 1)
leg$grobs[[1]] = gtable_add_cols(leg$grobs[[1]], w, 1)
# Construct grob containing p.breaks
# Begin with the z.score grob, then make relevant changes
p.values = leg$grobs[[1]]$grobs[[3]]
p.values[c("label", "x", "hjust")] = list(p.breaks, unit(1, "npc"), 1)
# Put the p.values grob into the legend gtable
leg$grobs[[1]] = gtable_add_grob(leg$grobs[[1]], p.values, t=4, l=2,
name = "p.values", clip = "off")
# Put 'p' and 'z' labels into the legend gtable
leg$grobs[[1]] = gtable_add_grob(leg$grobs[[1]], list(textGrob("p"), textGrob("z")),
t=2, l=c(2,6), clip = "off")
# Drop the current legend title
leg$grobs[[1]]$grobs[[4]] = nullGrob()
# Put the legend back into the plot,
# and make sure the relevant column is wide enough to take the new legend
g$grobs[[index]] = leg
g$widths[8] = g$widths[8] + sum(leg$grobs[[1]]$widths[2:3])
# Draw the plot
grid.newpage()
grid.draw(g)
Not precisely what you described, but you could put both p values and z values into the same labels on one side of the legend:
z.breaks = c(-2,0,2)
p.breaks = pnorm(abs(z.breaks),lower.tail = F)
ggplot(clustered.mat.df,aes(x=condition,y=process)) +
geom_tile(aes(fill = z.score)) +
scale_fill_gradient2("z score (p value)", high="darkred",low="darkblue",
breaks = z.breaks,
labels = paste0(z.breaks, ' (p = ', round(p.breaks,2), ')') ) +
theme_bw() +
theme(legend.key = element_blank(),
legend.position = 'right',
panel.border = element_blank(),
strip.background = element_blank(),
axis.text.x=element_text(angle=45,vjust=0.5))

How to properly create this kind of plot [duplicate]

This question already has answers here:
Simplest way to plot changes in ranking between two ordered lists in R?
(4 answers)
Closed 7 years ago.
I want to show the connections between a number of people, organizations or whatever:
Var1 Var2 Freq
1 F A 5
2 F B 38
3 B C 10
4 E C 28
5 A D 8
6 B D 21
7 A E 50
8 A F 34
9 D F 50
10 E F 14
I couldn't find any examples for this kind of plot, so I started from scratch. However, I'm struggling with the labels for the frequency values. Any ideas how to fix that?
MWE:
### Sample data ###
# Gerate names
names <- LETTERS[1:6]
# Generate all possible permutations
df = expand.grid(rep(list(names), 2))
rownames(df) <- NULL
# Drop some of the permutations
df <- df[df$Var1 != df$Var2, ]
df <- df[-sample(1:nrow(df), nrow(df) * 2/3), ]
# Add a column with random frequency values
df$Freq <- sample(1:50, nrow(df), replace=T)
### Prepare sample data for ggplot ####
# Add a column with the row numbers (used for grouping)
df$Pair <- 1:nrow(df)
# Convert data frame to long format
df.from <- df[, -which(names(df) %in% c("Var2"))]
df.from$Type <- "From"
colnames(df.from) <- c("Name", "Freq", "Pair", "Type")
df.to <- df[, -which(names(df) %in% c("Var1"))]
df.to$Type <- "To"
colnames(df.to) <- c("Name", "Freq", "Pair", "Type")
df2 <- rbind(df.from, df.to)
### Plot ###
library(ggplot2)
library(scales)
p <- ggplot()
p <- p + geom_text(aes(x = "From", y = names, label = names), hjust = 1, vjust = 0.5)
p <- p + geom_text(aes(x = "To", y = names, label = names), hjust = 0, vjust = 0.5)
p <- p + geom_line(data = df2, aes(x = Type, y = Name, group = Pair))
p <- p + geom_text(data = df2[df2$Type == "To", ], aes(x = Type, y = Name, group = Pair, label = Freq), hjust = 3, vjust = 0.5)
p <- p + scale_y_discrete(name = "", limits = rev(factor(names, levels = sort(names))))
p <- p + scale_x_discrete(name = "", limits = c("From", "To"))
p
to me the request:
to show the connections between a number of people, organizations or whatever
sounds like a desire to graph the the network plot. Using the network package:
#Construct a sparse graph
m<-matrix(rbinom(100,1,1.5/9),10)
diag(m)<-0
g<-network(m)
#Plot the graph
plot(g)
You could get the following
Alternatively, this may be more relevant to your problem, you may consider making use of the qgraph package. For example the code below:
require(qgraph)
set.seed(1)
adj = matrix(sample(0:1, 10^2, TRUE, prob = c(0.8, 0.2)), nrow = 10, ncol = 10)
qgraph(adj)
title("Unweighted and directed graphs", line = 2.5)
Would return this beautiful network graph:
If you are you looking for more examples just refer to this excellent page by Sacha Epskam on how to use qgraph.

ggplot2 - fail to apply color with scale_fill_manual inside a loop [closed]

This question is unlikely to help any future visitors; it is only relevant to a small geographic area, a specific moment in time, or an extraordinarily narrow situation that is not generally applicable to the worldwide audience of the internet. For help making this question more broadly applicable, visit the help center.
Closed 9 years ago.
I'm running a loop to get, at each sub setting of my data set, a map and apply a given palette (and respective legend) accordingly.
People tend to dislike the use of for() loops and maximize vectorization of their approaches. I don't know the best way to vectorize processes with this particular data set.
In this particular case, I'm handling a relatively large data set (distribution species Atlas) that is particularly complex since different methodologies were used and different options must be passed for each species, considering a particular season, different set of observations, etc.
Species may be present at one season and missed into another (They may be a breeder, a resident or a migrant). Maps should be created for all cases (seasons), empty when absent. Additional data (besides those from field work) may be available and used.
Map Legend must accommodate all variations, besides presenting variable in interest (abundances) in a custom discrete scale.
By running a loop I feel (to my limited expertise) I can more easily retain and control the several needed objects, while stepping into the flux I created to produce the pieces of interest and finally create sets of species distributions maps.
My Problem is that I'm storing each resulting ggplot in a list() object. Each species at each season will be stored in a list. The
issue I'm facing is related to scale_fill_manual when used
inside a loop.
The behavior is strange since I get the maps done but with colors applied only to the last ggplot output. Nonetheless all values still being correctly identified in the legend.
to exemplify:
Packages
if (!require(ggplot2)) install.packages("ggplot2",
repos = "http://cran.r-project.org"); library(ggplot2)
if (!require(grid)) install.packages("grid",
repos = "http://cran.r-project.org"); library(grid)
if (!require(RColorBrewer)) install.packages("RColorBrewer",
repos = "http://cran.r-project.org"); library(RColorBrewer)
if (!require(reshape)) install.packages("reshape",
repos = "http://cran.r-project.org"); library(reshape)
A simple example first
#Create a list of colors to be used with scale_manual
palette.l <- list()
palette.l[[1]] <- c('red', 'blue', 'green')
palette.l[[2]] <- c('pink', 'blue', 'yellow')
# Store each ggplot in a list object
plot.l <- list()
#Loop it
for(i in 1:2){
plot.l[[i]] <- qplot(mpg, wt, data = mtcars, colour = factor(cyl)) +
scale_colour_manual(values = palette.l[[i]])
}
In my session plot.l[1] will be painted with colors from palette.l[2].
My particular case
Functions
Arrange Plots
ArrangeGraph <- function(..., nrow=NULL, ncol=NULL, as.table=FALSE) {
dots <- list(...)
n <- length(dots)
if(is.null(nrow) & is.null(ncol)) { nrow = floor(n/2) ; ncol = ceiling(n/nrow)}
if(is.null(nrow)) { nrow = ceiling(n/ncol)}
if(is.null(ncol)) { ncol = ceiling(n/nrow)}
## NOTE see n2mfrow in grDevices for possible alternative
grid.newpage()
pushViewport(viewport(layout=grid.layout(nrow,ncol)))
ii.p <- 1
for(ii.row in seq(1, nrow)) {
ii.table.row <- ii.row
if(as.table) {ii.table.row <- nrow - ii.table.row + 1}
for(ii.col in seq(1, ncol)) {
ii.table <- ii.p
if(ii.p > n) break
print(dots[[ii.table]], vp=VPortLayout(ii.table.row, ii.col))
ii.p <- ii.p + 1
}
}
}
ViewPort
VPortLayout <- function(x, y) viewport(layout.pos.row=x, layout.pos.col=y)
Species data sets
bd.aves.1 <- structure(list(quad = c("K113", "K114", "K114", "K114", "K114",...
due to limited body character number limit, please download entire code from
https://docs.google.com/open?id=0BxSZDr4eTnb9R09iSndzZjBMS28
Species list
list.esp.1 <- c("Sylv mela", "Saxi rube","Ocea leuc")#
# download from the above link
Some taxonomy and other data
txcon.1 <- structure(list(id = c(156L, 359L, 387L), grupo = c("Aves", "Aves",#
# download from the above link
Seasons
kSeason.1 <- c("Inverno", "Primavera", "Outono")
A Sample Grid
grid500.df.1 <- structure(list(id = c("K113", "K113", "K113", "K113", "K113",#...
# download from the above link
Additional Mapping elements
Shoreline
coastline.df.1 <- structure(list(long = c(182554.963670234, 180518, 178865.39,#...
# download from the above link
Labels placement adjustments
kFacx1 <- c(9000, -13000, -10000, -12000)
The R Code
for(i in listsp.1) { # LOOP 1 - Species
# Set up objects
sist.i <- list() # Sistematic observations
nsist.i <- list() # Non-Sistematic observations
breaks.nind.1 <- list() # Breaks on abundances
## Grid and merged dataframe
spij.1 <- list() # Stores a dataframe for sp i at season j
## Palette build
classes.1 <- list()
cllevels.1 <- list()
palette.nind.1 <- list() # Color palette
## Maps
grid500ij.1 <- list() # Grid for species i at season j
map.dist.ij.1 <- NULL
for(j in 1:length(kSeason.1)) { # LOOP 2 - Seasons
# j assume each season: Inverno, Primavera, Outono
# Sistematic occurences ===================================================
sist.i.tmp <- nrow(subset(bd.aves.1, esp == i & cod_tipo %in% sistematica &
periodo == kSeason.1[j]))
if (sist.i.tmp!= 0) { # There is sistematic entries, Then:
sist.i[[j]]<- ddply(subset(bd.aves.1,
esp == i & cod_tipo %in% sistematica &
periodo == kSeason.1[j]),
.(periodo, quad), summarise, nind = sum(n_ind),
codnid = max(cod_nidi))
} else { # No Sistematic entries, Then:
sist.i[[j]] <- data.frame('quad' = NA, 'periodo' = NA, 'nind' = NA,
'codnid' = NA, stringsAsFactors = F)
}
# Additional Entries (RS1) e other non-sistematic entries (biblio) =======
nsist.tmp.i = nrow(subset(bd.aves.1, esp == i & !cod_tipo %in% sistematica &
periodo == kSeason.1[j]))
if (nsist.tmp.i != 0) { # RS1 and biblio entries, Then:
nsist.i[[j]] <- subset(bd.aves.1,
esp == i & !cod_tipo %in% sistematica &
periodo == kSeason.1[j] &
!quad %in% if (nrow(sist.i[[j]]) != 0) {
subset(sist.i[[j]],
select = quad)$quad
} else NA,
select = c(quad, periodo, cod_tipo, cod_nidi)
)
names(nsist.i[[j]])[4] <- 'codnid'
} else { # No RS1 and biblio entries, Then:
nsist.i[[j]] = data.frame('quad' = NA, 'periodo' = NA, 'cod_tipo' = NA,
'codnid' = NA, stringsAsFactors = F)
}
# Quantile breaks =========================================================
if (!is.na(sist.i[[j]]$nind[1])) {
breaks.nind.1[[j]] <- c(0,
unique(
ceiling(
quantile(unique(
subset(sist.i[[j]], is.na(nind) == F)$nind),
q = seq(0, 1, by = 0.25)))))
} else {
breaks.nind.1[[j]] <- 0
}
# =========================================================================
# Build Species dataframe and merge to grid
# =========================================================================
if (!is.na(sist.i[[j]]$nind[1])) { # There are Sistematic entries, Then:
spij.1[[j]] <- merge(unique(subset(grid500df.1, select = id)),
sist.i[[j]],
by.x = 'id', by.y = 'quad', all.x = T)
# Adjust abundances when equals to NA ===================================
spij.1[[j]]$nind[is.na(spij.1[[j]]$nind) == T] <- 0
# Break abundances to create discrete variable ==========================
spij.1[[j]]$cln <- if (length(breaks.nind.1[[j]]) > 2) {
cut(spij.1[[j]]$nind, breaks = breaks.nind.1[[j]],
include.lowest = T, right = F)
} else {
cut2(spij.1[[j]]$nind, g = 2)
}
# Variable Abundance ====================================================
classes.1[[j]] = nlevels(spij.1[[j]]$cln)
cllevels.1[[j]] = levels(spij.1[[j]]$cln)
# Color Palette for abundances - isolated Zero class (color #FFFFFF) ====
if (length(breaks.nind.1[[j]]) > 2) {
palette.nind.1[[paste(kSeason.1[j])]] = c("#FFFFFF", brewer.pal(length(
cllevels.1[[j]]) - 1, "YlOrRd"))
} else {
palette.nind.1[[paste(kSeason.1[j])]] = c(
"#FFFFFF", brewer.pal(3, "YlOrRd"))[1:classes.1[[j]]]
}
names(palette.nind.1[[paste(kSeason.1[j])]])[1 : length(
palette.nind.1[[paste(kSeason.1[j])]])] <- cllevels.1[[j]]
# Add RS1 and bilbio values to palette ==================================
palette.nind.1[[paste(kSeason.1[j])]][length(
palette.nind.1[[paste(kSeason.1[j])]]) + 1] <- '#CCC5AF'
names(palette.nind.1[[paste(kSeason.1[j])]])[length(
palette.nind.1[[paste(kSeason.1[j])]])] <- 'Suplementar'
palette.nind.1[[paste(kSeason.1[j])]][length(
palette.nind.1[[paste(kSeason.1[j])]]) + 1] <- '#ADCCD7'
names(palette.nind.1[[paste(kSeason.1[j])]])[length(
palette.nind.1[[paste(kSeason.1[j])]])] <- 'Bibliografia'
# Merge species i dataframe to grid map =================================
grid500ij.1[[j]] <- subset(grid500df.1, select = c(id, long, lat, order))
grid500ij.1[[j]]$cln = merge(grid500ij.1[[j]],
spij.1[[j]],
by.x = 'id', by.y = 'id', all.x = T)$cln
# Adjust factor levels of cln variable - Non-Sistematic data ============
levels(grid500ij.1[[j]]$cln) <- c(levels(grid500ij.1[[j]]$cln), 'Suplementar',
'Bibliografia')
if (!is.na(nsist.i[[j]]$quad[1])) {
grid500ij.1[[j]]$cln[grid500ij.1[[j]]$id %in% subset(
nsist.i[[j]], cod_tipo == 'RS1', select = quad)$quad] <- 'Suplementar'
grid500ij.1[[j]]$cln[grid500ij.1[[j]]$id %in% subset(
nsist.i[[j]], cod_tipo == 'biblio', select = quad)$quad] <- 'Bibliografia'
}
} else { # No Sistematic entries, Then:
if (!is.na(nsist.i[[j]]$quad[1])) { # RS1 or Biblio entries, Then:
grid500ij.1[[j]] <- grid500df
grid500ij.1[[j]]$cln <- '0'
grid500ij.1[[j]]$cln <- factor(grid500ij.1[[j]]$cln)
levels(grid500ij.1[[j]]$cln) <- c(levels(grid500ij.1[[j]]$cln),
'Suplementar', 'Bibliografia')
grid500ij.1[[j]]$cln[grid500ij.1[[j]]$id %in% subset(
nsist.i[[j]], cod_tipo == 'RS1',
select = quad)$quad] <- 'Suplementar'
grid500ij.1[[j]]$cln[grid500ij.1[[j]]$id %in% subset(
nsist.i[[j]],cod_tipo == 'biblio',
select = quad)$quad] <- 'Bibliografia'
} else { # No entries, Then:
grid500ij.1[[j]] <- grid500df
grid500ij.1[[j]]$cln <- '0'
grid500ij.1[[j]]$cln <- factor(grid500ij.1[[j]]$cln)
levels(grid500ij.1[[j]]$cln) <- c(levels(grid500ij.1[[j]]$cln),
'Suplementar', 'Bibliografia')
}
} # End of Species dataframe build
# Distribution Map for species i at season j =============================
if (!is.na(sist.i[[j]]$nind[1])) { # There is sistematic entries, Then:
map.dist.ij.1[[paste(kSeason.1[j])]] <- ggplot(grid500ij.1[[j]],
aes(x = long, y = lat)) +
geom_polygon(aes(group = id, fill = cln), colour = 'grey80') +
coord_equal() +
scale_x_continuous(limits = c(100000, 180000)) +
scale_y_continuous(limits = c(-4000, 50000)) +
scale_fill_manual(
name = paste("LEGEND",
'\nSeason: ', kSeason.1[j],
'\n% of Occupied Cells : ',
sprintf("%.1f%%", (length(unique(
grid500ij.1[[j]]$id[grid500ij.1[[j]]$cln != levels(
grid500ij.1[[j]]$cln)[1]]))/12)*100), # percent
sep = ""
),
# Set Limits
limits = names(palette.nind.1[[j]])[2:length(names(palette.nind.1[[j]]))],
values = palette.nind.1[[j]][2:length(names(palette.nind.1[[j]]))],
drop = F) +
opts(
panel.background = theme_rect(),
panel.grid.major = theme_blank(),
panel.grid.minor = theme_blank(),
axis.ticks = theme_blank(),
title = txcon.1$especie[txcon.1$esp == i],
plot.title = theme_text(size = 10, face = 'italic'),
axis.text.x = theme_blank(),
axis.text.y = theme_blank(),
axis.title.x = theme_blank(),
axis.title.y = theme_blank(),
legend.title = theme_text(hjust = 0,size = 10.5),
legend.text = theme_text(hjust = -0.2, size = 10.5)
) +
# Shoreline
geom_path(inherit.aes = F, aes(x = long, y = lat),
data = coastline.df.1, colour = "#997744") +
# Add localities
geom_point(inherit.aes = F, aes(x = x, y = y), colour = 'grey20',
data = localidades, size = 2) +
# Add labels
geom_text(inherit.aes = F, aes(x = x, y = y, label = c('Burgau',
'Sagres')),
colour = "black",
data = data.frame(x = c(142817 + kFacx1[1], 127337 + kFacx1[4]),
y = c(11886, 3962), size = 3))
} else { # NO sistematic entries,then:
map.dist.ij.1[[paste(kSeason.1[j])]] <- ggplot(grid500ij.1[[j]],
aes(x = long, y = lat)) +
geom_polygon(aes.inherit = F, aes(group = id, fill = cln),
colour = 'grey80') +
#scale_color_manual(values = kCorLimiteGrid) +
coord_equal() +
scale_x_continuous(limits = c(100000, 40000)) +
scale_y_continuous(limits = c(-4000, 180000)) +
scale_fill_manual(
name = paste('LEGENDA',
'\nSeason: ', kSeason.1[j],
'\n% of Occupied Cells :',
sprintf("%.1f%%", (length(unique(
grid500ij.1[[j]]$id[grid500ij.1[[j]]$cln != levels(
grid500ij.1[[j]]$cln)[1]]))/12 * 100)), # percent
sep = ''),
limits = names(kPaletaNsis)[2:length(names(kPaletaNsis))],
values = kPaletaNsis[2:length(names(kPaletaNsis))],
drop = F) +
opts(
panel.background = theme_rect(),
panel.grid.major = theme_blank(),
panel.grid.minor = theme_blank(),
title = txcon.1$especie[txcon.1$esp == i],
plot.title = theme_text(size = 10, face = 'italic'),
axis.ticks = theme_blank(),
axis.text.x = theme_blank(),
axis.text.y = theme_blank(),
axis.title.x = theme_blank(),
axis.title.y = theme_blank(),
legend.title = theme_text(hjust = 0,size = 10.5),
legend.text = theme_text(hjust = -0.2, size = 10.5)
) +
# Add Shoreline
geom_path(inherit.aes = F, data = coastline.df.1,
aes(x = long, y = lat),
colour = "#997744") +
# Add Localities
geom_point(inherit.aes = F, aes(x = x, y = y),
colour = 'grey20',
data = localidades, size = 2) +
# Add labels
geom_text(inherit.aes = F, aes(x = x, y = y,
label = c('Burgau', 'Sagres')),
colour = "black",
data = data.frame(x = c(142817 + kFacx1[1],
127337 + kFacx1[4],),
y = c(11886, 3962)),
size = 3)
} # End of Distribution map building for esp i and j seasons
} # Fim do LOOP 2: j Estacoes
# Print Maps
png(file = paste('panel_species',i,'.png', sep = ''), res = 96,
width = 800, height = 800)
ArrangeGraph(map.dist.ij.1[[paste(kSeason.1[3])]],
map.dist.ij.1[[paste(kSeason.1[2])]],
map.dist.ij.1[[paste(kSeason.1[1])]],
ncol = 2, nrow = 2)
dev.off()
graphics.off()
} # End of LOOP 1
map.dist.ij.1[[paste(kSeason.1[3])]] is the only with color palette applied to polygons, but the legend items is well defined for each j map.
Output using R Code
As we see, Legends are OK but not colored.
Hope not missing anything. Sorry for some lost Portuguese terminology.
Honestly, I have not looked much at your code for your specific problem--a bit too much to wade through!--but for your demo example, adding print(plot.l[[i]]) in your loop.
#Create a list of colors to be used with scale_manual
palette.l <- list()
palette.l[[1]] <- c('red', 'blue', 'green')
palette.l[[2]] <- c('pink', 'blue', 'yellow')
# Store each ggplot in a list object
plot.l <- list()
# Loop it
for(i in 1:2) {
plot.l[[i]] <- qplot(mpg, wt, data = mtcars, colour = factor(cyl)) +
scale_colour_manual(values = palette.l[[i]])
print(plot.l[[i]]) ### Added to your loop
}
In the case of your minimal example, though, this also works (without first having to create an empty list to store your plots) and I think it at least looks a lot cleaner. I'm not sure if something similar can be adapted to suit your larger scenario.
#Create a list of colors to be used with scale_manual
palette.l <- list(c('red', 'blue', 'green'),
c('pink', 'blue', 'yellow'))
p <- qplot(mpg, wt, data = mtcars, colour = factor(cyl))
# Use lapply and "force" to get your plots in a list
plot.l <- lapply(palette.l,
function(x) {
force(x)
p + scale_color_manual(values = x)
})

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