I have a list of values and a list of ggplots. I would like to attach the values from the list on to the ggplots. Is there a good way to do that?
Here's what I have for the list of ggplots:
p.list <- lapply(sort(unique(ind_steps$AnimalID)), function(i){
ggplot(ind_steps[ind_steps$AnimalID == i,], aes(x = t2, y = NSD)) +
geom_line() + theme_bw() +
theme(axis.text.x = element_text(angle = 90)) +
scale_x_datetime(date_breaks = '10 days', date_labels = '%y%j') +
facet_grid( ~ AnimalID, scales = "free") +
scale_colour_manual(values=hcl(seq(15,365,length.out=4)[match(i, sort(unique(ind_steps$AnimalID)))], 100, 65))
})
Assuming I have another list the same length as this one, and each one has a single value in each list.
I want to pair the ggplots with the list of values, and have the values show up in each respective plot. My expected output would be to have each value from the list of values be on each respective plot within the list of plots.
Since you don't provide any example data here I put an example with the iris built-in dataset. You can add values to plots with geom_text or geom_label (if I well understood what you want). For example, here we add the R^2 values to all the plot in a list:
library(ggplot2)
data(iris)
rsq <- lapply(1:length(unique(iris$Species)), function(i) {
cor(iris[iris$Species == unique(iris$Species)[i], "Sepal.Length"], iris[iris$Species == unique(iris$Species)[i], "Petal.Length"])^2
})
p.list <- lapply(1:length(unique(iris$Species)), function(i) {
ggplot(iris[iris$Species == unique(iris$Species)[i], ], aes(x = Sepal.Length, y = Petal.Length)) +
geom_point() + theme_bw()+
geom_text(aes(x=min(Sepal.Length),y=max(Petal.Length),label=paste0("R= ",round(rsq[[i]],2))))
})
library(gridExtra)
grid.arrange(p.list[[1]],p.list[[2]],p.list[[3]],nrow=3)
which return :
Related
I have a csv file which looks like the following:
Name,Count1,Count2,Count3
application_name1,x1,x2,x3
application_name2,x4,x5,x6
The x variables represent numbers and the applications_name variables represent names of different applications.
Now I would like to make a barplot for each row by using ggplot2. The barplot should have the application_name as title. The x axis should show Count1, Count2, Count3 and the y axis should show the corresponding values (x1, x2, x3).
I would like to have a single barplot for each row, because I have to store the different plots in different files. So I guess I cannot use "melt".
I would like to have something like:
for each row in rows {
print barplot in file
}
Thanks for your help.
You can use melt to rearrange your data and then use either facet_wrap or facet_grid to get a separate plot for each application name
library(ggplot2)
library(reshape2)
# example data
mydf <- data.frame(name = paste0("name",1:4), replicate(5,rpois(4,30)))
names(mydf)[2:6] <- paste0("count",1:5)
# rearrange data
m <- melt(mydf)
# if you are wanting to export each plot separately
# I used facet_wrap as a quick way to add the application name as a plot title
for(i in levels(m$name)) {
p <- ggplot(subset(m, name==i), aes(variable, value, fill = variable)) +
facet_wrap(~ name) +
geom_bar(stat="identity", show_guide=FALSE)
ggsave(paste0("figure_",i,".pdf"), p)
}
# or all plots in one window
ggplot(m, aes(variable, value, fill = variable)) +
facet_wrap(~ name) +
geom_bar(stat="identity", show_guide=FALSE)
I didn't see #user20650's nice answer before preparing this. It's almost identical, except that I use plyr::d_ply to save things instead of a loop. I believe dplyr::do() is another good option (you'd group_by(Name) first).
yourData <- data.frame(Name = sample(letters, 10),
Count1 = rpois(10, 20),
Count2 = rpois(10, 10),
Count3 = rpois(10, 8))
library(reshape2)
yourMelt <- melt(yourData, id.vars = "Name")
library(ggplot2)
# Test a function on one piece to develope graph
ggplot(subset(yourMelt, Name == "a"), aes(x = variable, y = value)) +
geom_bar(stat = "identity") +
labs(title = subset(yourMelt, Name == 'a')$Name)
# Wrap it up, with saving to file
bp <- function(dat) {
myPlot <- ggplot(dat, aes(x = variable, y = value)) +
geom_bar(stat = "identity") +
labs(title = dat$Name)
ggsave(filname = paste0("path/to/save/", dat$Name, "_plot.pdf"),
myPlot)
}
library(plyr)
d_ply(yourMelt, .variables = "Name", .fun = bp)
I trying to make boxplots with ggplot2.
The code I have to make the boxplots with the format that I want is as follows:
p <- ggplot(mg_data, aes(x=Treatment, y=CD68, color=Treatment)) +
geom_boxplot(mg_data, mapping=aes(x=Treatment, y=CD68))
p+ theme_classic() + geom_jitter(shape=16, position=position_jitter(0.2))
I can was able to use the following code to make looped boxplots:
variables <- mg_data %>%
select(10:17)
for(i in variables) {
print(ggplot(mg_data, aes(x = Treatment, y = i, color=Treatment)) +
geom_boxplot())
}
With this code I get the boxplots however, they do not have the name label of what variable is being select for the y-axis, unlike the original code when not using the for loop. I also do not know how to add the formating code to the loop:
p + theme_classic() + geom_jitter(shape=16, position=position_jitter(0.2))
Here is a way. I have tested with built-in data set iris, just change the data name and selected columns and it will work.
suppressPackageStartupMessages({
library(dplyr)
library(ggplot2)
})
variables <- iris %>%
select(1:4) %>%
names()
for(i in variables) {
g <- ggplot(iris, aes(x = Species, y = get(i), color=Species)) +
geom_boxplot() +
ylab(i)
print(g)
}
Edit
Answering to a comment by user TarJae, reproduced here because answers are less deleted than comments:
Could you please expand with saving all four files. Many thanks.
The code above can be made to save the plots with a ggsave instruction at the loop end. The filename is the variable name and the plot is the default, the return value of last_plot().
for(i in variables) {
g <- ggplot(iris, aes(x = Species, y = get(i), color=Species)) +
geom_boxplot() +
ylab(i)
print(g)
ggsave(paste0(i, ".png"), device = "png")
}
Try this:
variables <- mg_data %>%
colnames() %>%
`[`(10:17)
for (i in variables) {
print(ggplot(mg_data, aes(
x = Treatment, y = {{i}}, color = Treatment
)) +
geom_boxplot())
}
Another option is to use lapply. It's approximately the same as using a loop, but it hides the actual looping part and can make your code look a little cleaner.
variables = iris %>%
select(1:4) %>%
names()
lapply(variables, function(x) {
ggplot(iris, aes(x = Species, y = get(x), color=Species)) +
geom_boxplot() + ylab(x)
})
I'm trying to create boxplots with descriptive information (mean, count, etc.).
I found a lot of examples of how to add the numbers for one boxplot with different groups, but I didn't found a way to add those numbers for multiple boxplots grid (facet_wrap).
for example, this article describes how to add numbers for one boxplot - I'm trying to do the same for multiple boxplots
library(reshape2)
library(ggplot2)
df.m <- melt(iris, id.var = "Species")
p <- ggplot(data = df.m, aes(x=variable, y=value)) +
geom_boxplot(aes(fill=Species))
p + facet_wrap( ~ variable, scales="free")
and on top of this plot - I want to add the relevant descriptive information on top of each box.
Create the function that makes counts and means
stat_box_data <- function(y) {
return(
data.frame(
y = 0.5+1.1*max(y), #may need to modify this depending on your data
label = paste('count =', length(y), '\n',
'mean =', round(mean(y), 1), '\n')
)
)
}
)
}
df.m <- melt(iris, id.var = "Species")
You may want to use this or something similar if you have large outliers instead of the y=0.5... bit above:
y=quantile(y,probs=0.95)*1.1,
Plot the data and use stat_summary with your custom function
ggplot(data = df.m, aes(x=Species, y=value)) +
geom_boxplot(aes(fill=Species))+
stat_summary(
fun.data = stat_box_data,
geom = "text",
hjust = 0.5,
vjust = 0.9
) +
facet_wrap( ~ variable, scales="free")
I asked a question yesterday about annotating the x-axis with N in a faceted plot using a minimal example that turns out to be too simple, relative to my real problem. The answer given there works in the case of complete data, but if you have missing facets you would like to preserve, the combination of facet_wrap options drop=FALSE and scales="free_x" triggers an error: "Error in if (zero_range(from) || zero_range(to)) { : missing value where TRUE/FALSE needed"
Here is a new, less-minimal example. The goal here is to produce a large graph with two panels using grid.arrange; the first showing absolute values over time by treatment group; the second showing the change from baseline over time by treatment group. In the second panel, we need a blank facet when vis=1.
# setup
library(ggplot2)
library(plyr)
library(gridExtra)
trt <- factor(rep(LETTERS[1:2],150),ordered=TRUE)
vis <- factor(c(rep(1,150),rep(2,100),rep(3,50)),ordered=TRUE)
id <- c(c(1:150),c(1:100),c(1:50))
val <- rnorm(300)
data <- data.frame(id,trt,vis,val)
base <- with(subset(data,vis==1),data.frame(id,trt,baseval=val))
data <- merge(data,base,by="id")
data <- transform(data,chg=ifelse(vis==1,NA,val-baseval))
data.sum <- ddply(data, .(vis, trt), summarise, N=length(na.omit(val)))
data <- merge(data,data.sum)
data <- transform(data, trtN=paste(trt,N,sep="\n"))
mytheme <- theme_bw() + theme(panel.margin = unit(0, "lines"), strip.background = element_blank())
# no missing facets
plot.a <- ggplot(data) + geom_boxplot(aes(x=trtN,y=val,group=trt,colour=trt), show.legend=FALSE) +
facet_wrap(~ vis, drop=FALSE, switch="x", nrow=1, scales="free_x") +
labs(x="Visit") + mytheme
# first facet should be blank
plot.b <- ggplot(data) + geom_boxplot(aes(x=trtN,y=chg,group=trt,colour=trt), show.legend=FALSE) +
facet_wrap(~ vis, drop=FALSE, switch="x", nrow=1, scales="free_x") +
labs(x="Visit") + mytheme
grid.arrange(plot.a,plot.b,nrow=2)
You can add a blank layer to draw all the facets in your second plot. The key is that you need a variable that exists for every level of vis to use as your y variable. In your case you can simply use the variable you used in your first plot.
ggplot(data) +
geom_boxplot(aes(x = trtN, y = chg, group = trt, colour = trt), show.legend = FALSE) +
geom_blank(aes(x = trtN, y = val)) +
facet_wrap(~ vis, switch = "x", nrow = 1, scales = "free_x") +
labs(x="Visit") + mytheme
If your variables have different ranges, you can set the y limits using the overall min and max of your boxplot y variable.
+ scale_y_continuous(limits = c(min(data$chg, na.rm = TRUE), max(data$chg, na.rm = TRUE)))
I have a csv file which looks like the following:
Name,Count1,Count2,Count3
application_name1,x1,x2,x3
application_name2,x4,x5,x6
The x variables represent numbers and the applications_name variables represent names of different applications.
Now I would like to make a barplot for each row by using ggplot2. The barplot should have the application_name as title. The x axis should show Count1, Count2, Count3 and the y axis should show the corresponding values (x1, x2, x3).
I would like to have a single barplot for each row, because I have to store the different plots in different files. So I guess I cannot use "melt".
I would like to have something like:
for each row in rows {
print barplot in file
}
Thanks for your help.
You can use melt to rearrange your data and then use either facet_wrap or facet_grid to get a separate plot for each application name
library(ggplot2)
library(reshape2)
# example data
mydf <- data.frame(name = paste0("name",1:4), replicate(5,rpois(4,30)))
names(mydf)[2:6] <- paste0("count",1:5)
# rearrange data
m <- melt(mydf)
# if you are wanting to export each plot separately
# I used facet_wrap as a quick way to add the application name as a plot title
for(i in levels(m$name)) {
p <- ggplot(subset(m, name==i), aes(variable, value, fill = variable)) +
facet_wrap(~ name) +
geom_bar(stat="identity", show_guide=FALSE)
ggsave(paste0("figure_",i,".pdf"), p)
}
# or all plots in one window
ggplot(m, aes(variable, value, fill = variable)) +
facet_wrap(~ name) +
geom_bar(stat="identity", show_guide=FALSE)
I didn't see #user20650's nice answer before preparing this. It's almost identical, except that I use plyr::d_ply to save things instead of a loop. I believe dplyr::do() is another good option (you'd group_by(Name) first).
yourData <- data.frame(Name = sample(letters, 10),
Count1 = rpois(10, 20),
Count2 = rpois(10, 10),
Count3 = rpois(10, 8))
library(reshape2)
yourMelt <- melt(yourData, id.vars = "Name")
library(ggplot2)
# Test a function on one piece to develope graph
ggplot(subset(yourMelt, Name == "a"), aes(x = variable, y = value)) +
geom_bar(stat = "identity") +
labs(title = subset(yourMelt, Name == 'a')$Name)
# Wrap it up, with saving to file
bp <- function(dat) {
myPlot <- ggplot(dat, aes(x = variable, y = value)) +
geom_bar(stat = "identity") +
labs(title = dat$Name)
ggsave(filname = paste0("path/to/save/", dat$Name, "_plot.pdf"),
myPlot)
}
library(plyr)
d_ply(yourMelt, .variables = "Name", .fun = bp)