Here is my MWE code.
\documentclass{beamer}
\begin{document}
<<setup, include=FALSE>>=
# smaller font size for chunks
opts_chunk$set(size = 'footnotesize')
options(width=60)
#
\begin{frame}[fragile]
\frametitle{Test1}
<<boring-random>>=
y <- c(5, 7, 15, 17, 17, 19)
Trt <- gl(n = 3, k = 2, length = 3 * 2, labels = paste("Trt",
1:3, sep = ""), ordered = FALSE)
Data <- data.frame(Trt, y)
Fit1 <- aov(formula = y ~ Trt, data = Data, contrasts = list(Trt = "contr.sum"))
ANOVA1 <- anova(Fit1)
Coeffs1 <- coefficients(Fit1)
#
\end{frame}
\end{document}
I'm struggling to keep the R chunk code within the Beamer frame. I wonder what is the efficient way to manage the R chunk codes such that they stay inside the Beamer frame. Thanks
The best approach is to turn off the tidy option by tidy=FALSE, and manually break your lines.
<<boring-random, tidy=FALSE>>=
y <- c(5, 7, 15, 17, 17, 19)
Trt <- gl(n = 3, k = 2, length = 3 * 2, labels = paste("Trt",
1:3, sep = ""), ordered = FALSE)
Data <- data.frame(Trt, y)
Fit1 <- aov(formula = y ~ Trt, data = Data,
contrasts = list(Trt = "contr.sum"))
ANOVA1 <- anova(Fit1)
Coeffs1 <- coefficients(Fit1)
#
This will always work. The other way is to set smaller width in options() (knitr FAQ 8), and you probably need to try a few times for an ideal width. In your case, 60 is apparently too big.
Related
I created a heatmap and a pca plot and tried to merge them as single figure. But the are not displayed as single figure.
library(factoextra)
library(FactoMineR)
library(pheatmap)
library(RColorBrewer)
library(ggpubr)
# make test matrix
test = matrix(rnorm(200), 20, 10)
test[1:10, seq(1, 10, 2)] = test[1:10, seq(1, 10, 2)] + 3
test[11:20, seq(2, 10, 2)] = test[11:20, seq(2, 10, 2)] + 2
test[15:20, seq(2, 10, 2)] = test[15:20, seq(2, 10, 2)] + 4
colnames(test) = paste("Test", 1:10, sep = "")
rownames(test) = paste("Gene", 1:20, sep = "")
# define the annotation
annotation_row = data.frame(GeneClass = factor(rep(c("Path1", "Path2", "Path3"), c(10, 4, 6))),
AdditionalAnnotation = c(rep("random1", 10), rep("random2", 10)))
rownames(annotation_row) = paste("Gene", 1:20, sep = "")
a=pheatmap(test, annotation_row = annotation_row)
# creating pca
# Compute PCA with ncp = 3
res.pca <- PCA(test, ncp = 3, graph = FALSE)
# Compute hierarchical clustering on principal components
res.hcpc <- HCPC(res.pca, graph = FALSE)
# Principal components + tree
b=plot(res.hcpc, choice = "3D.map")
#arranging in a single plot
ggarrange(a$gtable, b, labels = c("A", "B"))
The output was without pca:
plot (or plot.HCPC) returns NULL and therefore b is NULL.
And from ?ggarrange, it expects a list of plots to be arranged into the grid. The plots can be either ggplot2 plot objects or arbitrary gtables.
So one option could be using as.ggplot() function from {ggplotify} package to convert that base plot to ggplot object and then pass it to ggarrange.
b <- ggplotify::as.ggplot(~plot(res.hcpc, choice = "3D.map"))
#arranging in a single plot
ggarrange(a$gtable, b, labels = c("A", "B"))
I am trying to save a ggsurvplot with risk.table using ggsave. However, the output off ggsave is always just the risk.table. I also tried this and this. None is working.
library(data.table)
library(survival)
library(survminer)
OS <- c(c(1:100), seq(1, 75, length = 50), c(1:50))
dead <- rep(1, times = 200)
variable <- c(rep(0, times = 100), rep(1, times = 50), rep(2, times = 50))
dt <- data.table(OS = OS,
dead = dead,
variable = variable)
survfit <- survfit(Surv(OS, dead) ~ variable, data = dt)
ggsurvplot(survfit, data = dt,
risk.table = TRUE)
ggsave("test.png")
The main issue is that a ggsurvplot object is a list of plots. Hence, when using ggsave only the last plot or element of the list is saved.
There is already a GitHub issue on that topic with several workarounds, e.g. using one of the more recent suggestions this works fine for me
library(survival)
library(survminer)
OS <- c(c(1:100), seq(1, 75, length = 50), c(1:50))
dead <- rep(1, times = 200)
variable <- c(rep(0, times = 100), rep(1, times = 50), rep(2, times = 50))
dt <- data.frame(OS = OS,
dead = dead,
variable = variable)
survfit <- survfit(Surv(OS, dead) ~ variable, data = dt)
# add method to grid.draw
grid.draw.ggsurvplot <- function(x){
survminer:::print.ggsurvplot(x, newpage = FALSE)
}
p <- ggsurvplot(survfit, data = dt, risk.table = TRUE)
ggsave("test.png", p, height = 6, width = 6)
I'm using a Cox regression model considering my variable trough splines transformation. All is working nice until the subsequent nomogram... as expected, the scale of my variable is also transformed but I'd like to add some custom ticks inside the region between values 0 and 2 (I guess is the transformed one). Any idea, if you please?
Here's my code...
data <- source("https://pastebin.com/raw/rGtUSTLz")$value
ddist <- datadist(data)
options(datadist = "ddist")
fit <- cph(Surv(time, event) ~ rcs(var, 3), data = data, surv = T, x = T, y = T)
surv <- Survival(fit)
plot(nomogram(fit,
fun = list(function(x) surv(times = 10, lp = x),
function(x) surv(times = 30, lp = x),
function(x) surv(times = 60, lp = x)),
funlabel = paste("c", 1:3), lp = T))
... and these are the real and the desired outputs.
Thanks in advance for your help!
I have had this issue too. My answer is a work around using another package, regplot. Alternatively, if you know what the point values are at the tick marks you want plotted, then you can supply those instead of using the output from regplot. Basically, you need to modify the tick marks and points that are output from the nomogram function and supplied to plot the nomogram.
This method also provides a way to remove points / tick marks by editing the nomogram output.
data <- source("https://pastebin.com/raw/rGtUSTLz")$value
ddist <- datadist(data)
options(datadist = "ddist")
fit <- cph(Surv(time, event) ~ rcs(var, 3), data = data, surv = T, x = T, y = T)
surv <- Survival(fit)
nom1 <- nomogram(fit, fun = list(function(x) surv(times = 10, lp = x),
function(x) surv(times = 30, lp = x),
function(x) surv(times = 60, lp = x)),
funlabel = paste("c", 1:3), lp = T)
library(regplot)
# call regplot with points = TRUE to get output
regplot(fit, fun = list(function(x) surv(times = 10, lp = x),
function(x) surv(times = 30, lp = x),
function(x) surv(times = 60, lp = x)),
funlabel = paste("c", 1:3), points = TRUE)
# look at the points supplied through regplot and take those.
nom1_edit <- nom1
# now we edit the ticks supplied for var and their corresponding point value
nom1_edit[[1]][1] <- list(c(0, 0.06, 0.15, 0.3, 2,4,6,8,10,12,14,16))
nom1_edit[[1]][2] <- list(c(0, 10, 21, 32, 42.41191, 50.63878, 58.86565,
67.09252, 75.31939, 83.54626, 91.77313, 100.00000))
nom1_edit$var$points <- c(0, 10, 21, 32, 42.41191, 50.63878, 58.86565,
67.09252, 75.31939, 83.54626, 91.77313, 100.00000)
# plot the edited nomogram with new points
plot(nom1_edit)
I am dealing with a function that takes a big data frame (36 rows and 194 columns) which performs a Principal Component Analysis and then generates a list of plots where I have the combination of 26 Principal Components which are 325 in total, using 'expand.grid'.
My problem is that when I am using ggarrange(), from ggpubr, to merge all the plots in only one pdf file, this file is empty.
My code:
a = 26
row.pairs = 325
PC.Graph <- function(df, col1, col2, tag, id){
df1 <- df[,-c(col1:col2)]
pca <- prcomp(df1, scale. = T)
pc.summ <- summary(pca)
a <- sum(pc.summ$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()
How can we custom the position of the panels/subplot in ggplot2?
Concretely I have a grouped times series and I want to produce 1 subplot per time series with custom positions of the subplot, not necessarily in a grid.
The facet_grid() or facet_wrap() functions do not provide a full customization of the position of the panel as it uses grid.
library(tidyverse)
df = data.frame(group = LETTERS[1:5],
x = c(1,2,3,1.5,2.5),
y =c(2,1,2,3,3),
stringsAsFactors = F)%>%
group_by(group)%>%
expand_grid(time = 1:20)%>%
ungroup()%>%
mutate(dv = rnorm(n()))%>%
arrange(group,time)
## plot in grid
df%>%
ggplot()+
geom_line(aes(x=time,y=dv))+
facet_grid(~group)
## plot with custom x, y position
## Is there an equivalent of facet_custom()?
df%>%
ggplot()+
geom_line(aes(x=time,y=dv))+
facet_custom(~group, x.subplot = x, y.subplot = y)
FYI: This dataset is only an example. My data are EEG data where each group represents an electrode (up to 64) and I want to plot the EEG signals of each electrode accordingly to the position of the electrode on the head.
Well, I guess this would not really be a 'facet plot' any more. I therefore don't think there is a specific function out there.
But you can use the fantastic patchwork package for that, in particular the layout option in wrap_plots.
As the main package author Thomas describes in the vignette, the below option using area() may be a bit verbose, but it would give you full programmatic options about positioning all your plots.
library(tidyverse)
library(patchwork)
mydf <- data.frame(
group = LETTERS[1:5],
x = c(1, 2, 3, 1.5, 2.5),
y = c(2, 1, 2, 3, 3),
stringsAsFactors = F
) %>%
group_by(group) %>%
expand_grid(time = 1:20) %>%
ungroup() %>%
mutate(dv = rnorm(n())) %>%
arrange(group, time)
## plot in grid
mylist <-
mydf %>%
split(., .$group)
p_list <-
map(1:length(mylist), function(i){
ggplot(mylist[[i]]) +
geom_line(aes(x = time, y = dv)) +
ggtitle(names(mylist)[i])
}
)
layout <- c(
area(t = 1, l = 1, b = 2, r = 2),
area(t = 2, l = 3, b = 3, r = 4),
area(t = 3, l = 5, b = 4, r = 6),
area(t = 4, l = 3, b = 5, r = 4),
area(t = 5, l = 1, b = 6, r = 2)
)
wrap_plots(p_list, design = layout)
#> result not shown, it's the same as below
For a more programmatic approach, one option is to create the required "patch_area" object manually.
t = 1:5
b = t+1
l = c(1,3,5,3,1)
r = l+1
list_area <- list(t = t, b = b, l = l, r = r)
class(list_area) <- "patch_area"
wrap_plots(p_list, design = list_area)
Created on 2020-04-22 by the reprex package (v0.3.0)