Fastest way to plot a line graph from a matrix in R - r

Question

Is there a way I can speed up the following code to plot a line graph in R from a matrix? The matrix is quite big, around 3000 rows by 60 columns, sometimes the data is bigger.
I currently use matlines or ggplot for example:
library(ggplot2)
library(reshape2)
## using ggplot2
data <- matrix(rnorm(3000*60), ncol = 60)
df <- as.data.frame(data)
df$id <- rownames(df)
plot_data <- melt(df, id.var = "id")
ggplot(plot_data, aes(x=variable,y=value,group=id)) + geom_point() + geom_line()
## using matlines
plot(0, ylim = range(data), xlim = c(1, ncol(data)), ylab = "tmp",
type = "n", xaxt = "n", xlab = "")
axis(1, at = seq(ncol(data)), labels = colnames(data), las = 2)
matlines(t(data))
I used Sys.time() to get an idea of timings and it's pretty slow in both cases, around 22 seconds for ggplot and 24 seconds for matlines. I am building this into a Shiny app and it takes a very long time to render (similarly in the console).
Any help greatly appreciated. Thank you.
> sessionInfo()
R version 4.0.0 (2020-04-24)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS High Sierra 10.13.6
Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
locale:
[1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8
attached base packages:
[1] stats4 parallel stats graphics grDevices utils datasets methods base
other attached packages:
[1] reshape2_1.4.4 ggplot2_3.3.1 circlize_0.4.9 shinyhelper_0.3.2 colorspace_1.4-1
[6] colourpicker_1.0 shinythemes_1.1.2 DT_0.13 shiny_1.4.0.2 dplyr_1.0.0
[11] pRoloc_1.29.0 BiocParallel_1.22.0 MLInterfaces_1.68.0 cluster_2.1.0 annotate_1.66.0
[16] XML_3.99-0.3 AnnotationDbi_1.50.0 IRanges_2.22.2 MSnbase_2.14.2 ProtGenerics_1.20.0
[21] S4Vectors_0.26.1 mzR_2.22.0 Rcpp_1.0.4.6 Biobase_2.48.0 BiocGenerics_0.34.0

Answer 1

You may be able to decrease your rendering time by reducing the number of colors and linetypes in your plot. For example, compare default matlines, which cycles through 5 linetypes and 6 colors:
system.time({
plot(0, ylim = range(data), xlim = c(1, ncol(data)), ylab = "tmp",
type = "n", xaxt = "n", xlab = "")
axis(1, at = seq(ncol(data)), labels = colnames(data), las = 2)
matlines(t(data))
})
# user system elapsed
# 0.66 3.50 4.15
to one where we only use one linetype and one color
system.time({
plot(0, ylim = range(data), xlim = c(1, ncol(data)), ylab = "tmp",
type = "n", xaxt = "n", xlab = "")
axis(1, at = seq(ncol(data)), labels = colnames(data), las = 2)
matlines(t(data), col = 1, lty = 1)
})
# user system elapsed
# 0.23 0.58 0.83
As others have mentioned, other than the performance aspect, simplifying the plot may be good in and of itself.

Answer 2

I just want to highlight how expensive drawing the points is if you have to draw a lot of them in ggplot2. Here are some different benchmarks with different types of points. If you omit the points you can draw the plot in 1/3rd of the time. Having periods . as points in 40% of the time. If you definitely need proper points, I recommend shape = 16.
library(ggplot2)
library(reshape2)
data <- matrix(rnorm(3000*60), ncol = 60)
df <- as.data.frame(data)
df$id <- rownames(df)
plot_data <- melt(df, id.var = "id")
bm <- microbenchmark::microbenchmark(
baseline = {
g <- ggplot(plot_data, aes(x=variable,y=value,group=id)) +
geom_point() +
geom_line()
print(g)
},
nopoint = {
g <- ggplot(plot_data, aes(x=variable,y=value,group=id)) +
geom_line()
print(g)
},
periodpoint = {
g <- ggplot(plot_data, aes(x=variable,y=value,group=id)) +
geom_point(shape = ".") +
geom_line()
print(g)
},
shape16point = {
g <- ggplot(plot_data, aes(x=variable,y=value,group=id)) +
geom_point(shape = 16) +
geom_line()
print(g)
},
times = 10
)
df <- data.frame(
x = bm$expr,
y = bm$time / 1e9 # because measured in nanoseconds
)
ggplot(df, aes(x, y)) +
geom_boxplot() +
scale_y_continuous(limits = c(0, NA), name = "Time (s)")
Created on 2020-06-27 by the reprex package (v0.3.0)
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