A R language ggplot2 package liked grammar of graphics library for R# language programming.
The R# language is another scientific computing language which is designed for .NET runtime, R# is evolved from the R language. There is a famous graphics library called ggplot2 in R language, so keeps the same, there is a graphics library called ggplot was developed for R# language.
require(ggplot); const volcano = read.csv(`${@dir}/log2FC.csv`); const foldchange = 1.5; # create color factor for scatter points volcano[, "factor"] = ifelse(volcano[, "log2FC"] > log2(foldchange), "Up", "Not Sig"); volcano[, "factor"] = ifelse(volcano[, "log2FC"] < -log2(foldchange), "Down", volcano[, "factor"]); volcano[, "factor"] = ifelse(volcano[, "p.value"] < 0.05, volcano[, "factor"], "Not Sig"); # transform of the pvalue scale volcano[, "p.value"] = -log10(volcano[, "p.value"]); print("peeks of the raw data:"); print(head(volcano)); print("count of the factors:"); print(`Up: ${sum("Up" == volcano[, "factor"])}`); print(`Not Sig: ${sum("Not Sig" == volcano[, "factor"])}`); print(`Down: ${sum("Down" == volcano[, "factor"])}`); # [1] "peeks of the raw data:" # ID p.value log2FC factor # <mode> <string> <double> <double> <string> # [1, ] "Q5XJ10" 9.81 -0.205 "Not Sig" # [2, ] "A8WG05" 5.21 0.0472 "Not Sig" # [3, ] "Q8JH71" 11.6 -0.38 "Not Sig" # [4, ] "Q7T3L3" 8.85 0.165 "Not Sig" # [5, ] "Q567C8" 21 0.837 "Up" # [6, ] "Q92005" 0.091 -0.00514 "Not Sig" # # [1] "count of the factors:" # [1] "Up: 90" # [1] "Not Sig: 2643" # [1] "Down: 93" bitmap(file = `${@dir}/volcano.png`, size = [3000, 3000]) { # create ggplot layers and tweaks via ggplot style options ggplot(volcano, aes(x = "log2FC", y = "p.value"), padding = "padding:250px 500px 250px 300px;") + geom_point(aes(color = "factor"), color = "black", shape = "circle", size = 25) + scale_colour_manual(values = list( Up = "red", "Not Sig" = "gray", Down = "steelblue" )) + geom_text(aes(label = "ID"), which = ~(factor != "Not Sig") && (p.value >= 15) ) + geom_hline(yintercept = -log10(0.05), color = "red", line.width = 5, linetype = "dash") + geom_vline(xintercept = log2(foldchange), color = "red", line.width = 5, linetype = "dash") + geom_vline(xintercept = -log2(foldchange), color = "red", line.width = 5, linetype = "dash") + labs(x = "log2(FoldChange)", y = "-log10(P-value)") + ggtitle("Volcano Plot (A vs B)") + scale_x_continuous(labels = "F2") + scale_y_continuous(labels = "F2") ; }
import ggplot x = seq(-5, 5, by = 0.2) y = sin(x) input = data.frame(x = x, y = y) def plotfile(filepath): print("previews of the plot table:") print(input, max.print = 13) plt = ggplot(input, aes(x = "x", y = "y"), padding = "padding: 200px 500px 200px 200px;", width = 2400, height = 1600) plt = plt + geom_line(width = 8, show.legend = TRUE, color = "Jet") bitmap(plt, file = filepath) plotfile(`${@dir}/line_sin_py.png`) # --=== Create Elegant Data Visualisations Using the Grammar of Graphics ===-- # * * # * ggplot is an open-source data visualization * # * package for the statistical programming * # * language R#. * # * * # ----===== R# author: xieguigang <xie.guigang@gcmodeller.org> ======---- # github: https://github.com/rsharp-lang/ggplot # [1] "previews of the plot table:" # x y # --------------------------- # <mode> <double> <double> # [1, ] -5 0.958924 # [2, ] -4.8 0.996165 # [3, ] -4.6 0.993691 # [4, ] -4.4 0.951602 # [5, ] -4.2 0.871576 # [6, ] -4 0.756802 # [7, ] -3.8 0.611858 # [8, ] -3.6 0.44252 # [9, ] -3.4 0.255541 # [10, ] -3.2 0.0583741 # [11, ] -3 -0.14112 # [12, ] -2.8 -0.334988 # [13, ] -2.6 -0.515501 # [ reached 'max' / getOption("max.print") -- omitted 37 rows ]
ggplot(groups, aes(x = "tags", y = "data", color = "colors"), padding = "padding: 250px 100px 250px 300px;") + geom_violin(width = 1) + geom_jitter(width = 0.3) + ggtitle(name) + ylab("Intensity") + xlab("") + scale_y_continuous(labels = "G2") + theme(axis.text.x = element_text(angle = 45)) ;
ggplot(groups, aes(x = "tags", y = "data", color = "colors"), padding = "padding: 250px 100px 250px 300px;") + geom_boxplot(width = 0.8) + geom_jitter(width = 0.3) + ggtitle(name) + ylab("Intensity") + xlab("") + scale_y_continuous(labels = "G2") + theme(axis.text.x = element_text(angle = 45)) ;
ggplot(groups, aes(x = "tags", y = "data", color = "colors"), padding = "padding: 250px 100px 250px 300px;") + geom_barplot(width = 0.8) + geom_jitter(width = 0.3) + ggtitle(name) + ylab("Intensity") + xlab("") + scale_y_continuous(labels = "G2") + theme(axis.text.x = element_text(angle = 45)) ;
rendering a 3d chart in ggplot package is just simply enough as create a 2d chart plot. we just needs add a data mapping of the z axis at here!
# create ggplot layers and tweaks via ggplot style options ggplot(data, aes(x = "X", y = "Y", z = "Z"), padding = "padding:250px 500px 100px 100px;") # use scatter points for visual our data + geom_point(aes(color = "class"), color = "paper", shape = "triangle", size = 20) + ggtitle("Scatter UMAP 3D") # use the default white theme from ggplot + theme_default() # use a 3d camera to rotate the charting plot # and adjust view distance + view_camera(angle = [31.5,65,125], fov = 100000) ;
just change the function theme_default to theme_black, then we can get a cool 3d scatter plot in black theme:
ggplot(data, aes(x = "X", y = "Y", z = "Z"), padding = "padding:250px 500px 100px 100px;") + geom_point(aes(color = "class"), color = "paper", shape = "triangle", size = 20) + ggtitle("Scatter UMAP 3D") # use the black theme from ggplot package + theme_black() + view_camera(angle = [31.5,65,125], fov = 100000) ;
- read the online help document.
- blog article about ggplot implements: https://stack.xieguigang.me/2021/implements-ggplot-package-for-rsharp-language/