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 <[email protected]> ======----
# 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/