# grid stats4 stats graphics grDevices utils datasets # LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C # LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
# LAPACK: /home/biocbuild/bbs-3.16-bioc/R/lib/libRlapack.so # BLAS: /home/biocbuild/bbs-3.16-bioc/R/lib/libRblas.so Library( "pasilla") pasCts 2, 8, 1)), labSize = 6.0, shape = c( 6, 6, 19, 16), title = "DESeq2 results", subtitle = "Differential expression", caption = bquote( ~Log ~ "fold change cutoff, 2 p-value cutoff, 10e-4"), legendPosition = "right", legendLabSize = 14, col = c( "gre圓0", "forestgreen", "royalblue", "red2"), colAlpha = 0.9, drawConnectors = TRUE, hline = c( 10e-8), widthConnectors = 0.5) p1 Our client roster includes Fortune 500 and NYSE listed companies in the USA and India.P1 2.5, 'gold', 'black')) lour <- 'black' names(lour) <- 'high' names(lour) <- 'mid' names(lour) <- 'low' p2 <- EnhancedVolcano(res, lab = rownames(res), x = 'log2FoldChange', y = 'pvalue', selectLab = rownames(res), xlab = bquote( ~Log ~ 'fold change'), title = 'Custom shape & colour over-ride', pCutoff = 10e-14, FCcutoff = 1.0, pointSize = 5.5, labSize = 0.0, shapeCustom = keyvals.shape, colCustom = lour, colAlpha = 1, legendPosition = 'right', legendLabSize = 15, legendIconSize = 5.0, drawConnectors = TRUE, widthConnectors = 0.5, colConnectors = 'grey50', gridlines.major = TRUE, gridlines.minor = FALSE, border = 'full', borderWidth = 1.0, borderColour = 'black') library(gridExtra) library(grid) grid.arrange(p1, p2, ncol= 2, top = textGrob( 'EnhancedVolcano', just = c( 'center'), gp = gpar( fontsize = 32))) Perceptive Analytics provides data analytics, data visualization, business intelligence and reporting services to e-commerce, retail, healthcare and pharmaceutical industries. Chaitanya Sagar, Vishnu Reddy and Saneesh Veetil contributed to this article. Don’t you?īio: This article was contributed by Perceptive Analytics. I have used the map of India as the base geographical region the same process can be applied to any geographical base and data.Īfter going to the article, I am sure you will agree to my point with which I started the article – choropleth maps are the best bets when we want to leave a strong impression on the audience in 15 seconds. The above examples show the flexibility and the convenience that choropleth maps provide us in presenting a measurement on geographical base.
Grid.arrange(plot1, plot2, plot3, plot4, plot5) Labs(title="Sex Ratio (per '000 males) in India")Ĭalling the library ‘gridExtra’ and using the function grid.arrange() to present all the 5 graphs at once. Scale_fill_distiller(name="Sex Ratio", palette = "Set3")+ Labs(title="Population Density in India")Īes(x = long, y = lat, group = group, fill = sex_ratio), Scale_fill_distiller(name="Population Density", palette = "Set3")+ Labs(title="Area (in '000 sq km) in India")Īes(x = long, y = lat, group = group, fill = pop_density), Scale_fill_distiller(name="Area (in '000 Sq Km)", palette = "Set3")+ Labs(title="Decadal growth (in %) in India")Īes(x = long, y = lat, group = group, fill = area/1000), Scale_fill_distiller(name="Decadal Growth (in %)", palette = "Set3")+ Scale_fill_distiller(name="Population (in '000)", palette = "Set3")+Īes(x = long, y = lat, group = group, fill = growth*100), Aes(x = long, y = lat, group = group, fill = pop/1000),
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