Moreover, before we start using ggplot, we will set some global configurations that we wish to apply to all our plots. The first configuration is the plot size and resolution. The second is the theme. library(ggplot2)options(repr.plot.width = 10, repr.plot.height = 6, repr.plot.res ...
未指定颜色的时候ggplot2会使用其默认配色方案，我们可以通过scale_color_manual()手动指定配色，另外可以...
with green edges showing positive weights and red edges showing negative weights. The color saturation and the width of the edges are proportional to the absolute weight and scale relative to the strongest weight in the graph. OS—occupational status; MoCA—Montreal Cognitive Assessment; GDS—Geriatr...
例子:现在我们来看看降序图 library(ggplot2)# Create the data frame.gfg.data<-data.frame(GFG_Id=c(1:7),GFG_Name=c("Damon","Joe","Jen","Ryan","Bonnie","Stefan","William"),GFG_Sal=c(6200,5152,6110,7290,8485,7654,2341))print(gfg.data)# GGPLOTx<-ggplot(gfg.data,aes(x=reorder(G...
Instead of thefigsizeargument, we can alsosetthe height and width of a figure. These can be done either via theset()function with thefigheightandfigwidthargument, or via theset_figheight()andset_figwidth()functions. The former allows you to write one line for multiple arguments while the ...
The red dotted line represents the trend in ∆SOS, fitted using the geom_smooth function from the R package ggplot2. Extended Data Fig. 4 Effect of late spring frost (LSF) on annual gross primary productivity (GPPnext) and net primary productivity in the next year (NPPnext) across ...
Moreover, some analyses were performed using R v4.2.286 with helper packages: tidyr 1.3.087, tidyverse 1.3.288, tibble 3.1.889, data.table 1.14.690, dplyr 1.1.091, gridExtra 2.392 and ggplot2 3.4.193. Data sources and cleaning Tree species occurrence data were downloaded from 13 ...
# Generate grouped histogramsp4<-ggplot(subset(dist_fields,!is.na(dist_nearest)),aes(x=dist_nearest))+theme_bw()+xlab("Grouped Hamming distance")+ylab("Count")+geom_histogram(color="white",binwidth=0.02)+geom_vline(xintercept=0.12,color="firebrick",linetype=2)+facet_grid(sample_id~.,...
b <- ggplot(mydata, aes(x = forcats::fct_reorder(CountryCount, !!RCx_P5rc, .fun = mean, .desc = TRUE), y = !!RCx_P5rc)) + geom_boxplot(aes(fill = `Median risk (2020)`, weight = Weight), varwidth = TRUE, outlier.size=0.5) + scale_fill_gradient2(midpoint = 3, low =...
result = ggplot(input, aes(x=log2fc, y=name, fill = phylum)) + geom_point(pch = 21, size = 4, color = "black") + labs(x="log2(Fold Change)", y="Taxa", fill="Phylum") + scale_y_discrete(limits = factor(input$name)) + ...