使用guides()命令或者使用个性化的guides参数,像guide_colourbar() 或者 guide_legend(). guide_colourbar guide_colorbar (原文链接:http://ggplot2.tidyverse.org/reference/guide_colourbar.html) Colour bar guide shows continuous color scales mapped onto values. Colour bar is available with scale_fill and...
没有特别系统的学习 tidy evaluation 这方面的高级操作，最近有空准备补一补，学习下这方面的知识。
library(ggplot2) library(reshape2) df <- melt(outer(1:4, 1:4), varnames = c("X1", "X2")) p1 <- ggplot(df, aes(X1, X2)) + geom_tile(aes(fill = value)) p2 <- p1 + geom_point(aes(size = value)) p1 + scale_fill_continuous(guide = "colorbar") # 默认形式 p1 + g...
The colorbar guide (guide_colourbar) converts units into cm and then uses them as mm. Reproducible example: library(ggplot2) p1 <- ggplot(iris, aes(Sepal.Width, Sepal.Length, color = Petal.Width)) + geom_point() + scale_color_distiller(name = "gap width = 1cm") + theme(legend.sp...
guide_area()函数:将图例放在对应位置 示例 1)直接组合图表: library(ggplot2)p1<-ggplot(mtcars)+geom_point(aes(mpg,disp,colour=factor(gear)))p2<-ggplot(mtcars)+geom_boxplot(aes(gear,disp,group=gear))p3<-ggplot(mtcars)+geom_bar(aes(gear))+facet_wrap(~cyl)#Guidesarebydefaultkeptbeesidetheir...
Once you've figured out how to create the standard scatter plots, bar charts, and line graphs in ggplot, the next step to really elevate your graphs is to master working with color. Strategic use of color can really help your graphs to stand out and make
我可以提供以下适合特定情况的解决方案：
split_senate |> filter(splits == 2) |> group_by(year, congress) |> summarize(n = n()) |> ggplot() + geom_bar(aes(x = year, y = n), color = 'white', fill = 'steelblue', stat = 'identity') + theme_minimal() + theme(axis.text.x = element_text(angle = 90, hjust =...
The model was implemented in the R package pomp114, operating in R version 4.4.1115. All figures were created with the R package ggplot116, and the data for the maps was obtained from Natural Earth (https://www.naturalearthdata.com). Other packages used for specific vignettes are cited bel...
The coefficient of determination R2, which is numerically equal to the square of Pearson correlation coefficient, was obtained using lm() function of R26 and plotting was done using ggplot2 library of R. The R2 for all the 51 pools have been reported in Supplementary Table S2. AF calculation...