scale_fill_distiller(palette = "RdBu",direction = 1) ggsave("3_vennplot.jpg",width = 5, height = 4) ##'@2维 ggVennDiagram(x[1:2],label = "count", label_alpha = 0)+ scale_fill_distiller(palette = "Set3",direction = 1) ggsave("2_vennplot.jpg",width = 5, height = 4) 更...
<- data.frame(x = 1:10, y = 1:10, z = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)) # 绘制散点图,并使用RColorBrewer包的"Spectral"配色方案来设置点的颜色 ggplot(data, aes(x = x, y = y, color = z)) + geom_point(size = 5) + scale_color_distiller(palette = "Spectral"...
p5 <- erupt + scale_fill_distiller(palette = "RdPu") p6 <- erupt + scale_fill_distiller(palette = "YlOrBr") #scico颜色盘 library(scico) p7 <- erupt + scico::scale_fill_scico(palette = "bilbao") # the default p8 <- erupt + scico::scale_fill_scico(palette = "vik") p9 ...
2-7维恩图的绘制 ##'@7维ggVennDiagram(x[1:7],label="count",label_alpha=0)+scale_fill_distiller(palette="Accent",direction=1)ggsave("7_vennplot.jpg",width=5,height=4)##'@6维ggVennDiagram(x[1:6],label="count",label_alpha=0)+scale_fill_distiller(palette="RdBu",direction=1)ggsave(...
scale_fill_distiller(palette = "RdYlBu", limits = c(-1,1), guide = FALSE) + xlab("") + ylab("Difference from 1900-2000 (ºC)") + theme_minimal(base_size = 16, base_family = "Georgia") 结果应该是: 该文件warming.csv包含字段year和value,后者是全球年平均气温,相比于1900-2000的平均...
scale_y_continuous(limits = c(-0.5,1)) + scale_fill_distiller(palette = "RdYlBu", limits = c(-1,1), guide = FALSE) + xlab("") + ylab("Difference from 1900-2000 (ºC)") + theme_minimal(base_size = 16, base_family = "Georgia") ...
ggplot2有着自己独特的图形语法,这套语法归纳起来包括:数据(data)、转换(transformation)、度量(scale)、坐标系(coordinate)、元素(element)、指引(guide)、显示(display)等一系列独立的步骤,我们就是通过将这些步骤组合搭配,才实现个性化的统计绘图。相较于其他的绘图包,ggplot2的精髓在于“+”的引入,理论上他能够在...
scale_fill_distiller(palette ="Set2",direction=1) + expand_limits(x = world$long, y = world$lat) + xlab("经度") + ylab("纬度") 这里做的并不像之前的图那么优雅,因为这个图的数据复杂一些。map_data("world")这个数据对于中国边界的经纬度数据是由一些问题的,如果要出版的话要尤为注意: ...
scale_fill_distiller(palette = "Spectral", trans = "log10", breaks = breaks_vec, labels = labels_vec)我还想将图例标题
scale_y_continuous(expand = c(0, 0)) + theme( legend.position='none' ) 自定义色彩风格: 使用scale_fill_distiller()函数 1 2 3 4 5 6 7 8 9 # Call the palette with a number ggplot(data, aes(x=x, y=y) ) + stat_density_2d(aes(fill = ..density..), geom = "raster", conto...