R+ geom_texthline(yintercept = 50, label = "R Language and Medical Student", hjust = 0.1, linewidth=1.5, color = "#FF4040") + geom_textvline(xintercept = 7, label = "R Language and Medical Student", hjust = 0.9, linetype = 2, linewidth=1.5, vjust = 1.3, color = "#00868B...
hjust = 0.8, color = "red4") + geom_textvline(xintercept = 6, label = "vline", hjust = 0.8, color = "blue4", linetype = 2, vjust = 1.3) + geom_textabline(slope = 15, intercept = -100, label = "abline", color = "green4", hjust = 0.6, vjust = -0.2)+ theme_bw()...
geom_density函数的变量设置 G g plot():底层绘图函数。DATA 为数据集,主要是数据框格式的数据集;MAPPING 变量的视觉通道映射,用来表示变量 x 和 y,还可以用来控制颜色(color),大小(size)或形状(shape)等视觉通道;STAT 表示统计变换,与 stat_xxx()相对应,默认为“identity”(无数据变换);POSITION 表示绘图数据...
qplot(carat , data = diamonds , geom ="density") ##密度图 qplot(carat , data = diamonds , geom ="density" , color = color) ##密度图,设置颜色 ##对于密度曲线而言,adjust参数控制了曲线的平滑程度(adjust取值越大,曲线越平滑) qplot(carat , data = diamonds , geom ="density" , adjust = ...
ggplot(aes(x='x'), data=diamonds)+geom_density(color='red') 4.其他 geom_boxplot()箱型图 geom_violin()小提琴图 但是目前我对这两种图理解不深,以后再补充。 5.图形叠加 ggplot(aes(x='wt', y='mpg', color='factor(cyl)'), data=mtcars)+geom_point(color='blue', alpha=0.5)+geom_lin...
geom_density_line( mapping = NULL, data = NULL, stat = "density", position = "identity", ..., na.rm = FALSE, show.legend = NA, inherit.aes = TRUE) 参数说明: mapping : aes()files()创建的美学映射集。如有规定,以及继承.aes=TRUE(默认值),它与绘图顶层的默认映射相结合。如果没有绘图...
除此之外,你还可以使用color和fill参数来设置密度曲线和填充区域的颜色,linetype参数来设置密度曲线的线型,size参数来设置密度曲线的粗细,以及其他一些参数来调整标签、图例等元素的外观。 总之,geom_density函数的参数可以帮助你定制出符合你需求的密度图,通过调整这些参数,你可以改变密度图的外观和行为,使其更好地展示...
color: "#457FFB" } } } } ] (1)设置折线线条颜色...lineStyle: { color: "#F29C1B", } (2)设置折线折点颜色 lineStyl...
geom_density(aes(x = var2, y = -..density..), fill="#404080") + geom_label(aes(x=4.5, y=-0.25, label="variable2"), color="#404080") + xlab("value of x")+ theme_classic() Base plot 1 2 3 4 5 6 7 8 9 10
(x)/2))) df$V1 <- as.numeric(df$V1) df <- df %>% group_by(V2, V3) %>% summarise(mumean = mean(V1)) %>% right_join(df) df %>% ggplot(aes(x= V1, color = V2)) + geom_density(aes(fill = V2)) + facet_grid(V3 ~ V2) + theme_bw() + geom_vline(data = df...