...用来分组的变量(因子) index.cond 列表,设定面板的展示顺序 key(或auto.key) 函数,添加分组变量的图例符号 layout 两元素数值型向量,设定面板的摆放方式(行数和列数);如有需要...Split/position 数值型向量,在一页上绘制多幅图形 Type 字符型向量,设定一个或多个散点图...
使用split.plot = T生成每个分组的单独小提琴图 代码语言:javascript 代码运行次数:0 运行 AI代码解释 p<-VlnPlot(sce.all.int, features = g2,stack=T, pt.size=0,flip = T,add.noise = T, split.by = 'stim', group.by = "celltype", cols = c("#78C2C4","#C73E3A"), split.plot = T...
注意如果想要每种cluster/celltype是一种颜色的话使用split.by参数。 my36colors <-c('#E5D2DD', '#53A85F', '#F1BB72', '#F3B1A0', '#D6E7A3', '#57C3F3', '#476D87', '#E95C59', '#E59CC4', '#AB3282', '#23452F', '#BD956A', '#8C549C', '#585658', '#9FA3A8', '...
group=sex)) + geom_line(aes(linetype=sex, color=sex, size=sex))+ geom_point()+ scale_linetype_manual(values=c("twodash", "dotted"))+ scale_color_manual(values=c('#999999','#E69F00'))+ scale_size_manual(values=c(1, 1.5))+ theme(legend.position="top") ...
"pvalue","qvalue")] dt$Description <- sapply(strsplit(dt$Description," - "),"[",1) ...
ggplot(df, aes(x=dose, y=len, group=supp)) + geom_line(aes(linetype=supp))+ geom_point() 修改线型、点的形状以及颜色 ggplot(df, aes(x=dose, y=len, group=supp)) + geom_line(aes(linetype=supp, color = supp))+ geom_point(aes(shape=supp, color = supp)) ...
(data) # 显示数据的长度 # 删除缺失值 data_clean <- na.omit(data) # 合并数据 data_merged <- merge(data1, data2, by = "id") # 分割数据 data_split <- split(data, data$group) # 提取数据 data_sub <- subset(data, price > 100) # 转换数据类型 data$price <- as.numeric(data$...
so.Plot(fmri, x="timepoint", y="signal", color="event").add(so.Line(), so.Agg(), group="subject") 自定义Mark和Stat对象 class PeakAnnotation(so.Mark): def _plot_split(self, keys, data, ax, kws): ix = data["y"].idxmax() ax.annotate( "The peak", data.loc[ix, ["x", ...
Facet with one discrete variable. Split by the levels of the group “supp” # Split in vertical directionp + facet_grid(rows = vars(supp))# Split in horizontal directionp + facet_grid(cols = vars(supp)) Facet with multiple variables. Split by the levels of two grouping variables: “dos...
ames_plot <- testing(split) model1 <- lm(Sale_Price ~ Central_Air, data = ames_plot) p1 <- model1 %>% broom::augment() %>% arrange(Central_Air) %>% mutate(House = row_number()) %>% ggplot(aes(House, Sale_Price, color = Central_Air)) + ...