【ggplot2】不同方法画half -小提琴图

参考：

效果图：

每一个分组画出一半小提琴图, 放一起。
列举了三种方法：

geom_ploygon : 画出密度多边形（难度：**）
gghalves 画出half 效果（难度：*）
geom图层语法 (难度：***)

image.png

实践

模拟数据

set.seed(20160229)

my_data = data.frame(
  y=c(rnorm(1000), rnorm(1000, 0.5), rnorm(1000, 1), rnorm(1000, 1.5)),
  x=c(rep('a', 2000), rep('b', 2000)),
  m=c(rep('i', 1000), rep('j', 2000), rep('i', 1000))
)

方法1 geom_ploygon

library(dplyr)

pdat <- my_data %>%
  group_by(x, m) %>% summarise(loc=density(.$y)$x,
                               dens=density(.$y)$y)

pdat$dens <- ifelse(pdat$m == 'i', pdat$dens * -1, pdat$dens)
pdat$dens <- ifelse(pdat$x == 'b', pdat$dens + 1, pdat$dens)


ggplot(pdat, aes(dens, loc, fill = m, group = interaction(m, x))) + 
  geom_polygon() +
  scale_x_continuous(breaks = 0:1, labels = c('a', 'b')) +
  ylab('density') +
  theme_minimal() +
  theme(axis.title.x = element_blank())

方法2 gghalves

library(gghalves)
ggplot(iris, aes(x = Species, y = Sepal.Width)) + 
  geom_half_point()+
  geom_point()



head(my_data)
ggplot()+
  geom_half_violin(data=my_data %>% filter(x=="a" & m=="i"),
                   aes(x=x,y=y,fill=m) ,side = "l")+
  geom_half_violin(data=my_data %>% filter(x=="a" & m=="j"),
                   aes(x=x,y=y,fill=m) ,side = "r")+
  geom_half_violin(data=my_data %>% filter(x=="b" & m=="i"),
                   aes(x=x,y=y,fill=m) ,side = "l")+
  geom_half_violin(data=my_data %>% filter(x=="b" & m=="j"),
                   aes(x=x,y=y,fill=m) ,side = "r")+
  theme_classic()

方法3 geom

GeomSplitViolin <- ggproto("GeomSplitViolin", GeomViolin, 
                           draw_group = function(self, data, ..., draw_quantiles = NULL) {
                             data <- transform(data, xminv = x - violinwidth * (x - xmin), xmaxv = x + violinwidth * (xmax - x))
                             grp <- data[1, "group"]
                             newdata <- plyr::arrange(transform(data, x = if (grp %% 2 == 1) xminv else xmaxv), if (grp %% 2 == 1) y else -y)
                             newdata <- rbind(newdata[1, ], newdata, newdata[nrow(newdata), ], newdata[1, ])
                             newdata[c(1, nrow(newdata) - 1, nrow(newdata)), "x"] <- round(newdata[1, "x"])
                             
                             if (length(draw_quantiles) > 0 & !scales::zero_range(range(data$y))) {
                               stopifnot(all(draw_quantiles >= 0), all(draw_quantiles <=
                                                                         1))
                               quantiles <- ggplot2:::create_quantile_segment_frame(data, draw_quantiles)
                               aesthetics <- data[rep(1, nrow(quantiles)), setdiff(names(data), c("x", "y")), drop = FALSE]
                               aesthetics$alpha <- rep(1, nrow(quantiles))
                               both <- cbind(quantiles, aesthetics)
                               quantile_grob <- GeomPath$draw_panel(both, ...)
                               ggplot2:::ggname("geom_split_violin", grid::grobTree(GeomPolygon$draw_panel(newdata, ...), quantile_grob))
                             }
                             else {
                               ggplot2:::ggname("geom_split_violin", GeomPolygon$draw_panel(newdata, ...))
                             }
                           })

geom_split_violin <- function(mapping = NULL, data = NULL, stat = "ydensity", position = "identity", ..., 
                              draw_quantiles = NULL, trim = TRUE, scale = "area", na.rm = FALSE, 
                              show.legend = NA, inherit.aes = TRUE) {
  layer(data = data, mapping = mapping, stat = stat, geom = GeomSplitViolin, 
        position = position, show.legend = show.legend, inherit.aes = inherit.aes, 
        params = list(trim = trim, scale = scale, draw_quantiles = draw_quantiles, na.rm = na.rm, ...))
}


ggplot(my_data, aes(x, y, fill = m)) + geom_split_violin()

Tips

gl函数：因子生成函数
interaction 函数：因子组合函数，有点类似expand.grid 函数功能。

# a <- gl(2, 4, 8)   
# b <- gl(2, 2, 8, labels = c("ctrl", "treat"))
# s <- gl(2, 1, 8, labels = c("M", "F"))
# interaction(a, b)   因子组合函数

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