stackVlnPlot: Get stack violin plot for a normal matrix
In Tong-Chen/YSX: For Yishengxin Training

Description Usage Arguments Value Examples

Get stack violin plot for a normal matrix

1	stackVlnPlot(data, x, y, facets, fill = NULL)

`data`	At least three columns needed. Gene Expr Cluster Sox2 2 1 Sox2 1.5 1 Sox2 1.2 1 Sox2 1.2 1 Sox2 20 2 Sox2 21 2 Sox2 22 2 Sox2 23 2 Sox2 0.4 3 Sox2 0.2 3 Sox3 2 1 Sox3 2 2 Sox3 2 3
`x`	List of name-value pairs in the form `aesthetic = variable` describing which variables in the layer data should be mapped to which aesthetics used by the paired geom/stat. The expression `variable` is evaluated within the layer data, so there is no need to refer to the original dataset (i.e., use `ggplot(df, aes(variable))` instead of `ggplot(df, aes(df$variable))`). The names for x and y aesthetics are typically omitted because they are so common; all other aesthetics must be named.
`y`	List of name-value pairs in the form `aesthetic = variable` describing which variables in the layer data should be mapped to which aesthetics used by the paired geom/stat. The expression `variable` is evaluated within the layer data, so there is no need to refer to the original dataset (i.e., use `ggplot(df, aes(variable))` instead of `ggplot(df, aes(df$variable))`). The names for x and y aesthetics are typically omitted because they are so common; all other aesthetics must be named.
`facets`	A set of variables or expressions quoted by `vars()` and defining faceting groups on the rows or columns dimension. The variables can be named (the names are passed to `labeller`). For compatibility with the classic interface, can also be a formula or character vector. Use either a one sided formula, `~a + b`, or a character vector, `c("a", "b")`.

a ggplot2 object

random_v <- c(rnorm(10, mean=1, sd=0.1), rnorm(10, mean=5), rnorm(20, mean=10),
              rnorm(10, mean=10), rnorm(10, mean=0.2, sd=0.01), rnorm(20, mean=1))
data <- data.frame(Gene=c(paste0('SOX', rep(2,40)), paste0('SOX', rep(3,40))),
                   Expr=random_v, Cluster=rep(c(rep(1,10), rep(2,10),rep(3,20)),2))
stackVlnPlot(data, x="Cluster", y="Expr", facets="Gene")