pipe: Pipe graphics

In plotGrouper: Shiny app GUI wrapper for ggplot with built-in statistical analysis

Description

Like dplyr, ggvis also uses the pipe function, %>% to turn function composition into a series of imperative statements.

Arguments

lhs, rhs

A visualisation and a function to apply to it

Examples

# Instead of
dplyr::mutate(dplyr::filter(iris, Species == "versicolor"),
"Sample" = paste0(Species, dplyr::row_number()))
# You can write
dplyr::filter(iris, Species == "versicolor") %>%
dplyr::mutate("Sample" = paste0(Species, "_", dplyr::row_number()))

plotGrouper documentation built on Nov. 8, 2020, 5:25 p.m.