These geometries are based on geom_abline(), geom_hline() and geom_vline().

geom_abline_interactive(...)

geom_hline_interactive(...)

geom_vline_interactive(...)

Arguments

...

arguments passed to base function, plus any of the interactive_parameters().

Details for geom_*_interactive functions

The interactive parameters can be supplied with two ways:

  • As aesthetics with the mapping argument (via aes()). In this way they can be mapped to data columns and apply to a set of geometries.

  • As plain arguments into the geom_*_interactive function. In this way they can be set to a scalar value.

See also

Examples

# add diagonal interactive reference lines to a ggplot ------- library(ggplot2) library(ggiraph) p <- ggplot(mtcars, aes(wt, mpg)) + geom_point() g <- p + geom_abline_interactive(intercept = 20, tooltip = 20) x <- girafe(ggobj = g) if (interactive()) print(x) l <- coef(lm(mpg ~ wt, data = mtcars)) g <- p + geom_abline_interactive( intercept = l[[1]], slope = l[[2]], tooltip = paste("intercept:", l[[1]], "\nslope:", l[[2]]), data_id="abline" ) x <- girafe(ggobj = g) x <- girafe_options(x = x, opts_hover(css = "cursor:pointer;fill:orange;stroke:orange;")) if (interactive()) print(x) # add horizontal interactive reference lines to a ggplot ------- library(ggplot2) library(ggiraph) if( requireNamespace("dplyr", quietly = TRUE)){ g1 <- ggplot(economics, aes(x = date, y = unemploy)) + geom_point() + geom_line() gg_hline1 <- g1 + geom_hline_interactive( aes(yintercept = mean(unemploy), tooltip = round(mean(unemploy), 2)), size = 3) x <- girafe(ggobj = gg_hline1) if( interactive() ) print(x) } dataset <- data.frame( x = c(1, 2, 5, 6, 8), y = c(3, 6, 2, 8, 7), vx = c(1, 1.5, 0.8, 0.5, 1.3), vy = c(0.2, 1.3, 1.7, 0.8, 1.4), year = c(2014, 2015, 2016, 2017, 2018) ) dataset$clickjs <- rep(paste0("alert(\"", mean(dataset$y), "\")"), 5) g2 <- ggplot(dataset, aes(x = year, y = y)) + geom_point() + geom_line() gg_hline2 <- g2 + geom_hline_interactive( aes(yintercept = mean(y), tooltip = round(mean(y), 2), data_id = y, onclick = clickjs)) x <- girafe(ggobj = gg_hline2) if( interactive() ) print(x) # add vertical interactive reference lines to a ggplot ------- library(ggplot2) library(ggiraph) if (requireNamespace("dplyr", quietly = TRUE)) { g1 <- ggplot(diamonds, aes(carat)) + geom_histogram() gg_vline1 <- g1 + geom_vline_interactive( aes(xintercept = mean(carat), tooltip = round(mean(carat), 2), data_id = carat), size = 3) x <- girafe(ggobj = gg_vline1) if( interactive() ) print(x) }
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
dataset <- data.frame(x = rnorm(100)) dataset$clickjs <- rep(paste0("alert(\"", round(mean(dataset$x), 2), "\")"), 100) g2 <- ggplot(dataset, aes(x)) + geom_density(fill = "#000000", alpha = 0.7) gg_vline2 <- g2 + geom_vline_interactive( aes(xintercept = mean(x), tooltip = round(mean(x), 2), data_id = x, onclick = clickjs), color = "white") x <- girafe(ggobj = gg_vline2) x <- girafe_options(x = x, opts_hover(css = "cursor:pointer;fill:orange;stroke:orange;") ) if( interactive() ) print(x)