These geometries are based on geom_rect() and geom_tile(). See the documentation for those functions for more details.

geom_rect_interactive(...)

geom_tile_interactive(...)

Arguments

...

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

Note

Converting a raster to svg elements could inflate dramatically the size of the svg and make it unreadable in a browser. Function geom_tile_interactive should be used with caution, total number of rectangles should be small.

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 interactive polygons to a ggplot ------- library(ggplot2) library(ggiraph) dataset = data.frame( x1 = c(1, 3, 1, 5, 4), x2 = c(2, 4, 3, 6, 6), y1 = c( 1, 1, 4, 1, 3), y2 = c( 2, 2, 5, 3, 5), t = c( 'a', 'a', 'a', 'b', 'b'), r = c( 1, 2, 3, 4, 5), tooltip = c("ID 1", "ID 2", "ID 3", "ID 4", "ID 5"), uid = c("ID 1", "ID 2", "ID 3", "ID 4", "ID 5"), oc = rep("alert(this.getAttribute(\"data-id\"))", 5) ) gg_rect = ggplot() + scale_x_continuous(name="x") + scale_y_continuous(name="y") + geom_rect_interactive(data=dataset, mapping = aes(xmin = x1, xmax = x2, ymin = y1, ymax = y2, fill = t, tooltip = tooltip, onclick = oc, data_id = uid ), color="black", alpha=0.5) + geom_text(data=dataset, aes(x = x1 + ( x2 - x1 ) / 2, y = y1 + ( y2 - y1 ) / 2, label = r ), size = 4 ) x <- girafe(ggobj = gg_rect) if( interactive() ) print(x) # add interactive tiles to a ggplot ------- library(ggplot2) library(ggiraph) df <- data.frame( id = rep(c("a", "b", "c", "d", "e"), 2), x = rep(c(2, 5, 7, 9, 12), 2), y = rep(c(1, 2), each = 5), z = factor(rep(1:5, each = 2)), w = rep(diff(c(0, 4, 6, 8, 10, 14)), 2) ) p <- ggplot(df, aes(x, y, tooltip = id)) + geom_tile_interactive(aes(fill = z)) x <- girafe(ggobj = p) if( interactive() ) print(x) # correlation dataset ---- cor_mat <- cor(mtcars) diag( cor_mat ) <- NA var1 <- rep( row.names(cor_mat), ncol(cor_mat) ) var2 <- rep( colnames(cor_mat), each = nrow(cor_mat) ) cor <- as.numeric(cor_mat) cor_mat <- data.frame( var1 = var1, var2 = var2, cor = cor, stringsAsFactors = FALSE ) cor_mat[["tooltip"]] <- sprintf("<i>`%s`</i> vs <i>`%s`</i>:</br><code>%.03f</code>", var1, var2, cor) p <- ggplot(data = cor_mat, aes(x = var1, y = var2) ) + geom_tile_interactive(aes(fill = cor, tooltip = tooltip), colour = "white") + scale_fill_gradient2_interactive(low = "#BC120A", mid = "white", high = "#BC120A", limits = c(-1, 1), data_id = "cormat", tooltip = "cormat") + coord_equal() x <- girafe(ggobj = p) if( interactive() ) print(x)