In gvarunkumar/plotly: Create Interactive Web Graphics via 'plotly.js'

knitr::opts_chunk$set(
  message = FALSE, 
  warning = FALSE,
  comment = "#>",
  fig.width = 7
)
# someday we may want to do pngs to reduce size...
# printly <- function(p, options, ...) { 
#   p <- layout(p, height = options$fig.height, width = options$fig.width)
#   u <- plotly::plotly_POST(p, filename = Sys.time())$url
#   sprintf(
#     "<a href='%s'> <img src='%s.png' style='%s' /> </a>", 
#     u, u, "max-width:100%"
#   )
# }
# assignInNamespace("knit_print.plotly_hash", printly, asNamespace("plotly"))
# assignInNamespace("knit_print.plotly_built", printly, asNamespace("plotly"))

Introduction

The subplot() function provides a flexible interface for arranging multiple plotly plots in a single view. The simplest way to use it is to pass plotly visualizations directly to subplot().

library(plotly)
p1 <- plot_ly(economics, x = ~date, y = ~unemploy, name = "unemploy")
p2 <- plot_ly(economics, x = ~date, y = ~uempmed, name = "uempmed")
subplot(p1, p2)

Although subplot() accepts an arbitrary number of plot objects, passing a list of plots can save typing and redundant code when dealing with a large number of plots. To demonstrate, let's create one time series for each variable in the economics dataset and share the x-axis so that zoom/pan events are synchronized across each series:

vars <- setdiff(names(economics), "date")
plots <- lapply(vars, function(var) {
  plot_ly(x = economics$date, y = economics[[var]], name = var)
})
subplot(plots, nrows = length(plots), shareX = TRUE, titleX = FALSE)

# this works too, but I'm not sure we should advertise...
elong <- tidyr::gather(economics, variable, value, -date)
elong$id <- as.integer(factor(elong$variable))
p <- plot_ly(elong, x = ~date, y = ~value, color = ~variable, yaxis = ~paste0("y", id))
subplot(p, nrows = 5, shareX = TRUE)

Conceptually, subplot() provides a way to place a collection of plots into a table with a given number of rows and columns. The number of rows (and, by consequence, the number of columns) is specified via the nrows argument. By default each row/column shares an equal proportion of the overall height/width, but as shown in the diagram below, that default can be changed via the heights and widths arguments.

This flexibility is quite useful for a number of visualizations, for example, a joint density plot (the new heatmaply package is another good example).

x <- rnorm(100)
y <- rnorm(100)
m <- list(color = "black")
s <- subplot(
  plot_ly(x = x, type = "histogram", marker = m), 
  plotly_empty(), 
  plot_ly(x = x, y = y, mode = "markers",  marker = m), 
  plot_ly(y = y, type = "histogram", marker = m),
  nrows = 2, heights = c(0.2, 0.8), widths = c(0.8, 0.2), 
  shareX = TRUE, shareY = TRUE, titleX = FALSE, titleY = FALSE
)
layout(s, showlegend = FALSE)

Note that, since subplot() returns a plotly object, any layout attribute can be modified downstream via layout().

Recursive subplots

The subplot() function is designed to work recursively so that you can have subplots of subplots. This idea is useful when your desired layout doesn't conform to the table structure described in the previous section. In fact, you can think of a subplot of subplots like a spreadsheet with merged cells.

plotList <- function(nplots) {
  # TODO: use new images infrastructure to overlay an R image on each plot
  lapply(seq_len(nplots), function(x) plot_ly())
}
s1 <- subplot(plotList(6), nrows = 2, shareX = TRUE, shareY = TRUE)
s2 <- subplot(plotList(2), shareY = TRUE)
subplot(s1, s2, plot_ly(), nrows = 3, margin = 0.04, heights = c(0.6, 0.3, 0.1))

The concept is particularly useful when you want plot(s) in a given row to have different widths from plot(s) in another row.

# specify some map projection/options
g <- list(
  scope = 'usa',
  projection = list(type = 'albers usa'),
  lakecolor = toRGB('white')
)
# create a map of population density
density <- state.x77[, "Population"] / state.x77[, "Area"]
map <- plot_ly(
  z = ~density, 
  text = state.name, locations = state.abb,
  type = 'choropleth', locationmode = 'USA-states', geo = "geo"
) %>% layout(geo = g)
# create a bunch of horizontal bar charts 
vars <- colnames(state.x77)
barcharts <- lapply(vars, function(var) {
  plot_ly(x = state.x77[, var], y = state.name, type = "bar", 
          orientation = "h", name = var) %>%
    layout(showlegend = FALSE, hovermode = "y",
           yaxis = list(showticklabels = FALSE))
})
subplot(
  subplot(barcharts, margin = 0.01), map, 
  nrows = 2, heights = c(0.3, 0.7)
)

ggplot2 subplots

The subplot() function also understands ggplot2 objects, and converts them to an interactive web-based version via ggplotly() before arranging them in the final layout.

e <- tidyr::gather(economics, variable, value, -date)
gg1 <- ggplot(e, aes(date, value)) + geom_line() +
  facet_wrap(~variable, scales = "free_y", ncol = 1)
gg2 <- ggplot(e, aes(factor(1), value)) + geom_violin() +
  facet_wrap(~variable, scales = "free_y", ncol = 1) + 
  theme(axis.text = element_blank(), axis.ticks = element_blank())
subplot(gg1, gg2) %>% layout(margin = list(l = 50))

This infrastructure allows ggplotly() to understand ggmatrix objects -- the class of object returned by the ggpairs() function in the GGally package.

pm <- GGally::ggpairs(iris)
ggplotly(pm)

gvarunkumar/plotly documentation built on May 17, 2019, 9:29 a.m.