Advanced Topic Presentation: Plotly
IST 719 - Information Visualization
Joshua A. Gaze
31-May-2022
What is Plotly?
Plotly is an open-source graphing library that allows for the creation of interactive plots and charts.
The package for using plotly in R is the following:
library(plotly)library(tidyverse)How to create an object using plotly
In R, there are two ways in which to create any kind of plotly object 1. Transforming a ggplot2 object (via ggplotly() )into a plotly object or.. 2. Directly intializing a plotly object with plot_ly(), plot_geo(), or plot_mapbox().
Both having their strengths/weaknesses.
Fundamentally, both approaches lay on the foundation of the “Grammar of Graphics” that’s powered by the JavaScript graphing library plotly.js
Comparing ggplot2 with plotly
Recall what we have seen before in class using ggplot2
library(ggplot2)
ggplot(mpg, aes(displ, hwy)) +
geom_point()
Now see the increased functionality of the same plot within plotly
library(plotly)
m <- highlight_key(mpg)
p <- ggplot(m, aes(displ, hwy)) + geom_point()
gg <- highlight(ggplotly(p), "plotly_selected")
crosstalk::bscols(gg, DT::datatable(m))Features with plotly
Using a slidebar
x <- seq(0,10, length.out = 1000)
# create data
aval <- list()
for(step in 1:11){
aval[[step]] <-list(visible = FALSE,
name = paste0('v = ', step),
x=x,
y=sin(step*x))
}
aval[3][[1]]$visible = TRUE
# create steps and plot all traces
steps <- list()
fig <- plot_ly()
for (i in 1:11) {
fig <- add_lines(fig,x=aval[i][[1]]$x, y=aval[i][[1]]$y, visible = aval[i][[1]]$visible,
name = aval[i][[1]]$name, type = 'scatter', mode = 'lines', hoverinfo = 'name',
line=list(color='00CED1'), showlegend = FALSE)
step <- list(args = list('visible', rep(FALSE, length(aval))),
method = 'restyle')
step$args[[2]][i] = TRUE
steps[[i]] = step
}
# add slider control to plot
fig <- fig %>%
layout(sliders = list(list(active = 3,
currentvalue = list(prefix = "Frequency: "),
steps = steps)))
figplot_ly(diamonds, x = ~cut, color = ~clarity, colors = "Accent")# create three visualizations of the diamonds dataset
plot_ly(diamonds, x = ~cut)plot_ly(diamonds, x = ~cut, y = ~clarity)plot_ly(diamonds, x = ~cut, color = ~clarity, colors = "Accent")# produces red bars with black outline
plot_ly(
diamonds, x = ~cut,
color = I("red"),
stroke = I("black"),
span = I(2)
)diamonds %>%
dplyr::count(cut) %>%
plot_ly() %>%
add_bars(x = ~cut, y = ~n)p <- ggplot(diamonds, aes(x = log(carat), y = log(price))) +
geom_hex(bins = 100)
ggplotly(p)p <- ggplot(diamonds, aes(x = log(price), color = clarity)) +
geom_freqpoly()
ggplotly(p)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.p <- ggplot(diamonds, aes(x = log(price), color = clarity)) +
geom_freqpoly(stat = "density") +
facet_wrap(~cut)
ggplotly(p)p <- ggplot(diamonds, aes(x=clarity, y=log(price), color=clarity)) +
ggforce::geom_sina(alpha = 0.1) +
stat_summary(fun.data = "mean_cl_boot", color = "black") +
facet_wrap(~cut)
# WebGL is a lot more efficient at rendering lots of points
toWebGL(ggplotly(p))Using plotly for boxplots
p <- plot_ly(diamonds, y = ~price, color = I("black"),
alpha = 0.1, boxpoints = "suspectedoutliers")
p1 <- p %>% add_boxplot(x = "Overall")
p2 <- p %>% add_boxplot(x = ~cut)
subplot(p1, p2, shareY = TRUE,
widths = c(0.2, 0.8), margin = 0) %>%
hide_legend()plot_ly(diamonds, x = ~price, y = ~interaction(clarity, cut)) %>%
add_boxplot(color = ~clarity) %>%
layout(yaxis = list(title = ""))library(plotly)
library(dplyr)
# airport locations
air <- read.csv('https://plotly-r.com/data-raw/airport_locations.csv')
# flights between airports
flights <- read.csv('https://plotly-r.com/data-raw/flight_paths.csv')
flights$id <- seq_len(nrow(flights))
# map projection
geo <- list(
projection = list(
type = 'orthographic',
rotation = list(lon = -100, lat = 40, roll = 0)
),
showland = TRUE,
landcolor = toRGB("gray95"),
countrycolor = toRGB("gray80")
)
plot_geo(color = I("red")) %>%
add_markers(
data = air, x = ~long, y = ~lat, text = ~airport,
size = ~cnt, hoverinfo = "text", alpha = 0.5
) %>%
add_segments(
data = group_by(flights, id),
x = ~start_lon, xend = ~end_lon,
y = ~start_lat, yend = ~end_lat,
alpha = 0.3, size = I(1), hoverinfo = "none"
) %>%
layout(geo = geo, showlegend = FALSE)Using choropleths
density <- state.x77[, "Population"] / state.x77[, "Area"]
g <- list(
scope = 'usa',
projection = list(type = 'albers usa'),
lakecolor = toRGB('white')
)
plot_geo() %>%
add_trace(
z = ~density, text = state.name, span = I(0),
locations = state.abb, locationmode = 'USA-states'
) %>%
layout(geo = g)