About you

https://plot.ly/ggplot2/geom_density/

Same data, another look

https://plot.ly/r/parallel-coordinates-plot/

Is there anything in particular that you would like covered?

• I'm most interested in linking and animating views without shiny. Already pretty well acquainted with R/RStudio/Plotly, but hoping to learn how to use Plotly more efficiently and extend my current usage with animation, crosstalk, etc. I use Plotly most often within flexdashboard applications, so most interested in non-Shiny options.
• I want to learn how to animate my plotly graphs/charts.
• More shiny dev
• Creating documents with data visualizations in Markdown files. I've been working to get better with Markdown, but if there's a way to include interactive visualizations and/or animations in Markdown files that can be sent to readers that would be great. I work in consulting so I often need to send reports to executives and including interactive data visualizations in those reports would be incredibly useful for me.
• Looks good! Stoked for the class. For me, I don't use rmarkdown and rarely use ggplot2 - so those could be dropped from the list.
• Looks like all of Day1 currently would be old news. Wouldn't half a day of R/RStudio/ggplot2 suffice for your audience?

Is there anything else that we can help you with at the workshop?

• I'd like to know how plotly performs with displaying large data sets
• Plotting large amounts of time series data efficiently
• Plotly custom controls and mapping options.
• Learn how to apply interactive filters in Plotly if that's supported (e.g. click on a single bar within a bar chart on the left which filters the heat-map to the right).
• More advanced visualization techniques such as a chord diagram or a sunburst
  • Check out sunburstR
• Suggestions/hints on how to create clean, good code would be especially appreciated.
  • Check out tidyverse
• Extended breakout session time for help on specific project i am bringing

Is there anything else that we can help you with at the workshop?

• I'd like to know how plotly performs with displaying large data sets
• Plotting large amounts of time series data efficiently
• Plotly custom controls and mapping options.
• Learn how to apply interactive filters in Plotly if that's supported (e.g. click on a single bar within a bar chart on the left which filters the heat-map to the right).
• More advanced visualization techniques such as a chord diagram or a sunburst
  • Check out sunburstR
• Suggestions/hints on how to create clean, good code would be especially appreciated.
  • Check out tidyverse
• Extended breakout session time for help on specific project i am bringing

A minimal bar chart

• Every plotly chart is powered by plotly.js, plus some extra R/JS magic 🎩 🐰.
• How/why did plot_ly() draw a bar chart? What if we want something different?

library(plotly)
plot_ly(x = c("A", "B"), y = 1:2)

The scatter trace type is quite general. It provides the foundation for many charts (e.g., polygons, ribbons, filled areas, etc), extends to different coordinate systems (e.g, scatter3d, scattergeo, and scattermapbox), and rendering systems (e.g., scattergl)

subplot(shareY = TRUE,
  plot_ly(x = 1:2, y = 1:2),
  plot_ly(x = 1:2, y = 1:2, mode = "lines"),
  plot_ly(x = 1:2, y = 1:2, mode = "markers+lines"),
  plot_ly(x = 1:2, y = 1:2, text = 1:2, mode = "text"),
  plot_ly(x = 1:2, y = 1:2, text = 1:2, mode = "markers+lines+text")
)

Which visualization is better?

subplot(shareX = TRUE, nrows = 2,
  plot_ly(logs, x = ~date, y = ~package, z = ~count, type = "heatmap"),
  plot_ly(logs, x = ~date, y = ~count, color = ~package, mode = "lines")
)

Famous question: which is larger (and by how much)? A or B?

Position is best, especially along common scale and baseline

A more general guideline from Cleveland and McGill

Again, which is better?

With all my installed.packages(), yikes!

plot_ly(logz, x = ~date, y = ~count) %>% group_by(package) %>% add_lines(alpha=0.3)

Can improve a bit with interaction

library(crosstalk)
SharedData$new(logz, ~package, "Select package(s)") %>% 
  plot_ly(x = ~date, y = ~count) %>% group_by(package) %>% add_lines(alpha=0.3) %>% 
  highlight(dynamic = TRUE, selectize = TRUE, persistent = TRUE)

heatmaply is awesome for visualizing a numeric matrices!

What about long time series?

• Tens of thousands points is responsive with SVG

y <- sample(c(-1, 1), 1e4, TRUE)
x <- seq(Sys.time(), Sys.time() + length(y) - 1, by = "1 sec")
plot_ly(x = x, y = cumsum(y)) %>% add_lines() %>% rangeslider()

What about long time series?

• Hundreds of thousands time series points is responsive with WebGL (but no rangeslider (yet))

y <- sample(c(-1, 1), 1e5, TRUE)
x <- seq(Sys.time(), Sys.time() + length(y) - 1, by = "1 sec")
plot_ly(x = x, y = cumsum(y)) %>% add_lines() %>% toWebGL()

What about performance (beyond time-series)?

SVG vs Canvas, in general

• The Scalable in SVG, means scalable in terms of bounding box size.
  • No matter the context, your browser will struggle to render > 30,000 SVG elements.
  • This is why canvas based elements exist (the difference is similar to pdf vs png)

Time series doesn't scale well, even in a canvas context

• Time series has performance limitations that other data types don't (this is pretty universal).

High performance plotly charts

• scattergl -- basically same as scatter (with limitations), but responsive with >1M points.
• pointcloud -- more restricted than scattergl, but responsive w/ several million points.
• heatmapgl -- response with >1M cells in heatmap.

More time series tips

Have lots of long time series?

• Consider extracting/visualizing features from each series

  • Some useful packages: anomalous and tscognostics
  • See my work on visualizing pedestrian traffic with plotly

• Consider a tool like trelliscope for exploring many "groups"

Visualization of models/predictions?

• Start with forecast and/or mgcv for model fitting.
• Use a strategy similar to here to plot forecasts.

Is seasonality important?

• Consider "wrapping" your time-series
  • Wrap (i.e., group) your series by hand (get inspired by this paper)
  • Checkout out sugrrants (ggplotly() converts the ggplot2 plots)

Texas housing prices

library(dplyr)
tx <- txhousing %>%
  select(city, year, month, median) %>%
  filter(city %in% c("Galveston", "Midland", "Odessa", "South Padre Island"))
tx
#> # A tibble: 748 x 4
#>         city  year month median
#>        <chr> <int> <int>  <dbl>
#>  1 Galveston  2000     1  95000
#>  2 Galveston  2000     2 100000
#>  3 Galveston  2000     3  98300
#>  4 Galveston  2000     4 111100
#>  5 Galveston  2000     5  89200
#>  6 Galveston  2000     6 108600
#>  7 Galveston  2000     7  99000
#>  8 Galveston  2000     8  96200
#>  9 Galveston  2000     9 104000
#> 10 Galveston  2000    10 118800
#> # ... with 738 more rows

Wrap by year, facet by city

ggplot(tx, aes(month, median, group = year)) + geom_line() +
  facet_wrap(~city, ncol = 2)

Compare across cities within year and across years within city

TX <- SharedData$new(tx, ~year)
p <- ggplot(TX, aes(month, median, group = year)) + geom_line() +
  facet_wrap(~city, ncol = 2)
(gg <- ggplotly(p, tooltip = "year"))

Set selection mode and default selections

highlight(gg, "plotly_hover", defaultValues = "2006")

Make comparisons with dynamic brush

highlight(gg, dynamic = TRUE, persistent = TRUE, selectize = TRUE)

Customize the appearance of selections

highlight(
  gg, dynamic = TRUE, persistent = TRUE, 
  selected = attrs_selected(mode = "markers+lines", marker = list(symbol = "x"))
)

Automate queries via animation

p <- ggplot(tx, aes(month, median)) + 
  geom_line(aes(group = year), alpha = 0.2) +
  geom_line(aes(frame = year), color = "red") +
  facet_wrap(~city, ncol = 2)
ggplotly(p)