Simple Example

# not on CRAN, so install with devtools
#  devtools::install_github("timelyportfolio/parcoords")
library(parcoords)

data(mtcars)

# just plain old stock parallel coordinates
#   don't worry its easy to make a whole lot better
parcoords( mtcars )

If you are familiar with MASS function parcoord, I’m sure you are not that impressed, so we’ll turn on some interactivity in the next example.

Brush On and Reorder

Two of interactive behaviors provided by parallel-coordinates are a brush to filter data and reorderable axes. Try both of these in this revised version of the above example.

parcoords(
  mtcars
  , brush = "1d-axes" # 2d-strums are really neat
  , reorderable = TRUE
)

Queue Render for Bigger Data and Some Color

parallel-coordinates handles bigger data well since the lines are drawn on the canvas. Set queue = T to progressively render the chart on build and interaction. Here is one example and another from the parallel-coordinates site. The diamonds dataset from ggplot2 will give us big enough data to test the progressive render and prove that we are in R. I’ll sample the diamonds to reduce the size of the HTML, but feel free to remove the sample and use the entire dataset on your local machine.

data( diamonds, package = "ggplot2" )
parcoords(
  diamonds[sample(1:nrow(diamonds),1000),]
  , rownames = F # turn off rownames from the data.frame
  , brushMode = "2D-strums"
  , reorderable = T
  , queue = T
  , color = RColorBrewer::brewer.pal(4,"BuPu")[4]
)

I hope you noticed the fancy new brush type. I would improve the chart by using color to represent the data. Let’s try a little more complicated color specification using the color helper from d3.scale.category10(). For even more fun let’s put it in a dplyr chain.

library(dplyr)
diamonds[sample(1:nrow(diamonds),1000),] %>%
  mutate( carat = cut(carat, breaks=c(0,1,2,3,4,5), right = T)) %>%
  select( carat, color, cut, clarity, depth, table, price,  x, y, z) %>%
  parcoords(
    rownames = F # turn off rownames from the data.frame
    , brushMode = "2D-strums"
    , reorderable = T
    , queue = T
    , color = list(
      colorBy = "carat"
      ,colorScale = htmlwidgets::JS("d3.scale.category10()")
    )    
  )

While we are on the topic of color, I wanted to highlight the fine work by Gregor Aisch with chroma.js and palettes. We could use Jeroen Oom’s new V8 for this, but for now, let’s stick with what we have in R and d3.js using a d3.scale.threshold with colors supplied by RColorBrewer::brewer.pal.

diamonds[sample(1:nrow(diamonds),1000),] %>%
  select( carat, color, cut, clarity, depth, table, price,  x, y, z) %>%
  parcoords(
    rownames = F # turn off rownames from the data.frame
    , brushMode = "2D-strums"
    , reorderable = T
    , queue = T
    , color = list(
      colorBy = "carat"
      ,colorScale = htmlwidgets::JS(sprintf('
        d3.scale.threshold()
          .domain(%s)
          .range(%s)
        '
        ,jsonlite::toJSON(seq(0,round(max(diamonds$carat))))
        ,jsonlite::toJSON(RColorBrewer::brewer.pal(6,"PuBuGn"))
      ))
    )
  )