Aggregations in R

How to use aggregates in R with Plotly.

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Plotly is a free and open-source graphing library for R. We recommend you read our Getting Started guide for the latest installation or upgrade instructions, then move on to our Plotly Fundamentals tutorials or dive straight in to some Basic Charts tutorials.

Introduction

Aggregates are a type of transform that can be applied to values in a given expression. Available aggregations are:

Function	Description
`count`	Returns the quantity of items for each group.
`sum`	Returns the summation of all numeric values.
`avg`	Returns the average of all numeric values.
`median`	Returns the median of all numeric values.
`mode`	Returns the mode of all numeric values.
`rms`	Returns the rms of all numeric values.
`stddev`	Returns the standard deviation of all numeric values.
`min`	Returns the minimum numeric value for each group.
`max`	Returns the maximum numeric value for each group.
`first`	Returns the first numeric value for each group.
`last`	Returns the last numeric value for each group.

Basic Example

library(plotly)

fig <- plot_ly(
  type = 'scatter',
  x = diamonds$cut,
  y = diamonds$price,
  mode = 'markers',
  transforms = list(
    list(
      type = 'aggregate',
      groups = diamonds$cut,
      aggregations = list(
        list(
          target = 'y', func = 'sum', enabled = T
        )
      )
    )
  )
)

fig

Aggregate Functions

library(plotly)
library(listviewer)

s <- schema()
agg <- s$transforms$aggregate$attributes$aggregations$items$aggregation$func$values


l = list()
for (i in 1:length(agg)) {
  ll = list(method = "restyle",
            args = list('transforms[0].aggregations[0].func', agg[i]),
            label = agg[i]) 
  l[[i]] = ll
}

fig <- plot_ly(
  type = 'scatter',
  x = diamonds$cut,
  y = diamonds$price,
  mode = 'markers',
  marker = list(
    size = 10,
    color = 'blue',
    opacity = 0.8
  ),
  transforms = list(
    list(
      type = 'aggregate',
      groups = diamonds$cut,
      aggregations = list(
        list(
          target = 'y', func = 'avg', enabled = T
        )
      )
    )
  )
)
fig <- fig %>% layout(
    title = '<b>Plotly Aggregations</b><br>use dropdown to change aggregation',
    xaxis = list(title = 'Cut'),
    yaxis = list(title = 'Price ($)'),
    updatemenus = list(
      list(
        x = 0.25,
        y = 1.04,
        xref = 'paper',
        yref = 'paper',
        yanchor = 'top',
        buttons = l
      )
    )
  )

fig

Histogram Binning

library(plotly)

df <- read.csv("https://plotly.com/~public.health/17.csv", skipNul = TRUE, encoding = "UTF-8")

labels <- function(size, label) {
  list(
    args = c("xbins.size", size), 
    label = label, 
    method = "restyle"
  )
}

fig <- df %>%
  plot_ly(
    x = ~date,
    autobinx = FALSE, 
    autobiny = TRUE, 
    marker = list(color = "rgb(68, 68, 68)"), 
    name = "date", 
    type = "histogram", 
    xbins = list(
      end = "2016-12-31 12:00", 
      size = "M1", 
      start = "1983-12-31 12:00"
    )
  )
fig <- fig %>% layout(
  paper_bgcolor = "rgb(240, 240, 240)", 
  plot_bgcolor = "rgb(240, 240, 240)", 
  title = "<b>Shooting Incidents</b><br>use dropdown to change bin size",
  xaxis = list(
    type = 'date'
  ),
  yaxis = list(
    title = "Incidents"
  ),
  updatemenus = list(
    list(
      x = 0.1, 
      y = 1.15,
      active = 1, 
      showactive = TRUE,
      buttons = list(
        labels("D1", "Day"),
        labels("M1", "Month"),
        labels("M6", "Half Year"),
        labels("M12", "Year")
      )
    )
  )
)

fig

Mapping with Aggregations

library(plotly)

df <- read.csv("https://raw.githubusercontent.com/bcdunbar/datasets/master/worldhappiness.csv")

s <- schema()
agg <- s$transforms$aggregate$attributes$aggregations$items$aggregation$func$values


l = list()
for (i in 1:length(agg)) {
  ll = list(method = "restyle",
            args = list('transforms[0].aggregations[0].func', agg[i]),
            label = agg[i]) 
  l[[i]] = ll
}

fig <- df %>%
  plot_ly(
    type = 'choropleth',
    locationmode = 'country names',
    locations = ~Country,
    z = ~HappinessScore,
    autocolorscale = F,
    reversescale = T,
    colorscale = 'Portland', 
    transforms = list(list(
      type = 'aggregate',
      groups = ~Country,
      aggregations = list(
        list(target = 'z', func = 'sum', enabled = T)
      )
    ))
  )
fig <- fig %>% layout(
    title = "<b>World Happiness</b>",
    geo = list(
      showframe = F,
      showcoastlines = F
    ),
    updatemenus = list(
      list(
        x = 0.25,
        y = 1.04,
        xref = 'paper',
        yref = 'paper',
        yanchor = 'top',
        buttons = l
      )
    )
  )

fig

Reference

See https://plotly.com/r/reference/ for more information and options!

What About Dash?

Dash for R is an open-source framework for building analytical applications, with no Javascript required, and it is tightly integrated with the Plotly graphing library.

Learn about how to install Dash for R at https://dashr.plot.ly/installation.

Everywhere in this page that you see fig, you can display the same figure in a Dash for R application by passing it to the figure argument of the Graph component from the built-in dashCoreComponents package like this:

library(plotly)

fig <- plot_ly() 
# fig <- fig %>% add_trace( ... )
# fig <- fig %>% layout( ... ) 

library(dash)
library(dashCoreComponents)
library(dashHtmlComponents)

app <- Dash$new()
app$layout(
    htmlDiv(
        list(
            dccGraph(figure=fig) 
        )
     )
)

app$run_server(debug=TRUE, dev_tools_hot_reload=FALSE)