Aggregations in R

How to use aggregates in R with Plotly.

Plotly Studio: Transform any dataset into an interactive data application in minutes with AI. Try Plotly Studio now.

Note: We are retiring documentation for R, MATLAB, Julia, and F#. Learn more about this change here.

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