Calendar Heatmap

Available as stat_calendar_heatmap and ggplot_calendar_heatmap.

A calendar heatmap is a great way to visualise daily data. Its structure makes it easy to detect weekly, monthly, or seasonal patterns.

# creating some data
set.seed(1)
dtData = data.table(
      DateCol = seq(
         as.Date("1/01/2014", "%d/%m/%Y"),
         as.Date("31/12/2015", "%d/%m/%Y"),
         "days"
      ),
      ValueCol = runif(730)
   )
dtData[, ValueCol := ValueCol + (strftime(DateCol,"%u") %in% c(6,7) * runif(1) * 0.75), .I]
dtData[, ValueCol := ValueCol + (abs(as.numeric(strftime(DateCol,"%m")) - 6.5)) * runif(1) * 0.75, .I]

# base plot
p1 = ggplot_calendar_heatmap(
   dtData,
   'DateCol',
   'ValueCol'
)

# adding some formatting
p1 + 
   xlab('') + 
   ylab('') + 
   scale_fill_continuous(low = 'green', high = 'red') + 
   facet_wrap(~Year, ncol = 1)

# creating some categorical data
dtData[, CategCol := letters[1 + round(ValueCol * 7)]]

# base plot
p2 = ggplot_calendar_heatmap(
   dtData,
   'DateCol',
   'CategCol'
)

# adding some formatting
p2 + 
   xlab('') + 
   ylab('') + 
   facet_wrap(~Year, ncol = 1)

Horizon Plots

Available as stat_horizon and ggplot_horizon.

Imagine an area chart which has been chopped into multiple chunks of equal height. If you overlay these chunks one on top of the the other, and colour them to indicate which chunk it is, you get a horizon plot. Horizon plots are useful when visualising y values spanning a vast range but with a skewed distribution, and / or trying to highlight outliers without losing context of variation in the rest of the data.

# creating some data
set.seed(1)
dfData = data.frame(x = 1:1000, y = cumsum(rnorm(1000)))

# base plot
p1 = ggplot_horizon(dfData, 'x', 'y')


print("If you're seeing any vertical white stripes, it's a display thing.")

## [1] "If you're seeing any vertical white stripes, it's a display thing."

# adding some formatting
p1 + 
   xlab('') + 
   ylab('') + 
   scale_fill_continuous(low = 'green', high = 'red') + 
   coord_fixed( 0.5 * diff(range(dfData$x)) / diff(range(dfData$y)))

Steamgraphs

Available as stat_steamgraph.

A steamgraph is a more aesthetically appealing version of a stacked area chart. It tries to highlight the changes in the data by placing the groups with the most variance on the edges, and the groups with the least variance towards the centre. This feature in conjunction with the centred alignment of each of the contributing areas makes it easier for the viewer to compare the contribution of any of the components across time.

# creating some data
set.seed(10)
dfData = data.frame(
   Time = 1:1000,
   Signal = abs(
      c(
         cumsum(rnorm(1000, 0, 3)), 
         cumsum(rnorm(1000, 0, 4)), 
         cumsum(rnorm(1000, 0, 1)),
         cumsum(rnorm(1000, 0, 2))
      )
   ),
   VariableLabel = c(rep('Class A', 1000), rep('Class B', 1000), rep('Class C', 1000), rep('Class D', 1000))
)

# base plot
p1 = ggplot(dfData, aes(x = Time, y = Signal, group = VariableLabel, fill = VariableLabel)) +
  stat_steamgraph()


# adding some formatting
p1 + 
   xlab('') + 
   ylab('') + 
   coord_fixed( 0.2 * diff(range(dfData$Time)) / diff(range(dfData$Signal)))

Waterfall

Available as stat_waterfall and ggplot_waterfall.

Rather than the values itself, a waterfall plot tries to bring out the changes in the values.

# creating some data
set.seed(1)
dfData = data.frame(x = 1:100, y = cumsum(rnorm(100)))

# base plot
p1 = ggplot_waterfall(
   dtData = dfData,
   'x',
   'y'
)

# adding some formatting
p1 + 
   xlab('') + 
   ylab('')

Occurrence Dot Plot

Available as stat_occurrence.

This one is a favourite in infographics. For rare events, the reader would find it convenient to have the count of events encoded in the chart itself instead of having to map the value back to the Y axis.

# creating some data
set.seed(1)
dfData = data.table(x = 1:100, y = floor(4 * abs(rnorm(100, 0 , 0.4))))

# base plot
p1 = ggplot(dfData, aes(x =x, y = y) )+
   stat_occurrence()

# adding some formatting   
p1 +
   xlab('') + 
   ylab('') + 
   coord_fixed(ylim = c(0,1 + max(dfData$y)))