ggTimeSeries (version 1.0.2)

ggplot_horizon: Plot a time series as a horizon plot

Description

A horizon plot breaks the Y dimension down using colours. This is useful when visualising y values spanning a vast range and / or trying to highlight outliers without losing context of the rest of the data. Horizon plots are best viewed in an apsect ratio of very low vertical length.

Usage

ggplot_horizon(dtData, cXColumnName, cYColumnName, bandwidth = NULL,
  vcGroupingColumnNames = NULL)

Arguments

dtData

Data set which may include other columns apart from date and values.

cXColumnName

Column name of dates.

cYColumnName

Column name of values.

bandwidth

the width of one band of Y values. easier to differentiate between the bands.

vcGroupingColumnNames

The set of columns which together define the group for the chart to operate within If you plan to facet your plot, you should specify the same column names to this argument.

Value

Returns a gpplot friendly object which means the user can use ggplot scales, etc. to modify the look.

Cosmetic Tips

The minimalist look can be achieved by appending the following chunk of code to the example output object: + xlab(NULL) + ylab(NULL) + scale_fill_continuous(low = 'green', high = 'red') + theme( axis.text = element_blank(), axis.ticks = element_blank(), legend.position = 'none', strip.background = element_blank(), # strip.text = element_blank(), # useful if only one year of data plot.background = element_blank(), panel.border = element_blank(), panel.background = element_blank(), panel.grid = element_blank(), panel.border = element_blank() ) + coord_fixed( 0.5 * diff(range(dfData$x)) / diff(range(dfData$y)))

Also See

stat_horizon, a less polished but more flexible alternative.

Examples

# NOT RUN {
{
library(ggplot2)
set.seed(1)
dfData = data.frame(x = 1:1000, y = cumsum(rnorm(1000)))
p1 = ggplot_horizon(dfData, 'x', 'y')
p1
# add new geoms or colours
p1 +
geom_text(label = '!!!') +
scale_colour_continuous(low = 'red', high = 'green')
}
# }