The ggsankeyfier package needs data in a
data.frame in order to plot it. This
data.frame does not necessarily has the same format as the
data while you are processing it. In most cases data is probably
organised in a wide format with stages of the Sankey diagram in columns
of the data.frame. For plotting this needs to be converted
into a long format. Why and how to do this, is discussed below.
A wide format would be typically used when working with the data.
This can be best understood when the framework you wish to visualise
represents a collection of cause-effect chains. In those cases each
stage would represent a link in the chain. So in a wide format each
stage (or link in the cause-effect chain) would be represented by a
column in a data.frame. Also, in the wide format, each row
would represent a unique cause-effect chain. The wide format is
therefore suitable for describing such chains.
So why do we need the long format? This is because in a Sankey diagram, we want to visualise how information flows between stages. Moreover, we might even want to distinguish between the start and the end of such a flow. So, rather than the entire ‘chain’, the data format revolves around the flow ends. This requires a long format where each row contains information on a flow end.
Now, when do we work with either the wide or the long format? When
working with information on chains, it makes sense to work with a wide
format. When plotting with ggsankeyfier or modification of
flow information is required, a long format is more suitable.
This package comes with a function that allow you to pivot information with stages organised as columns (i.e., wide format) to a long format. All you need to do is specify which columns represent the stages, which numeric column quantifies the size of each flow and if there are specific aspects you wish to include as aesthetics in your plot.
This is illustrated with data from Piet et al.
(submitted), this data set describes risks to provide ecosystem
services via cause-effect chains (note that the package contains a
simplified, highly aggregated, version of the data). Where the services
are affected by activities that exert pressures onto the ecosystem
components that supply those services. Risk is expressed as a numeric
indicator (RCSES). Stages are formed by the columns named
"activity_type", "pressure_cat",
"biotic_group" and "service_division". This is
pivoted from its wide format to the long format required for plotting as
follows:
## first get a data set with a wide format:
data(ecosystem_services)
## pivot to long format for plotting:
es_long <-
pivot_stages_longer(
## the data.frame we wish to pivot:
data = ecosystem_services,
## the columns that represent the stages:
stages_from = c("activity_type", "pressure_cat",
"biotic_realm", "service_division"),
## the column that represents the size of the flows:
values_from = "RCSES"
)After pivoting to the long format as illustrated above you will note
two additional columns that contain information that was not available
in the wide format. Namely the columns edge_id and
connector.
These columns are added as the ggsankeyfier functions
need them for plotting the data in a Sankey diagram. More specifically,
these columns are required to distinguish between the head and tail of a
flow. This allows to apply different aesthetics to both ends of a flow
(not yet fully implemented) and to visualise feedback loops (see
vignette("loopdeloop")), which would otherwise not be
possible. The column connector should contain either
"from" (start of a flow) or "to" (end of a
flow). The edge_id contains a unique identifier for each
edge (flow), this determines which "from" should be
connected to which "to" connector. When applying the
pivot_stages_longer function, the "from" end
"to" connector for each edge will have an identical
value.