data(RS.data)
codeNames = c('Data','Technical.Constraints','Performance.Parameters',
'Client.and.Consultant.Requests','Design.Reasoning','Collaboration');
accum = ena.accumulate.data(
units = RS.data[,c("UserName","Condition")],
conversation = RS.data[,c("GroupName","ActivityNumber")],
metadata = RS.data[,c("CONFIDENCE.Change","CONFIDENCE.Pre","CONFIDENCE.Post","C.Change")],
codes = RS.data[,codeNames],
window.size.back = 4,
model = "A"
);
set = ena.make.set(accum);
### get mean network plots
first.game.lineweights = as.matrix(set$line.weights$Condition$FirstGame)
first.game.mean = colMeans(first.game.lineweights)
second.game.lineweights = as.matrix(set$line.weights$Condition$SecondGame)
second.game.mean = colMeans(second.game.lineweights)
subtracted.network = first.game.mean - second.game.mean
# Plot dimension 1 against ActivityNumber metadata
dim.by.activity = cbind(
as.matrix(set$points)[,1],
set$trajectories$ActivityNumber * .8/14-.4 #scale down to dimension 1
)
plot = ena.plot(set)
plot = ena.plot.network(plot, network = subtracted.network, legend.name="Network")
plot = ena.plot.trajectory(
plot,
points = dim.by.activity,
names = unique(set$model$unit.label),
by = set$trajectories$ENA_UNIT
);
if (FALSE) print(plot)
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