Nothing
codeNames = c("Data","Technical.Constraints","Performance.Parameters","Client.and.Consultant.Requests","Design.Reasoning","Collaboration")
accum = ena.accumulate.data(
units = RS.data[,c("Condition","UserName")],
conversation = RS.data[,c("Condition","GroupName")],
metadata = RS.data[,c("CONFIDENCE.Change","CONFIDENCE.Pre","CONFIDENCE.Post","C.Change","GroupName")],
codes = RS.data[,codeNames],
model = "EndPoint",
window.size.back = 4,
as.list = FALSE
);
set = ena.make.set(
enadata = accum
# ,rotation.by = ena.rotate.by.mean,
# rotation.params = list(FirstGame=accum$meta.data$Condition=="FirstGame", SecondGame=accum$meta.data$Condition=="SecondGame")
);
### Subset rotated points and plot Condition 1 Group Mean
first.game = set$meta.data$Condition == "FirstGame"
first.game.points = set$points.rotated[first.game,]
### Subset rotated points and plot Condition 2 Group Mean
second.game = set$meta.data$Condition == "SecondGame"
second.game.points = set$points.rotated[second.game,]
ena.conversations(set = set,
units = c("FirstGame.steven z"), units.by=c("Condition","UserName"),
conversation.by = c("Condition","GroupName"),
codes=codeNames,
window = 4
)
#first.game.mean = colMeans( first.game.points )
#second.game.mean = colMeans( second.game.points )
#first.game.ci = t.test(first.game.points, conf.level = 0.95)$conf.int
#second.game.ci = t.test(second.game.points, conf.level = 0.95)$conf.int
### get mean network plots
first.game.lineweights = set$line.weights[first.game,]
first.game.mean = colMeans(first.game.lineweights)
second.game.lineweights = set$line.weights[second.game,]
second.game.mean = colMeans(second.game.lineweights)
subtracted.network = first.game.mean - second.game.mean
#Plot subtracted network only
plot1 = rENA::ena.plot(set)
plot1 = rENA::ena.plot.network(plot1, network = subtracted.network)
#plot means only
plot2 = rENA::ena.plot(set)
plot2 = rENA::ena.plot.group(plot2, second.game.points, labels = "SecondGame", colors = "blue", confidence.interval = "box")
plot2 = rENA::ena.plot.group(plot2, first.game.points, labels = "FirstGame", colors = "red", confidence.interval = "box")
#plot both
plot3 = rENA::ena.plot(set)
plot3 = rENA::ena.plot.network(plot3, network = subtracted.network)
plot3 = rENA::ena.plot.group(plot3, first.game.points, labels = "FirstGame", colors = "red", confidence.interval = "box")
plot3 = rENA::ena.plot.group(plot3, second.game.points, labels = "SecondGame", colors = "blue", confidence.interval = "box")
dim.by.activity = cbind(
set$points.rotated[,1],
set$enadata$trajectories$step$ActivityNumber*.8/14-.4 #scale down to dimension 1
)
accum = ena.accumulate.data(
units = RS.data[,c("UserName","Condition")],
conversation = RS.data[,c("GroupName","ActivityNumber")],
metadata = RS.data[,c("CONFIDENCE.Change","CONFIDENCE.Pre","CONFIDENCE.Post","C.Change")],
codes = RS.data[,codeNames],
window.size.back = 4,
model = "A"
);
set = ena.make.set(accum);
plot = ena.plot(set)
plot = ena.plot.network(plot, network = subtracted.network, legend.name="Network", legend.include.edges = T)
dim.by.activity = cbind(
set$points.rotated[,1],
set$enadata$trajectories$step$ActivityNumber*.8/14-.4 #scale down to dimension 1
)
# plot = ena.plot.trajectory(
# plot,
# points = dim.by.activity,
# names = unique(set$enadata$units$UserName),
# by = set$enadata$units$UserName
# );
# print(plot)
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