Residual diagnostics in DHARMa for multilevel logistic regression

I'm running a multilevel logistic regression, and have been trying to look at residual diagnostics using the DHARMa package. My data is copy-pastable from here and the following code should run everything.

library(lme4)
library(DHARMa)

m1 <- glmer("error ~ 1 + year + categorisation + statistic + (1 | journalID)",
data = data,

simulationOutput <- simulateResiduals(fittedModel = m1)

# Main plot function from DHARMa, which gives
# Left: a qq-plot to detect overall deviations from the expected distribution
# Right: a plot of the residuals against the rank-transformed model predictions
plot(simulationOutput)

# Plotting standardized residuals against predictors
plotResiduals(simulationOutput, data$$year, xlab = "Year", main=NULL) plotResiduals(simulationOutput, data$$statistic, xlab = "Statistic Type", main=NULL)
plotResiduals(simulationOutput, data$$categorisation, xlab = "Category 1 or Category 2", main=NULL) plotResiduals(simulationOutput, data$$journalID, xlab = "Journal ID", main=NULL)

# Plotting standardized residuals against the predicted value
plotResiduals(simulationOutput, main=NULL)
plotResiduals(simulationOutput, fitted(m1), xlab = "fitted(m1)")


The following is the main residual plot function.

There follow plots of standardized residuals against my continuous predictor Year, a nominal factor with 4 levels (Statistic), and against the binary predictor Category.

There follows the default DHARMa plot of residuals against the predicted values. I also tried to plot the residuals against fitted(m1), excepting that they would be the same thing. However, they aren't.

My questions are:

1. Why can't I plot the residuals against the predictor I modelled as a random effect (journalID). When I try to do it I get the error message Error in plot.window(...) : need finite 'xlim' values.
2. Why is it that my two different ways of standardized residuals against the predicted value, plotResiduals(simulationOutput, main=NULL), and plotResiduals(simulationOutput, fitted(m1), xlab = "fitted(m1)"), give slightly different results. Aren't they the same thing?
3. The plot for my predictor statistic lists the levels of that factor as 1-4, while the actual names of the levels of that factor are "F", "chi","t","r". How can I work out which number maps onto which level of the factor?
4. Do my residuals look OK? Is there anything further I should be testing in relation to them?