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graph.r
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graph.r
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# ----------------------------------------------------------------------------------------------------
# David Phillips
#
# 3/1/2016
# Function that produces a time series graph from the results of its.r, bma.r or cpbma.r.
# Inputs:
# * itsOutput - list. standard output from its.r
# * quarterly - logical. whether points should be averaged by quarter
# Outputs:
# * p - a ggplot graph
# ----------------------------------------------------------------------------------------------------
# Define function
graph = function(itsOutput=NULL, quarterly=TRUE) {
# ---------------------------------------------------------------------------------
# Handle inputs
# 'unpack' arguments
for(arg in names(itsOutput)) assign(arg, itsOutput[[arg]])
# test
for(arg in c('data', 'outcome')) {
if (is.null(get(arg))) stop(paste('Must provide', arg))
}
# essential variables
essentialVars = c(outcome, 'moyr', paste0(outcome, '_pred'),
paste0(outcome, '_pred_upper'), paste0(outcome, '_pred_lower'),
paste0(outcome, '_pred_cf'), paste0(outcome, '_pred_cf_upper'),
paste0(outcome, '_pred_cf_lower'))
if (!all(essentialVars %in% names(data))) stop('Essential variables not in data')
# cutpoint(s)
C = length(cutpoint)
if (C>2) stop('ITS not set up to handle more than 2 cutpoints')
if (C==1) start = end = cutpoint
if (C==2) {
start = cutpoint[1]
end = cutpoint[2]
}
# identify if the input was bma or not based on the contents (could be better)
is_bma = 'stats' %in% names(itsOutput)
# ---------------------------------------------------------------------------------
# ----------------------------------------------------------------------------------------------------
# Average to quarter level if specified
graphData = copy(data)
if (quarterly) {
# collapse to quarter level
quarterData = copy(graphData)
quarterData[month(moyr)<4, moyr:=as.Date(paste0('0102', year(moyr)), '%d%m%Y')]
quarterData[month(moyr)>=4 & month(moyr)<7, moyr:=as.Date(paste0('0105', year(moyr)), '%d%m%Y')]
quarterData[month(moyr)>=7 & month(moyr)<10, moyr:=as.Date(paste0('0108', year(moyr)), '%d%m%Y')]
quarterData[month(moyr)>=10, moyr:=as.Date(paste0('0111', year(moyr)), '%d%m%Y')]
quarterData = quarterData[, list(mean=mean(get(outcome))), by='moyr']
# merge and replace
graphData = merge(graphData, quarterData, 'moyr', all=TRUE)
graphData[, (outcome):=mean]
}
# ----------------------------------------------------------------------------------------------------
# ----------------------------------------------------------------------------------------------------
# Formatting
# format effect size to be human readable
effect_size = exp(effect_size)*100
if (effect_size$effect[1]<100) {
effect_size[, interpretation:='% Reduction']
effect_size[, effect:=100-effect]
effect_size[, estimate:=c('Estimate', 'Upper', 'Lower')]
} else {
effect_size[, interpretation:='% Increase']
effect_size[, effect:=effect-100]
effect_size[, estimate:=c('Estimate', 'Lower', 'Upper')]
}
effect_size = data.frame(effect_size)
effect_size$effect = round(effect_size$effect, 1)
# rename variables so aes_string isn't necessary
setnames(graphData, c(outcome, paste0(outcome,'_pred'), paste0(outcome,'_pred_upper'),
paste0(outcome,'_pred_lower'), paste0(outcome,'_pred_cf')),
c('observed', 'trend', 'upper', 'lower', 'cf'))
# drop empty lead-in period (for XRCP)
first_nonmissing = min(graphData[!is.na(observed) & is.finite(observed)]$moyr)
graphData = graphData[moyr>=first_nonmissing]
# ----------------------------------------------------------------------------------------------------
# ----------------------------------------------------------------------------------------------------------------
# Graph settings
# colors
ciColor = '#E0E0E0'
cfColor = '#FAA61A'
lineColor = 'black'
pointColor = '#2D358E'
annoColor = '#C0C0C0'
# title
title = 'PCV Impact'
if (outcome=='ipd_cases') title=paste0(title, '\nAll IPD Cases')
if (outcome=='ipd_pcv10_serotype_cases') title=paste0(title, '\nPCV10 Serotypes')
if (outcome=='ipd_non_pcv10_serotype_cases') title=paste0(title, '\nNon-PCV10 Serotypes')
if (outcome=='xrcp_cases') title=paste0(title, '\nAll X-Ray Confirmed Cases')
# axis labels
if (quarterly) ylab = 'Means cases per month'
if (!quarterly) ylab = 'Cases'
if (quarterly) xlab = 'Quarter'
if (!quarterly) xlab = 'Month'
# annotation text
annotation = paste0(effect_size[1,1], effect_size[1,2], '\n95% CI: ',
effect_size[effect_size$estimate=='Lower',1], '% to ',
effect_size[effect_size$estimate=='Upper',1], '%')
# annotation location
if (effect_size[1,2]=='% Reduction') annotationY = min(graphData$observed, na.rm=TRUE)
if (effect_size[1,2]!='% Reduction') annotationY = max(graphData$observed, na.rm=TRUE)
annotationX = start-450
if (!is.null(newEffectDate)) annotationEnd = newEffectDate
if (is.null(newEffectDate)) annotationEnd = end
annotationLocation = data.frame(x=annotationX, y=annotationY, xend=annotationEnd, yend=graphData$trend[graphData$moyr==annotationEnd])
if (start==end) label1 = label2 = 'PCV Introduction'
if (start!=end) label1 = 'PCV Introduction'
if (start!=end) label2 = 'PCV Routinization'
# ----------------------------------------------------------------------------------------------------------------
# --------------------------------------------------------------------------------------------------------------------------------
# Actually make graph
p = ggplot(graphData, aes(x=moyr)) +
# add geoms
geom_ribbon(aes(ymax=upper, ymin=lower, fill='95% CI')) +
geom_line(aes(y=cf, color='Counterfactual'), linetype=2) +
geom_line(aes(y=trend, color='Trend')) +
geom_point(aes(y=observed, color='Observed Cases', linetype='Observed Cases')) +
# control colors
scale_fill_manual('', breaks=c('95% CI'), values=c(ciColor)) +
scale_color_manual('', breaks=c('Counterfactual', 'Trend', 'Observed Cases'), values=c(cfColor, pointColor, lineColor)) +
# override legend shapes
guides(color=guide_legend(override.aes=list(shape=c(NA,NA,16), linetype=c(2,1,0)))) +
# annotate the effect size
geom_segment(aes(x=x, y=y, xend=xend, yend=yend), data=annotationLocation, color=annoColor) +
annotate('text', label=annotation, x=annotationX, y=annotationY, size=3, vjust=.5, hjust=1) +
# place vertical line(s) at cutpoint(s)
geom_vline(xintercept=as.numeric(start), linetype=5, color=annoColor) +
geom_vline(xintercept=as.numeric(end), linetype=5, color=annoColor) +
annotate('text', label=label1, x=start, y=max(graphData$observed, na.rm=TRUE), size=3, hjust=1, vjust=-.25, angle=90) +
annotate('text', label=label2, x=end, y=max(graphData$observed, na.rm=TRUE), size=3, hjust=1, vjust=-.25, angle=90) +
# label and theme
labs(title=title, y=ylab, x=xlab) +
theme_bw()
p
# --------------------------------------------------------------------------------------------------------------------------------
# --------------
# Return output
return(p)
# --------------
}