Help with R code -- Funnel Plot

I am new to R and having difficulty getting my head around it, I am creating a custom Funnel Plot HTML Visual for PowerBI and the open source R code uses a weighted mean to calculate the baseline

I want the baseline Calculated by the mean , the R code I am using is fairly complex for me to decipher but I know it is something simple for an experienced R dev, if anybody can help

Line 254 p.fem <- weighted.mean(p[p.se>0], 1/p.se[p.se>0]^2)

File is here

############ User Parameters #########

Set of parameters from GUI

##PBI_PARAM Color of scatterplot points
#Type:string, Default:"orange", Range:NA, PossibleValues:"orange","blue","green","black"
pointsCol = "orange"
if(exists("settings_scatter_params_pointColor")){
pointsCol = settings_scatter_params_pointColor
}

#PBI_PARAM Transparency of scatterplot points
#Type:numeric, Default:0.4, Range:[0,1], PossibleValues:NA, Remarks: NA
transparency = 0.4
if(exists("settings_scatter_params_percentile")){
transparency = settings_scatter_params_percentile/100
}

##PBI_PARAM Color of baseline
#Type:string, Default:"blue", Range:NA, PossibleValues:"orange","blue","green","black"
lineColor = "blue"
if(exists("settings_funnel_params_lineColor")){
lineColor = settings_funnel_params_lineColor
}

#PBI_PARAM Sparsification of scatterplot points
#Type:bool, Default:TRUE, Range:NA, PossibleValues:NA, Remarks: NA
sparsify = TRUE
if(exists("settings_scatter_params_sparsify")){
sparsify = settings_scatter_params_sparsify
}

#PBI_PARAM Size of points on the plot
#Type:numeric, Default: 1 , Range:[0.1,5], PossibleValues:NA, Remarks: NA
pointCex = 1
if(exists("settings_scatter_params_weight")){
pointCex = min(50,max(settings_scatter_params_weight,1))/10
}

#PBI_PARAM Confidence level line
#Type:numeric, Default: 0.75 , Range:[0,1], PossibleValues:NA, Remarks: GUI input is predefined set of values
conf1 = 0.95
if(exists("settings_funnel_params_conf1")){
conf1 = as.numeric(settings_funnel_params_conf1)
}

#PBI_PARAM Confidence level line #2
#Type:numeric, Default: 0.95 , Range:[0,1], PossibleValues:NA, Remarks: NA
conf2 = 0.99
if(exists("settings_funnel_params_conf2")){
conf2 = as.numeric(settings_funnel_params_conf2)
}

axisXisPercentage = TRUE # ratio or percentage
if(exists("settings_axes_params_axisXisPercentage")){
axisXisPercentage = as.numeric(settings_axes_params_axisXisPercentage)
}

scaleXformat = "comma"
if(exists("settings_axes_params_scaleXformat")){
scaleXformat = settings_axes_params_scaleXformat
}

scaleYformat = "none"
if(exists("settings_axes_params_scaleYformat")){
scaleYformat = settings_axes_params_scaleYformat
}

#PBI_PARAM Size of labels on axes
sizeLabel = 12
if(exists("settings_axes_params_textSize")){
sizeLabel = settings_axes_params_textSize
}

#PBI_PARAM Size of ticks on axes
sizeTicks = 6
if(exists("settings_axes_params_sizeTicks")){
sizeTicks = as.numeric(settings_axes_params_sizeTicks)
}
#PBI_PARAM Size of labels on axes
colLabel = "gray"
if(exists("settings_axes_params_colLabel")){
colLabel = settings_axes_params_colLabel
}
##PBI_PARAM: export out data to HTML?
#Type:logical, Default:FALSE, Range:NA, PossibleValues:NA, Remarks: NA
keepOutData = FALSE
if(exists("settings_export_params_show"))
keepOutData = settings_export_params_show

##PBI_PARAM: method of export interface
#Type: string , Default:"copy", Range:NA, PossibleValues:"copy", "download", Remarks: NA
exportMethod = "copy"
if(exists("settings_export_params_method"))
exportMethod = settings_export_params_method

##PBI_PARAM: limit the out table exported
#Type: string , Default:1000, Range:NA, PossibleValues:"1000", "10000", Inf, Remarks: NA
limitExportSize = 1000
if(exists("settings_export_params_limitExportSize"))
limitExportSize = as.numeric(settings_export_params_limitExportSize)

###############Library Declarations###############

source('./r_files/flatten_HTML.r')
source('./r_files/utils.r')

libraryRequireInstall("ggplot2")
libraryRequireInstall("plotly")
libraryRequireInstall("scales")
libraryRequireInstall("caTools")

###############Internal parameters definitions#################

Set of parameters, which are not exported to GUI

#PBI_PARAM is vertical plot
verticalPlot = FALSE

#PBI_PARAM Minimal number of points for funnel plot
minPoints = 10

#PBI_PARAM Size of warnings font
sizeWarn = 11

###############Internal functions definitions#################

#paste tooltips together separated by

generateNiceTooltips = function(dataset)
{
myNames = names(dataset)
LMN = length(myNames)
s = 1; if(LMN > 2) s = 3

nms = myNames[s:LMN]
dta = dataset[,s:LMN]
niceTooltips = NULL

for (n in c(1:length(nms)))
{
if(length(nms) == 1)
niceTooltips = paste(nms," = ", dta, sep = "")
else
{
niceTooltips = paste(niceTooltips,nms[n]," = ", dta[,n], sep = "")
if(n < length(nms))
niceTooltips = paste(niceTooltips,"
", sep = "")
}
}
return(niceTooltips)
}

#tweak the limits of the axis
NiceLimitsAxis <- function(axisData, baseline =NULL, isPositive = TRUE)
{
limsA = c(min(axisData), max(axisData)) # default
if(is.null(baseline))
baseline = sum(limsA)/2

limsA = (limsA - mean(limsA)) * 1.3 + baseline # centralize
limsA[1] = min(limsA[1], min(axisData)) # include outliers
limsA[2] = max(limsA[2], max(axisData)) # include outliers
if(limsA[1] < 0 && isPositive) # don't include region far away from 0
{
temp = -0.02 * (limsA[2])
limsA[1] = max(temp, limsA[1])
}
return(limsA)
}
ConvertDF64encoding = function (df, withoutEncoding = FALSE)
{
header_row <- paste(names(df), collapse=", ")
tab <- apply(df, 1, function(x)paste(x, collapse=", "))

if(withoutEncoding){
text <- paste(c(header_row, tab), collapse="\n")
x <- text
}
else
{
text <- paste(c(header_row, tab), collapse="\n")
x <- caTools::base64encode(text)
}
return(x)
}

KeepOutDataInHTML = function(df, htmlFile = 'out.html', exportMethod = "copy", limitExportSize = 1000)
{
if(nrow(df)>limitExportSize)
df = df[1:limitExportSize,]

outDataString64 = ConvertDF64encoding(df)

linkElem = '\nexport\n'
updateLinkElem = paste(' ', sep =' ')
var64 = paste('', sep ="")
var64href = paste('', sep ="")

buttonElem = 'copy to clipboard'
funcScript = ''

if(exportMethod == "copy")
endOfBody = paste(var64,funcScript, buttonElem,'\n',sep ="")
else#"download"
endOfBody = paste(linkElem,var64, var64href,updateLinkElem,'\n',sep ="")

ReadFullFileReplaceString('out.html', 'out.html', '', endOfBody)

}

############# Input validation & initializations #############

if(conf2 < conf1)# swap
{ temp = conf1; conf1 = conf2; conf2 = temp}

validToPlot = TRUE

pbiWarning = ""

gpd = goodPlotDimension()

if(validToPlot && !gpd) # too small canvas
{
validToPlot = FALSE
pbiWarning1 = "Visual is "
pbiWarning1 = cutStr2Show(pbiWarning1, strCex = sizeWarn/6, partAvailable = 0.9)
pbiWarning2 = "too small "
pbiWarning2 = cutStr2Show(pbiWarning2, strCex = sizeWarn/6, partAvailable = 0.9)
pbiWarning<-paste(pbiWarning1, "
", pbiWarning2, sep="")
sizeWarn = 8 #smaller
}

if(validToPlot && (!exists("population") ||!exists("occurrence"))) # invalid input
{
validToPlot = FALSE
pbiWarning1 = "Both population and occurrence are required"
pbiWarning = cutStr2Show(pbiWarning1, strCex = sizeWarn/6, partAvailable = 0.9)
}

if(validToPlot)
{
population[is.na(population)] = 0
occurrence[is.na(occurrence)] = -1
population[is.null(population)] = 0
occurrence[is.null(occurrence)] = -1

#clean data
validData = rep(TRUE,nrow(population))
validData = as.logical(validData & (population > 1) & (occurrence >= 0) & (occurrence <= population ))
}

if(validToPlot && (sum(validData) < minPoints)) # not enough data samples
{
validToPlot = FALSE
pbiWarning1 = "Not enough data samples"
pbiWarning1 = cutStr2Show(pbiWarning1, strCex = sizeWarn/6, partAvailable = 0.9)
pbiWarning2 = "for funnel plot"
pbiWarning2 = cutStr2Show(pbiWarning2, strCex = sizeWarn/6, partAvailable = 0.9)
pbiWarning<-paste(pbiWarning1, "
", pbiWarning2, sep="")
}

if(validToPlot ) # check packages
{
si = sessionInfo()
namesPackages = c(names(si$otherPkgs), names(si$basePkgs),names(si$loadedOnly))
checkPackages = c("XML","plotly","ggplot2","htmlwidgets","scales")
flagAllPackages = prod(checkPackages %in% namesPackages)

if(!flagAllPackages)
warning("*** Some of the packages are missing ! ***")
}

############# Main code #####################

if(validToPlot)
{
if(!exists("tooltips"))
{
dataset = cbind(population,occurrence)
}else{
dataset = cbind(population,occurrence,tooltips)
}

dataset = dataset[validData,]# keep only valid
namesDS = names(dataset)

countValue = dataset[,1]
p = dataset[,2]/dataset[,1]
p.se <- sqrt((p*(1-p)) / (countValue))
df <- data.frame(p, countValue, p.se)

common effect (fixed effect model)

p.fem <- weighted.mean(p[p.se>0], 1/p.se[p.se>0]^2)

lower and upper limits, based on FEM estimator

zLow = qnorm(conf1)
zUp = qnorm(conf2)

mult = 1
entryWordLabelY = "Ratio of "
if(axisXisPercentage)
{
mult = 100
entryWordLabelY = "Percentage of "
}

number.seq <- seq(from = min(countValue), to = max(countValue), length.out= 1000)
number.llconf1 <- (p.fem - zLow * sqrt((p.fem*(1-p.fem)) / (number.seq)))mult
number.ulconf1 <- (p.fem + zLow * sqrt((p.fem
(1-p.fem)) / (number.seq)))mult
number.llconf2 <- (p.fem - zUp * sqrt((p.fem
(1-p.fem)) / (number.seq)))mult
number.ulconf2 <- (p.fem + zUp * sqrt((p.fem
(1-p.fem)) / (number.seq)))*mult

if(keepOutData)
{
exportDF = dataset
exportDF$p = p * mult
exportDF$llconf1 <- (p.fem - zLow * sqrt((p.fem*(1-p.fem)) / (countValue)))mult
exportDF$ulconf1 <- (p.fem + zLow * sqrt((p.fem
(1-p.fem)) / (countValue)))mult
exportDF$llconf2 <- (p.fem - zUp * sqrt((p.fem
(1-p.fem)) / (countValue)))mult
exportDF$ulconf2 <- (p.fem + zUp * sqrt((p.fem
(1-p.fem)) / (countValue)))*mult
}

yAxis = pmult
p.fem = p.fem
mult

dfCI <- data.frame(number.llconf1, number.ulconf1, number.llconf2, number.ulconf2, number.seq, p.fem)

#tweak the limits of the y-axis
limsY = NiceLimitsAxis(axisData = yAxis, baseline = p.fem)

xLabText = cutStr2Show( namesDS[1], strCex = sizeLabel/6, isH = TRUE, partAvailable = 0.85)
yLabText = cutStr2Show( paste(entryWordLabelY, namesDS[2], sep =""), strCex = sizeLabel/6, isH = FALSE, partAvailable = 0.85)

draw plot

if(sparsify)
drawPoints = SparsifyScatter(dataset)# remove points from dense regions
else
drawPoints = SparsifyScatter(dataset,minMaxPoints = c(Inf,Inf))

fp <- ggplot(aes(x = countValue[drawPoints], y = yAxis[drawPoints]), data = df[drawPoints,])
fp <- fp + geom_point(shape = 19, colour = alpha(pointsCol,transparency), size = pointCex*2 )

fp <- fp + geom_line(aes(x = number.seq, y = number.llconf1),linetype = 1, colour = "green",data = dfCI)
fp <- fp + geom_line(aes(x = number.seq, y = number.ulconf1),linetype = 1, colour = "green", data = dfCI)

fp <- fp + geom_line(aes(x = number.seq, y = number.llconf2),linetype = 2, colour = "red",data = dfCI)
fp <- fp + geom_line(aes(x = number.seq, y = number.ulconf2), linetype = 2, colour = "red",data = dfCI)

fp <- fp + geom_hline(aes(yintercept = p.fem), data = dfCI, colour = lineColor, linetype = 4)

if(scaleYformat %in% c("none"))
fp <- fp + scale_y_continuous(limits =limsY)

if(scaleYformat %in% c("comma"))
fp <- fp + scale_y_continuous(limits =limsY, labels = comma)

if(scaleYformat %in% c("scientific"))
fp <- fp + scale_y_continuous(limits =limsY, labels = scientific)

if(scaleXformat %in% c("comma"))
fp <- fp + scale_x_continuous(labels = comma)

if(scaleXformat %in% c("dollar"))
fp <- fp + scale_x_continuous(labels = dollar)

if(scaleXformat %in% c("scientific"))
fp <- fp + scale_x_continuous(labels = scientific)

fp <- fp + xlab(xLabText) + ylab(yLabText) + theme_bw()

if(verticalPlot)
fp <- fp + coord_flip()

}else{# empty plot
fp <- ggplot()
}

#add warning as title
fp = fp + labs (title = pbiWarning, caption = NULL) + theme_bw() +
theme(plot.title = element_text(hjust = 0.5, size = sizeWarn),
axis.title=element_text(size = sizeLabel, colour = colLabel),
axis.text=element_text(size = sizeTicks),
panel.border = element_blank(), axis.line = element_line())

if(!validToPlot) # remove box from empty plot
fp = fp + theme(axis.line = element_blank())

############# Create and save widget ###############

p = ggplotly(fp);

disabledButtonsList <- list('toImage', 'sendDataToCloud', 'zoom2d', 'pan', 'pan2d', 'select2d', 'lasso2d', 'hoverClosestCartesian', 'hoverCompareCartesian')
p$x$config$modeBarButtonsToRemove = disabledButtonsList

p <- config(p, staticPlot = FALSE, editable = FALSE, sendData = FALSE, showLink = FALSE,
displaylogo = FALSE, collaborate = FALSE, cloud=FALSE)

if(validToPlot)
{
layerScatter = 1 # first layer is scatter
ntt = generateNiceTooltips(dataset[drawPoints,])
#tooltips on scatter
p$x$data[[layerScatter]]$text = ntt

#tooltips on lines
p$x$data[[2]]$text = paste(as.character(conf1100),"% limits (l)",sep ="")
p$x$data[[3]]$text = paste(as.character(conf1
100),"% limits (u)",sep ="")
p$x$data[[4]]$text = paste(as.character(conf2100),"% limits (l)",sep ="")
p$x$data[[5]]$text = paste(as.character(conf2
100),"% limits (u)",sep ="")
p$x$data[[6]]$text = paste("baseline ", as.character(round(p.fem,4)), sep ="")

}

internalSaveWidget(p, 'out.html')

resolve bug in plotly (margin of 40 px)

ReadFullFileReplaceString('out.html', 'out.html', ',"padding":40,', ',"padding":0,')

if(keepOutData)
KeepOutDataInHTML(df = exportDF, htmlFile = 'out.html', exportMethod = exportMethod, limitExportSize = limitExportSize)

####################################################

Hi,

Welcome to the RStudio community!

Could you rephrase the question please, I don't know what the exact issue is you're trying to solve :slight_smile:

You highlight line 254, but I don't understand what it is you want changed or don't understand ...

Kind regards,
PJ

Hi Pj

I want the baseline to Calculate the mean , at the moment it calculates the weighted mean

NiceLimitsAxis function , has the variable p.fem passed as the baseline
limsY = NiceLimitsAxis(axisData = yAxis, baseline = p.fem)

Line 254 p.fem <- weighted.mean(p[p.se>0], 1/p.se[p.se>0]^2)

p.fem is defined on line 254, I presume I have to alter to calculate the mean vs the weighted mean?

Hi,

According to the weighted mean function, the first argument contains the values, the second one the weights. This means if you just want to use the mean, you only need the first argument that has the values. The code would look like this:

p.fem <- mean(p[p.se>0])

I am not familiar with funnel plots and the reason for using weighted mean in this function, but if you understand and just want to change this to normal mean, this is the way to do it :slight_smil

Hope this helps,
PJ

1 Like

Thanks PJ!

From reading up , Weighted means are very common especially when studying populations ; this Funnel plot was designed to illustrate Population and Mortality Rate for the given input parameters

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