#' ParseTrended
#'
#' Internal Function - Parses a trended report returned from the API
#'
#' @param report.data jsonlite formatted data frame of report data returned from the API
#'
#' @importFrom plyr rename
#'
#' @return Formatted data frame
#'
#' @family internal
#' @keywords internal
ParseTrended <- function(report.data) {
# jsonlite already makes this into a nice data frame for us
data <- report.data$report$data
elements <- report.data$report$elements
metrics <- report.data$report$metrics$id
formatted.df <- data.frame()
# We need to work our way down the nested data structure
# We've essentially got a ranked report for each date
for(i in 1:nrow(data)) {
if(nrow(elements)>1){
# if we have multiple elements, then build inner breakdowns
breakdown <- data[i,"breakdown"][[1]]
if(length(breakdown)>0) {
temp <- BuildInnerBreakdownsRecursively(breakdown,elements,metrics,1,c())
}
} else {
# if we have just one element, then we just process this, as we may have anomaly detection
temp <- data[i,"breakdown"][[1]]
if(!is.null(temp)&&length(temp)>0) {
counts.df <- ldply(temp$counts)
names(counts.df) <- metrics #assign names to counts.df
# check if we have anomaly detection
if("forecasts" %in% colnames(temp)) {
forecasts.df <- ldply(temp$forecasts)
names(forecasts.df) <- paste("forecast.",metrics,sep="")
counts.df <- cbind(counts.df,forecasts.df)
}
if("upperBounds" %in% colnames(temp)) {
upperBounds.df <- ldply(temp$upperBounds)
names(upperBounds.df) <- paste("upperBound.",metrics,sep="")
counts.df <- cbind(counts.df,upperBounds.df)
}
if("lowerBounds" %in% colnames(temp)) {
lowerBounds.df <- ldply(temp$lowerBounds)
names(lowerBounds.df) <- paste("lowerBound.",metrics,sep="")
counts.df <- cbind(counts.df,lowerBounds.df)
}
# convert all count columns to numeric
for(j in 1:ncol(counts.df)) {
counts.df[,j] <- as.numeric(counts.df[,j])
}
drops <- c("counts","forecasts","upperBounds","lowerBounds")
temp <- temp[,!(names(temp) %in% drops)]
temp <- cbind(temp,counts.df)
}
}
# build out the date columns and bind them to the left of the data frame
if(exists("temp")&&!is.null(temp)&&length(temp)>0) {
date.df <- data.frame(matrix(NA, nrow = nrow(temp), ncol = 5))
names(date.df) <- c("date_desc","datetime","year","month","day")
date.df$date_desc <- data[i,]$name
date.df$year <- data[i,]$year
date.df$month <- data[i,]$month
date.df$day <- data[i,]$day
if('hour' %in% colnames(data[i,])){
date.df$datetime <- strptime(paste(data[i,]$year,data[i,]$month,data[i,]$day,data[i,]$hour,sep="-"), "%Y-%m-%d-%H")
date.df$hour <- data[i,]$hour
} else {
date.df$datetime <- strptime(paste(data[i,]$year,data[i,]$month,data[i,]$day,sep="-"), "%Y-%m-%d")
}
temp <- cbind(date.df,temp)
# clean up redundant date field
drops <- c("date_desc","year","month","day")
temp <- temp[,!(names(temp) %in% drops)]
if(nrow(formatted.df)>0) {
formatted.df <- rbind(formatted.df,temp)
} else {
formatted.df <- temp
}
}
}
#Get segment
seg <- report.data$report$segments
#If segment null, make a dummy data frame
if(is.null(seg)){
seg <- data.frame(list("", ""))
names(seg) <- c("segment.id", "segment.name")
}
#If segment has values, concatenate all values with "AND". R puts the
#concatenated values in every single row, so I dedupe the dataframe
else{
names(seg) <- c("segment.id", "segment.name")
seg$segment.name<-(paste(as.list(seg$segment.name),collapse=" AND "))
seg$segment.id<-(paste(as.list(seg$segment.id),collapse=" AND "))
seg<-subset(seg,!duplicated(seg$segment.name))}
if(nrow(formatted.df) > 0){
formatted.df <- cbind(formatted.df, seg, row.names = NULL)
}
return(formatted.df)
}
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