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Avoid using eval for aggregating functions #1

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49 changes: 26 additions & 23 deletions R/ts_anom_detection.R
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Original file line number Diff line number Diff line change
Expand Up @@ -34,7 +34,7 @@
#' 99th percentile of the daily max values (p99).
#' @param title Title for the output plot.
#' @param verbose Enable debug messages.
#' @param na.rm Remove any NAs in timestamps.(default: FALSE)
#' @param na.rm Remove any NAs in timestamps.(default: FALSE)
#' @return The returned value is a list with the following components.
#' @return \item{anoms}{Data frame containing timestamps, values, and optionally expected values.}
#' @return \item{plot}{A graphical object if plotting was requested by the user. The plot contains
Expand Down Expand Up @@ -66,7 +66,7 @@ AnomalyDetectionTs <- function(x, max_anoms = 0.10, direction = 'pos',
alpha = 0.05, only_last = NULL, threshold = 'None',
e_value = FALSE, longterm = FALSE, piecewise_median_period_weeks = 2, plot = FALSE,
y_log = FALSE, xlabel = '', ylabel = 'count',
title = NULL, verbose=FALSE, na.rm = FALSE, aggregator = "mean", desired_gran = NULL){
title = NULL, verbose=FALSE, na.rm = FALSE, aggregator = mean, desired_gran = NULL){

# Check for supported inputs types
if(!is.data.frame(x)){
Expand All @@ -84,11 +84,11 @@ AnomalyDetectionTs <- function(x, max_anoms = 0.10, direction = 'pos',
if (any((names(x) == c("timestamp", "count")) == FALSE)) {
colnames(x) <- c("timestamp", "count")
}

if(!is.logical(na.rm)){
stop("na.rm must be either TRUE (T) or FALSE (F)")
}

# Deal with NAs in timestamps
if(any(is.na(x$timestamp))){
if(na.rm){
Expand Down Expand Up @@ -147,6 +147,9 @@ AnomalyDetectionTs <- function(x, max_anoms = 0.10, direction = 'pos',
} else {
title <- paste(title, " : ", sep="")
}
if(!is.function(aggregator)){
stop("aggregator must be a function")
}

# -- Main analysis: Perform S-H-ESD

Expand All @@ -163,30 +166,30 @@ AnomalyDetectionTs <- function(x, max_anoms = 0.10, direction = 'pos',
num_days_per_line <- 1
}

# Aggregate data to minutely if secondly --
# Aggregate data to minutely if secondly --
# Fixed bug to keep proper granularity saved on 'gran' variable
if((gran == "sec") || (gran == "ms")){
x <- format_timestamp(aggregate(x[2], format(x[1], "%Y-%m-%d %H:%M:00"), eval(parse(text=aggregator))))
x <- format_timestamp(aggregate(x[2], format(x[1], "%Y-%m-%d %H:%M:00"), FUN = aggregator))
gran = "min"
}

# Allow for aggregate data in a different granularity than the one defined arbitrarily:
if (!is.null(desired_gran)){
if (desired_gran == "day"){
x <- format_timestamp(aggregate(x[2], format(x[1], "%Y-%m-%d 00:00:00"), eval(parse(text=aggregator))))
gran = "day"
}
if (desired_gran == "hr"){
x <- format_timestamp(aggregate(x[2], format(x[1], "%Y-%m-%d %H:00:00"), eval(parse(text=aggregator))))
gran = "hr"
}
if (desired_gran == "min"){
x <- format_timestamp(aggregate(x[2], format(x[1], "%Y-%m-%d %H:%M:00"), eval(parse(text=aggregator))))
gran = "min"
}
if (desired_gran == "day"){
x <- format_timestamp(aggregate(x[2], by = format(x[1], "%Y-%m-%d 00:00:00"), FUN = aggregator))
gran = "day"
}
if (desired_gran == "hr"){
x <- format_timestamp(aggregate(x[2], by = format(x[1], "%Y-%m-%d %H:00:00"), FUN = aggregator))
gran = "hr"
}
if (desired_gran == "min"){
x <- format_timestamp(aggregate(x[2], by = format(x[1], "%Y-%m-%d %H:%M:00"), FUN = aggregator))
gran = "min"
}
}



period = switch(gran,
min = 1440,
Expand Down Expand Up @@ -363,10 +366,10 @@ AnomalyDetectionTs <- function(x, max_anoms = 0.10, direction = 'pos',

# Fix to make sure date-time is correct and that we retain hms at midnight
all_anoms[[1]] <- format(all_anoms[[1]], format="%Y-%m-%d %H:%M:%S")

# Store expected values if set by user
if(e_value) {
anoms <- data.frame(timestamp=all_anoms[[1]], anoms=all_anoms[[2]],
anoms <- data.frame(timestamp=all_anoms[[1]], anoms=all_anoms[[2]],
expected_value=subset(seasonal_plus_trend[[2]], as.POSIXlt(seasonal_plus_trend[[1]], tz="UTC") %in% all_anoms[[1]]),
stringsAsFactors=FALSE)
} else {
Expand Down