53 lines
2.1 KiB
R
53 lines
2.1 KiB
R
library(dplyr)
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library(tidyr)
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filter_by_day_segment <- function(data, day_segment) {
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if(day_segment %in% c("morning", "afternoon", "evening", "night"))
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data <- data %>% filter(local_day_segment == day_segment)
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return(data %>% group_by(local_date))
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}
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compute_bluetooth_feature <- function(data, feature, day_segment){
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data <- data %>% filter_by_day_segment(day_segment)
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if(feature %in% c("countscans", "uniquedevices")){
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data <- switch(feature,
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"countscans" = data %>% summarise(!!paste("bluetooth", day_segment, feature, sep = "_") := n()),
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"uniquedevices" = data %>% summarise(!!paste("bluetooth", day_segment, feature, sep = "_") := n_distinct(bt_address)))
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return(data)
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} else if(feature == "countscansmostuniquedevice"){
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# Get the most scanned device
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mostuniquedevice <- data %>%
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group_by(bt_address) %>%
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mutate(N=n()) %>%
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ungroup() %>%
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filter(N == max(N)) %>%
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head(1) %>% # if there are multiple device with the same amount of scans pick the first one only
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pull(bt_address)
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return(data %>%
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filter(bt_address == mostuniquedevice) %>%
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group_by(local_date) %>%
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summarise(!!paste("bluetooth", day_segment, feature, sep = "_") := n()) %>%
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replace(is.na(.), 0))
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}
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}
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base_bluetooth_features <- function(bluetooth_data, day_segment, requested_features){
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# Output dataframe
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features = data.frame(local_date = character(), stringsAsFactors = FALSE)
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# The name of the features this function can compute
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base_features_names <- c("countscans", "uniquedevices", "countscansmostuniquedevice")
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# The subset of requested features this function can compute
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features_to_compute <- intersect(base_features_names, requested_features)
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for(feature_name in features_to_compute){
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feature <- compute_bluetooth_feature(bluetooth_data, feature_name, day_segment)
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features <- merge(features, feature, by="local_date", all = TRUE)
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}
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features <- features %>% mutate_at(vars(contains("countscansmostuniquedevice")), list( ~ replace_na(., 0)))
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return(features)
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} |