61 lines
3.1 KiB
R
61 lines
3.1 KiB
R
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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else if(day_segment == "daily")
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return(data)
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else
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return(data %>% head(0))
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}
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base_sms_features <- function(sms, sms_type, 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("countmostfrequentcontact", "count", "distinctcontacts", "timefirstsms", "timelastsms")
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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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# Filter rows that belong to the sms type and day segment of interest
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sms <- sms %>% filter(message_type == ifelse(sms_type == "received", "1", ifelse(sms_type == "sent", 2, NA))) %>%
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filter_by_day_segment(day_segment)
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# If there are not features or data to work with, return an empty df with appropiate columns names
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if(length(features_to_compute) == 0)
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return(features)
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if(nrow(sms) < 1)
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return(cbind(features, read.csv(text = paste(paste("sms", sms_type, day_segment, features_to_compute, sep = "_"), collapse = ","), stringsAsFactors = FALSE)))
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for(feature_name in features_to_compute){
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if(feature_name == "countmostfrequentcontact"){
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# Get the number of messages for the most frequent contact throughout the study
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mostfrequentcontact <- sms %>%
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group_by(trace) %>%
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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 contacts with the same amount of messages pick the first one only
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pull(trace)
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feature <- sms %>%
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filter(trace == mostfrequentcontact) %>%
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group_by(local_date) %>%
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summarise(!!paste("sms", sms_type, day_segment, feature_name, sep = "_") := n()) %>%
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replace(is.na(.), 0)
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features <- merge(features, feature, by="local_date", all = TRUE)
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} else {
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feature <- sms %>%
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group_by(local_date)
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feature <- switch(feature_name,
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"count" = feature %>% summarise(!!paste("sms", sms_type, day_segment, feature_name, sep = "_") := n()),
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"distinctcontacts" = feature %>% summarise(!!paste("sms", sms_type, day_segment, feature_name, sep = "_") := n_distinct(trace)),
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"timefirstsms" = feature %>% summarise(!!paste("sms", sms_type, day_segment, feature_name, sep = "_") := first(local_hour) * 60 + first(local_minute)),
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"timelastsms" = feature %>% summarise(!!paste("sms", sms_type, day_segment, feature_name, sep = "_") := last(local_hour) * 60 + last(local_minute)))
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features <- merge(features, feature, by="local_date", all = TRUE)
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}
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}
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return(features)
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} |