79 lines
4.1 KiB
R
79 lines
4.1 KiB
R
library('tidyr')
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library('stringr')
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message_features_of_type <- function(messages, messages_type, time_segment, requested_features){
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# Output dataframe
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features = data.frame(local_segment = 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", "timefirstmessage", "timelastmessage")
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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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# 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(messages) < 1)
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return(cbind(features, read.csv(text = paste(paste(messages_type, 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 <- messages %>%
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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 <- messages %>%
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group_by(local_segment) %>%
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summarise(!!paste(messages_type, feature_name, sep = "_") := sum(trace == mostfrequentcontact))
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features <- merge(features, feature, by="local_segment", all = TRUE)
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} else {
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feature <- messages %>%
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group_by(local_segment)
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feature <- switch(feature_name,
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"count" = feature %>% summarise(!!paste(messages_type, feature_name, sep = "_") := n()),
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"distinctcontacts" = feature %>% summarise(!!paste(messages_type, feature_name, sep = "_") := n_distinct(trace)),
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"timefirstmessage" = feature %>% summarise(!!paste(messages_type, feature_name, sep = "_") := first(local_hour) * 60 + first(local_minute)),
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"timelastmessage" = feature %>% summarise(!!paste(messages_type, feature_name, sep = "_") := last(local_hour) * 60 + last(local_minute)))
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features <- merge(features, feature, by="local_segment", all = TRUE)
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}
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}
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return(features)
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}
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rapids_features <- function(sensor_data_files, time_segment, provider){
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messages_data <- read.csv(sensor_data_files[["sensor_data"]], stringsAsFactors = FALSE)
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messages_data <- messages_data %>% filter_data_by_segment(time_segment)
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messages_types = provider[["MESSAGES_TYPES"]]
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messages_features <- setNames(data.frame(matrix(ncol=1, nrow=0)), c("local_segment"))
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for(message_type in messages_types){
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# Filter rows that belong to the message type and time segment of interest
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message_type_label = ifelse(message_type == "received", "1", ifelse(message_type == "sent", "2", NA))
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if(is.na(message_type_label))
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stop(paste("Message type can online be received or sent but instead you typed: ", message_type, " in config[PHONE_MESSAGES][MESSAGES_TYPES]"))
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requested_features <- provider[["FEATURES"]][[message_type]]
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messages_of_type <- messages_data %>% filter(message_type == message_type_label)
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features <- message_features_of_type(messages_of_type, message_type, time_segment, requested_features)
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messages_features <- merge(messages_features, features, all=TRUE)
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}
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# Fill seleted columns with a high number
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time_cols <- select(messages_features, contains("timefirstmessages") | contains("timelastmessages")) %>%
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colnames(.)
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messages_features <- messages_features %>%
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mutate_at(., time_cols, ~replace(., is.na(.), 1500))
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# Fill NA values with 0
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messages_features <- messages_features %>% mutate_all(~replace(., is.na(.), 0))
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return(messages_features)
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} |