32 lines
1.4 KiB
R
32 lines
1.4 KiB
R
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source("packrat/init.R")
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library(dplyr)
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all_sensors <- snakemake@input[["all_sensors"]]
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bin_size <- snakemake@params[["bin_size"]]
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min_valid_hours <- snakemake@params[["min_valid_hours"]]
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min_bins_per_hour <- snakemake@params[["min_bins_per_hour"]]
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output_file <- snakemake@output[[1]]
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# Load all sensors and extract timestamps
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all_sensor_data <- data.frame(timestamp = c())
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for(sensor in all_sensors){
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sensor_data <- read.csv(sensor, stringsAsFactors = F) %>% select(local_date, local_hour, local_minute)
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all_sensor_data <- rbind(all_sensor_data, sensor_data)
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}
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phone_valid_sensed_days <- all_sensor_data %>%
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mutate(bin = (local_minute %/% bin_size) * bin_size) %>% # bin rows into bin_size-minute bins
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group_by(local_date, local_hour, bin) %>%
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summarise(minute_period = first(bin)) %>% #filter repeated bins (if rows were logged within bin_size minutes)
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ungroup() %>%
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group_by(local_date, local_hour) %>%
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summarise(bins = n()) %>% # Count how many bins there are per hour
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ungroup() %>%
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filter(bins >= min_bins_per_hour) %>% # Discard those hours where there were fewer than min_bins_per_hour
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group_by(local_date) %>%
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summarise(valid_hours = n()) %>% # Count how many valid hours each day has
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filter(valid_hours >= min_valid_hours) # Discard those days where there were fewer than min_valid_hours
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write.csv(phone_valid_sensed_days, output_file)
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