rapids/src/data/phone_valid_sensed_days.R

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2019-11-05 18:34:22 +01:00
source("packrat/init.R")
library(dplyr)
all_sensors <- snakemake@input[["all_sensors"]]
bin_size <- snakemake@params[["bin_size"]]
min_valid_hours <- snakemake@params[["min_valid_hours"]]
min_bins_per_hour <- snakemake@params[["min_bins_per_hour"]]
output_file <- snakemake@output[[1]]
# Load all sensors and extract timestamps
all_sensor_data <- data.frame(timestamp = c())
for(sensor in all_sensors){
sensor_data <- read.csv(sensor, stringsAsFactors = F) %>% select(local_date, local_hour, local_minute)
all_sensor_data <- rbind(all_sensor_data, sensor_data)
}
phone_valid_sensed_days <- all_sensor_data %>%
mutate(bin = (local_minute %/% bin_size) * bin_size) %>% # bin rows into bin_size-minute bins
group_by(local_date, local_hour, bin) %>%
summarise(minute_period = first(bin)) %>% #filter repeated bins (if rows were logged within bin_size minutes)
ungroup() %>%
group_by(local_date, local_hour) %>%
summarise(bins = n()) %>% # Count how many bins there are per hour
ungroup() %>%
filter(bins >= min_bins_per_hour) %>% # Discard those hours where there were fewer than min_bins_per_hour
group_by(local_date) %>%
summarise(valid_hours = n()) %>% # Count how many valid hours each day has
filter(valid_hours >= min_valid_hours) # Discard those days where there were fewer than min_valid_hours
write.csv(phone_valid_sensed_days, output_file, row.names = FALSE)