41 lines
1.6 KiB
R
41 lines
1.6 KiB
R
source("renv/activate.R")
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library("dplyr", warn.conflicts = F)
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library(tidyr)
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library(lubridate)
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all_sensors <- snakemake@input[["all_sensors"]]
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bin_size <- snakemake@params[["bin_size"]]
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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) %>%
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select(local_date, local_hour, local_minute) %>%
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mutate(sensor = basename(sensor))
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all_sensor_data <- rbind(all_sensor_data, sensor_data)
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}
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if(nrow(all_sensor_data) == 0){
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bins = seq(0, 59, by = bin_size)
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hours = seq(0, 23, 1)
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write.csv(crossing(hours, bins) %>% unite("hour_bin",hours, bins, sep = "_") %>% mutate(value = NA, local_date = NA) %>% pivot_wider(names_from = hour_bin, values_from=value) %>% head(0), output_file, row.names = FALSE)
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} else{
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phone_sensed_bins <- 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(sensor_count = n_distinct(sensor)) %>%
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ungroup() %>%
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mutate(local_date = lubridate::ymd(local_date)) %>%
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complete(local_date = seq.Date(min(local_date), max(local_date), by="day"),
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fill = list(local_hour = 0, bin = 0, sensor_count = 0)) %>%
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complete(nesting(local_date),
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local_hour = seq(0, 23, 1),
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bin = seq(0, 59, bin_size),
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fill = list(sensor_count=0)) %>%
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pivot_wider(names_from = c(local_hour, bin), values_from = sensor_count)
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write.csv(phone_sensed_bins, output_file, row.names = FALSE)
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}
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