55 lines
2.9 KiB
R
55 lines
2.9 KiB
R
source("renv/activate.R")
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library(dplyr)
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library(readr)
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library(tidyr)
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bin_size <- snakemake@params[["bin_size"]]
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timezone <- snakemake@params[["timezone"]]
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consecutive_threshold <- snakemake@params[["consecutive_threshold"]]
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time_since_valid_location <- snakemake@params[["time_since_valid_location"]]
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locations <- read_csv(snakemake@input[["locations"]], col_types = cols()) %>% filter(provider == "fused")
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phone_sensed_bins <- read_csv(snakemake@input[["phone_sensed_bins"]], col_types = cols(local_date = col_character()))
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if(nrow(locations) > 0){
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sensed_minute_bins <- phone_sensed_bins %>%
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pivot_longer(-local_date, names_to = c("hour", "bin"), names_sep = "_", values_to = "sensor_count") %>%
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mutate(hour = as.integer(hour), bin = as.integer(bin)) %>%
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complete(nesting(local_date, hour), bin = seq(0, 59,1)) %>%
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fill(sensor_count) %>%
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mutate(timestamp = as.numeric(as.POSIXct(paste0(local_date, " ", hour,":", bin,":00"), format = "%Y-%m-%d %H:%M:%S", tz = timezone)) * 1000 ) %>%
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filter(sensor_count > 0) %>%
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select(timestamp)
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resampled_locations <- locations %>%
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bind_rows(sensed_minute_bins) %>%
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mutate(provider = replace_na(provider, "resampled")) %>%
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arrange(timestamp) %>%
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# We group and therefore, fill in, missing rows that appear after a valid fused location record and exist
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# within consecutive_threshold minutes from each other
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mutate(consecutive_time_diff = c(1, diff(timestamp)),
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resample_group = cumsum(!is.na(double_longitude) | consecutive_time_diff > (1000 * 60 * consecutive_threshold))) %>%
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group_by(resample_group) %>%
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# drop rows that are logged after time_since_valid_location minutes from the last valid fused location
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filter((timestamp - first(timestamp) < (1000 * 60 * time_since_valid_location))) %>%
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fill(-timestamp, -resample_group) %>%
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select(-consecutive_time_diff) %>%
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drop_na(double_longitude, double_latitude, accuracy) %>%
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# Add local date_time
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mutate(utc_date_time = as.POSIXct(timestamp/1000, origin="1970-01-01", tz="UTC"),
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local_date_time = format(utc_date_time, tz = timezone, usetz = F)) %>%
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separate(local_date_time, c("local_date","local_time"), "\\s", remove = FALSE) %>%
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separate(local_time, c("local_hour", "local_minute"), ":", remove = FALSE, extra = "drop") %>%
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mutate(local_hour = as.numeric(local_hour), local_minute = as.numeric(local_minute)) %>%
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# Delete resampled rows that exist in the same minute as other original (fused) rows
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group_by(local_date, local_hour, local_minute) %>%
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mutate(n = n()) %>%
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filter(n == 1 | (n > 1 & provider == "fused")) %>%
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select(-n)
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write.csv(resampled_locations,snakemake@output[[1]], row.names = F)
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} else {
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write.csv(locations,snakemake@output[[1]], row.names = F)
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}
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