74 lines
3.9 KiB
R
74 lines
3.9 KiB
R
source("renv/activate.R")
|
|
library("dplyr", warn.conflicts = F)
|
|
library(readr)
|
|
library(tidyr)
|
|
|
|
consecutive_threshold <- snakemake@params[["consecutive_threshold"]]
|
|
time_since_valid_location <- snakemake@params[["time_since_valid_location"]]
|
|
locations_to_use <- snakemake@params[["locations_to_use"]]
|
|
|
|
phone_sensed_timestamps <- read_csv(snakemake@input[["phone_sensed_timestamps"]], col_types = cols_only(timestamp = col_double()))
|
|
locations <- read.csv(snakemake@input[["locations"]]) %>%
|
|
filter(double_latitude != 0 & double_longitude != 0) %>%
|
|
drop_na(double_longitude, double_latitude)
|
|
|
|
if(!locations_to_use %in% c("ALL", "FUSED_RESAMPLED", "GPS", "ALL_RESAMPLED")){
|
|
print("Unkown location filter, provide one of the following three: ALL, GPS, ALL_RESAMPLED, or FUSED_RESAMPLED")
|
|
quit(save = "no", status = 1, runLast = FALSE)
|
|
}
|
|
|
|
# keep the location row that has the best (lowest) accuracy if more than 1 row was logged within any 1 second
|
|
if(locations_to_use %in% c("FUSED_RESAMPLED", "ALL_RESAMPLED"))
|
|
locations <- locations %>% drop_na(double_longitude, double_latitude) %>%
|
|
mutate(minute_bin = timestamp %/% 1001) %>%
|
|
group_by(minute_bin) %>%
|
|
slice(which.min(accuracy)) %>%
|
|
ungroup() %>%
|
|
select(-minute_bin)
|
|
|
|
if(locations_to_use == "ALL"){
|
|
processed_locations <- locations
|
|
} else if(locations_to_use == "GPS"){
|
|
processed_locations <- locations %>% filter(provider == "gps")
|
|
} else if(locations_to_use %in% c("FUSED_RESAMPLED", "ALL_RESAMPLED")){
|
|
if (locations_to_use == "FUSED_RESAMPLED"){
|
|
locations <- locations %>% filter(provider == "fused")
|
|
providers_to_keep = c("fused")
|
|
} else if(locations_to_use == "ALL_RESAMPLED"){
|
|
providers_to_keep = c("fused", "gps", "network")
|
|
}
|
|
|
|
if(nrow(locations) > 0){
|
|
processed_locations <- locations %>%
|
|
distinct(timestamp, .keep_all = TRUE) %>%
|
|
bind_rows(phone_sensed_timestamps) %>%
|
|
arrange(timestamp) %>%
|
|
# We group and therefore, fill in, missing rows that appear after a valid fused location record and exist
|
|
# within consecutive_threshold minutes from each other
|
|
mutate(consecutive_time_diff = c(1, diff(timestamp)),
|
|
resample_group = cumsum(!is.na(double_longitude) | consecutive_time_diff > (1000 * 60 * consecutive_threshold))) %>%
|
|
group_by(resample_group) %>%
|
|
# Filter those rows that are further away than time_since_valid_location since the last fused location
|
|
mutate(time_from_fused = timestamp - first(timestamp)) %>%
|
|
filter(provider %in% providers_to_keep | (time_from_fused < (1000 * 60 * time_since_valid_location))) %>%
|
|
select(-consecutive_time_diff, -time_from_fused) %>%
|
|
# Summarise the period to resample for
|
|
summarise(across(timestamp, max, .names = "limit"), across(everything(), first)) %>%
|
|
# the limit will be equal to the next timestamp-1 or the last binded timestamp (limit) plus the consecutive_threshold buffer
|
|
# you can think of consecutive_threshold as the period a location row is valid for
|
|
mutate(limit = pmin(lead(timestamp, default = 9999999999999) - 1, limit + (1000 * 60 * consecutive_threshold)),
|
|
n_resample = (limit - timestamp)%/%60001,
|
|
n_resample = if_else(n_resample == 0, 1, n_resample)) %>%
|
|
drop_na(double_longitude, double_latitude) %>%
|
|
uncount(weights = n_resample, .id = "id") %>%
|
|
mutate(provider = if_else(id > 1, "resampled", provider),
|
|
id = id -1,
|
|
timestamp = timestamp + (id * 60000)) %>%
|
|
ungroup() %>%
|
|
select(-resample_group, -limit, -id)
|
|
} else {
|
|
processed_locations <- locations
|
|
}
|
|
}
|
|
write.csv(processed_locations,snakemake@output[[1]], row.names = F)
|