90 lines
4.4 KiB
R
90 lines
4.4 KiB
R
source("renv/activate.R")
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# Load Ian Barnett's code. Taken from https://scholar.harvard.edu/ibarnett/software/gpsmobility
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file.sources = list.files(c("src/features/location_barnett"), pattern="*.R$", full.names=TRUE, ignore.case=TRUE)
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sapply(file.sources,source,.GlobalEnv)
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library(dplyr)
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write_empty_file <- function(file_path, requested_features){
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write.csv(data.frame(local_date= character(),
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location_barnett_hometime= numeric(),
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location_barnett_disttravelled= numeric(),
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location_barnett_rog= numeric(),
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location_barnett_maxdiam= numeric(),
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location_barnett_maxhomedist= numeric(),
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location_barnett_siglocsvisited= numeric(),
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location_barnett_avgflightlen= numeric(),
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location_barnett_stdflightlen= numeric(),
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location_barnett_avgflightdur= numeric(),
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location_barnett_stdflightdur= numeric(),
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location_barnett_probpause= numeric(),
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location_barnett_siglocentropy= numeric(),
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location_barnett_minsmissing= numeric(),
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location_barnett_circdnrtn= numeric(),
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location_barnett_wkenddayrtn= numeric(),
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minutes_data_used= numeric()
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) %>% select(requested_features), file_path, row.names = F)
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}
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location <- read.csv(snakemake@input[["locations"]], stringsAsFactors = F)
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# The choice between RESAMPLE_FUSED and the original location data happens at the rule level in the function
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# optional_location_input in features.snakefile
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locations_to_use <- snakemake@params[["locations_to_use"]]
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accuracy_limit <- snakemake@params[["accuracy_limit"]]
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timezone <- snakemake@params[["timezone"]]
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minutes_data_used <- snakemake@params[["minutes_data_used"]]
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requested_features <- intersect(unlist(snakemake@params["features"], use.names = F),
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c("hometime","disttravelled","rog","maxdiam","maxhomedist","siglocsvisited","avgflightlen","stdflightlen","avgflightdur","stdflightdur","probpause","siglocentropy","minsmissing","circdnrtn","wkenddayrtn"))
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requested_features <- c("local_date", paste("location_barnett", requested_features, sep = "_"))
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if(minutes_data_used)
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requested_features <- c(requested_features, "minutes_data_used")
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if(!locations_to_use %in% c("ALL_EXCEPT_FUSED", "RESAMPLE_FUSED", "ALL")){
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print("Unkown filter, provide one of the following three: ALL, ALL_EXCEPT_FUSED, or RESAMPLE_FUSED")
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quit(save = "no", status = 1, runLast = FALSE)
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}
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# excludes fused and resample
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if(locations_to_use == "ALL_EXCEPT_FUSED")
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location <- location %>% filter(provider == "gps")
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# Remove 0,0 location coordinates
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location <- location %>% filter(double_latitude != 0 & double_longitude != 0)
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# Excludes datasets with less than 24 hours of data
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if(max(location$timestamp) - min(location$timestamp) < 86400000)
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location <- head(location, 0)
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if (nrow(location) > 1){
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# Count how many minutes of data we use to get location features
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# Some minutes have multiple fused rows
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location_minutes_used <- location %>%
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group_by(local_date, local_hour) %>%
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summarise(n_minutes = n_distinct(local_minute)) %>%
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group_by(local_date) %>%
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summarise(minutes_data_used = sum(n_minutes)) %>%
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select(local_date, minutes_data_used)
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location <- location %>%
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select(timestamp, latitude = double_latitude, longitude = double_longitude, altitude = double_altitude, accuracy)
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outputMobility <- MobilityFeatures(location, ACCURACY_LIM = accuracy_limit, tz = timezone)
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if(is.null(outputMobility)){
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write_empty_file(snakemake@output[[1]], requested_features)
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} else{
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# Copy index (dates) as a column
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features <- cbind(rownames(outputMobility$featavg), outputMobility$featavg)
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features <- as.data.frame(features)
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features[-1] <- lapply(lapply(features[-1], as.character), as.numeric)
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colnames(features)=c("local_date",tolower(paste("location_barnett", colnames(outputMobility$featavg), sep = "_")))
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# Add the minute count column
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features <- left_join(features, location_minutes_used, by = "local_date")
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write.csv(features %>% select(requested_features), snakemake@output[[1]], row.names = F)
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}
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} else {
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write_empty_file(snakemake@output[[1]], requested_features)
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}
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