41 lines
1.6 KiB
R
41 lines
1.6 KiB
R
source("renv/activate.R")
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library(RMySQL)
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library("dplyr", warn.conflicts = F)
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library(readr)
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library(stringr)
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library(yaml)
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participant_file <- snakemake@input[[1]]
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source <- snakemake@params[["source"]]
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sensor <- snakemake@params[["sensor"]]
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table <- snakemake@params[["table"]]
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sensor_file <- snakemake@output[[1]]
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participant <- read_yaml(participant_file)
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if(! "FITBIT" %in% names(participant)){
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stop(paste("The following participant file does not have a FITBIT section, create one manually or automatically (see the docs):", participant_file))
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}
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device_ids <- participant$FITBIT$DEVICE_IDS
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unified_device_id <- tail(device_ids, 1)
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# As opposed to phone data, we dont' filter by date here because data can still be in JSON format, we need to parse it first
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if(source$TYPE == "DATABASE"){
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dbEngine <- dbConnect(MySQL(), default.file = "./.env", group = source$DATABASE_GROUP)
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query <- paste0("SELECT * FROM ", table, " WHERE ",source$DEVICE_ID_COLUMN," IN ('", paste0(device_ids, collapse = "','"), "')")
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sensor_data <- dbGetQuery(dbEngine, query)
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dbDisconnect(dbEngine)
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sensor_data <- sensor_data %>%
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rename(device_id = source$DEVICE_ID_COLUMN) %>%
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mutate(device_id = unified_device_id) # Unify device_id
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if(FALSE) # For MoSHI use, we didn't split fitbit sensors into different tables
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sensor_data <- sensor_data %>% filter(fitbit_data_type == str_split(sensor, "_", simplify = TRUE)[[2]])
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# Droping duplicates on all columns except for _id or id
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sensor_data <- sensor_data %>% distinct(!!!syms(setdiff(names(sensor_data), c("_id", "id"))))
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write_csv(sensor_data, sensor_file)
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}
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