41 lines
1.6 KiB
R
41 lines
1.6 KiB
R
source("packrat/init.R")
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library(tidyr)
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library(dplyr)
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filter_participant_without_enough_days <- function(clean_metrics, participants_day_threshold){
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if("pid" %in% colnames(clean_metrics))
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clean_metrics <- clean_metrics %>% group_by(pid)
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clean_metrics <- clean_metrics %>%
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filter(n() >= participants_day_threshold) %>%
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ungroup()
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return(clean_metrics)
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}
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clean_metrics <- read.csv(snakemake@input[[1]])
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cols_nan_threshold <- snakemake@params[["cols_nan_threshold"]]
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drop_zero_variance_columns <- snakemake@params[["cols_var_threshold"]]
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rows_nan_threshold <- snakemake@params[["rows_nan_threshold"]]
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participants_day_threshold <- snakemake@params[["participants_day_threshold"]]
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# We have to do this before and after dropping rows, that's why is duplicated
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clean_metrics <- filter_participant_without_enough_days(clean_metrics, participants_day_threshold)
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# drop columns with a percentage of NA values above cols_nan_threshold
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if(nrow(clean_metrics))
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clean_metrics <- clean_metrics %>% select_if(~ sum(is.na(.)) / length(.) <= cols_nan_threshold )
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if(drop_zero_variance_columns)
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clean_metrics <- clean_metrics %>% select_if(grepl("pid|local_date",names(.)) | sapply(., n_distinct, na.rm = T) > 1)
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# drop rows with a percentage of NA values above rows_nan_threshold
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clean_metrics <- clean_metrics %>%
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mutate(percentage_na = rowSums(is.na(.)) / ncol(.)) %>%
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filter(percentage_na < rows_nan_threshold) %>%
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select(-percentage_na)
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clean_metrics <- filter_participant_without_enough_days(clean_metrics, participants_day_threshold)
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write.csv(clean_metrics, snakemake@output[[1]], row.names = FALSE)
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