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No.1

    pollutantmean <- function(directory, pollutant, ID = 1:332){
    
    # Get file names from directory argument
    files_list <- list.files(directory, full.names = TRUE)
    
    # Create a single data frame from all of the files
    dat <- lapply(files_list,
                  read.csv)
    dat <- do.call("rbind", dat)
    
    # Subset the data appropriately
    dat_subset <- dat[dat$ID %in% ID, ]
    
    # Get the mean of the two columns, return in a list
    lapply(dat_subset[c("nitrate", "sulfate")],
           mean,
           na.rm = TRUE)
    }

No 2

    complete <- function(directory, id = 1:332) {

    cvsfiles <- list.files(directory, full.names=TRUE)
    
    nobs <- c()
    for (i in id) {
            dat <- read.csv(files_list[i])
            nobs <- c(nobs, sum(complete.cases(dat)))
            }
    data.frame(id,nobs)
    
    }

    complete("specdata", 30:25)

No 3

    source("complete.R")
    corr <- function(directory, threshold = 0) {
    
    result <- numeric()
    
    # Get file names from directory argument
    files_list <- list.files(directory, full.names = TRUE)
    
    complete_df <- complete(directory)
    
    # subset rows that have number of observations greater than `threshold`
    usable_df <- complete_df[complete_df$nobs > threshold,]
    
    for(monitor_id in usable_df$id) {
            # read data frame for monitors past the threshold
            path <- sprintf("%s/%03d.csv", directory, monitor_id)
            df <- read.csv(path, header = TRUE, comment.char = "")
            
            correlation <- cor(x = df$sulfate, y = df$nitrate, use = "complete.obs")
            result <- c(result, correlation)
            }
    result
    }

    cr <- corr("specdata", 400)
    head(cr)

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