complexity.R

telemetry_dir <- here::here('data', 'telemetry')

# simplest?
files <- dir(telemetry_dir)
grep_files <- files[!(is.na(stringr::str_extract(files, 'GSM\\d*.csv')))]

df <- tibble::tibble()

for(i in seq_along(grep_files)) {
  d <- readr::read_csv(file.path(telemetry_dir, grep_files[i]))
  df <- rbind(df, d)
  rm(d)
}

# how I wrote it
files <- Filter(x = dir(telemetry_dir),
                f = function (x) !(is.na(stringr::str_extract(x, 'GSM\\d*.csv')))
)

dat <- Reduce('rbind', lapply(files, function (x) {
  readr::read_csv(file.path(telemetry_dir, x))
}))

# one liner
dat2 <- Reduce(
  f = 'rbind',
  x = lapply(
    X = dir(telemetry_dir)[!(is.na(stringr::str_extract(dir(telemetry_dir), 'GSM\\d*.csv')))],
    FUN = function (x) {
      readr::read_csv(file.path(telemetry_dir, x))
      })
  )

# as a function, and for clarity
read_telemetry <- function (directory, pattern) {
  ## get all files in a directory
  files <- dir(directory)
  
  ## filter files to return those matching the pattern
  files <- files[!(is.na(stringr::str_extract(files, pattern)))]
  
  ## loop over files, read from directory with read_csv and
  ## reduce into a data frame with the rbind function
  dat <- Reduce('rbind', lapply(files, function (x) {
    ## change this to any IO function, maybe sf::read_sf, read.table, etc...
    readr::read_csv(file.path(directory, x))
  }))
}

dat3 <- read_telemetry(directory = telemetry_dir, pattern = 'GSM\\d*.csv')