5.3 Charlie: external helpers

Move reusable data and logic out of the analysis script and into separate files.

Here is the content of beach-lookup-table.csv:

where,english
beach,US
coast,US
seashore,UK
seaside,UK

Here is the content of cleaning-helpers.R:

library(tidyverse)

localize_beach <- function(dat) {
  lookup_table <- read_csv(
    "beach-lookup-table.csv",
    col_types = cols(where = "c", english = "c")
  )
  left_join(dat, lookup_table)
}

f_to_c <- function(x) (x - 32) * 5/9

celsify_temp <- function(dat) {
  mutate(dat, temp = if_else(english == "US", f_to_c(temp), temp))
}

now <- Sys.time()
timestamp <- function(time) format(time, "%Y-%B-%d_%H-%M-%S")
outfile_path <- function(infile) {
  paste0(timestamp(now), "_", sub("(.*)([.]csv$)", "\\1_clean\\2", infile))
}

• High-level helper functions like localize_beach() and celsify_temp(), were added to the pre-existing helpers (f_to_c(), timestamp(), and outfile_path())

library(tidyverse)
source("cleaning-helpers.R")

infile <- "swim.csv"
dat <- read_csv(infile, col_types = cols(name = "c", where = "c", temp = "d"))

(dat <- dat %>% 
    localize_beach() %>% 
    celsify_temp())
#> Joining, by = "where"
#> # A tibble: 5 × 4
#>   name  where     temp english
#>   <chr> <chr>    <dbl> <chr>  
#> 1 Adam  beach     35   US     
#> 2 Bess  coast     32.8 US     
#> 3 Cora  seashore  28   UK     
#> 4 Dale  beach     29.4 US     
#> 5 Evan  seaside   31   UK

write_csv(dat, outfile_path(infile))

Script is now much shorter (and cleaner). However, whether it’s easier depends on personal preference and what “feels” easier to work with.

For more information, refer to the Tidyverse design guide