Clean, Consistent Column Names

clean_names <- function(.data, unique = FALSE) {
  n <- if (is.data.frame(.data)) colnames(.data) else .data

  n <- gsub("%+", "_pct_", n)
  n <- gsub("\\$+", "_dollars_", n)
  n <- gsub("\\++", "_plus_", n)
  n <- gsub("-+", "_minus_", n)
  n <- gsub("\\*+", "_star_", n)
  n <- gsub("#+", "_cnt_", n)
  n <- gsub("&+", "_and_", n)
  n <- gsub("@+", "_at_", n)

  n <- gsub("[^a-zA-Z0-9_]+", "_", n)
  n <- gsub("([A-Z][a-z])", "_\\1", n)
  n <- tolower(trimws(n))
  
  n <- gsub("(^_+|_+$)", "", n)
  
  n <- gsub("_+", "_", n)
  
  if (unique) n <- make.unique(n, sep = "_")
  
  if (is.data.frame(.data)) {
    colnames(.data) <- n
    .data
  } else {
    n
  }
}

clean_names(
  c(
    "  a", "a  ",
    "a %", "a", "$a", "$$$a", "GDP ($)", "GDP (us$)",
    "a (#)", "a & b", "#", "$",
    "a_cnt",
    "Aa&Bb", "camelCasePhrases",
    "AlphaBetaGamma", "Alpha       Beta", "Beta  !!! Gamma",
    "a + b", "a - b", "a * b"
  )
)

##  [1] "a"                  "a"                  "a_pct"             
##  [4] "a"                  "dollars_a"          "dollars_a"         
##  [7] "gdp_dollars"        "gdp_us_dollars"     "a_cnt"             
## [10] "a_and_b"            "cnt"                "dollars"           
## [13] "a_cnt"              "aa_and_bb"          "camel_case_phrases"
## [16] "alpha_beta_gamma"   "alpha_beta"         "beta_gamma"        
## [19] "a_plus_b"           "a_minus_b"          "a_star_b"

df <- read.csv("data-raw/source_data") %>%
  clean_names()

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