Fix default warning

[zkamvar]

This will fix #47

• no longer warns if there's no default to replace
• now has the option to warn if things are caught by the default

[zkamvar]

Currently clean_variable_spelling() has a warn = FALSE argument that will aggregate warnings and indicate which columns they belong to. This has not yet been passed to clean_variables() or clean_data():

set.seed(2019-02-19)
library("linelist")
toy_data <- messy_data(10)

messy_locations <- c("hopsital", "h\u00f4pital", "hospital", 
                     "m\u00e9dical", "clinic", 
                     "feild", "field", NA, "howdy")

toy_data$location <- factor(sample(messy_locations, 10, replace = TRUE))
toy_data <- tibble::as_tibble(toy_data)

wordlist <- data.frame(
  from  = c("hopsital", "hopital",  "medical", "feild", NA, ".default"),
  to = c("hospital", "hospital", "clinic",  "field", "unknown", "unknown"),
  variable = rep("location", 6),
  stringsAsFactors = FALSE
)

global_words <- data.frame(
  from = "not_a_case",
  to = "not a case",
  variable = ".global",
  stringsAsFactors = FALSE
)

cleaned_data <- clean_data(toy_data)

# clean_variable_spelling has a `warn` argument
clean_variable_spelling(cleaned_data, cbind(wordlist, global_words), warn = TRUE)
#> Warning in clean_variable_spelling(cleaned_data, cbind(wordlist, global_words), : The following warnings were found...
#>   location__:
#>   .... NA was present in the first column of d; replacing with the character 'NA' If you want to indicate missing data, use the '.missing' keyword.
#>   .... 'howdy' was changed to the default value ('unknown')
#> # A tibble: 10 x 9
#>    id    date_of_onset discharge  gender epi_case_defini… messy_dates   lat
#>    <fct> <date>        <date>     <fct>  <fct>            <date>      <dbl>
#>  1 mxtw… 2018-01-02    2018-01-12 female suspected        NA          14.1 
#>  2 fdco… 2018-01-04    2018-01-14 female confirmed        NA          14.4 
#>  3 iwbo… 2018-01-02    2018-01-12 female suspected        NA          16.1 
#>  4 uboe… 2018-01-02    2018-01-12 female suspected        2018-10-18  14.7 
#>  5 dbbc… 2018-01-11    2018-01-21 male   not_a_case       1989-12-24  12.3 
#>  6 zzgy… 2018-01-06    2018-01-16 male   not_a_case       2001-12-01  13.0 
#>  7 nilp… 2018-01-02    2018-01-12 female not_a_case       NA          13.6 
#>  8 rmwa… 2018-01-10    2018-01-20 female suspected        NA           9.27
#>  9 bfzo… 2018-01-11    2018-01-21 male   suspected        1989-12-24  13.0 
#> 10 atwa… 2018-01-06    2018-01-16 female not_a_case       1989-12-24  13.5 
#> # … with 2 more variables: lon <dbl>, location <fct>

# clean_data doesn't have this option yet
clean_data(cleaned_data, wordlists = cbind(wordlist, global_words))
#> # A tibble: 10 x 9
#>    id    date_of_onset discharge  gender epi_case_defini… messy_dates   lat
#>    <fct> <date>        <date>     <fct>  <fct>            <date>      <dbl>
#>  1 mxtw… 2018-01-02    2018-01-12 female suspected        NA          14.1 
#>  2 fdco… 2018-01-04    2018-01-14 female confirmed        NA          14.4 
#>  3 iwbo… 2018-01-02    2018-01-12 female suspected        NA          16.1 
#>  4 uboe… 2018-01-02    2018-01-12 female suspected        2018-10-18  14.7 
#>  5 dbbc… 2018-01-11    2018-01-21 male   not_a_case       1989-12-24  12.3 
#>  6 zzgy… 2018-01-06    2018-01-16 male   not_a_case       2001-12-01  13.0 
#>  7 nilp… 2018-01-02    2018-01-12 female not_a_case       NA          13.6 
#>  8 rmwa… 2018-01-10    2018-01-20 female suspected        NA           9.27
#>  9 bfzo… 2018-01-11    2018-01-21 male   suspected        1989-12-24  13.0 
#> 10 atwa… 2018-01-06    2018-01-16 female not_a_case       1989-12-24  13.5 
#> # … with 2 more variables: lon <dbl>, location <fct>

wordlist$from[is.na(wordlist$from)] <- ".missing"
clean_variable_spelling(cleaned_data, cbind(wordlist, global_words), warn = TRUE)
#> Warning in clean_variable_spelling(cleaned_data, cbind(wordlist, global_words), : The following warnings were found...
#>   location__:
#>   .... 'howdy' was changed to the default value ('unknown')
#> # A tibble: 10 x 9
#>    id    date_of_onset discharge  gender epi_case_defini… messy_dates   lat
#>    <fct> <date>        <date>     <fct>  <fct>            <date>      <dbl>
#>  1 mxtw… 2018-01-02    2018-01-12 female suspected        NA          14.1 
#>  2 fdco… 2018-01-04    2018-01-14 female confirmed        NA          14.4 
#>  3 iwbo… 2018-01-02    2018-01-12 female suspected        NA          16.1 
#>  4 uboe… 2018-01-02    2018-01-12 female suspected        2018-10-18  14.7 
#>  5 dbbc… 2018-01-11    2018-01-21 male   not_a_case       1989-12-24  12.3 
#>  6 zzgy… 2018-01-06    2018-01-16 male   not_a_case       2001-12-01  13.0 
#>  7 nilp… 2018-01-02    2018-01-12 female not_a_case       NA          13.6 
#>  8 rmwa… 2018-01-10    2018-01-20 female suspected        NA           9.27
#>  9 bfzo… 2018-01-11    2018-01-21 male   suspected        1989-12-24  13.0 
#> 10 atwa… 2018-01-06    2018-01-16 female not_a_case       1989-12-24  13.5 
#> # … with 2 more variables: lon <dbl>, location <fct>

^{Created on 2019-02-27 by the reprex package (v0.2.1)}

[zkamvar]

I've added the option warn_spelling = FALSE to clean_variables() and clean_data() so now you get get these warnings no matter what!