add .common_na_strings

[njtierney]

There are many other ways to represent missing values - I would like to include a bit of a mega string of values that naniar can search for, and also replace. This would cover things like:

• "N A "
• "NA "
• " NA"
• "N/A"
• "N / A"

etc.

[njtierney]

It would be ideal if this had some fancy regex I think.

[njtierney]

Current list of "NA' values

• "NA"
• "N A"
• "N/A"
• "NA "
• " NA"
• "N /A"
• "N / A"
• " N / A"
• "N / A "
• "na"
• "n a"
• "n/a"
• "na "
• " na"
• "n /a"
• "n / a"
• " a / a"
• "n / a "

[b-rodrigues]

Recently I've encountered "?".

[EdwinTh]

• "NULL"
• "null"
• "-999"
• "-"

[apear9]

"" is one i've encountered a few times when going from excel to R

[sinarueeger]

I'd add:

• "." (origin: data extracted from STATA)
• "-9" (when a variable is supposed to a range of values, e.g. [0,1,2])

[cimentadaj]

Also "99"

Beware of the "66", "77" and "88" which are usualy things like "Didn't want to respond" or 'Question skipped'

[TonyLadson]

I've seen 'NR' and 'nr' for not read.

…

On Tue, 29 May 2018 at 18:07, Jorge Cimentada ***@***.***> wrote: Also "99" Beware of the "66", "77" and "88" which are usualy things like "Didn't want to respond" or 'Question skipped' — You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub <#168 (comment)>, or mute the thread <https://github.com/notifications/unsubscribe-auth/AB4BzTnkAeNNgB0Nu10ewlQuQDKO-3n9ks5t3QGngaJpZM4UQEAZ> .

[njtierney]

Thank you everyone for your comments, really appreciate them!

I'm also linking in the twitter question which had some nice examples as well:

https://twitter.com/nj_tierney/status/1001340686409482240

[njtierney]

Thank you everyone for your comments! Here are common_na_strings and common_na_numbers in action:

library(naniar)

common_na_strings
#>  [1] "NA"     "N A"    "N/A"    "NA "    " NA"    "N /A"   "N / A" 
#>  [8] " N / A" "N / A " "na"     "n a"    "n/a"    "na "    " na"   
#> [15] "n /a"   "n / a"  " a / a" "n / a " "?"      "."      "NULL"  
#> [22] "null"   ""       "."
common_na_numbers
#> [1]    -9   -99  -999 -9999  9999    66    77    88

dat_ms <- tibble::tribble(~x,  ~y,    ~z,
                         1,   "A",   -100,
                         3,   "N/A", -99,
                         NA,  NA,    -98,
                         -99, "E",   -101,
                         -98, "F",   -1)

miss_scan_count(dat_ms, -99)
#> # A tibble: 3 x 2
#>   Variable     n
#>   <chr>    <int>
#> 1 x            1
#> 2 y            0
#> 3 z            1
miss_scan_count(dat_ms, c(-99,-98))
#> # A tibble: 3 x 2
#>   Variable     n
#>   <chr>    <int>
#> 1 x            2
#> 2 y            0
#> 3 z            2
miss_scan_count(dat_ms, c("-99","-98","N/A"))
#> # A tibble: 3 x 2
#>   Variable     n
#>   <chr>    <int>
#> 1 x            2
#> 2 y            1
#> 3 z            2
miss_scan_count(dat_ms, common_na_numbers)
#> # A tibble: 3 x 2
#>   Variable     n
#>   <chr>    <int>
#> 1 x            2
#> 2 y            0
#> 3 z            2
miss_scan_count(dat_ms, common_na_strings)
#> # A tibble: 3 x 2
#>   Variable     n
#>   <chr>    <int>
#> 1 x            4
#> 2 y            4
#> 3 z            5

Created on 2018-06-05 by the reprex package (v0.2.0).