Date: 2026-02-21

# Recoding to and from NA with mde

#### 2026-02-21

The goal of `mde` is to ease exploration of missingness.

Loading the package

    library(mde)

# Recoding as NA

- `recode_as_na`

As the name might imply, this converts any value or vector of values to `NA` i.e. we take a value such as “missing” or “NA” (not a real `NA` according to `R`) and convert it to R’s known handler for missing values (`NA`).

To use the function out of the box (with default arguments), one simply does something like:

    dummy_test <- data.frame(ID = c("A","B","B","A"),
                             values = c("n/a",NA,"Yes","No"))
    # Convert n/a and no to NA
    head(recode_as_na(dummy_test, value = c("n/a","No")))
    #>   ID values
    #> 1  A   <NA>
    #> 2  B   <NA>
    #> 3  B    Yes
    #> 4  A   <NA>

Great, but I want to do so for specific columns not the entire dataset. You can do this by providing column names to `subset_cols`.

    another_dummy <- data.frame(ID = 1:5, Subject = 7:11,
    Change = c("missing","n/a",2:4 ))
    # Only change values at the column Change
    head(recode_as_na(another_dummy, subset_cols = "Change", value = c("n/a","missing")))
    #>   ID Subject Change
    #> 1  1       7   <NA>
    #> 2  2       8   <NA>
    #> 3  3       9      2
    #> 4  4      10      3
    #> 5  5      11      4

To recode columns using RegEx,one can provide `pattern_type` and a target `pattern`. Currently supported `pattern_types` are `starts_with`, `ends_with`, `contains` and `regex`. See docs for more details.:

    # only change at columns that start with Solar
    head(recode_as_na(airquality,value=190,pattern_type="starts_with",pattern="Solar"))
    #>   Ozone Solar.R Wind Temp Month Day
    #> 1    41      NA  7.4   67     5   1
    #> 2    36     118  8.0   72     5   2
    #> 3    12     149 12.6   74     5   3
    #> 4    18     313 11.5   62     5   4
    #> 5    NA      NA 14.3   56     5   5
    #> 6    28      NA 14.9   66     5   6

    # recode at columns that start with O or S(case sensitive)
    head(recode_as_na(airquality,value=c(67,118),pattern_type="starts_with",pattern="S|O"))
    #>   Ozone Solar.R Wind Temp Month Day
    #> 1    41     190  7.4   67     5   1
    #> 2    36      NA  8.0   72     5   2
    #> 3    12     149 12.6   74     5   3
    #> 4    18     313 11.5   62     5   4
    #> 5    NA      NA 14.3   56     5   5
    #> 6    28      NA 14.9   66     5   6

    # use my own RegEx
    head(recode_as_na(airquality,value=c(67,118),pattern_type="regex",pattern="(?i)^(s|o)"))
    #>   Ozone Solar.R Wind Temp Month Day
    #> 1    41     190  7.4   67     5   1
    #> 2    36      NA  8.0   72     5   2
    #> 3    12     149 12.6   74     5   3
    #> 4    18     313 11.5   62     5   4
    #> 5    NA      NA 14.3   56     5   5
    #> 6    28      NA 14.9   66     5   6

- `recode_as_na_if`

This function allows one to deliberately introduce missing values if a column meets a certain threshold of missing values. This is similar to `amputation` but is much more basic. It is only provided here because it is hoped it may be useful to someone for whatever reason.

    head(recode_as_na_if(airquality,sign="gt", percent_na=20))
    #>   Ozone Solar.R Wind Temp Month Day
    #> 1    NA     190  7.4   67     5   1
    #> 2    NA     118  8.0   72     5   2
    #> 3    NA     149 12.6   74     5   3
    #> 4    NA     313 11.5   62     5   4
    #> 5    NA      NA 14.3   56     5   5
    #> 6    NA      NA 14.9   66     5   6

- `recode_as_na_str`

This allows recoding as `NA` based on a string match.

    partial_match <- data.frame(A=c("Hi","match_me","nope"), B=c(NA, "not_me","nah"))

    recode_as_na_str(partial_match,"ends_with","ME", case_sensitive=FALSE)
    #>      A    B
    #> 1   Hi <NA>
    #> 2 <NA> <NA>
    #> 3 nope  nah

- `recode_as_na_for`

For all values greater/less/less or equal/greater or equal than some value, can I convert them to `NA`?!

Yes You Can! All we have to do is use `recode_as_na_for`:

    head(recode_as_na_for(airquality,criteria="gt",value=25))
    #>   Ozone Solar.R Wind Temp Month Day
    #> 1    NA      NA  7.4   NA     5   1
    #> 2    NA      NA  8.0   NA     5   2
    #> 3    12      NA 12.6   NA     5   3
    #> 4    18      NA 11.5   NA     5   4
    #> 5    NA      NA 14.3   NA     5   5
    #> 6    NA      NA 14.9   NA     5   6

To do so at specific columns, pass an optional `subset_cols` character vector:

    head(recode_as_na_for(airquality, value=40,subset_cols=c("Solar.R","Ozone"), criteria="gt"))
    #>   Ozone Solar.R Wind Temp Month Day
    #> 1    NA      NA  7.4   67     5   1
    #> 2    36      NA  8.0   72     5   2
    #> 3    12      NA 12.6   74     5   3
    #> 4    18      NA 11.5   62     5   4
    #> 5    NA      NA 14.3   56     5   5
    #> 6    28      NA 14.9   66     5   6

# Recoding NA as

- `recode_na_as`

Sometimes, for whatever reason, one would like to replace `NA`s with whatever value they would like. `recode_na_as` provides a very simple way to do just that.

    head(recode_na_as(airquality))
    #>   Ozone Solar.R Wind Temp Month Day
    #> 1    41     190  7.4   67     5   1
    #> 2    36     118  8.0   72     5   2
    #> 3    12     149 12.6   74     5   3
    #> 4    18     313 11.5   62     5   4
    #> 5     0       0 14.3   56     5   5
    #> 6    28       0 14.9   66     5   6

    # use NaN

    head(recode_na_as(airquality, value=NaN))
    #>   Ozone Solar.R Wind Temp Month Day
    #> 1    41     190  7.4   67     5   1
    #> 2    36     118  8.0   72     5   2
    #> 3    12     149 12.6   74     5   3
    #> 4    18     313 11.5   62     5   4
    #> 5   NaN     NaN 14.3   56     5   5
    #> 6    28     NaN 14.9   66     5   6

As a “bonus”, you can manipulate the data only at specific columns as shown here:

    head(recode_na_as(airquality, value=0, subset_cols="Ozone"))
    #>   Ozone Solar.R Wind Temp Month Day
    #> 1    41     190  7.4   67     5   1
    #> 2    36     118  8.0   72     5   2
    #> 3    12     149 12.6   74     5   3
    #> 4    18     313 11.5   62     5   4
    #> 5     0      NA 14.3   56     5   5
    #> 6    28      NA 14.9   66     5   6

The above also supports custom recoding similar to `recode_na_as`:

    head(mde::recode_na_as(airquality, value=0, pattern_type="starts_with",pattern="Solar"))
    #>   Ozone Solar.R Wind Temp Month Day
    #> 1    41     190  7.4   67     5   1
    #> 2    36     118  8.0   72     5   2
    #> 3    12     149 12.6   74     5   3
    #> 4    18     313 11.5   62     5   4
    #> 5    NA       0 14.3   56     5   5
    #> 6    28       0 14.9   66     5   6

- `column_based_recode`

Ever needed to change values in a given column based on the proportions of `NA`s in other columns(row-wise)?!. The goal of `column_based_recode` is to achieve just that. Let’s see how we could do this with a simple example:

    head(column_based_recode(airquality, values_from = "Wind", values_to="Wind", pattern_type = "regex", pattern = "Solar|Ozone"))
    #>   Ozone Solar.R Wind Temp Month Day
    #> 1    41     190  7.4   67     5   1
    #> 2    36     118  8.0   72     5   2
    #> 3    12     149 12.6   74     5   3
    #> 4    18     313 11.5   62     5   4
    #> 5    NA      NA  0.0   56     5   5
    #> 6    28      NA 14.9   66     5   6

- `custom_na_recode`

This allows recoding `NA` values with common stats functions such as `mean`,`max`,`min`,`sd`.

To use default values:

    head(custom_na_recode(airquality))
    #>      Ozone  Solar.R Wind Temp Month Day
    #> 1 41.00000 190.0000  7.4   67     5   1
    #> 2 36.00000 118.0000  8.0   72     5   2
    #> 3 12.00000 149.0000 12.6   74     5   3
    #> 4 18.00000 313.0000 11.5   62     5   4
    #> 5 42.12931 185.9315 14.3   56     5   5
    #> 6 28.00000 185.9315 14.9   66     5   6

To use select columns:

    head(custom_na_recode(airquality,func="mean",across_columns=c("Solar.R","Ozone")))
    #>      Ozone  Solar.R Wind Temp Month Day
    #> 1 41.00000 190.0000  7.4   67     5   1
    #> 2 36.00000 118.0000  8.0   72     5   2
    #> 3 12.00000 149.0000 12.6   74     5   3
    #> 4 18.00000 313.0000 11.5   62     5   4
    #> 5 42.12931 185.9315 14.3   56     5   5
    #> 6 28.00000 185.9315 14.9   66     5   6

To use a function from another package to perform replacements:

To perform a forward fill with `dplyr`’s `lead`:

    # use lag for a backfill
    head(custom_na_recode(airquality,func=dplyr::lead ))
    #>   Ozone Solar.R Wind Temp Month Day
    #> 1    41     190  7.4   67     5   1
    #> 2    36     118  8.0   72     5   2
    #> 3    12     149 12.6   74     5   3
    #> 4    18     313 11.5   62     5   4
    #> 5    23      99 14.3   56     5   5
    #> 6    28      19 14.9   66     5   6

To perform replacement by group:

    some_data <- data.frame(ID=c("A1","A1","A1","A2","A2", "A2"),A=c(5,NA,0,8,3,4),B=c(10,0,0,NA,5,6),C=c(1,NA,NA,25,7,8))

    head(custom_na_recode(some_data,func = "mean", grouping_cols = "ID"))
    #> # A tibble: 6 × 4
    #>   ID        A     B     C
    #>   <chr> <dbl> <dbl> <dbl>
    #> 1 A1      5    10       1
    #> 2 A1      2.5   0       1
    #> 3 A1      0     0       1
    #> 4 A2      8     5.5    25
    #> 5 A2      3     5       7
    #> 6 A2      4     6       8

Across specific columns:

    head(custom_na_recode(some_data,func = "mean", grouping_cols = "ID", across_columns = c("C", "A")))
    #> # A tibble: 6 × 4
    #>   ID        A     B     C
    #>   <chr> <dbl> <dbl> <dbl>
    #> 1 A1      5      10     1
    #> 2 A1      2.5     0     1
    #> 3 A1      0       0     1
    #> 4 A2      8      NA    25
    #> 5 A2      3       5     7
    #> 6 A2      4       6     8

- `recode_na_if`

Given a `data.frame` object, one can recode `NA`s as another value based on a grouping variable. In the example below, we replace all `NA`s in all columns with 0s if the ID is `A2` or `A3`

    some_data <- data.frame(ID=c("A1","A2","A3", "A4"),
                            A=c(5,NA,0,8), B=c(10,0,0,1),
                            C=c(1,NA,NA,25))

    head(recode_na_if(some_data,grouping_col="ID", target_groups=c("A2","A3"),
               replacement= 0))
    #> # A tibble: 4 × 4
    #>   ID        A     B     C
    #>   <chr> <dbl> <dbl> <dbl>
    #> 1 A1        5    10     1
    #> 2 A2        0     0     0
    #> 3 A3        0     0     0
    #> 4 A4        8     1    25

# Dropping Missing Values

- `drop_na_if`

Suppose you wanted to drop any column that has a percentage of `NA`s greater than or equal to a certain value? `drop_na_if` does just that.

We can drop any columns that have greater than or equal(gteq) to 24% of the values missing from `airquality`:

    head(drop_na_if(airquality, sign="gteq",percent_na = 24))
    #>   Solar.R Wind Temp Month Day
    #> 1     190  7.4   67     5   1
    #> 2     118  8.0   72     5   2
    #> 3     149 12.6   74     5   3
    #> 4     313 11.5   62     5   4
    #> 5      NA 14.3   56     5   5
    #> 6      NA 14.9   66     5   6

The above also supports less than or equal to(`lteq`), equal to(`eq`), greater than(`gt`) and less than(`lt`).

To keep certain columns despite fitting the target `percent_na` criteria, one can provide an optional `keep_columns` character vector.

    head(drop_na_if(airquality, percent_na = 24, keep_columns = "Ozone"))
    #>   Ozone Solar.R Wind Temp Month Day
    #> 1    41     190  7.4   67     5   1
    #> 2    36     118  8.0   72     5   2
    #> 3    12     149 12.6   74     5   3
    #> 4    18     313 11.5   62     5   4
    #> 5    NA      NA 14.3   56     5   5
    #> 6    28      NA 14.9   66     5   6

Compare the above result to the following:

    head(drop_na_if(airquality, percent_na = 24))
    #>   Solar.R Wind Temp Month Day
    #> 1     190  7.4   67     5   1
    #> 2     118  8.0   72     5   2
    #> 3     149 12.6   74     5   3
    #> 4     313 11.5   62     5   4
    #> 5      NA 14.3   56     5   5
    #> 6      NA 14.9   66     5   6

To drop groups that meet a set missingness criterion, we proceed as follows.

    grouped_drop <- structure(list(ID = c("A", "A", "B", "A", "B"),
              Vals = c(4, NA,  NA, NA, NA), Values = c(5, 6, 7, 8, NA)),
              row.names = c(NA, -5L), class = "data.frame")
    # Drop all columns for groups that meet a percent missingness of greater than or
    # equal to 67
    drop_na_if(grouped_drop,percent_na = 67,sign="gteq",
                                        grouping_cols = "ID")
    #> # A tibble: 3 × 3
    #>   ID     Vals Values
    #>   <chr> <dbl>  <dbl>
    #> 1 A         4      5
    #> 2 A        NA      6
    #> 3 A        NA      8

- `drop_row_if`

This is similar to `drop_na_if` but does operations rowwise not columnwise. Compare to the example above:

    # Drop rows with at least two NAs
    head(drop_row_if(airquality, sign="gteq", type="count" , value = 2))
    #> Dropped 2 rows.
    #>   Ozone Solar.R Wind Temp Month Day
    #> 1    41     190  7.4   67     5   1
    #> 2    36     118  8.0   72     5   2
    #> 3    12     149 12.6   74     5   3
    #> 4    18     313 11.5   62     5   4
    #> 6    28      NA 14.9   66     5   6
    #> 7    23     299  8.6   65     5   7

To drop based on percentages:

    # Drops 42 rows
    head(drop_row_if(airquality, type="percent", value=16, sign="gteq",
                     as_percent=TRUE))
    #> Dropped 42 rows.
    #>   Ozone Solar.R Wind Temp Month Day
    #> 1    41     190  7.4   67     5   1
    #> 2    36     118  8.0   72     5   2
    #> 3    12     149 12.6   74     5   3
    #> 4    18     313 11.5   62     5   4
    #> 7    23     299  8.6   65     5   7
    #> 8    19      99 13.8   59     5   8

For more details, please see the documentation of `drop_row_if`.

- `drop_na_at`

This provides a simple way to drop missing values only at specific columns. It currently only returns those columns with their missing values removed. See usage below. Further details are given in the documentation. It is currently case sensitive.

    head(drop_na_at(airquality,pattern_type = "starts_with","O"))
    #>   Ozone
    #> 1    41
    #> 2    36
    #> 3    12
    #> 4    18
    #> 5    28
    #> 6    23

- `drop_all_na`

This drops columns where all values are missing.

    test2 <- data.frame(ID= c("A","A","B","A","B"), Vals = c(4,rep(NA, 4)))

    drop_all_na(test2, grouping_cols="ID")
    #> # A tibble: 3 × 2
    #>   ID     Vals
    #>   <chr> <dbl>
    #> 1 A         4
    #> 2 A        NA
    #> 3 A        NA

Alternatively, we can drop groups where all variables are all NA.

    test2 <- data.frame(ID= c("A","A","B","A","B"), Vals = rep(NA, 5))

    head(drop_all_na(test2, grouping_cols = "ID"))
    #> # A tibble: 0 × 2
    #> # ℹ 2 variables: ID <chr>, Vals <lgl>

- `dict_recode`

If one would like to recode column values using a “dictionary”, `dict_recode` provides a simple way to do that. For example, if one would like to convert `NA` values in `Solar.R` to 520 and those in `Ozone` to 42, one simply calls the following:

    head(dict_recode(airquality, use_func="recode_na_as",
                     patterns = c("solar", "ozone"),
                     pattern_type="starts_with", values = c(520,42)))
    #>   Ozone Solar.R Wind Temp Month Day
    #> 1    41     190  7.4   67     5   1
    #> 2    36     118  8.0   72     5   2
    #> 3    12     149 12.6   74     5   3
    #> 4    18     313 11.5   62     5   4
    #> 5    42     520 14.3   56     5   5
    #> 6    28     520 14.9   66     5   6

# General Recoding

- For general recoding of values, one can use `recode_as_value` for example as shown below

    head(recode_as_value(airquality, value=c(67,118),replacement=NA,
                         pattern_type="starts_with",pattern="S|O"))
    #>   Ozone Solar.R Wind Temp Month Day
    #> 1    41     190  7.4   67     5   1
    #> 2    36      NA  8.0   72     5   2
    #> 3    12     149 12.6   74     5   3
    #> 4    18     313 11.5   62     5   4
    #> 5    NA      NA 14.3   56     5   5
    #> 6    28      NA 14.9   66     5   6

The above is a more general function that can do what the other functions do and may be more useful for development purposes.

Please note that the `mde` project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

For further exploration, please `browseVignettes("mde")`.

To raise an issue, please do so here

Thank you, feedback is always welcome :)