mde: Missing Data Explorer

The goal of mde is to ease exploration of missingness.

Installation

CRAN release


install.packages("mde")

Stable Development version


devtools::install_github("Nelson-Gon/mde")


devtools::install_github("Nelson-Gon/mde",  build_vignettes=TRUE)

Unstable Development version


devtools::install_github("Nelson-Gon/mde@develop")

Loading the package


library(mde)
#> Welcome to mde. This is mde version 0.3.2.
#>  Please file issues and feedback at https://www.github.com/Nelson-Gon/mde/issues
#> Turn this message off using 'suppressPackageStartupMessages(library(mde))'
#>  Happy Exploration :)

Exploring missingness

To get a simple missingness report, use na_summary:


na_summary(airquality)
#>   variable missing complete percent_complete percent_missing
#> 1      Day       0      153        100.00000        0.000000
#> 2    Month       0      153        100.00000        0.000000
#> 3    Ozone      37      116         75.81699       24.183007
#> 4  Solar.R       7      146         95.42484        4.575163
#> 5     Temp       0      153        100.00000        0.000000
#> 6     Wind       0      153        100.00000        0.000000

To sort this summary by a given column :


na_summary(airquality,sort_by = "percent_complete")
#>   variable missing complete percent_complete percent_missing
#> 3    Ozone      37      116         75.81699       24.183007
#> 4  Solar.R       7      146         95.42484        4.575163
#> 1      Day       0      153        100.00000        0.000000
#> 2    Month       0      153        100.00000        0.000000
#> 5     Temp       0      153        100.00000        0.000000
#> 6     Wind       0      153        100.00000        0.000000

To sort by percent_missing instead:

na_summary(airquality, sort_by = "percent_missing")
#>   variable missing complete percent_complete percent_missing
#> 1      Day       0      153        100.00000        0.000000
#> 2    Month       0      153        100.00000        0.000000
#> 5     Temp       0      153        100.00000        0.000000
#> 6     Wind       0      153        100.00000        0.000000
#> 4  Solar.R       7      146         95.42484        4.575163
#> 3    Ozone      37      116         75.81699       24.183007

To sort the above in descending order:

na_summary(airquality, sort_by="percent_missing", descending = TRUE)
#>   variable missing complete percent_complete percent_missing
#> 3    Ozone      37      116         75.81699       24.183007
#> 4  Solar.R       7      146         95.42484        4.575163
#> 1      Day       0      153        100.00000        0.000000
#> 2    Month       0      153        100.00000        0.000000
#> 5     Temp       0      153        100.00000        0.000000
#> 6     Wind       0      153        100.00000        0.000000

To exclude certain columns from the analysis:


na_summary(airquality, exclude_cols = c("Day", "Wind"))
#>   variable missing complete percent_complete percent_missing
#> 1    Month       0      153        100.00000        0.000000
#> 2    Ozone      37      116         75.81699       24.183007
#> 3  Solar.R       7      146         95.42484        4.575163
#> 4     Temp       0      153        100.00000        0.000000

To include or exclude via regex match:

na_summary(airquality, regex_kind = "inclusion",pattern_type = "starts_with", pattern = "O|S")
#>   variable missing complete percent_complete percent_missing
#> 1    Ozone      37      116         75.81699       24.183007
#> 2  Solar.R       7      146         95.42484        4.575163

na_summary(airquality, regex_kind = "exclusion",pattern_type = "regex", pattern = "^[O|S]")
#>   variable missing complete percent_complete percent_missing
#> 1      Day       0      153              100               0
#> 2    Month       0      153              100               0
#> 3     Temp       0      153              100               0
#> 4     Wind       0      153              100               0

To get this summary by group:


test2 <- data.frame(ID= c("A","A","B","A","B"), Vals = c(rep(NA,4),"No"),ID2 = c("E","E","D","E","D"))

na_summary(test2,grouping_cols = c("ID","ID2"))
#> # A tibble: 2 × 7
#>   ID    ID2   variable missing complete percent_complete percent_missing
#>   <chr> <chr> <chr>      <dbl>    <dbl>            <dbl>           <dbl>
#> 1 B     D     Vals           1        1               50              50
#> 2 A     E     Vals           3        0                0             100


na_summary(test2, grouping_cols="ID")
#> Warning in na_summary.data.frame(test2, grouping_cols = "ID"): All non grouping
#> values used. Using select non groups is currently not supported
#> # A tibble: 4 × 6
#>   ID    variable missing complete percent_complete percent_missing
#>   <chr> <chr>      <dbl>    <dbl>            <dbl>           <dbl>
#> 1 A     Vals           3        0                0             100
#> 2 A     ID2            0        3              100               0
#> 3 B     Vals           1        1               50              50
#> 4 B     ID2            0        2              100               0

get_na_counts

This provides a convenient way to show the number of missing values column-wise. It is relatively fast(tests done on about 400,000 rows, took a few microseconds.)

To get the number of missing values in each column of airquality, we can use the function as follows:


get_na_counts(airquality)
#>   Ozone Solar.R Wind Temp Month Day
#> 1    37       7    0    0     0   0

The above might be less useful if one would like to get the results by group. In that case, one can provide a grouping vector of names in grouping_cols.


test <- structure(list(Subject = structure(c(1L, 1L, 2L, 2L), .Label = c("A", 
"B"), class = "factor"), res = c(NA, 1, 2, 3), ID = structure(c(1L, 
1L, 2L, 2L), .Label = c("1", "2"), class = "factor")), class = "data.frame", row.names = c(NA, 
-4L))

get_na_counts(test, grouping_cols = "ID")
#> # A tibble: 2 × 3
#>   ID    Subject   res
#>   <fct>   <int> <int>
#> 1 1           0     1
#> 2 2           0     0

percent_missing

This is a very simple to use but quick way to take a look at the percentage of data that is missing column-wise.



percent_missing(airquality)
#>      Ozone  Solar.R Wind Temp Month Day
#> 1 24.18301 4.575163    0    0     0   0

We can get the results by group by providing an optional grouping_cols character vector.


percent_missing(test, grouping_cols = "Subject")
#> # A tibble: 2 × 3
#>   Subject   res    ID
#>   <fct>   <dbl> <dbl>
#> 1 A          50     0
#> 2 B           0     0

To exclude some columns from the above exploration, one can provide an optional character vector in exclude_cols.


percent_missing(airquality,exclude_cols = c("Day","Temp"))
#>      Ozone  Solar.R Wind Month
#> 1 24.18301 4.575163    0     0

sort_by_missingness

This provides a very simple but relatively fast way to sort variables by missingness. Unless otherwise stated, this does not currently support arranging grouped percents.

Usage:



sort_by_missingness(airquality, sort_by = "counts")
#>   variable percent
#> 1     Wind       0
#> 2     Temp       0
#> 3    Month       0
#> 4      Day       0
#> 5  Solar.R       7
#> 6    Ozone      37

To sort in descending order:


sort_by_missingness(airquality, sort_by = "counts", descend = TRUE)
#>   variable percent
#> 1    Ozone      37
#> 2  Solar.R       7
#> 3     Wind       0
#> 4     Temp       0
#> 5    Month       0
#> 6      Day       0

To use percentages instead:


sort_by_missingness(airquality, sort_by = "percents")
#>   variable   percent
#> 1     Wind  0.000000
#> 2     Temp  0.000000
#> 3    Month  0.000000
#> 4      Day  0.000000
#> 5  Solar.R  4.575163
#> 6    Ozone 24.183007

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 NAs 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 NAs 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 NAs as another value based on a grouping variable. In the example below, we replace all NAs 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 NAs

drop_na_if

Suppose you wanted to drop any column that has a percentage of NAs 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")
#> Warning: Returning data frames from `filter()` expressions was deprecated in dplyr
#> 1.0.8.
#> ℹ Please use `if_any()` or `if_all()` instead.
#> ℹ The deprecated feature was likely used in the mde package.
#>   Please report the issue at <https://github.com/Nelson-Gon/mde/issues>.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
#> generated.
#> # 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(test, grouping_cols = "ID"))
#> # A tibble: 4 × 3
#>   Subject   res ID   
#>   <fct>   <dbl> <fct>
#> 1 A          NA 1    
#> 2 A           1 1    
#> 3 B           2 2    
#> 4 B           3 2

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 :)