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The goal of mde is to ease exploration of missingness.

Loading the package


library(mde)

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 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 x 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 x 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

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 x 3
#>   ID    Subject   res
#>   <fct>   <int> <int>
#> 1 1           0     1
#> 2 2           0     0

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 x 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

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

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 :)