Get insight onto where missing values will enter when joining your data frames

Enter data frames and a joining key and generate a table that shows you the variable, number of NA values in the data frames, and number of NA values introduced when full joining the data frames. Additionally, see this output in two different bar plots called 'Coded Missing Values Within Datasets' and 'Missing Values In Datasets Resulting From Joins'. This function enables you to specify as many data frames as you wish. You specify the keys, where each data frame must have at least one of the keys in it. The function gives insight into missing values and if they come from your current data or result from the join.

get_all_missing(..., df_names = NULL, keys)

Arguments

...	enter as many data frames as you would like
df_names	the data frame names
keys	the primary key in your data frames

Examples

get_all_missing(demographics, math, ell, keys = c("student_id", "year"))
#> [[1]]
#> # A tibble: 9 x 3
#>   variable                         coded_missing join_missing
#>   <chr>                                    <int>        <int>
#> 1 name_demographics                            0            0
#> 2 sex_demographics                             0            0
#> 3 learning_disability_demographics             0            0
#> 4 learning_disability_math                     0         6000
#> 5 math_score_math                           4761         6000
#> 6 name_ell                                     0        20517
#> 7 sex_ell                                      0        20517
#> 8 learning_disability_ell                      0        20517
#> 9 ell_score_ell                             2904        20517
#> 
#> [[2]]
#> 
#> [[3]]
#> 
get_all_missing(list(demographics, math, ell),
     df_names = c("demographics", "math", "ell"),
     keys = c("student_id", "year"))
#> [[1]]
#> # A tibble: 9 x 3
#>   variable                         coded_missing join_missing
#>   <chr>                                    <int>        <int>
#> 1 name_demographics                            0            0
#> 2 sex_demographics                             0            0
#> 3 learning_disability_demographics             0            0
#> 4 learning_disability_math                     0         6000
#> 5 math_score_math                           4761         6000
#> 6 name_ell                                     0        20517
#> 7 sex_ell                                      0        20517
#> 8 learning_disability_ell                      0        20517
#> 9 ell_score_ell                             2904        20517
#> 
#> [[2]]
#> 
#> [[3]]
#>