R/add-cols.R
add_miss_cluster.Rd
A way to extract the cluster of missingness that a group belongs to.
For example, if you use vis_miss(airquality, cluster = TRUE)
, you can
see some clustering in the data, but you do not have a way to identify
the cluster. Future work will incorporate the seriation
package to
allow for better control over the clustering from the user.
add_miss_cluster(data, cluster_method = "mcquitty", n_clusters = 2)
data | a dataframe |
---|---|
cluster_method | character vector of the agglomeration method to use,
the default is "mcquitty". Options are taken from |
n_clusters | numeric the number of clusters you expect. Defaults to 2. |
bind_shadow()
add_any_miss()
add_label_missings()
add_label_shadow()
add_miss_cluster()
add_n_miss()
add_prop_miss()
add_shadow_shift()
cast_shadow()
add_miss_cluster(airquality)#> # A tibble: 153 x 7 #> Ozone Solar.R Wind Temp Month Day miss_cluster #> <int> <int> <dbl> <int> <int> <int> <int> #> 1 41 190 7.4 67 5 1 1 #> 2 36 118 8 72 5 2 1 #> 3 12 149 12.6 74 5 3 1 #> 4 18 313 11.5 62 5 4 1 #> 5 NA NA 14.3 56 5 5 2 #> 6 28 NA 14.9 66 5 6 1 #> 7 23 299 8.6 65 5 7 1 #> 8 19 99 13.8 59 5 8 1 #> 9 8 19 20.1 61 5 9 1 #> 10 NA 194 8.6 69 5 10 2 #> # … with 143 more rowsadd_miss_cluster(airquality, n_clusters = 3)#> # A tibble: 153 x 7 #> Ozone Solar.R Wind Temp Month Day miss_cluster #> <int> <int> <dbl> <int> <int> <int> <int> #> 1 41 190 7.4 67 5 1 1 #> 2 36 118 8 72 5 2 1 #> 3 12 149 12.6 74 5 3 1 #> 4 18 313 11.5 62 5 4 1 #> 5 NA NA 14.3 56 5 5 2 #> 6 28 NA 14.9 66 5 6 1 #> 7 23 299 8.6 65 5 7 1 #> 8 19 99 13.8 59 5 8 1 #> 9 8 19 20.1 61 5 9 1 #> 10 NA 194 8.6 69 5 10 3 #> # … with 143 more rowsadd_miss_cluster(airquality, cluster_method = "ward.D", n_clusters = 3)#> # A tibble: 153 x 7 #> Ozone Solar.R Wind Temp Month Day miss_cluster #> <int> <int> <dbl> <int> <int> <int> <int> #> 1 41 190 7.4 67 5 1 1 #> 2 36 118 8 72 5 2 1 #> 3 12 149 12.6 74 5 3 1 #> 4 18 313 11.5 62 5 4 1 #> 5 NA NA 14.3 56 5 5 2 #> 6 28 NA 14.9 66 5 6 2 #> 7 23 299 8.6 65 5 7 1 #> 8 19 99 13.8 59 5 8 1 #> 9 8 19 20.1 61 5 9 1 #> 10 NA 194 8.6 69 5 10 3 #> # … with 143 more rows