Description Usage Arguments Details Value Examples
cluster_elements() takes as imput a 'tbl' formatted as | <element> | <feature> | <value> | <...> | and identify clusters in the data.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 | cluster_elements(
.data,
.element,
.feature,
.value,
method,
of_elements = TRUE,
transform = NULL,
action = "add",
...
)
## S4 method for signature 'spec_tbl_df'
cluster_elements(
.data,
.element,
.feature,
.value,
method,
of_elements = TRUE,
transform = NULL,
action = "add",
...
)
## S4 method for signature 'tbl_df'
cluster_elements(
.data,
.element,
.feature,
.value,
method,
of_elements = TRUE,
transform = NULL,
action = "add",
...
)
|
.data |
A 'tbl' formatted as | <element> | <feature> | <value> | <...> | |
.element |
The name of the element column (normally elements). |
.feature |
The name of the feature column (normally features) |
.value |
The name of the column including the numerical value the clustering is based on (normally feature value) |
method |
A character string. The cluster algorithm to use, ay the moment k-means is the only algorithm included. |
of_elements |
A boolean. In case the input is a nanny object, it indicates Whether the element column will be element or feature column |
transform |
A function to use to tranforma the data internalli (e.g., log1p) |
action |
A character string. Whether to join the new information to the input tbl (add), or just get the non-redundant tbl with the new information (get). |
... |
Further parameters passed to the function kmeans |
maturing
identifies clusters in the data, normally of elements. This function returns a tibble with additional columns for the cluster annotation. At the moment only k-means clustering is supported, the plan is to introduce more clustering methods.
A tbl object with additional columns with cluster labels
A tbl object with additional columns with cluster labels
A tbl object with additional columns with cluster labels
1 | cluster_elements(mtcars_tidy, car_model, feature, value, method="kmeans", centers = 2)
|
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