This function returns an output list of data and plots that
come from using the K-Means
clustering algorithm on a time series data.
Usage
ts_feature_cluster_plot(
.data,
.date_col,
.value_col,
...,
.center = 3,
.facet_ncol = 3,
.smooth = FALSE
)
Arguments
- .data
The data passed must be the output of the
ts_feature_cluster()
function.- .date_col
The date column.
- .value_col
The column that holds the value of the time series that the featurs were built from.
- ...
This is where you can place grouping variables that are passed off to
dplyr::group_by()
- .center
An integer of the chosen amount of centers from the
ts_feature_cluster()
function.- .facet_ncol
This is passed to the
timetk::plot_time_series()
function.- .smooth
This is passed to the
timetk::plot_time_series()
function and is set to a default of FALSE.
Details
This function will return a list object output. The function itself
requires that the ts_feature_cluster()
be passed to it as it will look for
a specific attribute internally.
The output of this function includes the following:
Data Section
original_data
kmm_data_tbl
user_item_tbl
cluster_tbl
Plots
static_plot
plotly_plot
K-Means Object
k-means object
See also
Other Clustering:
ts_feature_cluster()
Examples
library(dplyr)
data_tbl <- ts_to_tbl(AirPassengers) %>%
mutate(group_id = rep(1:12, 12))
output <- ts_feature_cluster(
.data = data_tbl,
.date_col = date_col,
.value_col = value,
group_id,
.features = c("acf_features","entropy"),
.scale = TRUE,
.prefix = "ts_",
.centers = 3
)
ts_feature_cluster_plot(
.data = output,
.date_col = date_col,
.value_col = value,
.center = 2,
group_id
)
#> Joining with `by = join_by(group_id)`