summarize_covar: Covariance summarizer
In sparklyr.flint: Sparklyr Extension for 'Flint'

Description Usage Arguments Value See Also Examples

Compute covariance between values from 'xcolumn' and 'ycolumn' within each time window or within each group of records with identical timestamps, and store results in a new column named '<xcolumn>_<ycolumn>_covariance'

1	summarize_covar(ts_rdd, xcolumn, ycolumn, window = NULL, key_columns = list())

`ts_rdd`	Timeseries RDD being summarized
`xcolumn`	Column representing the first random variable
`ycolumn`	Column representing the second random variable
`window`	Either an R expression specifying time windows to be summarized (e.g., 'in_past("1h")' to summarize data from looking behind 1 hour at each time point, 'in_future("5s")' to summarize data from looking forward 5 seconds at each time point), or 'NULL' to compute aggregate statistics on records grouped by timestamps
`key_columns`	Optional list of columns that will form an equivalence relation associating each record with the time series it belongs to (i.e., any 2 records having equal values in those columns will be associated with the same time series, and any 2 records having differing values in those columns are considered to be from 2 separate time series and will therefore be summarized separately) By default, 'key_colums' is empty and all records are considered to be part of a single time series.

A TimeSeriesRDD containing the summarized result

Other summarizers: ols_regression(), summarize_avg(), summarize_corr2(), summarize_corr(), summarize_count(), summarize_dot_product(), summarize_ema_half_life(), summarize_ewma(), summarize_geometric_mean(), summarize_kurtosis(), summarize_max(), summarize_min(), summarize_nth_central_moment(), summarize_nth_moment(), summarize_product(), summarize_quantile(), summarize_skewness(), summarize_stddev(), summarize_sum(), summarize_var(), summarize_weighted_avg(), summarize_weighted_corr(), summarize_weighted_covar(), summarize_z_score()

library(sparklyr)
library(sparklyr.flint)

sc <- try_spark_connect(master = "local")

if (!is.null(sc)) {
  sdf <- copy_to(sc, tibble::tibble(t = seq(10), u = rnorm(10), v = rnorm(10)))
  ts <- fromSDF(sdf, is_sorted = TRUE, time_unit = "SECONDS", time_column = "t")
  ts_covar <- summarize_covar(ts, xcolumn = "u", ycolumn = "v", window = in_past("3s"))
} else {
  message("Unable to establish a Spark connection!")
}