General purpose metadata filtering for mts time series objects

A generalized metadata filter for mts objects to choose rows/cases where conditions are true. Multiple conditions are combined with & or separated by a comma. Only rows where the condition evaluates to TRUE are kept. Rows where the condition evaluates to FALSE or NA are dropped.

If an empty mts object is passed in, it is immediately returned, allowing for multiple filtering steps to be piped together and only checking for an empty mts object at the end of the pipeline.

mts_filterMeta(mts, ...)

Arguments

mts

mts object.

...

Logical predicates defined in terms of the variables in mts$meta.

Value

A subset of the incoming mts time series object. (A list with meta and data dataframes.)

Note

Filtering is done on variables in mts$meta.

See also

mts_filterData

Examples

library(MazamaTimeSeries)

# Filter for all labels with "SCSH"
scap <-
  example_mts %>%
  mts_filterMeta(communityRegion == "El Monte")

dplyr::select(scap$meta, ID, label, longitude, latitude, communityRegion)
#>                         ID   label longitude latitude communityRegion
#> 36fa039140645de8_2504 2504 SCEM_03 -118.0335 34.06491        El Monte
#> 173ff64a55da1183_2693 2693 SCEM_04 -118.0114 34.05451        El Monte
#> 055497925c615bbd_2452 2452 SCEM_05 -118.0023 34.07729        El Monte
#> 6db0b260ed58bea0_2713 2713 scem_06 -118.0313 34.07163        El Monte
#> 8d9ad84c05e66fcb_2496 2496 SCEM_07 -118.0717 34.08501        El Monte

head(scap$data)
#>              datetime 36fa039140645de8_2504 173ff64a55da1183_2693
#> 1 2019-07-01 07:00:00              12.89900              12.01483
#> 2 2019-07-01 08:00:00              13.89150              12.37000
#> 3 2019-07-01 09:00:00              14.83433              12.48583
#> 4 2019-07-01 10:00:00              15.84750              13.00417
#> 5 2019-07-01 11:00:00              16.29417              14.23800
#> 6 2019-07-01 12:00:00              16.91367              15.00083
#>   055497925c615bbd_2452 6db0b260ed58bea0_2713 8d9ad84c05e66fcb_2496
#> 1                    NA              11.94833              15.24183
#> 2                    NA              13.12333              15.25800
#> 3                    NA              14.48250              14.16783
#> 4                    NA              14.91900              13.29867
#> 5                    NA              15.56867              13.46133
#> 6                    NA              16.07867              13.63450