A generalized data filter for mts objects to
choose rows/cases where conditions are true. Multiple conditions should be
combined with &
or separated by a comma. Only rows where the condition
evaluates to TRUE are kept. Rows where the condition evaluates to NA
are dropped.
mts_filter(mts, ...)
mts | mts object. |
---|---|
... | Logical predicates defined in terms of the variables in
|
A subset of the incoming mts
.
Filtering is done on variables in mts$data
.
library(MazamaTimeSeries) # Are there any times when data exceeded 150? sapply(example_mts$data, function(x) { any(x > 150, na.rm = TRUE) })#> datetime 4af81ed0f6ec7acd_4734 c3ac53faf335f19f_5030 #> TRUE FALSE FALSE #> 776e00b070251a36_23237 ec61b544c1dbfe21_2448 70e599f772b22877_4670 #> FALSE FALSE TRUE #> e0d610f7f219e39f_5080 2ee691d37eaa1aff_3529 ecd1d2c35c1c18cf_3505 #> FALSE FALSE FALSE #> 05533d5b998ac7bf_3525 91299b887fface5f_3515 c316bcf7a85c8242_3617 #> FALSE FALSE TRUE #> a812772f2ef60b6f_3527 70f70265dbeb7f36_3551 f1eb28176ffa3c00_3487 #> FALSE TRUE TRUE #> 28a43b2d6854838f_4725 da4cadd2d6ea5302_4686 d68d5d33331df58f_4689 #> FALSE TRUE TRUE #> 2de587fc7359dd00_4662 07702ba2209dec22_4702 1fb48f53c052893a_4692 #> FALSE FALSE TRUE #> ada194ce98ce4bed_5184 a3eb0d42e5c78f8b_5228 065b6608aa9e6136_5218 #> FALSE FALSE FALSE #> 36fa039140645de8_2504 173ff64a55da1183_2693 055497925c615bbd_2452 #> TRUE TRUE FALSE #> 6db0b260ed58bea0_2713 8d9ad84c05e66fcb_2496 30b3a848f934811d_4746 #> TRUE TRUE FALSE #> bf7350cd6c2ec4c1_4727 452fd84452485d47_23233 bde3b79da492ff98_23429 #> FALSE TRUE FALSE #> 76a49e0074377eb5_23427 8c345724ee28a6ef_23253 85a2219173d7e0e6_22695 #> TRUE TRUE FALSE #> f4b229bfc8882d90_22687 dfed9bdf5bbdeee0_23093 d76f0e830185fe4c_23291 #> FALSE FALSE FALSE# Filter for all times where da4cadd2d6ea5302_4686 > 150 very_unhealthy <- example_mts %>% mts_filter(da4cadd2d6ea5302_4686 > 150) # Show all data dplyr::glimpse(very_unhealthy$data)#> Rows: 2 #> Columns: 39 #> $ datetime <dttm> 2019-07-05 04:00:00, 2019-07-05 05:00:00 #> $ `4af81ed0f6ec7acd_4734` <dbl> NA, NA #> $ c3ac53faf335f19f_5030 <dbl> NA, NA #> $ `776e00b070251a36_23237` <dbl> NA, NA #> $ ec61b544c1dbfe21_2448 <dbl> NA, NA #> $ `70e599f772b22877_4670` <dbl> 148.8535, 183.8073 #> $ e0d610f7f219e39f_5080 <dbl> NA, NA #> $ `2ee691d37eaa1aff_3529` <dbl> NA, NA #> $ ecd1d2c35c1c18cf_3505 <dbl> NA, NA #> $ `05533d5b998ac7bf_3525` <dbl> NA, NA #> $ `91299b887fface5f_3515` <dbl> NA, NA #> $ c316bcf7a85c8242_3617 <dbl> 126.7995, 186.3358 #> $ a812772f2ef60b6f_3527 <dbl> NA, NA #> $ `70f70265dbeb7f36_3551` <dbl> 119.4782, 175.1087 #> $ f1eb28176ffa3c00_3487 <dbl> 152.6133, 183.1000 #> $ `28a43b2d6854838f_4725` <dbl> 97.1760, 119.9163 #> $ da4cadd2d6ea5302_4686 <dbl> 172.8510, 212.0263 #> $ d68d5d33331df58f_4689 <dbl> 130.5768, 167.7328 #> $ `2de587fc7359dd00_4662` <dbl> NA, NA #> $ `07702ba2209dec22_4702` <dbl> 59.57717, 98.66717 #> $ `1fb48f53c052893a_4692` <dbl> 120.2220, 155.4523 #> $ ada194ce98ce4bed_5184 <dbl> 128.2922, NA #> $ a3eb0d42e5c78f8b_5228 <dbl> 105.7192, NA #> $ `065b6608aa9e6136_5218` <dbl> 95.7381, 138.4382 #> $ `36fa039140645de8_2504` <dbl> 104.0478, 202.0847 #> $ `173ff64a55da1183_2693` <dbl> 147.5515, 157.8998 #> $ `055497925c615bbd_2452` <dbl> NA, NA #> $ `6db0b260ed58bea0_2713` <dbl> 87.96833, 176.65567 #> $ `8d9ad84c05e66fcb_2496` <dbl> 81.28483, 108.92633 #> $ `30b3a848f934811d_4746` <dbl> NA, 144.8405 #> $ bf7350cd6c2ec4c1_4727 <dbl> NA, NA #> $ `452fd84452485d47_23233` <dbl> 241.8179, 175.3150 #> $ bde3b79da492ff98_23429 <dbl> 84.70883, 136.29433 #> $ `76a49e0074377eb5_23427` <dbl> 112.7997, 192.6018 #> $ `8c345724ee28a6ef_23253` <dbl> 120.3878, 176.0627 #> $ `85a2219173d7e0e6_22695` <dbl> NA, NA #> $ f4b229bfc8882d90_22687 <dbl> NA, NA #> $ dfed9bdf5bbdeee0_23093 <dbl> NA, NA #> $ d76f0e830185fe4c_23291 <dbl> NA, NA