This is how you can filter your dataframe but I would recommend keeping all in the same dataframe and add a new variable to differentiate.
library(dplyr)
library(lubridate)
past_90 <- tibble::tribble(
~Date, ~Time, ~Blood.Sugar, ~Systolic, ~Diastolic, ~Pulse, ~Weight, ~Comments,
"2019-04-24", "1899-12-31 17:39:33", 104, 120, 68, 60, NA, "Chose not to inject insulin",
"2019-06-02", "1899-12-31 07:48:06", 92, 130, 71, 49, NA, NA,
"2019-04-12", "1899-12-31 18:04:05", 119, 115, 67, 55, NA, "Injected 5 units of Novolog",
"2019-04-16", "1899-12-31 17:42:34", 129, 112, 61, 56, NA, "Injected 5 units of Novolog",
"2019-06-27", "1899-12-31 05:51:41", 108, 128, 69, 50, NA, NA,
"2019-05-08", "1899-12-31 17:36:33", 97, 130, 72, 56, NA, "Did not inject insulin",
"2019-04-27", "1899-12-31 17:41:14", 125, 124, 76, 56, NA, "\"average\" readings",
"2019-05-31", "1899-12-31 05:47:15", 93, 125, 70, 55, NA, NA,
"2019-05-21", "1899-12-31 05:50:46", 132, 125, 67, 59, NA, NA,
"2019-05-26", "1899-12-31 17:23:11", 106, 134, 83, 59, NA, "Chose not to inject insulin"
)
past_90 %>%
filter(hour(Time) < 12)
#> # A tibble: 4 x 8
#> Date Time Blood.Sugar Systolic Diastolic Pulse Weight Comments
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <lgl> <chr>
#> 1 2019-06… 1899-12-31… 92 130 71 49 NA <NA>
#> 2 2019-06… 1899-12-31… 108 128 69 50 NA <NA>
#> 3 2019-05… 1899-12-31… 93 125 70 55 NA <NA>
#> 4 2019-05… 1899-12-31… 132 125 67 59 NA <NA>
# I recommend this instead
past_90 %>%
mutate(noon = case_when(hour(Time) < 12 ~ "before",
TRUE ~ "after")) %>%
arrange(noon, Time) %>%
select(noon, everything())
#> # A tibble: 10 x 9
#> noon Date Time Blood.Sugar Systolic Diastolic Pulse Weight Comments
#> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <lgl> <chr>
#> 1 after 2019-… 1899… 106 134 83 59 NA Chose n…
#> 2 after 2019-… 1899… 97 130 72 56 NA Did not…
#> 3 after 2019-… 1899… 104 120 68 60 NA Chose n…
#> 4 after 2019-… 1899… 125 124 76 56 NA "\"aver…
#> 5 after 2019-… 1899… 129 112 61 56 NA Injecte…
#> 6 after 2019-… 1899… 119 115 67 55 NA Injecte…
#> 7 before 2019-… 1899… 93 125 70 55 NA <NA>
#> 8 before 2019-… 1899… 132 125 67 59 NA <NA>
#> 9 before 2019-… 1899… 108 128 69 50 NA <NA>
#> 10 before 2019-… 1899… 92 130 71 49 NA <NA>