Sometimes it is important to know what the census was on any given day, or what
the average length of stay is on given day, including for those patients that
are not yet discharged. This can be easily achieved. This will return one
record for every account so the data will still need to be summarized. If there
are multiple entries per day then those records will show up and you will
therefore have multiple entries in the column date in the resulting tibble.
If you want to aggregate from there you should be able to do so easily.
If you have a record where the .start_date_col is filled in but the corresponding
end_date is null then the end date will be set equal to Sys.Date()
If a record has a start_date that is NA then it will be discarded.
This function can take a little bit of time to run while the join comparison runs.
Usage
ts_census_los_daily_tbl(
.data,
.keep_nulls_only = FALSE,
.start_date_col,
.end_date_col,
.by_time = "day"
)Arguments
- .data
The data you want to pass to the function
- .keep_nulls_only
A boolean that will keep only those records that have a NULL end date, meaning the patient is still admitted. The default is FALSE which brings back all records.
- .start_date_col
The column containing the start date for the record
- .end_date_col
The column containing the end date for the record.
- .by_time
How you want the data presented, defaults to day and should remain that way unless you need more granular data.
Details
Requires a dataset that has at least a start date column and an end date column
Takes a single boolean parameter
Examples
library(healthyR)
library(healthyR.data)
library(dplyr)
df <- healthyR_data
df_tbl <- df %>%
filter(ip_op_flag == "I") %>%
select(visit_start_date_time, visit_end_date_time) %>%
timetk::filter_by_time(.date_var = visit_start_date_time, .start_date = "2020")
ts_census_los_daily_tbl(
.data = df_tbl
, .keep_nulls_only = FALSE
, .start_date_col = visit_start_date_time
, .end_date_col = visit_end_date_time
)
#> # A tibble: 45,572 × 5
#> date visit_start_date_time visit_end_date_time los census
#> <date> <date> <date> <int> <dbl>
#> 1 2020-01-01 2020-01-01 2020-01-02 1 1
#> 2 2020-01-01 2020-01-01 2020-01-02 1 1
#> 3 2020-01-01 2020-01-01 2020-01-02 1 1
#> 4 2020-01-01 2020-01-01 2020-01-03 2 1
#> 5 2020-01-01 2020-01-01 2020-01-03 2 1
#> 6 2020-01-01 2020-01-01 2020-01-03 2 1
#> 7 2020-01-01 2020-01-01 2020-01-04 3 1
#> 8 2020-01-01 2020-01-01 2020-01-04 3 1
#> 9 2020-01-01 2020-01-01 2020-01-04 3 1
#> 10 2020-01-01 2020-01-01 2020-01-05 4 1
#> # ℹ 45,562 more rows