ts_alos_plt {healthyR}

Plot ALOS - Average Length of Stay

Description

Plot ALOS - Average Length of Stay

Usage

ts_alos_plt(.data, .date_col, .value_col, .by_grouping, .interactive)

Arguments

.data

The time series data you need to pass

.date_col

The date column

.value_col

The value column

.by_grouping

How you want the data summarized - "sec", "min", "hour", "day", "week", "month", "quarter" or "year"

.interactive

TRUE or FALSE. TRUE returns a plotly plot and FALSE returns a static ggplot2 plot

Details

• Expects a tibble with a date time column and a value column

• Uses timetk for underlying sumarization and plot

• If .by_grouping is missing it will default to "day"

• A static ggplot2 object is return if the .interactive function is FALSE otherwise a plotly plot is returned.

Value

A timetk time series plot

Author(s)

Steven P. Sanderson II, MPH

See Also

Other Plotting Functions: diverging_bar_plt(), diverging_lollipop_plt(), gartner_magic_chart_plt(), los_ra_index_plt(), ts_median_excess_plt(), ts_plt(), ts_readmit_rate_plt()

Examples

library(healthyR)
library(healthyR.data)
library(timetk)
library(dplyr)
library(purrr)

# Make A Series of Dates ----
data_tbl <- healthyR_data

df_tbl <- data_tbl %>%
    filter(ip_op_flag == "I") %>%
    select(visit_end_date_time, length_of_stay) %>%
    summarise_by_time(
        .date_var = visit_end_date_time
        , .by     = "day"
        , visits  = mean(length_of_stay, na.rm = TRUE)
    ) %>%
    filter_by_time(
        .date_var     = visit_end_date_time
        , .start_date = "2012"
        , .end_date   = "2019"
    ) %>%
    set_names("Date","Values")

ts_alos_plt(
  .data = df_tbl
  , .date_col = Date
  , .value_col = Values
  , .by = "month"
  , .interactive = FALSE
)