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< section id="introduction" class="level1">
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Introduction
There are many approaches to modeling time series data in R. One of the types of data that we might come across is a nested time series. This means the data is grouped simply by one or more keys. There are many methods in which to accomplish this task. This will be a quick post, but if you want a longer more detailed and quite frankly well written out one, then this is a really good article
< section id="exampmle" class="level1">Exampmle
Let’s just get to it with a very simple example, the motivation here isn’t to be all encompassing, but rather to just showcase it is possible for those who may not know it is.
library(healthyR.data)
library(dplyr)
library(timetk)
ts_tbl <- healthyR_data |>
filter(ip_op_flag == "I") |>
select(visit_end_date_time, service_line, length_of_stay) |>
mutate(visit_end_date_time = as.Date(visit_end_date_time)) |>
group_by(service_line) |>
summarise_by_time(
.date_var = visit_end_date_time,
.by = "month",
los = mean(length_of_stay)
) |>
ungroup()
glimpse(ts_tbl)
Rows: 2,148 Columns: 3 $ service_line <chr> "Alcohol Abuse", "Alcohol Abuse", "Alcohol Abuse",… $ visit_end_date_time <date> 2011-09-01, 2011-10-01, 2011-11-01, 2011-12-01, 2… $ los <dbl> 3.666667, 3.181818, 4.380952, 3.464286, 3.677419, …
library(forecast) library(broom) library(tidyr) glanced_models <- ts_tbl |> nest_by(service_line) |> mutate(AA = list(auto.arima(data$los))) |> mutate(perf = list(glance(AA))) |> unnest(cols = c(perf)) glanced_models |> select(-data)
# A tibble: 23 × 7 # Groups: service_line [23] service_line AA sigma logLik AIC BIC nobs <chr> <list> <dbl> <dbl> <dbl> <dbl> <int> 1 Alcohol Abuse <fr_ARIMA> 2.22 -241. 493. 506. 109 2 Bariatric Surgery For Obesity <fr_ARIMA> 0.609 -80.1 168. 178. 88 3 CHF <fr_ARIMA> 0.963 -152. 309. 314. 110 4 COPD <fr_ARIMA> 0.987 -155. 315. 320. 110 5 CVA <fr_ARIMA> 1.50 -201. 407. 412. 110 6 Carotid Endarterectomy <fr_ARIMA> 6.27 -166. 335. 339. 51 7 Cellulitis <fr_ARIMA> 1.07 -163. 329. 335. 110 8 Chest Pain <fr_ARIMA> 0.848 -139. 281. 287. 110 9 GI Hemorrhage <fr_ARIMA> 1.21 -179. 361. 366. 111 10 Joint Replacement <fr_ARIMA> 1.65 -196. 396. 401. 102 # … with 13 more rows
Voila!
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