fix rt aggregate table

episodes/quantify-transmissibility.Rmd

@@ -96,21 +96,24 @@ This tutorial illustrates the usage of `epinow()` to estimate the time-varying r
 To illustrate the functions of `EpiNow2` we will use outbreak data of the start of the COVID-19 pandemic from the United Kingdom. The data are available in the R package `{incidence2}`. 
 
 ```{r}
-head(incidence2::covidregionaldataUK)
+dplyr::as_tibble(incidence2::covidregionaldataUK)
 ```
 
 To use the data, we must format the data to have two columns:
 
 + `date` : the date (as a date object see `?is.Date()`),
 + `confirm` : number of confirmed cases on that date.
 
-```{r}
-cases <- aggregate(
-  cases_new ~ date,
-  data = incidence2::covidregionaldataUK[, c("date", "cases_new")],
-  FUN = sum
-)
-colnames(cases) <- c("date", "confirm")
+Let's use `{dplyr}` for this:
+
+```{r, warning = FALSE, message = FALSE}
+library(dplyr)
+
+cases <- incidence2::covidregionaldataUK %>%
+  select(date, cases_new) %>%
+  group_by(date) %>%
+  summarise(confirm = sum(cases_new, na.rm = TRUE)) %>%
+  ungroup()
 ```
 
 
@@ -142,9 +145,9 @@ The number of delays and type of delay is a flexible input that depends on the d
 
 | Data source        | Delay(s) |
 | ------------- |-------------|
+|Time of symptom onset      |Incubation period |
 |Time of case report      |Incubation period + time from symptom onset to case notification |
 |Time of hospitalisation   |Incubation period + time from symptom onset to hospitalisation     |
-|Time of symptom onset      |Incubation period |
 
 </center>
 
@@ -317,14 +320,47 @@ generation_time_fixed <- dist_spec(
 
 ```{r, message = FALSE, eval = TRUE}
 reported_cases <- cases[1:90, ]
+
+estimates <- epinow(
+  reported_cases = reported_cases,
+  generation_time = generation_time_opts(generation_time_fixed),
+  delays = delay_opts(
+    incubation_period_fixed + reporting_delay_fixed
+  ),
+  rt = rt_opts(
+    prior = list(mean = rt_log_mean, sd = rt_log_sd)
+  )
+)
+```
+
+::::::::::::::::::::::::::::::::: spoiler
+
+### Reduce computation time
+
+Using an appropriate number of samples and chains is crucial for ensuring convergence and obtaining reliable estimates in Bayesian computations using Stan. Inadequate sampling or insufficient chains may lead to issues such as divergent transitions, impacting the accuracy and stability of the inference process.
+
+For the purpose of this tutorial, we can add more configuration details to get an useful output in less time. You can specify a fixed number of `samples` and `chains` to the `stan` argument using the `stan_opts()` function:
+
+The code in the previous chunk can take around 10 minutes. We expect this chunk below to take approximately 3 minutes:
+
+```{r,eval=FALSE}
 estimates <- epinow(
+  # same code as previous chunk
   reported_cases = reported_cases,
   generation_time = generation_time_opts(generation_time_fixed),
-  delays = delay_opts(incubation_period_fixed + reporting_delay_fixed),
-  rt = rt_opts(prior = list(mean = rt_log_mean, sd = rt_log_sd))
+  delays = delay_opts(
+    incubation_period_fixed + reporting_delay_fixed
+  ),
+  rt = rt_opts(
+    prior = list(mean = rt_log_mean, sd = rt_log_sd)
+  ),
+  # [new] set a fixed number of samples and chains
+  stan = stan_opts(samples = 1000, chains = 3)
 )
 ```
 
+:::::::::::::::::::::::::::::::::
+
 ### Results
 
 We can extract and visualise estimates of the effective reproduction number through time:
@@ -402,8 +438,12 @@ To find regional estimates, we use the same inputs as `epinow()` to the function
 estimates_regional <- regional_epinow(
   reported_cases = regional_cases,
   generation_time = generation_time_opts(generation_time_fixed),
-  delays = delay_opts(incubation_period_fixed + reporting_delay_fixed),
-  rt = rt_opts(prior = list(mean = rt_log_mean, sd = rt_log_sd))
+  delays = delay_opts(
+    incubation_period_fixed + reporting_delay_fixed
+  ),
+  rt = rt_opts(
+    prior = list(mean = rt_log_mean, sd = rt_log_sd)
+  )
 )
 
 estimates_regional$summary$summarised_results$table