These functions determine which items in a vector can be considered (the start of) a new episode, based on the argument episode_days. This can be used to determine clinical episodes for any epidemiological analysis. The get_episode() function returns the index number of the episode per group, while the is_new_episode() function returns values TRUE/FALSE to indicate whether an item in a vector is the start of a new episode.

get_episode(x, episode_days, ...)

is_new_episode(x, episode_days, ...)



vector of dates (class Date or POSIXt), will be sorted internally to determine episodes


required episode length in days, can also be less than a day or Inf, see Details


ignored, only in place to allow future extensions


  • get_episode(): a double vector

  • is_new_episode(): a logical vector


Dates are first sorted from old to new. The oldest date will mark the start of the first episode. After this date, the next date will be marked that is at least episode_days days later than the start of the first episode. From that second marked date on, the next date will be marked that is at least episode_days days later than the start of the second episode which will be the start of the third episode, and so on. Before the vector is being returned, the original order will be restored.

The first_isolate() function is a wrapper around the is_new_episode() function, but is more efficient for data sets containing microorganism codes or names and allows for different isolate selection methods.

The dplyr package is not required for these functions to work, but these functions do support variable grouping and work conveniently inside dplyr verbs such as filter(), mutate() and summarise().

Stable Lifecycle

The lifecycle of this function is stable. In a stable function, major changes are unlikely. This means that the unlying code will generally evolve by adding new arguments; removing arguments or changing the meaning of existing arguments will be avoided.

If the unlying code needs breaking changes, they will occur gradually. For example, a argument will be deprecated and first continue to work, but will emit an message informing you of the change. Next, typically after at least one newly released version on CRAN, the message will be transformed to an error.

Read more on Our Website!

On our website you can find a comprehensive tutorial about how to conduct AMR data analysis, the complete documentation of all functions and an example analysis using WHONET data.

See also


# `example_isolates` is a data set available in the AMR package.
# See ?example_isolates.

get_episode(example_isolates$date, episode_days = 60)    # indices
is_new_episode(example_isolates$date, episode_days = 60) # TRUE/FALSE

# filter on results from the third 60-day episode only, using base R
example_isolates[which(get_episode(example_isolates$date, 60) == 3), ]

# the functions also work for less than a day, e.g. to include one per hour:
              Sys.time() + 60 * 60),
            episode_days = 1/24)

# \donttest{
if (require("dplyr")) {
  # is_new_episode() can also be used in dplyr verbs to determine patient
  # episodes based on any (combination of) grouping variables:
  example_isolates %>%
    mutate(condition = sample(x = c("A", "B", "C"), 
                              size = 2000,
                              replace = TRUE)) %>% 
    group_by(condition) %>%
    mutate(new_episode = is_new_episode(date, 365))
  example_isolates %>%
    group_by(hospital_id, patient_id) %>%
              new_index = get_episode(date, 60),
              new_logical = is_new_episode(date, 60))
  example_isolates %>%
    group_by(hospital_id) %>% 
    summarise(patients = n_distinct(patient_id),
              n_episodes_365 = sum(is_new_episode(date, episode_days = 365)),
              n_episodes_60  = sum(is_new_episode(date, episode_days = 60)),
              n_episodes_30  = sum(is_new_episode(date, episode_days = 30)))
  # grouping on patients and microorganisms leads to the same
  # results as first_isolate() when using 'episode-based':
  x <- example_isolates %>%
    filter_first_isolate(include_unknown = TRUE,
                         method = "episode-based")
  y <- example_isolates %>%
    group_by(patient_id, mo) %>%
    filter(is_new_episode(date, 365))

  identical(x$patient_id, y$patient_id)
  # but is_new_episode() has a lot more flexibility than first_isolate(),
  # since you can now group on anything that seems relevant:
  example_isolates %>%
    group_by(patient_id, mo, hospital_id, ward_icu) %>%
    mutate(flag_episode = is_new_episode(date, 365))
# }