--- title: "An introduction to incidence2" output: markdown::html_format: options: toc: true number_sections: true vignette: > %\VignetteIndexEntry{An introduction to incidence2} %\VignetteEngine{knitr::knitr} %\VignetteEncoding{UTF-8} %\VignetteDepends{outbreaks, ggplot2, ciTools, tidyr} --- ```{r, include = FALSE} knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.align = "center" ) data.table::setDTthreads(2) ``` ## What does it do? **incidence2** is an R package that implements functions to compute, handle and visualise *incidence* data. It aims to be intuitive to use for both interactive data exploration and as part of more robust outbreak analytic pipelines. The package is based around objects of the namesake class, `incidence2`. These objects are a [`tibble`](https://cran.r-project.org/package=tibble) subclass with some additional invariants. That is, an `incidence2` object must: - have one column representing the date index (this does not need to be a `Date` object but must have an inherent ordering over time); - have one column representing the count variable (i.e. what is being counted) and one variable representing the associated count; - have zero or more columns representing groups; - not have duplicated rows with regards to the date, group and count variables. ## Functions at a glance To create and work with `incidence2` objects we provide a number of functions: - `incidence()`: for the construction of incidence objects from both linelists and pre-aggregated data sets. - `regroup()`: regroup incidence from different groups into one global incidence time series. - `incidence_()` and `regroup_()`: These work similar to their aforementioned namesakes but also add support for [tidy-select](https://dplyr.tidyverse.org/reference/dplyr_tidy_select.html) semantics in their arguments. - `plot.incidence2()`: generate simple plots with reasonable defaults. - `cumulate()`: calculate the cumulative incidence over time. - `complete_dates()`: ensure every possible combination of date and groupings is represented with a count. - `keep_first()`, `keep_last()`: keep the rows corresponding to the first (or last) set of grouped dates (ordered by time) from an `incidence2` object. - `keep_peaks()`; keep the rows corresponding to the maximum count value for each grouping of an `incidence2` object. A convenience wrapper around this, `first_peak()` keeps returns the earliest occurring peak row. - `bootstrap_incidence()`; sampling (with replacement and optional randomisation) from incidence2 objects. - `estimate_peak()`; estimate the peak of an epidemic curve using bootstrapped samples of the available data. - Accessors for underlying variables: `get_date_index()`, `get_count_variable()`, `get_count_value()`, `get_groups()`, `get_count_value_name()`, `get_count_variable_name()`, `get_date_index_name()` and `get_group_names()`. - Methods for common base R generics: - `as.data.frame.incidence2()` - `$<-.incidence2()` - `[.incidence2()` - `[<-.incidence2()` - `names<-.incidence2()` - `split.incidence2()` - `rbind.incidence2()` - Methods for generics from the wider R package ecosystem, including: - `mutate.incidence2()` - `summarise.incidence2()` - `nest.incidence2()` - `as_tibble.incidence2()` - `as.data.table.incidence2()` ## Basic Usage Examples in the vignette utilise three different sets of data: - A synthetic linelist generated from a simulated Ebola Virus Disease (EVD) outbreak and available in the [outbreaks](https://cran.r-project.org/package=outbreaks) package. - A pre-aggregated time-series of Covid cases, tests, hospitalisations, and deaths for UK regions that is included within the incidence2 package. - 136 cases of influenza A H7N9 in China. Again available in the outbreaks package. ### Computing incidence from a linelist Broadly speaking, we refer to data with one row of observations (e.g. 'Sex', 'Date of symptom onset', 'Date of Hospitalisation') per individual as a linelist ```{r} library(incidence2) # linelist from the simulated ebola outbreak (removing some missing entries) ebola <- subset(outbreaks::ebola_sim_clean$linelist ,!is.na(hospital)) str(ebola) ``` To compute daily incidence we pass to `incidence()` our linelist data frame as well as the name of a column in the data that we can use to index over time. Whilst we refer to this index as the `date_index` there is no restriction on it's type, save the requirement that is has an inherent ordering. ```{r} (daily_incidence <- incidence(ebola, date_index = "date_of_onset")) ``` incidence2 also provides a simple plot method (see `help("plot.incidence2")`) built upon [ggplot2](https://cran.r-project.org/package=ggplot2). ```{r} #| fig.height: 5 #| dpi: 90 #| fig.alt: > #| Bar chart of daily incidence covering the period April 2014 to April 2015 #| inclusive. The graph appears to peaks around September 2014. plot(daily_incidence) ``` The daily data is quite noisy, so it may be worth grouping the dates prior to calculating the incidence. One way to do this is to utilise functions from the [grates](https://cran.r-project.org/package=grates) package. incidence2 depends on the grates package so all of it's functionality is available directly to users. Here we use the `as_isoweek()` function to convert the 'date of onset' to an isoweek (a week starting on a Monday) before proceeding to calculate the incidence: ```{r} #| fig.alt: > #| Bar chart of weekly incidence covering 2014-W15 to 2015-W18 inclusive. #| The graph peaks at 2014-W38. The "descent" from the peak tapers off #| slower than the initial "ascent". (weekly_incidence <- ebola |> mutate(date_of_onset = as_isoweek(date_of_onset)) |> incidence(date_index = "date_of_onset")) plot(weekly_incidence, border_colour = "white") ``` As this sort of date grouping is often required we have chosen to integrate this within the `incidence()` function via the `interval` parameter. `interval` can take any of the following values: - 'day' or 'daily' (mapping to `Date` objects); - 'week(s)', 'isoweek(s)' or 'weekly' (mapping to `grates_isoweek`); - 'epiweek(s)' (mapping to `grates_epiweek`); - 'month(s)', 'yearmonth(s)' or 'monthly' (`grates_yearmonth`); - 'quarter(s)', 'yearquarter(s)' or 'quarterly' (`grates_yearquarter`); - 'year(s)' or 'yearly' (`grates_year`). As an example, the following is equivalent to the `weekly_incidence` output above: ```{r} (dat <- incidence(ebola, date_index = "date_of_onset", interval = "isoweek")) # check equivalent identical(dat, weekly_incidence) ``` If we wish to aggregate by specified groups we can use the `groups` argument. For instance, to compute the weekly incidence by gender: ```{r} (weekly_incidence_gender <- incidence( ebola, date_index = "date_of_onset", groups = "gender", interval = "isoweek" )) ``` For grouped data, the plot method will create a faceted plot across groups unless a fill variable is specified: ```{r} #| fig.alt: > #| Two bar charts (side by side) of weekly incidence covering 2014-W15 to #| 2015-W18 inclusive. Females are on the left, Males the right. The graphs #| peak between 2014-W35 and 2014-W45. The "descent" from the peak tapers off #| slower than the initial "ascent". plot(weekly_incidence_gender, border_colour = "white", angle = 45) ``` ```{r} #| fig.alt: > #| Bar chart of weekly incidence covering 2014-W15 to 2015-W18 inclusive. #| The graph peaks at 2014-W38. The "descent" from the peak tapers off #| slower than the initial "ascent". The graph is "filled" by the number of #| male versus female but it is hard to descern the difference. plot(weekly_incidence_gender, border_colour = "white", angle = 45, fill = "gender") ``` `incidence()` also supports multiple date inputs and allows renaming via the use of named vectors: ```{r} (weekly_multi_dates <- incidence( ebola, date_index = c( onset = "date_of_onset", infection = "date_of_infection" ), interval = "isoweek", groups = "gender" )) ``` For a quick, high-level, overview of grouped data we can use the `summary()` method: ```{r} summary(weekly_multi_dates) ``` When multiple date indices are given, they are used for rows of the resultant plot, unless the resultant variable is used to fill: ```{r} #| fig.alt: > #| Four bar charts arranged in a 2 by 2 grid. The top row represents incidence #| by date of infection, the bottom row by date of onset. Each row is arranged #| with females in the left plots and males the right. The graphs all peak #| between 2014-W35 and 2014-W45. The "descent" from the peaks tapers off #| slower than the initial "ascent". plot(weekly_multi_dates, angle = 45, border_colour = "white") ``` ```{r} #| fig.alt: > #| Two bar charts (side by side) of weekly incidence covering 2014-W15 to #| 2015-W18 inclusive. Females are on the left, Males the right. The graphs #| peak between 2014-W35 and 2014-W45. The "descent" from the peak tapers off #| slower than the initial "ascent". The graph is "filled" by the incidence #| according to date of onset and the incidence accorsing to date of #| infection. plot(weekly_multi_dates, angle = 45, border_colour = "white", fill = "count_variable") ``` ### Computing incidence from pre-aggregated data In terms of this package, pre-aggregated data, is data where we have a single column representing time and associated counts linked to those times (still optionally split by characteristics). The included Covid data set is in this wide format with multiple count values given for each day. ```{r} covid <- subset( covidregionaldataUK, !region %in% c("England", "Scotland", "Northern Ireland", "Wales") ) str(covid) ``` Like with our linelist data, `incidence()` requires us to specify a `date_index` column and optionally our `groups` and/or `interval`. In addition we must now also provide the `counts` variable(s) that we are interested in. Before continuing, take note of the missing values in output above. Where these occur in one of the count variables, `incidence()` warns users: ```{r} monthly_covid <- incidence( covid, date_index = "date", groups = "region", counts = "cases_new", interval = "yearmonth" ) monthly_covid ``` Whilst we could have let `incidence()` ignore missing values (equivalent to setting `sum(..., na.rm=TRUE)`), we prefer that users make an explicit choice on how these should (or should not) be imputed. For example, to treat missing values as zero counts we can simply replace them in the data prior to calling `incidence()`: ```{r} #| fig.alt: > #| Nine bar charts arranged in a 3 by 3 grid representing incidence new covid #| cases by month across nine English regions. Each plot goes from the start #| of 2020 to mid 2021. In each plot we see an increase in cases towards the #| end of 2020 and in to early 2021. (monthly_covid <- covid |> tidyr::replace_na(list(cases_new = 0)) |> incidence( date_index = "date", groups = "region", counts = "cases_new", interval = "yearmonth" )) plot(monthly_covid, nrow = 3, angle = 45, border_colour = "white") ``` ### Plotting in style of European Programme for Intervention Epidemiology Training (EPIET) For small datasets it is convention of EPIET to display individual cases as rectangles. We can do this by setting `show_cases = TRUE` in the call to `plot()` which will display each case as an individual square with a white border. ```{r} #| fig.height: 3 #| fig.alt: > #| Bar chart of daily incidence covering the period 2014-07-08 to 2014-07-16 #| inclusive. It shows 21 cases, with each case represented by an individual #| square. dat <- ebola[160:180, ] (small <- incidence( dat, date_index = "date_of_onset", date_names_to = "date" )) plot(small, show_cases = TRUE, angle = 45, n_breaks = 10) ``` ```{r} #| fig.height: 3 #| fig.alt: > #| Bar chart of daily incidence covering the period 2014-07-08 to 2014-07-16 #| inclusive. It shows 21 cases, with each case represented by an individual #| square filled with a colour based on an individuals gender. There is a peak #| on 2014-07-13 with 5 cases. (small_gender <- incidence( dat, date_index = "date_of_onset", groups = "gender", date_names_to = "date" )) plot(small_gender, show_cases = TRUE, angle = 45, n_breaks = 10, fill = "gender") ``` ## Support for tidy-select semantics When working interactively it can feel a little onerous constantly having to quote inputs for column names. To alleviate this we include the functions `incidence_()` and `regroup_()` which both support [tidy-select](https://dplyr.tidyverse.org/reference/dplyr_tidy_select.html) semantics in their column arguments (i.e. `date_index`, `groups` and `counts`). For now we have chosen to distinguish the functions via the post-fix underscore and have a preference for the standard version for non-interactive (e.g. programmatic usage). This could change over time if users feel having two similar functions is confusing. ## Working with incidence objects On top of the incidence constructor function and the basic plotting, printing and summary we provide a number of other useful functions and integrations for working with incidence2 objects. **Note:** The following sections utilise tidy-select semantics and hence use the post-fix version of the incidence function (`incidence_()`) ### `regroup()` If you've created a grouped incidence object but now want to change the internal grouping, you can `regroup()` to the desired aggregation: ```{r} # generate an incidence object with 3 groups (x <- incidence_( ebola, date_index = date_of_onset, groups = c(gender, hospital, outcome), interval = "isoweek" )) # regroup to just two groups regroup_(x, c(gender, outcome)) # standard (non-tidy-select) version regroup(x, c("gender", "outcome")) # drop all groups regroup(x) ``` ### `complete_dates()` Sometimes your incidence data does not span consecutive units of time, or different groupings may cover different periods. To this end we provide a `complete_dates()` function which ensures a complete and identical range of dates are given counts (by default filling with a 0 value). ```{r} dat <- data.frame( dates = as.Date(c("2020-01-01", "2020-01-04")), gender = c("male", "female") ) (incidence <- incidence_(dat, date_index = dates, groups = gender)) complete_dates(incidence) ``` ### `keep_first()`, `keep_last()` and `keep_peaks()` Once your data is grouped by date, you can select the first or last few entries based on a particular date grouping using `keep_first()` and `keep_last()`: ```{r} weekly_incidence <- incidence_( ebola, date_index = date_of_onset, groups = hospital, interval = "isoweek" ) keep_first(weekly_incidence, 3) keep_last(weekly_incidence, 3) ``` Similarly `keep_peaks()`returns the rows corresponding to the maximum count value for each grouping of an `incidence2` object: ```{r} keep_peaks(weekly_incidence) ``` ### Bootstrapping and estimating peaks `estimate_peak()` returns an estimate of the peak of an epidemic curve using bootstrapped samples of the available data. It is a wrapper around two functions: - Firstly, the imaginatively named, `first_peak()`, that returns the earliest occurring peak row per group; and, - Secondly, `bootstrap_incidence()` which samples (with replacement and optional randomisation) from incidence2 objects. Note that the bootstrapping approach used for estimating the peak time makes the following assumptions: - the total number of event is known (no uncertainty on total incidence); - dates with no events (zero incidence) will never be in bootstrapped datasets; and - the reporting is assumed to be constant over time, i.e. every case is equally likely to be reported. ```{r} #| fig.alt: > #| Bar chart of daily incidence covering the period March 2013 to August 2013 #| inclusive. The graph appears to peak around the start of April. influenza <- incidence_( outbreaks::fluH7N9_china_2013, date_index = date_of_onset, groups = province ) # across provinces (we suspend progress bar for markdown) estimate_peak(influenza, progress = FALSE) |> select(-count_variable) # regrouping for overall peak plot(regroup(influenza)) estimate_peak(regroup(influenza), progress = FALSE) |> select(-count_variable) # return the first peak of the grouped and ungrouped data first_peak(influenza) first_peak(regroup(influenza)) # bootstrap a single sample bootstrap_incidence(influenza) ``` ### `cumulate()` You can use `cumulate()` to easily generate cumulative incidences: ```{r} #| fig.alt: > #| Fives graphs representing cumulative weekly incidence covering 2014-W15 to #| 2015-W18 inclusive. Each graph represents a hospital in the data set. The #| five graphs fill a 3 by 2 grid with the bottom-right square being left #| blank. (y <- cumulate(weekly_incidence)) plot(y, angle = 45, nrow = 3) ``` ## Building on incidence2 The benefit incidence2 brings is not in the functionality it provides (which is predominantly wrapping around the functionality of other packages) but in the guarantees incidence2 objects give to a user about the underlying object structure and invariants that must hold. To make these objects easier to build upon we give sensible behaviour when the invariants are broken, an interface to access the variables underlying the `incidence2` object and methods, for popular group-aware generics, that implicitly utilise the underlying grouping structure. ### Class preservation As mentioned at the beginning of the vignetted, by definition, `incidence2` objects must: - have one column representing the date index (this does not need to be a `Date` object but must have an inherent ordering over time); - have one column representing the count variable (i.e. what is being counted) and one variable representing the associated count; - have zero or more columns representing groups; - not have duplicated rows with regards to the date, group and count variables. Due to these requirements it is important that these objects preserve (or drop) their structure appropriately under the range of different operations that can be applied to data frames. By this we mean that if an operation is applied to an incidence2 object then as long as the invariants of the object are preserved (i.e. required columns and uniqueness of rows) then the object will retain it's incidence class. If the invariants are not preserved then a `tibble` will be returned instead. ```{r} # create a weekly incidence object weekly_incidence <- incidence_( ebola, date_index = date_of_onset, groups = c(gender, hospital), interval = "isoweek" ) # filtering preserves class weekly_incidence |> subset(gender == "f" & hospital == "Rokupa Hospital") |> class() class(weekly_incidence[c(1L, 3L, 5L), ]) # Adding columns preserve class weekly_incidence$future <- weekly_incidence$date_index + 999L class(weekly_incidence) weekly_incidence |> mutate(past = date_index - 999L) |> class() # rename preserve class names(weekly_incidence)[names(weekly_incidence) == "date_index"] <- "isoweek" str(weekly_incidence) # select returns a data frame unless all date, count and group variables are # preserved in the output str(weekly_incidence[,-1L]) str(weekly_incidence[, -6L]) # duplicating rows will drop the class but only if duplicate rows class(rbind(weekly_incidence, weekly_incidence)) class(rbind(weekly_incidence[1:5, ], weekly_incidence[6:10, ])) ``` ### Accessing variable information We provide multiple accessors to easily access information about an `incidence2` object's structure: ```{r} # the name of the date_index variable of x get_date_index_name(weekly_incidence) # alias for `get_date_index_name()` get_dates_name(weekly_incidence) # the name of the count variable of x get_count_variable_name(weekly_incidence) # the name of the count value of x get_count_value_name(weekly_incidence) # the name(s) of the group variable(s) of x get_group_names(weekly_incidence) # the date_index variable of x str(get_date_index(weekly_incidence)) # alias for get_date_index str(get_dates(weekly_incidence)) # the count variable of x str(get_count_variable(weekly_incidence)) # the count value of x str(get_count_value(weekly_incidence)) # list of the group variable(s) of x str(get_groups(weekly_incidence)) ``` ### Grouping aware methods incidence2 provides methods for popular group-aware generics from both base R and the wider package ecosystem: - base: `split()`. - [dplyr](https://cran.r-project.org/package=dplyr): `mutate()` and `summarise()`. - [tidyr](https://cran.r-project.org/package=tidyr): `nest()`. When called on incidence2 objects, these methods will utilise the underlying grouping structure without the user needing to explicitly state what it is. This makes it very easy to utilise in analysis pipelines. #### Example fitting a poisson model ```{r} #| fig.alt: > #| Fives bar charts representing weekly incidence covering 2014-W15 to #| 2014-W35 inclusive. Each graph represents a hospital in the data set. The #| five graphs fill a 3 by 2 grid with the bottom-right square being left #| blank. On top of each bar graph is a line along with associated confidence #| intervals showing an increasing trend over the displayed weeks. # first twenty weeks of the ebola data set across hospitals dat <- incidence_(ebola, date_of_onset, groups = hospital, interval = "isoweek") dat <- keep_first(dat, 20L) # fit a poisson model to the grouped data (fitted <- dat |> nest(.key = "data") |> mutate( model = lapply( data, function(x) glm(count ~ date_index, data = x, family = poisson) ) )) # Add confidence intervals to the result (intervals <- fitted |> mutate(result = Map( function(data, model) { data |> ciTools::add_ci( model, alpha = 0.05, names = c("lower_ci", "upper_ci") ) |> as_tibble() }, data, model )) |> select(hospital, result) |> unnest(result)) # plot plot(dat, angle = 45) + ggplot2::geom_line( ggplot2::aes(date_index, y = pred), data = intervals, inherit.aes = FALSE ) + ggplot2::geom_ribbon( ggplot2::aes(date_index, ymin = lower_ci, ymax = upper_ci), alpha = 0.2, data = intervals, inherit.aes = FALSE, fill = "#BBB67E" ) ``` #### Example - Adding a rolling average ```{r} #| fig.alt: > #| Fives bar charts representing weekly incidence covering 2014-W15 to #| 2015-W18 inclusive. Each graph represents a hospital in the data set. The #| five graphs fill a 3 by 2 grid with the bottom-right square being left #| blank. On top of each bar graph is a line along that displays the rolling #| average. weekly_incidence |> regroup_(hospital) |> mutate(rolling_average = data.table::frollmean(count, n = 3L, align = "right")) |> plot(border_colour = "white", angle = 45) + ggplot2::geom_line(ggplot2::aes(x = date_index, y = rolling_average)) ```