This vignette documents to types of conversion which can be made using the incidence class:

Exporting results

To export results, we first compute bi-weekly incidence by gender from the simulated Ebola data used in the overview vignette:

library(outbreaks)
library(incidence)
dat <- ebola_sim$linelist$date_of_onset
i_14 <- incidence(dat, interval = 14, groups = ebola_sim$linelist$gender)
i_14
#> <incidence object>
#> [5888 cases from days 2014-04-07 to 2015-04-20]
#> [2 groups: f, m]
#> 
#> $counts: matrix with 28 rows and 2 columns
#> $n: 5888 cases in total
#> $dates: 28 dates marking the left-side of bins
#> $interval: 14 days
#> $timespan: 379 days
plot(i_14, border = "white")

To export the data to a data.frame, one simply needs:

as.data.frame(i_14)
#>         dates   f   m
#> 1  2014-04-07   1   1
#> 2  2014-04-21   8   1
#> 3  2014-05-05  17  13
#> 4  2014-05-19  17  18
#> 5  2014-06-02  20  25
#> 6  2014-06-16  27  25
#> 7  2014-06-30  29  44
#> 8  2014-07-14  73  48
#> 9  2014-07-28  90  79
#> 10 2014-08-11 122 139
#> 11 2014-08-25 189 184
#> 12 2014-09-08 290 278
#> 13 2014-09-22 316 286
#> 14 2014-10-06 270 272
#> 15 2014-10-20 246 273
#> 16 2014-11-03 202 195
#> 17 2014-11-17 154 161
#> 18 2014-12-01 136 150
#> 19 2014-12-15 124 115
#> 20 2014-12-29  88 108
#> 21 2015-01-12 112  95
#> 22 2015-01-26  86  82
#> 23 2015-02-09  76  73
#> 24 2015-02-23  76  75
#> 25 2015-03-09  57  71
#> 26 2015-03-23  63  42
#> 27 2015-04-06  45  42
#> 28 2015-04-20  28  31

The first column contains the dates marking the (inclusive) left side of the time intervals used for computing incidence, and the other columns give counts for the different groups. This function also has an option for exporting data as a ‘long’ format, i.e. with a column for ‘groups’ and a column for counts. This format can be useful especially when working with ggplot2, which expect data in this shape:

df <- as.data.frame(i_14, long = TRUE)
head(df)
#>        dates counts groups
#> 1 2014-04-07      1      f
#> 2 2014-04-21      8      f
#> 3 2014-05-05     17      f
#> 4 2014-05-19     17      f
#> 5 2014-06-02     20      f
#> 6 2014-06-16     27      f
tail(df)
#>         dates counts groups
#> 51 2015-02-09     73      m
#> 52 2015-02-23     75      m
#> 53 2015-03-09     71      m
#> 54 2015-03-23     42      m
#> 55 2015-04-06     42      m
#> 56 2015-04-20     31      m

## example of custom plot using steps:
library(ggplot2)
ggplot(df, aes(x = dates, y = counts)) + geom_step(aes(color = groups))

Finally, note that when ISO weeks are used, these are also reported in the output:

i_7 <- incidence(dat, interval = 7)
i_7
#> <incidence object>
#> [5888 cases from days 2014-04-07 to 2015-04-27]
#> [5888 cases from ISO weeks 2014-W15 to 2015-W18]
#> 
#> $counts: matrix with 56 rows and 1 columns
#> $n: 5888 cases in total
#> $dates: 56 dates marking the left-side of bins
#> $interval: 7 days
#> $timespan: 386 days
plot(i_7, border = "white")

head(as.data.frame(i_7))
#>        dates isoweeks counts
#> 1 2014-04-07 2014-W15      1
#> 2 2014-04-14 2014-W16      1
#> 3 2014-04-21 2014-W17      5
#> 4 2014-04-28 2014-W18      4
#> 5 2014-05-05 2014-W19     12
#> 6 2014-05-12 2014-W20     18
tail(as.data.frame(i_7))
#>         dates isoweeks counts
#> 51 2015-03-23 2015-W13     51
#> 52 2015-03-30 2015-W14     54
#> 53 2015-04-06 2015-W15     42
#> 54 2015-04-13 2015-W16     45
#> 55 2015-04-20 2015-W17     40
#> 56 2015-04-27 2015-W18     19

Importing pre-computed incidence

The function as.incidence facilitates the conversion of pre-computed incidences to an incidence object. Typically, the input will be imported into R from a .csv file or other spreadsheet formats.

as.incidence is a generic with methods for several types of objects (see ?as.incidence). The main method is matrix, as other types are coerced to matrix first and then passed to as.incidence.matrix:

args(incidence:::as.incidence.matrix)
#> function (x, dates = NULL, interval = NULL, isoweeks = TRUE, 
#>     ...) 
#> NULL

The only mandatory argument x is a table of counts, with time intervals in rows and groups in columns; if there are no groups, then the column doesn’t need a name; but if there are several groups, then columns should be named to indicate group labels. Optionally, dates can be provided to indicate the (inclusive) lower bounds of the time intervals, corresponding to the rows of x; most sensible date formats will do; if indicated as a character string, make sure the format is YYYY-mm-dd, e.g. 2017-04-01 for the 1st April 2017.

Let us illustrate the conversion using a simple vector of incidence:

vec <- c(1,2,3,0,3,2,4,1,2,1)

i <- as.incidence(vec)
i
#> <incidence object>
#> [19 cases from days 1 to 10]
#> 
#> $counts: matrix with 10 rows and 1 columns
#> $n: 19 cases in total
#> $dates: 10 dates marking the left-side of bins
#> $interval: 1 day
#> $timespan: 10 days

plot(vec, type = "s")

plot(i, border = "white")

Assuming the above incidences are computed weekly, we would then use:

i <- as.incidence(vec, interval = 7)
i
#> <incidence object>
#> [19 cases from days 1 to 64]
#> 
#> $counts: matrix with 10 rows and 1 columns
#> $n: 19 cases in total
#> $dates: 10 dates marking the left-side of bins
#> $interval: 7 days
#> $timespan: 64 days
plot(i, border = "white")

Note that in this case, incidences have been treated as per week, and corresponding dates in days have been computed during the conversion (the first day is always ‘1’), so that the first days of weeks 1, 2, 3… are:

i$dates
#>  [1]  1  8 15 22 29 36 43 50 57 64

In practice, it is best to provide the actual dates marking the lower bounds of the time intervals. We can illustrate this by a round trip using the example of the previous section:

## convertion: incidence --> data.frame:
i_14
#> <incidence object>
#> [5888 cases from days 2014-04-07 to 2015-04-20]
#> [2 groups: f, m]
#> 
#> $counts: matrix with 28 rows and 2 columns
#> $n: 5888 cases in total
#> $dates: 28 dates marking the left-side of bins
#> $interval: 14 days
#> $timespan: 379 days
df <- as.data.frame(i_14)
head(df)
#>        dates  f  m
#> 1 2014-04-07  1  1
#> 2 2014-04-21  8  1
#> 3 2014-05-05 17 13
#> 4 2014-05-19 17 18
#> 5 2014-06-02 20 25
#> 6 2014-06-16 27 25
tail(df)
#>         dates  f  m
#> 23 2015-02-09 76 73
#> 24 2015-02-23 76 75
#> 25 2015-03-09 57 71
#> 26 2015-03-23 63 42
#> 27 2015-04-06 45 42
#> 28 2015-04-20 28 31

## conversion: data.frame --> incidence
new_i <- as.incidence(df[,-1], df$dates)
new_i
#> <incidence object>
#> [5888 cases from days 2014-04-07 to 2015-04-20]
#> [2 groups: f, m]
#> 
#> $counts: matrix with 28 rows and 2 columns
#> $n: 5888 cases in total
#> $dates: 28 dates marking the left-side of bins
#> $interval: 14 days
#> $timespan: 379 days

## check round trip
identical(new_i, i_14)
#> [1] TRUE

Conversions to and from the incidence class

Thibaut Jombart

2017-11-12

Exporting results

Importing pre-computed incidence