Conversions to and from the incidence class

Thibaut Jombart, Zhian N. Kamvar

2020-03-25

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


Exporting results

To export results, we first compute semi-weekly incidence (with weeks starting on Sunday, the beginning of the CDC epiweek) 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 = "2 epiweeks", groups = ebola_sim$linelist$gender)
i_14
#> <incidence object>
#> [5888 cases from days 2014-04-06 to 2015-04-19]
#> [5888 cases from MMWR weeks 2014-W15 to 2015-W16]
#> [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: 2 weeks
#> $timespan: 379 days
#> $cumulative: FALSE
plot(i_14, border = "white")

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

as.data.frame(i_14)
#>         dates    weeks   f   m
#> 1  2014-04-06 2014-W15   1   1
#> 2  2014-04-20 2014-W17   7   1
#> 3  2014-05-04 2014-W19  16  11
#> 4  2014-05-18 2014-W21  19  20
#> 5  2014-06-01 2014-W23  18  22
#> 6  2014-06-15 2014-W25  27  28
#> 7  2014-06-29 2014-W27  26  43
#> 8  2014-07-13 2014-W29  72  43
#> 9  2014-07-27 2014-W31  89  78
#> 10 2014-08-10 2014-W33 115 133
#> 11 2014-08-24 2014-W35 181 188
#> 12 2014-09-07 2014-W37 280 266
#> 13 2014-09-21 2014-W39 319 282
#> 14 2014-10-05 2014-W41 279 282
#> 15 2014-10-19 2014-W43 249 265
#> 16 2014-11-02 2014-W45 211 202
#> 17 2014-11-16 2014-W47 152 166
#> 18 2014-11-30 2014-W49 138 148
#> 19 2014-12-14 2014-W51 122 127
#> 20 2014-12-28 2014-W53  94 101
#> 21 2015-01-11 2015-W02 112  96
#> 22 2015-01-25 2015-W04  85  83
#> 23 2015-02-08 2015-W06  76  71
#> 24 2015-02-22 2015-W08  75  81
#> 25 2015-03-08 2015-W10  59  70
#> 26 2015-03-22 2015-W12  64  45
#> 27 2015-04-05 2015-W14  44  40
#> 28 2015-04-19 2015-W16  32  33

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    weeks counts groups
#> 1 2014-04-06 2014-W15      1      f
#> 2 2014-04-20 2014-W17      7      f
#> 3 2014-05-04 2014-W19     16      f
#> 4 2014-05-18 2014-W21     19      f
#> 5 2014-06-01 2014-W23     18      f
#> 6 2014-06-15 2014-W25     27      f
tail(df)
#>         dates    weeks counts groups
#> 51 2015-02-08 2015-W06     71      m
#> 52 2015-02-22 2015-W08     81      m
#> 53 2015-03-08 2015-W10     70      m
#> 54 2015-03-22 2015-W12     45      m
#> 55 2015-04-05 2015-W14     40      m
#> 56 2015-04-19 2015-W16     33      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 = "week")
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: 1 week
#> $timespan: 386 days
#> $cumulative: FALSE
plot(i_7, border = "white")

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

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
#> $cumulative: FALSE
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 2014-04-06 to 2015-04-19]
#> [5888 cases from MMWR weeks 2014-W15 to 2015-W16]
#> [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: 2 weeks
#> $timespan: 379 days
#> $cumulative: FALSE
df <- as.data.frame(i_14)
head(df)
#>        dates    weeks  f  m
#> 1 2014-04-06 2014-W15  1  1
#> 2 2014-04-20 2014-W17  7  1
#> 3 2014-05-04 2014-W19 16 11
#> 4 2014-05-18 2014-W21 19 20
#> 5 2014-06-01 2014-W23 18 22
#> 6 2014-06-15 2014-W25 27 28
tail(df)
#>         dates    weeks  f  m
#> 23 2015-02-08 2015-W06 76 71
#> 24 2015-02-22 2015-W08 75 81
#> 25 2015-03-08 2015-W10 59 70
#> 26 2015-03-22 2015-W12 64 45
#> 27 2015-04-05 2015-W14 44 40
#> 28 2015-04-19 2015-W16 32 33

## conversion: data.frame --> incidence
new_i <- as.incidence(df[group_names(i_14)], df$dates, interval = "2 epiweeks")
new_i
#> <incidence object>
#> [5888 cases from days 2014-04-06 to 2015-04-19]
#> [5888 cases from MMWR weeks 2014-W15 to 2015-W16]
#> [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: 2 weeks
#> $timespan: 379 days
#> $cumulative: FALSE

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