Prepares the input for life.table(). An individual based approach is supported as well
as already pooled data (e. g. from an already existing life table). In the latter case, the user
has to specify a numerical variable (dec) which defines the count for each age class.
If no life table exists, this function will process a dataframe including the age ranges of
individuals or groups of individuals to discrete the age classes. The age range is spread to
single years. agebeg has to be specified for the beginning of an age range, as well
as ageend for the end of an age range. If a data-set with year-known individuals is
used, ageend can be omitted but then the parameter agerange has to left on its
default value (included). The method defines in which way the single years between
the different age classes are split. If the data set comprises a grouping variable (e.g., sex),
this can be specified with group.
Usage
prep.life.table(
x,
dec = NA,
agebeg,
ageend = NA,
group = NA,
method = "Standard",
agerange = "included"
)Arguments
- x
single dataframe containing sex age and quantity of deceased (individuals or group of individuals).
- dec
column name (as character) of the count of deceased, optional.
- agebeg
column name (as character) for the beginning of an age range.
- ageend
column name (as character) for the end of an age range, optional.
- group
column name (as character) of the grouping field (e.g., sex), optional. Default setup is:
NA.- method
character string, optional. Default options is
Standard, which will create age classes beginning with 1 year, up to 4 years, followed by steps of 5 years (1,4,5,5,...) until the maximum age is reached.Equal5will create age classes with an even distribution, stepped by 5 years (5,5,...) until the maximum age is reached. If method is a single numeric, this number will be repeated until the maximum age is reached. Thereby, it is possible to create a year-wise life table.- agerange
character string, optional. Default setup is:
included. If the age ranges from "20 to 40" and "40 to 60",excludedwill exclude the year 40 from "20 to 40", to prevent overlapping age classes.includedis for age ranges like "20 to 39" where the year 39 is meant to be counted.
Value
A list of input parameter needed for the function life.table.
x or Age: age interval.
a: years within x.
Dx: number of deaths within x.
Examples
# Separate age ranges in your data set.
df <- dplyr::mutate(
tidyr::separate(
replace(
magdalenenberg,
magdalenenberg=="60-x", "60-69"
),
a,
c("from", "to")
),
from = as.numeric(from),
to = as.numeric(to)
)
# Apply prep.life.table to a data set containing the age ranges.
magda_prep <- prep.life.table(
df,
dec = "Dx",
agebeg = "from",
ageend = "to",
method = "Equal5",
agerange = "included"
)
# Create a life.table.
life.table(magda_prep)
#>
#> mortAAR life table (n = 111 individuals)
#>
#> Life expectancy at birth (e0): 32.196
#>
#> x a Ax Dx dx lx qx Lx Tx ex rel_popx
#> 1 0--4 5 1.667 3.79 3.414 100.000 3.414 488.619 3219.632 32.196 15.176
#> 2 5--9 5 2.500 4.62 4.162 96.586 4.309 472.523 2731.014 28.276 14.676
#> 3 10--14 5 2.500 4.54 4.090 92.423 4.425 451.892 2258.491 24.436 14.036
#> 4 15--19 5 2.500 4.21 3.793 88.333 4.294 432.185 1806.599 20.452 13.423
#> 5 20--24 5 2.500 14.99 13.505 84.541 15.974 388.941 1374.414 16.257 12.080
#> 6 25--29 5 2.500 20.61 18.568 71.036 26.138 308.761 985.473 13.873 9.590
#> 7 30--34 5 2.500 17.20 15.495 52.468 29.533 223.604 676.712 12.897 6.945
#> 8 35--39 5 2.500 14.39 12.964 36.973 35.063 152.455 453.108 12.255 4.735
#> 9 40--44 5 2.500 6.68 6.018 24.009 25.066 105.000 300.653 12.523 3.261
#> 10 45--49 5 2.500 4.04 3.640 17.991 20.230 80.856 195.653 10.875 2.511
#> 11 50--54 5 2.500 5.49 4.946 14.351 34.463 59.392 114.797 7.999 1.845
#> 12 55--59 5 2.500 5.72 5.153 9.405 54.789 34.144 55.405 5.891 1.060
#> 13 60--64 5 2.500 2.36 2.126 4.252 50.000 15.946 21.261 5.000 0.495
#> 14 65--69 5 2.500 2.36 2.126 2.126 100.000 5.315 5.315 2.500 0.165