Gives user more control over their lifetable compared to the life_expectancy() function. Allows the user to add in the central death rate and proportion surviving to age x. Allows the user to omit accessory columns which are used to calculate life expectancy.
Usage
lifetable(
data,
age,
pop,
deaths,
includeAllSteps = TRUE,
includeCDR = TRUE,
includePS = TRUE,
...
)Arguments
- data
The mortality dataset, includes an age grouping variable,
- age
The age grouping variable, must be categorical
- pop
Population of each age group, must be numeric
- deaths
The midyear number of deaths at each age group, must be numeric
- includeAllSteps
If false, will only include the proportion surviving to age x and life expectancy for age x
- includeCDR
If true, will include the central death rate for each age group
- includePS
If true, will include the proportion surviving for each age group
- ...
Other optional grouping variables (can be race, gender, etc.)
Examples
# Running lifetable() and choosing not to include CentralDeathRate and
# ProportionToSurvive (optional columns) in the output dataset
lifetable(mortality2, "age_group", "population", "deaths", FALSE, TRUE, TRUE)
#> # A tibble: 85 × 6
#> age_group deaths population CentralDeathRate PropToSurvive LifeExpectancy
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 < 1 year 23161 3970145 0.00583 1 75.9
#> 2 1 year 1568 3995008 0.000392 0.994 75.3
#> 3 2 years 1046 3992154 0.000262 0.994 74.4
#> 4 3 years 791 3982074 0.000199 0.994 73.4
#> 5 4 years 640 3987656 0.000160 0.993 72.4
#> 6 5 years 546 4032515 0.000135 0.993 71.4
#> 7 6 years 488 4029655 0.000121 0.993 70.4
#> 8 7 years 511 4029991 0.000127 0.993 69.4
#> 9 8 years 483 4159114 0.000116 0.993 68.4
#> 10 9 years 462 4178524 0.000111 0.993 67.4
#> # ℹ 75 more rows