EASR: Generate EASR data from sex and age-group data.

In seanob01PHS/list.utils: Common Utility Packages in LIST

Generate EASR data from sex and age-group data.

Description

This takes a dataframe df with some sort of count variable split over sex and age-group columns with population data for each sex, age-group split. These columns are grouped over and accumulated in to EASR (European Age Standardised Rate) and crude rate columns. Total count and total population variables are also provided in the output. The EASR will be based on a dataframe that describes a typical (standard) European split of age-grouping and sex demographics. The default for this dataframe is at /conf/linkage/output/lookups/Unicode/Populations/Standard/ESP2013_by_sex.sav.

Usage

EASR(
  df,
  n_col = "n",
  pop_col = "pop",
  age_group_col = "age_group",
  sex_col = "sex",
  multiply = 1,
  standard_pop_lookup = NULL,
  verbose_colnames = TRUE,
  include_crude_rate = TRUE
)

Arguments

df	Dataframe containging n_col, pop_col, age_group_col and sex_col for each observation.
n_col	Name of column containing the number of occurences of the measure. E.g. 'n_hospital_stays'.
pop_col	Name of column containing popultaion data.
age_group_col	Name of column containing age group data.
sex_col	Name of column containing sex data.
multiply	Number to multiply EASR and crude rate by. E.g. multiply=1000 will give rate per 1000 people.
standard_pop_lookup	Tibble describing the standard population sex and age-group demographics.
verbose_colnames	Option to include the value of n_col in the names of the EASR and Crude Rate output columns.
include_crude_rate	Option to include the Crude Rate output column.

Details

If the user provides column names of n_col, pop_col, age_group_col or sex_col, they must be character vectors and not tidyselections.

The user can provide the standard population dataframe using the standard_pop_lookup argument. This must have exactly these columns: age_group, sex and easr_standard_pop. The format and values of age_group_col and sex_col in df must match that of the relevant columns in standard_pop_lookup.

If using the default standard_pop_lookup, the age-group column must be in 5-year age groups and be formatted as follows c("0-4", "5-9", "10-14", ..."90plus"). Similarly, using that default, the sex column must be a numeric vector where 1=Male, 2=Female.

The output column names will depend on the value of n_col and verbose_colnames. If n_col='n_attendances' and verbose_colnames=TRUE, then the EASR and crude rate column names will be 'easr_n_attendances' and 'crude_rate_n_attendances' respectively.

If verbose_colenames=FALSE then the EASR and crude rate column names will be easr and crude_rate.

Value

A dataframe.

Examples

> df <- tibble(indicator = rep("sample indicator", 38),
             sex = factor(c(rep(1, 19), rep(2, 19))),
             age_group = factor(rep(make_age_group_labels(c(seq(0,90,5),Inf)), 2)),
             pop = rep(c(100,200), 19),
             n = c(rep(c(1,2,3), 12), c(1,2))
             )

> df
# A tibble: 38 × 5
   indicator          sex age_group   pop     n
   <chr>            <dbl> <chr>     <dbl> <dbl>
 1 sample indicator     1 0-4         100     1
 2 sample indicator     1 5-9         200     2
 3 sample indicator     1 10-14       100     3
 4 sample indicator     1 15-19       200     1
 5 sample indicator     1 20-24       100     2
 6 sample indicator     1 25-29       200     3
 7 sample indicator     1 30-34       100     1
 8 sample indicator     1 35-39       200     2
 9 sample indicator     1 40-44       100     3
10 sample indicator     1 45-49       200     1
# … with 28 more rows

> EASR(df)
# A tibble: 1 × 5
 indicator        total_n total_pop easr_n crude_rate_n
 <chr>              <dbl>     <dbl>  <dbl>        <dbl>
1 sample indicator      75      5700 0.0150       0.0132

seanob01PHS/list.utils documentation built on March 26, 2022, 12:41 p.m.