SangerTools (version 1.0.2)

standardised_rates_df: Standardised Prevalence Rates.

Description

Standardisation will be performed for all unique values in the column passed to `split_by`. If input data frame does not contain age bands or age bands are not of class factor, it is recommended to use age_bandizer or age_bandizer_2. After the function has run, the output can be copied using excel_clip or written to a database using df_to_sql. Alternatively, if you are interested in seeing the effects of age confounding; consider joining the outputs of this function with the output from crude_rates using a left_join

Usage

standardised_rates_df(
  df,
  Split_by,
  Condition,
  Population_Standard,
  Granular = FALSE,
  ...
)

Arguments

df

a tidy data frame in standard Master Patient Index format ie SangerTools::PopHealthData.

Split_by

A column name within df for which the standardised rates will be calculated for.

Condition

A Health condition flag denoted by 1 & 0; where 1 denotes the patient being positive for the health condition.

Population_Standard

Population Standard Weight used for Standardising; default set to NULL; which denotes use of Age Structure of df.

Granular

Takes a boolean value. If set to TRUE will output a tibble with Standardised Rates using values provided in `Split_col` and `...`By default is set to FALSE.

...

Variables used to standardise by; Must always have Age band for age standardisation, additional variables are optional and should be passed separated by commas.

Value

A tibble containing standardised Prevalence Rates by specified group.

Examples

# NOT RUN {
library(SangerTools)
health_data <- SangerTools::age_bandizer(df = SangerTools::master_patient_index,
                                         Age_col=Age)
df_rates <- standardised_rates_df(
  df = health_data,
  Split_by = Locality,
  Condition = Diabetes,
  Population_Standard = NULL,
  Granular = TRUE,
  Ageband
)
print(df_rates)
# }