diseasemapping (version 2.0.6)

formatPopulation-methods: Format a population data set

Description

The formatCases funtion formats the population data set. Reshape the population data set to "long" format, add in 4 columns : GROUP, POPULATION, sex and age.

Usage

# S4 method for data.frame
formatPopulation(
popdata, aggregate.by = NULL, breaks = NULL, ...
)
# S4 method for SpatVector
formatPopulation(
popdata, aggregate.by = NULL, breaks = NULL, ...
)
# S4 method for list
formatPopulation(
popdata, aggregate.by = NULL, breaks = NULL,  
	years=as.integer(names(popdata)), year.range=NULL, 
	 time="YEAR", 
        personYears=TRUE,...
)

Value

A data frame or spatial object, matching the input.

Arguments

popdata

population data set. It can be a data frame, list, database connection, or spatial polygon data frame

aggregate.by

if want to view the data set from a macro way, could aggregate the data set by age or sex or other variables

breaks

age breaks the user want to use. i.e breaks = c(10, 20, 30 ,40, 60, Inf).

time

the time variable, i.e years

personYears

convert populations to person-years

years

a vector with the year of each dataset

year.range

two dimensional vector with first and last year

...

additional arguments

Author

Patrick Brown

Details

After using the formatPopulation function, it will return the population data set in the same class as the original data set. i.e if a spatial polygon data frame has been put into the formatPopulation function, it will return a spatial polygon data frame. If aggregate.by is not NULL, the number of cases will be sum up by the groups define in aggregate.by. The "Group" column contains information of sex and age groups,in the format of M.55, denotes male, year 55. The "POPULATION" column is a numeric column, denotes the size of population for the particular age and sex group. The "age" column will be a "character" column contains ages in a cut format. i.e [50,55), denotes age 50. The cut breaks will get from the breaks of population data set or define by user. The sex column will have two levels "M" and "F" as factors.

Examples

data('kentucky')
kentucky = terra::unwrap(kentucky)
head(terra::values(kentucky))
poptry <- formatPopulation(kentucky, breaks = c(seq(0, 80, by=10), Inf))
head(poptry)
poptryagg <- formatPopulation(kentucky, 
  breaks = c(seq(0, 80, by=10), Inf), 
  aggregate.by=c("sex", "age"))
head(poptryagg)