rSPARCS (version 0.1.1)

exposure_lag: Calculate Individual and Cumulative Lags for Exposure

Description

Calculate individual and cumulative lag exposure for specific variables. Cumulative lag exposure was calculated by using moving average.

Usage

exposure_lag(data,var,maxlag,ID,Date,lag_suffix)

Value

It returns a dataframe with calculated individual and cumulative lag exposures. 'var_cu_lag5' means the moving average from lag 0 to lag 5 days. 'var_si_lag5' means the exposure 5 days ago.

Arguments

data

A dataframe.

var

Variable names in the dataframe to specify variables to be used for the lag calculation.

maxlag

A number. The max day for calculating the lag exposure.

ID

A variable name. The exposure station ID.

Date

A variable name. A variable indicating the date of exposure measurement.

lag_suffix

A two-length vector indicating the cumulative lag or the individual lag. The first was the suffix for cumulative lag exposure. The second was for individual lag exposure. Default: c('_cu_lag','_si_lag')

References

Deng X, Friedman S, Ryan I, et al. The independent and synergistic impacts of power outages and floods on hospital admissions for multiple diseases [published online ahead of print, 2022 Mar 5]. Sci Total Environ. 2022;828:154305. doi:10.1016/j.scitotenv.2022.154305

Examples

data=data.frame(
  ID=rep(1:5,each=5),
  Date=seq(as.Date('2022-01-01'),as.Date('2022-01-05'),by='1 day'),
  x=rnorm(25)
)

exposure_lag(data,var='x',maxlag=3,ID='ID',Date='Date')