exposure_lag: Calculate Individual and Cumulative Lags for Exposure

In rSPARCS: Sites, Population, and Records Cleaning Skills

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)

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')

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.

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')

rSPARCS documentation built on Nov. 21, 2023, 9:07 a.m.