In geomarker-io/ecat: Calculate Estimates of ECAT Exposure at Point Locations in the Cincinnati, OH Area

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(ecat)
library(dplyr)
library(tidyr)

Example 1: Using calculate_ecat() and calculate_scaling_factors() and manually applying scaling factors to ECAT estimates.

d <- tibble::tribble(
  ~id,         ~lon,        ~lat,
    809089L, -84.69127387, 39.24710734,
    813233L, -84.47798287, 39.12005904,
    814881L, -84.47123583,  39.2631309,
    799697L, -84.41741798, 39.18541228,
    799698L, -84.41395064, 39.18322447
  )

my_dates <- data.frame(start_date = as.Date(c("2010-01-08", "2012-06-08", "2010-01-09", "2015-04-09", "2010-01-10")),
                       end_date = as.Date(c("2010-02-08", "2012-07-08", "2010-02-09", "2015-05-09", "2010-02-10")))

d %>% 
  mutate(unscaled_ecat = calculate_ecat(. , return.LU.vars = FALSE), 
         scaling_factors = calculate_scaling_factors(my_dates), 
         scaled_ecat = unscaled_ecat * scaling_factors)

Example 2: Using the add_scaled_ecat() wrapper function to automatically apply scaling factors to ECAT estimates.

A common use case is calculating monthly exposures. For example, we may have a pair of coordinates recorded annually for each participant. In the data below, we have 2 unique ids, with lat/lon recorded once per year.

d <- tibble::tribble(
  ~id,         ~lon,        ~lat,        ~date,
    809089L, -84.69127387, 39.24710734, as.Date("2010-01-08"),
    809089L, -84.69127387, 39.24710734, as.Date("2011-01-08"),
    809089L, -84.69127387, 39.24710734, as.Date("2012-01-08"),
    799697L, -84.41741798, 39.18541228, as.Date("2011-01-10"),
    799697L, -84.41741798, 39.18541228, as.Date("2012-02-10")
  )

We want to scale the ecat measurements to monthly exposures between these dates, but we need start_date and end_date columns that represent the monthly time periods we want to average over.

d <- d %>% 
  mutate(from = date,
         to = from + lubridate::years(1)) %>% 
  group_by(id, date) %>% 
  nest() %>% 
  mutate(dates = purrr::map(data, ~seq.Date(from = .x$from, 
                                     to = .x$to, 
                                     by = '3 months'))) %>% 
  unnest(cols=c('data', 'dates')) %>% 
  dplyr::select(-from, -to) %>% 
  rename(start_date = dates) %>% 
  mutate(end_date = lead(start_date)) %>% 
  filter(!is.na(end_date)) %>% 
  ungroup()

The entire tibble with columns called id, lon, lat, start_date, and end_date are passed as the only argument to the add_scaled_ecat function.

d %>% 
  mutate(scaled_ecat = add_scaled_ecat(.))

geomarker-io/ecat documentation built on May 15, 2020, 5:58 p.m.