Group level aggregations check
group_check.Rmd
library(data.table)
library(cityClimateHealth)
data("ma_exposure")
data("ma_deaths")
exposure_columns <- list(
"date" = "date",
"exposure" = "tmax_C",
"geo_unit" = "TOWN20",
"geo_unit_grp" = "COUNTY20"
)
TOWNLIST <- c('CHELSEA', 'EVERETT', 'REVERE', 'MALDEN')
exposure <- subset(ma_exposure, TOWN20 %in% TOWNLIST)
# create outcome table
outcome_columns <- list(
"date" = "date",
"outcome" = "daily_deaths",
"geo_unit" = "TOWN20",
"geo_unit_grp" = "COUNTY20"
)
deaths <- subset(ma_deaths, TOWN20 %in% TOWNLIST)Check 1
grp_level = False
exposure_mat <- make_exposure_matrix(exposure,
exposure_columns,
grp_level = F,
time_subset = list(month = 5:9))
#> -- NA values automatically removed
#> strata dt_by = 'day', setting strata as geo_unit:yr:mn:dow
deaths_tbl <- make_outcome_table(deaths,
outcome_columns,
grp_level = F,
time_subset = list(month = 5:9))
#> Missing outcome values introduced by xgrid were set to 0;
#> assumes that every time in the dataset should have an outcome value
#> strata dt_by = 'day', setting strata as geo_unit:yr:mn:dow
#> Warning in make_outcome_table(deaths, outcome_columns, grp_level = F, time_subset = list(month = 5:9)): 2020 in data years, Outcome counts likely impacted by the
#> COVID-19 Pandemic. Be sure to include a covariate adjustment
#> or exclude this year from analysis.
# checks
stopifnot(all(deaths_tbl$match_strata %in% exposure_mat$match_strata ))
stopifnot(all(deaths_tbl$strata %in% exposure_mat$strata ))
# with factor
outcome_columns <- list(
"date" = "date",
"outcome" = "daily_deaths",
"factor" = "age_grp",
"geo_unit" = "TOWN20",
"geo_unit_grp" = "COUNTY20"
)
deaths_tbl <- make_outcome_table(deaths,
outcome_columns,
grp_level = F,
time_subset = list(month = 5:9))
#> > Combined factor is age_grp
#> Missing outcome values introduced by xgrid were set to 0;
#> assumes that every time in the dataset should have an outcome value
#> strata dt_by = 'day', setting strata as geo_unit:yr:mn:dow
#> Warning in make_outcome_table(deaths, outcome_columns, grp_level = F, time_subset = list(month = 5:9)): 2020 in data years, Outcome counts likely impacted by the
#> COVID-19 Pandemic. Be sure to include a covariate adjustment
#> or exclude this year from analysis.
# checks
stopifnot(all(deaths_tbl$match_strata %in% exposure_mat$match_strata ))
stopifnot(all(deaths_tbl$strata %in% exposure_mat$strata ))
# with spatial collapse
deaths$isCHELSEA = ifelse(deaths$TOWN20 == 'CHELSEA', "isChelsea", "notChelsea")
outcome_columns <- list(
"date" = "date",
"outcome" = "daily_deaths",
"factor" = "isCHELSEA",
"geo_unit" = "TOWN20",
"geo_unit_grp" = "COUNTY20"
)
deaths_tbl <- make_outcome_table(deaths,
outcome_columns,
factor_is_spatial = T,
grp_level = F,
time_subset = list(month = 5:9))
#> > Combined factor is isCHELSEA
#> Missing outcome values introduced by xgrid were set to 0;
#> assumes that every time in the dataset should have an outcome value
#> strata dt_by = 'day', setting strata as geo_unit:yr:mn:dow
#> Warning in make_outcome_table(deaths, outcome_columns, factor_is_spatial = T, : 2020 in data years, Outcome counts likely impacted by the
#> COVID-19 Pandemic. Be sure to include a covariate adjustment
#> or exclude this year from analysis.
# checks
stopifnot(all(deaths_tbl$match_strata %in% exposure_mat$match_strata ))
stopifnot(all(deaths_tbl$strata %in% exposure_mat$strata ))
# with temporal collapse
deaths$fctWeekend = ifelse(wday(deaths$date) %in% c(6,7), "weekend", "weekday")
outcome_columns <- list(
"date" = "date",
"outcome" = "daily_deaths",
"factor" = "fctWeekend",
"geo_unit" = "TOWN20",
"geo_unit_grp" = "COUNTY20"
)
deaths_tbl <- make_outcome_table(deaths,
outcome_columns,
factor_is_temporal = T,
grp_level = F,
time_subset = list(month = 5:9))
#> > Combined factor is fctWeekend
#> strata dt_by = 'day', setting strata as geo_unit:yr:mn:dow
#> Warning in make_outcome_table(deaths, outcome_columns, factor_is_temporal = T, : 2020 in data years, Outcome counts likely impacted by the
#> COVID-19 Pandemic. Be sure to include a covariate adjustment
#> or exclude this year from analysis.
# checks
stopifnot(all(deaths_tbl$match_strata %in% exposure_mat$match_strata ))
stopifnot(all(deaths_tbl$strata %in% exposure_mat$strata ))Check 2
grp_level = True and keep unit-level exposures and outcomes
exposure_mat <- make_exposure_matrix(exposure,
exposure_columns,
grp_level = T,
keep_unit_exposures = T,
time_subset = list(month = 5:9))
#> -- NA values automatically removed
#> strata dt_by = 'day', setting strata as geo_unit:yr:mn:dow
deaths_tbl <- make_outcome_table(deaths,
outcome_columns,
grp_level = T,
keep_unit_outcomes = T,
time_subset = list(month = 5:9))
#> > Combined factor is fctWeekend
#> Missing outcome values introduced by xgrid were set to 0;
#> assumes that every time in the dataset should have an outcome value
#> strata dt_by = 'day', setting strata as geo_unit:yr:mn:dow
#> Warning in make_outcome_table(deaths, outcome_columns, grp_level = T, keep_unit_outcomes = T, : 2020 in data years, Outcome counts likely impacted by the
#> COVID-19 Pandemic. Be sure to include a covariate adjustment
#> or exclude this year from analysis.
# checks
stopifnot(all(deaths_tbl$match_strata %in% exposure_mat$match_strata ))
stopifnot(all(deaths_tbl$strata %in% exposure_mat$strata ))
# with factor
outcome_columns <- list(
"date" = "date",
"outcome" = "daily_deaths",
"factor" = "age_grp",
"geo_unit" = "TOWN20",
"geo_unit_grp" = "COUNTY20"
)
deaths_tbl <- make_outcome_table(deaths,
outcome_columns,
grp_level = T,
keep_unit_outcomes = T,
time_subset = list(month = 5:9))
#> > Combined factor is age_grp
#> Missing outcome values introduced by xgrid were set to 0;
#> assumes that every time in the dataset should have an outcome value
#> strata dt_by = 'day', setting strata as geo_unit:yr:mn:dow
#> Warning in make_outcome_table(deaths, outcome_columns, grp_level = T, keep_unit_outcomes = T, : 2020 in data years, Outcome counts likely impacted by the
#> COVID-19 Pandemic. Be sure to include a covariate adjustment
#> or exclude this year from analysis.
# checks
stopifnot(all(deaths_tbl$match_strata %in% exposure_mat$match_strata ))
stopifnot(all(deaths_tbl$strata %in% exposure_mat$strata ))
# with spatial collapse
deaths$isCHELSEA = ifelse(deaths$TOWN20 == 'CHELSEA', "isChelsea", "notChelsea")
outcome_columns <- list(
"date" = "date",
"outcome" = "daily_deaths",
"factor" = "isCHELSEA",
"geo_unit" = "TOWN20",
"geo_unit_grp" = "COUNTY20"
)
deaths_tbl <- make_outcome_table(deaths,
outcome_columns,
factor_is_spatial = T,
grp_level = T,
keep_unit_outcomes = T,
time_subset = list(month = 5:9))
#> > Combined factor is isCHELSEA
#> Missing outcome values introduced by xgrid were set to 0;
#> assumes that every time in the dataset should have an outcome value
#> strata dt_by = 'day', setting strata as geo_unit:yr:mn:dow
#> Warning in make_outcome_table(deaths, outcome_columns, factor_is_spatial = T, : 2020 in data years, Outcome counts likely impacted by the
#> COVID-19 Pandemic. Be sure to include a covariate adjustment
#> or exclude this year from analysis.
# checks
stopifnot(all(deaths_tbl$match_strata %in% exposure_mat$match_strata ))
stopifnot(all(deaths_tbl$strata %in% exposure_mat$strata ))
# with temporal collapse
deaths$fctWeekend = ifelse(wday(deaths$date) %in% c(6,7), "weekend", "weekday")
outcome_columns <- list(
"date" = "date",
"outcome" = "daily_deaths",
"factor" = "fctWeekend",
"geo_unit" = "TOWN20",
"geo_unit_grp" = "COUNTY20"
)
deaths_tbl <- make_outcome_table(deaths,
outcome_columns,
factor_is_temporal = T,
grp_level = T,
keep_unit_outcomes = T,
time_subset = list(month = 5:9))
#> > Combined factor is fctWeekend
#> strata dt_by = 'day', setting strata as geo_unit:yr:mn:dow
#> Warning in make_outcome_table(deaths, outcome_columns, factor_is_temporal = T, : 2020 in data years, Outcome counts likely impacted by the
#> COVID-19 Pandemic. Be sure to include a covariate adjustment
#> or exclude this year from analysis.
# checks
stopifnot(all(deaths_tbl$match_strata %in% exposure_mat$match_strata ))
stopifnot(all(deaths_tbl$strata %in% exposure_mat$strata ))Check 3
grp_level = True and don’t keep unit-level exposures and outcomes
exposure_mat <- make_exposure_matrix(exposure,
exposure_columns,
grp_level = T,
keep_unit_exposures = F,
time_subset = list(month = 5:9))
#> -- NA values automatically removed
#> > grp_level == TRUE and keep_unit == FALSE, so
#> aggregating to geo_unit_grp and using geo_unit_grp as strata
#> strata dt_by = 'day', setting strata as geo_unit:yr:mn:dow
deaths_tbl <- make_outcome_table(deaths,
outcome_columns,
grp_level = T,
keep_unit_outcomes = F,
time_subset = list(month = 5:9))
#> > Combined factor is fctWeekend
#> > grp_level == TRUE and keep_unit == FALSE, so
#> aggregating to geo_unit_grp and using geo_unit_grp as strata
#> Missing outcome values introduced by xgrid were set to 0;
#> assumes that every time in the dataset should have an outcome value
#> strata dt_by = 'day', setting strata as geo_unit:yr:mn:dow
#> Warning in make_outcome_table(deaths, outcome_columns, grp_level = T, keep_unit_outcomes = F, : 2020 in data years, Outcome counts likely impacted by the
#> COVID-19 Pandemic. Be sure to include a covariate adjustment
#> or exclude this year from analysis.
# checks
stopifnot(all(deaths_tbl$match_strata %in% exposure_mat$match_strata ))
stopifnot(all(deaths_tbl$strata %in% exposure_mat$strata ))
# with factor
outcome_columns <- list(
"date" = "date",
"outcome" = "daily_deaths",
"factor" = "age_grp",
"geo_unit" = "TOWN20",
"geo_unit_grp" = "COUNTY20"
)
deaths_tbl <- make_outcome_table(deaths,
outcome_columns,
grp_level = T,
keep_unit_outcomes = F,
time_subset = list(month = 5:9))
#> > Combined factor is age_grp
#> > grp_level == TRUE and keep_unit == FALSE, so
#> aggregating to geo_unit_grp and using geo_unit_grp as strata
#> Missing outcome values introduced by xgrid were set to 0;
#> assumes that every time in the dataset should have an outcome value
#> strata dt_by = 'day', setting strata as geo_unit:yr:mn:dow
#> Warning in make_outcome_table(deaths, outcome_columns, grp_level = T, keep_unit_outcomes = F, : 2020 in data years, Outcome counts likely impacted by the
#> COVID-19 Pandemic. Be sure to include a covariate adjustment
#> or exclude this year from analysis.
# checks
stopifnot(all(deaths_tbl$match_strata %in% exposure_mat$match_strata ))
stopifnot(all(deaths_tbl$strata %in% exposure_mat$strata ))
# with spatial collapse
deaths$isCHELSEA = ifelse(deaths$TOWN20 == 'CHELSEA', "isChelsea", "notChelsea")
outcome_columns <- list(
"date" = "date",
"outcome" = "daily_deaths",
"factor" = "isCHELSEA",
"geo_unit" = "TOWN20",
"geo_unit_grp" = "COUNTY20"
)
deaths_tbl <- make_outcome_table(deaths,
outcome_columns,
factor_is_spatial = T,
grp_level = T,
keep_unit_outcomes = F,
time_subset = list(month = 5:9))
#> > Combined factor is isCHELSEA
#> > grp_level == TRUE and keep_unit == FALSE, so
#> aggregating to geo_unit_grp and using geo_unit_grp as strata
#> Missing outcome values introduced by xgrid were set to 0;
#> assumes that every time in the dataset should have an outcome value
#> strata dt_by = 'day', setting strata as geo_unit:yr:mn:dow
#> Warning in make_outcome_table(deaths, outcome_columns, factor_is_spatial = T, : 2020 in data years, Outcome counts likely impacted by the
#> COVID-19 Pandemic. Be sure to include a covariate adjustment
#> or exclude this year from analysis.
# checks
stopifnot(all(deaths_tbl$match_strata %in% exposure_mat$match_strata ))
stopifnot(all(deaths_tbl$strata %in% exposure_mat$strata ))
# with temporal collapse
deaths$fctWeekend = ifelse(wday(deaths$date) %in% c(6,7), "weekend", "weekday")
outcome_columns <- list(
"date" = "date",
"outcome" = "daily_deaths",
"factor" = "fctWeekend",
"geo_unit" = "TOWN20",
"geo_unit_grp" = "COUNTY20"
)
deaths_tbl <- make_outcome_table(deaths,
outcome_columns,
factor_is_temporal = T,
grp_level = T,
keep_unit_outcomes = F,
time_subset = list(month = 5:9))
#> > Combined factor is fctWeekend
#> > grp_level == TRUE and keep_unit == FALSE, so
#> aggregating to geo_unit_grp and using geo_unit_grp as strata
#> strata dt_by = 'day', setting strata as geo_unit:yr:mn:dow
#> Warning in make_outcome_table(deaths, outcome_columns, factor_is_temporal = T, : 2020 in data years, Outcome counts likely impacted by the
#> COVID-19 Pandemic. Be sure to include a covariate adjustment
#> or exclude this year from analysis.
# checks
stopifnot(all(deaths_tbl$match_strata %in% exposure_mat$match_strata ))
stopifnot(all(deaths_tbl$strata %in% exposure_mat$strata ))