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1_make_summary_table.R
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1_make_summary_table.R
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## make table 1 and table A1: summary statistics for key variables by metro status, BEA region, and state
setwd(dirname(rstudioapi::getSourceEditorContext()$path))
source("prelim_plot.R")
## load data ----
dat_xc_county <- read_rds(file = "../final_data/fitted_and_actual_deaths_county_sets_2020_W2020_wash_6_3_countyrow.rds")
dat_xc_county <- dat_xc_county %>%
group_by(state, cs_name) %>%
mutate(metroname_mode = Mode(metroname))
dat_xc_countyset <- dat_xc_county %>%
distinct(state, cs_name, .keep_all = TRUE) %>%
select(-county_code, -county_fips, -county_name, -metroname)
negative_excess_death <- dat_xc_countyset %>%
filter(excess_deaths_2020 < 0)
## exclude counties with negative excess mortality
dat_xc_countyset <- dat_xc_countyset %>%
filter(excess_deaths_2020 >= 0)
## Prep data to calculate weighted mean ----
dat_xc_countyset_sum <- dat_xc_countyset %>%
ungroup() %>%
transmute(
year, state, cs_name,
region_bea, region_group_bea, metroname_mode, cens_pop_est,
fitted_deaths_2020,
all_cause_deaths_2020,
actual_pred_ratio = all_cause_deaths_2020 / fitted_deaths_2020,
excess_deaths_2020,
excess_death_rate_2020 = excess_death_rate_2020 * 100,
covid_deaths_2020,
covid_excess_ratio = covid_deaths_2020 / excess_deaths_2020
)
## All ----
mean_all <- dat_xc_countyset_sum %>%
summarise(across(c(
"covid_deaths_2020", "fitted_deaths_2020",
"all_cause_deaths_2020", "cens_pop_est"
), ~ sum(.x, na.rm = TRUE)),
n = n()
) %>%
ungroup() %>%
mutate(
offset = cens_pop_est / 100000,
excess_deaths_2020 = all_cause_deaths_2020 - fitted_deaths_2020,
unassigned_deaths = excess_deaths_2020 - covid_deaths_2020
) %>%
mutate(
excess_death_rate = excess_deaths_2020 / offset,
prop_assigned = covid_deaths_2020 / excess_deaths_2020 * 100,
obs_exp_ratio = all_cause_deaths_2020 / fitted_deaths_2020,
col1 = "All"
) %>%
select(
col1, fitted_deaths_2020, all_cause_deaths_2020,
obs_exp_ratio, excess_deaths_2020, excess_death_rate,
covid_deaths_2020, prop_assigned, n
)
## Metro ----
mean_metro <- dat_xc_countyset_sum %>%
group_by(metroname_mode) %>%
summarise(across(c(
"covid_deaths_2020", "fitted_deaths_2020",
"all_cause_deaths_2020", "cens_pop_est"
), ~ sum(.x, na.rm = TRUE)),
n = n()
) %>%
ungroup() %>%
mutate(
offset = cens_pop_est / 100000,
excess_deaths_2020 = all_cause_deaths_2020 - fitted_deaths_2020,
unassigned_deaths = excess_deaths_2020 - covid_deaths_2020
) %>%
mutate(
excess_death_rate = excess_deaths_2020 / offset,
prop_assigned = covid_deaths_2020 / excess_deaths_2020 * 100,
obs_exp_ratio = all_cause_deaths_2020 / fitted_deaths_2020
) %>%
select(
metroname_mode, fitted_deaths_2020, all_cause_deaths_2020,
obs_exp_ratio, excess_deaths_2020, excess_death_rate,
covid_deaths_2020, prop_assigned, n
)
colnames(mean_metro)[1] <- "col1"
## BEA region ----
mean_region <- dat_xc_countyset_sum %>%
group_by(region_bea) %>%
summarise(across(c(
"covid_deaths_2020", "fitted_deaths_2020",
"all_cause_deaths_2020", "cens_pop_est"
), ~ sum(.x, na.rm = TRUE)),
n = n()
) %>%
ungroup() %>%
mutate(
offset = cens_pop_est / 100000,
excess_deaths_2020 = all_cause_deaths_2020 - fitted_deaths_2020,
unassigned_deaths = excess_deaths_2020 - covid_deaths_2020
) %>%
mutate(
excess_death_rate = excess_deaths_2020 / offset,
prop_assigned = covid_deaths_2020 / excess_deaths_2020 * 100,
obs_exp_ratio = all_cause_deaths_2020 / fitted_deaths_2020
) %>%
select(
region_bea, fitted_deaths_2020, all_cause_deaths_2020,
obs_exp_ratio, excess_deaths_2020, excess_death_rate,
covid_deaths_2020, prop_assigned, n
)
colnames(mean_region)[1] <- "col1"
## BEA region * metro ----
mean_region_metro <- dat_xc_countyset_sum %>%
group_by(region_bea, metroname_mode) %>%
summarise(across(c(
"covid_deaths_2020", "fitted_deaths_2020",
"all_cause_deaths_2020", "cens_pop_est"
), ~ sum(.x, na.rm = TRUE)),
n = n()
) %>%
ungroup() %>%
mutate(
offset = cens_pop_est / 100000,
excess_deaths_2020 = all_cause_deaths_2020 - fitted_deaths_2020,
unassigned_deaths = excess_deaths_2020 - covid_deaths_2020
) %>%
mutate(
excess_death_rate = excess_deaths_2020 / offset,
prop_assigned = covid_deaths_2020 / excess_deaths_2020 * 100,
obs_exp_ratio = all_cause_deaths_2020 / fitted_deaths_2020
) %>%
select(
region_bea, metroname_mode, fitted_deaths_2020, all_cause_deaths_2020,
obs_exp_ratio, excess_deaths_2020, excess_death_rate,
covid_deaths_2020, prop_assigned, n
)
colnames(mean_region_metro)[1] <- "col1"
## State ----
mean_state <- dat_xc_countyset_sum %>%
group_by(state) %>%
summarise(across(c(
"covid_deaths_2020", "fitted_deaths_2020",
"all_cause_deaths_2020", "cens_pop_est"
), ~ sum(.x, na.rm = TRUE)),
n = n()
) %>%
ungroup() %>%
mutate(
offset = cens_pop_est / 100000,
excess_deaths_2020 = all_cause_deaths_2020 - fitted_deaths_2020,
unassigned_deaths = excess_deaths_2020 - covid_deaths_2020
) %>%
mutate(
excess_death_rate = excess_deaths_2020 / offset,
prop_assigned = covid_deaths_2020 / excess_deaths_2020 * 100,
obs_exp_ratio = all_cause_deaths_2020 / fitted_deaths_2020
) %>%
select(
state, fitted_deaths_2020, all_cause_deaths_2020,
obs_exp_ratio, excess_deaths_2020, excess_death_rate,
covid_deaths_2020, prop_assigned, n
)
colnames(mean_state)[1] <- "col1"
## combine all in one table ----
mean_table <- bind_rows(mean_all, mean_metro, mean_region, mean_region_metro, mean_state) %>%
mutate(
fitted_deaths_2020 = round(fitted_deaths_2020, 0),
excess_deaths_2020 = round(excess_deaths_2020, 0),
excess_death_rate = round(excess_death_rate, 1),
prop_assigned = round(prop_assigned, 1),
obs_exp_ratio = round(obs_exp_ratio, 2)
) %>%
select(col1, metroname_mode, everything())
mean_table <- as.data.frame(mean_table)
kt <- kbl(mean_table, "latex",
booktabs = T, linesep = "",
col.names = c(
"BEA Region", "Metro-Nonmetro Status",
"Predicted", "Actual", "Predicted/Expected",
"Excess",
"Excess per 1000",
"Covid-19",
"Proportion", "N"
),
format.args = list(big.mark = ","),
row.names = TRUE
)
kt <- gsub(".*midrule", "", kt)
kt <- gsub("bottomrule.*$", "", kt)
table_out <- substr(kt, 1, nchar(kt) - 1)
save_kable(table_out, file = paste0(outpath, "tab1_input.tex"), keep_tex = TRUE)