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table_2.R
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table_2.R
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######################################
# This script
# - produces a table with the number of patients fully vaccinated (2 doses + 2 weeks) in
# selected clinical and demographic groups
# - saves table as html
######################################
# Preliminaries ----
## Import libraries
library('tidyverse')
library('here')
library('glue')
library('gt')
library('gtsummary')
library('reshape2')
## Import custom user functions
source(here("analysis", "functions.R"))
## Create output directory
fs::dir_create(here::here("output", "tables"))
## Import data
data_processed <- read_rds(here::here("output", "data", "data_processed.rds"))
## Format data
data_processed <- data_processed %>%
mutate(group = ifelse(care_home_65plus == 1, 1, NA),
group = ifelse(is.na(group) & ageband == 3, 2, group),
group = ifelse(is.na(group) & hscworker == 1, 3, group),
group = ifelse(is.na(group) & ageband == 2, 4, group),
group = ifelse(is.na(group) & shielded == 1, 5, group),
group = ifelse(is.na(group) & age >=50 & age <70, 6, group),
group = ifelse(is.na(group), 7, group),
group = factor(group),
ageband3 = cut(
age,
breaks = c(16, 50, 60, 70, 80, Inf),
labels = c("16-50", "50-59", "60-69", "70-79", "80+"),
right = FALSE),
imd = as.character(imd),
imd = ifelse(imd %in% c("1 most deprived", 2:4, "5 least deprived"), imd, "Unknown"),
imd = fct_case_when(
imd == "1 most deprived" ~ "1 most deprived",
imd == 2 ~ "2",
imd == 3 ~ "3",
imd == 4 ~ "4",
imd == "5 least deprived" ~ "5 least deprived",
imd == "Unknown" ~ "Unknown",
#TRUE ~ "Unknown",
TRUE ~ NA_character_
),
region = as.character(region),
region = fct_case_when(
region == "London" ~ "London",
region == "East of England" ~ "East of England",
region == "East Midlands" ~ "East Midlands",
region == "North East" ~ "North East",
region == "North West" ~ "North West",
region == "South East" ~ "South East",
region == "South West" ~ "South West",
region == "West Midlands" ~ "West Midlands",
region == "Yorkshire and the Humber" ~ "Yorkshire and the Humber",
#TRUE ~ "Unknown"
TRUE ~ NA_character_),
bpcat = as.character(bpcat),
bpcat = ifelse(bpcat == "Normal", "Normal", ifelse(bpcat %in% c("Elevated", "High"), "Elevated/high", "Unknown")),
bpcat = fct_case_when(
bpcat == "Normal" ~ "Normal",
bpcat == "Elevated/high" ~ "Elevated/high",
bpcat == "Unknown" ~ "Unknown",
#TRUE ~ "Unknown",
TRUE ~ NA_character_),
organ_transplant_old = organ_transplant,
end_stage_renal_old = end_stage_renal,
organ_transplant = ifelse(organ_transplant_old == 1 & end_stage_renal_old == 0, "without RRT",
ifelse(organ_transplant_old == 1 & end_stage_renal_old == 1, "with RRT", NA)),
end_stage_renal = ifelse(organ_transplant_old == 0 & end_stage_renal_old == "without organ transplant", 1,
ifelse(organ_transplant_old == 1 & end_stage_renal_old == 1, "with organ transplant", NA))
) %>%
group_by(patient_id) %>%
mutate(follow_up_time = (follow_up_time_vax2 - 14)) %>%
ungroup()
# Table 2 ----
## Counts
counts0 <- data_processed %>%
mutate(time_since_fully_vaccinated = cut(follow_up_time_vax2 - 14,
breaks = c(0, 28, 56, 84, Inf),
labels = c("0-4 weeks", "4-8 weeks", "8-12 weeks", "12+ weeks"),
right = FALSE),
time_between_vaccinations = cut(tbv,
breaks = c(0, 42, 84, Inf),
labels = c("6 weeks or less", "6-12 weeks", "12 weeks or more"),
right = FALSE),
smoking_status = ifelse(is.na(smoking_status), "M", smoking_status)) %>%
select(ageband3,
sex,
bmi,
smoking_status,
ethnicity,
imd,
region,
asthma,
asplenia,
bpcat,
cancer,
diabetes,
chd,
haem_cancer,
immunosuppression,
chronic_kidney_disease,
learning_disability,
cld,
chronic_neuro_dis_inc_sig_learn_dis,
chronic_resp_dis,
end_stage_renal,
sev_mental_ill,
organ_transplant,
time_since_fully_vaccinated,
time_between_vaccinations,
prior_covid_cat) %>%
tbl_summary()
counts0$inputs$data <- NULL
counts <- counts0$table_body %>%
select(group = variable, variable = label, count = stat_0) %>%
separate(count, c("count","perc"), sep = "([(])") %>%
# mutate(count = as.numeric(count),
# perc = gsub('.{2}$', '', perc)) %>%
mutate(count = gsub(" ", "", count),
count = as.numeric(gsub(",", "", count))) %>%
filter(!(is.na(count))) %>%
select(-perc) %>%
filter(!(group == "prior_covid_cat" & variable == "Unknown"),
!(group == "organ_transplant" & variable == "Unknown"),
!(group == "end_stage_renal" & variable == "Unknown"))
## Positive test rates
positive_test_rates <- calculate_rates(group = "covid_positive_test",
follow_up = "time_to_positive_test",
data = data_processed,
Y = 1000,
dig = 0,
variables = c("ageband3", "sex", "bmi", "smoking_status", "ethnicity",
"imd", "region", "asthma", "asplenia", "bpcat", "chd",
"chronic_neuro_dis_inc_sig_learn_dis", "chronic_resp_dis",
"chronic_kidney_disease", "end_stage_renal","cld",
"diabetes", "immunosuppression", "learning_disability",
"sev_mental_ill", "organ_transplant", "time_since_fully_vaccinated",
"time_between_vaccinations", "prior_covid_cat", "cancer", "haem_cancer"))
table2 <- left_join(counts, positive_test_rates, by = c("group", "variable"))
## Hospitalisation rates
hospitalisation_rates <- calculate_rates(group = "covid_hospital_admission",
follow_up = "time_to_hospitalisation",
data = data_processed,
Y = 1000,
dig = 0,
variables = c("ageband3", "sex", "bmi", "smoking_status", "ethnicity",
"imd", "region", "asthma", "asplenia", "bpcat", "chd",
"chronic_neuro_dis_inc_sig_learn_dis", "chronic_resp_dis",
"chronic_kidney_disease", "end_stage_renal","cld",
"diabetes", "immunosuppression", "learning_disability",
"sev_mental_ill", "organ_transplant", "time_since_fully_vaccinated",
"time_between_vaccinations", "prior_covid_cat", "cancer", "haem_cancer"))
table2 <- left_join(table2, hospitalisation_rates, by = c("group", "variable"))
## Critical care with COVID rates
critial_care_rates <- calculate_rates(group = "covid_hospitalisation_critical_care",
follow_up = "time_to_hospitalisation",
data = data_processed,
Y = 1000,
dig = 0,
variables = c("ageband3", "sex", "bmi", "smoking_status", "ethnicity",
"imd", "region", "asthma", "asplenia", "bpcat", "chd",
"chronic_neuro_dis_inc_sig_learn_dis", "chronic_resp_dis",
"chronic_kidney_disease", "end_stage_renal","cld",
"diabetes", "immunosuppression", "learning_disability",
"sev_mental_ill", "organ_transplant", "time_since_fully_vaccinated",
"time_between_vaccinations", "prior_covid_cat", "cancer", "haem_cancer"))
table2 <- left_join(table2, critial_care_rates, by = c("group", "variable"))
## Death rates
death_rates <- calculate_rates(group = "covid_death",
follow_up = "time_to_covid_death",
data = data_processed,
Y = 1000,
dig = 0,
variables = c("ageband3", "sex", "bmi", "smoking_status", "ethnicity",
"imd", "region", "asthma", "asplenia", "bpcat", "chd",
"chronic_neuro_dis_inc_sig_learn_dis", "chronic_resp_dis",
"chronic_kidney_disease", "end_stage_renal","cld",
"diabetes", "immunosuppression", "learning_disability",
"sev_mental_ill", "organ_transplant", "time_since_fully_vaccinated",
"time_between_vaccinations", "prior_covid_cat", "cancer", "haem_cancer"))
table2 <- left_join(table2, death_rates, by = c("group", "variable"))
colnames(table2) = c("Variable", "level",
"Fully vaccinated",
"Positive COVID test", "PYs_1", "rate_1", "lci_1", "uci_1",
"Hospitalised with COVID", "PYs_2", "rate_2", "lci_2", "uci_2",
"Critical care with COVID", "PYs_3", "rate_3", "lci_3", "uci_3",
"COVID Deaths", "PYs_4", "rate_4", "lci_4", "uci_4")
## Counts of tests and positivity rate
test_counts <- data_processed %>%
mutate(time_since_fully_vaccinated = cut(follow_up_time_vax2 - 14,
breaks = c(0, 28, 56, 84, Inf),
labels = c("0-4 weeks", "4-8 weeks", "8-12 weeks", "12+ weeks"),
right = FALSE),
time_between_vaccinations = cut(tbv,
breaks = c(0, 42, 84, Inf),
labels = c("6 weeks or less", "6-12 weeks", "12 weeks or more"),
right = FALSE),
smoking_status = ifelse(is.na(smoking_status), "M", smoking_status),
smoking_status = ifelse(is.na(smoking_status), "N&M", smoking_status),
asthma = ifelse(asthma == 1, "asthma", NA),
asplenia = ifelse(asplenia == 1, "asplenia", NA),
cancer = ifelse(cancer == 1, "cancer", NA),
haem_cancer = ifelse(haem_cancer == 1, "haem_cancer", NA),
chd = ifelse(chd == 1, "chd", NA),
chronic_neuro_dis_inc_sig_learn_dis = ifelse(chronic_neuro_dis_inc_sig_learn_dis == 1, "chronic_neuro_dis_inc_sig_learn_dis", NA),
chronic_resp_dis = ifelse(chronic_resp_dis == 1, "chronic_resp_dis", NA),
#chronic_kidney_disease = ifelse(chronic_kidney_disease == 1, "chronic_kidney_disease", NA),
end_stage_renal = ifelse(end_stage_renal == 1, "end_stage_renal", NA),
cld = ifelse(cld == 1, "cld", NA),
diabetes = ifelse(diabetes == 1, "diabetes", NA),
immunosuppression = ifelse(immunosuppression == 1, "immunosuppression", NA),
learning_disability = ifelse(learning_disability == 1, "learning_disability", NA),
sev_mental_ill = ifelse(sev_mental_ill == 1, "sev_mental_ill", NA),
organ_transplant = ifelse(organ_transplant == 1, "organ_transplant", NA)) %>%
select(tests_conducted_any,
tests_conducted_positive,
ageband3,
sex,
bmi,
smoking_status,
ethnicity,
imd,
region,
asthma,
asplenia,
bpcat,
cancer,
diabetes,
chd,
haem_cancer,
immunosuppression,
chronic_kidney_disease,
learning_disability,
cld,
chronic_neuro_dis_inc_sig_learn_dis,
chronic_resp_dis,
end_stage_renal,
sev_mental_ill,
organ_transplant,
time_since_fully_vaccinated,
time_between_vaccinations,
prior_covid_cat) %>%
melt(id.var = c("tests_conducted_any", "tests_conducted_positive")) %>%
group_by(variable, value) %>%
summarise(n = n(),
test_0 = sum(is.na(tests_conducted_any)),
test_1 = sum(tests_conducted_any == 1, na.rm = T),
test_2 = sum(tests_conducted_any == 2, na.rm = T),
test_3 = sum(tests_conducted_any > 2, na.rm = T),
tests_conducted_any = sum(tests_conducted_any, na.rm = TRUE),
tests_conducted_positive = sum(tests_conducted_positive, na.rm = TRUE)) %>%
ungroup() %>%
mutate(positivy = tests_conducted_positive/tests_conducted_any*100) %>%
select(Variable = variable, level = value, test_0, test_1, test_2, test_3, tests_conducted_any, positivy)
## Follow-up time
follow_up <- data_processed %>%
mutate(time_since_fully_vaccinated = cut(follow_up_time_vax2 - 14,
breaks = c(0, 28, 56, 84, Inf),
labels = c("0-4 weeks", "4-8 weeks", "8-12 weeks", "12+ weeks"),
right = FALSE),
time_between_vaccinations = cut(tbv,
breaks = c(0, 42, 84, Inf),
labels = c("6 weeks or less", "6-12 weeks", "12 weeks or more"),
right = FALSE),
smoking_status = ifelse(is.na(smoking_status), "M", smoking_status),
smoking_status = ifelse(is.na(smoking_status), "N&M", smoking_status),
asthma = ifelse(asthma == 1, "asthma", NA),
asplenia = ifelse(asplenia == 1, "asplenia", NA),
cancer = ifelse(cancer == 1, "cancer", NA),
haem_cancer = ifelse(haem_cancer == 1, "haem_cancer", NA),
chd = ifelse(chd == 1, "chd", NA),
chronic_neuro_dis_inc_sig_learn_dis = ifelse(chronic_neuro_dis_inc_sig_learn_dis == 1, "chronic_neuro_dis_inc_sig_learn_dis", NA),
chronic_resp_dis = ifelse(chronic_resp_dis == 1, "chronic_resp_dis", NA),
#chronic_kidney_disease = ifelse(chronic_kidney_disease == 1, "chronic_kidney_disease", NA),
end_stage_renal = ifelse(end_stage_renal == 1, "end_stage_renal", NA),
cld = ifelse(cld == 1, "cld", NA),
diabetes = ifelse(diabetes == 1, "diabetes", NA),
immunosuppression = ifelse(immunosuppression == 1, "immunosuppression", NA),
learning_disability = ifelse(learning_disability == 1, "learning_disability", NA),
sev_mental_ill = ifelse(sev_mental_ill == 1, "sev_mental_ill", NA),
organ_transplant = ifelse(organ_transplant == 1, "organ_transplant", NA)) %>%
select(follow_up_time,
ageband3,
sex,
bmi,
smoking_status,
ethnicity,
imd,
region,
asthma,
asplenia,
bpcat,
cancer,
diabetes,
chd,
haem_cancer,
immunosuppression,
chronic_kidney_disease,
learning_disability,
cld,
chronic_neuro_dis_inc_sig_learn_dis,
chronic_resp_dis,
end_stage_renal,
sev_mental_ill,
organ_transplant,
time_since_fully_vaccinated,
time_between_vaccinations,
prior_covid_cat) %>%
melt(id.var = c("follow_up_time")) %>%
group_by(variable, value) %>%
summarise(quantile = scales::percent(c(0.25, 0.5, 0.75)),
fu = quantile(follow_up_time, c(0.25, 0.5, 0.75))) %>%
mutate(fu = round(fu, digits = 0)) %>%
pivot_wider(id_cols = c("variable", "value"), names_from = quantile, values_from = fu) %>%
mutate(fu = paste(`50%`, " (", `25%`, "-", `75%`, ")", sep = "")) %>%
select(Variable = variable, level = value, fu)
## Combine tables
table2 <- left_join(table2, test_counts, by = c("Variable", "level")) %>%
left_join(follow_up, by = c("Variable", "level")) %>%
mutate(test_0 = round(test_0/`Fully vaccinated`*100, digits = 0),
test_1 = round(test_1/`Fully vaccinated`*100, digits = 0),
test_2 = round(test_2/`Fully vaccinated`*100, digits = 0),
test_3 = round(test_3/`Fully vaccinated`*100, digits = 0),
positivy = round(positivy, digits = 2)) %>%
mutate() %>%
select("Variable", "level", "Fully vaccinated", "fu", "test_0", "test_1", "test_2", "test_3",
"Positive COVID test", "positivy", "PYs_1", "rate_1", "lci_1", "uci_1",
"Hospitalised with COVID", "PYs_2", "rate_2", "lci_2", "uci_2",
"Critical care with COVID", "PYs_3", "rate_3", "lci_3", "uci_3",
"COVID Deaths", "PYs_4", "rate_4", "lci_4", "uci_4")
# Redaction ----
## Redact values < 8
threshold = 8
table2_redacted <- table2 %>%
mutate(`Fully vaccinated` = ifelse(`Fully vaccinated` < threshold, NA, as.numeric(`Fully vaccinated`)),
fu = ifelse(is.na(`Fully vaccinated`), NA, fu),
test_0 = ifelse(is.na(`Fully vaccinated`), NA, test_0),
test_1 = ifelse(is.na(`Fully vaccinated`), NA, test_1),
test_2 = ifelse(is.na(`Fully vaccinated`), NA, test_2),
test_3 = ifelse(is.na(`Fully vaccinated`), NA, test_3),
`Positive COVID test` = ifelse(`Positive COVID test` < threshold, NA, `Positive COVID test`),
positivy = ifelse(`Positive COVID test` < threshold, NA, positivy),
PYs_1 = ifelse(`Positive COVID test` < threshold, NA, PYs_1),
rate_1 = ifelse(`Positive COVID test` < threshold, NA, rate_1),
lci_1 = ifelse(`Positive COVID test` < threshold, NA, lci_1),
uci_1 = ifelse(`Positive COVID test` < threshold, NA, uci_1),
`Hospitalised with COVID` = ifelse(`Hospitalised with COVID` < threshold, NA, `Hospitalised with COVID`),
PYs_2 = ifelse(`Hospitalised with COVID` < threshold, NA, PYs_2),
rate_2 = ifelse(`Hospitalised with COVID` < threshold, NA, rate_2),
lci_2 = ifelse(`Hospitalised with COVID` < threshold, NA, lci_2),
uci_2 = ifelse(`Hospitalised with COVID` < threshold, NA, uci_2),
`Critical care with COVID` = ifelse(`Critical care with COVID` < threshold, NA, `Critical care with COVID`),
PYs_3 = ifelse(`Critical care with COVID` < threshold, NA, PYs_3),
rate_3 = ifelse(`Critical care with COVID` < threshold, NA, rate_3),
lci_3 = ifelse(`Critical care with COVID` < threshold, NA, lci_3),
uci_3 = ifelse(`Critical care with COVID` < threshold, NA, uci_3),
`COVID Deaths` = ifelse(`COVID Deaths` < threshold, NA, `COVID Deaths`),
PYs_4 = ifelse(is.na(`COVID Deaths`), NA, PYs_4),
rate_4 = ifelse(is.na(`COVID Deaths`), NA, rate_4),
lci_4 = ifelse(is.na(`COVID Deaths`), NA, lci_4),
uci_4 = ifelse(is.na(`COVID Deaths`), NA, uci_4)) %>%
mutate(test_0 = ifelse(test_0/100*`Fully vaccinated` < threshold, NA, test_0),
test_1 = ifelse(test_1/100*`Fully vaccinated` < threshold, NA, test_1),
test_2 = ifelse(test_2/100*`Fully vaccinated` < threshold, NA, test_2),
test_3 = ifelse(test_3/100*`Fully vaccinated` < threshold, NA, test_3))
## Round to nearest 5
table2_redacted <- table2_redacted %>%
mutate(`Fully vaccinated` = plyr::round_any(`Fully vaccinated`, 5),
`Positive COVID test` = plyr::round_any(`Positive COVID test`, 5),
`Hospitalised with COVID` = plyr::round_any(`Hospitalised with COVID`, 5),
`Critical care with COVID` = plyr::round_any(`Critical care with COVID`, 5),
`COVID Deaths` = plyr::round_any(`COVID Deaths`, 5))
## Formatting
table2_redacted <- table2_redacted %>%
mutate(Positive_test_count = paste(`Positive COVID test`, " (", PYs_1, ")", sep = ""),
Positive_test_rate = paste(rate_1, " (", lci_1, "-", uci_1, ")", sep = ""),
Hospitalised_count = paste(`Hospitalised with COVID`, " (", PYs_2, ")", sep = ""),
Hospitalised_rate = paste(rate_2, " (", lci_2, "-", uci_2, ")", sep = ""),
Critial_Care_count = paste(`Critical care with COVID`, " (", PYs_3, ")", sep = ""),
Critial_Care_rate = paste(rate_3, " (", lci_3, "-", uci_3, ")", sep = ""),
Death_count = paste(`COVID Deaths`, " (", PYs_4, ")", sep = ""),
Death_rate = paste(rate_4, " (", lci_4, "-", uci_4, ")", sep = "")) %>%
select(Variable, level, "Fully vaccinated", Follow_up = fu, test_0, test_1, test_2, test_3,
Positive_test_count, Positivy = positivy, Positive_test_rate, Hospitalised_count, Hospitalised_rate,
Critial_Care_count, Critial_Care_rate, Death_count, Death_rate)
# Save as html ----
gt::gtsave(gt(table2), here::here("output","tables", "table2.html"))
gt::gtsave(gt(table2_redacted), here::here("output","tables", "table2_redacted.html"))