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articles-tables.R
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articles-tables.R
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characteristics.names <- function(onlyvars = FALSE) {
if (onlyvars == TRUE)
return(c("age", "female", "bmi", "sys", "dias", "htn", "smoker",
"diabetes", "leisure", "bptreat", "koltreat"))
list("n" = "N",
"age" = "Age, y (SD)",
"female" = "Female, N (%)",
"bmi" = "BMI, kg/m² (SD)",
"sys" = "Systolic BP, mmHg (SD)",
"dias" = "Diastolic BP, mmHg (SD)",
"htn" = "Hypertension, N (%)",
"smoker" = "Current smoker, N (%)",
"diabetes" = "Diabetes mellitus, N (%)",
"leisure" = "Exercise, N (%)",
"bptreat" = "Antihypertensive medication, N (%)",
"koltreat" = "Lipid medication, N (%)",
"1" = " Light",
"2" = " Moderate",
"3" = " Heavy",
"4" = " Competitive")
}
characteristicsTableFull <- function(dset, test = TRUE) {
table.sub <- characteristicsTable(dset, "cohort", test = test) %>% select(-test)
table.tot <- characteristicsTable(dset)
dplyr::full_join(table.tot, table.sub, by = "rowname")
}
characteristicsTable <- function(dset, strata, test = FALSE) {
nostrata <- missing(strata)
characteristics <- tableone::CreateTableOne(
strata = strata,
data = dset,
vars = characteristics.names(TRUE),
factorVars = getfactorvariables(dset,
characteristics.names(TRUE)),
test = test)
print(characteristics,
exact = "stage",
quote = FALSE,
noSpaces = TRUE,
printToggle = FALSE,
digits = 1,
pDigits = 3,
contDigits=1) %>%
as.data.frame %>%
tibble::rownames_to_column(var = "rowname") %>%
format(justify = "left", trim = TRUE) %>%
mutate(rowname = characteristics.names()[gsub("^ *([A-Za-z_0-9]+).*", "\\1", rowname)] %>%
unlist(use.names=F))
}
tableone <- function(characteristics) {
flextable(data = characteristics) %>%
# set_header_labels(rowname = "Characteristics",
# Overall = paste0("Cases, n=", dim(dset)[1])) %>%
flextable::width(j = 1, width = 3) %>%
flextable::width(j = 2, width = 1.2) %>%
flextable::border(border = fp_border(width=0), part="body") %>%
flextable::border(border = fp_border(width=0), part="header") %>%
flextable::border(part="header", border.bottom = fp_border(width=1)) %>%
flextable::border(i = nrow(characteristics),
part="body", border.bottom = fp_border(width=1)) %>%
flextable::bold(bold = FALSE, part = "header") %>%
flextable::bold(bold = FALSE, part = "body") %>%
flextable::fontsize(size = 12, part = "header") %>%
flextable::fontsize(size = 12, part = "body") %>%
flextable::align(align = "center", part = "all") %>%
flextable::align(align = "left", part = "header", j = 1) %>%
flextable::align(align = "left", part = "body", j = 1)
}
oddsratiotable <- function(df, modelres, regularisation, htn = "HT") {
riskmodel.results <- createoddstable(df, modelres, names(regularisation), htn = htn)
regruns.hits <- purrr::map_df(regularisation, ~as.data.frame(.x), .id="id") %>%
dplyr::filter(grepl("mzid", term)) %>%
group_by(id) %>%
summarize(n = n()) %>%
spread(id, n)
typology.tbl4 <- data.frame(
col_keys = colnames(riskmodel.results),
what = c( "", "",
rep(paste("Risk score based on", regruns.hits$fwdbonf, "eicosanoids selected",
"using forward selection with Bonferroni-based",
"threshold"), 3),
rep(paste("Risk score based on", regruns.hits$lasso ,"eicosanoids selected using LASSO"), 3),
rep(paste("Risk score based on", regruns.hits$fwdaic ,"eicosanoids selected using forward",
"selection aiming at maximum model AIC"), 3)),
measure = c("", "N of individuals",
rep(c("N with HTN",
"Unadjusted OR (95% CI)",
"Adjusted OR (95% CI)"), 3)),
stringsAsFactors = FALSE)
typologyformatter(
data = riskmodel.results,
typology = typology.tbl4,
font = 10) %>%
flextable::width(j = 1, width = 0.8) %>%
flextable::width(j = 2, width = 0.8) %>%
flextable::width(j = c(3, 6, 9), width = 0.5) %>%
flextable::width(j = c(4:5, 7:8, 10:11), width = 1.3)
}
smalloddsratiotable <- function(df, modelresbp, modelreshtn, regularisation, htn = "HT") {
riskmodel.results <- cbind(createoddstable(df, modelresbp, "fwdbonf", htn = htn) %>%
rename(fwdbonf.unadjusted.mean_ci_sbp = fwdbonf.unadjusted.mean_ci,
fwdbonf.adjusted.mean_ci_sbp = fwdbonf.adjusted.mean_ci),
createoddstable(df, modelreshtn, "fwdbonf", htn = htn)[4:5])
typology.tbl4 <- data.frame(
col_keys = colnames(riskmodel.results),
what = c( "", "", "",
rep(paste("Odds for systolic blood pressure"), 2),
rep(paste("Odds for hypertension"), 2)),
measure = c("", "N of individuals",
"N with HTN",
rep(c("Unadjusted OR (95% CI)",
"Adjusted OR (95% CI)"), 2)),
stringsAsFactors = FALSE)
typologyformatter(
data = riskmodel.results,
typology = typology.tbl4,
font = 10) %>%
flextable::width(j = 1, width = 0.8) %>%
flextable::width(j = 2, width = 0.8)
# flextable::width(j = c(3, 6), width = 0.5) %>%
# flextable::width(j = c(4:5, 7:8), width = 1.3)
}
myspread <- function(ret, list = c2l("mean_ci", "p.value"), key = "response", by = "term") {
keys <- unique(ret[[key]])
columns <- lapply(keys, function(x) paste0(x, "_", list)) %>% unlist
lapply(list, function(x) ret %>%
dplyr::select(term, key, x) %>%
tidyr::spread(key, x) %>%
dplyr::rename_at(vars(-term), ~paste0(., "_", x))) %>%
purrr::reduce(full_join, by = by) %>%
dplyr::select(one_of(by %union% columns))
}
mykable <- function(df) {
knitr::kable(df, format = ifelse(exists("rmdgeneration"), "html", "markdown")) %>%
{ if (exists("rmdgeneration") == TRUE)
kable_styling(., bootstrap_options = "striped", full_width = F)
else
.}
}
improveprob.tidy <- function(x) {
data.frame(index = c("NRI", "IDI"),
estimate = c(x$nri, x$idi),
conf.low = c(x$nri - qnorm(1- 0.05/2)*x$se.nri,
x$idi - qnorm(1- 0.05/2)*x$se.idi),
conf.high = c(x$nri + qnorm(1- 0.05/2)*x$se.nri,
x$idi + qnorm(1- 0.05/2)*x$se.idi))
}
roc.tidy <- function(df, models) {
lapply(models, function(model) {
roc.ret <- pROC::roc(myoutcomes(df), fitted(model))
roc.ci <- pROC::ci(roc.ret)
attributes(roc.ci) <- NULL
roc.p <- wilcox.test(myoutcomes(df), fitted(model), paired=FALSE)$p.value
data.frame(estimate = roc.ci[2],
conf.low = roc.ci[1],
conf.high = roc.ci[3],
p.value = roc.p)
}) %>%
purrr::map_df(~as.data.frame(.x), .id="model")
}
myresulttable <- function(list) {
lapply(list, function(x) {
broom::tidy(x) %>%
mutate(conf.low = estimate - qnorm(0.975) * std.error,
conf.high = estimate + qnorm(0.975) * std.error,
mean_ci = sprintf("%.2f (%.2f–%.2f)%s",
estimate,
conf.low,
conf.high,
gtools::stars.pval(p.value))) %>%
select(term, mean_ci)
}) %>%
purrr::map_df(., ~as.data.frame(.x), .id = "model") %>%
spread(model, mean_ci)
}
typologyformatter <- function(data, font = 12, typology, left = c(1), hleft = c(1)) {
flex <- flextable(data = data) %>%
flextable::theme_booktabs() %>%
flextable::border(border = fp_border(width=0), part="body") %>%
flextable::border(border = fp_border(width=0), part="header") %>%
flextable::border(part="header", border.bottom = fp_border(width=1))
if (!missing(typology)) {
flex <- flex %>%
set_header_df(mapping = typology, key = "col_keys") %>%
merge_h(part = "header") %>%
flextable::border(part="header", border.bottom = fp_border(width=1))
if (missing(hleft)) {
hleft <- c(2)
}
}
flex %>%
flextable::border(i = nrow(data), part="body", border.bottom = fp_border(width=1)) %>%
flextable::bold(bold = FALSE, part = "header") %>%
flextable::bold(bold = FALSE, part = "body") %>%
flextable::fontsize(size = font, part = "header") %>%
flextable::fontsize(size = font, part = "body") %>%
flextable::align(align = "center", part = "all") %>%
flextable::align(align = "left", part = "header", j = left, i = hleft) %>%
flextable::align(align = "left", part = "body", j = left)
}