library(tidyverse)
library(ggplot2)
library(janitor)
library(readr)
library(readxl)
library(openxlsx)
library(httr2)
library(httr)
library(jsonlite)
library(forcats)
library(rlang)
library(knitr)
library(broom)
library(dplyr)
library(tidyr)
library(stringr)
#setwd("C:/Users/gois0001/OneDrive - Universiteit Utrecht/Documents/Cartesius_data")
set.seed(123)
unsupported_questions <- character()
fmt_nN_pct <- function(n, N_total, Percent) paste0(n, "/", N_total, " (", Percent, "%)")
make_freq_table <- function(df, strat_col = NULL, strat_label = NULL, level_col) {
# df already has: n, N_total, Percent, plus level_col and optionally strat_col
out <- df %>%
mutate(`n/N (%)` = fmt_nN_pct(n, N_total, Percent)) %>%
select(any_of(c(strat_col, level_col)), `n/N (%)`)
if (!is.null(strat_col) && !is.null(strat_label)) {
names(out)[names(out) == strat_col] <- strat_label
}
out
}
safe_factor <- function(x) as.factor(x)
safe_numeric <- function(x) suppressWarnings(as.numeric(x))
safe_has_col <- function(df, col) col %in% names(df)
# For checkbox: TRUE if == 1, else FALSE/NA
checkbox_to_logical <- function(x) {
# x may be numeric, character "1"/"0", logical
if (is.logical(x)) return(x)
x_num <- suppressWarnings(as.numeric(x))
ifelse(is.na(x_num), NA, x_num == 1)
}
## plot helper:
plot_cfg_default <- list(
# Colors: use named vectors so you can add more buildings later
building_colors = c(Total = rgb(48, 81, 67, maxColorValue = 255),
Solo = "#7ac3d1",
Track = "#659b81"),
gender_colors = c(Man = "lightblue", Vrouw = "lightpink", Anders = "lightgreen"),
# If you want to relabel factor levels later, set e.g. c("Nee"="No","Ja"="Yes")
level_labels = NULL,
# Control order of levels in radio plots
level_order = NULL,
# Numeric plotting defaults
n_breaks = 6,
# Checkbox plotting defaults
checkbox_show_top = Inf # set e.g. 10 to show only top 10 options
)
## plot helpers 2:
apply_level_labels <- function(x, label_map = NULL, level_order = NULL) {
x <- as.factor(x)
if (!is.null(label_map)) {
lv <- levels(x)
levels(x) <- ifelse(lv %in% names(label_map), unname(label_map[lv]), lv)
}
if (!is.null(level_order)) {
x <- factor(as.character(x), levels = level_order)
}
x
}
plot_radio_side_by_side <- function(df, var, building_col, cfg, main_prefix = "") {
# panels: Total + each building present in df
buildings <- sort(unique(na.omit(df[[building_col]])))
panels <- c("Total", buildings)
n_panels <- length(panels)
oldpar <- par(no.readonly = TRUE)
on.exit(par(oldpar), add = TRUE)
par(mfrow = c(1, n_panels), mar = c(7, 4, 4, 1))
for (p in panels) {
dat <- if (p == "Total") df else df[df[[building_col]] == p, , drop = FALSE]
# remove NA in var for this panel
x <- dat[[var]]
x <- x[!is.na(x)]
# if no data: draw empty panel with message
if (length(x) == 0) {
plot.new()
title(main = paste0(main_prefix, ifelse(main_prefix == "", "", " "), p))
text(0.5, 0.5, "No data", cex = 1)
next
}
tab <- table(x)
# still guard: empty table
if (length(tab) == 0) {
plot.new()
title(main = paste0(main_prefix, ifelse(main_prefix == "", "", " "), p))
text(0.5, 0.5, "No data", cex = 1)
next
}
col_use <- cfg$building_colors[[p]]
if (is.null(col_use)) col_use <- "grey80"
barplot(tab,
main = paste0(main_prefix, ifelse(main_prefix == "", "", " "), p),
col = col_use,
las = 2)
}
}
plot_numeric_side_by_side <- function(df, var, building_col, cfg, main_prefix = "") {
buildings <- sort(unique(na.omit(df[[building_col]])))
panels <- c("Total", buildings)
n_panels <- length(panels)
vals <- suppressWarnings(as.numeric(df[[var]]))
vals <- vals[is.finite(vals)]
if (length(vals) == 0) {
oldpar <- par(no.readonly = TRUE)
on.exit(par(oldpar), add = TRUE)
par(mfrow = c(1, n_panels), mar = c(7, 4, 4, 1))
for (p in panels) {
plot.new()
title(main = paste0(main_prefix, ifelse(main_prefix == "", "", " "), p))
text(0.5, 0.5, "No numeric data", cex = 1)
}
return(invisible(NULL))
}
# If values are all integers and limited in number, treat as slider-like
is_integerish <- all(abs(vals - round(vals)) < 1e-8)
n_unique_vals <- length(unique(vals))
if (is_integerish && n_unique_vals <= 15) {
brks <- seq(min(vals) - 0.5, max(vals) + 0.5, by = 1)
} else {
brks <- pretty(vals, n = cfg$n_breaks)
}
oldpar <- par(no.readonly = TRUE)
on.exit(par(oldpar), add = TRUE)
par(mfrow = c(1, n_panels), mar = c(7, 4, 4, 1))
for (p in panels) {
dat <- if (p == "Total") df else df[df[[building_col]] == p, , drop = FALSE]
x <- suppressWarnings(as.numeric(dat[[var]]))
x <- x[is.finite(x)]
if (length(x) == 0) {
plot.new()
title(main = paste0(main_prefix, ifelse(main_prefix == "", "", " "), p))
text(0.5, 0.5, "No data", cex = 1)
next
}
col_use <- cfg$building_colors[[p]]
if (is.null(col_use)) col_use <- "grey80"
hist(
x,
breaks = brks,
main = paste0(main_prefix, ifelse(main_prefix == "", "", " "), p),
xlab = var,
col = col_use
)
}
}
plot_checkbox_side_by_side <- function(sel_df, vars, building_col, cfg, main_prefix = "") {
buildings <- sort(unique(na.omit(sel_df[[building_col]])))
panels <- c("Total", buildings)
n_panels <- length(panels)
oldpar <- par(no.readonly = TRUE)
on.exit(par(oldpar), add = TRUE)
par(mfrow = c(1, n_panels), mar = c(9, 4, 4, 1))
for (p in panels) {
dat <- if (p == "Total") sel_df else sel_df[sel_df[[building_col]] == p, , drop = FALSE]
if (nrow(dat) == 0) {
plot.new()
title(main = paste0(main_prefix, ifelse(main_prefix == "", "", " "), p))
text(0.5, 0.5, "No data", cex = 1)
next
}
counts <- sapply(vars, function(v) sum(dat[[v]] %in% TRUE, na.rm = TRUE))
if (all(!is.finite(counts)) || length(counts) == 0) {
plot.new()
title(main = paste0(main_prefix, ifelse(main_prefix == "", "", " "), p))
text(0.5, 0.5, "No data", cex = 1)
next
}
ord <- order(counts, decreasing = TRUE)
if (is.finite(cfg$checkbox_show_top)) ord <- ord[seq_len(min(cfg$checkbox_show_top, length(ord)))]
counts <- counts[ord]
# if all zero, still draw but show message
if (all(counts == 0)) {
plot.new()
title(main = paste0(main_prefix, ifelse(main_prefix == "", "", " "), p))
text(0.5, 0.5, "All zero", cex = 1)
next
}
col_use <- cfg$building_colors[[p]]
if (is.null(col_use)) col_use <- "grey80"
barplot(counts,
main = paste0(main_prefix, ifelse(main_prefix == "", "", " "), p),
col = col_use,
las = 2)
}
}
order_question_codes <- function(qcodes) {
# Split into: prefix letters, then a sequence of numeric or non-numeric tokens
# Example WON3.2.1 -> prefix=WON, tokens=c(3, ".", 2, ".", 1)
parse_one <- function(x) {
prefix <- sub("^([A-Za-z]+).*$", "\\1", x)
rest <- sub("^[A-Za-z]+", "", x)
# tokenize into numbers and non-numbers
tokens <- regmatches(rest, gregexpr("[0-9]+|[^0-9]+", rest))[[1]]
tokens_num <- suppressWarnings(as.numeric(tokens))
# for ordering: numbers as numbers, non-numbers as character
list(prefix = prefix, tokens = tokens, tokens_num = tokens_num)
}
parsed <- lapply(qcodes, parse_one)
prefixes <- vapply(parsed, `[[`, "", "prefix")
# Build an ordering key:
# prefix first, then token by token where numeric tokens are ordered numerically
max_len <- max(vapply(parsed, function(p) length(p$tokens), 0L))
key_df <- data.frame(prefix = prefixes, stringsAsFactors = FALSE)
for (i in seq_len(max_len)) {
tok_i <- vapply(parsed, function(p) if (length(p$tokens) >= i) p$tokens[i] else "", "")
num_i <- suppressWarnings(as.numeric(tok_i))
# numeric flag: TRUE if token is purely numeric
is_num <- !is.na(num_i)
# store two columns per position: numeric value (or Inf), and character token (or "")
key_df[[paste0("n", i)]] <- ifelse(is_num, num_i, Inf)
key_df[[paste0("c", i)]] <- ifelse(is_num, "", tok_i)
}
# order by prefix, then n1,c1,n2,c2,...
ord_cols <- c("prefix", as.vector(rbind(paste0("n", seq_len(max_len)), paste0("c", seq_len(max_len)))))
qcodes[do.call(order, c(key_df[ord_cols], list(na.last = TRUE)))]
}
# -------- Main runner for one question --------
report_one_question <- function(working_df, alg_map, question_code,
building_col = "Gebouw2",
building_label = "Gebouw",
gender_col = "geslacht",
age_col = "leeftijd",
plot_cfg = plot_cfg_default,
make_plots = TRUE) {
q_rows <- alg_map %>% dplyr::filter(question_code == !!question_code)
if (nrow(q_rows) == 0) return(invisible(NULL))
q_format <- unique(q_rows$question_format)
q_text <- unique(q_rows$question_text_NL)
if (length(q_format) != 1) {
cat("\n\n### ", question_code, "\n\n", sep = "")
cat("Multiple formats detected in codebook for this question. Skipping.\n\n")
return(invisible(NULL))
}
# Header should be the Dutch question text
hdr <- q_text[1]
if (is.na(hdr) || trimws(hdr) == "") hdr <- question_code # fallback
cat("\n\n### ", hdr, "\n\n", sep = "")
cat("*(Code: ", question_code, ")*\n\n", sep = "")
vars <- q_rows$variable_name_canonical
vars <- vars[vars %in% names(working_df)]
if (length(vars) == 0) {
cat("No matching columns found in working_df for this question.\n\n")
return(invisible(NULL))
}
if (gender_col %in% names(working_df)) working_df[[gender_col]] <- as.factor(working_df[[gender_col]])
if (age_col %in% names(working_df)) working_df[[age_col]] <- suppressWarnings(as.numeric(working_df[[age_col]]))
# ---- RADIO ----
if (q_format == "radio") {
v <- vars[1]
working_df[[v]] <- as.factor(working_df[[v]])
cat("#### Total\n\n")
total <- working_df %>%
dplyr::filter(!is.na(.data[[v]])) %>%
dplyr::count(.data[[v]]) %>%
dplyr::mutate(N_total = sum(n), Percent = round(100 * n / N_total, 1)) %>%
dplyr::rename(level = !!v)
print(knitr::kable(make_freq_table(total, level_col = "level")))
cat("\n\n#### By building\n\n")
by_b <- working_df %>%
dplyr::filter(!is.na(.data[[v]]), !is.na(.data[[building_col]])) %>%
dplyr::count(.data[[building_col]], .data[[v]]) %>%
dplyr::group_by(.data[[building_col]]) %>%
dplyr::mutate(N_total = sum(n), Percent = round(100 * n / N_total, 1)) %>%
dplyr::ungroup() %>%
dplyr::rename(level = !!v, strat = !!building_col)
print(knitr::kable(make_freq_table(by_b, strat_col = "strat", strat_label = building_label, level_col = "level")))
if (isTRUE(make_plots)) {
cat("\n\n#### Plot\n\n")
# DEBUG LINE
cat("\nDEBUG plotting question:", question_code,
"| variable:", v,
"| non-NA rows:", sum(!is.na(working_df[[v]])), "\n")
working_df[[v]] <- apply_level_labels(
working_df[[v]],
plot_cfg$level_labels,
plot_cfg$level_order
)
plot_radio_side_by_side(
df = working_df %>% filter(!is.na(.data[[v]]), !is.na(.data[[building_col]])),
var = v,
building_col = building_col,
cfg = plot_cfg,
main_prefix = question_code
)
}
if (gender_col %in% names(working_df)) {
cat("\n\n#### By gender\n\n")
by_g <- working_df %>%
dplyr::filter(!is.na(.data[[v]]), !is.na(.data[[gender_col]])) %>%
dplyr::count(.data[[gender_col]], .data[[v]]) %>%
dplyr::group_by(.data[[gender_col]]) %>%
dplyr::mutate(N_total = sum(n), Percent = round(100 * n / N_total, 1)) %>%
dplyr::ungroup() %>%
dplyr::rename(level = !!v, strat = !!gender_col)
print(knitr::kable(make_freq_table(by_g, strat_col = "strat", level_col = "level")))
}
if (age_col %in% names(working_df)) {
cat("\n\n#### Age by response\n\n")
age_tbl <- working_df %>%
dplyr::filter(!is.na(.data[[v]]), !is.na(.data[[age_col]])) %>%
dplyr::group_by(.data[[v]]) %>%
dplyr::summarise(
N = dplyr::n(),
Mean = mean(.data[[age_col]]),
SD = sd(.data[[age_col]]),
Median = median(.data[[age_col]]),
Min = min(.data[[age_col]]),
Max = max(.data[[age_col]]),
.groups = "drop"
) %>%
dplyr::rename(level = !!v)
print(knitr::kable(age_tbl, digits = 2))
}
return(invisible(NULL))
}
# ---- NUMERIC ----
if (q_format %in% c("numeric", "slider")) {
v <- vars[1]
working_df[[v]] <- suppressWarnings(as.numeric(working_df[[v]]))
cat("#### Total\n\n")
total_num <- working_df %>%
dplyr::filter(!is.na(.data[[v]])) %>%
dplyr::summarise(
N = dplyr::n(),
Mean = mean(.data[[v]]),
SD = sd(.data[[v]]),
Median = median(.data[[v]]),
Min = min(.data[[v]]),
Max = max(.data[[v]])
)
print(knitr::kable(total_num, digits = 2))
cat("\n\n#### By building\n\n")
by_b_num <- working_df %>%
dplyr::filter(!is.na(.data[[v]]), !is.na(.data[[building_col]])) %>%
dplyr::group_by(.data[[building_col]]) %>%
dplyr::summarise(
N = dplyr::n(),
Mean = mean(.data[[v]]),
SD = sd(.data[[v]]),
Median = median(.data[[v]]),
Min = min(.data[[v]]),
Max = max(.data[[v]]),
.groups = "drop"
) %>%
dplyr::rename(!!building_label := !!building_col)
print(knitr::kable(by_b_num, digits = 2))
if (isTRUE(make_plots)) {
cat("\n\n#### Plot\n\n")
cat("\nDEBUG numeric plot:", question_code,
"| variable:", v,
"| non-NA rows:", sum(!is.na(working_df[[v]])), "\n")
plot_numeric_side_by_side(
df = working_df %>% dplyr::filter(!is.na(.data[[v]]), !is.na(.data[[building_col]])),
var = v,
building_col = building_col,
cfg = plot_cfg,
main_prefix = question_code
)
}
if (gender_col %in% names(working_df)) {
cat("\n\n#### By gender\n\n")
by_g_num <- working_df %>%
dplyr::filter(!is.na(.data[[v]]), !is.na(.data[[gender_col]])) %>%
dplyr::group_by(.data[[gender_col]]) %>%
dplyr::summarise(
N = dplyr::n(),
Mean = mean(.data[[v]]),
SD = sd(.data[[v]]),
Median = median(.data[[v]]),
Min = min(.data[[v]]),
Max = max(.data[[v]]),
.groups = "drop"
) %>%
dplyr::rename(geslacht = !!gender_col)
print(knitr::kable(by_g_num, digits = 2))
}
return(invisible(NULL))
}
# ---- CHECKBOX ----
# ---- CHECKBOX ----
if (q_format == "checkbox") {
# logical TRUE if selected
sel_df <- working_df %>%
dplyr::select(dplyr::any_of(c(building_col, gender_col, age_col)),
dplyr::all_of(vars)) %>%
dplyr::mutate(dplyr::across(dplyr::all_of(vars), checkbox_to_logical))
# Denominator: respondents who answered at least one of the checkbox options
answered_any <- rowSums(!is.na(dplyr::select(sel_df, dplyr::all_of(vars)))) > 0
N_base <- sum(answered_any, na.rm = TRUE)
cat("#### Multiple choice options\n\n")
cat("Denominator (respondents with at least one checkbox answered): ", N_base, "\n\n", sep = "")
# ---- Total per option table ----
total_opt <- tibble::tibble(option = vars) %>%
dplyr::mutate(
n = sapply(option, function(v) sum(sel_df[[v]] %in% TRUE, na.rm = TRUE)),
N_total = N_base,
Percent = ifelse(N_total == 0, NA, round(100 * n / N_total, 1))
)
print(knitr::kable(make_freq_table(total_opt, level_col = "option")))
# ---- By building per option table ----
if (building_col %in% names(sel_df)) {
by_building_list <- lapply(sort(unique(na.omit(sel_df[[building_col]]))), function(b) {
tmp <- sel_df %>% dplyr::filter(.data[[building_col]] == b)
answered_any_b <- rowSums(!is.na(dplyr::select(tmp, dplyr::all_of(vars)))) > 0
N_b <- sum(answered_any_b, na.rm = TRUE)
tibble::tibble(
strat = as.character(b),
option = vars,
n = sapply(vars, function(v) sum(tmp[[v]] %in% TRUE, na.rm = TRUE)),
N_total = N_b,
Percent = ifelse(N_total == 0, NA, round(100 * n / N_total, 1))
)
})
by_b <- dplyr::bind_rows(by_building_list)
cat("\n\n#### By building\n\n")
print(knitr::kable(
make_freq_table(by_b, strat_col = "strat", strat_label = building_label, level_col = "option")
))
}
# ---- Plots (ALG3.1 style) ----
if (isTRUE(make_plots)) {
# Helper: choose pie if few categories, else barplot
plot_pie_or_bar <- function(counts_named, main, col_use = NULL, max_pie_cats = 8) {
counts_named <- counts_named[counts_named > 0]
if (length(counts_named) == 0) {
plot.new()
title(main = main)
text(0.5, 0.5, "No selections", cex = 1)
return(invisible(NULL))
}
if (length(counts_named) <= max_pie_cats) {
pie(counts_named,
labels = names(counts_named),
main = main)
} else {
# readable alternative when too many categories
barplot(counts_named,
las = 2,
main = main,
col = if (!is.null(col_use)) col_use else "grey80")
}
}
# Total counts
counts_total <- sapply(vars, function(v) sum(sel_df[[v]] %in% TRUE, na.rm = TRUE))
counts_total <- sort(counts_total, decreasing = TRUE)
cat("\n\n#### Plots\n\n")
if (building_col %in% names(sel_df)) {
b_levels <- sort(unique(na.omit(sel_df[[building_col]])))
panels <- c("Total", b_levels)
} else {
panels <- "Total"
}
oldpar <- par(no.readonly = TRUE)
on.exit(par(oldpar), add = TRUE)
par(mfrow = c(1, length(panels)), mar = c(9, 4, 4, 1))
# Total panel
plot_pie_or_bar(
counts_named = counts_total,
main = paste0(question_code, " – Total"),
col_use = plot_cfg$building_colors[["Total"]]
)
# Per building panels
if (building_col %in% names(sel_df)) {
for (b in b_levels) {
tmp <- sel_df %>% dplyr::filter(.data[[building_col]] == b)
counts_b <- sapply(vars, function(v) sum(tmp[[v]] %in% TRUE, na.rm = TRUE))
counts_b <- sort(counts_b, decreasing = TRUE)
plot_pie_or_bar(
counts_named = counts_b,
main = paste0(question_code, " – ", b),
col_use = plot_cfg$building_colors[[as.character(b)]]
)
}
}
par(oldpar)
}
# ---- Combinations (ALG3.1 style) ----
cat("\n\n#### Combinations (multi selection)\n\n")
comb_df <- sel_df %>%
dplyr::mutate(
n_sel = rowSums(dplyr::across(dplyr::all_of(vars), ~ .x %in% TRUE), na.rm = TRUE),
combination = apply(dplyr::select(., dplyr::all_of(vars)), 1, function(row) {
picked <- vars[which(row %in% TRUE)]
paste(picked, collapse = " + ")
})
)
combo_tbl <- comb_df %>%
dplyr::filter(n_sel >= 2) %>%
dplyr::count(combination, sort = TRUE)
if (nrow(combo_tbl) == 0) {
cat("No multi selection combinations found.\n\n")
} else {
print(knitr::kable(combo_tbl))
}
# ---- Optional UpSet: keep if you still want it ----
# UpSet only makes sense if >= 2 indicator variables
if (length(vars) >= 2) {
if (requireNamespace("ComplexUpset", quietly = TRUE)) {
upset_df <- sel_df %>%
dplyr::select(dplyr::any_of(building_col), dplyr::all_of(vars)) %>%
dplyr::mutate(dplyr::across(dplyr::all_of(vars), ~ .x %in% TRUE))
cat("\n\n#### UpSet plot\n\n")
p_total <- ComplexUpset::upset(
upset_df,
intersect = vars,
base_annotations = list("Intersection size" = ComplexUpset::intersection_size(text = list(size = 3))),
set_sizes = ComplexUpset::upset_set_size()
) + ggplot2::ggtitle(paste0(question_code, " – UpSet"))
print(p_total)
} else {
cat("\n\n#### UpSet plot\n\n")
cat("Skipped: ComplexUpset not installed.\n\n")
}
}
return(invisible(NULL))
}
cat("Unsupported question_format: ", q_format, "\n\n", sep = "")
# store for later summary
unsupported_questions <<- c(
unsupported_questions,
paste0(question_code, " – ", q_text[1], " (", q_format, ")")
)
invisible(NULL)
}
report_checkbox_group <- function(working_df,
q_rows,
building_col = "Gebouw2",
building_label = "Gebouw",
gender_col = "geslacht",
age_col = "leeftijd",
plot_cfg = plot_cfg_default,
make_plots = TRUE) {
# q_rows is the subset of proj_map for ONE checkbox question (all options)
q_text <- unique(q_rows$question_text_NL)
q_code <- unique(q_rows$question_code)
q_format <- unique(q_rows$question_format)
# Header
hdr <- q_text[1]
if (is.na(hdr) || trimws(hdr) == "") hdr <- q_code[1]
cat("\n\n### ", hdr, "\n\n", sep = "")
cat("*(Code: ", q_code[1], ")*\n\n", sep = "")
vars <- unique(q_rows$variable_name_canonical)
vars <- vars[vars %in% names(working_df)]
if (length(vars) == 0) {
cat("No matching columns found in working_df for this question.\n\n")
return(invisible(NULL))
}
sel_df <- working_df %>%
dplyr::select(dplyr::any_of(c(building_col, gender_col, age_col)),
dplyr::all_of(vars)) %>%
dplyr::mutate(dplyr::across(dplyr::all_of(vars), checkbox_to_logical))
answered_any <- rowSums(!is.na(dplyr::select(sel_df, dplyr::all_of(vars)))) > 0
N_base <- sum(answered_any, na.rm = TRUE)
cat("#### Total (per option)\n\n")
total_opt <- tibble::tibble(option = vars) %>%
dplyr::mutate(
n = sapply(option, function(v) sum(sel_df[[v]] %in% TRUE, na.rm = TRUE)),
N_total = N_base,
Percent = ifelse(N_total == 0, NA, round(100 * n / N_total, 1))
)
print(knitr::kable(make_freq_table(total_opt, level_col = "option")))
if (building_col %in% names(sel_df)) {
cat("\n\n#### By building (per option)\n\n")
by_building_list <- lapply(sort(unique(na.omit(sel_df[[building_col]]))), function(g) {
tmp <- sel_df %>% dplyr::filter(.data[[building_col]] == g)
answered_any_g <- rowSums(!is.na(dplyr::select(tmp, dplyr::all_of(vars)))) > 0
N_g <- sum(answered_any_g, na.rm = TRUE)
tibble::tibble(
strat = as.character(g),
option = vars,
n = sapply(vars, function(v) sum(tmp[[v]] %in% TRUE, na.rm = TRUE)),
N_total = N_g,
Percent = ifelse(N_total == 0, NA, round(100 * n / N_total, 1))
)
})
by_b <- dplyr::bind_rows(by_building_list)
print(knitr::kable(make_freq_table(by_b, strat_col = "strat", strat_label = building_label, level_col = "option")))
}
if (isTRUE(make_plots)) {
cat("\n\n#### Plots\n\n")
sanitize_counts <- function(x) {
x <- as.numeric(x)
x[!is.finite(x)] <- 0
x[is.na(x)] <- 0
x
}
counts_total <- sapply(vars, function(v) sum(sel_df[[v]] %in% TRUE, na.rm = TRUE))
counts_total <- sanitize_counts(counts_total)
if (length(counts_total) == 0 || sum(counts_total) <= 0) {
cat("No selections to plot for this checkbox question.\n\n")
} else {
pie(
counts_total,
labels = vars,
main = paste0(q_code[1], " – Total")
)
}
if (building_col %in% names(sel_df)) {
b_levels <- sort(unique(na.omit(sel_df[[building_col]])))
if (length(b_levels) == 0) {
cat("No building information available for plots.\n\n")
} else {
oldpar <- par(no.readonly = TRUE)
on.exit(par(oldpar), add = TRUE)
par(mfrow = c(1, length(b_levels)))
for (b in b_levels) {
tmp <- sel_df %>% dplyr::filter(.data[[building_col]] == b)
counts_b <- sapply(vars, function(v) sum(tmp[[v]] %in% TRUE, na.rm = TRUE))
counts_b <- sanitize_counts(counts_b)
if (length(counts_b) == 0 || sum(counts_b) <= 0) {
plot.new()
title(main = paste0(q_code[1], " – ", b))
text(0.5, 0.5, "No selections", cex = 1)
} else {
pie(
counts_b,
labels = vars,
main = paste0(q_code[1], " – ", b)
)
}
}
}
}
}
cat("\n\n#### Combinations (multi selection)\n\n")
comb_df <- sel_df %>%
dplyr::rowwise() %>%
dplyr::mutate(
combination = paste(vars[which(dplyr::c_across(dplyr::all_of(vars)) %in% TRUE)], collapse = " + "),
n_sel = sum(dplyr::c_across(dplyr::all_of(vars)) %in% TRUE, na.rm = TRUE)
) %>%
dplyr::ungroup() %>%
dplyr::filter(n_sel >= 2) %>%
dplyr::count(combination, sort = TRUE)
if (nrow(comb_df) == 0) cat("No multi selection combinations found.\n\n")
else print(knitr::kable(comb_df))
invisible(NULL)
}
make_project_map <- function(codebook, project_code) {
codebook %>%
dplyr::filter(project_code == !!project_code) %>%
dplyr::select(
variable_name_canonical,
question_format,
question_number,
question_code,
question_text_NL,
question_dependency,
parent_question,
dependency_requirement
) %>%
dplyr::distinct()
}
count_unique_questions <- function(codebook, project_code) {
codebook %>%
filter(project_code == !!project_code) %>%
summarise(n = n_distinct(question_text_NL)) %>%
pull(n)
}
run_project_report <- function(project_code,
project_title,
working_df,
codebook,
plot_cfg,
skip_formats = c("calculation", "string", "textarea"),
skip_question_codes = character(0),
building_col = "Gebouw2",
building_label = "Gebouw",
gender_col = "geslacht",
age_col = "leeftijd",
make_plots = TRUE) {
unsupported_questions <<- character()
proj_map <- make_project_map(codebook, project_code)
n_unique <- count_unique_questions(codebook, project_code)
cat("\n\n## ", project_title, "\n\n", sep = "")
cat("In this section, we are looking at ", n_unique, " questions.\n\n", sep = "")
# question codes for this project, excluding unsupported formats
qcodes <- proj_map %>%
pull(question_code) %>%
unique()
qcodes <- order_question_codes(qcodes)
# de-duplicate checkbox questions: keep only the first code per checkbox question_text_NL
checkbox_first_codes <- proj_map %>%
dplyr::filter(question_format == "checkbox") %>%
dplyr::group_by(question_text_NL) %>%
dplyr::summarise(first_code = dplyr::first(question_code), .groups = "drop") %>%
dplyr::pull(first_code)
# keep:
# - all non-checkbox codes
# - only the first checkbox code per checkbox question
qcodes <- c(
qcodes[!qcodes %in% proj_map$question_code[proj_map$question_format == "checkbox"]],
checkbox_first_codes
)
qcodes <- unique(qcodes)
qcodes <- order_question_codes(qcodes)
skip_codes <- proj_map %>%
filter(question_format %in% skip_formats) %>%
pull(question_code) %>%
unique()
qcodes <- setdiff(qcodes, skip_codes)
qcodes <- setdiff(qcodes, skip_question_codes)
for (qc in qcodes) {
q_rows <- proj_map %>% dplyr::filter(question_code == qc)
q_format <- unique(q_rows$question_format)
# checkbox questions need special handling:
# group all checkbox options that belong to the same question_text_NL
if (length(q_format) == 1 && q_format == "checkbox") {
q_text <- unique(q_rows$question_text_NL)[1]
# get all rows in this project that share this question text and are checkbox
group_rows <- proj_map %>%
dplyr::filter(question_format == "checkbox", question_text_NL == q_text)
report_checkbox_group(
working_df = working_df,
q_rows = group_rows,
building_col = building_col,
building_label = building_label,
gender_col = gender_col,
age_col = age_col,
plot_cfg = plot_cfg,
make_plots = make_plots
)
} else {
# everything else stays as you already do it
report_one_question(
working_df = working_df,
alg_map = proj_map,
question_code = qc,
building_col = building_col,
building_label = building_label,
gender_col = gender_col,
age_col = age_col,
plot_cfg = plot_cfg,
make_plots = make_plots
)
}
}
if (length(unsupported_questions) > 0) {
cat("\n\n### Questions not analysed (unsupported format)\n\n")
unsupported_unique <- unique(unsupported_questions)
cat(paste0("- ", unsupported_unique, collapse = "\n"), "\n\n")
}
invisible(NULL)
}
Context and background
information
This is a summary report of all questions within the Cartesius
vragenlijst. Made considering the questionnaire applied in summer 2025.
Contains a summary of the answers and initial statistical analysis
Inputs expected in environment:
raw_questionnaire : Castor questionnaire data
export
codebook : Questionnaire codebook, created and edited
with the english translations. Refer to the script
“2026_codebook_cartesius.R”
Note: All code for this report is available. On the right corners,
there is a button named “show”. If you want to know how a particular
chunk is made, just click on show code.
Loading of the data:
# Load data first
questionnaire_answer <- read.csv(
file = path_questionnaire,
sep = ";",
header = T,
stringsAsFactors = FALSE,
na.strings = c("", "NA"),
check.names = FALSE
)
codebook <- read.xlsx(
xlsxFile= path_codebook,
sheet = "codebook"
)
building_info <- read.csv(
file = path_building,
sep = ";",
stringsAsFactors = FALSE,
na.strings = c("", "NA")
)
Codebook
application
Just to exemplify why the codebook is created, beyond its
applicability in making the questionnaires/data structure: it’s also
easier for data analysis! A quick example is how we can standardize the
questions so that they are easier to handle while analyzing the
data/coding:
This is how we see the names of certain columns now:
## [1] "RedenVH#Studie"
## [2] "RedenVH#Werk"
## [3] "RedenVH#Financiële reden"
## [4] "RedenVH#Je vorige woning"
## [5] "RedenVH#De woonomgeving of buurt van je vorige woning"
This can be quite tiring to type, and often is hard to visualize, if
you want to plot or select multiple things. So, in the codebook, we
standardize and make them smaller. You can either call it by the
question code, which contains the “main topic” before the question, and
the question number. In our example, the questions we see are actually
multiple answer options from question #3 on the Woning topic:
## [1] "WON3B" "WON3C" "WON3D" "WON3E" "WON3F"
This makes it easier to code them. And then, when we want to see
what’s the question about, we can look back at the long structure of the
question, like the version above.
For this script, know that all questions are used as question codes,
and then for plotting/more info, I use the actual variable name and/or
the question. Because in the codebook you can see both the question text
and, in this case, the variables, which represent the answer
options:
## [1] "Wat was de reden met betrekking tot je oude woning/adres die je heeft doen/moeten besluiten te verhuizen?"
## [2] "Wat was de reden met betrekking tot je oude woning/adres die je heeft doen/moeten besluiten te verhuizen?"
## [3] "Wat was de reden met betrekking tot je oude woning/adres die je heeft doen/moeten besluiten te verhuizen?"
## [4] "Wat was de reden met betrekking tot je oude woning/adres die je heeft doen/moeten besluiten te verhuizen?"
## [5] "Wat was de reden met betrekking tot je oude woning/adres die je heeft doen/moeten besluiten te verhuizen?"
## [1] "reden_vh_studie"
## [2] "reden_vh_werk"
## [3] "reden_vh_financiele_reden"
## [4] "reden_vh_je_vorige_woning"
## [5] "reden_vh_de_woonomgeving_of_buurt_van_je_vorige_woning"
So, because they are multiple answers, the question is the same
across all variables, and the variables are coded like
“reden-verhuis-actuale rede”.
Hope this helps! Let’s move on!
Joining the data and
initial cleaning
# Build named vector: old_name -> new_name
map_old_to_new <- setNames(codebook$variable_name_canonical, codebook$variable_name_NL)
# Keep only mappings that exist in questionnaire_answer
map_old_to_new <- map_old_to_new[names(map_old_to_new) %in% names(questionnaire_answer)]
# Optional: warn if multiple old names map to the same new name
dup_new <- map_old_to_new[duplicated(unname(map_old_to_new))]
if (length(dup_new) > 0) {
warning(
"Some question_code values are duplicated; making names unique may be required. Duplicated new names include: ",
paste(unique(unname(dup_new)), collapse = ", ")
)
}
# Rename
questionnaire_answer_renamed <- questionnaire_answer %>%
dplyr::rename_with(~ ifelse(.x %in% names(map_old_to_new), map_old_to_new[.x], .x))
building_info <- building_info %>% rename(`Castor Participant ID` = cartesius_id)
building_info <- building_info %>% distinct(`Castor Participant ID`, .keep_all = TRUE)
working_df <- questionnaire_answer_renamed %>%
left_join(building_info, by = "Castor Participant ID") %>%
mutate(
Gebouw2 = if_else(grepl("Track", Gebouw), "Track", "Solo")
)
## from this part on we are set with working with the dataframe named as "working_df". It contains all data from the questionnaires, and it's paired with building information!
At this point, in the code, it has already loaded and merged all the
data from the questionnaire to the data regarding the buildings. This
way, we can look if there are differences in between buildings as well.
Additionally, it’s good to take a look at some of the questionnaire
stats, so we can inform ourselves better.
Questionnaire
The first survey invitation was sent on 25-08-2025, and the latest
was sent on 10-10-2025. Out of the 63 participants to which a
questionnaire was sent, 48 have started, but of those 4 have not
finished to answer and 44 have fully completed the questionnaire.
The participants replied to this survey between 25-08-2025 15:43:01
and 10-10-2025 21:41:39. The average time between sending out a survey
and having a participant complete it was 25.14 hours.
In regards to how many participants have repeated measurements, at
the latest update, this is the distribution of participants who have
participated in each questionnaire:
Now, the questionnaire is divided into 8 parts, or “projects”. Let’s
dive into each of them: Overzicht van projecten:
- Toestemmingsformulier
- Algemeen
- Wonen
- Hinder geluid en geur
- Veiligheid en Overlast
- Contact met buren
- Gezondheid en Leefstijl
- Chronische aandoeningen en langdurige ziekten
Algemeen
In this section, we have general information. In total, we are
looking at 14 questions in this section alone. Let’s dive in:
Age and
gender:
# Ensure correct types
working_df$leeftijd <- suppressWarnings(as.numeric(working_df$leeftijd))
working_df$geslacht <- as.factor(working_df$geslacht)
# ---------- AGE SUMMARY ----------
age_summary_total <- working_df %>%
summarise(
Group = "Total",
N = sum(!is.na(leeftijd)),
Mean = mean(leeftijd, na.rm = TRUE),
SD = sd(leeftijd, na.rm = TRUE),
Min = min(leeftijd, na.rm = TRUE),
Max = max(leeftijd, na.rm = TRUE)
)
age_summary_building <- working_df %>%
group_by(Gebouw2) %>%
summarise(
Group = as.character(first(Gebouw2)),
N = sum(!is.na(leeftijd)),
Mean = mean(leeftijd, na.rm = TRUE),
SD = sd(leeftijd, na.rm = TRUE),
Min = min(leeftijd, na.rm = TRUE),
Max = max(leeftijd, na.rm = TRUE),
.groups = "drop"
)
age_summary <- bind_rows(age_summary_total, age_summary_building)
# ---------- GENDER SUMMARY ----------
gender_summary_total <- working_df %>%
filter(!is.na(geslacht)) %>%
count(geslacht) %>%
mutate(
Group = "Total",
N_total = sum(n),
Percent = round(100 * n / N_total, 1)
)
gender_summary_building <- working_df %>%
filter(!is.na(geslacht)) %>%
group_by(Gebouw2, geslacht) %>%
summarise(n = n(), .groups = "drop") %>%
group_by(Gebouw2) %>%
mutate(
Group = as.character(first(Gebouw2)),
N_total = sum(n),
Percent = round(100 * n / N_total, 1)
) %>%
ungroup()
# Combine
gender_summary <- bind_rows(
gender_summary_total %>% select(Group, geslacht, n, Percent),
gender_summary_building %>% select(Group, geslacht, n, Percent)
)
gender_wide <- gender_summary %>%
pivot_wider(
names_from = geslacht,
values_from = c(n, Percent),
names_sep = "_"
)
gender_display <- gender_wide %>%
mutate(
Anders = ifelse(is.na(n_Anders), NA_character_,
paste0(n_Anders, " (", format(Percent_Anders, nsmall = 1), "%)")),
Man = ifelse(is.na(n_Man), NA_character_,
paste0(n_Man, " (", format(Percent_Man, nsmall = 1), "%)")),
Vrouw = ifelse(is.na(n_Vrouw), NA_character_,
paste0(n_Vrouw, " (", format(Percent_Vrouw, nsmall = 1), "%)"))
) %>%
select(Group, Anders, Man, Vrouw)
cat("Age Summary\n\n")
## Age Summary
kable(age_summary %>% select(-Gebouw2), digits = 2)
| Total |
47 |
31.00 |
4.62 |
24 |
54 |
| Solo |
13 |
31.85 |
2.82 |
28 |
38 |
| Track |
34 |
30.68 |
5.14 |
24 |
54 |
cat("Gender Summary\n\n")
## Gender Summary
kable(gender_display, digits = 2)
| Total |
1 (2.1%) |
19 (40.4%) |
27 (57.4%) |
| Solo |
NA |
6 (46.2%) |
7 (53.8%) |
| Track |
1 (2.9%) |
13 (38.2%) |
20 (58.8%) |
Now, we looked into the distribution of gender and age in a table.
Statistically, let’s check if there is something relevant:
#Shapiro–Wilk test:
# Overall
age_shapiro_total <- shapiro.test(working_df$leeftijd)
# By building
age_shapiro_by_building <- working_df %>%
group_by(Gebouw2) %>%
summarise(
W = shapiro.test(leeftijd)$statistic,
p_value = shapiro.test(leeftijd)$p.value,
.groups = "drop"
)
age_shapiro_total
##
## Shapiro-Wilk normality test
##
## data: working_df$leeftijd
## W = 0.7833, p-value = 6.839e-07
age_shapiro_by_building
## # A tibble: 2 × 3
## Gebouw2 W p_value
## <chr> <dbl> <dbl>
## 1 Solo 0.945 0.524
## 2 Track 0.743 0.00000242
In this case, we checked whether the age is normally distributed. It
doesn’t seem so, only in the solo building. But this has been done with
very low numbers, so we can’t say too much into it. Now let’s check the
correlations between gender and age. Keep in mind we have 01 person that
doesn’t identify with binary man/women. In theory, we need to remove
them, as they do not represent a statistically relevant group. But
before doing so, let’s check it by running t-test (for binary
man/women), wilcox (also binary) and kruskal (taking the non-binary
person):
# check for the correlation between age and gender. but for this we need to look into gender as man/vrouw (removing the 1 anders) and looking without removing the "anders":
df_binary_gender <- working_df %>%
filter(geslacht %in% c("Man", "Vrouw")) %>%
mutate(
geslacht = droplevels(as.factor(geslacht)),
leeftijd = as.numeric(leeftijd)
)
t_test <- t.test(leeftijd ~ geslacht, data = df_binary_gender)
# Non-parametric
wilcox_test <- wilcox.test(leeftijd ~ geslacht, data = df_binary_gender)
t_test
##
## Welch Two Sample t-test
##
## data: leeftijd by geslacht
## t = 0.70373, df = 26.907, p-value = 0.4876
## alternative hypothesis: true difference in means between group Man and group Vrouw is not equal to 0
## 95 percent confidence interval:
## -2.061780 4.213827
## sample estimates:
## mean in group Man mean in group Vrouw
## 31.63158 30.55556
wilcox_test
##
## Wilcoxon rank sum test with continuity correction
##
## data: leeftijd by geslacht
## W = 269.5, p-value = 0.7792
## alternative hypothesis: true location shift is not equal to 0
kruskal_result <- kruskal.test(leeftijd ~ geslacht, data = working_df)
kruskal_result
##
## Kruskal-Wallis rank sum test
##
## data: leeftijd by geslacht
## Kruskal-Wallis chi-squared = 0.1779, df = 2, p-value = 0.9149
Now, here’s a look at the gender and age data:
oldpar <- par(no.readonly = TRUE)
par(mfrow = c(1, 2), mar = c(5, 6, 3, 2))
# Total histogram (custom colour)
hist(working_df$leeftijd,
main = "Age Distribution - Total",
xlab = "Age",
col = rgb(48, 81, 67, maxColorValue = 255),
breaks = 5)
# Pyramid: Solo vs Track (manual rectangles so nothing overflows)
age_vals <- df_binary_gender$leeftijd
brks <- pretty(age_vals, n = 5)
bins <- cut(age_vals, breaks = brks, include.lowest = TRUE, right = FALSE)
all_bins <- levels(bins)
solo_counts <- table(bins[df_binary_gender$Gebouw2 == "Solo"])
track_counts <- table(bins[df_binary_gender$Gebouw2 == "Track"])
solo_vec <- as.numeric(solo_counts[all_bins]); solo_vec[is.na(solo_vec)] <- 0
track_vec <- as.numeric(track_counts[all_bins]); track_vec[is.na(track_vec)] <- 0
maxc <- max(c(solo_vec, track_vec))
ypos <- seq_along(all_bins)
plot(0, 0,
type = "n",
xlim = c(-maxc, maxc),
ylim = c(0.5, length(all_bins) + 0.5),
xaxt = "n",
yaxt = "n",
xlab = "Frequency",
ylab = "",
main = "Age Distribution - Solo vs Track")
axis(1)
axis(2, at = ypos, labels = all_bins, las = 2, cex.axis = 0.8)
abline(v = 0, lwd = 1.2)
for (i in seq_along(ypos)) {
# Solo left
rect(-solo_vec[i], ypos[i] - 0.4,
0, ypos[i] + 0.4,
col = "#7ac3d1", border = NA)
# Track right
rect(0, ypos[i] - 0.4,
track_vec[i], ypos[i] + 0.4,
col = "#659b81", border = NA)
}
legend("topright", legend = c("Solo", "Track"),
fill = c("#7ac3d1", "#659b81"), bty = "n")
par(oldpar)
# -------------------------
# 2) Gender distribution (binary only): Total, Solo, Track (3 plots side by side)
# -------------------------
oldpar <- par(no.readonly = TRUE)
par(mfrow = c(1, 3), mar = c(4, 5, 3, 1))
# Common age breaks across all panels for comparability
brks <- pretty(df_binary_gender$leeftijd, n = 5)
plot_gender_age_pyramid <- function(dat, main_title) {
bins <- cut(dat$leeftijd, breaks = brks, include.lowest = TRUE, right = FALSE)
all_bins <- levels(bins)
men_counts <- table(bins[dat$geslacht == "Man"])
wom_counts <- table(bins[dat$geslacht == "Vrouw"])
men_vec <- as.numeric(men_counts[all_bins]); men_vec[is.na(men_vec)] <- 0
wom_vec <- as.numeric(wom_counts[all_bins]); wom_vec[is.na(wom_vec)] <- 0
maxc <- max(c(men_vec, wom_vec))
ypos <- seq_along(all_bins)
plot(0, 0,
type = "n",
xlim = c(-maxc, maxc),
ylim = c(0.5, length(all_bins) + 0.5),
xaxt = "n",
yaxt = "n",
xlab = "Frequency",
ylab = "",
main = main_title)
axis(1)
axis(2, at = ypos, labels = all_bins, las = 2, cex.axis = 0.8)
abline(v = 0, lwd = 1.2)
# Draw rectangles manually
for (i in seq_along(ypos)) {
# Men (left)
rect(-men_vec[i], ypos[i] - 0.4,
0, ypos[i] + 0.4,
col = "lightblue", border = NA)
# Women (right)
rect(0, ypos[i] - 0.4,
wom_vec[i], ypos[i] + 0.4,
col = "lightpink", border = NA)
}
legend("topright",
legend = c("Man", "Vrouw"),
fill = c("lightblue", "lightpink"),
bty = "n")
}
# Total
plot_gender_age_pyramid(df_binary_gender, "Gender age pyramid - Total")
# Solo
plot_gender_age_pyramid(subset(df_binary_gender, Gebouw2 == "Solo"), "Gender age pyramid - Solo")
# Track
plot_gender_age_pyramid(subset(df_binary_gender, Gebouw2 == "Track"), "Gender age pyramid - Track")
par(oldpar)
# -------------------------
# 3) Keep your age by gender boxplots (including Anders) as is
# -------------------------
oldpar <- par(no.readonly = TRUE)
par(mfrow = c(1, 2), mar = c(5, 4, 3, 1))
boxplot(leeftijd ~ geslacht,
data = working_df,
main = "Age by Gender",
xlab = "Gender",
ylab = "Age",
col = c("lightgreen", "lightblue", "lightpink"))
boxplot(leeftijd ~ geslacht,
data = df_binary_gender,
main = "Age by Gender (Man vs Vrouw)",
xlab = "Gender",
ylab = "Age",
col = c("lightblue", "lightpink"))
par(oldpar)
# -------------------------
# 4) Grouped boxplots: grouped by gender, coloured by building
# -------------------------
# Restrict to binary here as well, to avoid Anders=1 distorting grouped layout
df_bg <- df_binary_gender
# Build a combined factor with controlled order: for each gender, Solo then Track
df_bg$g_build <- interaction(df_bg$geslacht, df_bg$Gebouw2, sep = " · ", drop = TRUE)
# Ensure consistent ordering: Man·Solo, Man·Track, Vrouw·Solo, Vrouw·Track
desired_order <- c("Man · Solo", "Man · Track", "Vrouw · Solo", "Vrouw · Track")
df_bg$g_build <- factor(as.character(df_bg$g_build), levels = desired_order)
# Colours by building (alternating within gender)
cols <- ifelse(grepl("Solo$", levels(df_bg$g_build)), "#7ac3d1", "#659b81")
boxplot(leeftijd ~ g_build,
data = df_bg,
main = "Age by Gender, coloured by Building",
xlab = "",
ylab = "Age",
col = cols,
las = 2,
xaxt = "n")
legend("topright", legend = c("Solo", "Track"),
fill = c( "#7ac3d1", "#659b81"), bty = "n")
# Add group separators and labels (optional but helps readability)
abline(v = 2.5, lty = 2)
mtext("Man", side = 1, at = 1.5, line = 4)
mtext("Vrouw", side = 1, at = 3.5, line = 4)
# -------------------------
# 5) Interaction plot (keep, but use binary df)
# -------------------------
interaction.plot(df_binary_gender$geslacht,
df_binary_gender$Gebouw2,
df_binary_gender$leeftijd,
col = c("#7ac3d1", "#659b81"),
lwd = 2,
ylab = "Mean Age",
xlab = "Gender",
trace.label = "Building")
Living
situation
Now, we have a few questions regarding the general living situation
of the participants: Woon je samen met één of meerdere personen?, Met
welke personen woon je momenteel samen?, Hoeveel mensen wonen er samen
in je huishouden, inclusief jezelf?, Welke situatie is op jou van
toepassing?
The first one is a nested question. Which means that depending on
their answers, they will have a distinct follow-up question.
Now, let’s dive in:
# Ensure factor
working_df$woon_sit <- as.factor(working_df$woon_sit)
# ---- Helper: turn counts into "vv/tt (pp%)" and keep only selected columns ----
fmt_count_table <- function(df, strat_col = NULL, level_col, total_by = NULL, strat_label = NULL) {
# df must contain at least: level_col, n, N_total, Percent
out <- df %>%
mutate(`n/N (%)` = paste0(n, "/", N_total, " (", Percent, "%)")) %>%
select(any_of(c(strat_col, level_col)), `n/N (%)`)
# Optional rename of stratification column
if (!is.null(strat_col) && !is.null(strat_label)) {
names(out)[names(out) == strat_col] <- strat_label
}
out
}
# ---- Living situation summary by building + total ----
alg3_summary <- working_df %>%
filter(!is.na(woon_sit)) %>%
count(Gebouw2, woon_sit) %>%
group_by(Gebouw2) %>%
mutate(
N_total = sum(n),
Percent = round(100 * n / N_total, 1)
) %>%
ungroup()
alg3_total <- working_df %>%
filter(!is.na(woon_sit)) %>%
count(woon_sit) %>%
mutate(
Gebouw2 = "Total",
N_total = sum(n),
Percent = round(100 * n / N_total, 1)
)
alg3_all <- bind_rows(alg3_total, alg3_summary)
kable(
fmt_count_table(
df = alg3_all,
strat_col = "Gebouw2",
strat_label = "Gebouw",
level_col = "woon_sit"
)
)
| Total |
Ja |
19/47 (40.4%) |
| Total |
Nee |
28/47 (59.6%) |
| Solo |
Ja |
10/13 (76.9%) |
| Solo |
Nee |
3/13 (23.1%) |
| Track |
Ja |
9/34 (26.5%) |
| Track |
Nee |
25/34 (73.5%) |
# ---- Plots ----
par(mfrow = c(1, 3), mar = c(4, 4, 3, 1))
barplot(table(working_df$woon_sit),
main = "Living Together - Total",
col = rgb(48, 81, 67, maxColorValue = 255))
barplot(table(working_df$woon_sit[working_df$Gebouw2 == "Solo"]),
main = "Living Together - Solo",
col = "#7ac3d1")
barplot(table(working_df$woon_sit[working_df$Gebouw2 == "Track"]),
main = "Living Together - Track",
col = "#659b81")
par(mfrow = c(1,1))
# ---- Gender by living situation ----
gender_living <- working_df %>%
filter(!is.na(woon_sit), !is.na(geslacht)) %>%
count(geslacht, woon_sit) %>%
group_by(woon_sit) %>%
mutate(
N_total = sum(n),
Percent = round(100 * n / N_total, 1)
) %>%
ungroup()
kable(
fmt_count_table(
df = gender_living,
strat_col = "geslacht",
level_col = "woon_sit"
)
)
| Anders |
Ja |
1/19 (5.3%) |
| Man |
Ja |
10/19 (52.6%) |
| Man |
Nee |
9/28 (32.1%) |
| Vrouw |
Ja |
8/19 (42.1%) |
| Vrouw |
Nee |
19/28 (67.9%) |
# ---- Age by living situation (unchanged) ----
age_living <- working_df %>%
filter(!is.na(woon_sit), !is.na(leeftijd)) %>%
group_by(woon_sit) %>%
summarise(
N = n(),
Mean = mean(leeftijd),
SD = sd(leeftijd),
Median = median(leeftijd),
Min = min(leeftijd),
Max = max(leeftijd),
.groups = "drop"
)
kable(age_living, digits = 2)
| Ja |
19 |
31.74 |
6.14 |
30.0 |
26 |
54 |
| Nee |
28 |
30.50 |
3.24 |
30.5 |
24 |
37 |
boxplot(leeftijd ~ woon_sit,
data = working_df,
main = "Age by Living Situation",
xlab = "Living Situation",
ylab = "Age",
col = c("#7ac3d1", "#659b81"))
# ---- ALG3.1 multiple selection categories ----
alg31_vars <- codebook %>%
filter(grepl("^ALG3\\.1", question_code)) %>%
pull(variable_name_canonical)
alg31_N_total <- working_df %>%
filter(!is.na(woon_sit)) %>%
nrow()
alg31_df <- tibble(Category = alg31_vars) %>%
mutate(
n = sapply(Category, function(v) sum(working_df[[v]] == 1, na.rm = TRUE)),
N_total = alg31_N_total,
Percent = round(100 * n / N_total, 1)
) %>%
mutate(`n/N (%)` = paste0(n, "/", N_total, " (", Percent, "%)")) %>%
select(Category, `n/N (%)`)
kable(alg31_df)
| woon_sit_samen_met_een_partner_echtgenoot_of_echtgenote |
16/47 (34%) |
| woon_sit_samen_met_kind_eren_jonger_dan4jaar |
1/47 (2.1%) |
| woon_sit_samen_met_kind_eren_van4t_m11jaar |
0/47 (0%) |
| woon_sit_samen_met_kind_eren_van12t_m17jaar |
0/47 (0%) |
| woon_sit_samen_met_kind_eren_van18jaar_of_ouder |
1/47 (2.1%) |
| woon_sit_samen_met_mijn_ouder_s_verzorger_s |
0/47 (0%) |
| woon_sit_samen_met_een_andere_volwassene_andere_volwassenen |
2/47 (4.3%) |
# ---- Prepare counts of selections ----
alg31_counts_total <- sapply(alg31_vars, function(v) {
sum(working_df[[v]] == 1, na.rm = TRUE)
})
# Pie chart total
pie(
alg31_counts_total,
labels = alg31_vars,
main = "ALG3.1 Living Arrangement Categories - Total",
col = rainbow(length(alg31_vars))
)
# ---- Pie charts per building ----
gebouwen <- unique(working_df$Gebouw2)
par(mfrow = c(1, length(gebouwen)))
for(g in gebouwen){
subset_df <- working_df %>% filter(Gebouw2 == g)
counts <- sapply(alg31_vars, function(v){
sum(subset_df[[v]] == 1, na.rm = TRUE)
})
pie(
counts,
labels = alg31_vars,
main = paste("ALG3.1 -", g),
col = rainbow(length(alg31_vars))
)
}
par(mfrow = c(1,1))
# ---- Detect combinations ----
alg31_combinations <- working_df %>%
rowwise() %>%
mutate(
combination = paste(
alg31_vars[which(c_across(all_of(alg31_vars)) == 1)],
collapse = " + "
)
) %>%
ungroup()
# Keep only rows with multiple selections
alg31_multi <- alg31_combinations %>%
filter(str_detect(combination, "\\+"))
# Count frequencies
combination_freq <- alg31_multi %>%
count(combination, sort = TRUE)
kable(combination_freq)
| woon_sit_samen_met_een_partner_echtgenoot_of_echtgenote
+ woon_sit_samen_met_kind_eren_jonger_dan4jaar |
1 |
Hoeveel mensen
wonen er samen in je huishouden, inclusief jezelf?
(Code: ALG3.2)
By building
| Solo |
10 |
2 |
0.47 |
2 |
1 |
3 |
| Track |
9 |
2 |
0.00 |
2 |
2 |
2 |
Plot
DEBUG numeric plot: ALG3.2 | variable: aantal_gezin | non-NA rows: 19
By gender
| Anders |
1 |
2.00 |
NA |
2 |
2 |
2 |
| Man |
10 |
1.90 |
0.32 |
2 |
1 |
2 |
| Vrouw |
8 |
2.12 |
0.35 |
2 |
2 |
3 |
Welke situatie is
op jou van toepassing?
(Code: ALG3.3)
Total
| Alleen |
23/28 (82.1%) |
| Relatie |
5/28 (17.9%) |
By building
| Solo |
Alleen |
2/3 (66.7%) |
| Solo |
Relatie |
1/3 (33.3%) |
| Track |
Alleen |
21/25 (84%) |
| Track |
Relatie |
4/25 (16%) |
Plot
DEBUG plotting question: ALG3.3 | variable: woon_sit_alleen | non-NA
rows: 28
By gender
| Man |
Alleen |
8/9 (88.9%) |
| Man |
Relatie |
1/9 (11.1%) |
| Vrouw |
Alleen |
15/19 (78.9%) |
| Vrouw |
Relatie |
4/19 (21.1%) |
Age by
response
| Alleen |
23 |
30.48 |
3.4 |
30 |
24 |
37 |
| Relatie |
5 |
30.60 |
2.7 |
31 |
27 |
34 |
Gaat/gaan je
kind(eren) naar het Kindcentrum Cartesius?
(Code: ALG4.1)
Total
| Nee |
1/2 (50%) |
| NeeInt |
1/2 (50%) |
By building
| Solo |
NeeInt |
1/1 (100%) |
| Track |
Nee |
1/1 (100%) |
Plot
DEBUG plotting question: ALG4.1 | variable: kindcentrum | non-NA
rows: 2
By gender
| Man |
Nee |
1/1 (100%) |
| Vrouw |
NeeInt |
1/1 (100%) |
Age by
response
| Nee |
1 |
54 |
NA |
54 |
54 |
54 |
| NeeInt |
1 |
35 |
NA |
35 |
35 |
35 |
Wat is je hoogst
afgeronde opleiding?
(Code: ALG5)
Total
| HogerBeroeps |
14/47 (29.8%) |
| MiddelbaarBeroeps |
2/47 (4.3%) |
| Wetenschap |
31/47 (66%) |
By building
| Solo |
HogerBeroeps |
2/13 (15.4%) |
| Solo |
Wetenschap |
11/13 (84.6%) |
| Track |
HogerBeroeps |
12/34 (35.3%) |
| Track |
MiddelbaarBeroeps |
2/34 (5.9%) |
| Track |
Wetenschap |
20/34 (58.8%) |
Plot
DEBUG plotting question: ALG5 | variable: opleiding | non-NA rows: 47
By gender
| Anders |
Wetenschap |
1/1 (100%) |
| Man |
HogerBeroeps |
4/19 (21.1%) |
| Man |
MiddelbaarBeroeps |
1/19 (5.3%) |
| Man |
Wetenschap |
14/19 (73.7%) |
| Vrouw |
HogerBeroeps |
10/27 (37%) |
| Vrouw |
MiddelbaarBeroeps |
1/27 (3.7%) |
| Vrouw |
Wetenschap |
16/27 (59.3%) |
Age by
response
| HogerBeroeps |
14 |
33.00 |
7.21 |
31.0 |
24 |
54 |
| MiddelbaarBeroeps |
2 |
25.50 |
0.71 |
25.5 |
25 |
26 |
| Wetenschap |
31 |
30.45 |
2.43 |
30.0 |
26 |
35 |
Welke van de
volgende omschrijvingen past het beste bij je?
(Code: ALG6)
By building
| Solo |
Werkend |
13/13 (100%) |
| Track |
Werkend |
34/34 (100%) |
Plot
DEBUG plotting question: ALG6 | variable: maatspos | non-NA rows: 47
By gender
| Anders |
Werkend |
1/1 (100%) |
| Man |
Werkend |
19/19 (100%) |
| Vrouw |
Werkend |
27/27 (100%) |
Age by
response
| Werkend |
47 |
31 |
4.62 |
30 |
24 |
54 |
Heb je de laatste
12 maanden moeite gehad om van het inkomen van je huishouden rond te
komen?
(Code: ALG7)
Total
| EnigeMoeite |
8/47 (17%) |
| GeenMoeite |
16/47 (34%) |
| Opletten |
23/47 (48.9%) |
By building
| Solo |
EnigeMoeite |
1/13 (7.7%) |
| Solo |
GeenMoeite |
7/13 (53.8%) |
| Solo |
Opletten |
5/13 (38.5%) |
| Track |
EnigeMoeite |
7/34 (20.6%) |
| Track |
GeenMoeite |
9/34 (26.5%) |
| Track |
Opletten |
18/34 (52.9%) |
Plot
DEBUG plotting question: ALG7 | variable: rondkomen | non-NA rows: 47
By gender
| Anders |
EnigeMoeite |
1/1 (100%) |
| Man |
EnigeMoeite |
4/19 (21.1%) |
| Man |
GeenMoeite |
6/19 (31.6%) |
| Man |
Opletten |
9/19 (47.4%) |
| Vrouw |
EnigeMoeite |
3/27 (11.1%) |
| Vrouw |
GeenMoeite |
10/27 (37%) |
| Vrouw |
Opletten |
14/27 (51.9%) |
Age by
response
| EnigeMoeite |
8 |
33.38 |
8.68 |
30.5 |
26 |
54 |
| GeenMoeite |
16 |
30.19 |
3.71 |
30.0 |
24 |
38 |
| Opletten |
23 |
30.74 |
2.93 |
31.0 |
26 |
37 |
Hoeveel dagen per
week ben je gemiddeld genomen overdag (09:00 – 17:00 uur) thuis?
(Code: ALG8)
Total
| 0 |
1/47 (2.1%) |
| 1 |
2/47 (4.3%) |
| 2 |
14/47 (29.8%) |
| 3 |
14/47 (29.8%) |
| 4 |
13/47 (27.7%) |
| 5 |
1/47 (2.1%) |
| 6 |
2/47 (4.3%) |
By building
| Solo |
2 |
5/13 (38.5%) |
| Solo |
3 |
2/13 (15.4%) |
| Solo |
4 |
6/13 (46.2%) |
| Track |
0 |
1/34 (2.9%) |
| Track |
1 |
2/34 (5.9%) |
| Track |
2 |
9/34 (26.5%) |
| Track |
3 |
12/34 (35.3%) |
| Track |
4 |
7/34 (20.6%) |
| Track |
5 |
1/34 (2.9%) |
| Track |
6 |
2/34 (5.9%) |
Plot
DEBUG plotting question: ALG8 | variable: dagen_thuis | non-NA rows:
47
By gender
| Anders |
4 |
1/1 (100%) |
| Man |
0 |
1/19 (5.3%) |
| Man |
2 |
5/19 (26.3%) |
| Man |
3 |
4/19 (21.1%) |
| Man |
4 |
8/19 (42.1%) |
| Man |
6 |
1/19 (5.3%) |
| Vrouw |
1 |
2/27 (7.4%) |
| Vrouw |
2 |
9/27 (33.3%) |
| Vrouw |
3 |
10/27 (37%) |
| Vrouw |
4 |
4/27 (14.8%) |
| Vrouw |
5 |
1/27 (3.7%) |
| Vrouw |
6 |
1/27 (3.7%) |
Age by
response
| 0 |
1 |
32.00 |
NA |
32.0 |
32 |
32 |
| 1 |
2 |
31.50 |
7.78 |
31.5 |
26 |
37 |
| 2 |
14 |
32.71 |
6.70 |
31.5 |
26 |
54 |
| 3 |
14 |
29.43 |
2.62 |
30.0 |
24 |
34 |
| 4 |
13 |
31.23 |
3.32 |
30.0 |
27 |
38 |
| 5 |
1 |
30.00 |
NA |
30.0 |
30 |
30 |
| 6 |
2 |
28.00 |
4.24 |
28.0 |
25 |
31 |
Wonen
In this section, we are looking at 33 questions.
Wat was de
belangrijkste reden waarom je vanwege je vorige woning bent
verhuisd?
(Code: WON3.2)
Total
| AndrRed |
7/19 (36.8%) |
| Iso |
1/19 (5.3%) |
| SlOnderh |
3/19 (15.8%) |
| TeKlein |
6/19 (31.6%) |
| TuinKlein |
1/19 (5.3%) |
| TypWon |
1/19 (5.3%) |
By building
| Solo |
SlOnderh |
1/3 (33.3%) |
| Solo |
TuinKlein |
1/3 (33.3%) |
| Solo |
TypWon |
1/3 (33.3%) |
| Track |
AndrRed |
7/16 (43.8%) |
| Track |
Iso |
1/16 (6.2%) |
| Track |
SlOnderh |
2/16 (12.5%) |
| Track |
TeKlein |
6/16 (37.5%) |
Plot
DEBUG plotting question: WON3.2 | variable: won_vh | non-NA rows: 19
By gender
| Anders |
AndrRed |
1/1 (100%) |
| Man |
AndrRed |
1/6 (16.7%) |
| Man |
SlOnderh |
1/6 (16.7%) |
| Man |
TeKlein |
3/6 (50%) |
| Man |
TuinKlein |
1/6 (16.7%) |
| Vrouw |
AndrRed |
5/12 (41.7%) |
| Vrouw |
Iso |
1/12 (8.3%) |
| Vrouw |
SlOnderh |
2/12 (16.7%) |
| Vrouw |
TeKlein |
3/12 (25%) |
| Vrouw |
TypWon |
1/12 (8.3%) |
Age by
response
| AndrRed |
7 |
28.29 |
1.80 |
28.0 |
26 |
31 |
| Iso |
1 |
30.00 |
NA |
30.0 |
30 |
30 |
| SlOnderh |
3 |
31.33 |
3.79 |
33.0 |
27 |
34 |
| TeKlein |
6 |
30.50 |
4.28 |
30.5 |
25 |
37 |
| TuinKlein |
1 |
31.00 |
NA |
31.0 |
31 |
31 |
| TypWon |
1 |
28.00 |
NA |
28.0 |
28 |
28 |
Waarom was de
vorige woonomgeving de reden voor verhuizing?
(Code: WON3.3A)
Total (per
option)
| omgev_vh_te_weinig_parkeerruimte_of_parkeerproblemen |
0/63 (0%) |
| omgev_vh_te_weinig_voorzieningen_zoals_winkels_scholen_of_openbaar_vervoer |
0/63 (0%) |
| omgev_vh_het_type_woningen_in_de_buurt |
0/63 (0%) |
| omgev_vh_slecht_onderhoud_in_de_buurt |
1/63 (1.6%) |
| omgev_vh_veranderde_buurtsamenstelling |
0/63 (0%) |
| omgev_vh_onveiligheid_door_criminaliteit |
0/63 (0%) |
| omgev_vh_overlast_door_bewoners |
3/63 (4.8%) |
| omgev_vh_overlast_zoals_rommel_op_straat_vernielingen_stank_of_lawaai |
4/63 (6.3%) |
| omgev_vh_andere_reden |
1/63 (1.6%) |
By building (per
option)
| Solo |
omgev_vh_te_weinig_parkeerruimte_of_parkeerproblemen |
0/17 (0%) |
| Solo |
omgev_vh_te_weinig_voorzieningen_zoals_winkels_scholen_of_openbaar_vervoer |
0/17 (0%) |
| Solo |
omgev_vh_het_type_woningen_in_de_buurt |
0/17 (0%) |
| Solo |
omgev_vh_slecht_onderhoud_in_de_buurt |
0/17 (0%) |
| Solo |
omgev_vh_veranderde_buurtsamenstelling |
0/17 (0%) |
| Solo |
omgev_vh_onveiligheid_door_criminaliteit |
0/17 (0%) |
| Solo |
omgev_vh_overlast_door_bewoners |
0/17 (0%) |
| Solo |
omgev_vh_overlast_zoals_rommel_op_straat_vernielingen_stank_of_lawaai |
0/17 (0%) |
| Solo |
omgev_vh_andere_reden |
0/17 (0%) |
| Track |
omgev_vh_te_weinig_parkeerruimte_of_parkeerproblemen |
0/46 (0%) |
| Track |
omgev_vh_te_weinig_voorzieningen_zoals_winkels_scholen_of_openbaar_vervoer |
0/46 (0%) |
| Track |
omgev_vh_het_type_woningen_in_de_buurt |
0/46 (0%) |
| Track |
omgev_vh_slecht_onderhoud_in_de_buurt |
1/46 (2.2%) |
| Track |
omgev_vh_veranderde_buurtsamenstelling |
0/46 (0%) |
| Track |
omgev_vh_onveiligheid_door_criminaliteit |
0/46 (0%) |
| Track |
omgev_vh_overlast_door_bewoners |
3/46 (6.5%) |
| Track |
omgev_vh_overlast_zoals_rommel_op_straat_vernielingen_stank_of_lawaai |
4/46 (8.7%) |
| Track |
omgev_vh_andere_reden |
1/46 (2.2%) |
Plots
Combinations
(multi selection)
| omgev_vh_overlast_door_bewoners +
omgev_vh_overlast_zoals_rommel_op_straat_vernielingen_stank_of_lawaai |
1 |
| omgev_vh_slecht_onderhoud_in_de_buurt +
omgev_vh_overlast_door_bewoners +
omgev_vh_overlast_zoals_rommel_op_straat_vernielingen_stank_of_lawaai |
1 |
Wat was de reden
met betrekking tot je oude woning/adres die je heeft doen/moeten
besluiten te verhuizen?
(Code: WON3A)
Total (per
option)
| reden_vh_gezondheid_of_behoefte_aan_zorg |
1/63 (1.6%) |
| reden_vh_studie |
0/63 (0%) |
| reden_vh_werk |
6/63 (9.5%) |
| reden_vh_financiele_reden |
6/63 (9.5%) |
| reden_vh_je_vorige_woning |
19/63 (30.2%) |
| reden_vh_de_woonomgeving_of_buurt_van_je_vorige_woning |
6/63 (9.5%) |
| reden_vh_omdat_je_dicht_bij_familie_vrienden_of_kennissen_wilde_wonen |
3/63 (4.8%) |
| reden_vh_omdat_je_graag_in_een_andere_woonplaats_wilde_wonen |
8/63 (12.7%) |
| reden_vh_andere_reden |
15/63 (23.8%) |
By building (per
option)
| Solo |
reden_vh_gezondheid_of_behoefte_aan_zorg |
0/17 (0%) |
| Solo |
reden_vh_studie |
0/17 (0%) |
| Solo |
reden_vh_werk |
3/17 (17.6%) |
| Solo |
reden_vh_financiele_reden |
3/17 (17.6%) |
| Solo |
reden_vh_je_vorige_woning |
3/17 (17.6%) |
| Solo |
reden_vh_de_woonomgeving_of_buurt_van_je_vorige_woning |
0/17 (0%) |
| Solo |
reden_vh_omdat_je_dicht_bij_familie_vrienden_of_kennissen_wilde_wonen |
0/17 (0%) |
| Solo |
reden_vh_omdat_je_graag_in_een_andere_woonplaats_wilde_wonen |
3/17 (17.6%) |
| Solo |
reden_vh_andere_reden |
3/17 (17.6%) |
| Track |
reden_vh_gezondheid_of_behoefte_aan_zorg |
1/46 (2.2%) |
| Track |
reden_vh_studie |
0/46 (0%) |
| Track |
reden_vh_werk |
3/46 (6.5%) |
| Track |
reden_vh_financiele_reden |
3/46 (6.5%) |
| Track |
reden_vh_je_vorige_woning |
16/46 (34.8%) |
| Track |
reden_vh_de_woonomgeving_of_buurt_van_je_vorige_woning |
6/46 (13%) |
| Track |
reden_vh_omdat_je_dicht_bij_familie_vrienden_of_kennissen_wilde_wonen |
3/46 (6.5%) |
| Track |
reden_vh_omdat_je_graag_in_een_andere_woonplaats_wilde_wonen |
5/46 (10.9%) |
| Track |
reden_vh_andere_reden |
12/46 (26.1%) |
Plots
Combinations
(multi selection)
| reden_vh_je_vorige_woning +
reden_vh_de_woonomgeving_of_buurt_van_je_vorige_woning |
4 |
| reden_vh_financiele_reden +
reden_vh_je_vorige_woning |
2 |
| reden_vh_gezondheid_of_behoefte_aan_zorg +
reden_vh_werk |
1 |
| reden_vh_je_vorige_woning + reden_vh_andere_reden |
1 |
| reden_vh_je_vorige_woning +
reden_vh_de_woonomgeving_of_buurt_van_je_vorige_woning +
reden_vh_omdat_je_graag_in_een_andere_woonplaats_wilde_wonen |
1 |
| reden_vh_je_vorige_woning +
reden_vh_omdat_je_dicht_bij_familie_vrienden_of_kennissen_wilde_wonen +
reden_vh_omdat_je_graag_in_een_andere_woonplaats_wilde_wonen |
1 |
| reden_vh_omdat_je_graag_in_een_andere_woonplaats_wilde_wonen
+ reden_vh_andere_reden |
1 |
| reden_vh_werk + reden_vh_financiele_reden +
reden_vh_je_vorige_woning +
reden_vh_omdat_je_dicht_bij_familie_vrienden_of_kennissen_wilde_wonen |
1 |
| reden_vh_werk + reden_vh_je_vorige_woning |
1 |
| reden_vh_werk +
reden_vh_omdat_je_graag_in_een_andere_woonplaats_wilde_wonen |
1 |
Sinds wanneer woon
je in Cartesius?
(Code: WON4)
Unsupported question_format: date_partial
Wat was de reden
dat je naar Cartesius bent verhuisd?
(Code: WON5A)
Total (per
option)
| reden_vh_cart_vanwege_de_ambities_van_cartesius_op_het_gebied_van_een_gezonde_en_duurzame_leefomgeving |
9/63 (14.3%) |
| reden_vh_cart_omdat_er_een_woning_beschikbaar_was |
39/63 (61.9%) |
| reden_vh_cart_omdat_ik_graag_in_utrecht_wilde_wonen |
26/63 (41.3%) |
| reden_vh_cart_vanwege_de_woonomgeving_en_of_locatie_in_utrecht |
15/63 (23.8%) |
| reden_vh_cart_anders |
1/63 (1.6%) |
By building (per
option)
| Solo |
reden_vh_cart_vanwege_de_ambities_van_cartesius_op_het_gebied_van_een_gezonde_en_duurzame_leefomgeving |
5/17 (29.4%) |
| Solo |
reden_vh_cart_omdat_er_een_woning_beschikbaar_was |
13/17 (76.5%) |
| Solo |
reden_vh_cart_omdat_ik_graag_in_utrecht_wilde_wonen |
10/17 (58.8%) |
| Solo |
reden_vh_cart_vanwege_de_woonomgeving_en_of_locatie_in_utrecht |
4/17 (23.5%) |
| Solo |
reden_vh_cart_anders |
0/17 (0%) |
| Track |
reden_vh_cart_vanwege_de_ambities_van_cartesius_op_het_gebied_van_een_gezonde_en_duurzame_leefomgeving |
4/46 (8.7%) |
| Track |
reden_vh_cart_omdat_er_een_woning_beschikbaar_was |
26/46 (56.5%) |
| Track |
reden_vh_cart_omdat_ik_graag_in_utrecht_wilde_wonen |
16/46 (34.8%) |
| Track |
reden_vh_cart_vanwege_de_woonomgeving_en_of_locatie_in_utrecht |
11/46 (23.9%) |
| Track |
reden_vh_cart_anders |
1/46 (2.2%) |
Plots
Combinations
(multi selection)
| reden_vh_cart_omdat_er_een_woning_beschikbaar_was +
reden_vh_cart_omdat_ik_graag_in_utrecht_wilde_wonen |
9 |
| reden_vh_cart_omdat_er_een_woning_beschikbaar_was +
reden_vh_cart_omdat_ik_graag_in_utrecht_wilde_wonen +
reden_vh_cart_vanwege_de_woonomgeving_en_of_locatie_in_utrecht |
7 |
| reden_vh_cart_vanwege_de_ambities_van_cartesius_op_het_gebied_van_een_gezonde_en_duurzame_leefomgeving
+ reden_vh_cart_omdat_er_een_woning_beschikbaar_was +
reden_vh_cart_omdat_ik_graag_in_utrecht_wilde_wonen |
4 |
| reden_vh_cart_omdat_er_een_woning_beschikbaar_was +
reden_vh_cart_vanwege_de_woonomgeving_en_of_locatie_in_utrecht |
3 |
| reden_vh_cart_vanwege_de_ambities_van_cartesius_op_het_gebied_van_een_gezonde_en_duurzame_leefomgeving
+ reden_vh_cart_omdat_er_een_woning_beschikbaar_was +
reden_vh_cart_omdat_ik_graag_in_utrecht_wilde_wonen +
reden_vh_cart_vanwege_de_woonomgeving_en_of_locatie_in_utrecht |
2 |
| reden_vh_cart_vanwege_de_ambities_van_cartesius_op_het_gebied_van_een_gezonde_en_duurzame_leefomgeving
+ reden_vh_cart_omdat_ik_graag_in_utrecht_wilde_wonen |
2 |
| reden_vh_cart_omdat_er_een_woning_beschikbaar_was +
reden_vh_cart_anders |
1 |
| reden_vh_cart_vanwege_de_ambities_van_cartesius_op_het_gebied_van_een_gezonde_en_duurzame_leefomgeving
+ reden_vh_cart_vanwege_de_woonomgeving_en_of_locatie_in_utrecht |
1 |
Welke ambitie van
Cartesius spreekt je aan? Als geen van deze ambities je aanspreekt, kunt
je de vraag overslaan.
(Code: WON6A)
Total (per
option)
| amb_cart_ruimte_en_aandacht_voor_ontmoeting |
21/63 (33.3%) |
| amb_cart_ruimte_en_aandacht_voor_ontspanning |
10/63 (15.9%) |
| amb_cart_ruimte_en_aandacht_voor_beweging |
9/63 (14.3%) |
| amb_cart_groene_woonomgeving |
39/63 (61.9%) |
| amb_cart_autoluwe_omgeving |
15/63 (23.8%) |
| amb_cart_duurzaamheid |
21/63 (33.3%) |
| amb_cart_ruimte_en_aandacht_voor_biodiversiteit |
13/63 (20.6%) |
By building (per
option)
| Solo |
amb_cart_ruimte_en_aandacht_voor_ontmoeting |
6/17 (35.3%) |
| Solo |
amb_cart_ruimte_en_aandacht_voor_ontspanning |
3/17 (17.6%) |
| Solo |
amb_cart_ruimte_en_aandacht_voor_beweging |
3/17 (17.6%) |
| Solo |
amb_cart_groene_woonomgeving |
10/17 (58.8%) |
| Solo |
amb_cart_autoluwe_omgeving |
7/17 (41.2%) |
| Solo |
amb_cart_duurzaamheid |
11/17 (64.7%) |
| Solo |
amb_cart_ruimte_en_aandacht_voor_biodiversiteit |
3/17 (17.6%) |
| Track |
amb_cart_ruimte_en_aandacht_voor_ontmoeting |
15/46 (32.6%) |
| Track |
amb_cart_ruimte_en_aandacht_voor_ontspanning |
7/46 (15.2%) |
| Track |
amb_cart_ruimte_en_aandacht_voor_beweging |
6/46 (13%) |
| Track |
amb_cart_groene_woonomgeving |
29/46 (63%) |
| Track |
amb_cart_autoluwe_omgeving |
8/46 (17.4%) |
| Track |
amb_cart_duurzaamheid |
10/46 (21.7%) |
| Track |
amb_cart_ruimte_en_aandacht_voor_biodiversiteit |
10/46 (21.7%) |
Plots
Combinations
(multi selection)
| amb_cart_groene_woonomgeving +
amb_cart_autoluwe_omgeving + amb_cart_duurzaamheid |
3 |
| amb_cart_ruimte_en_aandacht_voor_ontmoeting +
amb_cart_groene_woonomgeving + amb_cart_autoluwe_omgeving +
amb_cart_duurzaamheid |
3 |
| amb_cart_groene_woonomgeving + amb_cart_duurzaamheid +
amb_cart_ruimte_en_aandacht_voor_biodiversiteit |
2 |
| amb_cart_ruimte_en_aandacht_voor_ontmoeting +
amb_cart_groene_woonomgeving + amb_cart_autoluwe_omgeving |
2 |
| amb_cart_ruimte_en_aandacht_voor_ontmoeting +
amb_cart_groene_woonomgeving + amb_cart_duurzaamheid |
2 |
| amb_cart_ruimte_en_aandacht_voor_ontmoeting +
amb_cart_groene_woonomgeving +
amb_cart_ruimte_en_aandacht_voor_biodiversiteit |
2 |
| amb_cart_ruimte_en_aandacht_voor_ontmoeting +
amb_cart_ruimte_en_aandacht_voor_ontspanning +
amb_cart_groene_woonomgeving |
2 |
| amb_cart_groene_woonomgeving +
amb_cart_autoluwe_omgeving |
1 |
| amb_cart_groene_woonomgeving +
amb_cart_autoluwe_omgeving +
amb_cart_ruimte_en_aandacht_voor_biodiversiteit |
1 |
| amb_cart_groene_woonomgeving +
amb_cart_duurzaamheid |
1 |
| amb_cart_groene_woonomgeving +
amb_cart_ruimte_en_aandacht_voor_biodiversiteit |
1 |
| amb_cart_ruimte_en_aandacht_voor_beweging +
amb_cart_groene_woonomgeving + amb_cart_autoluwe_omgeving +
amb_cart_duurzaamheid |
1 |
| amb_cart_ruimte_en_aandacht_voor_beweging +
amb_cart_groene_woonomgeving + amb_cart_autoluwe_omgeving +
amb_cart_duurzaamheid +
amb_cart_ruimte_en_aandacht_voor_biodiversiteit |
1 |
| amb_cart_ruimte_en_aandacht_voor_ontmoeting +
amb_cart_autoluwe_omgeving |
1 |
| amb_cart_ruimte_en_aandacht_voor_ontmoeting +
amb_cart_duurzaamheid |
1 |
| amb_cart_ruimte_en_aandacht_voor_ontmoeting +
amb_cart_groene_woonomgeving |
1 |
| amb_cart_ruimte_en_aandacht_voor_ontmoeting +
amb_cart_ruimte_en_aandacht_voor_beweging +
amb_cart_groene_woonomgeving |
1 |
| amb_cart_ruimte_en_aandacht_voor_ontmoeting +
amb_cart_ruimte_en_aandacht_voor_beweging + amb_cart_groene_woonomgeving
+ amb_cart_ruimte_en_aandacht_voor_biodiversiteit |
1 |
| amb_cart_ruimte_en_aandacht_voor_ontmoeting +
amb_cart_ruimte_en_aandacht_voor_ontspanning +
amb_cart_groene_woonomgeving + amb_cart_duurzaamheid +
amb_cart_ruimte_en_aandacht_voor_biodiversiteit |
1 |
| amb_cart_ruimte_en_aandacht_voor_ontmoeting +
amb_cart_ruimte_en_aandacht_voor_ontspanning +
amb_cart_groene_woonomgeving +
amb_cart_ruimte_en_aandacht_voor_biodiversiteit |
1 |
| amb_cart_ruimte_en_aandacht_voor_ontmoeting +
amb_cart_ruimte_en_aandacht_voor_ontspanning +
amb_cart_ruimte_en_aandacht_voor_beweging + amb_cart_groene_woonomgeving
+ amb_cart_duurzaamheid +
amb_cart_ruimte_en_aandacht_voor_biodiversiteit |
1 |
| amb_cart_ruimte_en_aandacht_voor_ontmoeting +
amb_cart_ruimte_en_aandacht_voor_ontspanning +
amb_cart_ruimte_en_aandacht_voor_beweging + amb_cart_groene_woonomgeving
+ amb_cart_ruimte_en_aandacht_voor_biodiversiteit |
1 |
| amb_cart_ruimte_en_aandacht_voor_ontspanning +
amb_cart_groene_woonomgeving + amb_cart_autoluwe_omgeving +
amb_cart_duurzaamheid |
1 |
| amb_cart_ruimte_en_aandacht_voor_ontspanning +
amb_cart_groene_woonomgeving + amb_cart_autoluwe_omgeving +
amb_cart_duurzaamheid +
amb_cart_ruimte_en_aandacht_voor_biodiversiteit |
1 |
| amb_cart_ruimte_en_aandacht_voor_ontspanning +
amb_cart_ruimte_en_aandacht_voor_beweging +
amb_cart_groene_woonomgeving |
1 |
| amb_cart_ruimte_en_aandacht_voor_ontspanning +
amb_cart_ruimte_en_aandacht_voor_beweging + amb_cart_groene_woonomgeving
+ amb_cart_duurzaamheid |
1 |
Hoe (on)tevreden
ben je met … je huidige woning in het algemeen?
(Code: WON8)
Total
| Neutraal |
3/46 (6.5%) |
| OnTevr |
2/46 (4.3%) |
| Tevr |
26/46 (56.5%) |
| ZOnTevr |
1/46 (2.2%) |
| ZTevr |
14/46 (30.4%) |
By building
| Solo |
Tevr |
10/13 (76.9%) |
| Solo |
ZTevr |
3/13 (23.1%) |
| Track |
Neutraal |
3/33 (9.1%) |
| Track |
OnTevr |
2/33 (6.1%) |
| Track |
Tevr |
16/33 (48.5%) |
| Track |
ZOnTevr |
1/33 (3%) |
| Track |
ZTevr |
11/33 (33.3%) |
Plot
DEBUG plotting question: WON8 | variable: t_woning | non-NA rows: 46
By gender
| Anders |
ZTevr |
1/1 (100%) |
| Man |
Tevr |
9/18 (50%) |
| Man |
ZOnTevr |
1/18 (5.6%) |
| Man |
ZTevr |
8/18 (44.4%) |
| Vrouw |
Neutraal |
3/27 (11.1%) |
| Vrouw |
OnTevr |
2/27 (7.4%) |
| Vrouw |
Tevr |
17/27 (63%) |
| Vrouw |
ZTevr |
5/27 (18.5%) |
Age by
response
| Neutraal |
3 |
30.33 |
0.58 |
30.0 |
30 |
31 |
| OnTevr |
2 |
28.50 |
3.54 |
28.5 |
26 |
31 |
| Tevr |
26 |
31.96 |
5.62 |
31.0 |
24 |
54 |
| ZOnTevr |
1 |
30.00 |
NA |
30.0 |
30 |
30 |
| ZTevr |
14 |
29.93 |
3.02 |
30.5 |
25 |
34 |
Hoe (on)tevreden
ben je met … de grootte van je woning?
(Code: WON9)
Total
| Neutraal |
6/46 (13%) |
| OnTevr |
1/46 (2.2%) |
| Tevr |
22/46 (47.8%) |
| ZTevr |
17/46 (37%) |
By building
| Solo |
Tevr |
7/13 (53.8%) |
| Solo |
ZTevr |
6/13 (46.2%) |
| Track |
Neutraal |
6/33 (18.2%) |
| Track |
OnTevr |
1/33 (3%) |
| Track |
Tevr |
15/33 (45.5%) |
| Track |
ZTevr |
11/33 (33.3%) |
Plot
DEBUG plotting question: WON9 | variable: t_gro_woning | non-NA rows:
46
By gender
| Anders |
ZTevr |
1/1 (100%) |
| Man |
Neutraal |
2/18 (11.1%) |
| Man |
Tevr |
11/18 (61.1%) |
| Man |
ZTevr |
5/18 (27.8%) |
| Vrouw |
Neutraal |
4/27 (14.8%) |
| Vrouw |
OnTevr |
1/27 (3.7%) |
| Vrouw |
Tevr |
11/27 (40.7%) |
| Vrouw |
ZTevr |
11/27 (40.7%) |
Age by
response
| Neutraal |
6 |
30.33 |
3.39 |
30.5 |
26 |
36 |
| OnTevr |
1 |
32.00 |
NA |
32.0 |
32 |
32 |
| Tevr |
22 |
31.86 |
5.67 |
30.5 |
26 |
54 |
| ZTevr |
17 |
30.18 |
3.64 |
30.0 |
24 |
38 |
Hoe (on)tevreden
ben je met … het aantal kamers van je woning?
(Code: WON10)
Total
| Neutraal |
12/46 (26.1%) |
| OnTevr |
4/46 (8.7%) |
| Tevr |
15/46 (32.6%) |
| ZTevr |
15/46 (32.6%) |
By building
| Solo |
Tevr |
8/13 (61.5%) |
| Solo |
ZTevr |
5/13 (38.5%) |
| Track |
Neutraal |
12/33 (36.4%) |
| Track |
OnTevr |
4/33 (12.1%) |
| Track |
Tevr |
7/33 (21.2%) |
| Track |
ZTevr |
10/33 (30.3%) |
Plot
DEBUG plotting question: WON10 | variable: t_aant_kam | non-NA rows:
46
By gender
| Anders |
ZTevr |
1/1 (100%) |
| Man |
Neutraal |
4/18 (22.2%) |
| Man |
OnTevr |
2/18 (11.1%) |
| Man |
Tevr |
9/18 (50%) |
| Man |
ZTevr |
3/18 (16.7%) |
| Vrouw |
Neutraal |
8/27 (29.6%) |
| Vrouw |
OnTevr |
2/27 (7.4%) |
| Vrouw |
Tevr |
6/27 (22.2%) |
| Vrouw |
ZTevr |
11/27 (40.7%) |
Age by
response
| Neutraal |
12 |
30.42 |
3.68 |
30 |
26 |
37 |
| OnTevr |
4 |
30.50 |
3.32 |
31 |
26 |
34 |
| Tevr |
15 |
33.27 |
6.24 |
32 |
29 |
54 |
| ZTevr |
15 |
29.47 |
3.07 |
30 |
24 |
34 |
Hoe (on)tevreden
bent je met … de bereikbaarheid van je woning voor jezelf?
(Code: WON11)
Total
| Neutraal |
10/45 (22.2%) |
| OnTevr |
14/45 (31.1%) |
| Tevr |
13/45 (28.9%) |
| ZOnTevr |
2/45 (4.4%) |
| ZTevr |
6/45 (13.3%) |
By building
| Solo |
Neutraal |
2/13 (15.4%) |
| Solo |
OnTevr |
7/13 (53.8%) |
| Solo |
Tevr |
1/13 (7.7%) |
| Solo |
ZTevr |
3/13 (23.1%) |
| Track |
Neutraal |
8/32 (25%) |
| Track |
OnTevr |
7/32 (21.9%) |
| Track |
Tevr |
12/32 (37.5%) |
| Track |
ZOnTevr |
2/32 (6.2%) |
| Track |
ZTevr |
3/32 (9.4%) |
Plot
DEBUG plotting question: WON11 | variable: t_bereik_zelf | non-NA
rows: 45
By gender
| Anders |
Tevr |
1/1 (100%) |
| Man |
Neutraal |
3/17 (17.6%) |
| Man |
OnTevr |
6/17 (35.3%) |
| Man |
Tevr |
4/17 (23.5%) |
| Man |
ZTevr |
4/17 (23.5%) |
| Vrouw |
Neutraal |
7/27 (25.9%) |
| Vrouw |
OnTevr |
8/27 (29.6%) |
| Vrouw |
Tevr |
8/27 (29.6%) |
| Vrouw |
ZOnTevr |
2/27 (7.4%) |
| Vrouw |
ZTevr |
2/27 (7.4%) |
Age by
response
| Neutraal |
10 |
30.10 |
2.88 |
30.0 |
25 |
34 |
| OnTevr |
14 |
32.43 |
7.13 |
31.5 |
24 |
54 |
| Tevr |
13 |
30.31 |
3.75 |
30.0 |
26 |
37 |
| ZOnTevr |
2 |
30.50 |
0.71 |
30.5 |
30 |
31 |
| ZTevr |
6 |
30.67 |
1.86 |
30.5 |
29 |
34 |
Hoe (on)tevreden
ben je met … de bereikbaarheid van je woning voor anderen?
(Code: WON12)
Total
| Neutraal |
12/45 (26.7%) |
| OnTevr |
27/45 (60%) |
| Tevr |
2/45 (4.4%) |
| ZOnTevr |
3/45 (6.7%) |
| ZTevr |
1/45 (2.2%) |
By building
| Solo |
Neutraal |
2/13 (15.4%) |
| Solo |
OnTevr |
8/13 (61.5%) |
| Solo |
Tevr |
1/13 (7.7%) |
| Solo |
ZOnTevr |
1/13 (7.7%) |
| Solo |
ZTevr |
1/13 (7.7%) |
| Track |
Neutraal |
10/32 (31.2%) |
| Track |
OnTevr |
19/32 (59.4%) |
| Track |
Tevr |
1/32 (3.1%) |
| Track |
ZOnTevr |
2/32 (6.2%) |
Plot
DEBUG plotting question: WON12 | variable: t_bereik_ander | non-NA
rows: 45
By gender
| Anders |
Neutraal |
1/1 (100%) |
| Man |
Neutraal |
4/17 (23.5%) |
| Man |
OnTevr |
10/17 (58.8%) |
| Man |
Tevr |
2/17 (11.8%) |
| Man |
ZOnTevr |
1/17 (5.9%) |
| Vrouw |
Neutraal |
7/27 (25.9%) |
| Vrouw |
OnTevr |
17/27 (63%) |
| Vrouw |
ZOnTevr |
2/27 (7.4%) |
| Vrouw |
ZTevr |
1/27 (3.7%) |
Age by
response
| Neutraal |
12 |
29.67 |
2.90 |
30.0 |
25 |
35 |
| OnTevr |
27 |
31.52 |
5.63 |
31.0 |
24 |
54 |
| Tevr |
2 |
29.50 |
2.12 |
29.5 |
28 |
31 |
| ZOnTevr |
3 |
31.33 |
1.53 |
31.0 |
30 |
33 |
| ZTevr |
1 |
34.00 |
NA |
34.0 |
34 |
34 |
Hoe (on)tevreden
ben je met … de buitenruimte van je woning (zoals je tuin of balkon als
je die hebt)?
(Code: WON13)
Total
| Neutraal |
8/45 (17.8%) |
| OnTevr |
4/45 (8.9%) |
| Tevr |
25/45 (55.6%) |
| ZOnTevr |
1/45 (2.2%) |
| ZTevr |
7/45 (15.6%) |
By building
| Solo |
Neutraal |
2/13 (15.4%) |
| Solo |
Tevr |
8/13 (61.5%) |
| Solo |
ZTevr |
3/13 (23.1%) |
| Track |
Neutraal |
6/32 (18.8%) |
| Track |
OnTevr |
4/32 (12.5%) |
| Track |
Tevr |
17/32 (53.1%) |
| Track |
ZOnTevr |
1/32 (3.1%) |
| Track |
ZTevr |
4/32 (12.5%) |
Plot
DEBUG plotting question: WON13 | variable: t_buiten | non-NA rows: 45
By gender
| Anders |
Tevr |
1/1 (100%) |
| Man |
Neutraal |
2/17 (11.8%) |
| Man |
Tevr |
10/17 (58.8%) |
| Man |
ZOnTevr |
1/17 (5.9%) |
| Man |
ZTevr |
4/17 (23.5%) |
| Vrouw |
Neutraal |
6/27 (22.2%) |
| Vrouw |
OnTevr |
4/27 (14.8%) |
| Vrouw |
Tevr |
14/27 (51.9%) |
| Vrouw |
ZTevr |
3/27 (11.1%) |
Age by
response
| Neutraal |
8 |
33.88 |
8.90 |
31.5 |
24 |
54 |
| OnTevr |
4 |
28.50 |
2.38 |
28.5 |
26 |
31 |
| Tevr |
25 |
30.84 |
3.21 |
31.0 |
26 |
38 |
| ZOnTevr |
1 |
30.00 |
NA |
30.0 |
30 |
30 |
| ZTevr |
7 |
29.71 |
2.69 |
30.0 |
25 |
33 |
Hoe (on)tevreden
ben je met … je huidige woonomgeving in het algemeen?
(Code: WON15)
Total
| Neutraal |
12/44 (27.3%) |
| OnTevr |
7/44 (15.9%) |
| Tevr |
15/44 (34.1%) |
| ZOnTevr |
8/44 (18.2%) |
| ZTevr |
2/44 (4.5%) |
By building
| Solo |
Neutraal |
7/13 (53.8%) |
| Solo |
OnTevr |
3/13 (23.1%) |
| Solo |
Tevr |
2/13 (15.4%) |
| Solo |
ZOnTevr |
1/13 (7.7%) |
| Track |
Neutraal |
5/31 (16.1%) |
| Track |
OnTevr |
4/31 (12.9%) |
| Track |
Tevr |
13/31 (41.9%) |
| Track |
ZOnTevr |
7/31 (22.6%) |
| Track |
ZTevr |
2/31 (6.5%) |
Plot
DEBUG plotting question: WON15 | variable: t_omg_woon | non-NA rows:
44
By gender
| Anders |
Neutraal |
1/1 (100%) |
| Man |
Neutraal |
4/17 (23.5%) |
| Man |
OnTevr |
2/17 (11.8%) |
| Man |
Tevr |
7/17 (41.2%) |
| Man |
ZOnTevr |
3/17 (17.6%) |
| Man |
ZTevr |
1/17 (5.9%) |
| Vrouw |
Neutraal |
7/26 (26.9%) |
| Vrouw |
OnTevr |
5/26 (19.2%) |
| Vrouw |
Tevr |
8/26 (30.8%) |
| Vrouw |
ZOnTevr |
5/26 (19.2%) |
| Vrouw |
ZTevr |
1/26 (3.8%) |
Age by
response
| Neutraal |
12 |
30.42 |
2.54 |
30 |
26 |
36 |
| OnTevr |
7 |
31.00 |
3.79 |
31 |
26 |
37 |
| Tevr |
15 |
30.20 |
3.34 |
30 |
25 |
35 |
| ZOnTevr |
8 |
33.62 |
9.05 |
31 |
24 |
54 |
| ZTevr |
2 |
29.00 |
1.41 |
29 |
28 |
30 |
Hoe (on)tevreden
ben je met … de voorzieningen in je woonomgeving?
(Code: WON16)
Total
| Neutraal |
11/44 (25%) |
| OnTevr |
18/44 (40.9%) |
| Tevr |
8/44 (18.2%) |
| ZOnTevr |
7/44 (15.9%) |
By building
| Solo |
Neutraal |
4/13 (30.8%) |
| Solo |
OnTevr |
5/13 (38.5%) |
| Solo |
Tevr |
1/13 (7.7%) |
| Solo |
ZOnTevr |
3/13 (23.1%) |
| Track |
Neutraal |
7/31 (22.6%) |
| Track |
OnTevr |
13/31 (41.9%) |
| Track |
Tevr |
7/31 (22.6%) |
| Track |
ZOnTevr |
4/31 (12.9%) |
Plot
DEBUG plotting question: WON16 | variable: t_omg_voorz | non-NA rows:
44
By gender
| Anders |
Neutraal |
1/1 (100%) |
| Man |
Neutraal |
4/17 (23.5%) |
| Man |
OnTevr |
7/17 (41.2%) |
| Man |
Tevr |
5/17 (29.4%) |
| Man |
ZOnTevr |
1/17 (5.9%) |
| Vrouw |
Neutraal |
6/26 (23.1%) |
| Vrouw |
OnTevr |
11/26 (42.3%) |
| Vrouw |
Tevr |
3/26 (11.5%) |
| Vrouw |
ZOnTevr |
6/26 (23.1%) |
Age by
response
| Neutraal |
11 |
29.73 |
2.37 |
30 |
26 |
33 |
| OnTevr |
18 |
32.50 |
6.10 |
31 |
27 |
54 |
| Tevr |
8 |
29.12 |
3.52 |
29 |
25 |
34 |
| ZOnTevr |
7 |
31.00 |
4.16 |
31 |
24 |
38 |
Hoe (on)tevreden
ben je met … de beschikbaarheid en bereikbaarheid van het openbaar
vervoer?
(Code: WON17)
Total
| Neutraal |
6/44 (13.6%) |
| OnTevr |
12/44 (27.3%) |
| Tevr |
15/44 (34.1%) |
| ZOnTevr |
7/44 (15.9%) |
| ZTevr |
4/44 (9.1%) |
By building
| Solo |
Neutraal |
1/13 (7.7%) |
| Solo |
OnTevr |
3/13 (23.1%) |
| Solo |
Tevr |
6/13 (46.2%) |
| Solo |
ZTevr |
3/13 (23.1%) |
| Track |
Neutraal |
5/31 (16.1%) |
| Track |
OnTevr |
9/31 (29%) |
| Track |
Tevr |
9/31 (29%) |
| Track |
ZOnTevr |
7/31 (22.6%) |
| Track |
ZTevr |
1/31 (3.2%) |
Plot
DEBUG plotting question: WON17 | variable: t_omg_ov | non-NA rows: 44
By gender
| Anders |
Neutraal |
1/1 (100%) |
| Man |
Neutraal |
1/17 (5.9%) |
| Man |
OnTevr |
3/17 (17.6%) |
| Man |
Tevr |
9/17 (52.9%) |
| Man |
ZOnTevr |
3/17 (17.6%) |
| Man |
ZTevr |
1/17 (5.9%) |
| Vrouw |
Neutraal |
4/26 (15.4%) |
| Vrouw |
OnTevr |
9/26 (34.6%) |
| Vrouw |
Tevr |
6/26 (23.1%) |
| Vrouw |
ZOnTevr |
4/26 (15.4%) |
| Vrouw |
ZTevr |
3/26 (11.5%) |
Age by
response
| Neutraal |
6 |
30.17 |
3.87 |
30 |
26 |
37 |
| OnTevr |
12 |
29.83 |
3.46 |
31 |
24 |
35 |
| Tevr |
15 |
30.67 |
2.89 |
30 |
26 |
38 |
| ZOnTevr |
7 |
33.86 |
9.44 |
31 |
25 |
54 |
| ZTevr |
4 |
31.50 |
3.00 |
32 |
28 |
34 |
Hoe (on)tevreden
ben je met … de hoeveelheid groen in je woonomgeving?
(Code: WON18)
Total
| Neutraal |
10/44 (22.7%) |
| OnTevr |
15/44 (34.1%) |
| Tevr |
8/44 (18.2%) |
| ZOnTevr |
6/44 (13.6%) |
| ZTevr |
5/44 (11.4%) |
By building
| Solo |
Neutraal |
1/13 (7.7%) |
| Solo |
OnTevr |
8/13 (61.5%) |
| Solo |
Tevr |
1/13 (7.7%) |
| Solo |
ZOnTevr |
3/13 (23.1%) |
| Track |
Neutraal |
9/31 (29%) |
| Track |
OnTevr |
7/31 (22.6%) |
| Track |
Tevr |
7/31 (22.6%) |
| Track |
ZOnTevr |
3/31 (9.7%) |
| Track |
ZTevr |
5/31 (16.1%) |
Plot
DEBUG plotting question: WON18 | variable: t_omg_groen | non-NA rows:
44
By gender
| Anders |
OnTevr |
1/1 (100%) |
| Man |
Neutraal |
1/17 (5.9%) |
| Man |
OnTevr |
6/17 (35.3%) |
| Man |
Tevr |
6/17 (35.3%) |
| Man |
ZOnTevr |
1/17 (5.9%) |
| Man |
ZTevr |
3/17 (17.6%) |
| Vrouw |
Neutraal |
9/26 (34.6%) |
| Vrouw |
OnTevr |
8/26 (30.8%) |
| Vrouw |
Tevr |
2/26 (7.7%) |
| Vrouw |
ZOnTevr |
5/26 (19.2%) |
| Vrouw |
ZTevr |
2/26 (7.7%) |
Age by
response
| Neutraal |
10 |
30.50 |
3.47 |
30.5 |
26 |
36 |
| OnTevr |
15 |
30.47 |
3.11 |
31.0 |
24 |
37 |
| Tevr |
8 |
31.12 |
2.85 |
32.0 |
25 |
34 |
| ZOnTevr |
6 |
35.33 |
9.87 |
31.5 |
27 |
54 |
| ZTevr |
5 |
27.80 |
1.30 |
28.0 |
26 |
29 |
Hoe (on)tevreden
ben je met … de parkeerplaatsen voor auto’s
(Code: WON19)
Total
| Neutraal |
8/44 (18.2%) |
| NVT |
6/44 (13.6%) |
| OnTevr |
8/44 (18.2%) |
| Tevr |
5/44 (11.4%) |
| ZOnTevr |
15/44 (34.1%) |
| ZTevr |
2/44 (4.5%) |
By building
| Solo |
Neutraal |
4/13 (30.8%) |
| Solo |
NVT |
2/13 (15.4%) |
| Solo |
OnTevr |
2/13 (15.4%) |
| Solo |
ZOnTevr |
4/13 (30.8%) |
| Solo |
ZTevr |
1/13 (7.7%) |
| Track |
Neutraal |
4/31 (12.9%) |
| Track |
NVT |
4/31 (12.9%) |
| Track |
OnTevr |
6/31 (19.4%) |
| Track |
Tevr |
5/31 (16.1%) |
| Track |
ZOnTevr |
11/31 (35.5%) |
| Track |
ZTevr |
1/31 (3.2%) |
Plot
DEBUG plotting question: WON19 | variable: t_omg_park_auto | non-NA
rows: 44
By gender
| Anders |
Neutraal |
1/1 (100%) |
| Man |
Neutraal |
2/17 (11.8%) |
| Man |
NVT |
1/17 (5.9%) |
| Man |
OnTevr |
2/17 (11.8%) |
| Man |
Tevr |
4/17 (23.5%) |
| Man |
ZOnTevr |
7/17 (41.2%) |
| Man |
ZTevr |
1/17 (5.9%) |
| Vrouw |
Neutraal |
5/26 (19.2%) |
| Vrouw |
NVT |
5/26 (19.2%) |
| Vrouw |
OnTevr |
6/26 (23.1%) |
| Vrouw |
Tevr |
1/26 (3.8%) |
| Vrouw |
ZOnTevr |
8/26 (30.8%) |
| Vrouw |
ZTevr |
1/26 (3.8%) |
Age by
response
| Neutraal |
8 |
30.00 |
2.27 |
30 |
26 |
34 |
| NVT |
6 |
30.83 |
4.58 |
30 |
27 |
38 |
| OnTevr |
8 |
30.12 |
2.95 |
30 |
24 |
34 |
| Tevr |
5 |
29.00 |
2.00 |
30 |
26 |
31 |
| ZOnTevr |
15 |
32.73 |
6.64 |
31 |
26 |
54 |
| ZTevr |
2 |
30.00 |
7.07 |
30 |
25 |
35 |
Hoe (on)tevreden
ben je met … de parkeerplekken voor fietsen
(Code: WON20)
Total
| Neutraal |
7/44 (15.9%) |
| OnTevr |
8/44 (18.2%) |
| Tevr |
21/44 (47.7%) |
| ZOnTevr |
5/44 (11.4%) |
| ZTevr |
3/44 (6.8%) |
By building
| Solo |
Neutraal |
1/13 (7.7%) |
| Solo |
OnTevr |
1/13 (7.7%) |
| Solo |
Tevr |
10/13 (76.9%) |
| Solo |
ZTevr |
1/13 (7.7%) |
| Track |
Neutraal |
6/31 (19.4%) |
| Track |
OnTevr |
7/31 (22.6%) |
| Track |
Tevr |
11/31 (35.5%) |
| Track |
ZOnTevr |
5/31 (16.1%) |
| Track |
ZTevr |
2/31 (6.5%) |
Plot
DEBUG plotting question: WON20 | variable: t_omg_park_fiets | non-NA
rows: 44
By gender
| Anders |
Tevr |
1/1 (100%) |
| Man |
Neutraal |
3/17 (17.6%) |
| Man |
OnTevr |
3/17 (17.6%) |
| Man |
Tevr |
8/17 (47.1%) |
| Man |
ZOnTevr |
1/17 (5.9%) |
| Man |
ZTevr |
2/17 (11.8%) |
| Vrouw |
Neutraal |
4/26 (15.4%) |
| Vrouw |
OnTevr |
5/26 (19.2%) |
| Vrouw |
Tevr |
12/26 (46.2%) |
| Vrouw |
ZOnTevr |
4/26 (15.4%) |
| Vrouw |
ZTevr |
1/26 (3.8%) |
Age by
response
| Neutraal |
7 |
29.71 |
3.20 |
29 |
27 |
35 |
| OnTevr |
8 |
29.25 |
2.96 |
30 |
26 |
34 |
| Tevr |
21 |
31.19 |
2.48 |
31 |
27 |
38 |
| ZOnTevr |
5 |
37.40 |
9.86 |
36 |
29 |
54 |
| ZTevr |
3 |
26.00 |
2.65 |
25 |
24 |
29 |
Hoe (on)tevreden
ben je met … de parkeerplekken voor bakfietsen, scooters en
fatbikes
(Code: WON21)
Total
| Neutraal |
4/44 (9.1%) |
| NVT |
28/44 (63.6%) |
| OnTevr |
4/44 (9.1%) |
| Tevr |
3/44 (6.8%) |
| ZOnTevr |
5/44 (11.4%) |
By building
| Solo |
Neutraal |
2/13 (15.4%) |
| Solo |
NVT |
6/13 (46.2%) |
| Solo |
OnTevr |
2/13 (15.4%) |
| Solo |
Tevr |
1/13 (7.7%) |
| Solo |
ZOnTevr |
2/13 (15.4%) |
| Track |
Neutraal |
2/31 (6.5%) |
| Track |
NVT |
22/31 (71%) |
| Track |
OnTevr |
2/31 (6.5%) |
| Track |
Tevr |
2/31 (6.5%) |
| Track |
ZOnTevr |
3/31 (9.7%) |
Plot
DEBUG plotting question: WON21 | variable: t_omg_park_bakfiets |
non-NA rows: 44
By gender
| Anders |
NVT |
1/1 (100%) |
| Man |
Neutraal |
3/17 (17.6%) |
| Man |
NVT |
9/17 (52.9%) |
| Man |
OnTevr |
2/17 (11.8%) |
| Man |
Tevr |
2/17 (11.8%) |
| Man |
ZOnTevr |
1/17 (5.9%) |
| Vrouw |
Neutraal |
1/26 (3.8%) |
| Vrouw |
NVT |
18/26 (69.2%) |
| Vrouw |
OnTevr |
2/26 (7.7%) |
| Vrouw |
Tevr |
1/26 (3.8%) |
| Vrouw |
ZOnTevr |
4/26 (15.4%) |
Age by
response
| Neutraal |
4 |
31.00 |
2.16 |
30.5 |
29 |
34 |
| NVT |
28 |
30.00 |
3.28 |
30.0 |
24 |
38 |
| OnTevr |
4 |
31.25 |
2.06 |
31.0 |
29 |
34 |
| Tevr |
3 |
30.33 |
2.52 |
30.0 |
28 |
33 |
| ZOnTevr |
5 |
36.40 |
10.74 |
35.0 |
26 |
54 |
Hoe (on)tevreden
ben je met … de bevolkingssamenstelling (type mensen in de buurt)?
(Code: WON22)
Total
| Neutraal |
10/44 (22.7%) |
| Tevr |
32/44 (72.7%) |
| ZTevr |
2/44 (4.5%) |
By building
| Solo |
Neutraal |
4/13 (30.8%) |
| Solo |
Tevr |
9/13 (69.2%) |
| Track |
Neutraal |
6/31 (19.4%) |
| Track |
Tevr |
23/31 (74.2%) |
| Track |
ZTevr |
2/31 (6.5%) |
Plot
DEBUG plotting question: WON22 | variable: t_omg_bewoners | non-NA
rows: 44
By gender
| Anders |
Tevr |
1/1 (100%) |
| Man |
Neutraal |
3/17 (17.6%) |
| Man |
Tevr |
12/17 (70.6%) |
| Man |
ZTevr |
2/17 (11.8%) |
| Vrouw |
Neutraal |
7/26 (26.9%) |
| Vrouw |
Tevr |
19/26 (73.1%) |
Age by
response
| Neutraal |
10 |
31.6 |
3.17 |
30.5 |
26 |
37 |
| Tevr |
32 |
31.0 |
5.18 |
30.5 |
24 |
54 |
| ZTevr |
2 |
27.0 |
2.83 |
27.0 |
25 |
29 |
Hoe (on)tevreden
ben je met … de binnentuin of binnenplaats bij je woning?
(Code: WON23)
Total
| Neutraal |
8/44 (18.2%) |
| NVT |
2/44 (4.5%) |
| OnTevr |
2/44 (4.5%) |
| Tevr |
23/44 (52.3%) |
| ZTevr |
9/44 (20.5%) |
By building
| Solo |
Neutraal |
4/13 (30.8%) |
| Solo |
OnTevr |
2/13 (15.4%) |
| Solo |
Tevr |
7/13 (53.8%) |
| Track |
Neutraal |
4/31 (12.9%) |
| Track |
NVT |
2/31 (6.5%) |
| Track |
Tevr |
16/31 (51.6%) |
| Track |
ZTevr |
9/31 (29%) |
Plot
DEBUG plotting question: WON23 | variable: t_omg_binnentuin | non-NA
rows: 44
By gender
| Anders |
NVT |
1/1 (100%) |
| Man |
Neutraal |
2/17 (11.8%) |
| Man |
NVT |
1/17 (5.9%) |
| Man |
OnTevr |
2/17 (11.8%) |
| Man |
Tevr |
8/17 (47.1%) |
| Man |
ZTevr |
4/17 (23.5%) |
| Vrouw |
Neutraal |
6/26 (23.1%) |
| Vrouw |
Tevr |
15/26 (57.7%) |
| Vrouw |
ZTevr |
5/26 (19.2%) |
Age by
response
| Neutraal |
8 |
31.38 |
5.01 |
30.5 |
24 |
38 |
| NVT |
2 |
42.50 |
16.26 |
42.5 |
31 |
54 |
| OnTevr |
2 |
31.00 |
2.83 |
31.0 |
29 |
33 |
| Tevr |
23 |
29.96 |
2.74 |
30.0 |
26 |
36 |
| ZTevr |
9 |
30.56 |
2.74 |
30.0 |
25 |
34 |
Hoe (on)tevreden
ben je met … de moestuinbakken in je buurt?
(Code: WON24)
Total
| Neutraal |
4/44 (9.1%) |
| NVT |
4/44 (9.1%) |
| Tevr |
16/44 (36.4%) |
| ZTevr |
20/44 (45.5%) |
By building
| Solo |
NVT |
1/13 (7.7%) |
| Solo |
Tevr |
8/13 (61.5%) |
| Solo |
ZTevr |
4/13 (30.8%) |
| Track |
Neutraal |
4/31 (12.9%) |
| Track |
NVT |
3/31 (9.7%) |
| Track |
Tevr |
8/31 (25.8%) |
| Track |
ZTevr |
16/31 (51.6%) |
Plot
DEBUG plotting question: WON24 | variable: t_omg_moestuin | non-NA
rows: 44
By gender
| Anders |
NVT |
1/1 (100%) |
| Man |
Neutraal |
2/17 (11.8%) |
| Man |
NVT |
1/17 (5.9%) |
| Man |
Tevr |
5/17 (29.4%) |
| Man |
ZTevr |
9/17 (52.9%) |
| Vrouw |
Neutraal |
2/26 (7.7%) |
| Vrouw |
NVT |
2/26 (7.7%) |
| Vrouw |
Tevr |
11/26 (42.3%) |
| Vrouw |
ZTevr |
11/26 (42.3%) |
Age by
response
| Neutraal |
4 |
35.25 |
12.69 |
30.5 |
26 |
54 |
| NVT |
4 |
30.25 |
5.12 |
29.5 |
25 |
37 |
| Tevr |
16 |
30.06 |
3.28 |
30.0 |
24 |
38 |
| ZTevr |
20 |
30.95 |
2.86 |
30.5 |
26 |
36 |
Hoe (on)tevreden
ben je met … de picknicktafels en bankjes in je buurt?
(Code: WON25)
Total
| Neutraal |
3/44 (6.8%) |
| OnTevr |
1/44 (2.3%) |
| Tevr |
18/44 (40.9%) |
| ZTevr |
22/44 (50%) |
By building
| Solo |
Neutraal |
1/13 (7.7%) |
| Solo |
Tevr |
7/13 (53.8%) |
| Solo |
ZTevr |
5/13 (38.5%) |
| Track |
Neutraal |
2/31 (6.5%) |
| Track |
OnTevr |
1/31 (3.2%) |
| Track |
Tevr |
11/31 (35.5%) |
| Track |
ZTevr |
17/31 (54.8%) |
Plot
DEBUG plotting question: WON25 | variable: t_omg_picknick | non-NA
rows: 44
By gender
| Anders |
ZTevr |
1/1 (100%) |
| Man |
Tevr |
8/17 (47.1%) |
| Man |
ZTevr |
9/17 (52.9%) |
| Vrouw |
Neutraal |
3/26 (11.5%) |
| Vrouw |
OnTevr |
1/26 (3.8%) |
| Vrouw |
Tevr |
10/26 (38.5%) |
| Vrouw |
ZTevr |
12/26 (46.2%) |
Age by
response
| Neutraal |
3 |
31.67 |
6.03 |
31.0 |
26 |
38 |
| OnTevr |
1 |
37.00 |
NA |
37.0 |
37 |
37 |
| Tevr |
18 |
31.06 |
6.17 |
30.5 |
25 |
54 |
| ZTevr |
22 |
30.50 |
3.11 |
30.0 |
24 |
36 |
Binnen in je
woning
(Code: WON26)
By building
| Solo |
13 |
4.38 |
1.98 |
4 |
1 |
8 |
| Track |
31 |
4.10 |
2.43 |
4 |
1 |
8 |
Plot
DEBUG numeric plot: WON26 | variable: koel_woning | non-NA rows: 44
By gender
| Anders |
1 |
3.00 |
NA |
3 |
3 |
3 |
| Man |
17 |
4.29 |
2.31 |
5 |
1 |
8 |
| Vrouw |
26 |
4.15 |
2.34 |
4 |
1 |
8 |
Buiten
(balkon/tuin/buurt)
(Code: WON27)
By building
| Solo |
13 |
4.85 |
2.54 |
6 |
1 |
8 |
| Track |
31 |
4.90 |
2.18 |
4 |
1 |
8 |
Plot
DEBUG numeric plot: WON27 | variable: koel_buiten | non-NA rows: 44
By gender
| Anders |
1 |
3.00 |
NA |
3.0 |
3 |
3 |
| Man |
17 |
5.41 |
2.00 |
6.0 |
3 |
8 |
| Vrouw |
26 |
4.62 |
2.42 |
4.5 |
1 |
8 |
Binnen in andere
gebouwen, zoals een buurthuis, supermarkt of bibliotheek (in je
buurt)
(Code: WON28)
By building
| Solo |
13 |
5.54 |
2.37 |
6 |
2 |
9 |
| Track |
31 |
5.03 |
2.07 |
5 |
1 |
10 |
Plot
DEBUG numeric plot: WON28 | variable: koel_anders | non-NA rows: 44
By gender
| Anders |
1 |
4.00 |
NA |
4.0 |
4 |
4 |
| Man |
17 |
5.76 |
2.46 |
6.0 |
2 |
10 |
| Vrouw |
26 |
4.85 |
1.91 |
5.5 |
1 |
8 |
Heb je
airconditioning in je woning?
(Code: WON29)
Total
| Ja |
1/44 (2.3%) |
| Nee |
43/44 (97.7%) |
By building
| Solo |
Nee |
13/13 (100%) |
| Track |
Ja |
1/31 (3.2%) |
| Track |
Nee |
30/31 (96.8%) |
Plot
DEBUG plotting question: WON29 | variable: koel_airco | non-NA rows:
44
By gender
| Anders |
Nee |
1/1 (100%) |
| Man |
Nee |
17/17 (100%) |
| Vrouw |
Ja |
1/26 (3.8%) |
| Vrouw |
Nee |
25/26 (96.2%) |
Age by
response
| Ja |
1 |
26.00 |
NA |
26 |
26 |
26 |
| Nee |
43 |
31.07 |
4.73 |
30 |
24 |
54 |
Vind je dat de
buurt waarin je woont in de afgelopen 12 maanden erop vooruit is gegaan,
erop achteruit is gegaan of gelijk is gebleven?
(Code: WON30)
Total
| Achteruit |
3/44 (6.8%) |
| Gelijk |
12/44 (27.3%) |
| Vooruit |
29/44 (65.9%) |
By building
| Solo |
Gelijk |
4/13 (30.8%) |
| Solo |
Vooruit |
9/13 (69.2%) |
| Track |
Achteruit |
3/31 (9.7%) |
| Track |
Gelijk |
8/31 (25.8%) |
| Track |
Vooruit |
20/31 (64.5%) |
Plot
DEBUG plotting question: WON30 | variable: brt_va | non-NA rows: 44
By gender
| Anders |
Achteruit |
1/1 (100%) |
| Man |
Achteruit |
1/17 (5.9%) |
| Man |
Gelijk |
5/17 (29.4%) |
| Man |
Vooruit |
11/17 (64.7%) |
| Vrouw |
Achteruit |
1/26 (3.8%) |
| Vrouw |
Gelijk |
7/26 (26.9%) |
| Vrouw |
Vooruit |
18/26 (69.2%) |
Age by
response
| Achteruit |
3 |
40.67 |
11.93 |
37 |
31 |
54 |
| Gelijk |
12 |
31.17 |
3.27 |
31 |
26 |
38 |
| Vooruit |
29 |
29.86 |
2.96 |
30 |
24 |
36 |
Denk je dat de
buurt waarin je woont de komende 12 maanden erop vooruit zal gaan, erop
achteruit zal gaan of gelijk zal blijven?
(Code: WON31)
Total
| Achteruit |
1/44 (2.3%) |
| Gelijk |
10/44 (22.7%) |
| Vooruit |
33/44 (75%) |
By building
| Solo |
Gelijk |
2/13 (15.4%) |
| Solo |
Vooruit |
11/13 (84.6%) |
| Track |
Achteruit |
1/31 (3.2%) |
| Track |
Gelijk |
8/31 (25.8%) |
| Track |
Vooruit |
22/31 (71%) |
Plot
DEBUG plotting question: WON31 | variable: brt_zva | non-NA rows: 44
By gender
| Anders |
Achteruit |
1/1 (100%) |
| Man |
Gelijk |
2/17 (11.8%) |
| Man |
Vooruit |
15/17 (88.2%) |
| Vrouw |
Gelijk |
8/26 (30.8%) |
| Vrouw |
Vooruit |
18/26 (69.2%) |
Age by
response
| Achteruit |
1 |
31.00 |
NA |
31 |
31 |
31 |
| Gelijk |
10 |
34.60 |
7.75 |
32 |
26 |
54 |
| Vooruit |
33 |
29.85 |
2.81 |
30 |
24 |
35 |
Hinder geluid en
geur
In this section, we are looking at 40 questions.
Hoor je wel eens
geluid van… verkeer op wegen waar je harder mag dan 50 km/uur?
(Code: HGG2)
Total
| Ja |
5/44 (11.4%) |
| Nee |
39/44 (88.6%) |
By building
| Solo |
Ja |
2/13 (15.4%) |
| Solo |
Nee |
11/13 (84.6%) |
| Track |
Ja |
3/31 (9.7%) |
| Track |
Nee |
28/31 (90.3%) |
Plot
DEBUG plotting question: HGG2 | variable: gel_b50 | non-NA rows: 44
By gender
| Anders |
Nee |
1/1 (100%) |
| Man |
Ja |
2/17 (11.8%) |
| Man |
Nee |
15/17 (88.2%) |
| Vrouw |
Ja |
3/26 (11.5%) |
| Vrouw |
Nee |
23/26 (88.5%) |
Age by
response
| Ja |
5 |
30.6 |
3.13 |
30 |
26 |
34 |
| Nee |
39 |
31.0 |
4.94 |
30 |
24 |
54 |
Ervaar je
geluidshinder door… verkeer op wegen waar je harder mag dan 50
km/uur?
(Code: HGG2.1)
By building
| Solo |
2 |
3.00 |
2.83 |
3 |
1 |
5 |
| Track |
3 |
4.33 |
3.79 |
6 |
0 |
7 |
Plot
DEBUG numeric plot: HGG2.1 | variable: gel_hind_b50 | non-NA rows: 5
By gender
| Man |
2 |
0.5 |
0.71 |
0.5 |
0 |
1 |
| Vrouw |
3 |
6.0 |
1.00 |
6.0 |
5 |
7 |
Ervaar je
slaapverstoring door… verkeer op wegen waar je harder mag dan 50
km/uur?
(Code: HGG2.2)
By building
| Solo |
2 |
2.50 |
3.54 |
2.5 |
0 |
5 |
| Track |
3 |
2.67 |
2.52 |
3.0 |
0 |
5 |
Plot
DEBUG numeric plot: HGG2.2 | variable: slaap_b50 | non-NA rows: 5
By gender
| Man |
2 |
0.00 |
0.00 |
0 |
0 |
0 |
| Vrouw |
3 |
4.33 |
1.15 |
5 |
3 |
5 |
Hoor je wel een
geluid van… verkeer op wegen waar je niet harder mag dan 50 km/uur?
(Code: HGG3)
Total
| Ja |
27/44 (61.4%) |
| Nee |
17/44 (38.6%) |
By building
| Solo |
Ja |
11/13 (84.6%) |
| Solo |
Nee |
2/13 (15.4%) |
| Track |
Ja |
16/31 (51.6%) |
| Track |
Nee |
15/31 (48.4%) |
Plot
DEBUG plotting question: HGG3 | variable: gel_o50 | non-NA rows: 44
By gender
| Anders |
Ja |
1/1 (100%) |
| Man |
Ja |
9/17 (52.9%) |
| Man |
Nee |
8/17 (47.1%) |
| Vrouw |
Ja |
17/26 (65.4%) |
| Vrouw |
Nee |
9/26 (34.6%) |
Age by
response
| Ja |
27 |
30.81 |
3.06 |
31 |
25 |
38 |
| Nee |
17 |
31.18 |
6.71 |
30 |
24 |
54 |
Ervaar je
geluidshinder door… verkeer op wegen waar je niet harder mag dan 50
km/uur?
(Code: HGG3.1)
By building
| Solo |
11 |
3.82 |
2.40 |
3 |
1 |
9 |
| Track |
16 |
3.44 |
2.34 |
3 |
0 |
7 |
Plot
DEBUG numeric plot: HGG3.1 | variable: gel_hind_o50 | non-NA rows: 27
By gender
| Anders |
1 |
3.00 |
NA |
3 |
3 |
3 |
| Man |
9 |
2.22 |
0.97 |
2 |
1 |
4 |
| Vrouw |
17 |
4.35 |
2.57 |
5 |
0 |
9 |
Ervaar je
slaapverstoring door… verkeer op wegen waar je niet harder mag dan 50
km/uur?
(Code: HGG3.2)
By building
| Solo |
11 |
1.45 |
2.46 |
0 |
0 |
8 |
| Track |
16 |
1.31 |
2.15 |
0 |
0 |
7 |
Plot
DEBUG numeric plot: HGG3.2 | variable: slaap_o50 | non-NA rows: 27
By gender
| Anders |
1 |
3.00 |
NA |
3 |
3 |
3 |
| Man |
9 |
0.11 |
0.33 |
0 |
0 |
1 |
| Vrouw |
17 |
1.94 |
2.59 |
1 |
0 |
8 |
Hoor je wel eens
geluid van… treinverkeer?
(Code: HGG4)
Total
| Ja |
40/44 (90.9%) |
| Nee |
4/44 (9.1%) |
By building
| Solo |
Ja |
10/13 (76.9%) |
| Solo |
Nee |
3/13 (23.1%) |
| Track |
Ja |
30/31 (96.8%) |
| Track |
Nee |
1/31 (3.2%) |
Plot
DEBUG plotting question: HGG4 | variable: gel_trein | non-NA rows: 44
By gender
| Anders |
Ja |
1/1 (100%) |
| Man |
Ja |
15/17 (88.2%) |
| Man |
Nee |
2/17 (11.8%) |
| Vrouw |
Ja |
24/26 (92.3%) |
| Vrouw |
Nee |
2/26 (7.7%) |
Age by
response
| Ja |
40 |
30.35 |
3.20 |
30.0 |
24 |
38 |
| Nee |
4 |
37.00 |
11.63 |
32.5 |
29 |
54 |
Ervaar je
geluidshinder door… treinverkeer?
(Code: HGG4.1)
By building
| Solo |
10 |
2.00 |
1.70 |
1.5 |
0 |
6 |
| Track |
30 |
5.17 |
2.89 |
5.0 |
1 |
10 |
Plot
DEBUG numeric plot: HGG4.1 | variable: gel_hind_trein | non-NA rows:
40
By gender
| Anders |
1 |
5.00 |
NA |
5.0 |
5 |
5 |
| Man |
15 |
4.47 |
3.20 |
3.0 |
0 |
10 |
| Vrouw |
24 |
4.29 |
2.94 |
3.5 |
1 |
10 |
Ervaar je
slaapverstoring door… treinverkeer?
(Code: HGG4.2)
By building
| Solo |
10 |
0.70 |
1.06 |
0 |
0 |
3 |
| Track |
30 |
4.23 |
3.18 |
3 |
0 |
10 |
Plot
DEBUG numeric plot: HGG4.2 | variable: slaap_trein | non-NA rows: 40
By gender
| Anders |
1 |
5.00 |
NA |
5.0 |
5 |
5 |
| Man |
15 |
3.27 |
3.69 |
1.0 |
0 |
10 |
| Vrouw |
24 |
3.33 |
2.97 |
2.5 |
0 |
10 |
Hoor je wel eens
geluid van… vliegverkeer?
(Code: HGG5)
Total
| Ja |
17/44 (38.6%) |
| Nee |
27/44 (61.4%) |
By building
| Solo |
Ja |
4/13 (30.8%) |
| Solo |
Nee |
9/13 (69.2%) |
| Track |
Ja |
13/31 (41.9%) |
| Track |
Nee |
18/31 (58.1%) |
Plot
DEBUG plotting question: HGG5 | variable: gel_vlieg | non-NA rows: 44
By gender
| Anders |
Ja |
1/1 (100%) |
| Man |
Ja |
6/17 (35.3%) |
| Man |
Nee |
11/17 (64.7%) |
| Vrouw |
Ja |
10/26 (38.5%) |
| Vrouw |
Nee |
16/26 (61.5%) |
Age by
response
| Ja |
17 |
30.65 |
3.26 |
30 |
26 |
38 |
| Nee |
27 |
31.15 |
5.52 |
31 |
24 |
54 |
Ervaar je
geluidshinder door… vliegverkeer?
(Code: HGG5.1)
By building
| Solo |
4 |
0.75 |
0.50 |
1 |
0 |
1 |
| Track |
13 |
3.00 |
2.31 |
2 |
0 |
7 |
Plot
DEBUG numeric plot: HGG5.1 | variable: gel_hind_vlieg | non-NA rows:
17
By gender
| Anders |
1 |
2.0 |
NA |
2.0 |
2 |
2 |
| Man |
6 |
2.0 |
2.10 |
1.5 |
0 |
5 |
| Vrouw |
10 |
2.8 |
2.49 |
1.5 |
0 |
7 |
Ervaar je
slaapverstoring door… vliegverkeer?
(Code: HGG5.2)
By building
| Solo |
4 |
0.25 |
0.50 |
0 |
0 |
1 |
| Track |
13 |
1.00 |
1.96 |
0 |
0 |
5 |
Plot
DEBUG numeric plot: HGG5.2 | variable: slaap_vlieg | non-NA rows: 17
By gender
| Anders |
1 |
0.0 |
NA |
0 |
0 |
0 |
| Man |
6 |
0.0 |
0.00 |
0 |
0 |
0 |
| Vrouw |
10 |
1.4 |
2.12 |
0 |
0 |
5 |
Hoor je wel eens
geluid van… buren?
(Code: HGG7)
Total
| Ja |
35/44 (79.5%) |
| Nee |
9/44 (20.5%) |
By building
| Solo |
Ja |
11/13 (84.6%) |
| Solo |
Nee |
2/13 (15.4%) |
| Track |
Ja |
24/31 (77.4%) |
| Track |
Nee |
7/31 (22.6%) |
Plot
DEBUG plotting question: HGG7 | variable: gel_buren | non-NA rows: 44
By gender
| Anders |
Ja |
1/1 (100%) |
| Man |
Ja |
12/17 (70.6%) |
| Man |
Nee |
5/17 (29.4%) |
| Vrouw |
Ja |
22/26 (84.6%) |
| Vrouw |
Nee |
4/26 (15.4%) |
Age by
response
| Ja |
35 |
31.74 |
4.88 |
31 |
26 |
54 |
| Nee |
9 |
27.89 |
2.52 |
29 |
24 |
31 |
Ervaar je
geluidshinder door… buren?
(Code: HGG7.1)
By building
| Solo |
11 |
3.09 |
1.87 |
3 |
0 |
7 |
| Track |
24 |
3.38 |
1.88 |
3 |
1 |
8 |
Plot
DEBUG numeric plot: HGG7.1 | variable: gel_hind_buren | non-NA rows:
35
By gender
| Anders |
1 |
4.00 |
NA |
4 |
4 |
4 |
| Man |
12 |
2.33 |
1.23 |
2 |
0 |
5 |
| Vrouw |
22 |
3.77 |
2.00 |
3 |
1 |
8 |
Ervaar je
slaapverstoring door… buren?
(Code: HGG7.2)
By building
| Solo |
11 |
2.00 |
1.55 |
2.0 |
0 |
4 |
| Track |
24 |
2.71 |
2.01 |
2.5 |
0 |
7 |
Plot
DEBUG numeric plot: HGG7.2 | variable: slaap_buren | non-NA rows: 35
By gender
| Anders |
1 |
4.00 |
NA |
4 |
4 |
4 |
| Man |
12 |
1.42 |
1.44 |
1 |
0 |
4 |
| Vrouw |
22 |
3.00 |
1.90 |
3 |
0 |
7 |
Hoor je wel eens
geluid van… bedrijven/industrie?
(Code: HGG8)
Total
| Ja |
12/44 (27.3%) |
| Nee |
32/44 (72.7%) |
By building
| Solo |
Ja |
4/13 (30.8%) |
| Solo |
Nee |
9/13 (69.2%) |
| Track |
Ja |
8/31 (25.8%) |
| Track |
Nee |
23/31 (74.2%) |
Plot
DEBUG plotting question: HGG8 | variable: gel_bedrijven | non-NA
rows: 44
By gender
| Anders |
Ja |
1/1 (100%) |
| Man |
Ja |
2/17 (11.8%) |
| Man |
Nee |
15/17 (88.2%) |
| Vrouw |
Ja |
9/26 (34.6%) |
| Vrouw |
Nee |
17/26 (65.4%) |
Age by
response
| Ja |
12 |
31.17 |
2.79 |
31 |
26 |
36 |
| Nee |
32 |
30.88 |
5.33 |
30 |
24 |
54 |
Ervaar je
geluidshinder door… bedrijven/industrie?
(Code: HGG8.1)
By building
| Solo |
4 |
4.25 |
2.50 |
3 |
3 |
8 |
| Track |
8 |
4.12 |
2.75 |
4 |
0 |
8 |
Plot
DEBUG numeric plot: HGG8.1 | variable: gel_hind_bedrijven | non-NA
rows: 12
By gender
| Anders |
1 |
2.0 |
NA |
2.0 |
2 |
2 |
| Man |
2 |
1.5 |
2.12 |
1.5 |
0 |
3 |
| Vrouw |
9 |
5.0 |
2.29 |
4.0 |
2 |
8 |
Ervaar je
slaapverstoring door… bedrijven/industrie?
(Code: HGG8.2)
By building
| Solo |
4 |
1.25 |
1.26 |
1.0 |
0 |
3 |
| Track |
8 |
3.00 |
2.83 |
2.5 |
0 |
7 |
Plot
DEBUG numeric plot: HGG8.2 | variable: slaap_bedrijven | non-NA rows:
12
By gender
| Anders |
1 |
0.00 |
NA |
0.0 |
0 |
0 |
| Man |
2 |
0.50 |
0.71 |
0.5 |
0 |
1 |
| Vrouw |
9 |
3.11 |
2.52 |
3.0 |
0 |
7 |
Hoor je wel eens
geluid van… bouwactiviteiten?
(Code: HGG9)
Total
| Ja |
40/44 (90.9%) |
| Nee |
4/44 (9.1%) |
By building
| Solo |
Ja |
13/13 (100%) |
| Track |
Ja |
27/31 (87.1%) |
| Track |
Nee |
4/31 (12.9%) |
Plot
DEBUG plotting question: HGG9 | variable: gel_bouw | non-NA rows: 44
By gender
| Anders |
Ja |
1/1 (100%) |
| Man |
Ja |
14/17 (82.4%) |
| Man |
Nee |
3/17 (17.6%) |
| Vrouw |
Ja |
25/26 (96.2%) |
| Vrouw |
Nee |
1/26 (3.8%) |
Age by
response
| Ja |
40 |
31.1 |
4.88 |
30.0 |
24 |
54 |
| Nee |
4 |
29.5 |
3.11 |
30.5 |
25 |
32 |
Ervaar je
geluidshinder door… bouwactiviteiten?
(Code: HGG9.1)
By building
| Solo |
13 |
6.77 |
2.09 |
7 |
3 |
10 |
| Track |
27 |
5.41 |
2.26 |
5 |
2 |
9 |
Plot
DEBUG numeric plot: HGG9.1 | variable: gel_hind_bouw | non-NA rows:
40
By gender
| Anders |
1 |
5.00 |
NA |
5 |
5 |
5 |
| Man |
14 |
5.07 |
2.27 |
5 |
2 |
8 |
| Vrouw |
25 |
6.32 |
2.23 |
6 |
2 |
10 |
Ervaar je
slaapverstoring door… bouwactiviteiten?
(Code: HGG9.2)
By building
| Solo |
13 |
4.85 |
3.24 |
5 |
0 |
10 |
| Track |
27 |
4.00 |
3.21 |
3 |
0 |
10 |
Plot
DEBUG numeric plot: HGG9.2 | variable: slaap_bouw | non-NA rows: 40
By gender
| Anders |
1 |
3.00 |
NA |
3 |
3 |
3 |
| Man |
14 |
3.07 |
2.84 |
3 |
0 |
7 |
| Vrouw |
25 |
5.00 |
3.29 |
6 |
0 |
10 |
Hoor je wel eens
geluid van… warmtepompen/airco’s?
(Code: HGG10)
Total
| Ja |
8/44 (18.2%) |
| Nee |
36/44 (81.8%) |
By building
| Solo |
Nee |
13/13 (100%) |
| Track |
Ja |
8/31 (25.8%) |
| Track |
Nee |
23/31 (74.2%) |
Plot
DEBUG plotting question: HGG10 | variable: gel_w_pomp | non-NA rows:
44
By gender
| Anders |
Nee |
1/1 (100%) |
| Man |
Ja |
1/17 (5.9%) |
| Man |
Nee |
16/17 (94.1%) |
| Vrouw |
Ja |
7/26 (26.9%) |
| Vrouw |
Nee |
19/26 (73.1%) |
Age by
response
| Ja |
8 |
30.50 |
3.34 |
30.5 |
26 |
37 |
| Nee |
36 |
31.06 |
5.03 |
30.0 |
24 |
54 |
Ervaar je
geluidshinder door… warmtepompen/airco’s?
(Code: HGG10.1)
Plot
DEBUG numeric plot: HGG10.1 | variable: gel_hind_w_pomp | non-NA
rows: 8
By gender
| Man |
1 |
0.00 |
NA |
0 |
0 |
0 |
| Vrouw |
7 |
3.71 |
2.06 |
4 |
1 |
6 |
Ervaar je
slaapverstoring door… warmtepompen/airco’s?
(Code: HGG10.2)
By building
| Track |
8 |
2.62 |
2.26 |
2.5 |
0 |
6 |
Plot
DEBUG numeric plot: HGG10.2 | variable: slaap_w_pomp | non-NA rows: 8
By gender
| Man |
1 |
0 |
NA |
0 |
0 |
0 |
| Vrouw |
7 |
3 |
2.16 |
3 |
0 |
6 |
Ruik je wel eens
de geur van… open haard / allesbrander / andere houtkachel?
(Code: HGG13)
Total
| Ja |
2/44 (4.5%) |
| Nee |
42/44 (95.5%) |
By building
| Solo |
Ja |
1/13 (7.7%) |
| Solo |
Nee |
12/13 (92.3%) |
| Track |
Ja |
1/31 (3.2%) |
| Track |
Nee |
30/31 (96.8%) |
Plot
DEBUG plotting question: HGG13 | variable: geur_open_haard | non-NA
rows: 44
By gender
| Anders |
Nee |
1/1 (100%) |
| Man |
Ja |
1/17 (5.9%) |
| Man |
Nee |
16/17 (94.1%) |
| Vrouw |
Ja |
1/26 (3.8%) |
| Vrouw |
Nee |
25/26 (96.2%) |
Age by
response
| Ja |
2 |
29.50 |
0.71 |
29.5 |
29 |
30 |
| Nee |
42 |
31.02 |
4.84 |
30.5 |
24 |
54 |
Ervaar je
geurhinder door… open haard / allesbrander / andere houtkachel?
(Code: HGG13.1)
By building
| Solo |
1 |
3 |
NA |
3 |
3 |
3 |
| Track |
1 |
6 |
NA |
6 |
6 |
6 |
Plot
DEBUG numeric plot: HGG13.1 | variable: geur_hind_open_haard | non-NA
rows: 2
By gender
| Man |
1 |
3 |
NA |
3 |
3 |
3 |
| Vrouw |
1 |
6 |
NA |
6 |
6 |
6 |
Ruik je wel eens
de geur van een… vuurkorf / barbecue / terrashaard?
(Code: HGG14)
Total
| Ja |
8/44 (18.2%) |
| Nee |
36/44 (81.8%) |
By building
| Solo |
Ja |
3/13 (23.1%) |
| Solo |
Nee |
10/13 (76.9%) |
| Track |
Ja |
5/31 (16.1%) |
| Track |
Nee |
26/31 (83.9%) |
Plot
DEBUG plotting question: HGG14 | variable: geur_vuurkorf | non-NA
rows: 44
By gender
| Anders |
Nee |
1/1 (100%) |
| Man |
Ja |
2/17 (11.8%) |
| Man |
Nee |
15/17 (88.2%) |
| Vrouw |
Ja |
6/26 (23.1%) |
| Vrouw |
Nee |
20/26 (76.9%) |
Age by
response
| Ja |
8 |
29.38 |
1.51 |
30 |
26 |
31 |
| Nee |
36 |
31.31 |
5.14 |
31 |
24 |
54 |
Ervaar je
geurhinder door… vuurkorf / barbecue / terrashaard?
(Code: HGG14.1)
By building
| Solo |
3 |
2.67 |
0.58 |
3 |
2 |
3 |
| Track |
5 |
4.00 |
3.74 |
6 |
0 |
8 |
Plot
DEBUG numeric plot: HGG14.1 | variable: geur_hind_vuurkorf | non-NA
rows: 8
By gender
| Man |
2 |
1.50 |
2.12 |
1.5 |
0 |
3 |
| Vrouw |
6 |
4.17 |
2.99 |
4.5 |
0 |
8 |
Ruik je wel eens
de geur van… riolering / waterzuivering?
(Code: HGG15)
Total
| Ja |
15/44 (34.1%) |
| Nee |
29/44 (65.9%) |
By building
| Solo |
Ja |
1/13 (7.7%) |
| Solo |
Nee |
12/13 (92.3%) |
| Track |
Ja |
14/31 (45.2%) |
| Track |
Nee |
17/31 (54.8%) |
Plot
DEBUG plotting question: HGG15 | variable: geur_riool | non-NA rows:
44
By gender
| Anders |
Nee |
1/1 (100%) |
| Man |
Ja |
6/17 (35.3%) |
| Man |
Nee |
11/17 (64.7%) |
| Vrouw |
Ja |
9/26 (34.6%) |
| Vrouw |
Nee |
17/26 (65.4%) |
Age by
response
| Ja |
15 |
29.93 |
2.69 |
30 |
26 |
36 |
| Nee |
29 |
31.48 |
5.48 |
31 |
24 |
54 |
Ervaar je
geurhinder door… riolering / waterzuivering?
(Code: HGG15.1)
By building
| Solo |
1 |
7.00 |
NA |
7 |
7 |
7 |
| Track |
14 |
5.43 |
1.83 |
6 |
2 |
8 |
Plot
DEBUG numeric plot: HGG15.1 | variable: geur_hind_riool | non-NA
rows: 15
By gender
| Man |
6 |
6.17 |
1.6 |
7 |
3 |
7 |
| Vrouw |
9 |
5.11 |
1.9 |
5 |
2 |
8 |
Ruik je wel eens
de geur van… bedrijven / industrie?
(Code: HGG16)
Total
| Ja |
20/44 (45.5%) |
| Nee |
24/44 (54.5%) |
By building
| Solo |
Ja |
7/13 (53.8%) |
| Solo |
Nee |
6/13 (46.2%) |
| Track |
Ja |
13/31 (41.9%) |
| Track |
Nee |
18/31 (58.1%) |
Plot
DEBUG plotting question: HGG16 | variable: geur_bedrijven | non-NA
rows: 44
By gender
| Anders |
Ja |
1/1 (100%) |
| Man |
Ja |
8/17 (47.1%) |
| Man |
Nee |
9/17 (52.9%) |
| Vrouw |
Ja |
11/26 (42.3%) |
| Vrouw |
Nee |
15/26 (57.7%) |
Age by
response
| Ja |
20 |
30.25 |
3.04 |
30.0 |
26 |
38 |
| Nee |
24 |
31.54 |
5.79 |
30.5 |
24 |
54 |
Ervaar je
geurhinder door… bedrijven / industrie?
(Code: HGG16.1)
By building
| Solo |
7 |
2.57 |
1.40 |
3 |
1 |
5 |
| Track |
13 |
3.69 |
2.87 |
4 |
0 |
8 |
Plot
DEBUG numeric plot: HGG16.1 | variable: geur_hind_bedrijven | non-NA
rows: 20
By gender
| Anders |
1 |
0.00 |
NA |
0.0 |
0 |
0 |
| Man |
8 |
1.75 |
0.89 |
1.5 |
1 |
3 |
| Vrouw |
11 |
4.73 |
2.41 |
5.0 |
0 |
8 |
Ruik je wel eens
de geur van… bouwactiviteiten?
(Code: HGG17)
Total
| Ja |
18/44 (40.9%) |
| Nee |
26/44 (59.1%) |
By building
| Solo |
Ja |
10/13 (76.9%) |
| Solo |
Nee |
3/13 (23.1%) |
| Track |
Ja |
8/31 (25.8%) |
| Track |
Nee |
23/31 (74.2%) |
Plot
DEBUG plotting question: HGG17 | variable: geur_bouw | non-NA rows:
44
By gender
| Anders |
Nee |
1/1 (100%) |
| Man |
Ja |
6/17 (35.3%) |
| Man |
Nee |
11/17 (64.7%) |
| Vrouw |
Ja |
12/26 (46.2%) |
| Vrouw |
Nee |
14/26 (53.8%) |
Age by
response
| Ja |
18 |
30.72 |
3.18 |
31 |
24 |
36 |
| Nee |
26 |
31.12 |
5.63 |
30 |
25 |
54 |
Ervaar je
geurhinder door… bouwactiviteiten?
(Code: HGG17.1)
By building
| Solo |
10 |
2.80 |
1.32 |
3 |
1 |
6 |
| Track |
8 |
3.75 |
2.12 |
3 |
2 |
8 |
Plot
DEBUG numeric plot: HGG17.1 | variable: geur_hind_bouw | non-NA rows:
18
By gender
| Man |
6 |
2.50 |
0.84 |
3 |
1 |
3 |
| Vrouw |
12 |
3.58 |
1.98 |
3 |
2 |
8 |
Ruik je wel eens
de geur van… wegverkeer?
(Code: HGG18)
Total
| Ja |
6/44 (13.6%) |
| Nee |
38/44 (86.4%) |
By building
| Solo |
Ja |
3/13 (23.1%) |
| Solo |
Nee |
10/13 (76.9%) |
| Track |
Ja |
3/31 (9.7%) |
| Track |
Nee |
28/31 (90.3%) |
Plot
DEBUG plotting question: HGG18 | variable: geur_wergverk | non-NA
rows: 44
By gender
| Anders |
Nee |
1/1 (100%) |
| Man |
Ja |
1/17 (5.9%) |
| Man |
Nee |
16/17 (94.1%) |
| Vrouw |
Ja |
5/26 (19.2%) |
| Vrouw |
Nee |
21/26 (80.8%) |
Age by
response
| Ja |
6 |
31.00 |
2.83 |
31.5 |
26 |
34 |
| Nee |
38 |
30.95 |
5.00 |
30.0 |
24 |
54 |
Ervaar je
geurhinder door… wegverkeer?
(Code: HGG18.1)
By building
| Solo |
3 |
4 |
1.00 |
4 |
3 |
5 |
| Track |
3 |
4 |
3.61 |
3 |
1 |
8 |
Plot
DEBUG numeric plot: HGG18.1 | variable: geur_hind_wegverk | non-NA
rows: 6
By gender
| Man |
1 |
1.0 |
NA |
1 |
1 |
1 |
| Vrouw |
5 |
4.6 |
2.07 |
4 |
3 |
8 |
Veiligheid en
overlast
In this section, we are looking at 13 questions.
Ik voel me veilig
in de buurt
(Code: VO1)
Total
| Eens |
25/44 (56.8%) |
| HEens |
11/44 (25%) |
| Neutraal |
6/44 (13.6%) |
| OnEens |
2/44 (4.5%) |
By building
| Solo |
Eens |
11/13 (84.6%) |
| Solo |
Neutraal |
2/13 (15.4%) |
| Track |
Eens |
14/31 (45.2%) |
| Track |
HEens |
11/31 (35.5%) |
| Track |
Neutraal |
4/31 (12.9%) |
| Track |
OnEens |
2/31 (6.5%) |
Plot
DEBUG plotting question: VO1 | variable: veiligh_veilig1 | non-NA
rows: 44
By gender
| Anders |
Neutraal |
1/1 (100%) |
| Man |
Eens |
9/17 (52.9%) |
| Man |
HEens |
6/17 (35.3%) |
| Man |
Neutraal |
1/17 (5.9%) |
| Man |
OnEens |
1/17 (5.9%) |
| Vrouw |
Eens |
16/26 (61.5%) |
| Vrouw |
HEens |
5/26 (19.2%) |
| Vrouw |
Neutraal |
4/26 (15.4%) |
| Vrouw |
OnEens |
1/26 (3.8%) |
Age by
response
| Eens |
25 |
32.16 |
5.44 |
31.0 |
26 |
54 |
| HEens |
11 |
28.64 |
3.23 |
28.0 |
24 |
34 |
| Neutraal |
6 |
30.33 |
3.27 |
30.0 |
26 |
36 |
| OnEens |
2 |
30.50 |
0.71 |
30.5 |
30 |
31 |
Ervaar je overlast
van … rommel en zwerfafval op straat
(Code: VO3)
Total
| BOverlast |
26/44 (59.1%) |
| GOverlast |
10/44 (22.7%) |
| VOverlast |
8/44 (18.2%) |
By building
| Solo |
BOverlast |
7/13 (53.8%) |
| Solo |
GOverlast |
3/13 (23.1%) |
| Solo |
VOverlast |
3/13 (23.1%) |
| Track |
BOverlast |
19/31 (61.3%) |
| Track |
GOverlast |
7/31 (22.6%) |
| Track |
VOverlast |
5/31 (16.1%) |
Plot
DEBUG plotting question: VO3 | variable: overl_rommel1 | non-NA rows:
44
By gender
| Anders |
GOverlast |
1/1 (100%) |
| Man |
BOverlast |
10/17 (58.8%) |
| Man |
GOverlast |
5/17 (29.4%) |
| Man |
VOverlast |
2/17 (11.8%) |
| Vrouw |
BOverlast |
16/26 (61.5%) |
| Vrouw |
GOverlast |
4/26 (15.4%) |
| Vrouw |
VOverlast |
6/26 (23.1%) |
Age by
response
| BOverlast |
26 |
31.42 |
5.78 |
30.5 |
24 |
54 |
| GOverlast |
10 |
29.70 |
2.83 |
31.0 |
25 |
33 |
| VOverlast |
8 |
31.00 |
2.33 |
30.0 |
29 |
36 |
Ervaar je overlast
van … hondenpoep
(Code: VO4)
Total
| BOverlast |
19/44 (43.2%) |
| GAntwoord |
2/44 (4.5%) |
| GOverlast |
22/44 (50%) |
| VOverlast |
1/44 (2.3%) |
By building
| Solo |
BOverlast |
4/13 (30.8%) |
| Solo |
GAntwoord |
1/13 (7.7%) |
| Solo |
GOverlast |
7/13 (53.8%) |
| Solo |
VOverlast |
1/13 (7.7%) |
| Track |
BOverlast |
15/31 (48.4%) |
| Track |
GAntwoord |
1/31 (3.2%) |
| Track |
GOverlast |
15/31 (48.4%) |
Plot
DEBUG plotting question: VO4 | variable: overl_hondenpoep1 | non-NA
rows: 44
By gender
| Anders |
GOverlast |
1/1 (100%) |
| Man |
BOverlast |
4/17 (23.5%) |
| Man |
GAntwoord |
1/17 (5.9%) |
| Man |
GOverlast |
12/17 (70.6%) |
| Vrouw |
BOverlast |
15/26 (57.7%) |
| Vrouw |
GAntwoord |
1/26 (3.8%) |
| Vrouw |
GOverlast |
9/26 (34.6%) |
| Vrouw |
VOverlast |
1/26 (3.8%) |
Age by
response
| BOverlast |
19 |
31.42 |
2.87 |
31.0 |
26 |
37 |
| GAntwoord |
2 |
26.50 |
2.12 |
26.5 |
25 |
28 |
| GOverlast |
22 |
31.00 |
6.05 |
30.5 |
24 |
54 |
| VOverlast |
1 |
30.00 |
NA |
30.0 |
30 |
30 |
Ervaar je overlast
van … trillingen
(Code: VO5)
Total
| BOverlast |
13/44 (29.5%) |
| GOverlast |
27/44 (61.4%) |
| VOverlast |
4/44 (9.1%) |
By building
| Solo |
BOverlast |
4/13 (30.8%) |
| Solo |
GOverlast |
9/13 (69.2%) |
| Track |
BOverlast |
9/31 (29%) |
| Track |
GOverlast |
18/31 (58.1%) |
| Track |
VOverlast |
4/31 (12.9%) |
Plot
DEBUG plotting question: VO5 | variable: overl_trilling1 | non-NA
rows: 44
By gender
| Anders |
BOverlast |
1/1 (100%) |
| Man |
BOverlast |
6/17 (35.3%) |
| Man |
GOverlast |
11/17 (64.7%) |
| Vrouw |
BOverlast |
6/26 (23.1%) |
| Vrouw |
GOverlast |
16/26 (61.5%) |
| Vrouw |
VOverlast |
4/26 (15.4%) |
Age by
response
| BOverlast |
13 |
31.00 |
2.71 |
31.0 |
25 |
36 |
| GOverlast |
27 |
31.07 |
5.55 |
30.0 |
24 |
54 |
| VOverlast |
4 |
30.00 |
4.97 |
28.5 |
26 |
37 |
Ervaar je overlast
van … parkeerproblemen
(Code: VO6)
Total
| BOverlast |
17/44 (38.6%) |
| GAntwoord |
2/44 (4.5%) |
| GOverlast |
14/44 (31.8%) |
| VOverlast |
11/44 (25%) |
By building
| Solo |
BOverlast |
6/13 (46.2%) |
| Solo |
GAntwoord |
1/13 (7.7%) |
| Solo |
GOverlast |
4/13 (30.8%) |
| Solo |
VOverlast |
2/13 (15.4%) |
| Track |
BOverlast |
11/31 (35.5%) |
| Track |
GAntwoord |
1/31 (3.2%) |
| Track |
GOverlast |
10/31 (32.3%) |
| Track |
VOverlast |
9/31 (29%) |
Plot
DEBUG plotting question: VO6 | variable: overl_parkeer1 | non-NA
rows: 44
By gender
| Anders |
BOverlast |
1/1 (100%) |
| Man |
BOverlast |
10/17 (58.8%) |
| Man |
GOverlast |
4/17 (23.5%) |
| Man |
VOverlast |
3/17 (17.6%) |
| Vrouw |
BOverlast |
6/26 (23.1%) |
| Vrouw |
GAntwoord |
2/26 (7.7%) |
| Vrouw |
GOverlast |
10/26 (38.5%) |
| Vrouw |
VOverlast |
8/26 (30.8%) |
Age by
response
| BOverlast |
17 |
30.12 |
2.80 |
30.0 |
24 |
34 |
| GAntwoord |
2 |
32.50 |
7.78 |
32.5 |
27 |
38 |
| GOverlast |
14 |
29.93 |
3.02 |
29.5 |
26 |
35 |
| VOverlast |
11 |
33.27 |
7.55 |
31.0 |
26 |
54 |
Ervaar je overlast
van … te hard rijden
(Code: VO7)
Total
| BOverlast |
13/44 (29.5%) |
| GOverlast |
10/44 (22.7%) |
| VOverlast |
21/44 (47.7%) |
By building
| Solo |
BOverlast |
3/13 (23.1%) |
| Solo |
GOverlast |
3/13 (23.1%) |
| Solo |
VOverlast |
7/13 (53.8%) |
| Track |
BOverlast |
10/31 (32.3%) |
| Track |
GOverlast |
7/31 (22.6%) |
| Track |
VOverlast |
14/31 (45.2%) |
Plot
DEBUG plotting question: VO7 | variable: overl_hard_rijden1 | non-NA
rows: 44
By gender
| Anders |
VOverlast |
1/1 (100%) |
| Man |
BOverlast |
6/17 (35.3%) |
| Man |
GOverlast |
3/17 (17.6%) |
| Man |
VOverlast |
8/17 (47.1%) |
| Vrouw |
BOverlast |
7/26 (26.9%) |
| Vrouw |
GOverlast |
7/26 (26.9%) |
| Vrouw |
VOverlast |
12/26 (46.2%) |
Age by
response
| BOverlast |
13 |
33.15 |
7.48 |
32 |
25 |
54 |
| GOverlast |
10 |
29.70 |
3.09 |
30 |
24 |
34 |
| VOverlast |
21 |
30.19 |
2.42 |
30 |
26 |
36 |
Ervaar je overlast
van … dronken mensen op straat
(Code: VO8)
Total
| BOverlast |
2/44 (4.5%) |
| GOverlast |
41/44 (93.2%) |
| VOverlast |
1/44 (2.3%) |
By building
| Solo |
BOverlast |
2/13 (15.4%) |
| Solo |
GOverlast |
11/13 (84.6%) |
| Track |
GOverlast |
30/31 (96.8%) |
| Track |
VOverlast |
1/31 (3.2%) |
Plot
DEBUG plotting question: VO8 | variable: overl_dronken1 | non-NA
rows: 44
By gender
| Anders |
VOverlast |
1/1 (100%) |
| Man |
BOverlast |
1/17 (5.9%) |
| Man |
GOverlast |
16/17 (94.1%) |
| Vrouw |
BOverlast |
1/26 (3.8%) |
| Vrouw |
GOverlast |
25/26 (96.2%) |
Age by
response
| BOverlast |
2 |
32.50 |
2.12 |
32.5 |
31 |
34 |
| GOverlast |
41 |
30.88 |
4.89 |
30.0 |
24 |
54 |
| VOverlast |
1 |
31.00 |
NA |
31.0 |
31 |
31 |
Ervaar je overlast
van … verwarde mensen op straat
(Code: VO9)
By building
| Solo |
GOverlast |
13/13 (100%) |
| Track |
GOverlast |
31/31 (100%) |
Plot
DEBUG plotting question: VO9 | variable: overl_verward1 | non-NA
rows: 44
By gender
| Anders |
GOverlast |
1/1 (100%) |
| Man |
GOverlast |
17/17 (100%) |
| Vrouw |
GOverlast |
26/26 (100%) |
Age by
response
| GOverlast |
44 |
30.95 |
4.74 |
30 |
24 |
54 |
Ervaar je overlast
van … drugsgebruik
(Code: VO10)
Total
| BOverlast |
4/44 (9.1%) |
| GOverlast |
40/44 (90.9%) |
By building
| Solo |
BOverlast |
1/13 (7.7%) |
| Solo |
GOverlast |
12/13 (92.3%) |
| Track |
BOverlast |
3/31 (9.7%) |
| Track |
GOverlast |
28/31 (90.3%) |
Plot
DEBUG plotting question: VO10 | variable: overl_drugsgebr1 | non-NA
rows: 44
By gender
| Anders |
GOverlast |
1/1 (100%) |
| Man |
BOverlast |
1/17 (5.9%) |
| Man |
GOverlast |
16/17 (94.1%) |
| Vrouw |
BOverlast |
3/26 (11.5%) |
| Vrouw |
GOverlast |
23/26 (88.5%) |
Age by
response
| BOverlast |
4 |
29.5 |
1.73 |
30.0 |
27 |
31 |
| GOverlast |
40 |
31.1 |
4.93 |
30.5 |
24 |
54 |
Ervaar je overlast
van … drugshandel
(Code: VO11)
Total
| BOverlast |
7/44 (15.9%) |
| GOverlast |
35/44 (79.5%) |
| VOverlast |
2/44 (4.5%) |
By building
| Solo |
BOverlast |
1/13 (7.7%) |
| Solo |
GOverlast |
12/13 (92.3%) |
| Track |
BOverlast |
6/31 (19.4%) |
| Track |
GOverlast |
23/31 (74.2%) |
| Track |
VOverlast |
2/31 (6.5%) |
Plot
DEBUG plotting question: VO11 | variable: overl_drugshandel1 | non-NA
rows: 44
By gender
| Anders |
BOverlast |
1/1 (100%) |
| Man |
BOverlast |
1/17 (5.9%) |
| Man |
GOverlast |
15/17 (88.2%) |
| Man |
VOverlast |
1/17 (5.9%) |
| Vrouw |
BOverlast |
5/26 (19.2%) |
| Vrouw |
GOverlast |
20/26 (76.9%) |
| Vrouw |
VOverlast |
1/26 (3.8%) |
Age by
response
| BOverlast |
7 |
31.00 |
3.37 |
31.0 |
27 |
36 |
| GOverlast |
35 |
31.03 |
5.11 |
30.0 |
24 |
54 |
| VOverlast |
2 |
29.50 |
2.12 |
29.5 |
28 |
31 |
Ervaar je overlast
van … rondhangende jongeren
(Code: VO12)
Total
| BOverlast |
7/44 (15.9%) |
| GOverlast |
37/44 (84.1%) |
By building
| Solo |
BOverlast |
3/13 (23.1%) |
| Solo |
GOverlast |
10/13 (76.9%) |
| Track |
BOverlast |
4/31 (12.9%) |
| Track |
GOverlast |
27/31 (87.1%) |
Plot
DEBUG plotting question: VO12 | variable: overl_rond_jong1 | non-NA
rows: 44
By gender
| Anders |
GOverlast |
1/1 (100%) |
| Man |
BOverlast |
2/17 (11.8%) |
| Man |
GOverlast |
15/17 (88.2%) |
| Vrouw |
BOverlast |
5/26 (19.2%) |
| Vrouw |
GOverlast |
21/26 (80.8%) |
Age by
response
| BOverlast |
7 |
30.71 |
4.15 |
30 |
26 |
38 |
| GOverlast |
37 |
31.00 |
4.89 |
30 |
24 |
54 |
Gezondheid en
leefstijl
In this section, we are looking at 38 questions.
Hoe lang ben je?
(zonder schoenen)
(Code: GLS1)
Total
| 43 |
178.98 |
9.7 |
179 |
162 |
203 |
By building
| Solo |
12 |
180.50 |
9.83 |
182 |
162 |
197 |
| Track |
31 |
178.39 |
9.74 |
176 |
163 |
203 |
Plot
DEBUG numeric plot: GLS1 | variable: lengte | non-NA rows: 43
By gender
| Anders |
1 |
164.00 |
NA |
164 |
164 |
164 |
| Man |
17 |
186.47 |
8.95 |
186 |
162 |
203 |
| Vrouw |
25 |
174.48 |
6.40 |
175 |
163 |
191 |
Hoeveel weeg je?
(zonder kleren)
(Code: GLS2)
By building
| Solo |
12 |
73.75 |
11.58 |
71 |
55 |
90 |
| Track |
31 |
73.23 |
12.71 |
72 |
52 |
97 |
Plot
DEBUG numeric plot: GLS2 | variable: gewicht | non-NA rows: 43
By gender
| Anders |
1 |
52.00 |
NA |
52 |
52 |
52 |
| Man |
17 |
81.12 |
11.09 |
83 |
55 |
97 |
| Vrouw |
25 |
68.96 |
9.99 |
66 |
55 |
92 |
Hoe is over het
algemeen je gezondheid?
(Code: GLS3)
Total
| Goed |
19/43 (44.2%) |
| Neutraal |
5/43 (11.6%) |
| Slecht |
1/43 (2.3%) |
| ZGoed |
18/43 (41.9%) |
By building
| Solo |
Goed |
6/12 (50%) |
| Solo |
Neutraal |
1/12 (8.3%) |
| Solo |
ZGoed |
5/12 (41.7%) |
| Track |
Goed |
13/31 (41.9%) |
| Track |
Neutraal |
4/31 (12.9%) |
| Track |
Slecht |
1/31 (3.2%) |
| Track |
ZGoed |
13/31 (41.9%) |
Plot
DEBUG plotting question: GLS3 | variable: gezondheid | non-NA rows:
43
By gender
| Anders |
Neutraal |
1/1 (100%) |
| Man |
Goed |
11/17 (64.7%) |
| Man |
ZGoed |
6/17 (35.3%) |
| Vrouw |
Goed |
8/25 (32%) |
| Vrouw |
Neutraal |
4/25 (16%) |
| Vrouw |
Slecht |
1/25 (4%) |
| Vrouw |
ZGoed |
12/25 (48%) |
Age by
response
| Goed |
19 |
31.79 |
5.80 |
31.0 |
27 |
54 |
| Neutraal |
5 |
28.40 |
2.70 |
29.0 |
24 |
31 |
| Slecht |
1 |
26.00 |
NA |
26.0 |
26 |
26 |
| ZGoed |
18 |
30.83 |
3.79 |
30.5 |
25 |
38 |
Rook je?
(Code: GLS4)
Total
| Nee |
41/43 (95.3%) |
| Sigaretten |
2/43 (4.7%) |
By building
| Solo |
Nee |
11/12 (91.7%) |
| Solo |
Sigaretten |
1/12 (8.3%) |
| Track |
Nee |
30/31 (96.8%) |
| Track |
Sigaretten |
1/31 (3.2%) |
Plot
DEBUG plotting question: GLS4 | variable: roken | non-NA rows: 43
By gender
| Anders |
Nee |
1/1 (100%) |
| Man |
Nee |
16/17 (94.1%) |
| Man |
Sigaretten |
1/17 (5.9%) |
| Vrouw |
Nee |
24/25 (96%) |
| Vrouw |
Sigaretten |
1/25 (4%) |
Age by
response
| Nee |
41 |
31.02 |
4.80 |
30.0 |
24 |
54 |
| Sigaretten |
2 |
27.50 |
2.12 |
27.5 |
26 |
29 |
Rook je elke
dag?
(Code: GLS4.1)
By building
| Solo |
Nee |
1/1 (100%) |
| Track |
Nee |
1/1 (100%) |
Plot
DEBUG plotting question: GLS4.1 | variable: roken_elke_dag | non-NA
rows: 2
By gender
| Man |
Nee |
1/1 (100%) |
| Vrouw |
Nee |
1/1 (100%) |
Age by
response
| Nee |
2 |
27.5 |
2.12 |
27.5 |
26 |
29 |
Heb je in de
laatste 12 maanden weleens alcohol gedronken? (bijvoorbeeld bier, wijn,
sterke drank, mixdrankjes of cocktails)
(Code: GLS5)
Total
| Ja |
39/43 (90.7%) |
| Nee |
4/43 (9.3%) |
By building
| Solo |
Ja |
11/12 (91.7%) |
| Solo |
Nee |
1/12 (8.3%) |
| Track |
Ja |
28/31 (90.3%) |
| Track |
Nee |
3/31 (9.7%) |
Plot
DEBUG plotting question: GLS5 | variable: alcohol | non-NA rows: 43
By gender
| Anders |
Ja |
1/1 (100%) |
| Man |
Ja |
15/17 (88.2%) |
| Man |
Nee |
2/17 (11.8%) |
| Vrouw |
Ja |
23/25 (92%) |
| Vrouw |
Nee |
2/25 (8%) |
Age by
response
| Ja |
39 |
31.05 |
4.94 |
31.0 |
24 |
54 |
| Nee |
4 |
29.00 |
1.41 |
29.5 |
27 |
30 |
Op hoeveel dagen
van de week drink je gemiddeld genomen alcohol?
(Code: GLS5.1)
Total
| 1Dag |
12/39 (30.8%) |
| 2Dagen |
9/39 (23.1%) |
| 3Dagen |
4/39 (10.3%) |
| 4Dagen |
1/39 (2.6%) |
| M1Dag |
13/39 (33.3%) |
By building
| Solo |
1Dag |
3/11 (27.3%) |
| Solo |
2Dagen |
2/11 (18.2%) |
| Solo |
3Dagen |
1/11 (9.1%) |
| Solo |
4Dagen |
1/11 (9.1%) |
| Solo |
M1Dag |
4/11 (36.4%) |
| Track |
1Dag |
9/28 (32.1%) |
| Track |
2Dagen |
7/28 (25%) |
| Track |
3Dagen |
3/28 (10.7%) |
| Track |
M1Dag |
9/28 (32.1%) |
Plot
DEBUG plotting question: GLS5.1 | variable: alcohol_elke_dag | non-NA
rows: 39
By gender
| Anders |
M1Dag |
1/1 (100%) |
| Man |
1Dag |
4/15 (26.7%) |
| Man |
2Dagen |
5/15 (33.3%) |
| Man |
3Dagen |
2/15 (13.3%) |
| Man |
4Dagen |
1/15 (6.7%) |
| Man |
M1Dag |
3/15 (20%) |
| Vrouw |
1Dag |
8/23 (34.8%) |
| Vrouw |
2Dagen |
4/23 (17.4%) |
| Vrouw |
3Dagen |
2/23 (8.7%) |
| Vrouw |
M1Dag |
9/23 (39.1%) |
Age by
response
| 1Dag |
12 |
29.83 |
3.33 |
30.5 |
25 |
36 |
| 2Dagen |
9 |
30.67 |
2.65 |
30.0 |
26 |
34 |
| 3Dagen |
4 |
35.00 |
2.94 |
35.0 |
32 |
38 |
| 4Dagen |
1 |
29.00 |
NA |
29.0 |
29 |
29 |
| M1Dag |
13 |
31.38 |
7.29 |
30.0 |
24 |
54 |
Hoeveel glazen
drink je gemiddeld genomen per week?
(Code: GLS5.2)
Total
| 1Glas |
3/39 (7.7%) |
| 2Glazen |
8/39 (20.5%) |
| 3Glazen |
5/39 (12.8%) |
| 4Glazen |
4/39 (10.3%) |
| 6Glazen |
4/39 (10.3%) |
| 7-10Glazen |
5/39 (12.8%) |
| M1Glas |
10/39 (25.6%) |
By building
| Solo |
1Glas |
1/11 (9.1%) |
| Solo |
2Glazen |
1/11 (9.1%) |
| Solo |
3Glazen |
3/11 (27.3%) |
| Solo |
7-10Glazen |
3/11 (27.3%) |
| Solo |
M1Glas |
3/11 (27.3%) |
| Track |
1Glas |
2/28 (7.1%) |
| Track |
2Glazen |
7/28 (25%) |
| Track |
3Glazen |
2/28 (7.1%) |
| Track |
4Glazen |
4/28 (14.3%) |
| Track |
6Glazen |
4/28 (14.3%) |
| Track |
7-10Glazen |
2/28 (7.1%) |
| Track |
M1Glas |
7/28 (25%) |
Plot
DEBUG plotting question: GLS5.2 | variable: alcohol_glazen | non-NA
rows: 39
By gender
| Anders |
M1Glas |
1/1 (100%) |
| Man |
1Glas |
2/15 (13.3%) |
| Man |
2Glazen |
3/15 (20%) |
| Man |
3Glazen |
2/15 (13.3%) |
| Man |
4Glazen |
1/15 (6.7%) |
| Man |
6Glazen |
2/15 (13.3%) |
| Man |
7-10Glazen |
4/15 (26.7%) |
| Man |
M1Glas |
1/15 (6.7%) |
| Vrouw |
1Glas |
1/23 (4.3%) |
| Vrouw |
2Glazen |
5/23 (21.7%) |
| Vrouw |
3Glazen |
3/23 (13%) |
| Vrouw |
4Glazen |
3/23 (13%) |
| Vrouw |
6Glazen |
2/23 (8.7%) |
| Vrouw |
7-10Glazen |
1/23 (4.3%) |
| Vrouw |
M1Glas |
8/23 (34.8%) |
Age by
response
| 1Glas |
3 |
29.33 |
3.21 |
28 |
27 |
33 |
| 2Glazen |
8 |
28.88 |
3.68 |
28 |
25 |
36 |
| 3Glazen |
5 |
30.60 |
2.19 |
30 |
28 |
34 |
| 4Glazen |
4 |
29.00 |
2.94 |
29 |
26 |
32 |
| 6Glazen |
4 |
33.25 |
2.99 |
33 |
30 |
37 |
| 7-10Glazen |
5 |
32.40 |
3.71 |
33 |
29 |
38 |
| M1Glas |
10 |
32.80 |
7.90 |
31 |
24 |
54 |
Hoeveel dagen per
week … eet je ontbijt?
(Code: GLS6)
By building
| Solo |
12 |
6.17 |
1.99 |
7 |
0 |
7 |
| Track |
31 |
6.16 |
1.66 |
7 |
1 |
7 |
Plot
DEBUG numeric plot: GLS6 | variable: voeding_ontbijt | non-NA rows:
43
By gender
| Anders |
1 |
7.00 |
NA |
7 |
7 |
7 |
| Man |
17 |
6.29 |
1.31 |
7 |
3 |
7 |
| Vrouw |
25 |
6.04 |
2.01 |
7 |
0 |
7 |
Hoeveel dagen per
week … eet je groente?
(Code: GLS7)
By building
| Solo |
12 |
6.75 |
0.45 |
7 |
6 |
7 |
| Track |
31 |
6.55 |
0.85 |
7 |
4 |
7 |
Plot
DEBUG numeric plot: GLS7 | variable: voeding_groente | non-NA rows:
43
By gender
| Anders |
1 |
7.00 |
NA |
7 |
7 |
7 |
| Man |
17 |
6.47 |
0.80 |
7 |
5 |
7 |
| Vrouw |
25 |
6.68 |
0.75 |
7 |
4 |
7 |
Hoeveel dagen per
week … eet je fruit?
(Code: GLS8)
By building
| Solo |
12 |
5.83 |
1.59 |
6.5 |
2 |
7 |
| Track |
31 |
4.94 |
2.13 |
5.0 |
0 |
7 |
Plot
DEBUG numeric plot: GLS8 | variable: voeding_fruit | non-NA rows: 43
By gender
| Anders |
1 |
2.00 |
NA |
2 |
2 |
2 |
| Man |
17 |
5.00 |
1.94 |
5 |
0 |
7 |
| Vrouw |
25 |
5.44 |
2.02 |
6 |
1 |
7 |
Hoeveel dagen per
week … eet je vlees?
(Code: GLS9)
By building
| Solo |
12 |
2.17 |
2.21 |
2.0 |
0 |
6 |
| Track |
30 |
2.43 |
2.60 |
1.5 |
0 |
7 |
Plot
DEBUG numeric plot: GLS9 | variable: voeding_vlees | non-NA rows: 42
By gender
| Anders |
1 |
0.00 |
NA |
0 |
0 |
0 |
| Man |
16 |
3.25 |
2.21 |
3 |
0 |
7 |
| Vrouw |
25 |
1.88 |
2.52 |
1 |
0 |
7 |
Hoeveel dagen per
week … eet je vis?
(Code: GLS10)
By building
| Solo |
12 |
0.58 |
0.67 |
0.5 |
0 |
2 |
| Track |
30 |
0.67 |
0.88 |
0.5 |
0 |
4 |
Plot
DEBUG numeric plot: GLS10 | variable: voeding_vis | non-NA rows: 42
By gender
| Anders |
1 |
0.00 |
NA |
0 |
0 |
0 |
| Man |
16 |
0.69 |
0.70 |
1 |
0 |
2 |
| Vrouw |
25 |
0.64 |
0.91 |
0 |
0 |
4 |
Hoeveel dagen per
week … eet je zuivel?
(Code: GLS11)
By building
| Solo |
12 |
5.42 |
2.02 |
6.5 |
1 |
7 |
| Track |
29 |
4.48 |
2.71 |
5.0 |
0 |
7 |
Plot
DEBUG numeric plot: GLS11 | variable: voeding_zuivel | non-NA rows:
41
By gender
| Anders |
1 |
5.00 |
NA |
5 |
5 |
5 |
| Man |
15 |
4.87 |
2.67 |
6 |
0 |
7 |
| Vrouw |
25 |
4.68 |
2.56 |
5 |
0 |
7 |
Hoeveel dagen per
week … eet je een zelfgemaakte warme maaltijd?
(Code: GLS12)
By building
| Solo |
12 |
5.67 |
1.15 |
5.5 |
4 |
7 |
| Track |
30 |
5.70 |
1.56 |
6.0 |
1 |
7 |
Plot
DEBUG numeric plot: GLS12 | variable: voeding_zelfgemaakt | non-NA
rows: 42
By gender
| Anders |
1 |
7.00 |
NA |
7 |
7 |
7 |
| Man |
16 |
5.19 |
1.64 |
5 |
1 |
7 |
| Vrouw |
25 |
5.96 |
1.24 |
6 |
2 |
7 |
Hoeveel dagen per
week … eet je een kant-en-klaar maaltijd of diepvriesmaaltijd?
(Code: GLS13)
By building
| Solo |
12 |
0.58 |
0.79 |
0 |
0 |
2 |
| Track |
30 |
0.57 |
1.01 |
0 |
0 |
5 |
Plot
DEBUG numeric plot: GLS13 | variable: voeding_kant_en_klaar | non-NA
rows: 42
By gender
| Anders |
1 |
0.00 |
NA |
0.0 |
0 |
0 |
| Man |
16 |
0.81 |
1.28 |
0.5 |
0 |
5 |
| Vrouw |
25 |
0.44 |
0.65 |
0.0 |
0 |
2 |
Hoeveel dagen per
week … ga je lopend naar werk of school?
(Code: GLS14)
Total
| 0Dagen |
40/42 (95.2%) |
| 3Dagen |
2/42 (4.8%) |
By building
| Solo |
0Dagen |
12/12 (100%) |
| Track |
0Dagen |
28/30 (93.3%) |
| Track |
3Dagen |
2/30 (6.7%) |
Plot
DEBUG plotting question: GLS14 | variable: beweging_lopen_werk |
non-NA rows: 42
By gender
| Anders |
0Dagen |
1/1 (100%) |
| Man |
0Dagen |
16/16 (100%) |
| Vrouw |
0Dagen |
23/25 (92%) |
| Vrouw |
3Dagen |
2/25 (8%) |
Age by
response
| 0Dagen |
40 |
31.05 |
4.87 |
30.5 |
24 |
54 |
| 3Dagen |
2 |
28.50 |
2.12 |
28.5 |
27 |
30 |
Hoeveel dagen per
week … maak je een wandeling in je vrije tijd?
(Code: GLS15)
Total
| 0Dagen |
8/42 (19%) |
| 1Dag |
11/42 (26.2%) |
| 2Dagen |
8/42 (19%) |
| 3Dagen |
3/42 (7.1%) |
| 4Dagen |
5/42 (11.9%) |
| 5Dag |
4/42 (9.5%) |
| 6Dag |
2/42 (4.8%) |
| 7Dag |
1/42 (2.4%) |
By building
| Solo |
0Dagen |
3/12 (25%) |
| Solo |
1Dag |
3/12 (25%) |
| Solo |
2Dagen |
3/12 (25%) |
| Solo |
3Dagen |
1/12 (8.3%) |
| Solo |
5Dag |
1/12 (8.3%) |
| Solo |
7Dag |
1/12 (8.3%) |
| Track |
0Dagen |
5/30 (16.7%) |
| Track |
1Dag |
8/30 (26.7%) |
| Track |
2Dagen |
5/30 (16.7%) |
| Track |
3Dagen |
2/30 (6.7%) |
| Track |
4Dagen |
5/30 (16.7%) |
| Track |
5Dag |
3/30 (10%) |
| Track |
6Dag |
2/30 (6.7%) |
Plot
DEBUG plotting question: GLS15 | variable: beweging_lopen_vrij |
non-NA rows: 42
By gender
| Anders |
1Dag |
1/1 (100%) |
| Man |
0Dagen |
4/16 (25%) |
| Man |
1Dag |
4/16 (25%) |
| Man |
2Dagen |
2/16 (12.5%) |
| Man |
3Dagen |
1/16 (6.2%) |
| Man |
4Dagen |
2/16 (12.5%) |
| Man |
5Dag |
3/16 (18.8%) |
| Vrouw |
0Dagen |
4/25 (16%) |
| Vrouw |
1Dag |
6/25 (24%) |
| Vrouw |
2Dagen |
6/25 (24%) |
| Vrouw |
3Dagen |
2/25 (8%) |
| Vrouw |
4Dagen |
3/25 (12%) |
| Vrouw |
5Dag |
1/25 (4%) |
| Vrouw |
6Dag |
2/25 (8%) |
| Vrouw |
7Dag |
1/25 (4%) |
Age by
response
| 0Dagen |
8 |
30.25 |
2.60 |
30.5 |
26 |
34 |
| 1Dag |
11 |
32.45 |
7.57 |
31.0 |
27 |
54 |
| 2Dagen |
8 |
30.75 |
2.82 |
30.5 |
27 |
34 |
| 3Dagen |
3 |
29.67 |
3.51 |
30.0 |
26 |
33 |
| 4Dagen |
5 |
29.40 |
5.22 |
30.0 |
24 |
37 |
| 5Dag |
4 |
30.25 |
3.10 |
31.0 |
26 |
33 |
| 6Dag |
2 |
29.50 |
0.71 |
29.5 |
29 |
30 |
| 7Dag |
1 |
38.00 |
NA |
38.0 |
38 |
38 |
Hoeveel dagen per
week … ga je met de fiets naar werk of school?
(Code: GLS16)
Total
| 0Dagen |
9/42 (21.4%) |
| 1Dag |
1/42 (2.4%) |
| 2Dagen |
2/42 (4.8%) |
| 3Dagen |
10/42 (23.8%) |
| 4Dagen |
9/42 (21.4%) |
| 5Dagen |
11/42 (26.2%) |
By building
| Solo |
0Dagen |
4/12 (33.3%) |
| Solo |
2Dagen |
1/12 (8.3%) |
| Solo |
3Dagen |
3/12 (25%) |
| Solo |
4Dagen |
2/12 (16.7%) |
| Solo |
5Dagen |
2/12 (16.7%) |
| Track |
0Dagen |
5/30 (16.7%) |
| Track |
1Dag |
1/30 (3.3%) |
| Track |
2Dagen |
1/30 (3.3%) |
| Track |
3Dagen |
7/30 (23.3%) |
| Track |
4Dagen |
7/30 (23.3%) |
| Track |
5Dagen |
9/30 (30%) |
Plot
DEBUG plotting question: GLS16 | variable: beweging_fiets_werk |
non-NA rows: 42
By gender
| Anders |
3Dagen |
1/1 (100%) |
| Man |
0Dagen |
2/16 (12.5%) |
| Man |
1Dag |
1/16 (6.2%) |
| Man |
2Dagen |
2/16 (12.5%) |
| Man |
3Dagen |
4/16 (25%) |
| Man |
4Dagen |
2/16 (12.5%) |
| Man |
5Dagen |
5/16 (31.2%) |
| Vrouw |
0Dagen |
7/25 (28%) |
| Vrouw |
3Dagen |
5/25 (20%) |
| Vrouw |
4Dagen |
7/25 (28%) |
| Vrouw |
5Dagen |
6/25 (24%) |
Age by
response
| 0Dagen |
9 |
30.89 |
3.41 |
31 |
26 |
38 |
| 1Dag |
1 |
54.00 |
NA |
54 |
54 |
54 |
| 2Dagen |
2 |
29.00 |
5.66 |
29 |
25 |
33 |
| 3Dagen |
10 |
30.40 |
2.84 |
30 |
27 |
36 |
| 4Dagen |
9 |
30.44 |
3.84 |
30 |
24 |
37 |
| 5Dagen |
11 |
30.09 |
2.70 |
30 |
26 |
34 |
Hoeveel dagen per
week … fiets je in je vrije tijd?
(Code: GLS17)
Total
| 0Dagen |
2/42 (4.8%) |
| 1Dag |
6/42 (14.3%) |
| 2Dagen |
7/42 (16.7%) |
| 3Dagen |
7/42 (16.7%) |
| 4Dagen |
5/42 (11.9%) |
| 5Dagen |
3/42 (7.1%) |
| 6Dagen |
5/42 (11.9%) |
| 7Dagen |
7/42 (16.7%) |
By building
| Solo |
1Dag |
2/12 (16.7%) |
| Solo |
2Dagen |
2/12 (16.7%) |
| Solo |
3Dagen |
3/12 (25%) |
| Solo |
4Dagen |
2/12 (16.7%) |
| Solo |
5Dagen |
2/12 (16.7%) |
| Solo |
6Dagen |
1/12 (8.3%) |
| Track |
0Dagen |
2/30 (6.7%) |
| Track |
1Dag |
4/30 (13.3%) |
| Track |
2Dagen |
5/30 (16.7%) |
| Track |
3Dagen |
4/30 (13.3%) |
| Track |
4Dagen |
3/30 (10%) |
| Track |
5Dagen |
1/30 (3.3%) |
| Track |
6Dagen |
4/30 (13.3%) |
| Track |
7Dagen |
7/30 (23.3%) |
Plot
DEBUG plotting question: GLS17 | variable: beweging_fiets_vrij |
non-NA rows: 42
By gender
| Anders |
4Dagen |
1/1 (100%) |
| Man |
0Dagen |
1/16 (6.2%) |
| Man |
1Dag |
3/16 (18.8%) |
| Man |
2Dagen |
2/16 (12.5%) |
| Man |
3Dagen |
2/16 (12.5%) |
| Man |
4Dagen |
2/16 (12.5%) |
| Man |
5Dagen |
1/16 (6.2%) |
| Man |
6Dagen |
1/16 (6.2%) |
| Man |
7Dagen |
4/16 (25%) |
| Vrouw |
0Dagen |
1/25 (4%) |
| Vrouw |
1Dag |
3/25 (12%) |
| Vrouw |
2Dagen |
5/25 (20%) |
| Vrouw |
3Dagen |
5/25 (20%) |
| Vrouw |
4Dagen |
2/25 (8%) |
| Vrouw |
5Dagen |
2/25 (8%) |
| Vrouw |
6Dagen |
4/25 (16%) |
| Vrouw |
7Dagen |
3/25 (12%) |
Age by
response
| 0Dagen |
2 |
28.50 |
2.12 |
28.5 |
27 |
30 |
| 1Dag |
6 |
34.17 |
9.75 |
30.5 |
29 |
54 |
| 2Dagen |
7 |
32.29 |
2.50 |
32.0 |
29 |
36 |
| 3Dagen |
7 |
28.43 |
2.37 |
29.0 |
24 |
31 |
| 4Dagen |
5 |
30.40 |
2.19 |
31.0 |
27 |
33 |
| 5Dagen |
3 |
34.67 |
2.89 |
33.0 |
33 |
38 |
| 6Dagen |
5 |
29.00 |
2.74 |
29.0 |
26 |
33 |
| 7Dagen |
7 |
30.14 |
4.60 |
31.0 |
25 |
37 |
Hoeveel dagen per
week … doe je aan sport?
(Code: GLS18)
Total
| 0Dagen |
2/43 (4.7%) |
| 1Dag |
6/43 (14%) |
| 2Dagen |
9/43 (20.9%) |
| 3Dagen |
11/43 (25.6%) |
| 4Dagen |
5/43 (11.6%) |
| 5Dag |
5/43 (11.6%) |
| 6Dag |
3/43 (7%) |
| 7Dag |
2/43 (4.7%) |
By building
| Solo |
1Dag |
1/12 (8.3%) |
| Solo |
2Dagen |
3/12 (25%) |
| Solo |
3Dagen |
4/12 (33.3%) |
| Solo |
4Dagen |
1/12 (8.3%) |
| Solo |
5Dag |
2/12 (16.7%) |
| Solo |
6Dag |
1/12 (8.3%) |
| Track |
0Dagen |
2/31 (6.5%) |
| Track |
1Dag |
5/31 (16.1%) |
| Track |
2Dagen |
6/31 (19.4%) |
| Track |
3Dagen |
7/31 (22.6%) |
| Track |
4Dagen |
4/31 (12.9%) |
| Track |
5Dag |
3/31 (9.7%) |
| Track |
6Dag |
2/31 (6.5%) |
| Track |
7Dag |
2/31 (6.5%) |
Plot
DEBUG plotting question: GLS18 | variable: beweging_sport | non-NA
rows: 43
By gender
| Anders |
0Dagen |
1/1 (100%) |
| Man |
1Dag |
4/17 (23.5%) |
| Man |
2Dagen |
2/17 (11.8%) |
| Man |
3Dagen |
5/17 (29.4%) |
| Man |
4Dagen |
3/17 (17.6%) |
| Man |
6Dag |
2/17 (11.8%) |
| Man |
7Dag |
1/17 (5.9%) |
| Vrouw |
0Dagen |
1/25 (4%) |
| Vrouw |
1Dag |
2/25 (8%) |
| Vrouw |
2Dagen |
7/25 (28%) |
| Vrouw |
3Dagen |
6/25 (24%) |
| Vrouw |
4Dagen |
2/25 (8%) |
| Vrouw |
5Dag |
5/25 (20%) |
| Vrouw |
6Dag |
1/25 (4%) |
| Vrouw |
7Dag |
1/25 (4%) |
Age by
response
| 0Dagen |
2 |
29.00 |
2.83 |
29.0 |
27 |
31 |
| 1Dag |
6 |
30.67 |
1.63 |
30.5 |
29 |
33 |
| 2Dagen |
9 |
28.78 |
2.05 |
28.0 |
26 |
32 |
| 3Dagen |
11 |
33.27 |
7.68 |
33.0 |
24 |
54 |
| 4Dagen |
5 |
28.00 |
2.45 |
29.0 |
25 |
31 |
| 5Dag |
5 |
32.40 |
2.79 |
31.0 |
30 |
37 |
| 6Dag |
3 |
31.33 |
6.11 |
30.0 |
26 |
38 |
| 7Dag |
2 |
32.00 |
2.83 |
32.0 |
30 |
34 |
Maak je gebruik
van een auto?
(Code: GLS19)
Total
| Ja |
20/44 (45.5%) |
| Nee |
24/44 (54.5%) |
By building
| Solo |
Ja |
7/13 (53.8%) |
| Solo |
Nee |
6/13 (46.2%) |
| Track |
Ja |
13/31 (41.9%) |
| Track |
Nee |
18/31 (58.1%) |
Plot
DEBUG plotting question: GLS19 | variable: auto_gebruik | non-NA
rows: 44
By gender
| Anders |
Nee |
1/1 (100%) |
| Man |
Ja |
9/17 (52.9%) |
| Man |
Nee |
8/17 (47.1%) |
| Vrouw |
Ja |
11/26 (42.3%) |
| Vrouw |
Nee |
15/26 (57.7%) |
Age by
response
| Ja |
20 |
32.30 |
5.7 |
31.0 |
26 |
54 |
| Nee |
24 |
29.83 |
3.5 |
29.5 |
24 |
38 |
Van welke auto
maak je gebruik?
(Code: GLS19.1A)
Total (per
option)
| auto_gebruik_welke_ik_gebruik_hiervoor_mijn_eigen_auto_of_leaseauto |
11/63 (17.5%) |
| auto_gebruik_welke_ik_gebruik_hiervoor_mijn_eigen_auto_of_leaseauto_en_deel_deze_auto_met_bekenden_familie_vrienden_buren |
3/63 (4.8%) |
| auto_gebruik_welke_ik_leen_hiervoor_een_auto_van_bekenden_familie_vrienden_buren |
1/63 (1.6%) |
| auto_gebruik_welke_ik_maak_gebruik_van_deelmobiliteit_huurauto_s |
7/63 (11.1%) |
By building (per
option)
| Solo |
auto_gebruik_welke_ik_gebruik_hiervoor_mijn_eigen_auto_of_leaseauto |
3/17 (17.6%) |
| Solo |
auto_gebruik_welke_ik_gebruik_hiervoor_mijn_eigen_auto_of_leaseauto_en_deel_deze_auto_met_bekenden_familie_vrienden_buren |
1/17 (5.9%) |
| Solo |
auto_gebruik_welke_ik_leen_hiervoor_een_auto_van_bekenden_familie_vrienden_buren |
0/17 (0%) |
| Solo |
auto_gebruik_welke_ik_maak_gebruik_van_deelmobiliteit_huurauto_s |
4/17 (23.5%) |
| Track |
auto_gebruik_welke_ik_gebruik_hiervoor_mijn_eigen_auto_of_leaseauto |
8/46 (17.4%) |
| Track |
auto_gebruik_welke_ik_gebruik_hiervoor_mijn_eigen_auto_of_leaseauto_en_deel_deze_auto_met_bekenden_familie_vrienden_buren |
2/46 (4.3%) |
| Track |
auto_gebruik_welke_ik_leen_hiervoor_een_auto_van_bekenden_familie_vrienden_buren |
1/46 (2.2%) |
| Track |
auto_gebruik_welke_ik_maak_gebruik_van_deelmobiliteit_huurauto_s |
3/46 (6.5%) |
Plots
Combinations
(multi selection)
| auto_gebruik_welke_ik_gebruik_hiervoor_mijn_eigen_auto_of_leaseauto
+ auto_gebruik_welke_ik_maak_gebruik_van_deelmobiliteit_huurauto_s |
1 |
| auto_gebruik_welke_ik_leen_hiervoor_een_auto_van_bekenden_familie_vrienden_buren
+ auto_gebruik_welke_ik_maak_gebruik_van_deelmobiliteit_huurauto_s |
1 |
Hoeveel dagen per
week maak je gebruik van een auto?
(Code: GLS19.2)
Total
| 1Dag |
2/20 (10%) |
| 4Dagen |
4/20 (20%) |
| 6Dagen |
1/20 (5%) |
| 7Dagen |
2/20 (10%) |
| M1Dag |
11/20 (55%) |
By building
| Solo |
4Dagen |
1/7 (14.3%) |
| Solo |
6Dagen |
1/7 (14.3%) |
| Solo |
M1Dag |
5/7 (71.4%) |
| Track |
1Dag |
2/13 (15.4%) |
| Track |
4Dagen |
3/13 (23.1%) |
| Track |
7Dagen |
2/13 (15.4%) |
| Track |
M1Dag |
6/13 (46.2%) |
Plot
DEBUG plotting question: GLS19.2 | variable: auto_gebruik_week |
non-NA rows: 20
By gender
| Man |
4Dagen |
1/9 (11.1%) |
| Man |
6Dagen |
1/9 (11.1%) |
| Man |
7Dagen |
1/9 (11.1%) |
| Man |
M1Dag |
6/9 (66.7%) |
| Vrouw |
1Dag |
2/11 (18.2%) |
| Vrouw |
4Dagen |
3/11 (27.3%) |
| Vrouw |
7Dagen |
1/11 (9.1%) |
| Vrouw |
M1Dag |
5/11 (45.5%) |
Age by
response
| 1Dag |
2 |
33.50 |
4.95 |
33.5 |
30 |
37 |
| 4Dagen |
4 |
33.00 |
2.16 |
32.5 |
31 |
36 |
| 6Dagen |
1 |
31.00 |
NA |
31.0 |
31 |
31 |
| 7Dagen |
2 |
40.00 |
19.80 |
40.0 |
26 |
54 |
| M1Dag |
11 |
30.55 |
1.69 |
30.0 |
28 |
34 |
In welke mate heb
je in de afgelopen 2 weken last gehad van problemen met slapen?
(Code: GLS20)
Total
| Beetje |
19/42 (45.2%) |
| HeelVeel |
3/42 (7.1%) |
| Niet |
11/42 (26.2%) |
| Nogal |
4/42 (9.5%) |
| Veel |
5/42 (11.9%) |
By building
| Solo |
Beetje |
5/12 (41.7%) |
| Solo |
Niet |
5/12 (41.7%) |
| Solo |
Nogal |
2/12 (16.7%) |
| Track |
Beetje |
14/30 (46.7%) |
| Track |
HeelVeel |
3/30 (10%) |
| Track |
Niet |
6/30 (20%) |
| Track |
Nogal |
2/30 (6.7%) |
| Track |
Veel |
5/30 (16.7%) |
Plot
DEBUG plotting question: GLS20 | variable: insom | non-NA rows: 42
By gender
| Anders |
Beetje |
1/1 (100%) |
| Man |
Beetje |
7/16 (43.8%) |
| Man |
Niet |
5/16 (31.2%) |
| Man |
Nogal |
3/16 (18.8%) |
| Man |
Veel |
1/16 (6.2%) |
| Vrouw |
Beetje |
11/25 (44%) |
| Vrouw |
HeelVeel |
3/25 (12%) |
| Vrouw |
Niet |
6/25 (24%) |
| Vrouw |
Nogal |
1/25 (4%) |
| Vrouw |
Veel |
4/25 (16%) |
Age by
response
| Beetje |
19 |
32.37 |
5.97 |
31 |
27 |
54 |
| HeelVeel |
3 |
28.00 |
3.46 |
30 |
24 |
30 |
| Niet |
11 |
29.09 |
3.21 |
29 |
25 |
34 |
| Nogal |
4 |
32.25 |
2.99 |
32 |
29 |
36 |
| Veel |
5 |
30.20 |
2.86 |
30 |
26 |
34 |
In welke mate
heeft je slaapprobleem je in de afgelopen 2 weken belemmerd bij je
dagelijks functioneren?
(Code: GLS20.1)
Total
| Beetje |
15/31 (48.4%) |
| Niet |
7/31 (22.6%) |
| Nogal |
4/31 (12.9%) |
| Veel |
5/31 (16.1%) |
By building
| Solo |
Beetje |
3/7 (42.9%) |
| Solo |
Niet |
3/7 (42.9%) |
| Solo |
Veel |
1/7 (14.3%) |
| Track |
Beetje |
12/24 (50%) |
| Track |
Niet |
4/24 (16.7%) |
| Track |
Nogal |
4/24 (16.7%) |
| Track |
Veel |
4/24 (16.7%) |
Plot
DEBUG plotting question: GLS20.1 | variable: insom_belem | non-NA
rows: 31
By gender
| Anders |
Beetje |
1/1 (100%) |
| Man |
Beetje |
6/11 (54.5%) |
| Man |
Niet |
3/11 (27.3%) |
| Man |
Veel |
2/11 (18.2%) |
| Vrouw |
Beetje |
8/19 (42.1%) |
| Vrouw |
Niet |
4/19 (21.1%) |
| Vrouw |
Nogal |
4/19 (21.1%) |
| Vrouw |
Veel |
3/19 (15.8%) |
Age by
response
| Beetje |
15 |
32.93 |
6.27 |
31.0 |
27 |
54 |
| Niet |
7 |
31.14 |
3.76 |
30.0 |
27 |
38 |
| Nogal |
4 |
31.75 |
2.87 |
30.5 |
30 |
36 |
| Veel |
5 |
28.00 |
2.92 |
29.0 |
24 |
31 |
Heb je in de
laatste 4 weken last gehad van stress?
(Code: GLS21)
Total
| Beetje |
26/42 (61.9%) |
| HeelVeel |
1/42 (2.4%) |
| Niet |
6/42 (14.3%) |
| Veel |
9/42 (21.4%) |
By building
| Solo |
Beetje |
8/12 (66.7%) |
| Solo |
Niet |
2/12 (16.7%) |
| Solo |
Veel |
2/12 (16.7%) |
| Track |
Beetje |
18/30 (60%) |
| Track |
HeelVeel |
1/30 (3.3%) |
| Track |
Niet |
4/30 (13.3%) |
| Track |
Veel |
7/30 (23.3%) |
Plot
DEBUG plotting question: GLS21 | variable: stress4weken | non-NA
rows: 42
By gender
| Anders |
Beetje |
1/1 (100%) |
| Man |
Beetje |
11/17 (64.7%) |
| Man |
HeelVeel |
1/17 (5.9%) |
| Man |
Niet |
2/17 (11.8%) |
| Man |
Veel |
3/17 (17.6%) |
| Vrouw |
Beetje |
14/24 (58.3%) |
| Vrouw |
Niet |
4/24 (16.7%) |
| Vrouw |
Veel |
6/24 (25%) |
Age by
response
| Beetje |
26 |
31.58 |
5.63 |
30.5 |
26 |
54 |
| HeelVeel |
1 |
31.00 |
NA |
31.0 |
31 |
31 |
| Niet |
6 |
30.17 |
3.06 |
30.5 |
25 |
34 |
| Veel |
9 |
29.67 |
2.87 |
30.0 |
24 |
34 |
Op welke gebieden
ervaarde je deze stress?
(Code: GLS21.1A)
Total (per
option)
| stress_gebieden_werk |
30/63 (47.6%) |
| stress_gebieden_studie |
2/63 (3.2%) |
| stress_gebieden_relatie |
13/63 (20.6%) |
| stress_gebieden_familie_of_vrienden |
8/63 (12.7%) |
| stress_gebieden_opvoeding_kinderen |
0/63 (0%) |
| stress_gebieden_wonen |
6/63 (9.5%) |
| stress_gebieden_gezondheid |
10/63 (15.9%) |
| stress_gebieden_mantelzorg |
0/63 (0%) |
| stress_gebieden_geldzaken |
8/63 (12.7%) |
| stress_gebieden_sociale_media |
0/63 (0%) |
| stress_gebieden_anders |
0/63 (0%) |
By building (per
option)
| Solo |
stress_gebieden_werk |
9/17 (52.9%) |
| Solo |
stress_gebieden_studie |
2/17 (11.8%) |
| Solo |
stress_gebieden_relatie |
4/17 (23.5%) |
| Solo |
stress_gebieden_familie_of_vrienden |
3/17 (17.6%) |
| Solo |
stress_gebieden_opvoeding_kinderen |
0/17 (0%) |
| Solo |
stress_gebieden_wonen |
1/17 (5.9%) |
| Solo |
stress_gebieden_gezondheid |
1/17 (5.9%) |
| Solo |
stress_gebieden_mantelzorg |
0/17 (0%) |
| Solo |
stress_gebieden_geldzaken |
2/17 (11.8%) |
| Solo |
stress_gebieden_sociale_media |
0/17 (0%) |
| Solo |
stress_gebieden_anders |
0/17 (0%) |
| Track |
stress_gebieden_werk |
21/46 (45.7%) |
| Track |
stress_gebieden_studie |
0/46 (0%) |
| Track |
stress_gebieden_relatie |
9/46 (19.6%) |
| Track |
stress_gebieden_familie_of_vrienden |
5/46 (10.9%) |
| Track |
stress_gebieden_opvoeding_kinderen |
0/46 (0%) |
| Track |
stress_gebieden_wonen |
5/46 (10.9%) |
| Track |
stress_gebieden_gezondheid |
9/46 (19.6%) |
| Track |
stress_gebieden_mantelzorg |
0/46 (0%) |
| Track |
stress_gebieden_geldzaken |
6/46 (13%) |
| Track |
stress_gebieden_sociale_media |
0/46 (0%) |
| Track |
stress_gebieden_anders |
0/46 (0%) |
Plots
Combinations
(multi selection)
| stress_gebieden_werk +
stress_gebieden_familie_of_vrienden |
4 |
| stress_gebieden_werk + stress_gebieden_relatie |
3 |
| stress_gebieden_werk + stress_gebieden_gezondheid |
2 |
| stress_gebieden_werk + stress_gebieden_gezondheid +
stress_gebieden_geldzaken |
2 |
| stress_gebieden_werk + stress_gebieden_relatie +
stress_gebieden_familie_of_vrienden |
2 |
| stress_gebieden_werk + stress_gebieden_relatie +
stress_gebieden_geldzaken |
2 |
| stress_gebieden_werk + stress_gebieden_studie |
2 |
| stress_gebieden_werk + stress_gebieden_wonen +
stress_gebieden_gezondheid + stress_gebieden_geldzaken |
2 |
| stress_gebieden_relatie +
stress_gebieden_familie_of_vrienden + stress_gebieden_gezondheid |
1 |
| stress_gebieden_relatie +
stress_gebieden_gezondheid |
1 |
| stress_gebieden_werk + stress_gebieden_relatie +
stress_gebieden_familie_of_vrienden + stress_gebieden_wonen +
stress_gebieden_geldzaken |
1 |
| stress_gebieden_werk + stress_gebieden_relatie +
stress_gebieden_wonen |
1 |
| stress_gebieden_werk + stress_gebieden_relatie +
stress_gebieden_wonen + stress_gebieden_gezondheid |
1 |
Voelde je je
zenuwachtig?
(Code: GLS22)
Total
| Nooit |
6/41 (14.6%) |
| Soms |
19/41 (46.3%) |
| Vaak |
4/41 (9.8%) |
| Zelden |
12/41 (29.3%) |
By building
| Solo |
Nooit |
1/11 (9.1%) |
| Solo |
Soms |
4/11 (36.4%) |
| Solo |
Vaak |
1/11 (9.1%) |
| Solo |
Zelden |
5/11 (45.5%) |
| Track |
Nooit |
5/30 (16.7%) |
| Track |
Soms |
15/30 (50%) |
| Track |
Vaak |
3/30 (10%) |
| Track |
Zelden |
7/30 (23.3%) |
Plot
DEBUG plotting question: GLS22 | variable: welb_zenuwachtig | non-NA
rows: 41
By gender
| Anders |
Soms |
1/1 (100%) |
| Man |
Nooit |
3/16 (18.8%) |
| Man |
Soms |
6/16 (37.5%) |
| Man |
Vaak |
1/16 (6.2%) |
| Man |
Zelden |
6/16 (37.5%) |
| Vrouw |
Nooit |
3/24 (12.5%) |
| Vrouw |
Soms |
12/24 (50%) |
| Vrouw |
Vaak |
3/24 (12.5%) |
| Vrouw |
Zelden |
6/24 (25%) |
Age by
response
| Nooit |
6 |
34.00 |
10.16 |
31 |
25 |
54 |
| Soms |
19 |
30.79 |
3.75 |
31 |
24 |
38 |
| Vaak |
4 |
28.75 |
1.26 |
29 |
27 |
30 |
| Zelden |
12 |
30.25 |
2.60 |
30 |
26 |
34 |
Zat je zo erg in
de put dat niets je kon opvrolijken?
(Code: GLS23)
Total
| Nooit |
17/41 (41.5%) |
| Soms |
10/41 (24.4%) |
| Vaak |
1/41 (2.4%) |
| Zelden |
13/41 (31.7%) |
By building
| Solo |
Nooit |
4/11 (36.4%) |
| Solo |
Soms |
2/11 (18.2%) |
| Solo |
Zelden |
5/11 (45.5%) |
| Track |
Nooit |
13/30 (43.3%) |
| Track |
Soms |
8/30 (26.7%) |
| Track |
Vaak |
1/30 (3.3%) |
| Track |
Zelden |
8/30 (26.7%) |
Plot
DEBUG plotting question: GLS23 | variable: welb_put | non-NA rows: 41
By gender
| Anders |
Zelden |
1/1 (100%) |
| Man |
Nooit |
7/16 (43.8%) |
| Man |
Soms |
3/16 (18.8%) |
| Man |
Zelden |
6/16 (37.5%) |
| Vrouw |
Nooit |
10/24 (41.7%) |
| Vrouw |
Soms |
7/24 (29.2%) |
| Vrouw |
Vaak |
1/24 (4.2%) |
| Vrouw |
Zelden |
6/24 (25%) |
Age by
response
| Nooit |
17 |
29.76 |
2.54 |
30.0 |
25 |
34 |
| Soms |
10 |
33.10 |
3.41 |
33.5 |
27 |
38 |
| Vaak |
1 |
26.00 |
NA |
26.0 |
26 |
26 |
| Zelden |
13 |
31.08 |
7.23 |
30.0 |
24 |
54 |
Voelde je je kalm
en rustig?
(Code: GLS24)
Total
| Meestal |
16/41 (39%) |
| Soms |
9/41 (22%) |
| Vaak |
13/41 (31.7%) |
| Voortdurend |
3/41 (7.3%) |
By building
| Solo |
Meestal |
5/11 (45.5%) |
| Solo |
Soms |
1/11 (9.1%) |
| Solo |
Vaak |
4/11 (36.4%) |
| Solo |
Voortdurend |
1/11 (9.1%) |
| Track |
Meestal |
11/30 (36.7%) |
| Track |
Soms |
8/30 (26.7%) |
| Track |
Vaak |
9/30 (30%) |
| Track |
Voortdurend |
2/30 (6.7%) |
Plot
DEBUG plotting question: GLS24 | variable: welb_kalm | non-NA rows:
41
By gender
| Anders |
Vaak |
1/1 (100%) |
| Man |
Meestal |
7/16 (43.8%) |
| Man |
Soms |
4/16 (25%) |
| Man |
Vaak |
4/16 (25%) |
| Man |
Voortdurend |
1/16 (6.2%) |
| Vrouw |
Meestal |
9/24 (37.5%) |
| Vrouw |
Soms |
5/24 (20.8%) |
| Vrouw |
Vaak |
8/24 (33.3%) |
| Vrouw |
Voortdurend |
2/24 (8.3%) |
Age by
response
| Meestal |
16 |
31.25 |
6.71 |
30 |
26 |
54 |
| Soms |
9 |
30.00 |
3.61 |
30 |
24 |
36 |
| Vaak |
13 |
31.46 |
2.85 |
31 |
27 |
38 |
| Voortdurend |
3 |
29.33 |
4.04 |
30 |
25 |
33 |
Voelde je je
neerslachtig en somber?
(Code: GLS25)
Total
| Nooit |
5/40 (12.5%) |
| Soms |
19/40 (47.5%) |
| Vaak |
3/40 (7.5%) |
| Zelden |
13/40 (32.5%) |
By building
| Solo |
Nooit |
1/11 (9.1%) |
| Solo |
Soms |
4/11 (36.4%) |
| Solo |
Zelden |
6/11 (54.5%) |
| Track |
Nooit |
4/29 (13.8%) |
| Track |
Soms |
15/29 (51.7%) |
| Track |
Vaak |
3/29 (10.3%) |
| Track |
Zelden |
7/29 (24.1%) |
Plot
DEBUG plotting question: GLS25 | variable: welb_somber | non-NA rows:
40
By gender
| Anders |
Soms |
1/1 (100%) |
| Man |
Nooit |
2/15 (13.3%) |
| Man |
Soms |
6/15 (40%) |
| Man |
Vaak |
1/15 (6.7%) |
| Man |
Zelden |
6/15 (40%) |
| Vrouw |
Nooit |
3/24 (12.5%) |
| Vrouw |
Soms |
12/24 (50%) |
| Vrouw |
Vaak |
2/24 (8.3%) |
| Vrouw |
Zelden |
7/24 (29.2%) |
Age by
response
| Nooit |
5 |
30.60 |
3.44 |
32 |
25 |
34 |
| Soms |
19 |
31.32 |
3.04 |
31 |
27 |
38 |
| Vaak |
3 |
28.00 |
5.29 |
26 |
24 |
34 |
| Zelden |
13 |
31.31 |
7.19 |
30 |
26 |
54 |
Voelde je je
gelukkig?
(Code: GLS26)
Total
| Meestal |
14/40 (35%) |
| Soms |
10/40 (25%) |
| Vaak |
13/40 (32.5%) |
| Voortdurend |
3/40 (7.5%) |
By building
| Solo |
Meestal |
5/11 (45.5%) |
| Solo |
Soms |
1/11 (9.1%) |
| Solo |
Vaak |
4/11 (36.4%) |
| Solo |
Voortdurend |
1/11 (9.1%) |
| Track |
Meestal |
9/29 (31%) |
| Track |
Soms |
9/29 (31%) |
| Track |
Vaak |
9/29 (31%) |
| Track |
Voortdurend |
2/29 (6.9%) |
Plot
DEBUG plotting question: GLS26 | variable: welb_gelukkig | non-NA
rows: 40
By gender
| Anders |
Vaak |
1/1 (100%) |
| Man |
Meestal |
8/15 (53.3%) |
| Man |
Soms |
2/15 (13.3%) |
| Man |
Vaak |
5/15 (33.3%) |
| Vrouw |
Meestal |
6/24 (25%) |
| Vrouw |
Soms |
8/24 (33.3%) |
| Vrouw |
Vaak |
7/24 (29.2%) |
| Vrouw |
Voortdurend |
3/24 (12.5%) |
Age by
response
| Meestal |
14 |
29.36 |
2.56 |
30.0 |
25 |
34 |
| Soms |
10 |
30.80 |
4.24 |
30.5 |
24 |
37 |
| Vaak |
13 |
32.77 |
7.01 |
31.0 |
27 |
54 |
| Voortdurend |
3 |
31.33 |
1.53 |
31.0 |
30 |
33 |
Heb je één of meer
langdurige ziekten of aandoeningen?
(Code: GLS27)
Total
| Ja |
9/41 (22%) |
| Nee |
32/41 (78%) |
By building
| Solo |
Ja |
2/11 (18.2%) |
| Solo |
Nee |
9/11 (81.8%) |
| Track |
Ja |
7/30 (23.3%) |
| Track |
Nee |
23/30 (76.7%) |
Plot
DEBUG plotting question: GLS27 | variable: chron_lang_ziek | non-NA
rows: 41
By gender
| Anders |
Ja |
1/1 (100%) |
| Man |
Ja |
2/15 (13.3%) |
| Man |
Nee |
13/15 (86.7%) |
| Vrouw |
Ja |
6/25 (24%) |
| Vrouw |
Nee |
19/25 (76%) |
Age by
response
| Ja |
9 |
30.22 |
2.64 |
30.0 |
26 |
36 |
| Nee |
32 |
31.06 |
5.32 |
30.5 |
24 |
54 |
Ben je vanwege
problemen met je gezondheid beperkt in je dagelijks leven?
(Code: GLS28)
Total
| Ernstig |
2/41 (4.9%) |
| Ja |
8/41 (19.5%) |
| Nee |
21/41 (51.2%) |
| NVT |
10/41 (24.4%) |
By building
| Solo |
Ja |
2/11 (18.2%) |
| Solo |
Nee |
6/11 (54.5%) |
| Solo |
NVT |
3/11 (27.3%) |
| Track |
Ernstig |
2/30 (6.7%) |
| Track |
Ja |
6/30 (20%) |
| Track |
Nee |
15/30 (50%) |
| Track |
NVT |
7/30 (23.3%) |
Plot
DEBUG plotting question: GLS28 | variable: chron_beperkt | non-NA
rows: 41
By gender
| Anders |
Ernstig |
1/1 (100%) |
| Man |
Ja |
3/15 (20%) |
| Man |
Nee |
9/15 (60%) |
| Man |
NVT |
3/15 (20%) |
| Vrouw |
Ernstig |
1/25 (4%) |
| Vrouw |
Ja |
5/25 (20%) |
| Vrouw |
Nee |
12/25 (48%) |
| Vrouw |
NVT |
7/25 (28%) |
Age by
response
| Ernstig |
2 |
30.50 |
0.71 |
30.5 |
30 |
31 |
| Ja |
8 |
30.00 |
3.38 |
29.0 |
26 |
36 |
| Nee |
21 |
32.05 |
5.56 |
31.0 |
27 |
54 |
| NVT |
10 |
29.20 |
4.39 |
28.5 |
24 |
38 |
Maak je gebruik
van één of meerdere hulpmiddelen?
(Code: GLS29)
Total
| Nooit |
37/41 (90.2%) |
| Soms |
2/41 (4.9%) |
| Zelden |
2/41 (4.9%) |
By building
| Solo |
Nooit |
9/11 (81.8%) |
| Solo |
Soms |
1/11 (9.1%) |
| Solo |
Zelden |
1/11 (9.1%) |
| Track |
Nooit |
28/30 (93.3%) |
| Track |
Soms |
1/30 (3.3%) |
| Track |
Zelden |
1/30 (3.3%) |
Plot
DEBUG plotting question: GLS29 | variable: lp_hlp_op | non-NA rows:
41
By gender
| Anders |
Nooit |
1/1 (100%) |
| Man |
Nooit |
14/15 (93.3%) |
| Man |
Soms |
1/15 (6.7%) |
| Vrouw |
Nooit |
22/25 (88%) |
| Vrouw |
Soms |
1/25 (4%) |
| Vrouw |
Zelden |
2/25 (8%) |
Age by
response
| Nooit |
37 |
31.08 |
5.02 |
30.0 |
24 |
54 |
| Soms |
2 |
27.50 |
2.12 |
27.5 |
26 |
29 |
| Zelden |
2 |
30.50 |
0.71 |
30.5 |
30 |
31 |
Chronische
aandoeningen en langdurige ziekten
In this section, we are looking at 22 questions.
Diabetes mellitus
(suikerziekte)
(Code: CAZ1.2)
Plot
DEBUG plotting question: CAZ1.2 | variable: ziekte_diabetes | non-NA
rows: 0
(De gevolgen van)
een beroerte, hersenbloeding of herseninfarct.
(Code: CAZ1.3)
Plot
DEBUG plotting question: CAZ1.3 | variable: ziekte_beroerte | non-NA
rows: 0
(De gevolgen van)
een hartinfarct of een andere ernsitge hartaandoening (zoals hartfalen
of angina pectoris)
(Code: CAZ1.4)
Plot
DEBUG plotting question: CAZ1.4 | variable: ziekte_hartinfarct |
non-NA rows: 0
Een vorm van kanker
(kwaadaardige aandoening)
(Code: CAZ1.5)
Plot
DEBUG plotting question: CAZ1.5 | variable: ziekte_kanker | non-NA
rows: 0
Migraine of
regelmatige ernstige hoofdpijn
(Code: CAZ1.6)
Total
| NietVastgesteld |
1/3 (33.3%) |
| Vastgesteld |
2/3 (66.7%) |
By building
| Solo |
Vastgesteld |
1/1 (100%) |
| Track |
NietVastgesteld |
1/2 (50%) |
| Track |
Vastgesteld |
1/2 (50%) |
Plot
DEBUG plotting question: CAZ1.6 | variable: ziekte_migraine | non-NA
rows: 3
By gender
| Vrouw |
NietVastgesteld |
1/3 (33.3%) |
| Vrouw |
Vastgesteld |
2/3 (66.7%) |
Age by
response
| NietVastgesteld |
1 |
30 |
NA |
30 |
30 |
30 |
| Vastgesteld |
2 |
28 |
2.83 |
28 |
26 |
30 |
Hoge bloeddruk
(Code: CAZ1.7)
By building
| Track |
Vastgesteld |
1/1 (100%) |
Plot
DEBUG plotting question: CAZ1.7 | variable: ziekte_bloeddruk | non-NA
rows: 1
By gender
| Vrouw |
Vastgesteld |
1/1 (100%) |
Age by
response
| Vastgesteld |
1 |
26 |
NA |
26 |
26 |
26 |
Vernauwing van de
bloedvaten in de buik of benen (geen spataderen)
(Code: CAZ1.8)
Plot
DEBUG plotting question: CAZ1.8 | variable: ziekte_bloedvaten |
non-NA rows: 0
Astma, chronische
bronchitis, longemfyseem of CARA/COPD
(Code: CAZ1.9)
By building
| Track |
Vastgesteld |
2/2 (100%) |
Plot
DEBUG plotting question: CAZ1.9 | variable: ziekte_long | non-NA
rows: 2
By gender
| Vrouw |
Vastgesteld |
2/2 (100%) |
Age by
response
| Vastgesteld |
2 |
28 |
2.83 |
28 |
26 |
30 |
Ernstige of
hardnekkige darmstoornissen langer dan 3 maanden
(Code: CAZ1.10)
By building
| Track |
Vastgesteld |
3/3 (100%) |
Plot
DEBUG plotting question: CAZ1.10 | variable: ziekte_darm | non-NA
rows: 3
By gender
| Man |
Vastgesteld |
1/1 (100%) |
| Vrouw |
Vastgesteld |
2/2 (100%) |
Age by
response
| Vastgesteld |
3 |
29 |
2.65 |
30 |
26 |
31 |
Chronisch
eczeem
(Code: CAZ1.11)
Plot
DEBUG plotting question: CAZ1.11 | variable: ziekte_eczeem | non-NA
rows: 0
Duizeligheid met
vallen
(Code: CAZ1.12)
By building
| Track |
Vastgesteld |
1/1 (100%) |
Plot
DEBUG plotting question: CAZ1.12 | variable: ziekte_vallen | non-NA
rows: 1
By gender
| Vrouw |
Vastgesteld |
1/1 (100%) |
Age by
response
| Vastgesteld |
1 |
26 |
NA |
26 |
26 |
26 |
Gewrichtsslijtage
(artrose, slijtagereuma) van heupen of knieën
(Code: CAZ1.13)
By building
| Track |
Vastgesteld |
1/1 (100%) |
Plot
DEBUG plotting question: CAZ1.13 | variable: ziekte_gewr_slijtage |
non-NA rows: 1
By gender
| Vrouw |
Vastgesteld |
1/1 (100%) |
Age by
response
| Vastgesteld |
1 |
26 |
NA |
26 |
26 |
26 |
Chronische
gewrichtsontsteking (ontstekingsreuma, chronische reuma, reumatoïde
artritis)
(Code: CAZ1.14)
By building
| Track |
Vastgesteld |
1/1 (100%) |
Plot
DEBUG plotting question: CAZ1.14 | variable: ziekte_gewr_ontsteking |
non-NA rows: 1
By gender
| Vrouw |
Vastgesteld |
1/1 (100%) |
Age by
response
| Vastgesteld |
1 |
26 |
NA |
26 |
26 |
26 |
Ernstige of
hardnekkige aandoening van de rug
(Code: CAZ1.15)
Plot
DEBUG plotting question: CAZ1.15 | variable: ziekte_rug | non-NA
rows: 0
Andere ernstige of
hardnekkige aandoening van de nek of schouder, elleboog, pols of
hand
(Code: CAZ1.16)
By building
| Track |
Vastgesteld |
2/2 (100%) |
Plot
DEBUG plotting question: CAZ1.16 | variable: ziekte_nek | non-NA
rows: 2
By gender
| Vrouw |
Vastgesteld |
2/2 (100%) |
Age by
response
| Vastgesteld |
2 |
28 |
2.83 |
28 |
26 |
30 |
Depressiviteit
(Code: CAZ1.17)
By building
| Track |
Vastgesteld |
1/1 (100%) |
Plot
DEBUG plotting question: CAZ1.17 | variable: ziekte_depressie |
non-NA rows: 1
By gender
| Vrouw |
Vastgesteld |
1/1 (100%) |
Age by
response
| Vastgesteld |
1 |
30 |
NA |
30 |
30 |
30 |
Overspannenheid,
nervositeit, stress, burn-out
(Code: CAZ1.18)
By building
| Track |
Vastgesteld |
2/2 (100%) |
Plot
DEBUG plotting question: CAZ1.18 | variable: ziekte_stress | non-NA
rows: 2
By gender
| Vrouw |
Vastgesteld |
2/2 (100%) |
Age by
response
| Vastgesteld |
2 |
28 |
2.83 |
28 |
26 |
30 |
Angststoornis
(Code: CAZ1.19)
By building
| Track |
Vastgesteld |
1/1 (100%) |
Plot
DEBUG plotting question: CAZ1.19 | variable: ziekte_angst | non-NA
rows: 1
By gender
| Vrouw |
Vastgesteld |
1/1 (100%) |
Age by
response
| Vastgesteld |
1 |
26 |
NA |
26 |
26 |
26 |
Welke ziekte of
aandoening heb je nu of in de afgelopen 12 maanden gehad?
(Code: CAZ1A)
Total (per
option)
| lijst_ziekten_diabetes_mellitus_suikerziekte |
0/63 (0%) |
| lijst_ziekten_de_gevolgen_van_een_beroerte_hersenbloeding_of_herseninfarct |
0/63 (0%) |
| lijst_ziekten_de_gevolgen_van_een_hartinfarct_of_een_andere_ernsitge_hartaandoening_zoals_hartfalen_of_angina_pectoris |
0/63 (0%) |
| lijst_ziekten_een_vorm_van_kanker_kwaadaardige_aandoening |
0/63 (0%) |
| lijst_ziekten_migraine_of_regelmatige_ernstige_hoofdpijn |
3/63 (4.8%) |
| lijst_ziekten_hoge_bloeddruk |
1/63 (1.6%) |
| lijst_ziekten_vernauwing_van_de_bloedvaten_in_de_buik_of_benen_geen_spataderen |
0/63 (0%) |
| lijst_ziekten_astma_chronische_bronchitis_longemfyseem_of_cara_copd |
2/63 (3.2%) |
| lijst_ziekten_ernstige_of_hardnekkige_darmstoornissen_langer_dan3maanden |
3/63 (4.8%) |
| lijst_ziekten_chronisch_eczeem |
0/63 (0%) |
| lijst_ziekten_duizeligheid_met_vallen |
1/63 (1.6%) |
| lijst_ziekten_gewrichtsslijtage_artrose_slijtagereuma_van_heupen_of_knieen |
1/63 (1.6%) |
| lijst_ziekten_chronische_gewrichtsontsteking_ontstekingsreuma_chronische_reuma_reumatoide_artritis |
1/63 (1.6%) |
| lijst_ziekten_ernstige_of_hardnekkige_aandoening_van_de_rug |
0/63 (0%) |
| lijst_ziekten_andere_ernstige_of_hardnekkige_aandoening_van_de_nek_of_schouder_elleboog_pols_of_hand |
2/63 (3.2%) |
| lijst_ziekten_depressiviteit |
1/63 (1.6%) |
| lijst_ziekten_overspannenheid_nervositeit_stress_burn_out |
2/63 (3.2%) |
| lijst_ziekten_angststoornis |
1/63 (1.6%) |
| lijst_ziekten_anders |
4/63 (6.3%) |
By building (per
option)
| Solo |
lijst_ziekten_diabetes_mellitus_suikerziekte |
0/17 (0%) |
| Solo |
lijst_ziekten_de_gevolgen_van_een_beroerte_hersenbloeding_of_herseninfarct |
0/17 (0%) |
| Solo |
lijst_ziekten_de_gevolgen_van_een_hartinfarct_of_een_andere_ernsitge_hartaandoening_zoals_hartfalen_of_angina_pectoris |
0/17 (0%) |
| Solo |
lijst_ziekten_een_vorm_van_kanker_kwaadaardige_aandoening |
0/17 (0%) |
| Solo |
lijst_ziekten_migraine_of_regelmatige_ernstige_hoofdpijn |
1/17 (5.9%) |
| Solo |
lijst_ziekten_hoge_bloeddruk |
0/17 (0%) |
| Solo |
lijst_ziekten_vernauwing_van_de_bloedvaten_in_de_buik_of_benen_geen_spataderen |
0/17 (0%) |
| Solo |
lijst_ziekten_astma_chronische_bronchitis_longemfyseem_of_cara_copd |
0/17 (0%) |
| Solo |
lijst_ziekten_ernstige_of_hardnekkige_darmstoornissen_langer_dan3maanden |
0/17 (0%) |
| Solo |
lijst_ziekten_chronisch_eczeem |
0/17 (0%) |
| Solo |
lijst_ziekten_duizeligheid_met_vallen |
0/17 (0%) |
| Solo |
lijst_ziekten_gewrichtsslijtage_artrose_slijtagereuma_van_heupen_of_knieen |
0/17 (0%) |
| Solo |
lijst_ziekten_chronische_gewrichtsontsteking_ontstekingsreuma_chronische_reuma_reumatoide_artritis |
0/17 (0%) |
| Solo |
lijst_ziekten_ernstige_of_hardnekkige_aandoening_van_de_rug |
0/17 (0%) |
| Solo |
lijst_ziekten_andere_ernstige_of_hardnekkige_aandoening_van_de_nek_of_schouder_elleboog_pols_of_hand |
0/17 (0%) |
| Solo |
lijst_ziekten_depressiviteit |
0/17 (0%) |
| Solo |
lijst_ziekten_overspannenheid_nervositeit_stress_burn_out |
0/17 (0%) |
| Solo |
lijst_ziekten_angststoornis |
0/17 (0%) |
| Solo |
lijst_ziekten_anders |
1/17 (5.9%) |
| Track |
lijst_ziekten_diabetes_mellitus_suikerziekte |
0/46 (0%) |
| Track |
lijst_ziekten_de_gevolgen_van_een_beroerte_hersenbloeding_of_herseninfarct |
0/46 (0%) |
| Track |
lijst_ziekten_de_gevolgen_van_een_hartinfarct_of_een_andere_ernsitge_hartaandoening_zoals_hartfalen_of_angina_pectoris |
0/46 (0%) |
| Track |
lijst_ziekten_een_vorm_van_kanker_kwaadaardige_aandoening |
0/46 (0%) |
| Track |
lijst_ziekten_migraine_of_regelmatige_ernstige_hoofdpijn |
2/46 (4.3%) |
| Track |
lijst_ziekten_hoge_bloeddruk |
1/46 (2.2%) |
| Track |
lijst_ziekten_vernauwing_van_de_bloedvaten_in_de_buik_of_benen_geen_spataderen |
0/46 (0%) |
| Track |
lijst_ziekten_astma_chronische_bronchitis_longemfyseem_of_cara_copd |
2/46 (4.3%) |
| Track |
lijst_ziekten_ernstige_of_hardnekkige_darmstoornissen_langer_dan3maanden |
3/46 (6.5%) |
| Track |
lijst_ziekten_chronisch_eczeem |
0/46 (0%) |
| Track |
lijst_ziekten_duizeligheid_met_vallen |
1/46 (2.2%) |
| Track |
lijst_ziekten_gewrichtsslijtage_artrose_slijtagereuma_van_heupen_of_knieen |
1/46 (2.2%) |
| Track |
lijst_ziekten_chronische_gewrichtsontsteking_ontstekingsreuma_chronische_reuma_reumatoide_artritis |
1/46 (2.2%) |
| Track |
lijst_ziekten_ernstige_of_hardnekkige_aandoening_van_de_rug |
0/46 (0%) |
| Track |
lijst_ziekten_andere_ernstige_of_hardnekkige_aandoening_van_de_nek_of_schouder_elleboog_pols_of_hand |
2/46 (4.3%) |
| Track |
lijst_ziekten_depressiviteit |
1/46 (2.2%) |
| Track |
lijst_ziekten_overspannenheid_nervositeit_stress_burn_out |
2/46 (4.3%) |
| Track |
lijst_ziekten_angststoornis |
1/46 (2.2%) |
| Track |
lijst_ziekten_anders |
3/46 (6.5%) |
Plots
Combinations
(multi selection)
| lijst_ziekten_migraine_of_regelmatige_ernstige_hoofdpijn +
lijst_ziekten_astma_chronische_bronchitis_longemfyseem_of_cara_copd +
lijst_ziekten_andere_ernstige_of_hardnekkige_aandoening_van_de_nek_of_schouder_elleboog_pols_of_hand
+ lijst_ziekten_depressiviteit +
lijst_ziekten_overspannenheid_nervositeit_stress_burn_out |
1 |
| lijst_ziekten_migraine_of_regelmatige_ernstige_hoofdpijn +
lijst_ziekten_hoge_bloeddruk +
lijst_ziekten_astma_chronische_bronchitis_longemfyseem_of_cara_copd +
lijst_ziekten_ernstige_of_hardnekkige_darmstoornissen_langer_dan3maanden
+ lijst_ziekten_duizeligheid_met_vallen +
lijst_ziekten_gewrichtsslijtage_artrose_slijtagereuma_van_heupen_of_knieen
+
lijst_ziekten_chronische_gewrichtsontsteking_ontstekingsreuma_chronische_reuma_reumatoide_artritis
+
lijst_ziekten_andere_ernstige_of_hardnekkige_aandoening_van_de_nek_of_schouder_elleboog_pols_of_hand
+ lijst_ziekten_overspannenheid_nervositeit_stress_burn_out +
lijst_ziekten_angststoornis |
1 |
Heb je de
vragenlijst volledig ingevuld?
(Code: CAZ2A)
Total (per
option)
| vragen_volledig_ja_hierbij_verklaar_ik_dat_ik_de_vragenlijst_volledig_heb_ingevuld |
44/63 (69.8%) |
By building (per
option)
| Solo |
vragen_volledig_ja_hierbij_verklaar_ik_dat_ik_de_vragenlijst_volledig_heb_ingevuld |
13/17 (76.5%) |
| Track |
vragen_volledig_ja_hierbij_verklaar_ik_dat_ik_de_vragenlijst_volledig_heb_ingevuld |
31/46 (67.4%) |
Plots
Combinations
(multi selection)
No multi selection combinations found.
