Create an fmt vector, the tabxplor cell

fmt vectors, of class tabxplor_fmt, powers tabxplor and tab tibbles. As a record, they stores all data necessary to calculate percentages, Chi2 metadata or confidence intervals, but also to format and color the table to help the user read it. You can access this data with vctrs::field, or change it with vctrs:field<-. Its per-cell fields are listed below. The other arguments are attributes, attached not to each value but to the whole vector, like scale, col_var, totcol or color. You can get them with attr and modify them with attr<-. Special functions listed below are made to facilitate programming with with tabxplor formatted numbers. taxplfmt vectors can use all standard operations, like +, -, sum(), or c(), using vctrs.

Usage

fmt(
  n = integer(),
  scale = "level_n",
  digits = rep(0L, length(n)),
  display = est_default_display(scale[1]),
  wn = rep(NA_real_, length(n)),
  pct = rep(NA_real_, length(n)),
  mean = rep(NA_real_, length(n)),
  diff = rep(NA_real_, length(n)),
  ratio = rep(NA_real_, length(n)),
  ctr = rep(NA_real_, length(n)),
  var = rep(NA_real_, length(n)),
  ci = rep(NA_real_, length(n)),
  ci_inf = rep(NA_real_, length(n)),
  ci_sup = rep(NA_real_, length(n)),
  pvalue = rep(NA_real_, length(n)),
  or = rep(NA_real_, length(n)),
  tot_n = rep(NA_real_, length(n)),
  n_eff = rep(NA_real_, length(n)),
  obs = rep(NA_real_, length(n)),
  gap_se = rep(NA_real_, length(n)),
  row_kind = rep("data", length(n)),
  in_tottab = rep(FALSE, length(n)),
  in_refrow = rep(FALSE, length(n)),
  in_totrow = NULL,
  comp_all = NA,
  ref = "",
  pct_type = "none",
  col_var = "",
  col_group = "",
  totcol = FALSE,
  refcol = FALSE,
  color = "",
  color_signif = "ignore",
  model_family = "",
  role = "",
  conf_level = NA_real_,
  degf = NA_real_,
  basis = "n",
  ci_method = "",
  ...
)

is_fmt(x)

Arguments

n

The underlying count, as an integer vector of length n(). It is used to calculate confidence intervals.

scale

What the column estimates, as a single string (an attribute, not a field): one key into the declared library of estimate scales. It says which field holds the estimate, what its null value is, whether the scale is additive or multiplicative, and which colour ladder it reads.

"level_n": counts
"level_pct": percentages (pct_type says of what)
"level_mean": means (from numeric variables)
"points": a difference between two percentages, in percentage points
"mean_diff": a difference between two means, in the outcome's own units
"raw_diff": a regression coefficient / marginal effect in the outcome's units
"pct_ratio", "mean_ratio": the ratio of two percentages / two means
"odds_ratio": a multiplicative effect (odds ratio, risk ratio, rate ratio)
"log_coef": a link-scale coefficient (a log-odds, a log-rate)
"mixed": what binding columns of unlike scales collapses to

digits

The number of digits, as an integer, or an integer vector the length of n.

display

The display type : the name of the field you want to show when printing the vector, as a single string or a character vector the length of n. Every accepted value is listed in Every display token below; a named layout or a {} template combining several (e.g. "\{pct\} (n=\{n\})") is also accepted — see tabxplor-display.

wn

The underlying weighted counts, as a double vector the length of n. It is used in certain operations on fmt, like means.

pct

The percentages, as a double vector the length of n. Calculate with tab_pct.

mean

The means, as a double vector the length of n.

diff

The differences (from totals or first cells), as a double vector the length of n. Used to set colors for means and row or col percentages. Built by tab.

ratio

The ratio to the reference (relative risk for percentages, mean ratio for means), as a double vector the length of n.

ctr

The contributions of cells to (sub)tables variances, as a double vector the length of n. Used to print colors when color = "contrib". The mean contribution of each (sub)table is written on total rows (then, colors don't print well without total rows). Built by tab. The cell's adjusted standardized residual is not a field of its own: it is recovered from pvalue and this field's sign, and readable with display = "resid" (see tab).

var

The cells variances, as a double vector the length of n. Used with scale = "level_mean" to calculate confidence intervals.

ci

The confidence interval half-width (margin of error), as a double vector the length of n. Kept for backward compatibility: it is stored as the symmetric bounds ci_inf/ci_sup and read back by get_ci().

ci_inf, ci_sup

The lower and upper bounds of the confidence interval, as double vectors the length of n. Built by tab.

pvalue

The per-cell significance p-value, as a double vector the length of n.

or

The odds ratio (for a 3+ level variable, the OR of each level versus the reference), as a double vector the length of n.

tot_n

The cell's own (unweighted) percentage base, as a double vector the length of n.

n_eff

The effective sample size used for this cell's confidence interval, p(1-p) / Var_design(p) (a mean: s^2 / Var_design(mean)): from survey::svyrecvar under a survey::svydesign, from the closed-form flat-design variance when the weighted basis is asked for (tab(design_effect = TRUE)), else NA (the CI falls back to the raw unweighted base). It records the base that was used: a finite value where the design or weights corrected it, NA where nothing did, and the raw count where a correction was asked for but this cell could not carry one. Populated for descriptive cells (a crosstab/mean cell, a tab_reg Obs_* column whose interval came from a closed form); a coefficient column, and any column whose interval came from a fit instead, carry none. A double vector the length of n. Non-displayed.

obs

The value this cell's estimate is COMPARED TO by the tab_reg colour measures "adjustment" / "between_groups", on the cell's own scale: the observed (crude) effect beside a model effect, or – under tab_vars with color = "between_groups" – the reference group's estimate. NA on cross-tables and wherever there is no counterpart (leaving those cells uncoloured). A double vector the length of n; displayable as display = "\{obs\}".

gap_se

The standard error of the GAP between this cell's estimate and obs, on the estimate's own test scale. Written by tab_reg where the two estimates are independent (tab_vars groups), so color = "between_groups" can honour color_signif; NA elsewhere. A double vector the length of n. Non-displayed.

row_kind

What kind of row the cell sits in — one of "data" (an ordinary body row), "total", and the synthetic display rows "n", "pct", "pvalue", "gof", "blank". A character vector the length of n. It supersedes the logical in_totrow field, kept as a soft-deprecated argument and read-only $in_totrow.

in_tottab

TRUE when the cell is part of a total table

in_refrow

TRUE when the cell is part of a reference row (cf. ref)

in_totrow

Use row_kind = "total".

comp_all

FALSE when the comparison level is the subtable/group, TRUE when it is the whole table

ref

The type of difference of the vector. Cf. tab.

pct_type

For a percentage column, what the percentage is a percentage OF, and hence which axis its reference lies on (as a single string): "row", "col", "all" (frequencies by subtable / group, i.e. by tab_vars), "all_tabs" (frequencies for the whole table), or "none" (counts, means, coefficients).

col_var

The name of the col_var used to calculate the vector

col_group

The sub-population this column's block belongs to: a level of a spread_vars variable (tab_spread), or a tab_reg tab_vars group. "" (the default) when the table was never spread. Together with col_var it identifies a column BLOCK: two blocks may show the same variable for two sub-populations, and exports head them on two lines.

totcol

TRUE when the vector is a total column

refcol

TRUE when the vector is a reference column

color

The colour measure, as a single string — how a cell's value is compared to colour it (significance is handled separately by color_signif):

"no": no colors are printed.
"diff" ("difference"): the cell's difference from the reference (a total, or the first cell when ref = "first") — percentage points for factors, a standardized difference for means.
"ratio": the ratio to the reference (relative risk for percentages, mean ratio).
"or" ("odds_ratio"): the odds ratio, for row/col percentages.
"contrib": the cell's contribution to the table's variance. Under color_signif = "guaranteed_effect" it switches to the absolute adjusted standardized residual — see tab.
"adjustment" / "between_groups": the two tab_reg measures, which compare a cell to another column rather than to a reference row. A hand-built column may carry them, provided it fills the obs field they score.

The value is validated and normalised: every accepted spelling — the discipline's acronyms included ("RD", "RR", "IRR", "RoM", "OR" and their lowercase twins) — is stored as its canonical measure name, and an unknown one is an error. The tabxplor 1.x combined strings "diff_ci" / "after_ci" still work but are superseded by the color + color_signif pair; here they resolve to their measure half only, so pass the significance policy through color_signif.

color_signif

How significance gates the color, as a single string ("ignore" / "grey_non_signif" / "guaranteed_effect"). See tab.

model_family

For regression tables (tab_reg): the column's model family ("binomial", "gaussian", "poisson", "multinomial", "ordinal"), as a single string. Empty ("") on cross-tables. Lets a table mix several outcomes with different families, each column keeping its own effect wording.

role

For regression tables (tab_reg): the column's role, "model" for a model-estimate column or "emp" for an empirical (crude) companion column. Empty ("") on cross-tables. Read by the colour legend to name each column's effect without matching its label.

conf_level

The confidence level this column's interval and thresholds were computed at, as a single number in (0, 1). NA (default) means "unknown" — the colour engine then falls back to options("tabxplor.conf_level"). Stored per COLUMN, because colours are resolved per column at print time and cannot see the table's conf_level argument.

degf

The degrees of freedom this column's interval is referred to. On a cross-table that is the survey design's #PSU - #strata, which matters below ~30 primary sampling units; on a regression it is the fitted model's own residual df (for an svyglm, degf + 1 - p), so a model column and its observed companion legitimately differ. NA (default) means "refer to the normal quantile".

basis

How this column's interval and significance were computed — "n" (the raw sample size), "weights" (the design effect of the weights), "design" (a full survey design), or "design_partial" (a design was given but its variance could not be computed). Default "n". A per-COLUMN fact, so a table states honestly what its numbers carry even after a pipeline drops the table's metadata; binding columns keeps the WEAKEST basis.

ci_method

Which interval ENGINE built this column's bounds — "wilson", "wald", "beta" (a cell proportion), "newcombe", "ac" (a difference of proportions), "katz" (a ratio of proportions), "welch", "student", "ols" (a difference of means), "robust", "quasipoisson", "poisson" (a ratio of means), "woolf", "wald_log", "profile"; "" (default) when the column carries no interval. Read back by the colour legend, so it always names the method the bounds were built with.

...

In fmt(), it exists only for the arguments retired in tabxplor 2.0.0: type is translated into scale + pct_type (see tabxplor-type), ci_type gets an error naming its replacement. In the accessor methods below, to add arguments in the future.

x

The object to test, to get a field in, or to modify.

Value

A vector of class tabxplor_fmt.

A logical vector.

Functions

is_fmt(): a test function for class fmt.

The fields of a cell

A fmt cell carries 21 fields. Many are NA when the quantity was not requested; read one with x$field or vctrs::field(), and see them all with vctrs::vec_data():

n — the unweighted count.
display — which field this cell shows (a bare name, or a {} template).
digits — how many decimals this cell prints.
wn — the weighted count.
pct — the percentage.
mean — the mean, on a numeric column variable.
diff — the difference from the reference cell (percentage points, or the outcome's own units).
ratio — the ratio to the reference cell (a relative risk, or a ratio of means).
ctr — the cell's contribution to the table's Chi-2.
var — the column's variance quantity – which one is given by its scale.
ci_inf — the lower bound of the confidence interval.
ci_sup — the upper bound of the confidence interval.
pvalue — the cell's own significance p-value, which the stars read.
or — the odds ratio against the ref2 level.
tot_n — the cell's own base — the count its percentage is computed on.
n_eff — the effective sample size its interval was computed on (weights or a survey design).
obs — tab_reg() only: the observed (crude) effect the modelled one is compared to.
gap_se — tab_reg() only: the standard error of the gap between the estimate and obs.
row_kind — what kind of row the cell sits in — see get_row_kind().
in_tottab — is the cell in a total table (logical).
in_refrow — is the cell in a reference row (logical).

Every display token

Generated from the package's own display table, so it cannot drift from what get_num() reads. Each of pct, n, wn, mean, diff, ratio, or, ctr, var, obs, pvalue shows the field of the same name, described above. The rest are composed or derived by the pipeline itself, and the last few are not meant to be typed:

est — the estimate, whatever this column estimates — an odds ratio, a risk difference, a coefficient, a percentage. The one token that means the same thing on every table.
base — the level the estimate sits on: the percentage, the mean or the count. On a plain percentage table it is the same number as est; beside a regression effect it is the adjusted prediction.
ci — the confidence interval of whatever the column compares, as [low;high].
moe — the margin of error — the same interval as ci, written as the half-width +/-x around the estimate. Void where the column compares a RATIO: a ratio's interval is symmetric on the LOG scale, so it has no half-width.
sd — the standard deviation, in the variable's own unit.
cv — the coefficient of variation — the standard deviation as a percentage of the mean.
resid — the adjusted standardized residual – whether the cell departs from independence. Derived from the p-value and the sign of ctr, so it is read-only.
coef — the estimate on the model's LINK scale — the coefficient a linear or log-link model fitted. The same number as est where the column is already additive, its logarithm where the column shows a ratio.
gap — how far adjustment moved the effect: the gap between the modelled estimate and its observed counterpart, on the estimate's own scale. What color = "adjustment" grades — readable in print and Excel, not only in an html tooltip.
gof — a model-fit statistic (N, R2, AIC, BIC, dispersion).
gof_warn — a model-fit statistic past the threshold its check is read against.
n_range — the unweighted base: one count, or a min-max range over the table.
blank — nothing: a cell masked by n_min.
rr — the legacy synonym of ratio, still accepted.
OR — the acronym spelling of or, still accepted.

Examples

library(dplyr)
#> 
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#> 
#>     filter, lag
#> The following objects are masked from ‘package:base’:
#> 
#>     intersect, setdiff, setequal, union

f <- fmt(n = c(7, 19, 2), pct = c(0.25, 0.679, 0.07),
         scale = "level_pct", pct_type = "row")
f
#> <fmt-row%[3]>
#> [1] 25% 68% 7% 

# To get the currently displayed field :
get_num(f)
#> [1] 0.250 0.679 0.070

# To modify the currently displayed field :
set_num(f, c(1, 0, 0))
#> <fmt-row%[3]>
#> [1] 100% 0%   0%  


# See all the underlying fields of a fmt vector (a data frame with a number of rows
#  equal to the length of the vector) :
vctrs::vec_data(f)
#>    n display digits wn   pct mean diff ratio ctr var ci_inf ci_sup pvalue or
#> 1  7     pct      0 NA 0.250   NA   NA    NA  NA  NA     NA     NA     NA NA
#> 2 19     pct      0 NA 0.679   NA   NA    NA  NA  NA     NA     NA     NA NA
#> 3  2     pct      0 NA 0.070   NA   NA    NA  NA  NA     NA     NA     NA NA
#>   tot_n n_eff obs gap_se row_kind in_tottab in_refrow
#> 1    NA    NA  NA     NA     data     FALSE     FALSE
#> 2    NA    NA  NA     NA     data     FALSE     FALSE
#> 3    NA    NA  NA     NA     data     FALSE     FALSE

# To get the numbers of digits :
vctrs::field(f, "digits")
#> [1] 0 0 0
f$digits
#> [1] 0 0 0

# To get the count :
vctrs::field(f, "n")
#> [1]  7 19  2
f$n
#> [1]  7 19  2

# To get the display :
vctrs::field(f, "display")
#> [1] "pct" "pct" "pct"
f$display
#> [1] "pct" "pct" "pct"

# To modify a field, you can use `dplyr::mutate` on the fmt vector,
# referring to the names of the columns of the underlying data.frame (`vctrs::vec_data`) :
vctrs::`field<-`(f, "pct", c(1, 0, 0))
#> <fmt-row%[3]>
#> [1] 100% 0%   0%  
mutate(f, pct = c(1, 0, 0))
#> <fmt-row%[3]>
#> [1] 100% 0%   0%  

# See all the attributes of a fmt vector :
attributes(f)
#> $names
#>  [1] "n"         "display"   "digits"    "wn"        "pct"       "mean"     
#>  [7] "diff"      "ratio"     "ctr"       "var"       "ci_inf"    "ci_sup"   
#> [13] "pvalue"    "or"        "tot_n"     "n_eff"     "obs"       "gap_se"   
#> [19] "row_kind"  "in_tottab" "in_refrow"
#> 
#> $scale
#> [1] "level_pct"
#> 
#> $comp_all
#> [1] NA
#> 
#> $ref
#> [1] ""
#> 
#> $pct_type
#> [1] "row"
#> 
#> $col_var
#> [1] ""
#> 
#> $col_group
#> [1] ""
#> 
#> $totcol
#> [1] FALSE
#> 
#> $refcol
#> [1] FALSE
#> 
#> $color
#> [1] ""
#> 
#> $color_signif
#> [1] "ignore"
#> 
#> $model_family
#> [1] ""
#> 
#> $role
#> [1] ""
#> 
#> $conf_level
#> [1] NA
#> 
#> $degf
#> [1] NA
#> 
#> $basis
#> [1] "n"
#> 
#> $ci_method
#> [1] ""
#> 
#> $class
#> [1] "tabxplor_fmt" "vctrs_rcrd"   "vctrs_vctr"  
#> 

# To modify the "pct_type" attribute of a fmt vector (what the percentage is a percentage OF) :
set_pct_type(f, "col")
#> <fmt-col%[3]>
#> [1] 25% 68% 7% 

# To modify the "color" attribute of a fmt vector :
set_color(f, "contrib")
#> <fmt-row%[3]>
#> [1] 25% 68% 7% 




tabs <- tab(starwars, sex, hair_color, gender, na = "drop", pct = "row",
            other_if_less_than = 5)

# To identify the total columns, and work with them :
is_totcol(tabs)
#> gender    sex  black  brown   none Others  Total 
#>  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE   TRUE 
tabs |> mutate(across(where(is_totcol), ~ "total column"))
#> # A tabxplor tab: 8 × 8
#> # Groups:         gender [3]
#>   gender    sex              black  brown   none Others Total          n
#>                             <row%> <row%> <row%> <row%>              <n>
#> 1 feminine  female             19%    31%    31%    19% total column  16
#> 2 feminine  Others              0%     0%   100%     0% total column   1
#> 3 feminine  Total feminine     18%    29%    35%    18% total column  17
#> 
#> 4 masculine male               15%    19%    49%    17% total column  59
#> 5 masculine none                0%     0%   100%     0% total column   2
#> 6 masculine Others              0%     0%     0%     0% total column   0
#> 7 masculine Total masculine    15%    18%    51%    16% total column  61
#> 
#> 8 Ensemble  Total Ensemble     15%    21%    47%    17% total column  78

# To identify the total rows, and work with them :
is_totrow(tabs)
#> [1] FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE
tabs |>
  mutate(across(
    where(is_fmt),
    ~ if_else(is_totrow(.), true = "into_total_row", false = "normal_cell")
  ))
#> # A tabxplor tab: 8 × 7
#> # Groups:         gender [3]
#>   gender    sex             black          brown          none      Others Total
#>                                                                                 
#> 1 feminine  female          normal_cell    normal_cell    normal_c… norma… norm…
#> 2 feminine  Others          normal_cell    normal_cell    normal_c… norma… norm…
#> 3 feminine  Total feminine  into_total_row into_total_row into_tot… into_… into…
#> 
#> 4 masculine male            normal_cell    normal_cell    normal_c… norma… norm…
#> 5 masculine none            normal_cell    normal_cell    normal_c… norma… norm…
#> 6 masculine Others          normal_cell    normal_cell    normal_c… norma… norm…
#> 7 masculine Total masculine into_total_row into_total_row into_tot… into_… into…
#> 
#> 8 Ensemble  Total Ensemble  into_total_row into_total_row into_tot… into_… into…

# To identify the total tables, and work with them :
tottabs <- is_tottab(tabs)
tabs |> tibble::add_column(tottabs) |>
  mutate(total = if_else(tottabs, "part of a total table", "normal cell"))
#> # A tabxplor tab: 8 × 9
#> # Groups:         gender [3]
#>   gender    sex              black  brown   none Others      Total tottabs total
#>                             <row%> <row%> <row%> <row%> <row% (n)> <lgl>        
#> 1 feminine  female             19%    31%    31%    19%  100% (16) FALSE   norm…
#> 2 feminine  Others              0%     0%   100%     0%  100% ( 1) FALSE   norm…
#> 3 feminine  Total feminine     18%    29%    35%    18%  100% (17) FALSE   norm…
#> 
#> 4 masculine male               15%    19%    49%    17%  100% (59) FALSE   norm…
#> 5 masculine none                0%     0%   100%     0%  100% ( 2) FALSE   norm…
#> 6 masculine Others              0%     0%     0%     0%    0% ( 0) FALSE   norm…
#> 7 masculine Total masculine    15%    18%    51%    16%  100% (61) FALSE   norm…
#> 
#> 8 Ensemble  Total Ensemble     15%    21%    47%    17%  100% (78) TRUE    part…

# To access the displayed numbers, as numeric vectors :
tabs |> mutate(across(where(is_fmt), get_num))
#> # A tabxplor tab: 8 × 7
#> # Groups:         gender [3]
#>   gender    sex             black brown  none Others Total
#>                             <dbl> <dbl> <dbl>  <dbl> <dbl>
#> 1 feminine  female          0.188 0.312 0.312  0.188     1
#> 2 feminine  Others          0     0     1      0         1
#> 3 feminine  Total feminine  0.176 0.294 0.353  0.176     1
#> 
#> 4 masculine male            0.153 0.186 0.492  0.169     1
#> 5 masculine none            0     0     1      0         1
#> 6 masculine Others          0     0     0      0         0
#> 7 masculine Total masculine 0.148 0.180 0.508  0.164     1
#> 
#> 8 Ensemble  Total Ensemble  0.154 0.205 0.474  0.167     1

# To access the displayed numbers, as character vectors (without colors) :
tabs |> mutate(across(where(is_fmt), format))
#> # A tabxplor tab: 8 × 7
#> # Groups:         gender [3]
#>   gender    sex             black brown none  Others Total
#>                                                           
#> 1 feminine  female          19%   31%   31%   19%    100% 
#> 2 feminine  Others          0%    0%    100%  0%     100% 
#> 3 feminine  Total feminine  18%   29%   35%   18%    100% 
#> 
#> 4 masculine male            15%   19%   49%   17%    100% 
#> 5 masculine none            0%    0%    100%  0%     100% 
#> 6 masculine Others          0%    0%    0%    0%     0%   
#> 7 masculine Total masculine 15%   18%   51%   16%    100% 
#> 
#> 8 Ensemble  Total Ensemble  15%   21%   47%   17%    100% 

# To access the (non-displayed) differences of the cells percentages from totals :
tabs |> mutate(across(where(is_fmt), ~ vctrs::field(., "diff")))
#> # A tabxplor tab: 8 × 7
#> # Groups:         gender [3]
#>   gender    sex                black    brown    none   Others Total
#>                                <dbl>    <dbl>   <dbl>    <dbl> <dbl>
#> 1 feminine  female           0.0110   0.0184  -0.0404  0.0110      0
#> 2 feminine  Others          -0.176   -0.294    0.647  -0.176       0
#> 3 feminine  Total feminine   0        0        0       0           0
#> 
#> 4 masculine male             0.00500  0.00611 -0.0167  0.00556     0
#> 5 masculine none            -0.148   -0.180    0.492  -0.164       0
#> 6 masculine Others          -0.148   -0.180   -0.508  -0.164      -1
#> 7 masculine Total masculine  0        0        0       0           0
#> 
#> 8 Ensemble  Total Ensemble   0        0        0       0           0


# To do more complex operations, like creating a new column with standard deviation and
# print it with 2 decimals, use `dplyr::mutate` on all the fmt columns of a table :

tab(forcats::gss_cat, race, c(age, tvhours), marital, digits = 1L, comp = "all",
    color = "auto") |>
  dplyr::mutate(dplyr::across( #Mutate over the whole table.
    c(age, tvhours),
    ~ dplyr::mutate(.,         #Mutate over each fmt vector's underlying data.frame.
                    var     = sqrt(var),
                    display = "var",
                    digits  = 2L) |>
      set_color("no"),
    .names = "{.col}_sd"
  ))
#> # A tabxplor tab: 25 × 6
#> # Groups:         marital [7]
#>    marital     race                          age       tvhours age_sd tvhours_sd
#>                                      <mean (cv)>   <mean (cv)> <mean> <mean-var>
#>  1 No answer   Other               34.0 (cv 25%) 2.0             8.49           
#>  2 No answer   Black               64.0                                         
#>  3 No answer   White               56.0 (cv 28%) 2.6 (cv  45%)  15.71       1.19
#>  4 No answer   Total No answer     52.4 (cv 32%) 2.6 (cv  44%)  16.51       1.13
#> 
#>  5 Never marr… Other               30.2 (cv 35%) 2.8 (cv  94%)  10.60       2.67
#>  6 Never marr… Black               34.5 (cv 35%) 4.2 (cv  82%)  12.14       3.39
#>  7 Never marr… White               34.4 (cv 41%) 2.8 (cv  93%)  14.29       2.56
#>  8 Never marr… Total Never married 33.9 (cv 40%) 3.1 (cv  92%)  13.47       2.86
#> 
#>  9 Separated   Other               42.5 (cv 30%) 3.3 (cv  99%)  12.97       3.26
#> 10 Separated   Black               46.2 (cv 29%) 5.1 (cv  93%)  13.36       4.73
#> 11 Separated   White               45.6 (cv 30%) 2.9 (cv  96%)  13.52       2.77
#> 12 Separated   Total Separated     45.3 (cv 30%) 3.5 (cv 101%)  13.43       3.60
#> 
#> 13 Divorced    Other               45.5 (cv 26%) 3.0 (cv  92%)  11.82       2.71
#> 14 Divorced    Black               51.0 (cv 25%) 4.3 (cv  88%)  12.67       3.74
#> 15 Divorced    White               51.6 (cv 26%) 2.9 (cv  85%)  13.22       2.43
#> 16 Divorced    Total Divorced      51.1 (cv 26%) 3.1 (cv  89%)  13.14       2.73
#> 
#> 17 Widowed     Other               64.5 (cv 23%) 4.2 (cv  67%)  14.84       2.79
#> 18 Widowed     Black               67.5 (cv 21%) 4.7 (cv  78%)  13.89       3.70
#> 19 Widowed     White               72.8 (cv 17%) 3.7 (cv  72%)  12.48       2.70
#> 20 Widowed     Total Widowed       71.7 (cv 18%) 3.9 (cv  74%)  13.00       2.90
#> 
#> 21 Married     Other               42.2 (cv 31%) 2.5 (cv  76%)  13.01       1.88
#> 22 Married     Black               46.4 (cv 29%) 3.8 (cv  81%)  13.40       3.06
#> 23 Married     White               49.7 (cv 31%) 2.6 (cv  78%)  15.24       1.98
#> 24 Married     Total Married       48.7 (cv 31%) 2.7 (cv  80%)  15.06       2.11
#> 
#> 25 Ensemble    Total Ensemble      47.2 (cv 37%) 3.0 (cv  87%)  17.29       2.59
#> # ratio (Total Ensemble): ÷2 ÷1.5 ÷1.2 ÷1.1 ×1.1 ×1.2 ×1.5 ×2

Create an `fmt` vector, the tabxplor cell