![[Superseded]](figures/lifecycle-superseded.svg)
Superseded (2.0.0) by tab(), the unified entry point: it accepts several row_vars /
col_vars and merges them into one table by default (output_list = TRUE gives the list shape
tab_many() returns).
tab_many() forwards everything to tab(), translating the five renamed arguments:
tab_many() | tab() |
chi2 = TRUE | test = TRUE |
totrow = FALSE | tot = "col" |
totcol = "no" | tot = "row" |
compact = TRUE | output_list = FALSE |
na_drop_all = c(a, b) | filter = !is.na(a) & !is.na(b) |
Everything else keeps its name and meaning — see tab().
Usage
tab_many(
data,
row_vars,
col_vars,
tab_vars,
wt,
...,
chi2,
totrow,
totcol,
compact,
na_drop_all,
filter
)Arguments
- data
A data frame, or a
survey::svydesign.- row_vars, col_vars, tab_vars, wt
The variable roles — see
tab(). Withdata, the only arguments that may be passed by position: everything else must be named, becausetab()'s argument order differs.- ...
Passed on to
tab().- chi2
![[Deprecated]](figures/lifecycle-deprecated.svg)
Use tab()'stest.- totrow, totcol
- Use
tab()'stot. A total row is always computed and exactly one total column is shown, so both are cosmetic;totcol = "each"and"all_col_vars"now give that same single total column instead of erroring. - compact
- Use
tab()'soutput_list(inverted). - na_drop_all
- <tidy-select>
Use
tab()'sfilter:na_drop_all = c(a, b)isfilter = !is.na(a) & !is.na(b). - filter
- A
dplyr::filterto apply to the data frame first — seetab(). Prefer filtering upstream.
Value
What tab() returns: a tabxplor_tab (a tabxplor_grouped_tab with
tab_vars), or a tabxplor_tabs list under output_list = TRUE / compact = FALSE.
Examples
# Make a summary table with many col_vars, showing only one specific level :
# \donttest{
library(dplyr)
first_lvs <- c("Married", "$25000 or more", "Strong republican", "Protestant")
data <- forcats::gss_cat |> mutate(across(
where(is.factor),
~ forcats::fct_relevel(., first_lvs[first_lvs %in% levels(.)])
))
tab_many(data, race, c(marital, rincome, partyid, relig, age, tvhours),
levels = "first", pct = "row", test = TRUE, color = "auto")
#> Warning: `tab_many()` was deprecated in tabxplor 2.0.0.
#> ℹ Please use `tab()` instead.
#> ℹ tab() accepts several row_vars / col_vars. It merges >=2 row_vars into one
#> table by default; pass output_list = TRUE for a list (tab_many()'s old
#> default).
#> | | Tests | marital | | rincome | | partyid | | relig | | age | | tvhours |
#> |:-----|:-----------------------|---------:|:-:|---------:|:-:|---------:|:-:|---------:|:-:|------------:|:-:|------------:|
#> | race | N | 21 483 | | 21 483 | | 21 483 | | 21 483 | | 21 407 | | 11 337 |
#> | | pvalue (Chi2, Welch F) | <0.01% | | <0.01% | | <0.01% | | <0.01% | | <0.01% | | <0.01% |
#> | | Cramér's V, eta2 | V = 0.16 | | V = 0.06 | | V = 0.15 | | V = 0.25 | | eta2 = 0.03 | | eta2 = 0.04 |
#>
#> # A tabxplor tab: 4 × 8
#> race n Married `$25000 or more` `Strong republican` Protestant
#> <n_range> <row%> <row%> <row%> <row%>
#> 1 Other 1 027- 1 959 48% 32% 4% 20%
#> 2 Black 1 700- 3 129 28% 28% 2% 73%
#> 3 White 8 610-16 395 51% 36% 13% 50%
#> 4 Total 11 337-21 483 47% 34% 11% 50%
#> # ℹ 2 more variables: age <mean (cv)>, tvhours <mean (cv)>
#> # marital, rincome, partyid, relig: difference (Total): -30 -15 -5 +5 +15 +30; bg ratio: ×2
#> # age, tvhours: ratio (Total): ÷4 ÷2 ÷1.15 ×1.15 ×2 ×4
# }
# Can be used with map and tribble to program several tables with different parameters
# all at once, in a readable way:
# \donttest{
library(purrr)
library(tibble)
pmap(
tribble(
~row_vars, ~col_vars , ~pct , ~filter , ~subtext ,
"race" , "marital" , "row", NULL , "Source: GSS 2000-2014",
"relig" , c("race", "age"), "row", "year %in% 2000:2010", "Source: GSS 2000-2010",
NA_character_, "race" , "no" , NULL , "Source: GSS 2000-2014",
),
.f = tab_many,
data = forcats::gss_cat, color = "auto", test = TRUE)
#> [[1]]
#> | | Tests | marital |
#> |:-----|:----------------|---------:|
#> | race | N | 21 483 |
#> | | pvalue (Chi2 !) | <0.01% |
#> | | Cramér's V | V = 0.15 |
#>
#> # A tabxplor tab: 4 × 8
#> race `No answer` `Never married` Separated Divorced Widowed Married
#> <row%> <row%> <row%> <row%> <row%> <row%>
#> 1 Other 0% 32% 6% 11% 4% 48%
#> 2 Black 0% 42% 6% 16% 8% 28%
#> 3 White 0% 21% 3% 16% 9% 51%
#> 4 Total 0% 25% 3% 16% 8% 47%
#> # ℹ 1 more variable: Total <row% (n)>
#> # difference (Total): -30 -15 -5 +5 +15 +30; bg ratio: ×2
#> # Source: GSS 2000-2014
#>
#> [[2]]
#> | | Tests | race | | age |
#> |:------|:-------------------------|---------:|:-:|------------:|
#> | relig | N | 16 971 | | 16 909 |
#> | | pvalue (Chi2 !, Welch F) | <0.01% | | <0.01% |
#> | | Cramér's V, eta2 | V = 0.26 | | eta2 = 0.04 |
#>
#> # A tabxplor tab: 16 × 6
#> relig Other Black White Total age
#> <row%> <row%> <row%> <row% (n_range)> <mean (cv)>
#> 1 No answer 12% 17% 71% 100% ( 70- 76) 47 (cv 32%)
#> 2 Don't know 29% 29% 43% 100% ( 7) 35 (cv 26%)
#> 3 Inter-nondenominational 2% 26% 72% 100% ( 103) 40 (cv 35%)
#> 4 Native american 67% 0% 33% 100% ( 15) 40 (cv 35%)
#> 5 Christian 12% 18% 70% 100% ( 433- 435) 39 (cv 32%)
#> 6 Orthodox-christian 1% 1% 98% 100% ( 80) 49 (cv 31%)
#> 7 Moslem/islam 41% 34% 24% 100% ( 82) 36 (cv 29%)
#> 8 Other eastern 21% 8% 71% 100% ( 24) 47 (cv 31%)
#> 9 Hinduism 84% 2% 14% 100% ( 48- 49) 38 (cv 34%)
#> 10 Buddhism 54% 7% 39% 100% ( 115) 44 (cv 38%)
#> 11 Other 11% 10% 79% 100% ( 172) 41 (cv 35%)
#> 12 None 9% 11% 81% 100% ( 2 607- 2 614) 41 (cv 38%)
#> 13 Jewish 2% 2% 96% 100% ( 319- 320) 52 (cv 35%)
#> 14 Catholic 18% 4% 78% 100% ( 4 055- 4 074) 46 (cv 37%)
#> 15 Protestant 3% 21% 76% 100% ( 8 779- 8 805) 49 (cv 35%)
#> 16 Total 9% 14% 77% 100% (16 909-16 971) 47 (cv 37%)
#> # race: difference (Total): -30 -15 -5 +5 +15 +30; bg ratio: ×2
#> # age: ratio (Total): ÷4 ÷2 ÷1.15 ×1.15 ×2 ×4
#> # Source: GSS 2000-2010
#>
#> [[3]]
#> # A tabxplor tab: 2 × 5
#> no_row_var Other Black White Total
#> <n> <n> <n> <n>
#> 1 no_row_var 1 959 3 129 16 395 21 483
#> 2 Total 1 959 3 129 16 395 21 483
#> # Source: GSS 2000-2014
#>
# }