Explanatory Factors in Data as List of Expressions
expl_fcts.RdCreate a list of defused expressions representing the names of all or a selection of explanatory factors or character vectors in a dataset.
Usage
expl_fcts(
.data,
...,
.named = FALSE,
.val = c("syms", "data_syms", "character")
)Arguments
- .data
a data frame, or a data frame extension (e.g. a
tibble).- ...
<
tidy-select> quoted name(s) of one or morefactorsorcharacter vectorsin.data, to be included in (or excluded from) the output.- .named
logical, whether to name the elements of the list. IfTRUE, unnamed inputs are automatically named withset_names(); defaultFALSE.- .val
the type of output required. The default
"syms"returns a list ofsymbols; the alternative"data_syms"returns a list ofsymbolsprefixed with the.datapronoun. The"character"option returns acharacter vector.
Value
A list of symbols representing the names of selected explanatory factors or character vectors in
.data; unless .val = "data_syms", in which case the symbols are prefixed with the .data
pronoun or .val = "character" whereupon the selected names are returned as a character vector instead.
Details
By default, expl_fcts() creates a list of symbols i.e.,
defused R expressions, representing the names of all or a selection of explanatory factors (or
character vectors) in .data, using syms() from package rlang. Alternatively,
if .val = "data_syms", a list of symbols prefixed with the .data pronoun is returned instead.
Finally, if .val = "character", expl_fcts() returns a character vector of the names of the explanatory factors
(or character vectors) in .data
Variables in .data may be selected for inclusion or exclusion using the ... argument and the
<tidy-select> syntax from package dplyr, including use of
“selection helpers”. If no ... arguments are supplied, all categorical variables in .data will
be included in the list.
A list of symbols returned by expl_fcts() may be “injected” into the ... arguments of
contingency_table(), xcontingency_table(),
binom_contingency() and other similar functions, using the
splice-operator !!!. If .val = "character", the functions all_of()
or any_of() should be used to wrap the resulting character vector of names instead of using
!!!. A list of symbols returned by expl_fcts() may also be used to provide a list argument with injection
support to lapply() (or purrr package map() functions), using the
injection-operator !! (see examples).
See also
!!, !!!, all_of,
any_of, set_names(), defused R expressions,
map() and symbol.
Other contingency_table:
binom_contingency(),
contingency_table()
Examples
(d <- list(
iv2 = list(g = c("a", "c", "e"), h = c("b", "d", "f")),
iv3 = list(i = c("a", "b", "c"), j = c("d", "e", "f")),
iv4 = list(k = c("a", "b"), l = c("c", "d"), m = c("e", "f"))
) |> add_grps(bernoulli_data(levels = 6), iv, .key = _))
#> ___________________________
#> Simulated Bernoulli Data: -
#>
#> # A tibble: 396 × 5
#> iv iv2 iv3 iv4 dv
#> <fct> <fct> <fct> <fct> <int>
#> 1 a g i k 0
#> 2 a g i k 0
#> 3 a g i k 0
#> 4 a g i k 0
#> 5 a g i k 1
#> 6 a g i k 0
#> 7 a g i k 0
#> 8 a g i k 0
#> 9 a g i k 1
#> 10 a g i k 0
#> # ℹ 386 more rows
d |> expl_fcts()
#> [[1]]
#> iv
#>
#> [[2]]
#> iv2
#>
#> [[3]]
#> iv3
#>
#> [[4]]
#> iv4
#>
d |> expl_fcts(.named = TRUE)
#> $iv
#> iv
#>
#> $iv2
#> iv2
#>
#> $iv3
#> iv3
#>
#> $iv4
#> iv4
#>
d |> expl_fcts(.val = "data_syms")
#> [[1]]
#> .data$iv
#>
#> [[2]]
#> .data$iv2
#>
#> [[3]]
#> .data$iv3
#>
#> [[4]]
#> .data$iv4
#>
d |> expl_fcts(.named = TRUE, .val = "data_syms")
#> $iv
#> .data$iv
#>
#> $iv2
#> .data$iv2
#>
#> $iv3
#> .data$iv3
#>
#> $iv4
#> .data$iv4
#>
d |> expl_fcts(.val = "character")
#> [1] "iv" "iv2" "iv3" "iv4"
d |> expl_fcts(.named = TRUE, .val = "character")
#> iv iv2 iv3 iv4
#> "iv" "iv2" "iv3" "iv4"
## Select or exclude factors
d |> expl_fcts(iv, iv3)
#> [[1]]
#> iv
#>
#> [[2]]
#> iv3
#>
d |> expl_fcts(!c(iv, iv3))
#> [[1]]
#> iv2
#>
#> [[2]]
#> iv4
#>
## Use {dplyr} selection helpers e.g., last_col(), num_range() and starts_with()
d |> expl_fcts(last_col(1L)) ## Offset of 1L used, since last column of d is dv
#> [[1]]
#> iv4
#>
d |> expl_fcts(!last_col())
#> [[1]]
#> iv
#>
#> [[2]]
#> iv2
#>
#> [[3]]
#> iv3
#>
#> [[4]]
#> iv4
#>
d |> expl_fcts(num_range("iv", 2:3))
#> [[1]]
#> iv2
#>
#> [[2]]
#> iv3
#>
d |> expl_fcts(!num_range("iv", 2:3))
#> [[1]]
#> iv
#>
#> [[2]]
#> iv4
#>
d |> expl_fcts(starts_with("iv"))
#> [[1]]
#> iv
#>
#> [[2]]
#> iv2
#>
#> [[3]]
#> iv3
#>
#> [[4]]
#> iv4
#>
## Negation of selection helper excludes all explanatory factors
d |> expl_fcts(!starts_with("iv"))
#> list()
## In following three examples, each triplet should give identical results
## Include all explanatory factors
d |> binom_contingency(dv)
#> _____________________________
#> Binomial Contingency Table: -
#>
#> # A tibble: 6 × 6
#> iv iv2 iv3 iv4 pn qn
#> * <fct> <fct> <fct> <fct> <int> <int>
#> 1 a g i k 28 38
#> 2 b h i k 27 39
#> 3 c g i l 19 47
#> 4 d h j l 23 43
#> 5 e g j m 7 59
#> 6 f h j m 3 63
d |> binom_contingency(dv, !!!expl_fcts(d))
#> _____________________________
#> Binomial Contingency Table: -
#>
#> # A tibble: 6 × 6
#> iv iv2 iv3 iv4 pn qn
#> * <fct> <fct> <fct> <fct> <int> <int>
#> 1 a g i k 28 38
#> 2 b h i k 27 39
#> 3 c g i l 19 47
#> 4 d h j l 23 43
#> 5 e g j m 7 59
#> 6 f h j m 3 63
d |> binom_contingency(dv, all_of(expl_fcts(d, .val = "character")))
#> _____________________________
#> Binomial Contingency Table: -
#>
#> # A tibble: 6 × 6
#> iv iv2 iv3 iv4 pn qn
#> * <fct> <fct> <fct> <fct> <int> <int>
#> 1 a g i k 28 38
#> 2 b h i k 27 39
#> 3 c g i l 19 47
#> 4 d h j l 23 43
#> 5 e g j m 7 59
#> 6 f h j m 3 63
## Include only iv and iv3
d |> binom_contingency(dv, iv, iv3)
#> _____________________________
#> Binomial Contingency Table: -
#>
#> # A tibble: 6 × 4
#> iv iv3 pn qn
#> * <fct> <fct> <int> <int>
#> 1 a i 28 38
#> 2 b i 27 39
#> 3 c i 19 47
#> 4 d j 23 43
#> 5 e j 7 59
#> 6 f j 3 63
d |> binom_contingency(dv, !!!expl_fcts(d, iv, iv3))
#> _____________________________
#> Binomial Contingency Table: -
#>
#> # A tibble: 6 × 4
#> iv iv3 pn qn
#> * <fct> <fct> <int> <int>
#> 1 a i 28 38
#> 2 b i 27 39
#> 3 c i 19 47
#> 4 d j 23 43
#> 5 e j 7 59
#> 6 f j 3 63
d |> binom_contingency(dv, all_of(expl_fcts(d, iv, iv3, .val = "character")))
#> _____________________________
#> Binomial Contingency Table: -
#>
#> # A tibble: 6 × 4
#> iv iv3 pn qn
#> * <fct> <fct> <int> <int>
#> 1 a i 28 38
#> 2 b i 27 39
#> 3 c i 19 47
#> 4 d j 23 43
#> 5 e j 7 59
#> 6 f j 3 63
## Exclude iv and iv3
d |> binom_contingency(dv, !c(iv, iv3))
#> _____________________________
#> Binomial Contingency Table: -
#>
#> # A tibble: 6 × 4
#> iv2 iv4 pn qn
#> * <fct> <fct> <int> <int>
#> 1 g k 28 38
#> 2 h k 27 39
#> 3 g l 19 47
#> 4 h l 23 43
#> 5 g m 7 59
#> 6 h m 3 63
d |> binom_contingency(dv, !!!expl_fcts(d, !c(iv, iv3)))
#> _____________________________
#> Binomial Contingency Table: -
#>
#> # A tibble: 6 × 4
#> iv2 iv4 pn qn
#> * <fct> <fct> <int> <int>
#> 1 g k 28 38
#> 2 h k 27 39
#> 3 g l 19 47
#> 4 h l 23 43
#> 5 g m 7 59
#> 6 h m 3 63
d |> binom_contingency(dv, all_of(expl_fcts(d, !c(iv, iv3), .val = "character")))
#> _____________________________
#> Binomial Contingency Table: -
#>
#> # A tibble: 6 × 4
#> iv2 iv4 pn qn
#> * <fct> <fct> <int> <int>
#> 1 g k 28 38
#> 2 h k 27 39
#> 3 g l 19 47
#> 4 h l 23 43
#> 5 g m 7 59
#> 6 h m 3 63
## Use with lapply, binom_contingency(), glm() and odds_ratio()
expl_fcts(d, .named = TRUE) |>
lapply(\(x) binom_contingency(d, dv, !!x))
#> $iv
#> _____________________________
#> Binomial Contingency Table: -
#>
#> # A tibble: 6 × 3
#> iv pn qn
#> * <fct> <int> <int>
#> 1 a 28 38
#> 2 b 27 39
#> 3 c 19 47
#> 4 d 23 43
#> 5 e 7 59
#> 6 f 3 63
#>
#> $iv2
#> _____________________________
#> Binomial Contingency Table: -
#>
#> # A tibble: 2 × 3
#> iv2 pn qn
#> * <fct> <int> <int>
#> 1 g 54 144
#> 2 h 53 145
#>
#> $iv3
#> _____________________________
#> Binomial Contingency Table: -
#>
#> # A tibble: 2 × 3
#> iv3 pn qn
#> * <fct> <int> <int>
#> 1 i 74 124
#> 2 j 33 165
#>
#> $iv4
#> _____________________________
#> Binomial Contingency Table: -
#>
#> # A tibble: 3 × 3
#> iv4 pn qn
#> * <fct> <int> <int>
#> 1 k 55 77
#> 2 l 42 90
#> 3 m 10 122
#>
expl_fcts(d, .named = TRUE) |>
lapply(\(x)
binom_contingency(d, dv, !!x) |>
glm(cbind(pn, qn) ~ ., binomial, data = _)
)
#> $iv
#>
#> Call: glm(formula = cbind(pn, qn) ~ ., family = binomial, data = binom_contingency(d,
#> dv, !!x))
#>
#> Coefficients:
#> (Intercept) ivb ivc ivd ive ivf
#> -0.30538 -0.06234 -0.60033 -0.32032 -1.82625 -2.73914
#>
#> Degrees of Freedom: 5 Total (i.e. Null); 0 Residual
#> Null Deviance: 49.2
#> Residual Deviance: 1.754e-14 AIC: 36.89
#>
#> $iv2
#>
#> Call: glm(formula = cbind(pn, qn) ~ ., family = binomial, data = binom_contingency(d,
#> dv, !!x))
#>
#> Coefficients:
#> (Intercept) iv2h
#> -0.98083 -0.02561
#>
#> Degrees of Freedom: 1 Total (i.e. Null); 0 Residual
#> Null Deviance: 0.01281
#> Residual Deviance: 4.441e-16 AIC: 15.01
#>
#> $iv3
#>
#> Call: glm(formula = cbind(pn, qn) ~ ., family = binomial, data = binom_contingency(d,
#> dv, !!x))
#>
#> Coefficients:
#> (Intercept) iv3j
#> -0.5162 -1.0932
#>
#> Degrees of Freedom: 1 Total (i.e. Null); 0 Residual
#> Null Deviance: 21.96
#> Residual Deviance: 6.595e-14 AIC: 14.83
#>
#> $iv4
#>
#> Call: glm(formula = cbind(pn, qn) ~ ., family = binomial, data = binom_contingency(d,
#> dv, !!x))
#>
#> Coefficients:
#> (Intercept) iv4l iv4m
#> -0.3365 -0.4257 -2.1650
#>
#> Degrees of Freedom: 2 Total (i.e. Null); 0 Residual
#> Null Deviance: 46.84
#> Residual Deviance: -6.706e-14 AIC: 20.59
#>
expl_fcts(d, .named = TRUE) |>
lapply(\(x)
binom_contingency(d, dv, !!x, .drop_zero = TRUE) |>
odds_ratio(.ind_var = !!x)
)
#> Waiting for profiling to be done...
#> Waiting for profiling to be done...
#> Waiting for profiling to be done...
#> Waiting for profiling to be done...
#> $iv
#> ____________________________
#> Estimates and Odds Ratios: -
#>
#> # A tibble: 6 × 7
#> parameter estimate se p_val odds_ratio ci[,"2.5%"] [,"97.5%"] sig
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <fct>
#> 1 (Intercept) -0.305 0.249 0.220 1 NA NA NS
#> 2 ivb -0.0623 0.353 0.860 0.940 0.469 1.88 NS
#> 3 ivc -0.600 0.369 0.103 0.549 0.263 1.12 NS
#> 4 ivd -0.320 0.359 0.372 0.726 0.357 1.46 NS
#> 5 ive -1.83 0.471 0.000106 0.161 0.0597 0.387 ***
#> 6 ivf -2.74 0.641 0.0000194 0.0646 0.0147 0.198 ***
#>
#> $iv2
#> ____________________________
#> Estimates and Odds Ratios: -
#>
#> # A tibble: 2 × 7
#> parameter estimate se p_val odds_ratio ci[,"2.5%"] [,"97.5%"] sig
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <fct>
#> 1 (Intercept) -0.981 0.160 0 1 NA NA ***
#> 2 iv2h -0.0256 0.226 0.910 0.975 0.625 1.52 NS
#>
#> $iv3
#> ____________________________
#> Estimates and Odds Ratios: -
#>
#> # A tibble: 2 × 7
#> parameter estimate se p_val odds_ratio ci[,"2.5%"] [,"97.5%"] sig
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <fct>
#> 1 (Intercept) -0.516 0.147 0.000441 1 NA NA ***
#> 2 iv3j -1.09 0.241 0.0000056 0.335 0.207 0.533 ***
#>
#> $iv4
#> ____________________________
#> Estimates and Odds Ratios: -
#>
#> # A tibble: 3 × 7
#> parameter estimate se p_val odds_ratio ci[,"2.5%"] [,"97.5%"] sig
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <fct>
#> 1 (Intercept) -0.336 0.177 0.0567 1 NA NA .
#> 2 iv4l -0.426 0.257 0.0978 0.653 0.393 1.08 .
#> 3 iv4m -2.16 0.373 0 0.115 0.0524 0.229 ***
#>
rm(d)