The goal of htcrosstabs is to easily create printable crosstabs from raw data frames.
You can install the development version of htcrosstabs like so:
devtools::install_github("HealthTree/htcrosstabs")And you can load it like so:
library(htcrosstabs)The fast way to create crosstabs is to pass your data frame into
crosstab_stacked(), telling it if you have a cohort column. Then pass
the object into kbl(); not the kableExtra one, the one that comes
with htcrosstabs. It works exactly the same way, but applies some
automatic formatting like adding auto-generated footnotes and row
groupings.
new_crosstab<- crosstab_stacked(
iris,
cohort_col_name="Species"
)
kbl(new_crosstab)A simple crosstab can be created from a one-column data frame.
sports<-sports_by_age[, "sport", drop=FALSE]
head(sports, 5)
#> sport#> 1 basketball#> 2 baseball#> 3 baseball#> 4 football#> 5 tennisnew_crosstab<- crosstab(sports)A crosstab can group data into cohorts by providing a two column data frame, one for the data and one for the cohorts. Simply provide the name of the cohort column when creating it.
head(sports_by_age, 5)
#> sport age#> 1 basketball child#> 2 baseball child#> 3 baseball child#> 4 football child#> 5 tennis childnew_crosstab<- crosstab(sports_by_age, cohort_col_name="age")Crosstabs can hold multiple data types: categorical, numeric, Likert-like, and multi-response. The data type is automatically determined when you create the crosstab.
Categorical crosstabs are the default data type. Character values, logical values, and factors will all be counted as categorical.
head(sports_by_age, 5) # Categorical#> sport age#> 1 basketball child#> 2 baseball child#> 3 baseball child#> 4 football child#> 5 tennis childnew_crosstab<- crosstab(sports_by_age, "age")Numeric crosstabs are the default when the data is numbers.
head(length_by_species, 5)
#> petal length species#> 1 1.4 setosa#> 2 1.4 setosa#> 3 1.3 setosa#> 4 1.5 setosa#> 5 1.4 setosanew_crosstab<- crosstab(length_by_species, "species")Likert crosstabs are created when categorical data is provided, along with a named vector to map values to numbers.
head(licorice_by_region, 5)
#> opinion region#> 1 neither west#> 2 dislikes west#> 3 dislikes west#> 4 likes west#> 5 likes westopinion_map<- c("likes"=1, "neither"=0, "dislikes"=-1)
new_crosstab<- crosstab(licorice_by_region, "region", var_map=opinion_map)Multi-response crosstabs are created by providing a list-column allowing
multiple answers per row. You can nest columns into the proper format
with nest_multi_col().
head(allergies_by_school, 5)
#> allergies school#> 1 1, 2 Brighton High#> 2 Brighton High#> 3 Brighton High#> 4 2, 1, 3 Brighton High#> 5 2, 1, 4, 3 Brighton Highnew_crosstab<- crosstab(allergies_by_school, "school")You can check the data type and whether it’s grouped or not with
is.crosstab.*() functions.
new_crosstab<- crosstab(length_by_species, "species")
is.crosstab.grouped(new_crosstab)
#> [1] TRUE
is.crosstab.categorical(new_crosstab)
#> [1] FALSE
is.crosstab.numeric(new_crosstab)
#> [1] TRUE
is.crosstab.likert(new_crosstab)
#> [1] FALSE
is.crosstab.multi(new_crosstab)
#> [1] FALSEYou can also, if needed, cast one data type to another using the
as.crosstab.*() functions. The details on how the conversion is done
can be found in the man pages by typing ?cast_crosstab.
new_crosstab<- crosstab(licorice_by_region, "region", opinion_map)
is.crosstab.likert(new_crosstab)
#> [1] TRUEnew_crosstab<- as.crosstab.num(new_crosstab)
is.crosstab.likert(new_crosstab)
#> [1] FALSEYou can see the data from the crosstabs by using data_table().
Ungrouped data has a dummy cohort column with the name of the final
output column.
new_crosstab<- crosstab(sports)
data_table(new_crosstab) |> head(5)
#> <Categorical Crosstab Data Object>#> # Variable name: sport#> sport default_cohort#> 1 basketball All#> 2 baseball All#> 3 baseball All#> 4 football All#> 5 tennis AllYou can extract useful values with the get_*() functions. You can get
values like the mean, standard deviation, median, iqr, percentages,
counts, etc. A full list is available at the bottom of this page, or in
the man pages with ?get_values.
new_crosstab<- crosstab(length_by_species, "species")
get_mean(new_crosstab)
#> species mean#> 1 All 3.8#> 2 setosa 1.5#> 3 versicolor 4.3#> 4 virginica 5.6
get_complete_total(new_crosstab)
#> species comp_total#> 1 All 150 / 150#> 2 setosa 50 / 50#> 3 versicolor 50 / 50#> 4 virginica 50 / 50There is also a function join_val() which lets you quickly and
automatically join different variable tables.
new_crosstab<- crosstab(length_by_species, "species")
join_val(
get_complete(new_crosstab),
get_mean(new_crosstab),
get_sd(new_crosstab)
)
#> species complete mean sd#> 1 All 150 3.8 1.8#> 2 setosa 50 1.5 0.2#> 3 versicolor 50 4.3 0.5#> 4 virginica 50 5.6 0.6new_crosstab<- crosstab(sports_by_age, "age")
join_val(
get_complete(new_crosstab),
get_count(new_crosstab),
get_proportion(new_crosstab)
)
#> age complete sport count prop#> 1 All 400 basketball 77 0.19#> 2 All 400 baseball 81 0.20#> 3 All 400 football 60 0.15#> 4 All 400 tennis 102 0.26#> 5 All 400 golf 80 0.20#> 6 child 100 basketball 21 0.21#> 7 child 100 baseball 33 0.33#> 8 child 100 football 15 0.15#> 9 child 100 tennis 23 0.23#> 10 child 100 golf 8 0.08#> 11 teen 100 basketball 22 0.22#> 12 teen 100 baseball 12 0.12#> 13 teen 100 football 21 0.21#> 14 teen 100 tennis 27 0.27#> 15 teen 100 golf 18 0.18#> 16 adult 100 basketball 24 0.24#> 17 adult 100 baseball 22 0.22#> 18 adult 100 football 9 0.09#> 19 adult 100 tennis 20 0.20#> 20 adult 100 golf 25 0.25#> 21 senior 100 basketball 10 0.10#> 22 senior 100 baseball 14 0.14#> 23 senior 100 football 15 0.15#> 24 senior 100 tennis 32 0.32#> 25 senior 100 golf 29 0.29You can also do statistical analysis quickly, like an ANOVA, chi-square,
or Rao-Scott corrected chi-square test with get_*_p_value() functions.
Post-hocs can be done with the get_*_posthoc() functions. Post-hocs
return a symmetric data frame/matrix of pairwise p-values, which are by
default corrected using the Benjamini-Hochberg correction. You can
change the correction method with method = ..., or you can disable
correction completely with p.adj = FALSE.
new_crosstab<- crosstab(length_by_species, "species")
get_anova_p_value(new_crosstab)
#> [1] 2.856777e-91
get_tukey_posthoc(new_crosstab)
#> setosa versicolor virginica#> setosa 1e+00 3e-15 3e-15#> versicolor 3e-15 1e+00 3e-15#> virginica 3e-15 3e-15 1e+00You can add rows with pre-formatted data by using the add_*_row()
functions. A full list of functions can be found at the bottom of this
page, or in the man pages with ?add_formatted_rows. You can also
extract the rows separate from the crosstab object with the
get_*_row() functions.
The order of the rows and columns is determined by the factor levels of the input data frame.
crosstab(length_by_species, "species") |> add_complete_total_row() |> add_mean_sd_row() |> add_med_q1_q3_row()
#> <Crosstab object>#> # Contains 1 table, grouped_by species#> ===============================#> Table: petal length [Numeric]#> ===============================#> Description All setosa versicolor virginica#> 1 Complete / Total 150 / 150 50 / 50 50 / 50 50 / 50#> 2 Mean ± SD 3.8 ± 1.8 1.5 ± 0.2 4.3 ± 0.5 5.6 ± 0.6#> 3 Med (Q1, Q3) 4.3 (1.6, 5.1) 1.5 (1.4, 1.6) 4.3 (4, 4.6) 5.6 (5.1, 5.9)
crosstab(sports_by_age, "age") |> add_complete_total_row() |> add_count_rows()
#> <Crosstab object>#> # Contains 1 table, grouped_by age#> ============================#> Table: sport [Categorical]#> ============================#> Description All child teen adult senior#> 1 Complete / Total 400 / 400 100 / 100 100 / 100 100 / 100 100 / 100#> 2 basketball 77 21 22 24 10#> 3 baseball 81 33 12 22 14#> 4 football 60 15 21 9 15#> 5 tennis 102 23 27 20 32#> 6 golf 80 8 18 25 29Certain types of data have common types of rows you usually want, so we
have preset templates you can use with add_*_table().
satisfaction_map<- c("very satisfied"=3, "somewhat satisfied"=2, "not satisfied"=1)
crosstab(satisfaction_by_company, "company", satisfaction_map) |>
add_likert_table()
#> <Crosstab object>#> # Contains 1 table, grouped_by company#> ==============================#> Table: satisfaction [Likert]#> ==============================#> Description All Apple Amazon Google Microsoft#> 1 Complete / Total 400 / 400 100 / 100 100 / 100 100 / 100 100 / 100#> 2 Mean ± SD 2.1 ± 0.8 2.1 ± 0.9 1.9 ± 0.8 2.6 ± 0.5 1.9 ± 0.8#> 3 very satisfied 155 (39%) 43 (43%) 23 (23%) 62 (62%) 27 (27%)#> 4 somewhat satisfied 141 (35%) 25 (25%) 39 (39%) 38 (38%) 39 (39%)#> 5 not satisfied 104 (26%) 32 (32%) 38 (38%) 0 (0%) 34 (34%)#> #> ANOVA Results:#> Description All Apple Amazon Google Microsoft#> 6 Dif. Apple - - NS (0.064) < 0.001 NS (0.314)#> 7 Dif. Amazon - - - < 0.001 NS (0.87)#> 8 Dif. Google - - - - < 0.001#> 9 Overall < 0.001 - - - -#> #> Pearson Chi-Square Results:#> Description All Apple Amazon Google Microsoft#> 10 Dif. Apple - - 0.012 < 0.001 0.04#> 11 Dif. Amazon - - - < 0.001 NS (0.763)#> 12 Dif. Google - - - - < 0.001#> 13 Overall < 0.001 - - - -
crosstab(allergies_by_school, "school") |> add_categorical_table()
#> <Crosstab object>#> # Contains 1 table, grouped_by school#> ===================================#> Table: allergies [Multi-Response]#> ===================================#> Description All Brighton High Cottonwood High Olympus High#> 1 Complete / Total 204 / 400 51 / 100 51 / 100 50 / 100#> 2 gluten 141 (69%) 38 (75%) 36 (71%) 32 (64%)#> 3 nuts 123 (60%) 34 (67%) 36 (71%) 24 (48%)#> 4 eggs 68 (33%) 24 (47%) 9 (18%) 17 (34%)#> 5 milk 75 (37%) 17 (33%) 22 (43%) 26 (52%)#> #> Alta High#> 1 52 / 100#> 2 35 (67%)#> 3 29 (56%)#> 4 18 (35%)#> 5 10 (19%)#> #> Rao-Scott Chi-Square Results:#> Description All Brighton High Cottonwood High Olympus High#> 6 Dif. Brighton High - - 0.007 0.029#> 7 Dif. Cottonwood High - - - 0.018#> 8 Dif. Olympus High - - - -#> 9 Overall < 0.001 - - -#> #> Alta High#> 6 NS (0.573)#> 7 0.007#> 8 0.007#> 9 -You can also choose add_default_table() and it will decide
automatically based on the data you passed in.
crosstab(length_by_species, "species") |> add_default_table()
#> <Crosstab object>#> # Contains 1 table, grouped_by species#> ===============================#> Table: petal length [Numeric]#> ===============================#> Description All setosa versicolor virginica#> 1 Complete / Total 150 / 150 50 / 50 50 / 50 50 / 50#> 2 Mean ± SD 3.8 ± 1.8 1.5 ± 0.2 4.3 ± 0.5 5.6 ± 0.6#> 3 Med (Q1, Q3) 4.3 (1.6, 5.1) 1.5 (1.4, 1.6) 4.3 (4, 4.6) 5.6 (5.1, 5.9)#> #> ANOVA Results:#> Description All setosa versicolor virginica#> 4 Dif. setosa - - < 0.001 < 0.001#> 5 Dif. versicolor - - - < 0.001#> 6 Overall < 0.001 - - -
crosstab(sports_by_age, "age") |> add_default_table()
#> <Crosstab object>#> # Contains 1 table, grouped_by age#> ============================#> Table: sport [Categorical]#> ============================#> Description All child teen adult senior#> 1 Complete / Total 400 / 400 100 / 100 100 / 100 100 / 100 100 / 100#> 2 basketball 77 (19%) 21 (21%) 22 (22%) 24 (24%) 10 (10%)#> 3 baseball 81 (20%) 33 (33%) 12 (12%) 22 (22%) 14 (14%)#> 4 football 60 (15%) 15 (15%) 21 (21%) 9 (9%) 15 (15%)#> 5 tennis 102 (26%) 23 (23%) 27 (27%) 20 (20%) 32 (32%)#> 6 golf 80 (20%) 8 (8%) 18 (18%) 25 (25%) 29 (29%)#> #> Pearson Chi-Square Results:#> Description All child teen adult senior#> 7 Dif. child - - 0.014 0.024 < 0.001#> 8 Dif. teen - - - 0.048 NS (0.07)#> 9 Dif. adult - - - - 0.025#> 10 Overall < 0.001 - - - -If you want stacked crosstabs, there are two ways to do it:
The first way is to make two crosstabs and manually stack them with
stack_crosstabs(). You can stack as many as you want.
ct1<-iris[, c("Sepal.Length", "Species")] |> crosstab("Species") |> add_default_table(anova=F)
ct2<-iris[, c("Petal.Length", "Species")] |> crosstab("Species") |> add_default_table(anova=F)
ct3<-iris[, c("Sepal.Width", "Species")] |> crosstab("Species") |> add_default_table(anova=F)
ct4<-iris[, c("Petal.Width", "Species")] |> crosstab("Species") |> add_default_table(anova=F)
stack_crosstabs(ct1, ct2, ct3, ct4)
#> <Crosstab object>#> # Contains 4 tables, grouped_by Species#> ===============================#> Table: Sepal.Length [Numeric]#> ===============================#> Description All setosa versicolor virginica#> 1 Complete / Total 150 / 150 50 / 50 50 / 50 50 / 50#> 2 Mean ± SD 5.8 ± 0.8 5 ± 0.4 5.9 ± 0.5 6.6 ± 0.6#> 3 Med (Q1, Q3) 5.8 (5.1, 6.4) 5 (4.8, 5.2) 5.9 (5.6, 6.3) 6.5 (6.2, 6.9)#> #> ===============================#> Table: Petal.Length [Numeric]#> ===============================#> Description All setosa versicolor virginica#> 4 Complete / Total 150 / 150 50 / 50 50 / 50 50 / 50#> 5 Mean ± SD 3.8 ± 1.8 1.5 ± 0.2 4.3 ± 0.5 5.6 ± 0.6#> 6 Med (Q1, Q3) 4.3 (1.6, 5.1) 1.5 (1.4, 1.6) 4.3 (4, 4.6) 5.6 (5.1, 5.9)#> #> ==============================#> Table: Sepal.Width [Numeric]#> ==============================#> Description All setosa versicolor virginica#> 7 Complete / Total 150 / 150 50 / 50 50 / 50 50 / 50#> 8 Mean ± SD 3.1 ± 0.4 3.4 ± 0.4 2.8 ± 0.3 3 ± 0.3#> 9 Med (Q1, Q3) 3 (2.8, 3.3) 3.4 (3.2, 3.7) 2.8 (2.5, 3) 3 (2.8, 3.2)#> #> ==============================#> Table: Petal.Width [Numeric]#> ==============================#> Description All setosa versicolor virginica#> 10 Complete / Total 150 / 150 50 / 50 50 / 50 50 / 50#> 11 Mean ± SD 1.2 ± 0.8 0.2 ± 0.1 1.3 ± 0.2 2 ± 0.3#> 12 Med (Q1, Q3) 1.3 (0.3, 1.8) 0.2 (0.2, 0.3) 1.3 (1.2, 1.5) 2 (1.8, 2.3)The second is to call crosstab_stacked() which runs through a data
frame and automatically applies the default table format.
crosstab_stacked(iris, "Species", anova=F)
#> <Crosstab object>#> # Contains 4 tables, grouped_by Species#> ===============================#> Table: Sepal.Length [Numeric]#> ===============================#> Description All setosa versicolor virginica#> 1 Complete / Total 150 / 150 50 / 50 50 / 50 50 / 50#> 2 Mean ± SD 5.8 ± 0.8 5 ± 0.4 5.9 ± 0.5 6.6 ± 0.6#> 3 Med (Q1, Q3) 5.8 (5.1, 6.4) 5 (4.8, 5.2) 5.9 (5.6, 6.3) 6.5 (6.2, 6.9)#> #> ==============================#> Table: Sepal.Width [Numeric]#> ==============================#> Description All setosa versicolor virginica#> 4 Complete / Total 150 / 150 50 / 50 50 / 50 50 / 50#> 5 Mean ± SD 3.1 ± 0.4 3.4 ± 0.4 2.8 ± 0.3 3 ± 0.3#> 6 Med (Q1, Q3) 3 (2.8, 3.3) 3.4 (3.2, 3.7) 2.8 (2.5, 3) 3 (2.8, 3.2)#> #> ===============================#> Table: Petal.Length [Numeric]#> ===============================#> Description All setosa versicolor virginica#> 7 Complete / Total 150 / 150 50 / 50 50 / 50 50 / 50#> 8 Mean ± SD 3.8 ± 1.8 1.5 ± 0.2 4.3 ± 0.5 5.6 ± 0.6#> 9 Med (Q1, Q3) 4.3 (1.6, 5.1) 1.5 (1.4, 1.6) 4.3 (4, 4.6) 5.6 (5.1, 5.9)#> #> ==============================#> Table: Petal.Width [Numeric]#> ==============================#> Description All setosa versicolor virginica#> 10 Complete / Total 150 / 150 50 / 50 50 / 50 50 / 50#> 11 Mean ± SD 1.2 ± 0.8 0.2 ± 0.1 1.3 ± 0.2 2 ± 0.3#> 12 Med (Q1, Q3) 1.3 (0.3, 1.8) 0.2 (0.2, 0.3) 1.3 (1.2, 1.5) 2 (1.8, 2.3)If you have two likert columns, you can pass multiple maps into
var_maps as a list, where the name of the list item is the same as the
column name.
perception_map<- c(
"overwhelmingly positive"=2, "somewhat positive"=1, "neutral"=0, "somewhat negative"=-1, "overwhelmingly negative"=-2
)
support_map<- c(
"very supportive"=4, "somewhat supportive"=3, "apathetic"=2, "somewhat demeaning"=1, "very demeaning"=0
)
# Names in map match their respective columnslikert_maps<-list(
uni_perception=perception_map,
prof_support=support_map
)
keep_cols<- c("university", "uni_perception", "prof_support")
crosstab_stacked(
df=students[, keep_cols],
cohort_col_name="university",
var_map=likert_maps,
anova=F,
chisq=F
)
#> <Crosstab object>#> # Contains 2 tables, grouped_by university#> ================================#> Table: uni_perception [Likert]#> ================================#> Description All UCLA Berkeley Stanford Yale#> 1 Complete / Total 3500 / 3500 500 / 500 500 / 500 500 / 500 500 / 500#> 2 Mean ± SD 0.8 ± 1.3 0.6 ± 1.5 0.4 ± 1.2 1.2 ± 1.2 1.1 ± 1.1#> 3 overwhelmingly positive 1343 (38%) 218 (44%) 90 (18%) 289 (58%) 230 (46%)#> 4 somewhat positive 849 (24%) 68 (14%) 160 (32%) 94 (19%) 160 (32%)#> 5 neutral 752 (21%) 109 (22%) 150 (30%) 62 (12%) 57 (11%)#> 6 somewhat negative 277 (8%) 25 (5%) 51 (10%) 30 (6%) 23 (5%)#> 7 overwhelmingly negative 279 (8%) 80 (16%) 49 (10%) 25 (5%) 30 (6%)#> #> MIT Texas Tech Alabama#> 1 500 / 500 500 / 500 500 / 500#> 2 0.5 ± 1.3 0.7 ± 1.2 0.9 ± 1.1#> 3 143 (29%) 168 (34%) 205 (41%)#> 4 99 (20%) 139 (28%) 129 (26%)#> 5 162 (32%) 111 (22%) 101 (20%)#> 6 47 (9%) 51 (10%) 50 (10%)#> 7 49 (10%) 31 (6%) 15 (3%)#> #> ==============================#> Table: prof_support [Likert]#> ==============================#> Description All UCLA Berkeley Stanford Yale#> 8 Complete / Total 3500 / 3500 500 / 500 500 / 500 500 / 500 500 / 500#> 9 Mean ± SD 3 ± 1.2 3.2 ± 1.3 2.8 ± 1.2 3.4 ± 1.1 3.2 ± 1.1#> 10 very supportive 1714 (49%) 292 (58%) 193 (39%) 332 (66%) 247 (49%)#> 11 somewhat supportive 836 (24%) 105 (21%) 112 (22%) 79 (16%) 159 (32%)#> 12 apathetic 466 (13%) 34 (7%) 118 (24%) 42 (8%) 41 (8%)#> 13 somewhat demeaning 271 (8%) 29 (6%) 47 (9%) 29 (6%) 30 (6%)#> 14 very demeaning 213 (6%) 40 (8%) 30 (6%) 18 (4%) 23 (5%)#> #> MIT Texas Tech Alabama#> 8 500 / 500 500 / 500 500 / 500#> 9 2.7 ± 1.3 3 ± 1.2 3 ± 1.2#> 10 172 (34%) 245 (49%) 233 (47%)#> 11 138 (28%) 115 (23%) 128 (26%)#> 12 89 (18%) 65 (13%) 77 (15%)#> 13 52 (10%) 52 (10%) 32 (6%)#> 14 49 (10%) 23 (5%) 30 (6%)The kableExtra package contains a lot of good stuff, so I made a
wrapper for the kbl() function which will recognize a crosstab object
and automatically apply formatting such as auto-generated footnotes.
new_crosstab<- crosstab_stacked(students, "university")
htcrosstabs::kbl(new_crosstab)Because 1) the output order of the rows and columns is determined by
factor levels, 2) multi-response data is handled in list-columns, and 3)
list-columns are a pain to factorize, I included wrappers for factor()
and levels() which include support for list-columns.
head(allergies_by_school, 5)
#> allergies school#> 1 1, 2 Brighton High#> 2 Brighton High#> 3 Brighton High#> 4 2, 1, 3 Brighton High#> 5 2, 1, 4, 3 Brighton Highnew_allergies<-factor(allergies_by_school$allergies, levels= c("nuts", "eggs", "milk", "gluten"))
levels(new_allergies)
#> [1] "nuts" "eggs" "milk" "gluten"I also included a function default_var_map() which will automatically
generate a likert map based on factor levels.
default_var_map(students$prof_support)
#> very supportive somewhat supportive apathetic somewhat demeaning #> 5 4 3 2 #> very demeaning #> 1crosstab() # Create crosstab
as.crosstab() # Create crosstab (Same as crosstab())
crosstab_stacked() # Create crosstab with rows from multi-column data frame
stack_crosstabs() # Combine 2+ crosstabs
crosstab_data() # Create internal data table (you shouldn't need this)# GETTERS
var_name() # The name of the variable column
var() # The variable column as a vector
var_levels() # The factor levels in the variable column
var_map() # The named numeric vector mapping variable to number (likert)
var_mapped() # The variable column mapped to its numeric representation
cohort_name() # The name of the cohort column
cohort() # The cohort column as a vector
cohort_levels() # Factor levels in the cohort column
combined_cohort_name() # Name of the combined cohort column in the output
desc_name() # Name of the leftmost description column in the output
data_table() # The internal data table containing the original data
get_raw_data() # The internal data table without combined cohort
index() # The vector keeping track of row groupings# SETTERS# Note that functions with a `<-` at the end are called on the left side of `<-` like this:# cohort_name(test_ct) <- "new name"# var_map(test_ct) <- c("agree" = 3, "neither" = 2, "disagree" = 1)
set_new_data() # Replace data table in current crosstab with a new one
`data_table<-`() # Replace data table in current crosstab with a new one
`var_name<-`() # Rename the variable column
`var<-`() # Set new values for the variable column
`var_levels<-`() # Set new variable factor levels
`var_map<-`() # Set new numeric values to map the variables to
`cohort_name<-`() # Rename the cohort column
`cohort<-`() # Set new values for the cohort column
`cohort_levels<-`() # Set new cohort factor levels
`index<-`() # Change the row grouping for the output tableis.crosstab() # Is this a crosstab object? (FALSE if not)
is.crosstab.grouped() # Is this a grouped crosstab? (FALSE if not)
is.crosstab.categorical() # Is this a categorical crosstab? (FALSE if not)
is.crosstab.numeric() # Is this a numeric crosstab? (FALSE if not)
is.crosstab.likert() # Is this a likert crosstab? (FALSE if not)
is.crosstab.multi() # Is this a multi-response crosstab? (FALSE if not)
is.crosstab.data() # Is this an internal crosstab_data table? (FALSE if not)
assert_crosstab() # Is this a crosstab object? (Errors if not)
assert_crosstab_grouped() # Is this a grouped crosstab? (Errors if not)
assert_crosstab_categorical() # Is this a categorical crosstab? (Errors if not)
assert_crosstab_numeric() # Is this a numeric crosstab? (Errors if not)
assert_crosstab_likert() # Is this a likert crosstab? (Errors if not)
assert_crosstab_multi() # Is this a multi-response crosstab? (Errors if not)
assert_crosstab_data() # Is this an internal crosstab_data table? (Errors if not)as.crosstab() # Cast data frame to crosstab
as.crosstab.cat() # Cast crosstab to categorical
as.crosstab.num() # Cast crosstab to numeric
as.crosstab.likert() # Cast crosstab to likert
as.crosstab.multi() # Cast crosstab to multi-responsejoin_val() # Quickly join multiple value tables
get_complete() # Get complete (not NA)
get_total() # Get total (including NA)
get_complete_total() # Get "complete / total"
get_max() # Get maximum
get_min() # Get minimum
get_mean() # Get mean
get_sd() # Get standard deviation
get_mean_sd() # Get "mean +/- sd"
get_med() # Get median
get_median() # Get median
get_q1() # Get first quartile
get_q3() # Get third quartile
get_q1_q3() # Get "Q1--Q3"
get_med_q1_q3() # Get "med (Q1, Q3)"
get_iqr() # Get IQR
get_iqr_q3_q1() # Get "IQR (Q3-Q1)"
get_count() # Get counts
get_prop() # Get proportions (out of complete)
get_proportion() # Get proportions (out of complete)
get_count_prop() # Get "count (proportion)"
get_percent() # Get percent
get_count_percent() # Get "count (percent%)"get_anova_p_value() # Get overall ANOVA p-value
get_anova_posthoc() # Get Tukey post-hoc p-values
get_tukey_posthoc() # Get Tukey post-hoc p-values
get_chisq_p_value() # Get overall chi-square p-value
get_chisq_posthoc() # Get chi-square post-hoc p_values
get_rao_scott_p_value() # Get overall Rao-Scott p-value
get_rao_scott_posthoc() # Get Rao-Scott post-hoc p-valuesadd_rows() # Add custom rows to the output table
get_complete_row() # Get formatted complete row
add_complete_row() # Get formatted complete row
get_total_row() # Get formatted total row
add_total_row() # Get formatted total row
get_complete_total_row() # Get formatted "complete / total row"
add_complete_total_row() # Get formatted "complete / total row"
get_mean_row() # Get formatted mean row
add_mean_row() # Get formatted mean row
get_sd_row() # Get formatted standard deviation row
add_sd_row() # Get formatted standard deviation row
get_mean_sd_row() # Get formatted "mean +/- sd" row
add_mean_sd_row() # Get formatted "mean +/- sd" row
get_med_row() # Get formatted median row
add_med_row() # Get formatted median row
get_median_row() # Get formatted median row
add_median_row() # Get formatted median row
get_q1_row() # Get formatted Q1 row
add_q1_row() # Get formatted Q1 row
get_q3_row() # Get formatted Q3 row
add_q3_row() # Get formatted Q3 row
get_q1_q3_row() # Get formatted "Q1--Q3" row
add_q1_q3_row() # Get formatted "Q1--Q3" row
get_med_q1_q3_row() # Get formatted "med (Q1, Q3)" row
add_med_q1_q3_row() # Get formatted "med (Q1, Q3)" row
get_iqr_row() # Get formatted IQR row
add_iqr_row() # Get formatted IQR row
get_iqr_q3_q1_row() # Get formatted "IQR (Q3-Q1)" row
add_iqr_q3_q1_row() # Get formatted "IQR (Q3-Q1)" row
get_count_rows() # Get formatted count rows
add_count_rows() # Get formatted count rows
get_prop_rows() # Get formatted proportion rows
add_prop_rows() # Get formatted proportion rows
get_proportion_rows() # Get formatted proportion rows
add_proportion_rows() # Get formatted proportion rows
get_count_prop_rows() # Get formatted "count (proportion)" rows
add_count_prop_rows() # Get formatted "count (proportion)" rows
get_percent_rows() # Get formatted percent rows
add_percent_rows() # Get formatted percent rows
get_count_percent_rows() # Get formatted "count (percent%)" rows
add_count_percent_rows() # Get formatted "count (percent%)" rows
get_anova_rows() # Get formatted ANOVA rows
add_anova_rows() # Get formatted ANOVA rows
get_chisq_rows() # Get formatted chi-square rows
add_chisq_rows() # Get formatted chi-square rows
get_rao_scott_rows() # Get formatted Rao-Scott rows
add_rao_scott_rows() # Get formatted Rao-Scott rowsadd_default_table() # Guess which table based on the data type
add_categorical_table() # Add default categorical table
add_numeric_table() # Add default numeric table
add_likert_table() # Add default likert tablekbl() # Wrapper for kableExtra::kbl(), automatically applies formatting
apply_footnotes() # Manually apply footnotes (automatically called by kbl())
apply_pack_rows() # Manually apply row groups (automatically called by kbl())
apply_table_sep() # Manually apply lines between tables (automatically called by kbl())factor() # Wrapper for base::factor(), supports lists
levels() # Wrapper for base::levels(), supports lists
`levels<-`() # Wrapper for base::`levels<-`(), supports lists
is.factorlist() # Is this a list of factor objects?
default_var_map() # Create a default likert map based on factor levels
nest_multi_col() # Nest column into proper multi-response form
to_long() # Pivot data from formatted row format to long format
to_wide() # Pivot data from 3-column long (description, cohort, variable) to formatted row