These objects are imported from other packages. Follow the links below to see their documentation.
- flextable
Usage
# S3 method for crosstable
as_flextable(
x,
keep_id = FALSE,
by_header = NULL,
autofit = TRUE,
compact = FALSE,
show_test_name = TRUE,
fontsizes = list(body = 11, subheaders = 11, header = 11),
padding_v = NULL,
remove_header_keys = FALSE,
header_show_n = FALSE,
header_show_n_pattern = "{.col} (N={.n})",
generic_labels = list(id = ".id", variable = "variable", value = "value", total =
"Total", label = "label", test = "test", effect = "effect"),
...
)Arguments
- x
the result of
crosstable().- keep_id
whether to keep the
.idcolumn.- by_header
a string to override the header if
xhas only onebystratum.- autofit
whether to use
flextable::autofit()on the table.- compact
whether to compact the table. If
TRUE, seect_compact.crosstable()to see how to usekeep_id.- show_test_name
in the
testcolumn, show the test name.- fontsizes
font sizes as a list of keys. Default to
list(body=11, subheaders=11, header=11). If set through arguments instead of options, all 3 names should be specified.- padding_v
vertical padding (body).
- remove_header_keys
if
TRUEandxhas severalbystrata, header will only display values.- header_show_n
numeric vector telling on which depth the group size should be indicated in the header. You can control the pattern using option
crosstable_options. Seecrosstable_options()for details about it. See example for use case.- header_show_n_pattern
glue pattern used when
header_show_n==TRUE..colis the name of the column and.nthe size of the group. Default to{.col} (N={.n}); you can use{.col_key}and{.col_val}whenbyhas multiple stratum.- generic_labels
names of the crosstable default columns. Useful for translation for instance.
- ...
unused.
Examples
#Crosstables
library(crosstable)
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
crosstable_options(crosstable_fontsize_header=14,
crosstable_fontsize_subheaders=10,
crosstable_fontsize_body=8)
crosstable(iris) %>% as_flextable()
#> a flextable object.
#> col_keys: `label`, `variable`, `value`
#> header has 1 row(s)
#> body has 19 row(s)
#> original dataset sample:
#> .id label variable value
#> 1 Sepal.Length Sepal.Length Min / Max 4.3 / 7.9
#> 2 Sepal.Length Sepal.Length Med [IQR] 5.8 [5.1;6.4]
#> 3 Sepal.Length Sepal.Length Mean (std) 5.8 (0.8)
#> 4 Sepal.Length Sepal.Length N (NA) 150 (0)
#> 5 Sepal.Width Sepal.Width Min / Max 2.0 / 4.4
crosstable(mtcars2, -model, by=c(am, vs)) %>% as_flextable(header_show_n=1:2)
#> a flextable object.
#> col_keys: `label`, `variable`, `am=auto & vs=straight`, `am=manual & vs=straight`, `am=auto & vs=vshaped`, `am=manual & vs=vshaped`
#> header has 2 row(s)
#> body has 42 row(s)
#> original dataset sample:
#> .id label variable am=auto & vs=straight
#> 1 mpg Miles/(US) gallon Min / Max 17.8 / 24.4
#> 2 mpg Miles/(US) gallon Med [IQR] 21.4 [18.6;22.1]
#> 3 mpg Miles/(US) gallon Mean (std) 20.7 (2.5)
#> 4 mpg Miles/(US) gallon N (NA) 7 (0)
#> 5 cyl Number of cylinders 4 3 (27.27%)
#> am=manual & vs=straight am=auto & vs=vshaped am=manual & vs=vshaped
#> 1 21.4 / 33.9 10.4 / 19.2 15.0 / 26.0
#> 2 30.4 [25.1;31.4] 15.2 [14.1;16.6] 20.4 [16.8;21.0]
#> 3 28.4 (4.8) 15.1 (2.8) 19.8 (4.0)
#> 4 7 (0) 12 (0) 6 (0)
#> 5 7 (63.64%) 0 (0%) 1 (9.09%)
crosstable(mtcars2, cols=c(mpg, cyl), by=am, effect=TRUE) %>%
as_flextable(keep_id=TRUE, autofit=FALSE)
#> a flextable object.
#> col_keys: `.id`, `label`, `variable`, `auto`, `manual`, `effect`
#> header has 2 row(s)
#> body has 7 row(s)
#> original dataset sample:
#> .id label variable auto manual
#> 1 mpg Miles/(US) gallon Min / Max 10.4 / 24.4 15.0 / 33.9
#> 2 mpg Miles/(US) gallon Med [IQR] 17.3 [14.9;19.2] 22.8 [21.0;30.4]
#> 3 mpg Miles/(US) gallon Mean (std) 17.1 (3.8) 24.4 (6.2)
#> 4 mpg Miles/(US) gallon N (NA) 19 (0) 13 (0)
#> 5 cyl Number of cylinders 4 3 (27.27%) 8 (72.73%)
#> effect
#> 1 Difference in means (t-test CI), ref='auto'\nmanual minus auto: 7.24 [3.64 to 10.85]
#> 2 Difference in means (t-test CI), ref='auto'\nmanual minus auto: 7.24 [3.64 to 10.85]
#> 3 Difference in means (t-test CI), ref='auto'\nmanual minus auto: 7.24 [3.64 to 10.85]
#> 4 Difference in means (t-test CI), ref='auto'\nmanual minus auto: 7.24 [3.64 to 10.85]
#> 5 Odds ratio [95% Wald CI], ref='manual vs auto'\n6 vs 4: 0.28 [0.03 to 1.99]\n8 vs 4: 0.06 [0.01 to 0.39]
crosstable(mtcars2, cols=c(mpg, cyl), by=am, effect=TRUE) %>%
as_flextable(compact=TRUE, header_show_n=TRUE)
#> a flextable object.
#> col_keys: `variable`, `auto`, `manual`, `effect`
#> header has 2 row(s)
#> body has 9 row(s)
#> original dataset sample:
#> variable auto manual
#> 1 Miles/(US) gallon
#> 2 Min / Max 10.4 / 24.4 15.0 / 33.9
#> 3 Med [IQR] 17.3 [14.9;19.2] 22.8 [21.0;30.4]
#> 4 Mean (std) 17.1 (3.8) 24.4 (6.2)
#> 5 N (NA) 19 (0) 13 (0)
#> effect
#> 1 Difference in means (t-test CI), ref='auto'\nmanual minus auto: 7.24 [3.64 to 10.85]
#> 2
#> 3
#> 4
#> 5
#Renaming (because why not?)
crosstable(mtcars2, -model, by=vs, total="both", test=TRUE, effect=TRUE) %>%
rename(ID=.id, math=variable, Tot=Total, lab=label, pval=test, fx=effect) %>%
as_flextable(by_header = "Engine shape",
generic_labels=list(id = "ID", variable = "math", total="Tot",
label = "lab", test = "pval", effect="fx"))
#> Warning: Be aware that automatic global testing should only be done in an exploratory
#> context, as it would cause extensive alpha inflation otherwise.
#> This warning is displayed once every 8 hours.
#> Warning: Problems occured when calculating crosstable effects (glm-logit):
#> ℹ "glm.fit: fitted probabilities numerically 0 or 1 occurred", "glm.fit:
#> algorithm did not converge", and "collapsing to unique 'x' values"
#> • You might want to check for complete separation or extreme outliers.
#> • Applying `forcats::fct_rev()` to some columns might help too.
#> a flextable object.
#> col_keys: `lab`, `math`, `straight`, `vshaped`, `Tot`, `fx`, `pval`
#> header has 2 row(s)
#> body has 47 row(s)
#> original dataset sample:
#> ID lab math straight vshaped
#> 1 mpg Miles/(US) gallon Min / Max 17.8 / 33.9 10.4 / 26.0
#> 2 mpg Miles/(US) gallon Med [IQR] 22.8 [21.4;29.6] 15.7 [14.8;19.1]
#> 3 mpg Miles/(US) gallon Mean (std) 24.6 (5.4) 16.6 (3.9)
#> 4 mpg Miles/(US) gallon N (NA) 14 (0) 18 (0)
#> 5 cyl Number of cylinders 4 10 (90.91%) 1 (9.09%)
#> Tot
#> 1 10.4 / 33.9
#> 2 19.2 [15.4;22.8]
#> 3 20.1 (6.0)
#> 4 32 (0)
#> 5 11 (34.38%)
#> fx
#> 1 Difference in means (t-test CI), ref='straight'\nvshaped minus straight: -7.94 [-11.27 to -4.61]
#> 2 Difference in means (t-test CI), ref='straight'\nvshaped minus straight: -7.94 [-11.27 to -4.61]
#> 3 Difference in means (t-test CI), ref='straight'\nvshaped minus straight: -7.94 [-11.27 to -4.61]
#> 4 Difference in means (t-test CI), ref='straight'\nvshaped minus straight: -7.94 [-11.27 to -4.61]
#> 5 Odds ratio [95% Wald CI], ref='vshaped vs straight'\n6 vs 4: 7.50 [0.72 to 180.02]\n8 vs 4: 3143660128.53 [0.00 to NA]
#> pval
#> 1 p value: <0.0001 \n(Two Sample t-test)
#> 2 p value: <0.0001 \n(Two Sample t-test)
#> 3 p value: <0.0001 \n(Two Sample t-test)
#> 4 p value: <0.0001 \n(Two Sample t-test)
#> 5 p value: <0.0001 \n(Fisher's Exact Test for Count Data)