Desctable

Introduction

Desctable is a comprehensive descriptive and comparative tables generator for R.

Every person doing data analysis has to create tables for descriptive summaries of data (a.k.a. Table.1), or comparative tables.

Many packages, such as the aptly named tableone, address this issue. However, they often include hard-coded behaviors, have outputs not easily manipulable with standard R tools, or their syntax are out-of-style (e.g. the argument order makes them difficult to use with the pipe (%>%)).

Enter desctable, a package built with the following objectives in mind:

• generate descriptive and comparative statistics tables with nesting
• keep the syntax as simple as possible
• have good reasonable defaults
• be entirely customizable, using standard R tools and functions
• produce the simplest (as a data structure) output possible
• provide helpers for different outputs
• integrate with “modern” R usage, and the tidyverse set of tools
• apply functional paradigms

Installation

Install from CRAN with

install.packages("desctable")

or install the development version from github with

devtools::install_github("maximewack/desctable")

Loading

library(desctable)

It is recommended to read this manual through its vignette:

vignette("desctable")

---

Descriptive tables

Simple usage

desctable uses and exports the pipe (%>%) operator (from packages magrittr and dplyr fame), though it is not mandatory to use it.

The single interface to the package is its eponymous desctable function.

When used on a data.frame, it returns a descriptive table:

iris %>%
  desctable()

##                         N        %     Mean        sd  Med IQR
## 1        Sepal.Length 150       NA       NA        NA 5.80 1.3
## 2         Sepal.Width 150       NA 3.057333 0.4358663 3.00 0.5
## 3        Petal.Length 150       NA       NA        NA 4.35 3.5
## 4         Petal.Width 150       NA       NA        NA 1.30 1.5
## 5             Species 150       NA       NA        NA   NA  NA
## 6     Species: setosa  50 33.33333       NA        NA   NA  NA
## 7 Species: versicolor  50 33.33333       NA        NA   NA  NA
## 8  Species: virginica  50 33.33333       NA        NA   NA  NA

desctable(mtcars)

##          N      Mean        sd     Med       IQR
## 1   mpg 32 20.090625 6.0269481  19.200   7.37500
## 2   cyl 32        NA        NA   6.000   4.00000
## 3  disp 32        NA        NA 196.300 205.17500
## 4    hp 32        NA        NA 123.000  83.50000
## 5  drat 32  3.596563 0.5346787   3.695   0.84000
## 6    wt 32        NA        NA   3.325   1.02875
## 7  qsec 32 17.848750 1.7869432  17.710   2.00750
## 8    vs 32        NA        NA   0.000   1.00000
## 9    am 32        NA        NA   0.000   1.00000
## 10 gear 32        NA        NA   4.000   1.00000
## 11 carb 32        NA        NA   2.000   2.00000

As you can see with these two examples, desctable describes every variable, with individual levels for factors. It picks statistical functions depending on the type and distribution of the variables in the data, and applies those statistical functions only on the relevant variables.

Output

The object produced by desctable is in fact a list of data.frames, with a “desctable” class.
Methods for reduction to a simple dataframe (as.data.frame, automatically used for printing), conversion to markdown (pander), and interactive html output with DT (datatable) are provided:

iris %>%
  desctable() %>%
  pander()

	N	%	Mean	sd	Med	IQR
Sepal.Length	150				5.8	1.3
Sepal.Width	150		3.1	0.44	3	0.5
Petal.Length	150				4.3	3.5
Petal.Width	150				1.3	1.5
Species	150
setosa	50	33
versicolor	50	33
virginica	50	33

To use pander you need to load the package yourself.

Calls to pander and datatable with “regular” dataframes will not be affected by the defaults used in the package, and you can modify these defaults for desctable objects.

The datatable wrapper function for desctable objects comes with some default options and formatting such as freezing the row names and table header, export buttons, and rounding of values. Both pander and datatable wrapper take a digits argument to set the number of decimals to show. (pander uses the digits, justify and missing arguments of pandoc.table, whereas datatable calls prettyNum with the digits parameter, and removes NA values. You can set digits = NULL if you want the full table and format it yourself)

Subsequent outputs in this README will use pander.

Advanced usage

desctable automatically chooses statistical functions if none is provided, using the following algorithm:

• always show N
• if there are factors, show %
• if there are normally distributed variables, show Mean and SD
• if there are non-normally distributed variables, show Median and IQR

For each variable in the table, compute the relevant statistical functions in that list (non-applicable functions will safely return NA).

You can specify the statistical functions yourself with the stats argument. This argument can either be:

• a function for automatic selection of appropriate statistical functions, depending on the data
• a named list of functions/formulas

The functions/formulas leverage the tidyverse way of working with anonymous functions, i.e.:

If a function, is is used as is. If a formula, e.g. ‘~ .x + 1’ or ~ . + 1, it is converted to a function. There are three ways to refer to the arguments:

• For a single argument function, use ‘.’
• For a two argument function, use ‘.x’ and ‘.y’
• For more arguments, use ‘..1’, ‘..2’, ‘..3’ etc

This syntax allows you to create very compact anonymous functions, and is the same as in the map family of functions from purrr.

Conditional formulas (condition ~ if_T | if F) from previous versions are no longer supported!

Automatic function

The default value for the stats argument is stats_auto, provided in the package.

Several other “automatic statistical functions” are defined in this package: stats_auto, stats_default, stats_normal, stats_nonnormal.

You can also provide your own automatic function, which needs to

• accept a dataframe as its argument (whether to use this dataframe or not in the function is your choice), and
• return a named list of statistical functions to use, as defined in the subsequent paragraphs.

# Strictly equivalent to iris %>% desctable() %>% pander()
iris %>%
  desctable(stats = stats_auto) %>%
  pander()

	N	%	Mean	sd	Med	IQR
Sepal.Length	150				5.8	1.3
Sepal.Width	150		3.1	0.44	3	0.5
Petal.Length	150				4.3	3.5
Petal.Width	150				1.3	1.5
Species	150
setosa	50	33
versicolor	50	33
virginica	50	33

For reference, here is the body of the stats_auto function in the package:

## function (data) 
## {
##     shapiro <- data %>% Filter(f = is.numeric) %>% lapply(is.normal) %>% 
##         unlist()
##     if (length(shapiro) == 0) {
##         normal <- F
##         nonnormal <- F
##     }
##     else {
##         normal <- any(shapiro)
##         nonnormal <- any(!shapiro)
##     }
##     fact <- data %>% lapply(is.factor) %>% unlist() %>% any()
##     if (fact & normal & !nonnormal) 
##         stats_normal(data)
##     else if (fact & !normal & nonnormal) 
##         stats_nonnormal(data)
##     else if (fact & !normal & !nonnormal) 
##         list(N = length, `%` = percent)
##     else if (!fact & normal & nonnormal) 
##         list(N = length, Mean = ~if (is.normal(.)) mean(.), sd = ~if (is.normal(.)) sd(.), 
##             Med = stats::median, IQR = ~if (!is.factor(.)) IQR(.))
##     else if (!fact & normal & !nonnormal) 
##         list(N = length, Mean = mean, sd = stats::sd)
##     else if (!fact & !normal & nonnormal) 
##         list(N = length, Med = stats::median, IQR = IQR)
##     else stats_default(data)
## }
## <bytecode: 0x56144fe581a8>
## <environment: namespace:desctable>

Statistical functions

Statistical functions can be any function defined in R that you want to use, such as length or mean.

The only condition is that they return a single numerical value. One exception is when they return a vector of length 1 + nlevels(x) when applied to factors, as is needed for the percent function.

As mentioned above, they need to be used inside a named list, such as

mtcars %>%
  desctable(stats = list("N" = length, "Mean" = mean, "SD" = sd)) %>%
  pander()

	N	Mean	SD
mpg	32	20	6
cyl	32	6.2	1.8
disp	32	231	124
hp	32	147	69
drat	32	3.6	0.53
wt	32	3.2	0.98
qsec	32	18	1.8
vs	32	0.44	0.5
am	32	0.41	0.5
gear	32	3.7	0.74
carb	32	2.8	1.6

The names will be used as column headers in the resulting table, and the functions will be applied safely on the variables (errors return NA, and for factors the function will be used on individual levels).

Several convenience functions are included in this package.

• percent, which prints percentages of levels in a factor
• IQR, which re-implements stats::IQR but works better with NA values
• is.normal, which tests for normality using the following method: length(na.omit(x)) > 30 & shapiro.test(x)$p.value > .1

Be aware that all functions will be used on variables stripped of their NA values! This is necessary for most statistical functions to be useful, and makes N (length) show only the number of observations in the dataset for each variable.

Labels

It is often the case that variable names are not “pretty” enough to be used as-is in a table.
Although you could still edit the variable labels in the table afterwards using sub-setting or string replacement functions, we provide a facility for this using the labels argument.

The labels argument is a named character vector associating variable names and labels.
You don’t need to provide labels for all the variables, and extra labels will be silently discarded. This allows you to define a “global” labels vector and use it for multiple tables even after variable selections.

mtlabels <- c(mpg  = "Miles/(US) gallon",
              cyl  = "Number of cylinders",
              disp = "Displacement (cu.in.)",
              hp   = "Gross horsepower",
              drat = "Rear axle ratio",
              wt   = "Weight (1000 lbs)",
              qsec = "¼ mile time",
              vs   = "V/S",
              am   = "Transmission",
              gear = "Number of forward gears",
              carb = "Number of carburetors")

mtcars %>%
  dplyr::mutate(am = factor(am, labels = c("Automatic", "Manual"))) %>%
  desctable(labels = mtlabels) %>%
  pander()

	N	%	Mean	sd	Med	IQR
Miles/(US) gallon	32		20	6	19	7.4
Number of cylinders	32				6	4
Displacement (cu.in.)	32				196	205
Gross horsepower	32				123	84
Rear axle ratio	32		3.6	0.53	3.7	0.84
Weight (1000 lbs)	32				3.3	1
¼ mile time	32		18	1.8	18	2
V/S	32				0	1
Transmission	32
Automatic	19	59
Manual	13	41
Number of forward gears	32				4	1
Number of carburetors	32				2	2

---

Comparative tables

Simple usage

Creating a comparative table (between groups defined by a factor) using desctable is as easy as creating a descriptive table.

It leverages the group_by function from dplyr:

iris %>%
  group_by(Species) %>%
  desctable() -> iris_by_Species

iris_by_Species

##                Species: setosa (n=50) / N Species: setosa (n=50) / Mean
## 1 Sepal.Length                         50                         5.006
## 2  Sepal.Width                         50                         3.428
## 3 Petal.Length                         50                            NA
## 4  Petal.Width                         50                            NA
##   Species: setosa (n=50) / sd Species: setosa (n=50) / Med
## 1                   0.3524897                          5.0
## 2                   0.3790644                          3.4
## 3                          NA                          1.5
## 4                          NA                          0.2
##   Species: setosa (n=50) / IQR Species: versicolor (n=50) / N
## 1                        0.400                             50
## 2                        0.475                             50
## 3                        0.175                             50
## 4                        0.100                             50
##   Species: versicolor (n=50) / Mean Species: versicolor (n=50) / sd
## 1                             5.936                       0.5161711
## 2                             2.770                       0.3137983
## 3                             4.260                       0.4699110
## 4                                NA                              NA
##   Species: versicolor (n=50) / Med Species: versicolor (n=50) / IQR
## 1                             5.90                            0.700
## 2                             2.80                            0.475
## 3                             4.35                            0.600
## 4                             1.30                            0.300
##   Species: virginica (n=50) / N Species: virginica (n=50) / Mean
## 1                            50                            6.588
## 2                            50                            2.974
## 3                            50                            5.552
## 4                            50                               NA
##   Species: virginica (n=50) / sd Species: virginica (n=50) / Med
## 1                      0.6358796                            6.50
## 2                      0.3224966                            3.00
## 3                      0.5518947                            5.55
## 4                             NA                            2.00
##   Species: virginica (n=50) / IQR    tests / p                  tests / test
## 1                           0.675 1.505059e-28 oneway.test(., var.equal = F)
## 2                           0.375 4.492017e-17 oneway.test(., var.equal = T)
## 3                           0.775 4.803974e-29                  kruskal.test
## 4                           0.500 3.261796e-29                  kruskal.test

The result is a table containing a descriptive sub-table for each level of the grouping factor (the statistical functions rules are applied to each sub-table independently), with the statistical tests performed, and their p values.

When displayed as a flat dataframe, the grouping header appears in each variable name.

You can also see the grouping headers by inspecting the resulting object, which is a nested list of dataframes, each dataframe being named after the grouping factor and its levels (with sample size for each).

str(iris_by_Species)

## List of 5
##  $ Variables                 :'data.frame':  4 obs. of  1 variable:
##   ..$ Variables: chr [1:4] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width"
##  $ Species: setosa (n=50)    :'data.frame':  4 obs. of  5 variables:
##   ..$ N   : int [1:4] 50 50 50 50
##   ..$ Mean: num [1:4] 5.01 3.43 NA NA
##   ..$ sd  : num [1:4] 0.352 0.379 NA NA
##   ..$ Med : num [1:4] 5 3.4 1.5 0.2
##   ..$ IQR : num [1:4] 0.4 0.475 0.175 0.1
##  $ Species: versicolor (n=50):'data.frame':  4 obs. of  5 variables:
##   ..$ N   : int [1:4] 50 50 50 50
##   ..$ Mean: num [1:4] 5.94 2.77 4.26 NA
##   ..$ sd  : num [1:4] 0.516 0.314 0.47 NA
##   ..$ Med : num [1:4] 5.9 2.8 4.35 1.3
##   ..$ IQR : num [1:4] 0.7 0.475 0.6 0.3
##  $ Species: virginica (n=50) :'data.frame':  4 obs. of  5 variables:
##   ..$ N   : int [1:4] 50 50 50 50
##   ..$ Mean: num [1:4] 6.59 2.97 5.55 NA
##   ..$ sd  : num [1:4] 0.636 0.322 0.552 NA
##   ..$ Med : num [1:4] 6.5 3 5.55 2
##   ..$ IQR : num [1:4] 0.675 0.375 0.775 0.5
##  $ tests                     :'data.frame':  4 obs. of  2 variables:
##   ..$ p   : num [1:4] 1.51e-28 4.49e-17 4.80e-29 3.26e-29
##   ..$ test: chr [1:4] "oneway.test(., var.equal = F)" "oneway.test(., var.equal = T)" "kruskal.test" "kruskal.test"
##  - attr(*, "class")= chr "desctable"

You can specify groups based on any variable, not only factors:

# With pander output
mtcars %>%
  group_by(cyl) %>%
  desctable() %>%
  pander()

	cyl: 4 (n=11) N	Med	IQR	cyl: 6 (n=7) N	Med	IQR	cyl: 8 (n=14) N	Med	IQR	tests p	test
mpg	11	26	7.6	7	20	2.4	14	15	1.8	2.6e-06	kruskal.test
disp	11	108	42	7	168	36	14	350	88	1.6e-06	kruskal.test
hp	11	91	30	7	110	13	14	192	65	3.3e-06	kruskal.test
drat	11	4.1	0.35	7	3.9	0.56	14	3.1	0.15	0.00075	kruskal.test
wt	11	2.2	0.74	7	3.2	0.62	14	3.8	0.48	1.1e-05	kruskal.test
qsec	11	19	1.4	7	18	2.4	14	17	1.5	0.0062	kruskal.test
vs	11	1	0	7	1	1	14	0	0	3.2e-05	kruskal.test
am	11	1	0.5	7	0	1	14	0	0	0.014	kruskal.test
gear	11	4	0	7	4	0.5	14	3	0	0.0062	kruskal.test
carb	11	2	1	7	4	1.5	14	3.5	1.8	0.0017	kruskal.test

You can also specify groups based on an expression

iris %>%
  group_by(Petal.Length > 5) %>%
  desctable() %>%
  pander()

	Petal.Length > 5: FALSE (n=108) N	%	Mean	sd	Med	IQR	Petal.Length > 5: TRUE (n=42) N	%	Mean	sd	Med	IQR	tests p	test
Sepal.Length	108				5.5	1	42				6.7	0.85	1.6e-15	wilcox.test
Sepal.Width	108		3.1	0.48	3	0.6	42				3	0.4	0.69	wilcox.test
Petal.Length	108				3.5	3	42				5.6	0.67	2.1e-21	wilcox.test
Petal.Width	108				1	1.2	42		2.1	0.28	2.1	0.47	1.6e-19	wilcox.test
Species	108						42						2.5e-26	fisher.test
setosa	50	46					0	0
versicolor	49	45					1	2.4
virginica	9	8.3					41	98

Multiple nested groups are also possible:

mtcars %>%
  dplyr::mutate(am = factor(am, labels = c("Automatic", "Manual"))) %>%
  group_by(vs, am, cyl) %>%
  desctable() %>%
  pander()

	vs: 0 (n=18) am: Automatic (n=12) cyl: 8 (n=12) N	Med	IQR	tests p	test	am: Manual (n=6) cyl: 4 (n=1) N	Med	IQR	cyl: 6 (n=3) N	Med	IQR	cyl: 8 (n=2) N	Med	IQR	tests p	test	vs: 1 (n=14) am: Automatic (n=7) cyl: 4 (n=3) N	Med	IQR	cyl: 6 (n=4) N	Med	IQR	tests p	test	am: Manual (n=7) cyl: 4 (n=7) N	Med	IQR	tests p	test
mpg	12	15	2.6		no.test	1	26	0	3	21	0.65	2	15	0.4	0.11	kruskal.test	3	23	1.5	4	19	1.7	0.057	wilcox.test	7	30	6.3		no.test
disp	12	355	113		no.test	1	120	0	3	160	7.5	2	326	25	0.11	kruskal.test	3	141	13	4	196	66	0.05	wilcox.test	7	79	24		no.test
hp	12	180	44		no.test	1	91	0	3	110	32	2	300	36	0.11	kruskal.test	3	95	18	4	116	14	0.05	wilcox.test	7	66	36		no.test
drat	12	3.1	0.11		no.test	1	4.4	0	3	3.9	0.14	2	3.9	0.34	0.33	kruskal.test	3	3.7	0.11	4	3.5	0.92	0.85	wilcox.test	7	4.1	0.2		no.test
wt	12	3.8	0.81		no.test	1	2.1	0	3	2.8	0.13	2	3.4	0.2	0.12	kruskal.test	3	3.1	0.36	4	3.4	0.061	0.05	wilcox.test	7	1.9	0.53		no.test
qsec	12	17	0.67		no.test	1	17	0	3	16	0.76	2	15	0.05	0.17	kruskal.test	3	20	1.4	4	19	0.89	0.23	wilcox.test	7	19	0.62		no.test
gear	12	3	0		no.test	1	5	0	3	4	0.5	2	5	0	0.29	kruskal.test	3	4	0.5	4	3.5	1	0.84	wilcox.test	7	4	0		no.test
carb	12	3	2		no.test	1	2	0	3	4	1	2	6	2	0.26	kruskal.test	3	2	0.5	4	2.5	3	0.85	wilcox.test	7	1	1		no.test

In the case of nested groups (a.k.a. sub-group analysis), statistical tests are performed only between the groups of the deepest grouping level.

Statistical tests are automatically selected depending on the data and the grouping factor.

Advanced usage

desctable automatically chooses statistical functions if none is provided, using the following algorithm:

• if the variable is a factor, use fisher.test
• if the grouping factor has only one level, use the provided no.test (which does nothing)
• if the grouping factor has two levels
  • and the variable presents homoskedasticity (p value for var.test > .1) and normality of distribution in both groups, use t.test(var.equal = T)
  • and the variable does not present homoskedasticity (p value for var.test < .1) but normality of distribution in both groups, use t.test(var.equal = F)
  • else use wilcox.test
• if the grouping factor has more than two levels
  • and the variable presents homoskedasticity (p value for bartlett.test > .1) and normality of distribution in all groups, use oneway.test(var.equal = T)
  • and the variable does not present homoskedasticity (p value for bartlett.test < .1) but normality of distribution in all groups, use oneway.test(var.equal = F)
  • else use kruskal.test

You can specify the statistical test functions yourself with the tests argument. This argument can either be:

• a function for automatic selection of appropriate statistical test functions, depending on the data
• a named list of statistical test functions

Please note that the statistical test functions must be given as formulas so as to capture the name of the test to display in the table. purrr style formulas are also accepted, as with the statistical functions. This also allows to specify optional arguments of such functions, and go around non-standard test functions (see Statistical test functions).

Automatic function

The default value for the tests argument is tests_auto, provided in the package.

You can also provide your own automatic function, which needs to

• accept a variable and a grouping factor as its arguments, and
• return a single-term formula containing a statistical test function.

This function will be used on every variable and every grouping factor to determine the appropriate test.

# Strictly equivalent to iris %>% group_by(Species) %>% desctable() %>% pander()
iris %>%
  group_by(Species) %>%
  desctable(tests = tests_auto) %>%
  pander()

	Species: setosa (n=50) N	Mean	sd	Med	IQR	Species: versicolor (n=50) N	Mean	sd	Med	IQR	Species: virginica (n=50) N	Mean	sd	Med	IQR	tests p	test
Sepal.Length	50	5	0.35	5	0.4	50	5.9	0.52	5.9	0.7	50	6.6	0.64	6.5	0.67	1.5e-28	oneway.test(., var.equal = F)
Sepal.Width	50	3.4	0.38	3.4	0.48	50	2.8	0.31	2.8	0.48	50	3	0.32	3	0.38	4.5e-17	oneway.test(., var.equal = T)
Petal.Length	50			1.5	0.18	50	4.3	0.47	4.3	0.6	50	5.6	0.55	5.5	0.78	4.8e-29	kruskal.test
Petal.Width	50			0.2	0.1	50			1.3	0.3	50			2	0.5	3.3e-29	kruskal.test

For reference, here is the body of the tests_auto function in the package:

## function (var, grp) 
## {
##     grp <- factor(grp)
##     if (nlevels(grp) < 2) 
##         ~no.test
##     else if (is.factor(var)) {
##         if (tryCatch(is.numeric(fisher.test(var ~ grp)$p.value), 
##             error = function(e) F)) 
##             ~fisher.test
##         else ~chisq.test
##     }
##     else {
##         all_normal <- all(tapply(var, grp, is.normal))
##         if (nlevels(grp) == 2) {
##             if (all_normal) {
##                 if (tryCatch(stats::var.test(var ~ grp)$p.value > 
##                   0.1, warning = function(e) F, error = function(e) F)) 
##                   ~t.test(., var.equal = T)
##                 else ~t.test(., var.equal = F)
##             }
##             else ~wilcox.test
##         }
##         else {
##             if (all_normal) {
##                 if (tryCatch(stats::bartlett.test(var ~ grp)$p.value > 
##                   0.1, warning = function(e) F, error = function(e) F)) 
##                   ~oneway.test(., var.equal = T)
##                 else ~oneway.test(., var.equal = F)
##             }
##             else ~kruskal.test
##         }
##     }
## }
## <bytecode: 0x56144cbe3948>
## <environment: namespace:desctable>

Statistical test functions

You can provide a named list of statistical functions, but here the mechanism is a bit different from the stats argument.

The list must contain either .auto or .default.

• .auto needs to be an automatic function, such as tests_auto. It will be used by default on all variables to select a test
• .default needs to be a single-term formula containing a statistical test function that will be used on all variables

You can also provide overrides to use specific tests for specific variables.
This is done using list items named as the variable and containing a single-term formula function.

iris %>%
  group_by(Petal.Length > 5) %>%
  desctable(tests = list(.auto   = tests_auto,
                         Species = ~chisq.test)) %>%
  pander()

	Petal.Length > 5: FALSE (n=108) N	%	Mean	sd	Med	IQR	Petal.Length > 5: TRUE (n=42) N	%	Mean	sd	Med	IQR	tests p	test
Sepal.Length	108				5.5	1	42				6.7	0.85	1.6e-15	wilcox.test
Sepal.Width	108		3.1	0.48	3	0.6	42				3	0.4	0.69	wilcox.test
Petal.Length	108				3.5	3	42				5.6	0.67	2.1e-21	wilcox.test
Petal.Width	108				1	1.2	42		2.1	0.28	2.1	0.47	1.6e-19	wilcox.test
Species	108						42						2.7e-24	chisq.test
setosa	50	46					0	0
versicolor	49	45					1	2.4
virginica	9	8.3					41	98

mtcars %>%
  dplyr::mutate(am = factor(am, labels = c("Automatic", "Manual"))) %>%
  group_by(am) %>%
  desctable(tests = list(.default = ~wilcox.test,
                         mpg      = ~t.test)) %>%
  pander()

	am: Automatic (n=19) N	Med	IQR	am: Manual (n=13) N	Med	IQR	tests p	test
mpg	19	17	4.2	13	23	9.4	0.0014	t.test
cyl	19	8	2	13	4	2	0.0039	wilcox.test
disp	19	276	164	13	120	81	0.00055	wilcox.test
hp	19	175	76	13	109	47	0.046	wilcox.test
drat	19	3.1	0.63	13	4.1	0.37	0.00014	wilcox.test
wt	19	3.5	0.41	13	2.3	0.84	4.3e-05	wilcox.test
qsec	19	18	2	13	17	2.1	0.27	wilcox.test
vs	19	0	1	13	1	1	0.36	wilcox.test
gear	19	3	0	13	4	1	7.6e-06	wilcox.test
carb	19	3	2	13	2	3	0.74	wilcox.test

Here’s an example of purrr style function:

iris %>%
  group_by(Petal.Length > 5) %>%
  desctable(tests = list(.auto = tests_auto,
                         Petal.Width = ~oneway.test(., var.equal = T)))

##                       Petal.Length > 5: FALSE (n=108) / N
## 1        Sepal.Length                                 108
## 2         Sepal.Width                                 108
## 3        Petal.Length                                 108
## 4         Petal.Width                                 108
## 5             Species                                 108
## 6     Species: setosa                                  50
## 7 Species: versicolor                                  49
## 8  Species: virginica                                   9
##   Petal.Length > 5: FALSE (n=108) / % Petal.Length > 5: FALSE (n=108) / Mean
## 1                                  NA                                     NA
## 2                                  NA                               3.066667
## 3                                  NA                                     NA
## 4                                  NA                                     NA
## 5                                  NA                                     NA
## 6                           46.296296                                     NA
## 7                           45.370370                                     NA
## 8                            8.333333                                     NA
##   Petal.Length > 5: FALSE (n=108) / sd Petal.Length > 5: FALSE (n=108) / Med
## 1                                   NA                                   5.5
## 2                            0.4800701                                   3.0
## 3                                   NA                                   3.5
## 4                                   NA                                   1.0
## 5                                   NA                                    NA
## 6                                   NA                                    NA
## 7                                   NA                                    NA
## 8                                   NA                                    NA
##   Petal.Length > 5: FALSE (n=108) / IQR Petal.Length > 5: TRUE (n=42) / N
## 1                                   1.0                                42
## 2                                   0.6                                42
## 3                                   3.0                                42
## 4                                   1.2                                42
## 5                                    NA                                42
## 6                                    NA                                 0
## 7                                    NA                                 1
## 8                                    NA                                41
##   Petal.Length > 5: TRUE (n=42) / % Petal.Length > 5: TRUE (n=42) / Mean
## 1                                NA                                   NA
## 2                                NA                                   NA
## 3                                NA                                   NA
## 4                                NA                             2.061905
## 5                                NA                                   NA
## 6                          0.000000                                   NA
## 7                          2.380952                                   NA
## 8                         97.619048                                   NA
##   Petal.Length > 5: TRUE (n=42) / sd Petal.Length > 5: TRUE (n=42) / Med
## 1                                 NA                                 6.7
## 2                                 NA                                 3.0
## 3                                 NA                                 5.6
## 4                          0.2802023                                 2.1
## 5                                 NA                                  NA
## 6                                 NA                                  NA
## 7                                 NA                                  NA
## 8                                 NA                                  NA
##   Petal.Length > 5: TRUE (n=42) / IQR    tests / p
## 1                               0.850 1.553676e-15
## 2                               0.400 6.927432e-01
## 3                               0.675 2.076978e-21
## 4                               0.475 3.982443e-24
## 5                                  NA 2.453675e-26
## 6                                  NA           NA
## 7                                  NA           NA
## 8                                  NA           NA
##                    tests / test
## 1                   wilcox.test
## 2                   wilcox.test
## 3                   wilcox.test
## 4 oneway.test(., var.equal = T)
## 5                   fisher.test
## 6                          <NA>
## 7                          <NA>
## 8                          <NA>

As with statistical functions, any statistical test function defined in R can be used.

The conditions are that the function

• accepts a formula (variable ~ grouping_variable) as a first positional argument (as is the case with most tests, like t.test), and
• returns an object with a p.value element.

Several convenience function are provided: formula versions for chisq.test and fisher.test using generic S3 methods (thus the behavior of standard calls to chisq.test and fisher.test are not modified), and ANOVA, a partial application of oneway.test with parameter var.equal = T.