# desctable usage vignette (deprecated)

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

# 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        % Min  Q1  Med     Mean  Q3 Max        sd IQR
    ## 1        Sepal.Length 150       NA 4.3 5.1 5.80 5.843333 6.4 7.9 0.8280661 1.3
    ## 2         Sepal.Width 150       NA 2.0 2.8 3.00 3.057333 3.3 4.4 0.4358663 0.5
    ## 3        Petal.Length 150       NA 1.0 1.6 4.35 3.758000 5.1 6.9 1.7652982 3.5
    ## 4         Petal.Width 150       NA 0.1 0.3 1.30 1.199333 1.8 2.5 0.7622377 1.5
    ## 5             Species 150       NA  NA  NA   NA       NA  NA  NA        NA  NA
    ## 6     Species: setosa  50 33.33333  NA  NA   NA       NA  NA  NA        NA  NA
    ## 7 Species: versicolor  50 33.33333  NA  NA   NA       NA  NA  NA        NA  NA
    ## 8  Species: virginica  50 33.33333  NA  NA   NA       NA  NA  NA        NA  NA

    desctable(mtcars)

    ##            Min        Q1     Med       Mean     Q3     Max          sd
    ## 1   mpg 10.400  15.42500  19.200  20.090625  22.80  33.900   6.0269481
    ## 2   cyl  4.000   4.00000   6.000   6.187500   8.00   8.000   1.7859216
    ## 3  disp 71.100 120.82500 196.300 230.721875 326.00 472.000 123.9386938
    ## 4    hp 52.000  96.50000 123.000 146.687500 180.00 335.000  68.5628685
    ## 5  drat  2.760   3.08000   3.695   3.596563   3.92   4.930   0.5346787
    ## 6    wt  1.513   2.58125   3.325   3.217250   3.61   5.424   0.9784574
    ## 7  qsec 14.500  16.89250  17.710  17.848750  18.90  22.900   1.7869432
    ## 8    vs  0.000   0.00000   0.000   0.437500   1.00   1.000   0.5040161
    ## 9    am  0.000   0.00000   0.000   0.406250   1.00   1.000   0.4989909
    ## 10 gear  3.000   3.00000   4.000   3.687500   4.00   5.000   0.7378041
    ## 11 carb  1.000   2.00000   2.000   2.812500   4.00   8.000   1.6152000
    ##          IQR
    ## 1    7.37500
    ## 2    4.00000
    ## 3  205.17500
    ## 4   83.50000
    ## 5    0.84000
    ## 6    1.02875
    ## 7    2.00750
    ## 8    1.00000
    ## 9    1.00000
    ## 10   1.00000
    ## 11   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 | % | Min | Q1 | Med | Mean | Q3 | Max | sd | IQR
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
Sepal.Length | 150 |  | 4.3 | 5.1 | 5.8 | 5.8 | 6.4 | 7.9 | 0.83 | 1.3
Sepal.Width | 150 |  | 2 | 2.8 | 3 | 3.1 | 3.3 | 4.4 | 0.44 | 0.5
Petal.Length | 150 |  | 1 | 1.6 | 4.3 | 3.8 | 5.1 | 6.9 | 1.8 | 3.5
Petal.Width | 150 |  | 0.1 | 0.3 | 1.3 | 1.2 | 1.8 | 2.5 | 0.76 | 1.5
Species | 150 |  |  |  |  |  |  |  |  |
setosa | 50 | 33 |  |  |  |  |  |  |  |
versicolor | 50 | 33 |  |  |  |  |  |  |  |
virginica | 50 | 33 |  |  |  |  |  |  |  |

    mtcars %>%
      desctable() %>%
      datatable()

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 vignette will use DT.

## 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() %>% datatable()
    iris %>%
      desctable(stats = stats_auto) %>%
      datatable()

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

    ## function (data)
    ## {
    ##     numeric <- data %>% lapply(is.numeric) %>% unlist() %>% any
    ##     fact <- data %>% lapply(is.factor) %>% unlist() %>% any()
    ##     stats <- list(Min = min, Q1 = ~quantile(., 0.25), Med = stats::median,
    ##         Mean = mean, Q3 = ~quantile(., 0.75), Max = max, sd = stats::sd,
    ##         IQR = IQR)
    ##     if (fact & numeric)
    ##         c(list(N = length, `%` = percent), stats)
    ##     else if (fact & !numeric)
    ##         list(N = length, `%` = percent)
    ##     else if (!fact & numeric)
    ##         stats
    ## }
    ## <bytecode: 0x55d58787c6b0>
    ## <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)) %>%
      datatable()

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) %>%
      datatable()

# 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) / Min Species: setosa (n=50) / Q1
    ## 1 Sepal.Length                          4.3                         4.8
    ## 2  Sepal.Width                          2.3                         3.2
    ## 3 Petal.Length                          1.0                         1.4
    ## 4  Petal.Width                          0.1                         0.2
    ##   Species: setosa (n=50) / Med Species: setosa (n=50) / Mean
    ## 1                          5.0                         5.006
    ## 2                          3.4                         3.428
    ## 3                          1.5                         1.462
    ## 4                          0.2                         0.246
    ##   Species: setosa (n=50) / Q3 Species: setosa (n=50) / Max
    ## 1                       5.200                          5.8
    ## 2                       3.675                          4.4
    ## 3                       1.575                          1.9
    ## 4                       0.300                          0.6
    ##   Species: setosa (n=50) / sd Species: setosa (n=50) / IQR
    ## 1                   0.3524897                        0.400
    ## 2                   0.3790644                        0.475
    ## 3                   0.1736640                        0.175
    ## 4                   0.1053856                        0.100
    ##   Species: versicolor (n=50) / Min Species: versicolor (n=50) / Q1
    ## 1                              4.9                           5.600
    ## 2                              2.0                           2.525
    ## 3                              3.0                           4.000
    ## 4                              1.0                           1.200
    ##   Species: versicolor (n=50) / Med Species: versicolor (n=50) / Mean
    ## 1                             5.90                             5.936
    ## 2                             2.80                             2.770
    ## 3                             4.35                             4.260
    ## 4                             1.30                             1.326
    ##   Species: versicolor (n=50) / Q3 Species: versicolor (n=50) / Max
    ## 1                             6.3                              7.0
    ## 2                             3.0                              3.4
    ## 3                             4.6                              5.1
    ## 4                             1.5                              1.8
    ##   Species: versicolor (n=50) / sd Species: versicolor (n=50) / IQR
    ## 1                       0.5161711                            0.700
    ## 2                       0.3137983                            0.475
    ## 3                       0.4699110                            0.600
    ## 4                       0.1977527                            0.300
    ##   Species: virginica (n=50) / Min Species: virginica (n=50) / Q1
    ## 1                             4.9                          6.225
    ## 2                             2.2                          2.800
    ## 3                             4.5                          5.100
    ## 4                             1.4                          1.800
    ##   Species: virginica (n=50) / Med Species: virginica (n=50) / Mean
    ## 1                            6.50                            6.588
    ## 2                            3.00                            2.974
    ## 3                            5.55                            5.552
    ## 4                            2.00                            2.026
    ##   Species: virginica (n=50) / Q3 Species: virginica (n=50) / Max
    ## 1                          6.900                             7.9
    ## 2                          3.175                             3.8
    ## 3                          5.875                             6.9
    ## 4                          2.300                             2.5
    ##   Species: virginica (n=50) / sd Species: virginica (n=50) / IQR    tests / p
    ## 1                      0.6358796                           0.675 8.918734e-22
    ## 2                      0.3224966                           0.375 1.569282e-14
    ## 3                      0.5518947                           0.775 4.803974e-29
    ## 4                      0.2746501                           0.500 3.261796e-29
    ##   tests / test
    ## 1 kruskal.test
    ## 2 kruskal.test
    ## 3 kruskal.test
    ## 4 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  8 variables:
    ##   ..$ Min : num [1:4] 4.3 2.3 1 0.1
    ##   ..$ Q1  : num [1:4] 4.8 3.2 1.4 0.2
    ##   ..$ Med : num [1:4] 5 3.4 1.5 0.2
    ##   ..$ Mean: num [1:4] 5.006 3.428 1.462 0.246
    ##   ..$ Q3  : num [1:4] 5.2 3.68 1.58 0.3
    ##   ..$ Max : num [1:4] 5.8 4.4 1.9 0.6
    ##   ..$ sd  : num [1:4] 0.352 0.379 0.174 0.105
    ##   ..$ IQR : num [1:4] 0.4 0.475 0.175 0.1
    ##  $ Species: versicolor (n=50):'data.frame':  4 obs. of  8 variables:
    ##   ..$ Min : num [1:4] 4.9 2 3 1
    ##   ..$ Q1  : num [1:4] 5.6 2.52 4 1.2
    ##   ..$ Med : num [1:4] 5.9 2.8 4.35 1.3
    ##   ..$ Mean: num [1:4] 5.94 2.77 4.26 1.33
    ##   ..$ Q3  : num [1:4] 6.3 3 4.6 1.5
    ##   ..$ Max : num [1:4] 7 3.4 5.1 1.8
    ##   ..$ sd  : num [1:4] 0.516 0.314 0.47 0.198
    ##   ..$ IQR : num [1:4] 0.7 0.475 0.6 0.3
    ##  $ Species: virginica (n=50) :'data.frame':  4 obs. of  8 variables:
    ##   ..$ Min : num [1:4] 4.9 2.2 4.5 1.4
    ##   ..$ Q1  : num [1:4] 6.23 2.8 5.1 1.8
    ##   ..$ Med : num [1:4] 6.5 3 5.55 2
    ##   ..$ Mean: num [1:4] 6.59 2.97 5.55 2.03
    ##   ..$ Q3  : num [1:4] 6.9 3.18 5.88 2.3
    ##   ..$ Max : num [1:4] 7.9 3.8 6.9 2.5
    ##   ..$ sd  : num [1:4] 0.636 0.322 0.552 0.275
    ##   ..$ IQR : num [1:4] 0.675 0.375 0.775 0.5
    ##  $ tests                     :'data.frame':  4 obs. of  2 variables:
    ##   ..$ p   : num [1:4] 8.92e-22 1.57e-14 4.80e-29 3.26e-29
    ##   ..$ test: chr [1:4] "kruskal.test" "kruskal.test" "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) Min | Q1 | Med | Mean | Q3 | Max | sd | IQR | cyl: 6 (n=7) Min | Q1 | Med | Mean | Q3 | Max | sd | IQR | cyl: 8 (n=14) Min | Q1 | Med | Mean | Q3 | Max | sd | IQR | tests p | test
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
mpg | 21 | 23 | 26 | 27 | 30 | 34 | 4.5 | 7.6 | 18 | 19 | 20 | 20 | 21 | 21 | 1.5 | 2.4 | 10 | 14 | 15 | 15 | 16 | 19 | 2.6 | 1.8 | 2.6e-06 | kruskal.test
disp | 71 | 79 | 108 | 105 | 121 | 147 | 27 | 42 | 145 | 160 | 168 | 183 | 196 | 258 | 42 | 36 | 276 | 302 | 350 | 353 | 390 | 472 | 68 | 88 | 1.6e-06 | kruskal.test
hp | 52 | 66 | 91 | 83 | 96 | 113 | 21 | 30 | 105 | 110 | 110 | 122 | 123 | 175 | 24 | 13 | 150 | 176 | 192 | 209 | 241 | 335 | 51 | 65 | 3.3e-06 | kruskal.test
drat | 3.7 | 3.8 | 4.1 | 4.1 | 4.2 | 4.9 | 0.37 | 0.35 | 2.8 | 3.4 | 3.9 | 3.6 | 3.9 | 3.9 | 0.48 | 0.56 | 2.8 | 3.1 | 3.1 | 3.2 | 3.2 | 4.2 | 0.37 | 0.15 | 0.00075 | kruskal.test
wt | 1.5 | 1.9 | 2.2 | 2.3 | 2.6 | 3.2 | 0.57 | 0.74 | 2.6 | 2.8 | 3.2 | 3.1 | 3.4 | 3.5 | 0.36 | 0.62 | 3.2 | 3.5 | 3.8 | 4 | 4 | 5.4 | 0.76 | 0.48 | 1.1e-05 | kruskal.test
qsec | 17 | 19 | 19 | 19 | 20 | 23 | 1.7 | 1.4 | 16 | 17 | 18 | 18 | 19 | 20 | 1.7 | 2.4 | 14 | 16 | 17 | 17 | 18 | 18 | 1.2 | 1.5 | 0.0062 | kruskal.test
vs | 0 | 1 | 1 | 0.91 | 1 | 1 | 0.3 | 0 | 0 | 0 | 1 | 0.57 | 1 | 1 | 0.53 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3.2e-05 | kruskal.test
am | 0 | 0.5 | 1 | 0.73 | 1 | 1 | 0.47 | 0.5 | 0 | 0 | 0 | 0.43 | 1 | 1 | 0.53 | 1 | 0 | 0 | 0 | 0.14 | 0 | 1 | 0.36 | 0 | 0.014 | kruskal.test
gear | 3 | 4 | 4 | 4.1 | 4 | 5 | 0.54 | 0 | 3 | 3.5 | 4 | 3.9 | 4 | 5 | 0.69 | 0.5 | 3 | 3 | 3 | 3.3 | 3 | 5 | 0.73 | 0 | 0.0062 | kruskal.test
carb | 1 | 1 | 2 | 1.5 | 2 | 2 | 0.52 | 1 | 1 | 2.5 | 4 | 3.4 | 4 | 6 | 1.8 | 1.5 | 2 | 2.2 | 3.5 | 3.5 | 4 | 8 | 1.6 | 1.8 | 0.0017 | kruskal.test

You can also specify groups based on an expression

    # With datatable output
    iris %>%
      group_by(Petal.Length > 5) %>%
      desctable() %>%
      datatable()

Multiple nested groups are also possible:

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

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 actepted, 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() %>% datatable()
    iris %>%
      group_by(Species) %>%
      desctable(tests = tests_auto) %>%
      datatable()

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 if (nlevels(grp) == 2)
    ##         ~wilcox.test
    ##     else ~kruskal.test
    ## }
    ## <bytecode: 0x55d5872051f0>
    ## <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)) %>%
      datatable()

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

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) / Min
    ## 1                                  NA                                   4.3
    ## 2                                  NA                                   2.0
    ## 3                                  NA                                   1.0
    ## 4                                  NA                                   0.1
    ## 5                                  NA                                    NA
    ## 6                           46.296296                                    NA
    ## 7                           45.370370                                    NA
    ## 8                            8.333333                                    NA
    ##   Petal.Length > 5: FALSE (n=108) / Q1 Petal.Length > 5: FALSE (n=108) / Med
    ## 1                                  5.0                                   5.5
    ## 2                                  2.8                                   3.0
    ## 3                                  1.5                                   3.5
    ## 4                                  0.2                                   1.0
    ## 5                                   NA                                    NA
    ## 6                                   NA                                    NA
    ## 7                                   NA                                    NA
    ## 8                                   NA                                    NA
    ##   Petal.Length > 5: FALSE (n=108) / Mean Petal.Length > 5: FALSE (n=108) / Q3
    ## 1                              5.5018519                                  6.0
    ## 2                              3.0666667                                  3.4
    ## 3                              3.0074074                                  4.5
    ## 4                              0.8638889                                  1.4
    ## 5                                     NA                                   NA
    ## 6                                     NA                                   NA
    ## 7                                     NA                                   NA
    ## 8                                     NA                                   NA
    ##   Petal.Length > 5: FALSE (n=108) / Max Petal.Length > 5: FALSE (n=108) / sd
    ## 1                                   7.0                            0.6386290
    ## 2                                   4.4                            0.4800701
    ## 3                                   5.0                            1.4885673
    ## 4                                   2.0                            0.6110292
    ## 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) / Min
    ## 1                                NA                                 5.8
    ## 2                                NA                                 2.5
    ## 3                                NA                                 5.1
    ## 4                                NA                                 1.4
    ## 5                                NA                                  NA
    ## 6                          0.000000                                  NA
    ## 7                          2.380952                                  NA
    ## 8                         97.619048                                  NA
    ##   Petal.Length > 5: TRUE (n=42) / Q1 Petal.Length > 5: TRUE (n=42) / Med
    ## 1                              6.325                                 6.7
    ## 2                              2.800                                 3.0
    ## 3                              5.300                                 5.6
    ## 4                              1.825                                 2.1
    ## 5                                 NA                                  NA
    ## 6                                 NA                                  NA
    ## 7                                 NA                                  NA
    ## 8                                 NA                                  NA
    ##   Petal.Length > 5: TRUE (n=42) / Mean Petal.Length > 5: TRUE (n=42) / Q3
    ## 1                             6.721429                              7.175
    ## 2                             3.033333                              3.200
    ## 3                             5.688095                              5.975
    ## 4                             2.061905                              2.300
    ## 5                                   NA                                 NA
    ## 6                                   NA                                 NA
    ## 7                                   NA                                 NA
    ## 8                                   NA                                 NA
    ##   Petal.Length > 5: TRUE (n=42) / Max Petal.Length > 5: TRUE (n=42) / sd
    ## 1                                 7.9                          0.5748958
    ## 2                                 3.8                          0.2968671
    ## 3                                 6.9                          0.4919857
    ## 4                                 2.5                          0.2802023
    ## 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.