desctable {desctable} | R Documentation |

Generate a statistics table with the chosen statistical functions, and tests if given a `"grouped"`

dataframe.

```
desctable(data, stats, tests, labels)
## Default S3 method:
desctable(data, stats = stats_auto, tests, labels = NULL)
## S3 method for class 'grouped_df'
desctable(data, stats = stats_auto, tests = tests_auto, labels = NULL)
```

`data` |
The dataframe to analyze |

`stats` |
A list of named statistics to apply to each element of the dataframe, or a function returning a list of named statistics |

`tests` |
A list of statistical tests to use when calling desctable with a grouped_df |

`labels` |
A named character vector of labels to use instead of variable names |

A desctable object, which prints to a table of statistics for all variables

labels is an option named character vector used to make the table prettier.

If given, the variable names for which there is a label will be replaced by their corresponding label.

Not all variables need to have a label, and labels for non-existing variables are ignored.

labels must be given in the form c(unquoted_variable_name = "label")

The stats can be a function which takes a dataframe and returns a list of statistical functions to use.

stats can also be a named list of statistical functions, or purrr::map like formulas.

The names will be used as column names in the resulting table. If an element of the list is a function, it will be used as-is for the stats.

The tests can be a function which takes a variable and a grouping variable, and returns an appropriate statistical test to use in that case.

tests can also be a named list of statistical test functions, associating the name of a variable in the data and a test to use specifically for that variable.

That test name must be expressed as a single-term formula (e.g. `~t.test`

), or a purrr::map like formula
(e.g. `~t.test(., var.equal = T)`

). You don't have to specify tests for all the variables: a default test for
all other variables can be defined with the name `.default`

, and an automatic test can be defined with the name `.auto`

.

If data is a grouped dataframe (using `group_by`

), subtables are created and statistic tests are performed over each sub-group.

The output is a desctable object, which is a list of named dataframes that can be further manipulated. Methods for printing, using in pander and DT are present. Printing reduces the object to a dataframe.

```
iris %>%
desctable()
# Does the same as stats_auto here
iris %>%
desctable(stats = list("N" = length,
"Mean" = ~ if (is.normal(.)) mean(.),
"sd" = ~ if (is.normal(.)) sd(.),
"Med" = stats::median,
"IQR" = ~ if(!is.factor(.)) IQR(.)))
# With labels
mtcars %>% desctable(labels = c(hp = "Horse Power",
cyl = "Cylinders",
mpg = "Miles per gallon"))
# With grouping on a factor
iris %>%
group_by(Species) %>%
desctable(stats = stats_default)
# With nested grouping, on arbitrary variables
mtcars %>%
group_by(vs, cyl) %>%
desctable()
# With grouping on a condition, and choice of tests
iris %>%
group_by(Petal.Length > 5) %>%
desctable(tests = list(.auto = tests_auto, Species = ~chisq.test))
```

[Package *desctable* version 0.3.0 Index]