This function describes a distribution by a set of indices (e.g., measures of centrality, dispersion, range, skewness, kurtosis).

```
describe_distribution(x, ...)
# S3 method for numeric
describe_distribution(
x,
centrality = "mean",
dispersion = TRUE,
iqr = TRUE,
range = TRUE,
quartiles = FALSE,
ci = NULL,
iterations = 100,
threshold = 0.1,
verbose = TRUE,
...
)
# S3 method for factor
describe_distribution(x, dispersion = TRUE, range = TRUE, verbose = TRUE, ...)
# S3 method for data.frame
describe_distribution(
x,
centrality = "mean",
dispersion = TRUE,
iqr = TRUE,
range = TRUE,
quartiles = FALSE,
include_factors = FALSE,
ci = NULL,
iterations = 100,
threshold = 0.1,
verbose = TRUE,
...
)
```

x | A numeric vector. |
---|---|

... | Additional arguments to be passed to or from methods. |

centrality | The point-estimates (centrality indices) to compute. Character (vector) or list with one or more of these options: |

dispersion | Logical, if |

iqr | Logical, if |

range | Return the range (min and max). |

quartiles | Return the first and third quartiles (25th and 75pth percentiles). |

ci | Confidence Interval (CI) level. Default is |

iterations | The number of bootstrap replicates for computing confidence
intervals. Only applies when |

threshold | For |

verbose | Toggle warnings and messages. |

include_factors | Logical, if |

A data frame with columns that describe the properties of the variables.

There is also a
`plot()`

-method
implemented in the
see-package.

```
describe_distribution(rnorm(100))
#> Mean | SD | IQR | Min | Max | Skewness | Kurtosis | n | n_Missing
#> --------------------------------------------------------------------
#> 0.06 | 1 | 1.5 | -2.8 | 2.5 | -0.36 | 0.11 | 100 | 0
data(iris)
describe_distribution(iris)
#> Variable | Mean | SD | IQR | Min | Max | Skewness | Kurtosis | n | n_Missing
#> -------------------------------------------------------------------------------------
#> Sepal.Length | 5.8 | 0.83 | 1.30 | 4.3 | 7.9 | 0.31 | -0.55 | 150 | 0
#> Sepal.Width | 3.1 | 0.44 | 0.52 | 2.0 | 4.4 | 0.32 | 0.23 | 150 | 0
#> Petal.Length | 3.8 | 1.77 | 3.52 | 1.0 | 6.9 | -0.27 | -1.40 | 150 | 0
#> Petal.Width | 1.2 | 0.76 | 1.50 | 0.1 | 2.5 | -0.10 | -1.34 | 150 | 0
describe_distribution(iris, include_factors = TRUE, quartiles = TRUE)
#> Variable | Mean | SD | IQR | Min | Max | Q1 | Q3 | Skewness | Kurtosis | n | n_Missing
#> ----------------------------------------------------------------------------------------------------------
#> Sepal.Length | 5.8 | 0.83 | 1.30 | 4.3 | 7.9 | 5.1 | 6.4 | 0.31 | -0.55 | 150 | 0
#> Sepal.Width | 3.1 | 0.44 | 0.52 | 2 | 4.4 | 2.8 | 3.3 | 0.32 | 0.23 | 150 | 0
#> Petal.Length | 3.8 | 1.77 | 3.52 | 1 | 6.9 | 1.6 | 5.1 | -0.27 | -1.40 | 150 | 0
#> Petal.Width | 1.2 | 0.76 | 1.50 | 0.1 | 2.5 | 0.3 | 1.8 | -0.10 | -1.34 | 150 | 0
#> Species | NA | NA | NA | setosa | virginica | NA | NA | 0.00 | -1.51 | 150 | 0
```