Description Usage Arguments Details Value Author(s) Examples

A contingency table returns the counts of all the combinations of
the modalities of two series in a table for which every modality of
the first series is a row and every modality of the second series
is a column. The `joint`

, `marginal`

and `conditional`

functions
compute these three distributions from the contingency table (by
indicating one series for the last two).

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 |

`data` |
a tibble, |

`x1, x2` |
the two series used the construct the contingency table, the distinct values of the first and the second will respectively be the rows and the columns of the contingency table, |

`weights` |
a series containing the weights that should be used to mimic the population, |

`freq` |
the frequencies (in the case where data is already contingency table), |

`total` |
if |

`xfirst1, xfirst2, xlast1, xlast2, wlast1, wlast2` |
see |

`x` |
the series on which the operation should be computed, |

`f` |
see |

`vals` |
see |

`cont_table`

actually returns a tibble in "long format", as the
`dplyr::count`

table does. As the returned object is of class
`cont_table`

, this is the `format`

and `print`

methods that turns
the tibble in a wide format before printing.

The `conditional`

and `joint`

functions return a `cont_table`

object, as the `marginal`

function returns a `freq_table`

object.

a tibble

Yves Croissant

1 2 3 4 5 6 7 8 9 10 | ```
library("dplyr")
# get a contingency table containing education and sex
cont_table(employment, education, sex)
# instead of counts, sum the weights
cont_table(employment, education, sex, weights = weights)
# get the joint distribution and the conditional and marginal
# distribution of sex
cont_table(employment, education, sex) %>% joint
cont_table(employment, education, sex) %>% marginal(sex)
cont_table(employment, education, sex) %>% conditional(sex)
``` |

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