# Summary method for pivotr

Source: `R/pivotr.R`
`summary.pivotr.Rd`

Summary method for pivotr

    # S3 method for pivotr
    summary(object, perc = FALSE, dec = 3, chi2 = FALSE, shiny = FALSE, ...)

## Arguments

object

  Return value from `pivotr`

perc

  Display numbers as percentages (TRUE or FALSE)

dec

  Number of decimals to show

chi2

  If TRUE calculate the chi-square statistic for the (pivot) table

shiny

  Did the function call originate inside a shiny app

...

  further arguments passed to or from other methods

## Details

See https://radiant-rstats.github.io/docs/data/pivotr.html for an example in Radiant

## See also

`pivotr` to create the pivot-table using dplyr

## Examples

    pivotr(diamonds, cvars = "cut") %>% summary(chi2 = TRUE)
    #> Pivot table
    #> Data        : diamonds
    #> Categorical : cut
    #>
    #>        cut n_obs
    #>       Fair   101
    #>       Good   275
    #>  Very Good   677
    #>    Premium   771
    #>      Ideal 1,176
    #>      Total 3,000
    #>
    #> Chi-squared: 1202.62 df(4), p.value < .001
    #> 0.0% of cells have expected values below 5
    pivotr(diamonds, cvars = "cut", tabsort = "desc(n_obs)") %>% summary()
    #> Pivot table
    #> Data        : diamonds
    #> Table sorted: desc(n_obs)
    #> Categorical : cut
    #>
    #>        cut n_obs
    #>      Ideal 1,176
    #>    Premium   771
    #>  Very Good   677
    #>       Good   275
    #>       Fair   101
    #>      Total 3,000
    #>
    pivotr(diamonds, cvars = "cut", tabfilt = "n_obs > 700") %>% summary()
    #> Pivot table
    #> Data        : diamonds
    #> Table filter: n_obs > 700
    #> Categorical : cut
    #>
    #>      cut n_obs
    #>  Premium   771
    #>    Ideal 1,176
    #>    Total 3,000
    #>
    pivotr(diamonds, cvars = "cut:clarity", nvar = "price") %>% summary()
    #> Pivot table
    #> Data        : diamonds
    #> Categorical : cut clarity
    #> Numeric     : price
    #> Function    : mean
    #>
    #>  clarity      Fair      Good Very_Good   Premium     Ideal     Total
    #>       I1 2,730.167 4,333.500 3,864.167 4,932.231 6,078.200 4,194.775
    #>      SI2 5,893.964 5,280.919 5,045.621 5,568.019 4,435.673 5,100.189
    #>      SI1 4,273.069 3,757.022 4,277.544 4,113.811 3,758.125 3,998.577
    #>      VS2 3,292.000 3,925.481 3,950.947 4,522.914 3,306.290 3,822.967
    #>      VS1 5,110.769 3,740.697 3,889.475 4,461.333 3,189.362 3,789.181
    #>     VVS2 2,030.500 4,378.167 2,525.193 3,580.581 3,665.181 3,337.820
    #>     VVS1 6,761.500 3,889.333 1,945.875 1,426.692 2,960.594 2,608.460
    #>       IF 3,205.000   817.250 4,675.867 2,361.333 1,961.344 2,411.697
    #>    Total 4,505.238 4,130.433 3,959.916 4,369.409 3,470.224 3,907.186
    #>