Make discretize more consistent when supplied missing values

[EmilHvitfeldt]

This PR aims to close #982 .

It makes it so discretize() will automatically remove missing values is keep_na = TRUE. It further updates some existing code to use call2(), modify_call() and eval_tidy().

library(modeldata)
library(recipes)
data(biomass)

biomass$carbon[1] = NA
summary(biomass$carbon)
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
#>   14.61   44.70   47.10   48.29   49.70   97.18       1

discretize(biomass$carbon, keep_na = TRUE)
#> Bins: 5 (includes missing category)
#> Breaks: -Inf, 44.7, 47.1, 49.7, Inf

discretize(biomass$carbon, keep_na = FALSE)
#> Error in quantile.default(x = c(NA, 49.5, 47.82, 45.1, 46.76, 45.4, 47.17, : missing values and NaN's not allowed if 'na.rm' is FALSE

discretize(biomass$carbon, keep_na = FALSE, na.rm = TRUE)
#> Bins: 4
#> Breaks: -Inf, 44.7, 47.1, 49.7, Inf

^{Created on 2022-05-20 by the reprex package (v2.0.1)}

[topepo]

It looks good but please update the news file

[github-actions]

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