require(stats)

#' Compute different averages
#'
#' @param x \code{numeric} vector of sample data
#' @param type \code{character} vector of length 1 specifying the average type
#' @return \code{centre} returns the sample average according to the chosen method.
#' @examples
#' centre(rcauchy(10), "mean")
#' @export
centre <- function(x, type) {
  switch(type,
         mean = mean(x),
         median = median(x),
         trimmed = mean(x, trim = .1))
}
x <- rcauchy(10)
centre(x, "mean")

library(ggplot2)

models <- tibble::tribble(
  ~model_name,    ~ formula,
  "length-width", Sepal.Length ~ Petal.Width + Petal.Length,
  "interaction",  Sepal.Length ~ Petal.Width * Petal.Length
)

iris %>% 
  nest_by(Species) %>% 
  left_join(models, by = character()) %>% 
  rowwise(Species, model_name) %>% 
  mutate(model = list(lm(formula, data = data))) %>% 
  summarise(broom::glance(model))
#> `summarise()` regrouping output by 'Species', 'model_name' (override with `.groups` argument)
#> # A tibble: 6 x 13
#> # Groups:   Species, model_name [6]
#>   Species model_name r.squared adj.r.squared sigma statistic  p.value    df
#>   <fct>   <chr>          <dbl>         <dbl> <dbl>     <dbl>    <dbl> <int>
#> 1 setosa  length-wi…     0.112        0.0739 0.339      2.96 6.18e- 2     3
#> 2 setosa  interacti…     0.133        0.0760 0.339      2.34 8.54e- 2     4
#> 3 versic… length-wi…     0.574        0.556  0.344     31.7  1.92e- 9     3
#> 4 versic… interacti…     0.577        0.549  0.347     20.9  1.11e- 8     4
#> 5 virgin… length-wi…     0.747        0.736  0.327     69.3  9.50e-15     3
#> 6 virgin… interacti…     0.757        0.741  0.323     47.8  3.54e-14     4
#> # … with 5 more variables: logLik <dbl>, AIC <dbl>, BIC <dbl>, deviance <dbl>,
#> #   df.residual <int>