Passing arguments to dplyr summarize function

I am trying to use the summarize function within dplyr to calculate summary statistics using a two argument function that passes a table and field name from a connected database. Unfortunately as soon as I wrap the summarize function with another function the results aren't correct. The end table is a dataframe that does not iterate through each row. I'll show the input/output below:

Summary Statistics Function library(dplyr)

data<-iris
data<- group_by(.data = data,Species)

SummaryStatistics <- function(table, field){
table %>%
summarise(count = n(),
          min = min(table[[field]], na.rm = T),
          mean = mean(table[[field]], na.rm = T, trim=0.05),
          median = median(table[[field]], na.rm = T))
}

SummaryStatistics(data, "Sepal.Length")

Output Table--Incorrect, it's just repeating the same calculation

     Species count   min     mean median
1     setosa    50   4.3 5.820588    5.8
2 versicolor    50   4.3 5.820588    5.8
3  virginica    50   4.3 5.820588    5.8

Correct Table/Desired Outcome--This is what the table should look like. When I run the summarize function outsize of the wrapper function, this is what it produces.

      Species count   min     mean median
 1     setosa    50   4.3 5.002174    5.0
 2 versicolor    50   4.9 5.934783    5.9
 3  virginica    50   4.9 6.593478    6.5

I hope this is easy to understand. I just can't grasp as to why the summary statistics work perfectly outside of the wrapper function, but as soon as I pass arguments to it, it will calculate the same thing for each row. Any help would be greatly appreciated.

Thanks, Kev

You need to use standard evaluation to use dplyr functions programmatically alongside lazyeval. The dplyrNSE vignette covers it fairly well.

library(dplyr)
library(lazyeval)

data <- group_by(iris, Species)

SummaryStatistics <- function(table, field){
  table %>%
    summarise_(count = ~n(),
              min = interp(~min(var, na.rm = T), var = as.name(field)),
              mean = interp(~mean(var, na.rm = T, trim=0.05), var = as.name(field)),
              median = interp(~median(var, na.rm = T), var = as.name(field)))
}

SummaryStatistics(data, "Sepal.Length")

# A tibble: 3 × 5
     Species count   min     mean median
      <fctr> <int> <dbl>    <dbl>  <dbl>
1     setosa    50   4.3 5.002174    5.0
2 versicolor    50   4.9 5.934783    5.9
3  virginica    50   4.9 6.593478    6.5