Passing arguments to dplyr summarize function

Question

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

Answer 1

You need to use Non-Standard Evaluation (NSE) to use dplyr functions programmatically alongside lazyeval . The dplyr NSE 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