combine multiple commands into one r function-CodePudding

I need to create a function that will return several things, I started with a real data example and was able to get the result, but not really sure how to combine all these commands into one r function. My code here:

# find all the cases with the given breakover and store in a data frame named find_id

find_id <- model%>%
      filter(breakover == 5)%>%
      select(id)

#With the returned id, inner join with another table

all_case <- sim_breakover %>% inner_join(find_id, by = "id")



#Filter down the inner joined table with the two conditions and return the section_eur column

all_case <- all_case %>%
      filter(wells_per_section == 5 & section_eur >55250, section_eur < 74750 )%>%
      select(id, well_eur, section_eur, incr_eur)

Below is the function I'm trying to come up with but not really sure how to finish, in my example 5 is breakover, section_eur is 65000, tolerance is 15%, both breakover and wells_per_section will equal 5:

match <- function(breakover, section_eur, tolerance) {
  find_id <- df %>% filter(breakover == breakover)%>%select()
}

CodePudding user response：

It is not entirely clear to me what you are looking for. The following function returns all records where the breakover = req_breakover and section_eur is within tolerance of req_eur. Hopefully this is sufficient for you to figure out the remaining steps.

match <- function(df, req_breakover, req_eur, tolerance) {
  lower_bound = req_eur * (1 - tolerance)
  upper_bound = req_eur * (1   tolerance)

  find_id <- df %>%
    filter(breakover == req_breakover) %>%
    select(id)

  all_case <- df %>%
    inner_join(find_id, by = "id")

  all_case <- all_case %>%
    filter(section_eur > lower_bound ,
           section_eur < upper_bound )%>%
    select(id, well_eur, section_eur, incr_eur)

  return(all_case)
}

You would call the function as follows:

output = match(model, 5, 65000, 0.15)

A more compact function would look like:

match <- function(df, req_breakover, req_eur, tolerance) {
  output <- df %>%
    filter(breakover == req_breakover
           section_eur > req_eur * (1 - tolerance),
           section_eur < req_eur * (1   tolerance))%>%
    select(id, well_eur, section_eur, incr_eur)

Note that the return command is unnecessary. I have included it for clarity.