How to feed a list of unquoted column names into `lapply` (so that I can use it with a `dplyr` function)

Question

I am trying to write a function in tidyverse/dplyr that I want to eventually use with lapply (or map). (I had been working on it to answer

Answer 1

Let me first point out that in your initial report function, you can use quo_name to convert the quosure into a string, which you can then use in mutate like the following:

library(dplyr)
library(rlang)

report <- function(report_cat){
  report_cat <- enquo(report_cat)

  sample_data %>%
    group_by(!!report_cat, YEAR) %>%
    summarize(num=n(),total=sum(AMOUNT)) %>%
    rename(REPORT_VALUE = !!report_cat) %>%
    mutate(REPORT_CATEGORY = quo_name(report_cat))
}

report(REPORT_CODE)

Now, to address your question of "how to feed a list of unquoted strings through lapply or map to make it work inside dplyr functions", I propose two ways of doing it.

1. Use rlang::sym to parse your strings and unquote it when feeding into lapply or map

library(purrr)

cat.list <- c("REPORT_CODE","PAYMENT_METHOD","INBOUND_CHANNEL","AMOUNT_CAT")

map_df(cat.list, ~report(!!sym(.)))    

or with syms you can parse all elements of a vector at once:

map_df(syms(cat.list), ~report(!!.))

Result:

# A tibble: 27 x 5
# Groups:   REPORT_VALUE [16]
   REPORT_VALUE  YEAR   num total REPORT_CATEGORY
                        
 1            J  FY14     1    25     REPORT_CODE
 2            Q  FY16     1     1     REPORT_CODE
 3            Q  FY17     1   100     REPORT_CODE
 4            R  FY17     1    50     REPORT_CODE
 5            R  FY18     2    75     REPORT_CODE
 6            S  FY17     2   400     REPORT_CODE
 7            S  FY18     2   530     REPORT_CODE
 8        Check  FY14     1    25  PAYMENT_METHOD
 9        Check  FY17     1    50  PAYMENT_METHOD
10        Check  FY18     2    55  PAYMENT_METHOD
# ... with 17 more rows 

2. Rewrite your report function by placing lapply or map inside so that report can do NSE

report <- function(...){
  report_cat <- quos(...)

  map_df(report_cat, function(x) sample_data %>%
             group_by(!!x, YEAR) %>%
             summarize(num=n(),total=sum(AMOUNT)) %>%
             rename(REPORT_VALUE = !!x) %>%
             mutate(REPORT_CATEGORY = quo_name(x)))
}

By placing map_df inside report, you can take advantage of quos, which converts ... to list of quosures. They are then fed into map_df and unquoted one by one using !!.

report(REPORT_CODE, PAYMENT_METHOD, INBOUND_CHANNEL, AMOUNT_CAT)

Another advantage of writing it like this is that you can also supply a vector of string symbols and splice them using !!! like the following:

report(!!!syms(cat.list))

Result:

# A tibble: 27 x 5
# Groups:   REPORT_VALUE [16]
   REPORT_VALUE  YEAR   num total REPORT_CATEGORY
                        
 1            J  FY14     1    25     REPORT_CODE
 2            Q  FY16     1     1     REPORT_CODE
 3            Q  FY17     1   100     REPORT_CODE
 4            R  FY17     1    50     REPORT_CODE
 5            R  FY18     2    75     REPORT_CODE
 6            S  FY17     2   400     REPORT_CODE
 7            S  FY18     2   530     REPORT_CODE
 8        Check  FY14     1    25  PAYMENT_METHOD
 9        Check  FY17     1    50  PAYMENT_METHOD
10        Check  FY18     2    55  PAYMENT_METHOD
# ... with 17 more rows