trying to make map* work with mutate to make new df columns from a list

map_mutate_problem_reprex.R

# load packages -----------------------------------------------------------

library(rlang)
library(dplyr)
library(tidyr)
library(magrittr)
library(purrr)
library(nomisr)



# set up initial list of tibbles ------------------------------------------

df <- list(
  district_population = tibble(
    dataset_title = "Population estimates - local authority based by single year",
    dataset_id = "NM_2002_1"
  ),
  jsa_claimants = tibble(
    dataset_title = "Jobseeker\'s Allowance with rates and proportions",
    dataset_id = "NM_1_1"
  )
)


# just use the first tibble for now, for testing --------------------------
# ideally I want to map across dfs through a list -------------------------

df <- df[[1]]

# generic add_columns function --------------------------------------------

nomis_add_columns <- function(dataset, new_column, function_name, these_vars_in_particular) {
  dataset %>%
  # map doesn't work if there's just one dataframe instead of a list 
  # map(~ mutate(., {{ new_column }} := function_name(!!!these_vars_in_particular)))
    mutate(., {{ new_column }} := function_name(!!!these_vars_in_particular))
}

# add 'geography' column function -----------------------------------------

get_geog_concepts <- function(dataset_id) {
  nomis_overview(id = dataset_id,
                 select = c("dimensions", "codes")) %>%
    pluck("value", 1, "dimension") %>%
    filter(concept == "geography") %>%
    pluck("types.type") %>%
    # if tibble already hds a geog_level column then this table would be
    # filtered by that:
    # filter(name == geog_level) %>%
    as.list              # required to obtain length = 1
}



# apply geography add column function -------------------------------------

# df %<>% nomis_add_columns(.,
#                           geography_types,
#                           get_geog_concepts,
#                           these_vars_in_particular = vars(dataset_id))


dput(df)
# structure(list(dataset_title = "Population estimates - local authority based by single year", 
#                dataset_id = "NM_2002_1", concepts = list(c("time", "gender", 
#                                                            "c_age", "measures"))),
#                                                           class = c("tbl_df", "tbl", "data.frame"),
#                                                           row.names = c(NA, -1L))

# nitty gritty functions --------------------------------------------------

get_concept_list <- function(df) {
  dataset_id <- pluck(df, "dataset_id")
  nomis_overview(id = dataset_id,
                 select = c("dimensions", "codes")) %>%
    pluck("value", 1, "dimension") %>%
    filter(!concept == "geography") %>%
    pull("concept")
}

get_concept_list(df)

# using toupper() here for simplicity - ultimately need to use
# get_concept_info()

get_concept_list(df) %>% 
  map(~ mutate(df, {{.x}} := toupper(.x)))
get_concept_list(df) %>% 
  map_chr(~ mutate(df, {{.x}} := toupper(.x)))
get_concept_list(df) %>% 
  map_df(~ mutate(df, {{.x}} := toupper(.x)))
get_concept_list(df) %>% 
  map_dfr(~ mutate(df, {{.x}} := toupper(.x)))
get_concept_list(df) %>% 
  map_dfc(~ mutate(df, {{.x}} := toupper(.x)))

# function to get info on each concept (except geography) -----------------

get_concept_info <- function(df, concept_name) {
  dataset_id <- pluck(df, "dataset_id")
  nomis_overview(id = dataset_id) %>%
    filter(name == "dimensions") %>%
    pluck("value", 1, "dimension") %>%
    filter(concept == concept_name) %>%
    pluck("codes.code", 1) %>%
    select(name, value) %>%
    nest(data = everything()) %>%
    as.list() %>%
    pluck("data")
}


# individual mutate works, for comparison ---------------------------------

# df %>% map(~ mutate(., time = get_concept_info(., concept_name = "time")))
df %>% mutate(., time = get_concept_info(df, "time"))