How to use map_dfr to clean different datasets

Question

I have written some code to clean a particular database but now I want to apply this code to other databases. I am having the following problems:

1. Function reads the database as an object class "character".
2. I am using an external database (UR_data) to filter data, is this the correct way to include this? UR_data contains only country names, which is used to subset the list of databases to be cleaned.
3. I need to save each cleaned database as [name_of_database]_cleaned, is this the correct way to include this?

#Databases to clean
 data_list <- c(Pre_primary_data, Primary_data, Lower_secondary_data, Higher_secondary_data)

dput(head(data_mod))
structure(list(Country = c("Afghanistan", "Bangladesh", "Benin", 
"Bhutan", "Burkina Faso", "Cabo Verde"), `2010` = c(295.32, NA, 
NA, 3747.64, 615.93, NA), `2011` = c(228.51, 438.31, NA, 4670.31, 
322.81, NA), `2012` = c(165.72, 430.68, NA, NA, 317.36, NA), 
    `2013` = c(201.43, NA, 311.84, NA, 290.36, NA), `2014` = c(217.26, 
    NA, NA, NA, 315.27, 974.33), `2015` = c(185.45, NA, NA, 3216.2, 
    365.42, 1142.58), `2016` = c(254.33, 540.51, NA, NA, 808.66, 
    1255.95), `2017` = c(242.6, NA, NA, NA, NA, 1278.25), `2018` = c(NA_real_, 
    NA_real_, NA_real_, NA_real_, NA_real_, NA_real_), `2019` = c(NA, 
    473.69, NA, NA, NA, 679.57), `2020` = c(NA, 420.54, NA, NA, 
    NA, NA), `2021` = c(NA_integer_, NA_integer_, NA_integer_, 
    NA_integer_, NA_integer_, NA_integer_)), class = c("rowwise_df", 
"tbl_df", "tbl", "data.frame"), row.names = c(NA, -6L), groups = structure(list(
    .rows = structure(list(1L, 2L, 3L, 4L, 5L, 6L), ptype = integer(0), class = c("vctrs_list_of", 
    "vctrs_vctr", "list"))), row.names = c(NA, -6L), class = c("tbl_df", 
"tbl", "data.frame")))

Each database consist of a column of Country names and a set of year columns indicate expenditure.

#Cleaning function
 data_clean <- function(data) {
      
        data %>%
          filter(data$Country %in% UR_data$Country) %>%
          arrange(data$Country)  %>% 
          na_if(.,0) %>%
          rowwise(.) %>%
          filter(!all(is.na(c_across(where(is.numeric))))) %>% 
          data.frame()-> paste0(data, "_cleaned", sep = "")
        
      }
   
#Applying map function
      map_dfr(data_list, data_clean)