R Apply Function for Formatting Many Datasets - r

Question

I would like to loop through nine data sets, perform calculations, and output a different file name.
Existing Code:
list <- c(corporate_service, finance, its, law, market_services, operations, president, member_services, System_Planning)
Calc <- function(list){
list %>% filter(Total_Flag == 1) %>%
select(Element, Amount, Total)
}
lapply(list, Calc)
I would like to loop through each dataset and apply the function above. More specifically, I would like to re-name each processed dataframe something different. Is there a way to do this? I should also note, this code has not worked for me - is there anything noticeably wrong?
Thanks

Answer 1

Avoid flooding your global environment with separate, similarly structured data frames in the first place. Instead continue to use a list of data frames. See #GregorThomas's best practices answer of why. In fact, a named list is preferable for better indexing.
# DEFINE A NAMED LIST OF DATA FRAMES
df_list <- list(corporate_service = corporate_service,
finance = finance,
its = its,
law = law,
market_services = market_services,
operations = operations,
president = president,
member_services = member_services,
system_planning = System_Planning)
# REMOVE ORIGINALS FROM GLOBAL ENVIRONMENT
rm(corporate_service, finance, its, law, market_services,
operations, president, member_services, System_Planning)
# REVIEW STRUCTURE
str(df_list)
Then define a method to interact with a single data frame (not list) and its list name. Then call it iteratively:
Calc <- function(df, nm) {
df <- select(filter(df, Total_Flag == 1), Element, Amount, Total)
write.csv(df, file.path("path", "to", "my", "destination", paste(nm, ".csv")))
return(df)
}
# ASSIGN TO A NEW LIST
new_df_list <- mapply(Calc, df_list, names(df_list), SIMPLIFY=FALSE)
new_df_list <- Map(Calc, df_list, names(df_list)) # EQUIVALENT WRAPPER TO ABOVE
To be clear, you lose no functionality of a data frame if it is stored in a larger container.
head(new_df_list$corporate_service)
tail(new_df_list$finance)
summary(new_df_list$its)
Such containers even help serialize same operations:
lapply(new_df_list, summary)
Even concatenate all data frame elements together with column of corresponding list name:
final_df <- dplyr::bind_rows(new_df_list, .id="division")
Overall, your organization and data management is enhanced since you only have to use a single, indexed object and not many that require ls, mget, get, eval, assign for dynamic operations.