Applying a custom function to multiple files and creating unique csv output in R

Question

I am a beginner user in R and have been compiling a code to create a custom function to execute a specific task on some data that I possess. The custom function is structured to identify missing data in a csv file and patch this using the mean value. Thereafter, I want to summarize the data by year and month and export this as a csv file. I have multiple csv files that are sitting in a folder and would like to perform this task on each of these files. Thus far, I am able to get the code to perform the task at hand but don't know how to write a unique output for each csv file that has been processed and save these to a new folder. I would also like to retain the original file name in the processed output but have the words "_processed" appended to it. Additionally, any suggestions on how this code can be improved are most welcome. Thanks in advance.

# Load all packages required by the script
library(tidyverse) # data science package
library(lubridate) # work with dates
library(dplyr)     # data manipulation (filter, summarize, mutate)
library(ggplot2)   # graphics
library(gridExtra) # tile several plots next to each other
library(scales)

# Set the working directory #
setwd("H:/Shaeden_Post_Doc/Genus_Exchange/GEE_Data/MODIS_Product_Data_Raw/Cold_Temperate_Moist")


#create a function to summarize data by year and month
#patch missing values using the average

summarize_by_month = function(df){
  
# counting unique, missing and mean values in the ET column
df %>% summarise(n = n_distinct(ET),
                   na = sum(is.na(ET)),
                   med = mean(ET, na.rm = TRUE))
  
# assign mean values to the missing data and modify the dataframe
df = df %>%
    mutate(ET = replace(ET,is.na(ET),mean(ET, na.rm = TRUE)))
df
  
#separate data into year, month and day  
df$date = as.Date(df$date,format="%Y/%m/%d")

#summarize by year and month 

df %>%
    mutate(year = format(date, "%Y"), month = format(date, "%m")) %>%
    group_by(year, month) %>%
    summarise(mean_monthly = mean(ET))

}

#import all files and execute custom function for each
file_list = list.files(pattern="AET", full.names=TRUE)
file_list

my_AET_files = lapply(file_list, read_csv)
monthly_AET = lapply(my_AET_files, summarize_by_month)
monthly_AET 

A link to the sample datasets is provided below https://drive.google.com/drive/folders/1pLHt-vT87lxzW2We-AS1PwVcne3ALP2d?usp=sharing

Answer 1

path<-"your_peferred_path/" #set a path to were you want to save the files

x<-list.files(pattern= "your_pattern") # create a list of your file names

name<-str_sub(x, start=xL, end=yL) #x & y being the part of the name you want to keep 

for (i in 1:length(monthly_AET)){
  write_excel_csv(monthly_AET[i], paste0(path, name, "_processed.csv")) # paste0 allows to create custom names from variables and static strings
}

note: this is only an assumption and may have to be tweaked to suit your needs

Answer 2

You can read, manipulate data and write the csv in the same function:

library(dplyr)

summarize_by_month = function(file) {
  df <- readr::read_csv(file)

  # assign mean values to the missing data and modify the dataframe
  df = df %>% mutate(ET = replace(ET,is.na(ET),mean(ET, na.rm = TRUE)))

  #separate data into year, month and day  
  df$date = as.Date(df$date,format="%Y/%m/%d")

  #summarize by year and month 
  new_df <- df %>%
    mutate(year = format(date, "%Y"), month = format(date, "%m")) %>%
    group_by(year, month) %>%
    summarise(mean_monthly = mean(ET))
  
     write.csv(new_df, sprintf('output_folder/%s_processed.csv', 
           tools::file_path_sans_ext(basename(file))), row.names = FALSE)
}

monthly_AET = lapply(file_list, summarize_by_month)