[英]Applying a custom function to multiple files and creating unique csv output in R
我是 R 的初学者,一直在编译代码以创建自定义 function 以对我拥有的某些数据执行特定任务。 自定义 function 的结构可识别 csv 文件中缺失的数据,并使用平均值对其进行修补。 此后,我想按年和月汇总数据并将其导出为 csv 文件。 我有多个 csv 文件位于一个文件夹中,我想对每个文件执行此任务。 到目前为止,我能够获得执行手头任务的代码,但不知道如何为每个已处理的 csv 文件编写一个唯一的 output 并将它们保存到新文件夹中。 我还想在处理后的 output 中保留原始文件名,但附加了“_processed”字样。 此外,欢迎就如何改进此代码提出任何建议。 提前致谢。
# 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
下面提供了示例数据集的链接https://drive.google.com/drive/folders/1pLHt-vT87lxzW2We-AS1PwVcne3ALP2d?usp=sharing
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
}
注意:这只是一个假设,可能需要根据您的需要进行调整
您可以在同一个 function 中读取、操作数据和写入 csv:
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)
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