提问人：Alexia k Boston 提问时间：7/18/2023 最后编辑：zx8754Alexia k Boston 更新时间：7/18/2023 访问量：49
修改多个txt文件的数据帧代码
Modify the code of dataframes for multiple txt files
问：
以下代码适用于数据帧 df1、df2。代码正在读取数据列 （var） 并查看每个数据帧，如果 var 列不存在，它会将其添加并将 NA 放在该特定列中。
dfs1 <- c('df1','df2')
var <- c('City_Name', 'Temp', 'Pres' , 'Wind_Hor' , 'Wind_Ver' , 'Rainf' , 'S_Moist')
lapply(dfs1, \(x) {
dfn <- get(x, envir = .GlobalEnv)
dfn[[var[which(is.na(match(var,names(dfn))))]]] <- NA
dfn <- dfn %>% select(all_of(var))
return(assign(x,dfn,envir = .GlobalEnv))
})
如果我有一个文件列表，我该如何修改上面的代码？
我试着跟着走
dfs1 <- list.files(path = 'D:/Test3', pattern = "*txt", recursive = TRUE)
var <- c('D/T', 'City_Name', 'Temp', 'Pres', 'Wind_Hor', 'Wind_Ver', 'Rainf', 'S_Moist')
lapply(dfs1, \(x) {
dfn <- get(x, envir = .GlobalEnv)
dfn[[var[which(is.na(match(var,names(dfn))))]]] <- NA
dfn <- dfn %>% select(all_of(var))
return(assign(x,dfn,envir = .GlobalEnv))
})
但它返回一个错误：
Error in get(x, envir = .GlobalEnv) :
object 'File/File1.txt' not found
任何人都可以回答如何修改文件的代码。
答：
2赞
Mburu
7/18/2023
#1
library(dplyr)
library(data.table)
# I find it easier to use data.table in this case when it comes to assigning NA's using dt[, (character_vector) := NA]
var <- c('City_Name', 'Temp', 'Pres' , 'Wind_Hor' , 'Wind_Ver' , 'Rainf' , 'S_Moist')
## comment 2 columns
df1 = data.frame(City_Name = "NYC",
Temp = 20,
Pres = 10,
#Wind_Hor = 5,
Wind_Ver = 5,
# Rainf = 10,
S_Moist = 5)
## Comment 3
df2 = data.frame(#City_Name = "NYC",
Temp = 15,
#Pres = 15,
Wind_Hor = 5,
Wind_Ver = 5,
Rainf = 15)
#S_Moist = 5)
## put the dfs as a list
dfs1 <- list(df1, df2)
## loop through
processed_dfs <- lapply(seq_along(dfs1), function(x) {
dfn = dfs1[[x]]
dfn_nms = names(dfn)
#get missing column names
var_missing = var[!var %in% dfn_nms]
setDT(dfn) # convert to data.table
dfn[, (var_missing) := NA] # asign NA to missing
dfn[, ..var] ## data.table select statement
})
## combine final output
## dplyr method
final_df <- bind_rows(processed_dfs)
## if you want final as data.table
final_df <- rbindlist(processed_dfs)
# I made the above code to make it reproducible if I wanted to combine it by from reading text files from my disk then
## this will give you the files
## you need to read in the files
## to read in files create file_paths
dfs1 <- list.files(path = 'D:/Test3',
pattern = "*txt",
recursive = TRUE)
## this will create file paths ie appending the name of the file to folder name
dfs1_file_paths = file.path( 'D:/Test3', dfs1)
var <- c('D/T', 'City_Name', 'Temp',
'Pres', 'Wind_Hor',
'Wind_Ver', 'Rainf',
'S_Moist')
processed_dfs <- lapply(seq_along(dfs1), function(x) {
file_x = dfs1_file_paths[[x]] ## file path i
## read the file
dfn <- fread(file_x) ## use can also use read.table but you need one more step to convert to data.table
dfn_nms = names(dfn)
#get missing column names
var_missing = var[!var %in% dfn_nms]
dfn[, (var_missing) := NA] # asign NA to missing
dfn[, ..var] ## data.table select statement
})
## combine final output
## dplyr method
final_df <- bind_rows(processed_dfs)
## if you want final as data.table
final_df <- rbindlist(processed_dfs)
# Hope this helps
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