如何使用lapply将列表的元素放入不同的数据帧 [英] How to use lapply to put elements of a list into different data frames

问题描述

我有一个称为db的数据帧列表；每个数据框都有其名称. 我使用:

I have a list of dataframes called db; each data frame has its name. I use:

lapply(names(db),
       function(x)write.csv(db[x],
                            file =paste0(x,'.csv')))

提取d.frame并将其保存到csv文件中. 现在，我尝试从列表中提取数据帧，并使用以下命令创建不同的数据帧:

to extract the d.frames and save them into csv files. Now i'm trying to extact from the list the dataframes and create different dataframes with this command:

lapply(names(db),
       function(x)as.data.frame(db[x]))

但是它没有将任何数据帧存储到工作空间中，如何将每个具有不同名称的df存储到工作空间中?

But it does not store any data frame into the workspace , how can I store each df with different names into the workspace?

推荐答案

我们用[[

lapply(names(db), function(x) write.csv(db[[x]],
        file =paste0(x,'.csv'), row.names=FALSE, quote= FALSE))

因为db[x]仍然是data.frame与length为1的list.

如果这些是大型数据集，则data.table中的fwrite函数将更有效

If these are big datasets, the fwrite function from data.table would be more efficient

library(data.table)
lapply(names(db), function(x) fwrite(db[[x]], file = paste0(x, ".csv")))

---

只是为了说明问题，

---

Just to illustrate the issue,

set.seed(24)
db <- setNames(lapply(1:3, function(i) as.data.frame(matrix(sample(1:9, 
                             5*4, replace=TRUE), ncol=4))), paste0("df", 1:3))

OP的方法与[[的区别在于OP的方法将write列名带有'db'的names前缀的文件，而[[则没有任何此类问题.

The difference between the OP's approach and the [[ is in the OP's approach it will write the files with column names that have a prefix from the names of 'db' whereas the [[ will not have any such problem.

关于第二个问题，即在全局环境中创建多个对象，我们可以直接在'db'上使用list2env

Regarding the second problem, that is creating multiple objects in the global environment, we can use list2env directly on the 'db'

list2env(db, envir = .GlobalEnv)

但是，不建议这样做，因为大多数操作都可以在list本身内完成.

But, this is not recommended as it most of the operations can be done within the list itself.

df1
#  V1 V2 V3 V4
#1  3  9  6  9
#2  3  3  4  2
#3  7  7  7  1
#4  5  8  7  5
#5  6  3  3  2

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