本文翻譯自：Create an empty data.frame
I'm trying to initialize a data.frame without any rows. 我正在嘗試初始化沒有任何行的data.frame。 Basically, I want to specify the data types for each column and name them, but not have any rows created as a result. 基本上，我想爲每一列指定數據類型併爲其命名，但因此沒有創建任何行。
The best I've been able to do so far is something like: 到目前爲止，我能做的最好的事情是：
df <- data.frame(Date=as.Date("01/01/2000", format="%m/%d/%Y"),
File="", User="", stringsAsFactors=FALSE)
df <- df[-1,]
Which creates a data.frame with a single row containing all of the data types and column names I wanted, but also creates a useless row which then needs to be removed. 這將創建一個data.frame，其中包含包含我想要的所有數據類型和列名的單行，而且還會創建一個無用的行，然後將其刪除。
Is there a better way to do this? 有一個更好的方法嗎？
#1樓
參考：https://stackoom.com/question/iqi7/創建一個空的data-frame
#2樓
Just initialize it with empty vectors: 只需使用空向量對其進行初始化：
df <- data.frame(Date=as.Date(character()),
File=character(),
User=character(),
stringsAsFactors=FALSE)
Here's an other example with different column types : 這是另一個具有不同列類型的示例：
df <- data.frame(Doubles=double(),
Ints=integer(),
Factors=factor(),
Logicals=logical(),
Characters=character(),
stringsAsFactors=FALSE)
str(df)
> str(df)
'data.frame': 0 obs. of 5 variables:
$ Doubles : num
$ Ints : int
$ Factors : Factor w/ 0 levels:
$ Logicals : logi
$ Characters: chr
NB : 注意：
Initializing a data.frame with an empty column of the wrong type does not prevent further additions of rows having columns of different types. 使用錯誤類型的空列初始化data.frame不會阻止進一步添加具有不同類型列的行。
This method is just a bit safer in the sense that you'll have the correct column types from the beginning, hence if your code relies on some column type checking, it will work even with a data.frame with zero rows. 從一開始就具有正確的列類型的意義上說，此方法稍微安全一些，因此，如果您的代碼依賴於某些列類型檢查，則即使行數爲零的data.frame使用。
#3樓
You can do it without specifying column types 您可以在不指定列類型的情況下進行操作
df = data.frame(matrix(vector(), 0, 3,
dimnames=list(c(), c("Date", "File", "User"))),
stringsAsFactors=F)
#4樓
You could use read.table with an empty string for the input text as follows: 您可以將read.table與空字符串一起用於輸入text ，如下所示：
colClasses = c("Date", "character", "character")
col.names = c("Date", "File", "User")
df <- read.table(text = "",
colClasses = colClasses,
col.names = col.names)
Alternatively specifying the col.names as a string: 或者將col.names指定爲字符串：
df <- read.csv(text="Date,File,User", colClasses = colClasses)
Thanks to Richard Scriven for the improvement 感謝Richard Scriven的改進
#5樓
If you are looking for shortness : 如果您正在尋找短缺：
read.csv(text="col1,col2")
so you don't need to specify the column names separately. 因此您無需單獨指定列名稱。 You get the default column type logical until you fill the data frame. 在填充數據框之前，您將獲得默認的邏輯列類型。
#6樓
The most efficient way to do this is to use structure to create a list that has the class "data.frame" : 實現此目的的最有效方法是使用structure創建具有"data.frame"類的列表：
structure(list(Date = as.Date(character()), File = character(), User = character()),
class = "data.frame")
# [1] Date File User
# <0 rows> (or 0-length row.names)
To put this into perspective compared to the presently accepted answer, here's a simple benchmark: 與當前接受的答案相比，這是一個簡單的基準：
s <- function() structure(list(Date = as.Date(character()),
File = character(),
User = character()),
class = "data.frame")
d <- function() data.frame(Date = as.Date(character()),
File = character(),
User = character(),
stringsAsFactors = FALSE)
library("microbenchmark")
microbenchmark(s(), d())
# Unit: microseconds
# expr min lq mean median uq max neval
# s() 58.503 66.5860 90.7682 82.1735 101.803 469.560 100
# d() 370.644 382.5755 523.3397 420.1025 604.654 1565.711 100