Advertisement
Advertisement


Create an empty data.frame


Question

I'm trying to initialize a data.frame without any rows. Basically, I want to specify the data types for each column and name them, but not have any rows created as a result.

The best I've been able to do so far is something like:

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.

Is there a better way to do this?

2018/08/29
1
493
8/29/2018 10:31:22 AM

Accepted Answer

Just initialize it with empty vectors:

df <- data.frame(Date=as.Date(character()),
                 File=character(), 
                 User=character(), 
                 stringsAsFactors=FALSE) 

Here's an other example with different column types :

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 

N.B. :

Initializing a data.frame with an empty column of the wrong type does not prevent further additions of rows having columns of different types.
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.

2015/06/11
664
6/11/2015 5:49:03 PM

If you already have an existent data frame, let's say df that has the columns you want, then you can just create an empty data frame by removing all the rows:

empty_df = df[FALSE,]

Notice that df still contains the data, but empty_df doesn't.

I found this question looking for how to create a new instance with empty rows, so I think it might be helpful for some people.

2016/05/03

You can do it without specifying column types

df = data.frame(matrix(vector(), 0, 3,
                dimnames=list(c(), c("Date", "File", "User"))),
                stringsAsFactors=F)
2015/10/10

You could use read.table with an empty string for the input text as follows:

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:

df <- read.csv(text="Date,File,User", colClasses = colClasses)

Thanks to Richard Scriven for the improvement

2016/09/28

Just declare

table = data.frame()

when you try to rbind the first line it will create the columns

2018/05/24

The most efficient way to do this is to use structure to create a list that has the class "data.frame":

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:

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
2015/06/20