R: losing column names when adding rows to an empty data frame - r

Question

I am just starting with R and encountered a strange behaviour: when inserting the first row in an empty data frame, the original column names get lost.
example:
a<-data.frame(one = numeric(0), two = numeric(0))
a
#[1] one two
#<0 rows> (or 0-length row.names)
names(a)
#[1] "one" "two"
a<-rbind(a, c(5,6))
a
# X5 X6
#1 5 6
names(a)
#[1] "X5" "X6"
As you can see, the column names one and two were replaced by X5 and X6.
Could somebody please tell me why this happens and is there a right way to do this without losing column names?
A shotgun solution would be to save the names in an auxiliary vector and then add them back when finished working on the data frame.
Thanks
Context:
I created a function which gathers some data and adds them as a new row to a data frame received as a parameter.
I create the data frame, iterate through my data sources, passing the data.frame to each function call to be filled up with its results.

Answer 1

The rbind help pages specifies that :
For ‘cbind’ (‘rbind’), vectors of zero
length (including ‘NULL’) are ignored
unless the result would have zero rows
(columns), for S compatibility.
(Zero-extent matrices do not occur in
S3 and are not ignored in R.)
So, in fact, a is ignored in your rbind instruction. Not totally ignored, it seems, because as it is a data frame the rbind function is called as rbind.data.frame :
rbind.data.frame(c(5,6))
# X5 X6
#1 5 6
Maybe one way to insert the row could be :
a[nrow(a)+1,] <- c(5,6)
a
# one two
#1 5 6
But there may be a better way to do it depending on your code.

Answer 2

was almost surrendering to this issue.
1) create data frame with stringsAsFactor set to FALSE or you run straight into the next issue
2) don't use rbind - no idea why on earth it is messing up the column names. simply do it this way:
df[nrow(df)+1,] <- c("d","gsgsgd",4)
df <- data.frame(a = character(0), b=character(0), c=numeric(0))
df[nrow(df)+1,] <- c("d","gsgsgd",4)
#Warnmeldungen:
#1: In `[<-.factor`(`*tmp*`, iseq, value = "d") :
# invalid factor level, NAs generated
#2: In `[<-.factor`(`*tmp*`, iseq, value = "gsgsgd") :
# invalid factor level, NAs generated
df <- data.frame(a = character(0), b=character(0), c=numeric(0), stringsAsFactors=F)
df[nrow(df)+1,] <- c("d","gsgsgd",4)
df
# a b c
#1 d gsgsgd 4

Answer 3

Workaround would be:
a <- rbind(a, data.frame(one = 5, two = 6))
?rbind states that merging objects demands matching names:
It then takes the classes of the
columns from the first data frame, and
matches columns by name (rather than
by position)

Answer 4

FWIW, an alternative design might have your functions building vectors for the two columns, instead of rbinding to a data frame:
ones <- c()
twos <- c()
Modify the vectors in your functions:
ones <- append(ones, 5)
twos <- append(twos, 6)
Repeat as needed, then create your data.frame in one go:
a <- data.frame(one=ones, two=twos)

Answer 5

One way to make this work generically and with the least amount of re-typing the column names is the following. This method doesn't require hacking the NA or 0.
rs <- data.frame(i=numeric(), square=numeric(), cube=numeric())
for (i in 1:4) {
calc <- c(i, i^2, i^3)
# append calc to rs
names(calc) <- names(rs)
rs <- rbind(rs, as.list(calc))
}
rs will have the correct names
> rs
i square cube
1 1 1 1
2 2 4 8
3 3 9 27
4 4 16 64
>
Another way to do this more cleanly is to use data.table:
> df <- data.frame(a=numeric(0), b=numeric(0))
> rbind(df, list(1,2)) # column names are messed up
> X1 X2
> 1 1 2
> df <- data.table(a=numeric(0), b=numeric(0))
> rbind(df, list(1,2)) # column names are preserved
a b
1: 1 2
Notice that a data.table is also a data.frame.
> class(df)
"data.table" "data.frame"

Answer 6

You can do this:
give one row to the initial data frame
df=data.frame(matrix(nrow=1,ncol=length(newrow))
add your new row and take out the NAS
newdf=na.omit(rbind(newrow,df))
but watch out that your newrow does not have NAs or it will be erased too.
Cheers
Agus

Answer 7

I use the following solution to add a row to an empty data frame:
d_dataset <-
data.frame(
variable = character(),
before = numeric(),
after = numeric(),
stringsAsFactors = FALSE)
d_dataset <-
rbind(
d_dataset,
data.frame(
variable = "test",
before = 9,
after = 12,
stringsAsFactors = FALSE))
print(d_dataset)
variable before after
1 test 9 12
HTH.
Kind regards
Georg

Answer 8

Researching this venerable R annoyance brought me to this page. I wanted to add a bit more explanation to Georg's excellent answer (https://stackoverflow.com/a/41609844/2757825), which not only solves the problem raised by the OP (losing field names) but also prevents the unwanted conversion of all fields to factors. For me, those two problems go together. I wanted a solution in base R that doesn't involve writing extra code but preserves the two distinct operations: define the data frame, append the row(s)--which is what Georg's answer provides.
The first two examples below illustrate the problems and the third and fourth show Georg's solution.
Example 1: Append the new row as vector with rbind
Result: loses column names AND coverts all variables to factors
my.df <- data.frame(
table = character(0),
score = numeric(0),
stringsAsFactors=FALSE
)
my.df <- rbind(
my.df,
c("Bob", 250)
)
my.df
X.Bob. X.250.
1 Bob 250
str(my.df)
'data.frame': 1 obs. of 2 variables:
$ X.Bob.: Factor w/ 1 level "Bob": 1
$ X.250.: Factor w/ 1 level "250": 1
Example 2: Append the new row as a data frame inside rbind
Result: keeps column names but still converts character variables to factors.
my.df <- data.frame(
table = character(0),
score = numeric(0),
stringsAsFactors=FALSE
)
my.df <- rbind(
my.df,
data.frame(name="Bob", score=250)
)
my.df
name score
1 Bob 250
str(my.df)
'data.frame': 1 obs. of 2 variables:
$ name : Factor w/ 1 level "Bob": 1
$ score: num 250
Example 3: Append the new row inside rbind as a data frame, with stringsAsFactors=FALSE
Result: problem solved.
my.df <- data.frame(
table = character(0),
score = numeric(0),
stringsAsFactors=FALSE
)
my.df <- rbind(
my.df,
data.frame(name="Bob", score=250, stringsAsFactors=FALSE)
)
my.df
name score
1 Bob 250
str(my.df)
'data.frame': 1 obs. of 2 variables:
$ name : chr "Bob"
$ score: num 250
Example 4: Like example 3, but adding multiple rows at once.
my.df <- data.frame(
table = character(0),
score = numeric(0),
stringsAsFactors=FALSE
)
my.df <- rbind(
my.df,
data.frame(
name=c("Bob", "Carol", "Ted"),
score=c(250, 124, 95),
stringsAsFactors=FALSE)
)
str(my.df)
'data.frame': 3 obs. of 2 variables:
$ name : chr "Bob" "Carol" "Ted"
$ score: num 250 124 95
my.df
name score
1 Bob 250
2 Carol 124
3 Ted 95

Answer 9

Instead of constructing the data.frame with numeric(0) I use as.numeric(0).
a<-data.frame(one=as.numeric(0), two=as.numeric(0))
This creates an extra initial row
a
# one two
#1 0 0
Bind the additional rows
a<-rbind(a,c(5,6))
a
# one two
#1 0 0
#2 5 6
Then use negative indexing to remove the first (bogus) row
a<-a[-1,]
a
# one two
#2 5 6
Note: it messes up the index (far left). I haven't figured out how to prevent that (anyone else?), but most of the time it probably doesn't matter.