How to append rows to an R data frame

user_2932774 0
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I have looked around StackOverflow, but I cannot find a solution specific to my problem, which involves appending rows to an R data frame.

I am initializing an empty 2-column data frame, as follows.

df = data.frame(x = numeric(), y = character())

Then, my goal is to iterate through a list of values and, in each iteration, append a value to the end of the list. I started with the following code.

for (i in 1:10) {
    df$x = rbind(df$x, i)
    df$y = rbind(df$y, toString(i))
}

I also attempted the functions c, append, and merge without success. Please let me know if you have any suggestions.

Update from comment: I don't presume to know how R was meant to be used, but I wanted to ignore the additional line of code that would be required to update the indices on every iteration and I cannot easily preallocate the size of the data frame because I don't know how many rows it will ultimately take. Remember that the above is merely a toy example meant to be reproducible. Either way, thanks for your suggestion!

rows dataframe append r merge
7 Answers

Update with purrr, tidyr & dplyr

As the question is already dated (6 years), the answers are missing a solution with newer packages tidyr and purrr. So for people working with these packages, I want to add a solution to the previous answers - all quite interesting, especially .

The biggest advantage of purrr and tidyr are better readability IMHO. purrr replaces lapply with the more flexible map() family, tidyr offers the super-intuitive method add_row - just does what it says :)

map_df(1:1000, function(x) { df %>% add_row(x = x, y = toString(x)) })

This solution is short and intuitive to read, and it's relatively fast:

system.time(
   map_df(1:1000, function(x) { df %>% add_row(x = x, y = toString(x)) })
)
   user  system elapsed 
   0.756   0.006   0.766

It scales almost linearly, so for 1e5 rows, the performance is:

system.time(
  map_df(1:100000, function(x) { df %>% add_row(x = x, y = toString(x)) })
)
   user  system elapsed 
 76.035   0.259  76.489

which would make it rank second right after data.table (if your ignore the placebo) in the benchmark by @Adam Ryczkowski:

nr  function      time
4   data.frame    228.251 
3   sqlite        133.716
2   data.table      3.059
1   rbindlist     169.998 
0   placebo         0.202
user_77690760

My solution is almost the same as the original answer but it doesn't worked for me.

So, I gave names for the columns and it works:

painel <- rbind(painel, data.frame("col1" = xtweets$created_at,
                                   "col2" = xtweets$text))
user_79706030
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