**Combining multiple dataframes into one

[hawken1]

I have four dataframes, "df1, df2, df3, and df4." Each has different times values corresponding to behavioral events. I want to do the following: I would first like to load all the dataframes into R. Then I would like to add a column using the mutate function with the specific dataframe (e.g. 1, 2, 3, or 4) to each dataframe. Finally, I would like to combine all the frames, after adding an individualized column to each, into one big dataframe so that I can perform other operations on this new dataframe.

The purpose of creating the new columns for each dataframe before combining them all into one, is so that I can refer back to the individual dataframes when I need to.

I hope this makes sense. Cheers.

[williaml]

Can you provide a reprex? It will make it easier to work out what you are after.

FAQ: How to do a minimal reproducible example ( reprex ) for beginners

A minimal reproducible example consists of the following items: A minimal dataset, necessary to reproduce the issue The minimal runnable code necessary to reproduce the issue, which can be run on the given dataset, and including the necessary information on the used packages. Let's quickly go over each one of these with examples: Minimal Dataset (Sample Data) You need to provide a data frame that is small enough to be (reasonably) pasted on a post, but big enough to reproduce your issue. Let's say, as an example, that you are working with the iris data frame head(iris) #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1 5.1 3.5 1.4 0.2…

[mitchelloharawild]

The dplyr::bind_rows() function might be what you're looking for. If you name each of your dataset inputs, you can add an identifier using the .id argument.

For example:

library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
df1 <- df2 <- df3 <- df4 <- band_instruments
bind_rows(
  `1` = df1,
  `2` = df2, 
  `3` = df3, 
  `4` = df4,
  .id = "identifier"
)
#> # A tibble: 12 x 3
#>    identifier name  plays 
#>    <chr>      <chr> <chr> 
#>  1 1          John  guitar
#>  2 1          Paul  bass  
#>  3 1          Keith guitar
#>  4 2          John  guitar
#>  5 2          Paul  bass  
#>  6 2          Keith guitar
#>  7 3          John  guitar
#>  8 3          Paul  bass  
#>  9 3          Keith guitar
#> 10 4          John  guitar
#> 11 4          Paul  bass  
#> 12 4          Keith guitar

^{Created on 2020-10-09 by the reprex package (v0.3.0)}

[hawken1]

Hey, so I've included the dput with a header of 100 (since they are pretty big dataframes) for three dataframes. I hope this helps. I am looking to create a new column for each that specifies the dataframe by its ID (1, 2, or 3) and then combine the three into one somehow.

[williaml]

Are you just trying to read in all the files, then add another column based on the source? Something like this using purrr, then adding the source indicator as shown.

Otherwise, the response from @mitchelloharawild should also work.

Import a Directory of CSV Files at Once Using {purrr} and {readr}