Merge/Join one data frame (X) to multiple data frames separately.

[saad1490]

Hi,
Let us say that we have some data frames named S1,S2,S3... that I want to join independently by column names to dataframe X. This is how they look like:

S1 <- data.frame(
      values = c (1:5), 
      numbers = c(11:15))
    S1
    
    
    S2 <- data.frame(
      values = c (1:5), 
      numbers = c(11:15))
    S2
    
    
    S3 <- data.frame(
      values = c (1:5), 
      numbers = c(16:20))
    S3

#Dataframe X

    X <- data.frame(
      values = c (26:30), 
      numbers = c(501:505))
    X
    
    S1
    S2
    S3
    X

Using rbind, it is really easy to merge/join them according to column names. As below:

 S1_X<- rbind(S1,X)
    
    S1_X
    
    
    S2_X<- rbind(S2,X)
    
    S2_X
    
    
    S3_X<- rbind(S3,X)
    
    S3_X

Now the problem is that I have 500 plus data frames (S1,S2,S3,S4,S5.....S500) that I want to join to dataframe "X" by any function. For example rbind. Is it possible to use some loop/function for that.

What I have best tried is create a list (nested list of dataframes) that I can later split into individual dataframes:

output = lapply(list(S1=S1,S2=S2,S3=S3),rbind,X)

The output is what I expected. So it works The only problem is, how do I specify S1 to S500?

Thank you in advance!

[scottyd22]

The code below generates a named list with each of the S* data frames bound to X. This approach assumes all 500 of your data frames will be created in your environment first, puts them all in a list using the ls() function, and then walks through each one, calling it to bind to X via eval(parse(text = )). Finally, if desired, you can name all of the newly formed data frames in the list to match the naming convention you shared (i.e. S1_X).

# some data frames
S1 <- data.frame(
  values = c (1:5), 
  numbers = c(11:15))

S2 <- data.frame(
  values = c (1:5), 
  numbers = c(11:15))

S3 <- data.frame(
  values = c (1:5), 
  numbers = c(16:20))

# create a list of the data frames
list_of_dataframes = ls()

# data frame to bind 
X <- data.frame(
  values = c (26:30), 
  numbers = c(501:505))

# function to bind a data frame to X
f = function(i) {
  rbind(eval(parse(text = i)), X)
}

# step through the list of data frames, binding each to X
output = lapply(list_of_dataframes, f)

# set the names of each newly bound data frame
names(output) = paste0(list_of_dataframes, '_X')

output
#> $S1_X
#>    values numbers
#> 1       1      11
#> 2       2      12
#> 3       3      13
#> 4       4      14
#> 5       5      15
#> 6      26     501
#> 7      27     502
#> 8      28     503
#> 9      29     504
#> 10     30     505
#> 
#> $S2_X
#>    values numbers
#> 1       1      11
#> 2       2      12
#> 3       3      13
#> 4       4      14
#> 5       5      15
#> 6      26     501
#> 7      27     502
#> 8      28     503
#> 9      29     504
#> 10     30     505
#> 
#> $S3_X
#>    values numbers
#> 1       1      16
#> 2       2      17
#> 3       3      18
#> 4       4      19
#> 5       5      20
#> 6      26     501
#> 7      27     502
#> 8      28     503
#> 9      29     504
#> 10     30     505

Created on 2022-09-14 with reprex v2.0.2.9000

[saad1490]

Thanks @scottyd22!!

I really appreciate your help.

Best regards

[martin.R]

An alternative and a more general solution would be to use Reduce() with merge() or purrr::reduce() with dplyr::*_join(), which allow for different dataframe structures as long as there is a key to join.

[Andrzej]

Could you please show that more general solution ?