Multiple df using tidyverse

I want to eventually combine multiple dataframes into one big dataframe, however, I want the identity of each dataframe to remain intact, and so I was thinking of using map_dfr to create a new column (with the header "Day_") for each file, and then somehow combining the files into one big dataframe, so I can later run the rest of the code. How might I go about doing this?

This can be done conveniently with dplyr.
I dont have your csv's so we'll go with iris dataset to generate example from.

#example frames
df_1<- slice(iris,1)
df_2 <- slice(iris,2)
df_3 <- slice(iris,3)

(getnames <- ls(pattern="df_"))

as_a_list <- map(getnames,
                 ~get(.)) %>% 

#binding and recording source
(result_df<- bind_rows(as_a_list,
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A slightly more concise solution:

files <- list.files(pattern = "df_") # adapt as required
result_df <- purrr::map_dfr(files, read.csv, .id = "file")

Hello, thanks for the response; when I go to do this, it gives me empty character values for "files" and also result_df is empty and says 0 observations of 0 variables

Sorry I was copying the pattern from the previous replier's example.

Try this:

files <- list.files(pattern = "Day") # adapt as required
result_df <- purrr::map_dfr(files, read.csv, .id = "file")
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I think map_dfr is similar to bind_rows in that the default is just to get a numeric index as the ID, which might be all that is needed in many cases.
Admittedly my example is a little overloaded / inelegant, but it is to accommodate for 'better naming' in the resulting dataset.
Im thinking that perhaps in your map_dfr approach something like

names(files) <- files

before the map_dfr step would cover that :slight_smile:

map_dfr() just combines map() and bind_rows() into one step.

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