Some googledrive functions are built to naturally handle multiple files, while others operate on a single file.
Functions that expect a single file:
-
drive_browse()
-
drive_cp()
drive_download()drive_ls()-
drive_mv()
drive_put()-
drive_rename()
drive_update()drive_upload()
Functions that allow multiple files:
In general, the principle is: if there are multiple parameters that are likely to vary across multiple files, the function is designed to take a single input. In order to use these function with multiple inputs, use them together with your favorite approach for iteration in R. Below is a worked example, focusing on tools in the tidyverse, namely the map() functions in purrr.
Upload multiple files, then rename them
Scenario: we have multiple local files we want to upload into a folder on Drive. Then we regret their original names and want to rename them.
Load packages.
Upload
Use the example files that ship with googledrive.
local_files <- drive_examples_local()
local_files <- set_names(local_files, basename(local_files))
local_files
#> chicken.csv
#> "/home/runner/work/_temp/Library/googledrive/extdata/example_files/chicken.csv"
#> chicken.jpg
#> "/home/runner/work/_temp/Library/googledrive/extdata/example_files/chicken.jpg"
#> chicken.pdf
#> "/home/runner/work/_temp/Library/googledrive/extdata/example_files/chicken.pdf"
#> chicken.txt
#> "/home/runner/work/_temp/Library/googledrive/extdata/example_files/chicken.txt"
#> imdb_latin1.csv
#> "/home/runner/work/_temp/Library/googledrive/extdata/example_files/imdb_latin1.csv"
#> markdown.md
#> "/home/runner/work/_temp/Library/googledrive/extdata/example_files/markdown.md"
#> r_about.html
#> "/home/runner/work/_temp/Library/googledrive/extdata/example_files/r_about.html"
#> r_logo.jpg
#> "/home/runner/work/_temp/Library/googledrive/extdata/example_files/r_logo.jpg"Create a folder on your Drive and upload all files into this folder by iterating over the local_files using purrr::map().
folder <- drive_mkdir("upload-into-me-article-demo")
#> Created Drive file:
#> • 'upload-into-me-article-demo' <id: 1I5R_fOodKZx4UNC2NwB1q0utPZR1Ydou>
#> With MIME type:
#> • 'application/vnd.google-apps.folder'
with_drive_quiet(
files <- map(local_files, ~ drive_upload(.x, path = folder))
)First, let’s confirm that we uploaded the files into the new folder.
drive_ls(folder)
#> # A dribble: 8 × 3
#> name id drive_resource
#> <chr> <drv_id> <list>
#> 1 r_logo.jpg 1bFk8cGyK1kSxOwGdUZxGwSXdCnjQk5W6 <named list [46]>
#> 2 r_about.html 1r7k_CELUgyJ0HLIK18CsdXn3gJ0Ee9R2 <named list [45]>
#> 3 markdown.md 1MrikNZxrzEr5u6qAevT1-sAhOlBDiHEY <named list [44]>
#> 4 imdb_latin1.csv 1Lg5zSebMWGDS16Tns5UvLJoivMnUm-4f <named list [44]>
#> 5 chicken.txt 1t61PM3vomBYvB_9xUnO8T6Su3cZuC85n <named list [45]>
#> 6 chicken.pdf 1BT0Eas51R_rh-cUcjN6YQz4roJa_52_0 <named list [45]>
#> 7 chicken.jpg 1Q7G6e3EGEPV7kz5VLLINa900ssQIegYP <named list [46]>
#> 8 chicken.csv 1fqAKfSblImDM4f_dDI2YD6c7yUfSS_CA <named list [44]>Now let’s reflect on the files object returned by this operation. files is a list of dribbles, one per uploaded file.
str(files, max.level = 1)
#> List of 8
#> $ chicken.csv : dribble [1 × 3] (S3: dribble/tbl_df/tbl/data.frame)
#> $ chicken.jpg : dribble [1 × 3] (S3: dribble/tbl_df/tbl/data.frame)
#> $ chicken.pdf : dribble [1 × 3] (S3: dribble/tbl_df/tbl/data.frame)
#> $ chicken.txt : dribble [1 × 3] (S3: dribble/tbl_df/tbl/data.frame)
#> $ imdb_latin1.csv: dribble [1 × 3] (S3: dribble/tbl_df/tbl/data.frame)
#> $ markdown.md : dribble [1 × 3] (S3: dribble/tbl_df/tbl/data.frame)
#> $ r_about.html : dribble [1 × 3] (S3: dribble/tbl_df/tbl/data.frame)
#> $ r_logo.jpg : dribble [1 × 3] (S3: dribble/tbl_df/tbl/data.frame)This would be a favorable data structure if you’ve got more map()ing to do, as you’ll see below.
But what if not? You can always row bind individual dribbles into one big dribble yourself with, e.g., dplyr::bind_rows().
bind_rows(files)
#> # A dribble: 8 × 3
#> name id drive_resource
#> <chr> <drv_id> <list>
#> 1 chicken.csv 1fqAKfSblImDM4f_dDI2YD6c7yUfSS_CA <named list [44]>
#> 2 chicken.jpg 1Q7G6e3EGEPV7kz5VLLINa900ssQIegYP <named list [46]>
#> 3 chicken.pdf 1BT0Eas51R_rh-cUcjN6YQz4roJa_52_0 <named list [44]>
#> 4 chicken.txt 1t61PM3vomBYvB_9xUnO8T6Su3cZuC85n <named list [44]>
#> 5 imdb_latin1.csv 1Lg5zSebMWGDS16Tns5UvLJoivMnUm-4f <named list [44]>
#> 6 markdown.md 1MrikNZxrzEr5u6qAevT1-sAhOlBDiHEY <named list [44]>
#> 7 r_about.html 1r7k_CELUgyJ0HLIK18CsdXn3gJ0Ee9R2 <named list [44]>
#> 8 r_logo.jpg 1bFk8cGyK1kSxOwGdUZxGwSXdCnjQk5W6 <named list [46]>Below we show another way to finesse this by using a variant of purrr::map() that does this for us, namely map_dfr().
Rename
Imagine that we now wish these file names had a date prefix. First, form the new names. We use glue::glue() for string interpolation but you could also use paste(). Second, we map over two inputs: the list of dribbles from above and the vector of new names.
(new_names <- glue("{Sys.Date()}_{basename(local_files)}"))
#> 2026-10-29_chicken.csv
#> 2026-10-29_chicken.jpg
#> 2026-10-29_chicken.pdf
#> 2026-10-29_chicken.txt
#> 2026-10-29_imdb_latin1.csv
#> 2026-10-29_markdown.md
#> 2026-10-29_r_about.html
#> 2026-10-29_r_logo.jpg
files_dribble <- map2_dfr(files, new_names, drive_rename)
#> Original file:
#> • 'chicken.csv' <id: 1fqAKfSblImDM4f_dDI2YD6c7yUfSS_CA>
#> Has been renamed:
#> • '2026-10-29_chicken.csv' <id: 1fqAKfSblImDM4f_dDI2YD6c7yUfSS_CA>
#> Original file:
#> • 'chicken.jpg' <id: 1Q7G6e3EGEPV7kz5VLLINa900ssQIegYP>
#> Has been renamed:
#> • '2026-10-29_chicken.jpg' <id: 1Q7G6e3EGEPV7kz5VLLINa900ssQIegYP>
#> Original file:
#> • 'chicken.pdf' <id: 1BT0Eas51R_rh-cUcjN6YQz4roJa_52_0>
#> Has been renamed:
#> • '2026-10-29_chicken.pdf' <id: 1BT0Eas51R_rh-cUcjN6YQz4roJa_52_0>
#> Original file:
#> • 'chicken.txt' <id: 1t61PM3vomBYvB_9xUnO8T6Su3cZuC85n>
#> Has been renamed:
#> • '2026-10-29_chicken.txt' <id: 1t61PM3vomBYvB_9xUnO8T6Su3cZuC85n>
#> Original file:
#> • 'imdb_latin1.csv' <id: 1Lg5zSebMWGDS16Tns5UvLJoivMnUm-4f>
#> Has been renamed:
#> • '2026-10-29_imdb_latin1.csv' <id: 1Lg5zSebMWGDS16Tns5UvLJoivMnUm-4f>
#> Original file:
#> • 'markdown.md' <id: 1MrikNZxrzEr5u6qAevT1-sAhOlBDiHEY>
#> Has been renamed:
#> • '2026-10-29_markdown.md' <id: 1MrikNZxrzEr5u6qAevT1-sAhOlBDiHEY>
#> Original file:
#> • 'r_about.html' <id: 1r7k_CELUgyJ0HLIK18CsdXn3gJ0Ee9R2>
#> Has been renamed:
#> • '2026-10-29_r_about.html' <id: 1r7k_CELUgyJ0HLIK18CsdXn3gJ0Ee9R2>
#> Original file:
#> • 'r_logo.jpg' <id: 1bFk8cGyK1kSxOwGdUZxGwSXdCnjQk5W6>
#> Has been renamed:
#> • '2026-10-29_r_logo.jpg' <id: 1bFk8cGyK1kSxOwGdUZxGwSXdCnjQk5W6>We use purrr::map2_dfr() to work through files, the list of dribbles (= Drive files), and new_names, the vector of new names, and row bind the returned dribbles into a single dribble holding all files.
Let’s check on the contents of this folder again to confirm the new names:
drive_ls(folder)
#> # A dribble: 8 × 3
#> name id drive_resource
#> <chr> <drv_id> <list>
#> 1 2026-10-29_r_logo.jpg 1bFk8cGyK1kSxOwGdUZxGwSXdCnjQk5W6 <named list [46]>
#> 2 2026-10-29_r_about.html 1r7k_CELUgyJ0HLIK18CsdXn3gJ0Ee9R2 <named list [45]>
#> 3 2026-10-29_markdown.md 1MrikNZxrzEr5u6qAevT1-sAhOlBDiHEY <named list [44]>
#> 4 2026-10-29_imdb_latin1.csv 1Lg5zSebMWGDS16Tns5UvLJoivMnUm-4f <named list [44]>
#> 5 2026-10-29_chicken.txt 1t61PM3vomBYvB_9xUnO8T6Su3cZuC85n <named list [45]>
#> 6 2026-10-29_chicken.pdf 1BT0Eas51R_rh-cUcjN6YQz4roJa_52_0 <named list [45]>
#> 7 2026-10-29_chicken.jpg 1Q7G6e3EGEPV7kz5VLLINa900ssQIegYP <named list [46]>
#> 8 2026-10-29_chicken.csv 1fqAKfSblImDM4f_dDI2YD6c7yUfSS_CA <named list [44]>Let’s confirm that, by using map2_df2() instead of map2(), we got a single dribble back, instead of a list of one-row dribbles:
files_dribble
#> # A dribble: 8 × 3
#> name id drive_resource
#> <chr> <drv_id> <list>
#> 1 2026-10-29_chicken.csv 1fqAKfSblImDM4f_dDI2YD6c7yUfSS_CA <named list [44]>
#> 2 2026-10-29_chicken.jpg 1Q7G6e3EGEPV7kz5VLLINa900ssQIegYP <named list [46]>
#> 3 2026-10-29_chicken.pdf 1BT0Eas51R_rh-cUcjN6YQz4roJa_52_0 <named list [45]>
#> 4 2026-10-29_chicken.txt 1t61PM3vomBYvB_9xUnO8T6Su3cZuC85n <named list [45]>
#> 5 2026-10-29_imdb_latin1.csv 1Lg5zSebMWGDS16Tns5UvLJoivMnUm-4f <named list [44]>
#> 6 2026-10-29_markdown.md 1MrikNZxrzEr5u6qAevT1-sAhOlBDiHEY <named list [44]>
#> 7 2026-10-29_r_about.html 1r7k_CELUgyJ0HLIK18CsdXn3gJ0Ee9R2 <named list [45]>
#> 8 2026-10-29_r_logo.jpg 1bFk8cGyK1kSxOwGdUZxGwSXdCnjQk5W6 <named list [46]>What if you wanted to get a list back, because your downstream operations include yet more map()ing? Then you would use map2().
files_list <- map2(files, new_names, drive_rename)Clean up
Our trashing function, drive_trash() is vectorized and can therefore operate on a multi-file dribble. We could trash these files like so:
drive_trash(files_dribble)If you’re absolutely sure of yourself and happy to do something irreversible, you could truly delete these files with drive_rm(), which is also vectorized:
drive_rm(files_dribble)Finally – and this is the code we will actually execute – the easiest way to delete these files is to delete their enclosing folder.
drive_rm(folder)
#> File deleted:
#> • 'upload-into-me-article-demo' <id: 1I5R_fOodKZx4UNC2NwB1q0utPZR1Ydou>