- Google Sheets R API
Google Sheets R API
googlesheets is going away fairly soon (in 2020)! It is not a good idea to write new code that uses it!
googlesheets (this package) wraps the Sheets v3 API, which is quite old and is gradually going offline, starting in March 2020. Full shutdown is planned for September 2020. When that happens, this R package will be of no use and will presumably be archived on CRAN in due course.
googlesheets4 is a new package that wraps the current Sheets v4 API. It is the successor to the googlesheets package. It is strongly advised to adopt googlesheets4 going forward. See the website for how to install googlesheets4 and to see basic usage:
Access and manage Google spreadsheets from R with
- Access a spreadsheet by its title, key or URL.
- Extract data or edit data.
- Create | delete | rename | copy | upload | download spreadsheets and worksheets.
- Upload local Excel workbook into a Google Sheet and vice versa.
googlesheets is inspired by
gspread, a Google Spreadsheets
The exuberant prose in this README is inspired by Tabletop.js: If you’ve ever wanted to get data in or out of a Google Spreadsheet from R without jumping through a thousand hoops, welcome home!
The released version is available on CRAN
googlesheets is no longer under active maintenance, development and support has shifted to:
- googledrive, available on CRAN. This package can handle all “whole file” operations for documents on Google Drive, including Sheets. It can work with Team Drives, it can upload/download entire Sheets (with conversions to/from other formats, such as csv and xlsx), and it can upload new media to an existing Sheet ID.
- googlesheets4, available on CRAN. This package wraps the Sheets API v4 and does “Sheets-aware” operations that involve concepts specific to Sheets, such as worksheets and cells. It is the successor to googlesheets.
Regard everything below here as legacy content.
googlesheets is designed for use with the
%>% pipe operator and, to
a lesser extent, the data-wrangling mentality of
dplyr. This README uses
both, but the examples in the help files emphasize usage with plain
vanilla R, if that’s how you roll.
internally but does not require the user to do so. You can make the
%>% pipe operator available in your own work by loading
Function naming convention
To play nicely with tab completion, we use consistent prefixes:
gs_= all functions in the package.
gs_ws_= all functions that operate on worksheets or tabs within a spreadsheet.
gd_= something to do with Google Drive, usually has a
gs_synonym, might one day migrate to a Drive client.
Here’s how to get a copy of a Gapminder-based Sheet we publish for practicing and follow along. You’ll be sent to the browser to authenticate yourself with Google at this point.
gs_gap() %>% gs_copy(to = "Gapminder") ## or, if you don't use pipes gs_copy(gs_gap(), to = "Gapminder")
Register a Sheet (in this case, by title):
gap <- gs_title("Gapminder") #> Sheet successfully identified: "Gapminder"
Here’s a registered
gap #> Spreadsheet title: Gapminder #> Spreadsheet author: gspreadr #> Date of googlesheets registration: 2018-06-28 20:31:39 GMT #> Date of last spreadsheet update: 2018-06-28 20:28:33 GMT #> visibility: private #> permissions: rw #> version: new #> #> Contains 5 worksheets: #> (Title): (Nominal worksheet extent as rows x columns) #> Africa: 625 x 6 #> Americas: 301 x 6 #> Asia: 397 x 6 #> Europe: 361 x 6 #> Oceania: 25 x 6 #> #> Key: 1vz6eeNH_rutBS2z6QtMq_rffRpqq3R_8Qevw7-vETC0 #> Browser URL: https://docs.google.com/spreadsheets/d/1vz6eeNH_rutBS2z6QtMq_rffRpqq3R_8Qevw7-vETC0/
Visit a registered
googlesheet in the browser:
gap %>% gs_browse() gap %>% gs_browse(ws = "Europe")
Read all the data in a worksheet:
africa <- gs_read(gap) #> Accessing worksheet titled 'Africa'. #> Parsed with column specification: #> cols( #> country = col_character(), #> continent = col_character(), #> year = col_double(), #> lifeExp = col_double(), #> pop = col_double(), #> gdpPercap = col_double() #> ) glimpse(africa) #> Observations: 624 #> Variables: 6 #> $ country <chr> "Algeria", "Algeria", "Algeria", "Algeria", "Algeria... #> $ continent <chr> "Africa", "Africa", "Africa", "Africa", "Africa", "A... #> $ year <dbl> 1952, 1957, 1962, 1967, 1972, 1977, 1982, 1987, 1992... #> $ lifeExp <dbl> 43.077, 45.685, 48.303, 51.407, 54.518, 58.014, 61.3... #> $ pop <dbl> 9279525, 10270856, 11000948, 12760499, 14760787, 171... #> $ gdpPercap <dbl> 2449.008, 3013.976, 2550.817, 3246.992, 4182.664, 49... africa #> # A tibble: 624 x 6 #> country continent year lifeExp pop gdpPercap #> <chr> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 Algeria Africa 1952 43.1 9279525 2449. #> 2 Algeria Africa 1957 45.7 10270856 3014. #> 3 Algeria Africa 1962 48.3 11000948 2551. #> 4 Algeria Africa 1967 51.4 12760499 3247. #> 5 Algeria Africa 1972 54.5 14760787 4183. #> 6 Algeria Africa 1977 58.0 17152804 4910. #> 7 Algeria Africa 1982 61.4 20033753 5745. #> 8 Algeria Africa 1987 65.8 23254956 5681. #> 9 Algeria Africa 1992 67.7 26298373 5023. #> 10 Algeria Africa 1997 69.2 29072015 4797. #> # ... with 614 more rows
Some of the many ways to target specific cells:
gap %>% gs_read(ws = 2, range = "A1:D8") gap %>% gs_read(ws = "Europe", range = cell_rows(1:4)) gap %>% gs_read(ws = "Africa", range = cell_cols(1:4))
readr-style control of data ingest – highly artificial example!
gap %>% gs_read(ws = "Oceania", col_names = paste0("Z", 1:6), na = c("1962", "1977"), col_types = "cccccc", skip = 1, n_max = 7) #> Accessing worksheet titled 'Oceania'. #> # A tibble: 7 x 6 #> Z1 Z2 Z3 Z4 Z5 Z6 #> <chr> <chr> <chr> <chr> <chr> <chr> #> 1 Australia Oceania 1952 69.12 8691212 10039.6 #> 2 Australia Oceania 1957 70.33 9712569 10949.65 #> 3 Australia Oceania <NA> 70.93 10794968 12217.23 #> 4 Australia Oceania 1967 71.1 11872264 14526.12 #> 5 Australia Oceania 1972 71.93 13177000 16788.63 #> 6 Australia Oceania <NA> 73.49 14074100 18334.2 #> 7 Australia Oceania 1982 74.74 15184200 19477.01
Create a new Sheet from an R object:
iris_ss <- gs_new("iris", input = head(iris, 3), trim = TRUE) #> Warning: At least one sheet matching "iris" already exists, so you may #> need to identify by key, not title, in future. #> Sheet "iris" created in Google Drive. #> Range affected by the update: "R1C1:R4C5" #> Worksheet "Sheet1" successfully updated with 20 new value(s). #> Accessing worksheet titled 'Sheet1'. #> Sheet successfully identified: "iris" #> Accessing worksheet titled 'Sheet1'. #> Worksheet "Sheet1" dimensions changed to 4 x 5. #> Worksheet dimensions: 4 x 5.
Edit some arbitrary cells and append a row:
iris_ss <- iris_ss %>% gs_edit_cells(input = c("what", "is", "a", "sepal", "anyway?"), anchor = "A2", byrow = TRUE) #> Range affected by the update: "R2C1:R2C5" #> Worksheet "Sheet1" successfully updated with 5 new value(s). iris_ss <- iris_ss %>% gs_add_row(input = c("sepals", "support", "the", "petals", "!!")) #> Row successfully appended.
Look at what we have wrought:
iris_ss %>% gs_read() #> Accessing worksheet titled 'Sheet1'. #> Parsed with column specification: #> cols( #> Sepal.Length = col_character(), #> Sepal.Width = col_character(), #> Petal.Length = col_character(), #> Petal.Width = col_character(), #> Species = col_character() #> ) #> # A tibble: 4 x 5 #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> <chr> <chr> <chr> <chr> <chr> #> 1 what is a sepal anyway? #> 2 4.9 3 1.4 0.2 setosa #> 3 4.7 3.2 1.3 0.2 setosa #> 4 sepals support the petals !!
Download this precious thing as csv (other formats are possible):
iris_ss %>% gs_download(to = "iris-ish-stuff.csv", overwrite = TRUE) #> Sheet successfully downloaded: #> /Users/jenny/rrr/googlesheets/iris-ish-stuff.csv
Download this precious thing as an Excel workbook (other formats are possible):
iris_ss %>% gs_download(to = "iris-ish-stuff.xlsx", overwrite = TRUE) #> Sheet successfully downloaded: #> /Users/jenny/rrr/googlesheets/iris-ish-stuff.xlsx
Upload a Excel workbook into a new Sheet:
gap_xlsx <- gs_upload(system.file("mini-gap", "mini-gap.xlsx", package = "googlesheets")) #> File uploaded to Google Drive: #> /Users/jenny/resources/R/library/googlesheets/mini-gap/mini-gap.xlsx #> As the Google Sheet named: #> mini-gap
Clean up our mess locally and on Google Drive:
gs_vecdel(c("iris", "Gapminder")) file.remove(c("iris-ish-stuff.csv", "iris-ish-stuff.xlsx"))
Remember, the vignette shows a lot more usage.
Overview of functions
|gs_title()||Register a Sheet by title|
|gs_key()||Register a Sheet by key|
|gs_url()||Register a Sheet by URL|
|gs_browse()||Visit a registered
|gs_read()||Read data and let
|gs_read_csv()||Read explicitly via the fast exportcsv link|
|gs_read_listfeed()||Read explicitly via the list feed|
|gs_read_cellfeed()||Read explicitly via the cell feed|
|gs_reshape_cellfeed()||Reshape cell feed data into a 2D thing|
|gs_simplify_cellfeed()||Simplify cell feed data into a 1D thing|
|gs_edit_cells()||Edit specific cells|
|gs_add_row()||Append a row to pre-existing data table|
|gs_new()||Create a new Sheet and optionally populate|
|gs_copy()||Copy a Sheet into a new Sheet|
|gs_rename()||Rename an existing Sheet|
|gs_ws_ls()||List the worksheets in a Sheet|
|gs_ws_new()||Create a new worksheet and optionally populate|
|gs_ws_rename()||Rename a worksheet|
|gs_ws_delete()||Delete a worksheet|
|gs_delete()||Delete a Sheet|
|gs_grepdel()||Delete Sheets with matching titles|
|gs_vecdel()||Delete the named Sheets|
|gs_upload()||Upload local file into a new Sheet|
|gs_download()||Download a Sheet into a local file|
|gs_auth()||Authorize the package|
|gs_deauth()||De-authorize the package|
|gs_user()||Get info about current user and auth status|
|gs_webapp_auth_url()||Facilitates auth by user of a Shiny app|
|gs_webapp_get_token()||Facilitates auth by user of a Shiny app|
|gs_gap()||Registers a public Gapminder-based Sheet (for practicing)|
|gs_gap_key()||Key of the Gapminder practice Sheet|
|gs_gap_url()||Browser URL for the Gapminder practice Sheet|
What the hell do I do with this?
googlesheets as a read/write CMS that you (or your less
R-obsessed friends) can edit through Google Docs, as well via R. It’s
like Christmas up in here.
Use a Google Form to conduct a survey, which populates a Google Sheet.
googleformrpackage provides an R API for Google Forms, allowing useRs to POST data securely to Google Forms without authentication. On CRAN and GitHub (README has lots of info and links to blog posts).
Gather data while you’re in the field in a Google Sheet, maybe with an iPhone or an Android device. Take advantage of data validation to limit the crazy on the way in. You do not have to be online to edit a Google Sheet! Work offline via the Chrome browser, the Sheets app for Android, or the Sheets app for iOS.
There are various ways to harvest web data directly into a Google Sheet. For example:
- IFTTT, which stands for “if this, then that”,
makes it easy to create recipes in which changes in one web service,
such as Gmail or Instagram, trigger another action, such as writing
to a Google Sheet.
- Martin Hawksey blog post about feeding a Google Sheet from IFTTT.
IMPORTXML(), IMPORTHTML(), IMPORTFEED(): Google Sheets offer functions to populate Sheets based on web data.
- Martin Hawksey offers TAGS, a free Google Sheet template to setup and run automated collection of search results from Twitter.
googlesheets to get all that data into R.
Use it in a Shiny app! Several example apps come with the package.
What other ideas do you have?