Google Sheets R API
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.
googlesheets is inspired by gspread, a Google Spreadsheets Python API
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!
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.
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:
- This blog post from Aylien.com has a simple example that uses the
=IMPORTXML()formula to populate a Google Sheet with restaurant reviews and ratings from TripAdvisor.
- Martin Hawksey offers TAGS, a free Google Sheet template to setup and run automated collection of search results from Twitter.
- Martin Hawksey also has a great blog post, Feeding Google Spreadsheets, that demonstrates how functions like
importXMLhelp you get data from the web into a Google Sheet with no programming.
- Martin Hawksey has another blog post about feeding a Google Sheet from IFTTT. IFTTT stands for "if this, then that" and it's "a web-based service that allows users to create chains of simple conditional statements, called 'recipes', which are triggered based on changes to other web services such as Gmail, Facebook, Instagram, and Craigslist" (from Wikipedia).
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?
The released version is available on CRAN
Or you can get the development version from GitHub (which currently depends on the development version of
If you use Windows, you may want to install the development version of
xml2. This will improve handling of encoding when reading Sheets:
Take a look at the vignette
Read the vignette on GitHub.
Slides from UseR2015
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.
dplyr 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
All functions start with
gs_, which plays nicely with tab completion. If the function has something to do with worksheets or tabs within a spreadsheet, then it will start with
First, 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 identifed: "Gapminder"
Here's a registered
gap #> Spreadsheet title: Gapminder #> Spreadsheet author: gspreadr #> Date of googlesheets registration: 2015-10-29 15:06:57 GMT #> Date of last spreadsheet update: 2015-03-23 20:34:08 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: 1HT5B8SgkKqHdqHJmn5xiuaC04Ngb7dG9Tv94004vezA #> Browser URL: https://docs.google.com/spreadsheets/d/1HT5B8SgkKqHdqHJmn5xiuaC04Ngb7dG9Tv94004vezA/
Read all the data in a worksheet:
africa <- gs_read(gap) #> Accessing worksheet titled "Africa" str(africa) #> Classes 'tbl_df', 'tbl' and 'data.frame': 624 obs. of 6 variables: #> $ country : chr "Algeria" "Algeria" "Algeria" "Algeria" ... #> $ continent: chr "Africa" "Africa" "Africa" "Africa" ... #> $ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ... #> $ lifeExp : num 43.1 45.7 48.3 51.4 54.5 ... #> $ pop : int 9279525 10270856 11000948 12760499 14760787 17152804 20033753 23254956 26298373 29072015 ... #> $ gdpPercap: num 2449 3014 2551 3247 4183 ... head(africa) #> Source: local data frame [6 x 6] #> #> country continent year lifeExp pop gdpPercap #> (chr) (chr) (int) (dbl) (int) (dbl) #> 1 Algeria Africa 1952 43.077 9279525 2449.008 #> 2 Algeria Africa 1957 45.685 10270856 3013.976 #> 3 Algeria Africa 1962 48.303 11000948 2550.817 #> 4 Algeria Africa 1967 51.407 12760499 3246.992 #> 5 Algeria Africa 1972 54.518 14760787 4182.664 #> 6 Algeria Africa 1977 58.014 17152804 4910.417
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))
Create a new Sheet:
iris_ss <- gs_new("iris", input = head(iris, 3), trim = TRUE) #> Warning in gs_new("iris", input = head(iris, 3), trim = TRUE): 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: "A1:E4" #> Worksheet "Sheet1" successfully updated with 20 new value(s). #> Accessing worksheet titled "Sheet1" #> Authorization will be used. #> Sheet successfully identifed: "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: "A2:E2" #> 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" #> Source: local data frame [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 (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
Clean up our mess:
gs_vecdel("iris", "Gapminder") file.remove("iris-ish-stuff.csv")
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_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_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_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|