Join 10350+ others. No spamming.
I promise!

Follow us at github.



jennybc/gspreadr

472

jennybc / googlesheets

R

Google Spreadsheets R API


READ ME

Build Status Coverage Status DOI CRAN version


Google Sheets R API

Access and manage Google spreadsheets from R with googlesheets.

Features:

  • 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?

Think of 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 importHTML, importFeed, and importXML help 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).

Use 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?

Install googlesheets

The released version is available on CRAN

install.packages("googlesheets")

Or you can get the development version from GitHub (which currently depends on the development version of readr):

devtools::install_github("hadley/readr")
devtools::install_github("jennybc/googlesheets")

If you use Windows, you may want to install the development version of xml2. This will improve handling of encoding when reading Sheets:

devtools::install_github("hadley/xml2")

Take a look at the vignette

Read the vignette on GitHub.

Slides from UseR2015

Slides for a talk in July 2015 at the UseR2015 conference

Load googlesheets

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. googlesheets uses dplyr internally but does not require the user to do so. You can make the %>% pipe operator available in your own work by loading dplyr or magrittr.

library("googlesheets")
suppressPackageStartupMessages(library("dplyr"))

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 gs_ws_.

Quick demo

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 googlesheet object:

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

fxn description
gs_ls() List Sheets
gs_title() Register a Sheet by title
gs_key() Register a Sheet by key
gs_url() Register a Sheet by URL
gs_gs() Re-register a googlesheet
gs_read() Read data and let googlesheets figure out how
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