• Google Sheets R API
  • Install googlesheets
  • Vignettes
  • Talks
  • Load googlesheets
  • Function naming convention
  • Quick demo
  • Overview of functions
  • What the hell do I do with this?

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.
• Upload local Excel workbook into a Google Sheet and vice versa.

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!

Install googlesheets

The released version is available on CRAN

install.packages("googlesheets")

Or you can get the development version from GitHub:

devtools::install_github("jennybc/googlesheets")

Vignettes

GitHub versions:

• Basic usage
• Formulas and formatted numbers
• Managing OAuth tokens

Talks

• Slides for a talk in July 2015 at useR! 2015
• Slides for an rOpenSci Community Call in March 2016

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

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.

Quick demo

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 googlesheet object:

gap
#>                   Spreadsheet title: Gapminder
#>                  Spreadsheet author: gspreadr
#>   Date of googlesheets registration: 2016-06-25 02:05:56 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/

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'.
#> No encoding supplied: defaulting to UTF-8.
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)
#> <tibble [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))

Full 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'.
#> No encoding supplied: defaulting to UTF-8.
#> <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: "A1:E4"
#> 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: "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'.
#> No encoding supplied: defaulting to UTF-8.
#> <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

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.

• The googleformr package 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.
  • Aylien.com blog post on using =IMPORTXML() to populate a Google Sheet with restaurant reviews and ratings from TripAdvisor.
  • Martin Hawksey blog post, Feeding Google Spreadsheets, shows how to scrape web data into a Google Sheet with no programming.
• Martin Hawksey offers TAGS, a free Google Sheet template to setup and run automated collection of search results from Twitter.

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?