README.md
In dakota1063/dddd: Manage Google Spreadsheets from R
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:
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: 2017-05-06 19:37:34 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'.
#> Parsed with column specification:
#> cols(
#> country = col_character(),
#> continent = col_character(),
#> year = col_integer(),
#> lifeExp = col_double(),
#> pop = col_integer(),
#> 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 <int> 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 <int> 9279525, 10270856, 11000948, 12760499, 14760787, 171...
#> $ gdpPercap <dbl> 2449.008, 3013.976, 2550.817, 3246.992, 4182.664, 49...
africa
#> # A tibble: 624 × 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
#> 7 Algeria Africa 1982 61.368 20033753 5745.160
#> 8 Algeria Africa 1987 65.799 23254956 5681.359
#> 9 Algeria Africa 1992 67.744 26298373 5023.217
#> 10 Algeria Africa 1997 69.152 29072015 4797.295
#> # ... 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))
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'.
#> # A tibble: 7 × 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 × 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
| 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_browse() | Visit a registered googlesheet in the browser |
| 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_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?
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?
dakota1063/dddd documentation built on May 25, 2019, 4:21 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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:
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 ags_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: 2017-05-06 19:37:34 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'.
#> Parsed with column specification:
#> cols(
#> country = col_character(),
#> continent = col_character(),
#> year = col_integer(),
#> lifeExp = col_double(),
#> pop = col_integer(),
#> 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 <int> 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 <int> 9279525, 10270856, 11000948, 12760499, 14760787, 171...
#> $ gdpPercap <dbl> 2449.008, 3013.976, 2550.817, 3246.992, 4182.664, 49...
africa
#> # A tibble: 624 × 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
#> 7 Algeria Africa 1982 61.368 20033753 5745.160
#> 8 Algeria Africa 1987 65.799 23254956 5681.359
#> 9 Algeria Africa 1992 67.744 26298373 5023.217
#> 10 Algeria Africa 1997 69.152 29072015 4797.295
#> # ... 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))
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'.
#> # A tibble: 7 × 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 × 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
| 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_browse() | Visit a registered googlesheet in the browser |
| 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_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?
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
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.- 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.
- Aylien.com blog post on using
- 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?
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.