This is the main "read" function of the googlesheets4 package. The goal is that read_sheet() is to a Google Sheet as readr::read_csv() is to a csv file or read_excel() is to an Excel spreadsheet. It's still under development, but is quite usable now. Note that googlesheets4 is not wired up for auth yet (happening soon!), so at the moment the target Sheet must be readable by anyone with a link (see examples for how to accomplish via googledrive).

read_sheet(ss, sheet = NULL, range = NULL, col_names = TRUE,
  col_types = NULL, na = "", trim_ws = TRUE, skip = 0,
  n_max = Inf, guess_max = min(1000, n_max), .name_repair = "unique")

sheets_read(ss, sheet = NULL, range = NULL, col_names = TRUE,
  col_types = NULL, na = "", trim_ws = TRUE, skip = 0,
  n_max = Inf, guess_max = min(1000, n_max), .name_repair = "unique")



Something that identifies a Google Sheet: its file ID, a URL from which we can recover the ID, or a dribble, which is how googledrive represents Drive files. Processed through as_sheets_id().


Sheet to read, as in "worksheet" or "tab". Either a string (the name of a sheet), or an integer (the position of the sheet). Ignored if the sheet is specified via range. If neither argument specifies the sheet, defaults to the first visible sheet.


A cell range to read from. If NULL, all non-empty cells are read. Otherwise specify range as described in Sheets A1 notation or using the helpers documented in cell-specification. Sheets uses fairly standard spreadsheet range notation, although a bit different from Excel. Examples of valid ranges: "Sheet1!A1:B2", "Sheet1!A:A", "Sheet1!1:2", "Sheet1!A5:A", "A1:B2", "Sheet1". Interpreted strictly, even if the range forces the inclusion of leading, trailing, or embedded empty rows or columns. Takes precedence over skip, n_max and sheet. Note range can be a named range, like "sales_data", without any cell reference.


TRUE to use the first row as column names, FALSE to get default names, or a character vector to provide column names directly. In all cases, names are processed through tibble::tidy_names(). If user provides col_types, col_names can have one entry per column or one entry per unskipped column.


Column types. Either NULL to guess all from the spreadsheet or a string of readr-style shortcodes, with one character or code per column. If exactly one col_type is specified, it is recycled. See Details for more.


Character vector of strings to interpret as missing values. By default, blank cells are treated as missing data.


Logical. Should leading and trailing whitespace be trimmed from cell contents?


Minimum number of rows to skip before reading anything, be it column names or data. Leading empty rows are automatically skipped, so this is a lower bound. Ignored if range is given.


Maximum number of data rows to read. Trailing empty rows are automatically skipped, so this is an upper bound on the number of rows in the returned tibble. Ignored if range is given.


Maximum number of data rows to use for guessing column types.


Handling of column names. By default, googlesheets4 ensures column names are not empty and are unique. There is full support for .name_repair as documented in tibble::tibble().


A tibble

Column specification

Column types must be specified in a single string of readr-style short codes, e.g. "cci?l" means "character, character, integer, guess, logical". This is not where googlesheets4's col spec will end up, but it gets the ball rolling in a way that is consistent with readr and doesn't reinvent any wheels.

Shortcodes for column types:

  • _ or -: Skip. Data in a skipped column is still requested from the API (the high-level functions in this package are rectangle-oriented), but is not parsed into the data frame output.

  • ?: Guess. A type is guessed for each cell and then a consensus type is selected for each column. If no atomic type is suitable for all cells, a list-column is created, in which each cell is converted to an R object of "best" type". If no column types are specified, i.e. col_types = NULL, all types are guessed.

  • l: Logical.

  • i: Integer. This type is never guessed from the data, because Sheets have no formal cell type for integers.

  • d or n: Numeric, in sense of "double".

  • D: Date. This type is never guessed from the data, because date cells are just serial datetimes that bear a "date" format.

  • t: Time of day. This type is never guessed from the data, because time cells are just serial datetimes that bear a "time" format. Not implemented yet; returns POSIXct.

  • T: Datetime, specifically POSIXct.

  • c: Character.

  • C: Cell. This type is unique to googlesheets4. This returns raw cell data, as an R list, which consists of everything sent by the Sheets API for that cell. Has S3 type of "CELL_SOMETHING" and "SHEETS_CELL". Mostly useful internally, but exposed for those who want direct access to, e.g., formulas and formats.

  • L: List, as in "list-column". Each cell is a length-1 atomic vector of its discovered type.

  • Still to come: duration (code will be :) and factor (code will be f).


ss <- sheets_example("deaths") read_sheet(ss, range = "A5:F15")
#> Reading from 'deaths'
#> Range "'arts'!A5:F15"
#> # A tibble: 10 x 6 #> Name Profession Age `Has kids` `Date of birth` `Date of death` #> <chr> <chr> <dbl> <lgl> <dttm> <dttm> #> 1 David Bo… musician 69 TRUE 1947-01-08 00:00:00 2016-01-10 00:00:00 #> 2 Carrie F… actor 60 TRUE 1956-10-21 00:00:00 2016-12-27 00:00:00 #> 3 Chuck Be… musician 90 TRUE 1926-10-18 00:00:00 2017-03-18 00:00:00 #> 4 Bill Pax… actor 61 TRUE 1955-05-17 00:00:00 2017-02-25 00:00:00 #> 5 Prince musician 57 TRUE 1958-06-07 00:00:00 2016-04-21 00:00:00 #> 6 Alan Ric… actor 69 FALSE 1946-02-21 00:00:00 2016-01-14 00:00:00 #> 7 Florence… actor 82 TRUE 1934-02-14 00:00:00 2016-11-24 00:00:00 #> 8 Harper L… author 89 FALSE 1926-04-28 00:00:00 2016-02-19 00:00:00 #> 9 Zsa Zsa … actor 99 TRUE 1917-02-06 00:00:00 2016-12-18 00:00:00 #> 10 George M… musician 53 FALSE 1963-06-25 00:00:00 2016-12-25 00:00:00
read_sheet(ss, range = "other!A5:F15", col_types = "ccilDD")
#> Reading from 'deaths'
#> Range "'other'!A5:F15"
#> # A tibble: 10 x 6 #> Name Profession Age `Has kids` `Date of birth` `Date of death` #> <chr> <chr> <int> <lgl> <date> <date> #> 1 Vera Rubin scientist 88 TRUE 1928-07-23 2016-12-25 #> 2 Mohamed Ali athlete 74 TRUE 1942-01-17 2016-06-03 #> 3 Morley Safer journalist 84 TRUE 1931-11-08 2016-05-19 #> 4 Fidel Castro politician 90 TRUE 1926-08-13 2016-11-25 #> 5 Antonin Scalia lawyer 79 TRUE 1936-03-11 2016-02-13 #> 6 Jo Cox politician 41 TRUE 1974-06-22 2016-06-16 #> 7 Janet Reno lawyer 78 FALSE 1938-07-21 2016-11-07 #> 8 Gwen Ifill journalist 61 FALSE 1955-09-29 2016-11-14 #> 9 John Glenn astronaut 95 TRUE 1921-07-28 2016-12-08 #> 10 Pat Summit coach 64 TRUE 1952-06-14 2016-06-28
#> Reading from 'test-gs-mini-gapminder'
#> Range "'Africa'"
#> # A tibble: 5 x 6 #> country continent year lifeExp pop gdpPercap #> <chr> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 Algeria Africa 1952 43.1 9279525 2449. #> 2 Angola Africa 1952 30.0 4232095 3521. #> 3 Benin Africa 1952 38.2 1738315 1063. #> 4 Botswana Africa 1952 47.6 442308 851. #> 5 Burkina Faso Africa 1952 32.0 4469979 543.
read_sheet( sheets_example("mini-gap"), sheet = "Europe", range = "A:D", col_types = "ccid" )
#> Reading from 'test-gs-mini-gapminder'
#> Range "'Europe'!A:D"
#> # A tibble: 5 x 4 #> country continent year lifeExp #> <chr> <chr> <int> <dbl> #> 1 Albania Europe 1952 55.2 #> 2 Austria Europe 1952 66.8 #> 3 Belgium Europe 1952 68 #> 4 Bosnia and Herzegovina Europe 1952 53.8 #> 5 Bulgaria Europe 1952 59.6
# NOT RUN { ## converts a local Excel file to a Google Sheet ## and shares it such that "anyone with a link can view" library(googledrive) local_xlsx <- readxl::readxl_example("deaths.xlsx") x <- drive_upload(local_xlsx, type = "spreadsheet") x <- drive_share(x, role = "reader", type = "anyone") drive_reveal(x, "permissions") # }