Skip to content

Read Sheets

Source: vignettes/articles/read-sheets.Rmd
read-sheets.Rmd

Basic Sheet reading is shown in the Get started article. Here we show how to target a specific (work)sheet or cell range, how to deal with column types, and how to get detailed cell data.

Auth

As a regular, interactive user, you can just let googlesheets4 prompt you for anything it needs re: auth.

Since this article is compiled noninteractively on a server, we have arranged for googlesheets4 to use a service account token (not shown).

read_sheet() and range_read() are synonyms

The main “read” function of the googlesheets4 package goes by two names, because we want it to make sense in two contexts:

  • read_sheet() evokes other table-reading functions, like readr::read_csv() and readxl::read_excel(). The sheet in this case refers to a Google (spread)Sheet.

  • range_read() is technically the right name according to the naming convention used throughout the googlesheets4 package, because we can read from an arbitrary cell range.

read_sheet() and range_read() are synonyms and you can use either one. Throughout this article, we’re going to use range_read().

Note: The first release of googlesheets used a sheets_ prefix everywhere, so we had sheets_read(). It still works, but it’s deprecated and will go away rather swiftly.

Specify the range and column types

Here we read from the “mini-gap” and “deaths” example Sheets to show some of the different ways to specify (work)sheet and cell ranges.

 range_read(gs4_example("mini-gap"), sheet = 2) #> ✔ Reading from mini-gap. #> ✔ Range ''Americas''. #> # A tibble: 5 × 6 #> country continent year lifeExp pop gdpPercap #> <chr> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 Argentina Americas 1952 62.5 17876956 5911. #> 2 Bolivia Americas 1952 40.4 2883315 2677. #> 3 Brazil Americas 1952 50.9 56602560 2109. #> 4 Canada Americas 1952 68.8 14785584 11367. #> 5 Chile Americas 1952 54.7 6377619 3940.  range_read(gs4_example("mini-gap"), sheet = "Oceania", n_max = 3) #> ✔ Reading from mini-gap. #> ✔ Range ''Oceania''. #> # A tibble: 3 × 6 #> country continent year lifeExp pop gdpPercap #> <chr> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 Australia Oceania 1952 69.1 8691212 10040. #> 2 New Zealand Oceania 1952 69.4 1994794 10557. #> 3 Australia Oceania 1957 70.3 9712569 10950.  range_read(gs4_example("deaths"), skip = 4, n_max = 10) #> ✔ Reading from deaths. #> ✔ Range 5:10000000. #> # A tibble: 10 × 6 #> Name Profession Age `Has kids` `Date of birth`  #> <chr> <chr> <dbl> <lgl> <dttm>  #>  1 David Bowie musician 69 TRUE 1947-01-08 00:00:00 #>  2 Carrie Fisher actor 60 TRUE 1956-10-21 00:00:00 #>  3 Chuck Berry musician 90 TRUE 1926-10-18 00:00:00 #>  4 Bill Paxton actor 61 TRUE 1955-05-17 00:00:00 #>  5 Prince musician 57 TRUE 1958-06-07 00:00:00 #>  6 Alan Rickman actor 69 FALSE 1946-02-21 00:00:00 #>  7 Florence Henderson actor 82 TRUE 1934-02-14 00:00:00 #>  8 Harper Lee author 89 FALSE 1926-04-28 00:00:00 #>  9 Zsa Zsa Gábor actor 99 TRUE 1917-02-06 00:00:00 #> 10 George Michael musician 53 FALSE 1963-06-25 00:00:00 #> # ℹ 1 more variable: `Date of death` <dttm>

The example below shows the use of range to specify both the (work)sheet and an A1-style cell range.

It also demonstrates how col_types gives control of column types, similar to how col_types works in readr and readxl. Note that currently there is only support for the “shortcode” style of column specification and we plan to align better with readr’s capabilities in a future release.

 range_read(  gs4_example("deaths"), range = "other!A5:F15", col_types = "?ci??D" ) #> ✔ Reading from deaths. #> ✔ Range ''other'!A5:F15'. #> # A tibble: 10 × 6 #> Name Profession Age `Has kids` `Date of birth`  #> <chr> <chr> <int> <lgl> <dttm>  #>  1 Vera Rubin scientist 88 TRUE 1928-07-23 00:00:00 #>  2 Mohamed Ali athlete 74 TRUE 1942-01-17 00:00:00 #>  3 Morley Safer journalist 84 TRUE 1931-11-08 00:00:00 #>  4 Fidel Castro politician 90 TRUE 1926-08-13 00:00:00 #>  5 Antonin Scalia lawyer 79 TRUE 1936-03-11 00:00:00 #>  6 Jo Cox politician 41 TRUE 1974-06-22 00:00:00 #>  7 Janet Reno lawyer 78 FALSE 1938-07-21 00:00:00 #>  8 Gwen Ifill journalist 61 FALSE 1955-09-29 00:00:00 #>  9 John Glenn astronaut 95 TRUE 1921-07-28 00:00:00 #> 10 Pat Summit coach 64 TRUE 1952-06-14 00:00:00 #> # ℹ 1 more variable: `Date of death` <date>

If you looked at the “deaths” spreadsheet in the browser (it’s here), you know that it has some of the typical features of real world spreadsheets: the main data rectangle has prose intended for human-consumption before and after it. That’s why we have to specify the range when we read from it.

We’ve designated the data rectangles as named ranges, which provides a very slick way to read them – definitely less brittle and mysterious than approaches like range = "other!A5:F15" or skip = 4, n_max = 10. A named range can be passed via the range = argument:

 gs4_example("deaths") %>%   range_read(range = "arts_data") #> ✔ Reading from deaths. #> ✔ Range arts_data. #> # A tibble: 10 × 6 #> Name Profession Age `Has kids` `Date of birth`  #> <chr> <chr> <dbl> <lgl> <dttm>  #>  1 David Bowie musician 69 TRUE 1947-01-08 00:00:00 #>  2 Carrie Fisher actor 60 TRUE 1956-10-21 00:00:00 #>  3 Chuck Berry musician 90 TRUE 1926-10-18 00:00:00 #>  4 Bill Paxton actor 61 TRUE 1955-05-17 00:00:00 #>  5 Prince musician 57 TRUE 1958-06-07 00:00:00 #>  6 Alan Rickman actor 69 FALSE 1946-02-21 00:00:00 #>  7 Florence Henderson actor 82 TRUE 1934-02-14 00:00:00 #>  8 Harper Lee author 89 FALSE 1926-04-28 00:00:00 #>  9 Zsa Zsa Gábor actor 99 TRUE 1917-02-06 00:00:00 #> 10 George Michael musician 53 FALSE 1963-06-25 00:00:00 #> # ℹ 1 more variable: `Date of death` <dttm>

The named ranges, if any exist, are part of the information returned by gs4_get().

Detailed cell data

range_read_cells() returns a data frame with one row per cell and it gives access to raw cell data sent by the Sheets API.

 (df <- range_read_cells(gs4_example("deaths"), range = "E5:E7")) #> ✔ Reading from deaths. #> ✔ Range E5:E7. #> # A tibble: 3 × 4 #> row col loc cell  #> <int> <int> <chr> <list>  #> 1 5 5 E5 <CELL_TEX> #> 2 6 5 E6 <CELL_DAT> #> 3 7 5 E7 <CELL_DAT> df$cell[[3]] #> $effectiveValue #> $effectiveValue$numberValue #> [1] 20749 #>  #>  #> $formattedValue #> [1] "10/21/1956" #>  #> $effectiveFormat #> $effectiveFormat$numberFormat #> $effectiveFormat$numberFormat$type #> [1] "DATE" #>  #> $effectiveFormat$numberFormat$pattern #> [1] "M/D/YYYY" #>  #>  #>  #> attr(,"class") #> [1] "CELL_DATE" "SHEETS_CELL"

Specify cell_data = "full", discard_empty = FALSE to get even more data if you, for example, need access to cell formulas or formatting.

spread_sheet() converts data in the “one row per cell” form into the data frame you get from range_read(), which involves reshaping and column typing.

 df %>% spread_sheet(col_types = "D") #> # A tibble: 2 × 1 #> `Date of birth` #> <date>  #> 1 1947-01-08  #> 2 1956-10-21 ## is same as ... range_read(gs4_example("deaths"), range = "E5:E7", col_types ="D") #> ✔ Reading from deaths. #> ✔ Range E5:E7. #> # A tibble: 2 × 1 #> `Date of birth` #> <date>  #> 1 1947-01-08  #> 2 1956-10-21

When speed matters

If your Sheet is so large that the speed of range_read() is causing problems, consider range_speedread(). It uses a special URL that allows a Sheet to be read as comma-separated values (CSV). Access via this URL doesn’t use the Sheets API (although range_speedread() still makes an API call to retrieve Sheet metadata). As an example, on a Sheet with around 57,000 rows and 25 columns (over 1.4 million cells), range_speedread() takes ~5 seconds, whereas range_read() takes closer to 3 minutes. Why wouldn’t we always take the faster option?!? Because the speed difference is imperceptible for many Sheets and there are some downsides (described later).

range_speedread() has much the same interface as range_read().

 gs4_example("gapminder") %>%   range_speedread(sheet = "Oceania", n_max = 3) #> ✔ Reading from gapminder, sheet Oceania. #> ℹ Export URL: #> <https://docs.google.com/spreadsheets/d/1U6Cf_qEOhiR9AZqTqS3mbMF3zt2db48ZP5v3rkrAEJY/export?format=csv&gid=1796776040> #> Rows: 3 Columns: 6 #> ── Column specification ─────────────────────────────────────────────── #> Delimiter: "," #> chr (2): country, continent #> dbl (4): year, lifeExp, pop, gdpPercap #>  #> ℹ Use `spec()` to retrieve the full column specification for this data. #> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message. #> # A tibble: 3 × 6 #> country continent year lifeExp pop gdpPercap #> <chr> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 Australia Oceania 1952 69.1 8691212 10040. #> 2 Australia Oceania 1957 70.3 9712569 10950. #> 3 Australia Oceania 1962 70.9 10794968 12217.

The output above reveals that, under the hood, range_speedread() calls an external function for CSV parsing (namely, readr::read_csv()). An important consequence is that all arguments around column type specification are passed along to the CSV-parsing function. Here is a demo using readr-style column specification:

 gs4_example("deaths") %>%   range_speedread(  range = "other!A5:F15",  col_types = readr::cols(  Age = readr::col_integer(),  `Date of birth` = readr::col_date("%m/%d/%Y"),  `Date of death` = readr::col_date("%m/%d/%Y")  )  ) #> ✔ Reading from deaths, sheet other, range A5:F15. #> ℹ Export URL: #> <https://docs.google.com/spreadsheets/d/1VTJjWoP1nshbyxmL9JqXgdVsimaYty21LGxxs018H2Y/export?format=csv&range=A5%3AF15&gid=278837031> #> # A tibble: 10 × 6 #> Name Profession Age `Has kids` `Date of birth` `Date of death` #> <chr> <chr> <int> <lgl> <date> <date>  #>  1 Vera Ru… scientist 88 TRUE 1928-07-23 2016-12-25  #>  2 Mohamed… athlete 74 TRUE 1942-01-17 2016-06-03  #>  3 Morley … journalist 84 TRUE 1931-11-08 2016-05-19  #>  4 Fidel C… politician 90 TRUE 1926-08-13 2016-11-25  #>  5 Antonin… lawyer 79 TRUE 1936-03-11 2016-02-13  #>  6 Jo Cox politician 41 TRUE 1974-06-22 2016-06-16  #>  7 Janet R… lawyer 78 FALSE 1938-07-21 2016-11-07  #>  8 Gwen If… journalist 61 FALSE 1955-09-29 2016-11-14  #>  9 John Gl… astronaut 95 TRUE 1921-07-28 2016-12-08  #> 10 Pat Sum… coach 64 TRUE 1952-06-14 2016-06-28

Compare that to how we would read the same data with range_read():

 gs4_example("deaths") %>%   range_read(range = "other_data", col_types = "??i?DD") #> ✔ Reading from deaths. #> ✔ Range other_data. #> # A tibble: 10 × 6 #> Name Profession Age `Has kids` `Date of birth` `Date of death` #> <chr> <chr> <int> <lgl> <date> <date>  #>  1 Vera Ru… scientist 88 TRUE 1928-07-23 2016-12-25  #>  2 Mohamed… athlete 74 TRUE 1942-01-17 2016-06-03  #>  3 Morley … journalist 84 TRUE 1931-11-08 2016-05-19  #>  4 Fidel C… politician 90 TRUE 1926-08-13 2016-11-25  #>  5 Antonin… lawyer 79 TRUE 1936-03-11 2016-02-13  #>  6 Jo Cox politician 41 TRUE 1974-06-22 2016-06-16  #>  7 Janet R… lawyer 78 FALSE 1938-07-21 2016-11-07  #>  8 Gwen If… journalist 61 FALSE 1955-09-29 2016-11-14  #>  9 John Gl… astronaut 95 TRUE 1921-07-28 2016-12-08  #> 10 Pat Sum… coach 64 TRUE 1952-06-14 2016-06-28

This example highlights two important differences:

  • range = "other!A5:F15" versus range = "other_data": range_speedread() can’t access a named range, whereas range_read() can.
  • readr::col_date("%m/%d/%Y") vs D: range_speedread() must parse a character representation of all cell data, including datetimes, whereas range_read() has access to the actual cell data and its type.

What’s the speed difference for something like the Africa sheet in the “gapminder” example Sheet? (around 625 rows x 6 columns, or 3700 cells)

 system.time(  gs4_example("gapminder") %>% range_speedread(sheet = "Africa") ) #> ✔ Reading from gapminder, sheet Africa. #> ℹ Export URL: #> <https://docs.google.com/spreadsheets/d/1U6Cf_qEOhiR9AZqTqS3mbMF3zt2db48ZP5v3rkrAEJY/export?format=csv&gid=780868077> #> Rows: 624 Columns: 6 #> ── Column specification ─────────────────────────────────────────────── #> Delimiter: "," #> chr (2): country, continent #> dbl (4): year, lifeExp, pop, gdpPercap #>  #> ℹ Use `spec()` to retrieve the full column specification for this data. #> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message. #> user system elapsed  #> 0.088 0.011 0.519 system.time(  gs4_example("gapminder") %>% range_read(sheet = "Africa") ) #> ✔ Reading from gapminder. #> ✔ Range ''Africa''. #> user system elapsed  #> 0.289 0.016 0.608

The modest difference above shows that the speed difference is unlikely to be a gamechanger in many settings.

Summary of how to think about range_speedread() vs range_read():

  • Both use auth (literally, send a token), unless there was a prior to call to gs4_deauth().
  • range_speedread() is faster, but it’s not noticeable for typical Sheets.
  • range_speedread() uses readr-style column type specification, which is actually more flexible than what range_read() currently does. In future googlesheets4 releases, we will adopt readr-style column type specification.
  • range_speedread() requires more detailed column type specification, because it cannot access unformatted cell data and the actual cell type, as range_read() can.
  • range_speedread() can’t access full cell data, e.g., formatting.
  • range_speedread() can’t work with named ranges.