Read Sheets

library(googlesheets4)

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.

For the full list of column types and how to specify them, see the Column specification section of the help for range_read().

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`     `Date of death`
#>    <chr>   <chr>      <int> <lgl>      <dttm>              <date>         
#>  1 Vera R… scientist     88 TRUE       1928-07-23 00:00:00 2016-12-25     
#>  2 Mohame… athlete       74 TRUE       1942-01-17 00:00:00 2016-06-03     
#>  3 Morley… journalist    84 TRUE       1931-11-08 00:00:00 2016-05-19     
#>  4 Fidel … politician    90 TRUE       1926-08-13 00:00:00 2016-11-25     
#>  5 Antoni… lawyer        79 TRUE       1936-03-11 00:00:00 2016-02-13     
#>  6 Jo Cox  politician    41 TRUE       1974-06-22 00:00:00 2016-06-16     
#>  7 Janet … lawyer        78 FALSE      1938-07-21 00:00:00 2016-11-07     
#>  8 Gwen I… journalist    61 FALSE      1955-09-29 00:00:00 2016-11-14     
#>  9 John G… astronaut     95 TRUE       1921-07-28 00:00:00 2016-12-08     
#> 10 Pat Su… coach         64 TRUE       1952-06-14 00:00:00 2016-06-28

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 Rubin  scientist     88 TRUE       1928-07-23      2016-12-25     
#>  2 Mohamed Ali athlete       74 TRUE       1942-01-17      2016-06-03     
#>  3 Morley Saf… journalist    84 TRUE       1931-11-08      2016-05-19     
#>  4 Fidel Cast… politician    90 TRUE       1926-08-13      2016-11-25     
#>  5 Antonin Sc… 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

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 Rubin  scientist     88 TRUE       1928-07-23      2016-12-25     
#>  2 Mohamed Ali athlete       74 TRUE       1942-01-17      2016-06-03     
#>  3 Morley Saf… journalist    84 TRUE       1931-11-08      2016-05-19     
#>  4 Fidel Cast… politician    90 TRUE       1926-08-13      2016-11-25     
#>  5 Antonin Sc… 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

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 
#>   0.123   0.004   0.411
system.time(
  gs4_example("gapminder") %>% range_read(sheet = "Africa")
)
#> ✔ Reading from gapminder.
#> ✔ Range ''Africa''.
#>    user  system elapsed 
#>   0.388   0.008   0.610

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.

Auth

read_sheet() and range_read() are synonyms

Specify the range and column types

Detailed cell data

When speed matters

`read_sheet()` and `range_read()` are synonyms