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.
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 synonymsThe 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.
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)
#> → Request failed [429]. Retry 1 happens in 100.5 seconds ...
#> ✔ Reading from mini-gap
#> ✔ Range 'Americas'
#> # A tibble: 5 x 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 x 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 5:5000000
#> # 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 00:00:00The 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().
range_read(
gs4_example("deaths"), range = "other!A5:F15", col_types = "?ci??D"
)
#> ✔ 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> <dttm> <date>
#> 1 Vera Rubin scientist 88 TRUE 1928-07-23 00:00:00 2016-12-25
#> 2 Mohamed Ali athlete 74 TRUE 1942-01-17 00:00:00 2016-06-03
#> 3 Morley Safer journalist 84 TRUE 1931-11-08 00:00:00 2016-05-19
#> 4 Fidel Castro politician 90 TRUE 1926-08-13 00:00:00 2016-11-25
#> 5 Antonin Scal… 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 Reno lawyer 78 FALSE 1938-07-21 00:00:00 2016-11-07
#> 8 Gwen Ifill journalist 61 FALSE 1955-09-29 00:00:00 2016-11-14
#> 9 John Glenn astronaut 95 TRUE 1921-07-28 00:00:00 2016-12-08
#> 10 Pat Summit coach 64 TRUE 1952-06-14 00:00:00 2016-06-28If 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 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 00:00:00The named ranges, if any exist, are part of the information returned by gs4_get().
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 x 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 x 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 x 1
#> `Date of birth`
#> <date>
#> 1 1947-01-08
#> 2 1956-10-21If 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
#> ✔ Export URL:
#> <https://docs.google.com/spreadsheets/d/1U6Cf_qEOhiR9AZqTqS3mbMF3zt2db48ZP5v3rkrAEJY/export?format=csv&gid=1796776040>
#>
#> ── Column specification ────────────────────────────────────────────────────────
#> cols(
#> country = col_character(),
#> continent = col_character(),
#> year = col_double(),
#> lifeExp = col_double(),
#> pop = col_double(),
#> gdpPercap = col_double()
#> )
#> # A tibble: 3 x 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 (currently readr::read_csv(), but it is likely to switch to vroom::vroom()). 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
#> ✔ Export URL:
#> <https://docs.google.com/spreadsheets/d/1VTJjWoP1nshbyxmL9JqXgdVsimaYty21LGxxs018H2Y/export?format=csv&range=A5%3AF15&gid=278837031>
#> # 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 2016-06-28Compare 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 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 2016-06-28This 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
#> ✔ Export URL:
#> <https://docs.google.com/spreadsheets/d/1U6Cf_qEOhiR9AZqTqS3mbMF3zt2db48ZP5v3rkrAEJY/export?format=csv&gid=780868077>
#>
#> ── Column specification ────────────────────────────────────────────────────────
#> cols(
#> country = col_character(),
#> continent = col_character(),
#> year = col_double(),
#> lifeExp = col_double(),
#> pop = col_double(),
#> gdpPercap = col_double()
#> )
#> user system elapsed
#> 0.076 0.000 0.755
system.time(
gs4_example("gapminder") %>% range_read(sheet = "Africa")
)
#> ✔ Reading from gapminder
#> ✔ Range 'Africa'
#> user system elapsed
#> 0.278 0.028 0.735The 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():
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.