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.
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).
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. Note also that col_types gives control of column types, similar to how col_types works in readr and readxl.
read_sheet(sheets_example("mini-gap"), sheet = 2) #> 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. read_sheet(sheets_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. read_sheet(sheets_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 00:00:00 read_sheet( sheets_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 2016-06-28
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:
sheets_example("deaths") %>% read_sheet(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 00:00:00
The named ranges, if any exist, are part of the information returned by sheets_get().
sheets_cells() returns a data frame with one row per cell and it gives access to raw cell data sent by the Sheets API.
(df <- sheets_cells(sheets_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 read_sheet(), 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 ... read_sheet(sheets_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 1956-10-21
If your Sheet is so large that the speed of sheets_read() is causing problems, consider sheets_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 sheets_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), sheets_speadread() takes ~5 seconds, whereas sheets_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).
sheets_speedread() has much the same interface as sheets_read().
sheets_example("gapminder") %>% sheets_speedread(sheet = "Oceania", n_max = 3) #> Reading from "gapminder", "Oceania" sheet #> Export URL: https://docs.google.com/spreadsheets/d/1U6Cf_qEOhiR9AZqTqS3mbMF3zt2db48ZP5v3rkrAEJY/export?format=csv&gid=1796776040 #> Parsed with 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, sheets_speadread() 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:
sheets_example("deaths") %>% sheets_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", "other" sheet, range "A5:F15" #> Export URL: https://docs.google.com/spreadsheets/d/1tuYKzSbLukDLe5ymf_ZKdQA8SfOyeMM7rmf6D6NJpxg/export?format=csv&range=A5%3AF15&gid=28655153 #> # 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
Compare that to how we would read the same data with sheets_read():
sheets_example("deaths") %>% sheets_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 2016-06-28
This example highlights two important differences:
range = "other!A5:F15" versus range = "other_data": sheets_speedread() can’t access a named range, whereas sheets_read() can.readr::col_date("%m/%d/%Y") vs D: sheets_speedread() must parse a character representation of all cell data, including datetimes, whereas sheets_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( sheets_example("gapminder") %>% sheets_speedread(sheet = "Africa") ) #> Reading from "gapminder", "Africa" sheet #> Export URL: https://docs.google.com/spreadsheets/d/1U6Cf_qEOhiR9AZqTqS3mbMF3zt2db48ZP5v3rkrAEJY/export?format=csv&gid=780868077 #> Parsed with column specification: #> cols( #> country = col_character(), #> continent = col_character(), #> year = col_double(), #> lifeExp = col_double(), #> pop = col_double(), #> gdpPercap = col_double() #> ) #> user system elapsed #> 0.050 0.001 0.357 system.time( sheets_example("gapminder") %>% sheets_read(sheet = "Africa") ) #> Reading from "gapminder" #> Range "'Africa'" #> user system elapsed #> 0.595 0.023 0.940
The modest difference above shows that the speed difference is unlikely to be a gamechanger in many settings.
Summary of how to think about sheets_speedread() vs sheets_read():
sheets_deauth().sheets_speedread() is faster, but it’s not noticeable for typical Sheets.sheets_speedread() uses readr-style column type specification, which is actually more flexible than what sheets_read() currently does. In future googlesheets4 releases, we will adopt readr-style column type specification.sheets_speedread() requires more detailed column type specification, because it cannot access unformatted cell data and the actual cell type, as sheets_read() can.sheets_speedread() can’t access full cell data, e.g., formatting.sheets_speedrad() can’t work with named ranges.