You probably only need to use two functions from this package:
read_ods and write_ods.
Write the data PlantGrowth (from the built-in datasets
package) as a new file plant.ods in the current working
directory of the user’s session.
write_ods(PlantGrowth, "plant.ods")You can then read it back from plant.ods
read_ods("plant.ods")
#> # A tibble: 30 × 2
#> weight group
#> <dbl> <chr>
#> 1 4.17 ctrl
#> 2 5.58 ctrl
#> 3 5.18 ctrl
#> 4 6.11 ctrl
#> 5 4.5 ctrl
#> 6 4.61 ctrl
#> 7 5.17 ctrl
#> 8 4.53 ctrl
#> 9 5.33 ctrl
#> 10 5.14 ctrl
#> # ℹ 20 more rowsUpdate and Append
You can append another sheet into an existing ods file with the sheet name being “mtcars_ods”.
write_ods(mtcars, "plant.ods", sheet = "mtcars_ods", append = TRUE)Read from a specific sheet. Notice row names are missing.
read_ods("plant.ods", sheet = "mtcars_ods")
#> # A tibble: 32 × 11
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4
#> 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4
#> 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
#> 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1
#> 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2
#> 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1
#> 7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4
#> 8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2
#> 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2
#> 10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4
#> # ℹ 22 more rowsYou can also integer for sheet, e.g. 2 for the second
sheet.
read_ods("plant.ods", sheet = 2)
#> # A tibble: 32 × 11
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4
#> 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4
#> 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
#> 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1
#> 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2
#> 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1
#> 7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4
#> 8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2
#> 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2
#> 10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4
#> # ℹ 22 more rowsUpdate an existing sheet and preserve row names
write_ods(mtcars, "plant.ods", sheet = "mtcars_ods", update = TRUE, row_names = TRUE)Notice the information from the sheet mtcars_ods is
updated.
read_ods("plant.ods", sheet = "mtcars_ods")
#> New names:
#> • `` -> `...1`
#> # A tibble: 32 × 12
#> ...1 mpg cyl disp hp drat wt qsec vs am gear carb
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Mazda RX4 21 6 160 110 3.9 2.62 16.5 0 1 4 4
#> 2 Mazda RX4 … 21 6 160 110 3.9 2.88 17.0 0 1 4 4
#> 3 Datsun 710 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
#> 4 Hornet 4 D… 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1
#> 5 Hornet Spo… 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2
#> 6 Valiant 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1
#> 7 Duster 360 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4
#> 8 Merc 240D 24.4 4 147. 62 3.69 3.19 20 1 0 4 2
#> 9 Merc 230 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2
#> 10 Merc 280 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4
#> # ℹ 22 more rowsRead from a specific range
read_ods("plant.ods", sheet = "mtcars_ods", range = "A1:C10")
#> New names:
#> • `` -> `...1`
#> # A tibble: 9 × 3
#> ...1 mpg cyl
#> <chr> <dbl> <dbl>
#> 1 Mazda RX4 21 6
#> 2 Mazda RX4 Wag 21 6
#> 3 Datsun 710 22.8 4
#> 4 Hornet 4 Drive 21.4 6
#> 5 Hornet Sportabout 18.7 8
#> 6 Valiant 18.1 6
#> 7 Duster 360 14.3 8
#> 8 Merc 240D 24.4 4
#> 9 Merc 230 22.8 4You cannot append to an existing sheet.
write_ods(iris, "plant.ods", sheet = "mtcars_ods", append = TRUE)
#> Error: Sheet mtcars_ods exists. Set update to TRUE is you want to update this sheet.You cannot update a missing sheet.
write_ods(iris, "plant.ods", sheet = "iris", update = TRUE)
#> Error: Sheet iris does not exist. Cannot update.Writing multiple sheets simultaneously
It is much faster to write data frames into the same file by putting them in a (named) list.
write_ods(list("iris" = iris, "plant" = PlantGrowth), "plant_multi.ods")
read_ods("plant_multi.ods", sheet = "plant")
#> # A tibble: 30 × 2
#> weight group
#> <dbl> <chr>
#> 1 4.17 ctrl
#> 2 5.58 ctrl
#> 3 5.18 ctrl
#> 4 6.11 ctrl
#> 5 4.5 ctrl
#> 6 4.61 ctrl
#> 7 5.17 ctrl
#> 8 4.53 ctrl
#> 9 5.33 ctrl
#> 10 5.14 ctrl
#> # ℹ 20 more rowsFlat ODS files (.xml or .fods)
Can be read with read_ods() 2 (note that the same
function is used to read flat files, no matter the extension). This has
the same behaviour and arguments as read_ods()
read_fods("plant.fods")write_ods() can be used to write Flat ODS files
write_ods(PlantGrowth, "plant.fods")Misc.
Use the function list_ods_sheets() to list out all
sheets in an (F)ODS file.
list_ods_sheets("plant.ods")
#> [1] "Sheet1" "mtcars_ods"readODS 2.0.0
Starting from 2.0.0, write_ods writes NA as
empty by default.
PlantGrowth2 <- tibble::as_tibble(PlantGrowth)
PlantGrowth2[1,1] <- NA
PlantGrowth2$group <- as.character(PlantGrowth2$group)
## NA is preseved; weight is still <dbl>
read_ods(write_ods(PlantGrowth2))
#> # A tibble: 30 × 2
#> weight group
#> <dbl> <chr>
#> 1 NA ctrl
#> 2 5.58 ctrl
#> 3 5.18 ctrl
#> 4 6.11 ctrl
#> 5 4.5 ctrl
#> 6 4.61 ctrl
#> 7 5.17 ctrl
#> 8 4.53 ctrl
#> 9 5.33 ctrl
#> 10 5.14 ctrl
#> # ℹ 20 more rowsIf you want NA to be written literally as the string
“NA”, use na_as_string. You should literally see the string
“NA” when the file is opened with LibreOffice, for example.
But the string “NA” messes up the automatic type inference of
read_ods.
## NA is preseved; but weight is now <chr>
read_ods(write_ods(PlantGrowth2, na_as_string = TRUE))
#> # A tibble: 30 × 2
#> weight group
#> <chr> <chr>
#> 1 NA ctrl
#> 2 5.58 ctrl
#> 3 5.18 ctrl
#> 4 6.11 ctrl
#> 5 4.5 ctrl
#> 6 4.61 ctrl
#> 7 5.17 ctrl
#> 8 4.53 ctrl
#> 9 5.33 ctrl
#> 10 5.14 ctrl
#> # ℹ 20 more rowsOf course you can fix this by specifying col_types.
## NA is preseved; but weight is now <chr>
read_ods(write_ods(PlantGrowth2, na_as_string = TRUE),
col_types = readr::cols(weight = readr::col_double()))
#> Warning: [0, 1]: expected a double, but got 'NA'
#> # A tibble: 30 × 2
#> weight group
#> <dbl> <chr>
#> 1 NA ctrl
#> 2 5.58 ctrl
#> 3 5.18 ctrl
#> 4 6.11 ctrl
#> 5 4.5 ctrl
#> 6 4.61 ctrl
#> 7 5.17 ctrl
#> 8 4.53 ctrl
#> 9 5.33 ctrl
#> 10 5.14 ctrl
#> # ℹ 20 more rowsSeveral functions were removed in readODS 2.0.0. Please consider the
API of readODS mature and there should not be any breaking
change until readODS 3.0.0.
ods_sheets
Please use list_ods_sheets(path = "plant.ods")
instead.
## ods_sheets("plant.ods")
list_ods_sheets("plant.ods")
#> [1] "Sheet1" "mtcars_ods"
get_num_sheets_in_ods and
getNrOfSheetsInODS
Please use list_ods_sheets
##get_num_sheets_in_ods("plant.ods")
length(list_ods_sheets("plant.ods"))
#> [1] 2
read.ods
Please use read_ods. In order to emulate the behaviours
of read.ods, the followings are recommended
## read.ods from 1.6 to 1.8
read_ods("plant.ods", col_names = FALSE, skip = 0, na = NULL, col_types = NA, as_tibble = FALSE)
#> New names:
#> • `` -> `...1`
#> • `` -> `...2`
#> ...1 ...2
#> 1 weight group
#> 2 4.17 ctrl
#> 3 5.58 ctrl
#> 4 5.18 ctrl
#> 5 6.11 ctrl
#> 6 4.5 ctrl
#> 7 4.61 ctrl
#> 8 5.17 ctrl
#> 9 4.53 ctrl
#> 10 5.33 ctrl
#> 11 5.14 ctrl
#> 12 4.81 trt1
#> 13 4.17 trt1
#> 14 4.41 trt1
#> 15 3.59 trt1
#> 16 5.87 trt1
#> 17 3.83 trt1
#> 18 6.03 trt1
#> 19 4.89 trt1
#> 20 4.32 trt1
#> 21 4.69 trt1
#> 22 6.31 trt2
#> 23 5.12 trt2
#> 24 5.54 trt2
#> 25 5.5 trt2
#> 26 5.37 trt2
#> 27 5.29 trt2
#> 28 4.92 trt2
#> 29 6.15 trt2
#> 30 5.8 trt2
#> 31 5.26 trt2
## read.ods older than 1.6
lapply(list_ods_sheets("plant.ods"),
function(x) read_ods(path = "plant.ods", sheet = x, col_names = FALSE, skip = 0, na = NULL, col_types = NA, as_tibble = FALSE))
#> New names:
#> New names:
#> • `` -> `...1`
#> • `` -> `...2`
#> [[1]]
#> ...1 ...2
#> 1 weight group
#> 2 4.17 ctrl
#> 3 5.58 ctrl
#> 4 5.18 ctrl
#> 5 6.11 ctrl
#> 6 4.5 ctrl
#> 7 4.61 ctrl
#> 8 5.17 ctrl
#> 9 4.53 ctrl
#> 10 5.33 ctrl
#> 11 5.14 ctrl
#> 12 4.81 trt1
#> 13 4.17 trt1
#> 14 4.41 trt1
#> 15 3.59 trt1
#> 16 5.87 trt1
#> 17 3.83 trt1
#> 18 6.03 trt1
#> 19 4.89 trt1
#> 20 4.32 trt1
#> 21 4.69 trt1
#> 22 6.31 trt2
#> 23 5.12 trt2
#> 24 5.54 trt2
#> 25 5.5 trt2
#> 26 5.37 trt2
#> 27 5.29 trt2
#> 28 4.92 trt2
#> 29 6.15 trt2
#> 30 5.8 trt2
#> 31 5.26 trt2
#>
#> [[2]]
#> ...1 ...2 ...3 ...4 ...5 ...6 ...7 ...8 ...9 ...10 ...11
#> 1 mpg cyl disp hp drat wt qsec vs am gear
#> 2 Mazda RX4 21 6 160 110 3.9 2.62 16.46 0 1 4
#> 3 Mazda RX4 Wag 21 6 160 110 3.9 2.875 17.02 0 1 4
#> 4 Datsun 710 22.8 4 108 93 3.85 2.32 18.61 1 1 4
#> 5 Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3
#> 6 Hornet Sportabout 18.7 8 360 175 3.15 3.44 17.02 0 0 3
#> 7 Valiant 18.1 6 225 105 2.76 3.46 20.22 1 0 3
#> 8 Duster 360 14.3 8 360 245 3.21 3.57 15.84 0 0 3
#> 9 Merc 240D 24.4 4 146.7 62 3.69 3.19 20 1 0 4
#> 10 Merc 230 22.8 4 140.8 95 3.92 3.15 22.9 1 0 4
#> 11 Merc 280 19.2 6 167.6 123 3.92 3.44 18.3 1 0 4
#> 12 Merc 280C 17.8 6 167.6 123 3.92 3.44 18.9 1 0 4
#> 13 Merc 450SE 16.4 8 275.8 180 3.07 4.07 17.4 0 0 3
#> 14 Merc 450SL 17.3 8 275.8 180 3.07 3.73 17.6 0 0 3
#> 15 Merc 450SLC 15.2 8 275.8 180 3.07 3.78 18 0 0 3
#> 16 Cadillac Fleetwood 10.4 8 472 205 2.93 5.25 17.98 0 0 3
#> 17 Lincoln Continental 10.4 8 460 215 3 5.424 17.82 0 0 3
#> 18 Chrysler Imperial 14.7 8 440 230 3.23 5.345 17.42 0 0 3
#> 19 Fiat 128 32.4 4 78.7 66 4.08 2.2 19.47 1 1 4
#> 20 Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4
#> 21 Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.9 1 1 4
#> 22 Toyota Corona 21.5 4 120.1 97 3.7 2.465 20.01 1 0 3
#> 23 Dodge Challenger 15.5 8 318 150 2.76 3.52 16.87 0 0 3
#> 24 AMC Javelin 15.2 8 304 150 3.15 3.435 17.3 0 0 3
#> 25 Camaro Z28 13.3 8 350 245 3.73 3.84 15.41 0 0 3
#> 26 Pontiac Firebird 19.2 8 400 175 3.08 3.845 17.05 0 0 3
#> 27 Fiat X1-9 27.3 4 79 66 4.08 1.935 18.9 1 1 4
#> 28 Porsche 914-2 26 4 120.3 91 4.43 2.14 16.7 0 1 5
#> 29 Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.9 1 1 5
#> 30 Ford Pantera L 15.8 8 351 264 4.22 3.17 14.5 0 1 5
#> 31 Ferrari Dino 19.7 6 145 175 3.62 2.77 15.5 0 1 5
#> 32 Maserati Bora 15 8 301 335 3.54 3.57 14.6 0 1 5
#> 33 Volvo 142E 21.4 4 121 109 4.11 2.78 18.6 1 1 4
#> ...12
#> 1 carb
#> 2 4
#> 3 4
#> 4 1
#> 5 1
#> 6 2
#> 7 1
#> 8 4
#> 9 2
#> 10 2
#> 11 4
#> 12 4
#> 13 3
#> 14 3
#> 15 3
#> 16 4
#> 17 4
#> 18 4
#> 19 1
#> 20 2
#> 21 1
#> 22 1
#> 23 2
#> 24 2
#> 25 4
#> 26 2
#> 27 1
#> 28 2
#> 29 2
#> 30 4
#> 31 6
#> 32 8
#> 33 2