The list of sheet names is especially useful when you want to iterate over
all of the sheets in a workbook. The vignette("readxl-workflows") article
provides several worked examples of this, showing how to combine readxl with
other packages in the tidyverse, such as purrr, or with base R functions like
lapply().
Examples
excel_sheets(readxl_example("datasets.xlsx"))
#> [1] "iris" "mtcars" "chickwts" "quakes"
excel_sheets(readxl_example("datasets.xls"))
#> [1] "iris" "mtcars" "chickwts" "quakes"
# To load all sheets in a workbook, use lapply()
path <- readxl_example("datasets.xls")
lapply(excel_sheets(path), read_excel, path = path)
#> [[1]]
#> # A tibble: 150 × 5
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 5.1 3.5 1.4 0.2 setosa
#> 2 4.9 3 1.4 0.2 setosa
#> 3 4.7 3.2 1.3 0.2 setosa
#> 4 4.6 3.1 1.5 0.2 setosa
#> 5 5 3.6 1.4 0.2 setosa
#> 6 5.4 3.9 1.7 0.4 setosa
#> 7 4.6 3.4 1.4 0.3 setosa
#> 8 5 3.4 1.5 0.2 setosa
#> 9 4.4 2.9 1.4 0.2 setosa
#> 10 4.9 3.1 1.5 0.1 setosa
#> # ℹ 140 more rows
#>
#> [[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 rows
#>
#> [[3]]
#> # A tibble: 71 × 2
#> weight feed
#> <dbl> <chr>
#> 1 179 horsebean
#> 2 160 horsebean
#> 3 136 horsebean
#> 4 227 horsebean
#> 5 217 horsebean
#> 6 168 horsebean
#> 7 108 horsebean
#> 8 124 horsebean
#> 9 143 horsebean
#> 10 140 horsebean
#> # ℹ 61 more rows
#>
#> [[4]]
#> # A tibble: 1,000 × 5
#> lat long depth mag stations
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 -20.4 182. 562 4.8 41
#> 2 -20.6 181. 650 4.2 15
#> 3 -26 184. 42 5.4 43
#> 4 -18.0 182. 626 4.1 19
#> 5 -20.4 182. 649 4 11
#> 6 -19.7 184. 195 4 12
#> 7 -11.7 166. 82 4.8 43
#> 8 -28.1 182. 194 4.4 15
#> 9 -28.7 182. 211 4.7 35
#> 10 -17.5 180. 622 4.3 19
#> # ℹ 990 more rows
#>