Read xls and xlsx files

Source: R/read_excel.R

Read xls and xlsx files

read_excel() calls excel_format() to determine if path is xls or xlsx, based on the file extension and the file itself, in that order. Use read_xls() and read_xlsx() directly if you know better and want to prevent such guessing.

read_excel(path, sheet = NULL, range = NULL, col_names = TRUE,
  col_types = NULL, na = "", trim_ws = TRUE, skip = 0,
  n_max = Inf, guess_max = min(1000, n_max),
  progress = readxl_progress(), .name_repair = "unique")

read_xls(path, sheet = NULL, range = NULL, col_names = TRUE,
  col_types = NULL, na = "", trim_ws = TRUE, skip = 0,
  n_max = Inf, guess_max = min(1000, n_max),
  progress = readxl_progress(), .name_repair = "unique")

read_xlsx(path, sheet = NULL, range = NULL, col_names = TRUE,
  col_types = NULL, na = "", trim_ws = TRUE, skip = 0,
  n_max = Inf, guess_max = min(1000, n_max),
  progress = readxl_progress(), .name_repair = "unique")

Arguments

path

Path to the xls/xlsx file.
sheet

Sheet to read. Either a string (the name of a sheet), or an integer (the position of the sheet). Ignored if the sheet is specified via range. If neither argument specifies the sheet, defaults to the first sheet.
range

A cell range to read from, as described in cell-specification. Includes typical Excel ranges like "B3:D87", possibly including the sheet name like "Budget!B2:G14", and more. Interpreted strictly, even if the range forces the inclusion of leading or trailing empty rows or columns. Takes precedence over skip, n_max and sheet.
col_names

TRUE to use the first row as column names, FALSE to get default names, or a character vector giving a name for each column. If user provides col_types as a vector, col_names can have one entry per column, i.e. have the same length as col_types, or one entry per unskipped column.
col_types

Either NULL to guess all from the spreadsheet or a character vector containing one entry per column from these options: "skip", "guess", "logical", "numeric", "date", "text" or "list". If exactly one col_type is specified, it will be recycled. The content of a cell in a skipped column is never read and that column will not appear in the data frame output. A list cell loads a column as a list of length 1 vectors, which are typed using the type guessing logic from col_types = NULL, but on a cell-by-cell basis.
na

Character vector of strings to interpret as missing values. By default, readxl treats blank cells as missing data.
trim_ws

Should leading and trailing whitespace be trimmed?
skip

Minimum number of rows to skip before reading anything, be it column names or data. Leading empty rows are automatically skipped, so this is a lower bound. Ignored if range is given.
n_max

Maximum number of data rows to read. Trailing empty rows are automatically skipped, so this is an upper bound on the number of rows in the returned tibble. Ignored if range is given.
guess_max

Maximum number of data rows to use for guessing column types.
progress

Display a progress spinner? By default, the spinner appears only in an interactive session, outside the context of knitting a document, and when the call is likely to run for several seconds or more. See readxl_progress() for more details.
.name_repair

Handling of column names. By default, readxl ensures column names are not empty and are unique. If the tibble package version is recent enough, there is full support for .name_repair as documented in tibble::tibble(). If an older version of tibble is present, readxl falls back to name repair in the style of tibble v1.4.2.

Value

A tibble

See also

cell-specification for more details on targetting cells with the range argument

Examples

datasets <- readxl_example("datasets.xlsx")
read_excel(datasets)
#> # A tibble: 150 x 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 
#> # … with 140 more rows

# Specify sheet either by position or by name
read_excel(datasets, 2)
#> # A tibble: 32 x 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
#> # … with 22 more rows
read_excel(datasets, "mtcars")
#> # A tibble: 32 x 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
#> # … with 22 more rows

# Skip rows and use default column names
read_excel(datasets, skip = 148, col_names = FALSE)
#> New names:
#> * `` -> ...1
#> * `` -> ...2
#> * `` -> ...3
#> * `` -> ...4
#> * `` -> ...5
#> # A tibble: 3 x 5
#>    ...1  ...2  ...3  ...4 ...5     
#>   <dbl> <dbl> <dbl> <dbl> <chr>    
#> 1   6.5   3     5.2   2   virginica
#> 2   6.2   3.4   5.4   2.3 virginica
#> 3   5.9   3     5.1   1.8 virginica

# Recycle a single column type
read_excel(datasets, col_types = "text")
#> # A tibble: 150 x 5
#>    Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#>    <chr>        <chr>       <chr>        <chr>       <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 
#> # … with 140 more rows

# Specify some col_types and guess others
read_excel(datasets, col_types = c("text", "guess", "numeric", "guess", "guess"))
#> # A tibble: 150 x 5
#>    Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#>    <chr>              <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 
#> # … with 140 more rows

# Accomodate a column with disparate types via col_type = "list"
df <- read_excel(readxl_example("clippy.xlsx"), col_types = c("text", "list"))
df
#> # A tibble: 4 x 2
#>   name                 value     
#>   <chr>                <list>    
#> 1 Name                 <chr [1]> 
#> 2 Species              <chr [1]> 
#> 3 Approx date of death <dttm [1]>
#> 4 Weight in grams      <dbl [1]> 
df$value
#> [[1]]
#> [1] "Clippy"
#> 
#> [[2]]
#> [1] "paperclip"
#> 
#> [[3]]
#> [1] "2007-01-01 UTC"
#> 
#> [[4]]
#> [1] 0.9
#> 
sapply(df$value, class)
#> [[1]]
#> [1] "character"
#> 
#> [[2]]
#> [1] "character"
#> 
#> [[3]]
#> [1] "POSIXct" "POSIXt" 
#> 
#> [[4]]
#> [1] "numeric"
#> 

# Limit the number of data rows read
read_excel(datasets, n_max = 3)
#> # A tibble: 3 x 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 

# Read from an Excel range using A1 or R1C1 notation
read_excel(datasets, range = "C1:E7")
#> # A tibble: 6 x 3
#>   Petal.Length Petal.Width Species
#>          <dbl>       <dbl> <chr>  
#> 1          1.4         0.2 setosa 
#> 2          1.4         0.2 setosa 
#> 3          1.3         0.2 setosa 
#> 4          1.5         0.2 setosa 
#> 5          1.4         0.2 setosa 
#> 6          1.7         0.4 setosa 
read_excel(datasets, range = "R1C2:R2C5")
#> # A tibble: 1 x 4
#>   Sepal.Width Petal.Length Petal.Width Species
#>         <dbl>        <dbl>       <dbl> <chr>  
#> 1         3.5          1.4         0.2 setosa 

# Specify the sheet as part of the range
read_excel(datasets, range = "mtcars!B1:D5")
#> # A tibble: 4 x 3
#>     cyl  disp    hp
#>   <dbl> <dbl> <dbl>
#> 1     6   160   110
#> 2     6   160   110
#> 3     4   108    93
#> 4     6   258   110

# Read only specific rows or columns
read_excel(datasets, range = cell_rows(102:151), col_names = FALSE)
#> New names:
#> * `` -> ...1
#> * `` -> ...2
#> * `` -> ...3
#> * `` -> ...4
#> * `` -> ...5
#> # A tibble: 50 x 5
#>     ...1  ...2  ...3  ...4 ...5     
#>    <dbl> <dbl> <dbl> <dbl> <chr>    
#>  1   6.3   3.3   6     2.5 virginica
#>  2   5.8   2.7   5.1   1.9 virginica
#>  3   7.1   3     5.9   2.1 virginica
#>  4   6.3   2.9   5.6   1.8 virginica
#>  5   6.5   3     5.8   2.2 virginica
#>  6   7.6   3     6.6   2.1 virginica
#>  7   4.9   2.5   4.5   1.7 virginica
#>  8   7.3   2.9   6.3   1.8 virginica
#>  9   6.7   2.5   5.8   1.8 virginica
#> 10   7.2   3.6   6.1   2.5 virginica
#> # … with 40 more rows
read_excel(datasets, range = cell_cols("B:D"))
#> # A tibble: 150 x 3
#>    Sepal.Width Petal.Length Petal.Width
#>          <dbl>        <dbl>       <dbl>
#>  1         3.5          1.4         0.2
#>  2         3            1.4         0.2
#>  3         3.2          1.3         0.2
#>  4         3.1          1.5         0.2
#>  5         3.6          1.4         0.2
#>  6         3.9          1.7         0.4
#>  7         3.4          1.4         0.3
#>  8         3.4          1.5         0.2
#>  9         2.9          1.4         0.2
#> 10         3.1          1.5         0.1
#> # … with 140 more rows

# Get a preview of column names
names(read_excel(readxl_example("datasets.xlsx"), n_max = 0))
#> [1] "Sepal.Length" "Sepal.Width"  "Petal.Length" "Petal.Width"  "Species"     

if (utils::packageVersion("tibble") > "1.4.2") {
  ## exploit full .name_repair flexibility from tibble

  ## "universal" names are unique and syntactic
  read_excel(
    readxl_example("deaths.xlsx"),
    range = "arts!A5:F15",
    .name_repair = "universal"
  )

  ## specify name repair as a built-in function
  read_excel(readxl_example("clippy.xlsx"), .name_repair = toupper)

  ## specify name repair as a custom function
  my_custom_name_repair <- function(nms) tolower(gsub("[.]", "_", nms))
  read_excel(
    readxl_example("datasets.xlsx"),
    .name_repair = my_custom_name_repair
  )

  ## specify name repair as an anonymous function
  read_excel(
    readxl_example("datasets.xlsx"),
    sheet = "chickwts",
    .name_repair = ~ substr(.x, start = 1, stop = 3)
  )
}
#> New names:
#> * `Has kids` -> Has.kids
#> * `Date of birth` -> Date.of.birth
#> * `Date of death` -> Date.of.death
#> # A tibble: 71 x 2
#>      wei fee      
#>    <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
#> # … with 61 more rows