openxlsx2 basic manual

Welcome to the basic manual to openxlsx2. In this manual you will learn how to use openxlsx2 to import data from xlsx-files to R as well as how to export data from R to xlsx, and how to import and modify these openxml workbooks in R. This package is based on the work of many contributors to openxlsx. It was mostly rewritten using pugixml and R6 making use of modern technology, providing a fresh and easy to use R package.

Over the years many people have worked on the tricky task to handle xls and xlsx files. Notably openxlsx, but there are countless other R-packages as well as third party libraries or calculation software capable of handling such files. Please feel free to use and test your files with other software and or let us know about your experience. Open an issue on github or write us a mail.

Importing data

Coming from openxlsx you might know about read.xlsx() (two functions, one for files and one for workbooks) and readWorkbook(). Functions that do different things, but mostly the same. In openxlsx2 we tried our best to reduce the complexity under the hood and for the user as well. In openxlsx2 they are replaced with read_xlsx(), wb_read() and they share the same underlying function wb_to_df().

For this example we will use example data provided by the package. You can locate it in our “inst/extdata” folder. The files are included with the package source and you can open them in any calculation software as well.

Basic import

We begin with the openxlsx2_example.xlsx file by telling R where to find this file on our system

file <- system.file("extdata", "openxlsx2_example.xlsx", package = "openxlsx2")

The object contains a path to the xlsx file and we pass this file to our function to read the workbook into R

# import workbook
library(openxlsx2)
wb_to_df(file)
#>     Var1 Var2 NA  Var3  Var4       Var5         Var6    Var7     Var8
#> 3   TRUE    1 NA     1     a 2023-05-29 3209324 This #DIV/0! 01:27:15
#> 4   TRUE   NA NA #NUM!     b 2023-05-23         <NA>       0 14:02:57
#> 5   TRUE    2 NA  1.34     c 2023-02-01         <NA> #VALUE! 23:01:02
#> 6  FALSE    2 NA  <NA> #NUM!       <NA>         <NA>       2 17:24:53
#> 7  FALSE    3 NA  1.56     e       <NA>         <NA>    <NA>     <NA>
#> 8  FALSE    1 NA   1.7     f 2023-03-02         <NA>     2.7 08:45:58
#> 9     NA   NA NA  <NA>  <NA>       <NA>         <NA>    <NA>     <NA>
#> 10 FALSE    2 NA    23     h 2023-12-24         <NA>      25     <NA>
#> 11 FALSE    3 NA  67.3     i 2023-12-25         <NA>       3     <NA>
#> 12    NA    1 NA   123  <NA> 2023-07-31         <NA>     122     <NA>

The output is created as a data frame and contains data types date, logical, numeric and character. The function to import the file to R, wb_to_df() provides similar options as the openxlsx functions read.xlsx() and readWorkbook() and a few new functions we will go through the options. As you might have noticed, we return the column of the xlsx file as the row name of the data frame returned. Per default the first sheet in the workbook is imported. If you want to switch this, either provide the sheet parameter with the correct index or provide the sheet name.

col_names - first row as column name

In the previous example the first imported row was used as column name for the data frame. This is the default behavior, but not always wanted or expected. Therefore this behavior can be disabled by the user.

# do not convert first row to column names
wb_to_df(file, col_names = FALSE)
#>        B    C  D     E     F          G            H       I        J
#> 2     NA Var2 NA  Var3  Var4       Var5         Var6    Var7     Var8
#> 3   TRUE    1 NA     1     a 2023-05-29 3209324 This #DIV/0! 01:27:15
#> 4   TRUE <NA> NA #NUM!     b 2023-05-23         <NA>       0 14:02:57
#> 5   TRUE    2 NA  1.34     c 2023-02-01         <NA> #VALUE! 23:01:02
#> 6  FALSE    2 NA  <NA> #NUM!       <NA>         <NA>       2 17:24:53
#> 7  FALSE    3 NA  1.56     e       <NA>         <NA>    <NA>     <NA>
#> 8  FALSE    1 NA   1.7     f 2023-03-02         <NA>     2.7 08:45:58
#> 9     NA <NA> NA  <NA>  <NA>       <NA>         <NA>    <NA>     <NA>
#> 10 FALSE    2 NA    23     h 2023-12-24         <NA>      25     <NA>
#> 11 FALSE    3 NA  67.3     i 2023-12-25         <NA>       3     <NA>
#> 12    NA    1 NA   123  <NA> 2023-07-31         <NA>     122     <NA>

detect_dates - convert cells to R dates

The creators of the openxml standard are well known for mistakenly treating something as a date and openxlsx2 has built in ways to identify a cell as a date and will try to convert the value for you, but unfortunately this is not always a trivial task and might fail. In such a case we provide an option to disable the date conversion entirely. In this case the underlying numerical value will be returned.

# do not try to identify dates in the data
wb_to_df(file, detect_dates = FALSE)
#>     Var1 Var2 NA  Var3  Var4  Var5         Var6    Var7       Var8
#> 3   TRUE    1 NA     1     a 45075 3209324 This #DIV/0! 0.06059028
#> 4   TRUE   NA NA #NUM!     b 45069         <NA>       0 0.58538194
#> 5   TRUE    2 NA  1.34     c 44958         <NA> #VALUE! 0.95905093
#> 6  FALSE    2 NA  <NA> #NUM!    NA         <NA>       2 0.72561343
#> 7  FALSE    3 NA  1.56     e    NA         <NA>    <NA>         NA
#> 8  FALSE    1 NA   1.7     f 44987         <NA>     2.7 0.36525463
#> 9     NA   NA NA  <NA>  <NA>    NA         <NA>    <NA>         NA
#> 10 FALSE    2 NA    23     h 45284         <NA>      25         NA
#> 11 FALSE    3 NA  67.3     i 45285         <NA>       3         NA
#> 12    NA    1 NA   123  <NA> 45138         <NA>     122         NA

show_formula - show formulas instead of results

Sometimes things might feel off. This can be because the openxml files are not updating formula results in the sheets unless they are opened in software that provides such functionality as certain tabular calculation software. Therefore the user might be interested in the underlying functions to see what is going on in the sheet. Using show_formula this is possible

# return the underlying Excel formula instead of their values
wb_to_df(file, show_formula = TRUE)
#>     Var1 Var2 NA  Var3  Var4       Var5         Var6            Var7     Var8
#> 3   TRUE    1 NA     1     a 2023-05-29 3209324 This            E3/0 01:27:15
#> 4   TRUE   NA NA #NUM!     b 2023-05-23         <NA>              C4 14:02:57
#> 5   TRUE    2 NA  1.34     c 2023-02-01         <NA>         #VALUE! 23:01:02
#> 6  FALSE    2 NA  <NA> #NUM!       <NA>         <NA>           C6+E6 17:24:53
#> 7  FALSE    3 NA  1.56     e       <NA>         <NA>            <NA>     <NA>
#> 8  FALSE    1 NA   1.7     f 2023-03-02         <NA>           C8+E8 08:45:58
#> 9     NA   NA NA  <NA>  <NA>       <NA>         <NA>            <NA>     <NA>
#> 10 FALSE    2 NA    23     h 2023-12-24         <NA>    SUM(C10,E10)     <NA>
#> 11 FALSE    3 NA  67.3     i 2023-12-25         <NA> PRODUCT(C11,E3)     <NA>
#> 12    NA    1 NA   123  <NA> 2023-07-31         <NA>         E12-C12     <NA>

dims - read specific dimension

Sometimes the entire worksheet contains to much data, in such case we provide functions to read only a selected dimension range. Such a range consists of either a specific cell like “A1” or a cell range in the notion used in the openxml standard

# read dimension without column names
wb_to_df(file, dims = "A2:C5", col_names = FALSE)
#>    A    B    C
#> 2 NA   NA Var2
#> 3 NA TRUE    1
#> 4 NA TRUE <NA>
#> 5 NA TRUE    2

Alternatively, if you don’t know the Excel sheet’s address, you can use wb_dims() to specify the dimension. See below or in?wb_dims for more details.

# read dimension without column names with `wb_dims()`
wb_to_df(file, dims = wb_dims(rows = 2:5, cols = 1:3), col_names = FALSE)
#>    A    B    C
#> 2 NA   NA Var2
#> 3 NA TRUE    1
#> 4 NA TRUE <NA>
#> 5 NA TRUE    2

cols - read selected columns

If you do not want to read a specific cell, but a cell range you can use the column attribute. This attribute takes a numeric vector as argument

# read selected cols
wb_to_df(file, cols = c("A:B", "G"))
#>    NA  Var1       Var5
#> 3  NA  TRUE 2023-05-29
#> 4  NA  TRUE 2023-05-23
#> 5  NA  TRUE 2023-02-01
#> 6  NA FALSE       <NA>
#> 7  NA FALSE       <NA>
#> 8  NA FALSE 2023-03-02
#> 9  NA    NA       <NA>
#> 10 NA FALSE 2023-12-24
#> 11 NA FALSE 2023-12-25
#> 12 NA    NA 2023-07-31

rows - read selected rows

The same goes with rows. You can select them using numeric vectors

# read selected rows
wb_to_df(file, rows = c(2, 4, 6))
#>    Var1 Var2 NA  Var3  Var4       Var5 Var6 Var7     Var8
#> 4  TRUE   NA NA #NUM!     b 2023-05-23   NA    0 14:02:57
#> 6 FALSE    2 NA  <NA> #NUM!       <NA>   NA    2 17:24:53

convert - convert input to guessed type

In xml exists no difference between value types. All values are per default characters. To provide these as numerics, logicals or dates, openxlsx2 and every other software dealing with xlsx files has to make assumptions about the cell type. This is especially tricky due to the notion of worksheets. Unlike in a data frame, a worksheet can have a wild mix of all types of data. Even though the conversion process from character to date or numeric is rather solid, sometimes the user might want to see the data without any conversion applied. This might be useful in cases where something unexpected happened or the import created warnings. In such a case you can look at the raw input data. If you want to disable date detection as well, please see the entry above.

# convert characters to numerics and date (logical too?)
wb_to_df(file, convert = FALSE)
#>     Var1 Var2   NA  Var3  Var4       Var5         Var6    Var7     Var8
#> 3   TRUE    1 <NA>     1     a 2023-05-29 3209324 This #DIV/0! 01:27:15
#> 4   TRUE <NA> <NA> #NUM!     b 2023-05-23         <NA>       0 14:02:57
#> 5   TRUE    2 <NA>  1.34     c 2023-02-01         <NA> #VALUE! 23:01:02
#> 6  FALSE    2 <NA>  <NA> #NUM!       <NA>         <NA>       2 17:24:53
#> 7  FALSE    3 <NA>  1.56     e       <NA>         <NA>    <NA>     <NA>
#> 8  FALSE    1 <NA>   1.7     f 2023-03-02         <NA>     2.7 08:45:58
#> 9   <NA> <NA> <NA>  <NA>  <NA>       <NA>         <NA>    <NA>     <NA>
#> 10 FALSE    2 <NA>    23     h 2023-12-24         <NA>      25     <NA>
#> 11 FALSE    3 <NA>  67.3     i 2023-12-25         <NA>       3     <NA>
#> 12  <NA>    1 <NA>   123  <NA> 2023-07-31         <NA>     122     <NA>

skip_empty_rows - remove empty rows

Even though openxlsx2 imports everything as requested, sometimes it might be helpful to remove empty lines from the data. These might be either left empty intentional or empty because they are were formatted, but the cell value was removed afterwards. This was added mostly for backward comparability, but the default has been changed to FALSE. The behavior has changed a bit as well. Previously empty cells were removed prior to the conversion to R data frames, now they are removed after the conversion and are removed only if they are completely empty

# erase empty rows from dataset
wb_to_df(file, sheet = 1, skip_empty_rows = TRUE) |> tail()
#>     Var1 Var2 NA Var3  Var4       Var5 Var6 Var7     Var8
#> 6  FALSE    2 NA <NA> #NUM!       <NA> <NA>    2 17:24:53
#> 7  FALSE    3 NA 1.56     e       <NA> <NA> <NA>     <NA>
#> 8  FALSE    1 NA  1.7     f 2023-03-02 <NA>  2.7 08:45:58
#> 10 FALSE    2 NA   23     h 2023-12-24 <NA>   25     <NA>
#> 11 FALSE    3 NA 67.3     i 2023-12-25 <NA>    3     <NA>
#> 12    NA    1 NA  123  <NA> 2023-07-31 <NA>  122     <NA>

skip_empty_cols - remove empty columns

The same for columns

# erase empty cols from dataset
wb_to_df(file, skip_empty_cols = TRUE)
#>     Var1 Var2  Var3  Var4       Var5         Var6    Var7     Var8
#> 3   TRUE    1     1     a 2023-05-29 3209324 This #DIV/0! 01:27:15
#> 4   TRUE   NA #NUM!     b 2023-05-23         <NA>       0 14:02:57
#> 5   TRUE    2  1.34     c 2023-02-01         <NA> #VALUE! 23:01:02
#> 6  FALSE    2  <NA> #NUM!       <NA>         <NA>       2 17:24:53
#> 7  FALSE    3  1.56     e       <NA>         <NA>    <NA>     <NA>
#> 8  FALSE    1   1.7     f 2023-03-02         <NA>     2.7 08:45:58
#> 9     NA   NA  <NA>  <NA>       <NA>         <NA>    <NA>     <NA>
#> 10 FALSE    2    23     h 2023-12-24         <NA>      25     <NA>
#> 11 FALSE    3  67.3     i 2023-12-25         <NA>       3     <NA>
#> 12    NA    1   123  <NA> 2023-07-31         <NA>     122     <NA>

row_names - keep rownames from input

Sometimes the data source might provide rownames as well. In such a case you can openxlsx2 to treat the first column as rowname

# convert first row to rownames
wb_to_df(file, sheet = 2, dims = "C6:G9", row_names = TRUE)
#>                mpg cyl disp  hp
#> Mazda RX4     21.0   6  160 110
#> Mazda RX4 Wag 21.0   6  160 110
#> Datsun 710    22.8   4  108  93

types - convert column to specific type

If the user know better than the software what type to expect in a worksheet, this can be provided via types. This parameter takes a named numeric. 0 is character, 1 is numeric and 2 is date

# define type of the data.frame
wb_to_df(file, cols = c(2, 5), types = c("Var1" = 0, "Var3" = 1))
#>     Var1   Var3
#> 3   TRUE   1.00
#> 4   TRUE    NaN
#> 5   TRUE   1.34
#> 6  FALSE     NA
#> 7  FALSE   1.56
#> 8  FALSE   1.70
#> 9   <NA>     NA
#> 10 FALSE  23.00
#> 11 FALSE  67.30
#> 12  <NA> 123.00

start_row - where to begin

Often the creator of the worksheet has used a lot of creativity and the data does not begin in the first row, instead it begins somewhere else. To define the row where to begin reading, define it via the start_row parameter

# start in row 5
wb_to_df(file, start_row = 5, col_names = FALSE)
#>        B  C  D      E     F          G  H       I        J
#> 5   TRUE  2 NA   1.34     c 2023-02-01 NA #VALUE! 23:01:02
#> 6  FALSE  2 NA     NA #NUM!       <NA> NA       2 17:24:53
#> 7  FALSE  3 NA   1.56     e       <NA> NA    <NA>     <NA>
#> 8  FALSE  1 NA   1.70     f 2023-03-02 NA     2.7 08:45:58
#> 9     NA NA NA     NA  <NA>       <NA> NA    <NA>     <NA>
#> 10 FALSE  2 NA  23.00     h 2023-12-24 NA      25     <NA>
#> 11 FALSE  3 NA  67.30     i 2023-12-25 NA       3     <NA>
#> 12    NA  1 NA 123.00  <NA> 2023-07-31 NA     122     <NA>

na.strings - define missing values

There is the “#N/A” string, but often the user will be faced with custom missing values and other values we are not interested. Such strings can be passed as character vector via na.strings

# na strings
wb_to_df(file, na.strings = "")
#>     Var1 Var2 NA  Var3  Var4       Var5         Var6    Var7     Var8
#> 3   TRUE    1 NA     1     a 2023-05-29 3209324 This #DIV/0! 01:27:15
#> 4   TRUE   NA NA #NUM!     b 2023-05-23         <NA>       0 14:02:57
#> 5   TRUE    2 NA  1.34     c 2023-02-01         <NA> #VALUE! 23:01:02
#> 6  FALSE    2 NA  <NA> #NUM!       <NA>         <NA>       2 17:24:53
#> 7  FALSE    3 NA  1.56     e       <NA>         <NA>    <NA>     <NA>
#> 8  FALSE    1 NA   1.7     f 2023-03-02         <NA>     2.7 08:45:58
#> 9     NA   NA NA  <NA>  <NA>       <NA>         <NA>    <NA>     <NA>
#> 10 FALSE    2 NA    23     h 2023-12-24         <NA>      25     <NA>
#> 11 FALSE    3 NA  67.3     i 2023-12-25         <NA>       3     <NA>
#> 12    NA    1 NA   123  <NA> 2023-07-31         <NA>     122     <NA>

Importing as workbook

In addition to importing directly from xlsx or xlsm files, openxlsx2 provides the wbWorkbook class used for importing and modifying entire the openxml files in R. This workbook class is the heart of openxlsx2 and probably the reason why you are reading this manual in the first place.

Importing a file into a workbook looks like this:

# the file we are going to load
file <- system.file("extdata", "openxlsx2_example.xlsx", package = "openxlsx2")
# loading the file into the workbook
wb <- wb_load(file = file)

The additional options wb_load() provides are for internal use: sheet loads only a selected sheet from the workbook and data_only reads only the data parts from a workbook and ignores any additional graphics or pivot tables. Both functions create workbook objects that can only be used to read data, and we do not recommend end users to use them. Especially not if they intend to re-export the workbook afterwards.

Once a workbook is imported, we provide several functions to interact with and modify it (the wb_to_df() function mentioned above works the same way for an imported workbook). It is possible to add new sheets and remove sheets, as well as to add or remove data. R-plots can be inserted and also the style of the workbook can be changed, new fonts, background colors and number formats. There is a wealth of options explained in the man pages and the additional style vignette (more vignettes to follow).

Exporting data

Exporting data frames or vectors

If you want to export a data frame from R, you can use write_xlsx() which will create an xlsx file. This file can be tweaked further. See ?write_xlsx to see all the options. (further explanation and examples will follow).

write_xlsx(x = mtcars, file = "mtcars.xlsx")

Exporting a wbWorkbook

Imported workbooks can be saved as xlsx or xlsm files with the wrapper wb_save() or with wb$save(). Both functions take the filename and an optional overwrite option. If the latter is set, an optional guard is provided to check if the file you want to write already exists. But be careful, this is optional. The default is to save the file and replace an existing file. Of course, on Windows, files that are locked (for example, if they were opened by another process) will not be replaced.

# replace the existing file
wb$save("mtcars.xlsx")

# do not overwrite the existing file
try(wb$save("mtcars.xlsx", overwrite = FALSE))

dims/ wb_dims()

In openxlsx2 functions that interact with worksheet cells are using dims as argument and require the users to provide these. dims are cells or cell ranges in A1 notation. The single argument dims hereby replaces col/row, cols/rows and xy. Since A1 notation is rather simple in the first few columns it might get confusing after the 26. Therefore we provide a wrapper to construct it:

# various options
wb_dims(from_row = 4)
#> [1] "A4"

wb_dims(rows = 4, cols = 4)
#> [1] "D4"
wb_dims(rows = 4, cols = "D")
#> [1] "D4"

wb_dims(rows = 4:10, cols = 5:9)
#> [1] "E4:I10"

wb_dims(rows = 4:10, cols = "A:D") # same as below
#> [1] "A4:D10"
wb_dims(rows = seq_len(7), cols = seq_len(4), from_row = 4)
#> [1] "A4:D10"
# 10 rows and 15 columns from indice B2.
wb_dims(rows = 1:10, cols = 1:15, from_col = "B", from_row = 2)
#> [1] "B2:P11"

# data + col names
wb_dims(x = mtcars)
#> [1] "A1:K33"
# only data
wb_dims(x = mtcars, select = "data")
#> [1] "A2:K33"

# The dims of the values of a column in `x`
wb_dims(x = mtcars, cols = "cyl")
#> [1] "B2:B33"
# a column in `x` with the column name
wb_dims(x = mtcars, cols = "cyl", select = "x")
#> [1] "B1:B33"
# rows in `x`
wb_dims(x = mtcars)
#> [1] "A1:K33"

# in a wb chain
wb <- wb_workbook()$
  add_worksheet()$
  add_data(x = mtcars)$
  add_fill(
    dims = wb_dims(x = mtcars, rows = 1:5), # only 1st 5 rows of x data
    color = wb_color("yellow")
  )$
  add_fill(
    dims = wb_dims(x = mtcars, select = "col_names"), # only column names
    color = wb_color("cyan2")
  )

# or if the data's first coord needs to be located in B2.

wb_dims_custom <- function(...) {
  wb_dims(x = mtcars, from_col = "B", from_row = 2, ...)
}
wb <- wb_workbook()$
  add_worksheet()$
  add_data(x = mtcars, dims = wb_dims_custom())$
  add_fill(
    dims = wb_dims_custom(rows = 1:5),
    color = wb_color("yellow")
  )$
  add_fill(
    dims = wb_dims_custom(select = "col_names"),
    color = wb_color("cyan2")
  )

A note on speed and memory usage

The current state of openxlsx2 is that it is reasonably fast. That is, it works well with reasonably large input data when reading or writing. It may not work well with data that tests the limits of the openxml specification. Things may slow down on the R side of things, and performance and usability will depend on the speed and size of the local operating system’s CPU and memory.

Note that there are at least two cases where openxlsx2 constructs potentially large data frames (i) when loading, openxlsx2 usually needs to read the entire input file into pugixml and convert it into long data frame(s), and wb_to_df() converts one long data frame into two data frames that construct the output object and (ii) when adding data to the workbook, openxlsx2 reshapes the input data frame into a long data frame and stores it in the workbook, and writes the entire worksheet into a pugixml file that is written when it is complete. Applying cell styles, date conversions etc. will further slow down the process and finally the sheets will be zipped to provide the xlsx output.

Therefore, if you are faced with an unreasonably large dataset, either give yourself enough time, use another package to write the xlsx output (openxlsx2 was not written with the intention of working with maximum memory efficiency), and by all means use other ways to store data (binary file formats or a database). However, we are always happy to improve, so if you have found a way to improve what we are currently doing, please let us know and open an issue or a pull request.