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 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>
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 Var1 Var2
#> 3 NA TRUE 1
#> 4 NA TRUE <NA>
#> 5 NA TRUE 2Alternatively, 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.
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
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
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.