# openxlsx2 read to data frame

## 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    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 Var1 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>