[R] R interpreting numeric field as a boolean field

[CALUM POLWART]

And your other option - recode what gets imported. It may well be you will
actually want the blanks to be NAs for instance rather than blank. I'm
assuming the True and False are >$0 and $0 from your description. (Or maybe
vice versa). So I'd have made my column name something like
"OverZeroDollars" and then your data makes sense... Depends what data
processing comes next.

On Tue, 30 Jan 2024, 19:41 Duncan Murdoch, <murdoch.duncan using gmail.com> wrote:

> If you are using the read_excel() function from the readxl package, then
> there's an argument named col_types that lets you specify the types to use.
>
> You could specify col_types = "numeric" to read all columns as numeric
> columns.  If some columns are different types, you should specify a
> vector of type names, with one entry per column.  Allowable names are
> "skip", "guess", "logical", "numeric", "date", "text" or "list".  You'll
> have to read the docs to find out what some of those do.
>
> Duncan Murdoch
>
> On 30/01/2024 1:40 p.m., Paul Bernal wrote:
> > Dear friend Duncan,
> >
> > Thank you so much for your kind reply. Yes, that is exactly what is
> > happening, there are a lot of NA values at the start, so R assumes that
> > the field is of type boolean. The challenge that I am facing is that I
> > want to read into R an Excel file that has many sheets (46 in this case)
> > but I wanted to combine all 46 sheets into a single dataframe (since the
> > columns are exactly the same for all 46 sheets). The rio package does
> > this nicely, the problem is that, once I have the full dataframe (which
> > amounts to roughly 2.98 million rows total), I cannot change the data
> > type from boolean to numeric. I tried doing dataset$my_field =
> > as.numeric(dataset$my_field), I also tried to do dataset <-
> > dataset[complete.cases(dataset), ], didn't work either.
> >
> > The only thing that worked for me was to take a single sheed and through
> > the read_excel function use the guess_max parameter and set it to a
> > sufficiently large number (a number >= to the total amount of the full
> > merged dataset). I want to automate the merging of the N number of Excel
> > sheets so that I don't have to be manually doing it. Unless there is a
> > way to accomplish something similar to what rio's package function
> > import_list does, that is able to keep the field's numeric data type
> nature.
> >
> > Cheers,
> > Paul
> >
> > El mar, 30 ene 2024 a las 12:23, Duncan Murdoch
> > (<murdoch.duncan using gmail.com <mailto:murdoch.duncan using gmail.com>>) escribió:
> >
> >     On 30/01/2024 11:10 a.m., Paul Bernal wrote:
> >      > Dear friends,
> >      >
> >      > Hope you are doing well. I am currently using R version 4.3.2,
> >     and I have a
> >      > .xlsx file that has 46 sheets on it. I basically combined  all 46
> >     sheets
> >      > and read them as a single dataframe in R using package rio.
> >      >
> >      > I read a solution using package readlx, as suggested in a
> >     StackOverflow
> >      > discussion as follows:
> >      > df <- read_excel(path = filepath, sheet = sheet_name, guess_max =
> >     100000).
> >      > Now, when you have so many sheets (46 in my case) in an Excel
> >     file, the rio
> >      > methodology is more practical.
> >      >
> >      > This is what I did:
> >      > path =
> >      >
> >
>  "C:/Users/myuser/Documents/DataScienceF/Forecast_and_Econometric_Analysis_FIGI
> >      > (4).xlsx"
> >      > figidat = import_list(path, rbind = TRUE) #here figidat refers to
> >     my dataset
> >      >
> >      > Now, it successfully imports and merges all records, however,
> >     some fields
> >      > (despite being numeric), R interprets as a boolean field.
> >      >
> >      > Here is the structure of the field that is causing me problems (I
> >     apologize
> >      > for the length):
> >      > structure(list(StoreCharges = c(NA, NA, NA, NA, NA, NA, NA, NA,
> >      > NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
> >      > NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
> >      > NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
> >      > NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
> >      > NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
> >      > NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
> >      > NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
> >      > NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
> >     ...
> >      > FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, NA, NA,
> >      > FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,
> >      > FALSE, FALSE, FALSE)), class = c("tbl_df", "tbl", "data.frame"
> >      > ), row.names = c(NA, -7033L))
> >      >
> >      > As you can see, when I do the dput, it gives me a bunch of TRUE
> >     and FALSE
> >      > values, when in reality I have records with value $0, records
> >     with amounts
> >      >> $0 and also a bunch of blank records.
> >      >
> >      > Any help will be greatly appreciated.
> >
> >     I don't know how read_excel() determines column types, but some
> >     functions look only at the first n rows to guess the type.  It
> appears
> >     you have a lot of NA values at the start.  That is a logical value,
> so
> >     that might be what is going wrong.
> >
> >     In read.table() and related functions, you can specify the types of
> >     column explicitly.  It sounds as though that's what you should do if
> >     read_excel() offers that as a possibility.
> >
> >     Duncan Murdoch
> >
>
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