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R interpreting numeric field as a boolean field

5 messages · Paul Bernal, Duncan Murdoch, Calum Polwart

Original
Paul Bernal
# 
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,
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, 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,
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FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,
FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,
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
Any help will be greatly appreciated.

Cheers,
Paul
Duncan Murdoch
#
On 30/01/2024 11:10 a.m., Paul Bernal wrote:
...
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
Paul Bernal
# 
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 at gmail.com>)
escribi?:

  
  


  


      

    

    

      
      

        


  

        

      Duncan Murdoch
    

    

      
      #
    

  


  

      
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:

            

      
      
      
    

  
  


  


      

    

    

      
      

        


  

        

      Calum Polwart
    

    

      
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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 at gmail.com> wrote: