A small number of columns in the data I need to work with are strings, the
rest numbers. I'm using read_excel() from the readxl package to get the
data ; right after it, the string columns are of type chr and the rest num.
I'm tasked with finding out which columns are integers. From an advice, I
tried saving the spreadsheet content into a CSV then loading that, which
works like a charm ; the chr columns are the same but now a large portion
of num is now instead int. Is there a way to skip writing and reading a CSV
and get the same transformation? Perhaps some way to break the spreadsheet
data (eg XLdata <- read_excel(...)), then put it back together without any
writing to a file (eg XLdataReformed <- reform(XLdata)) ?
In addition, from is.integer() documentation I ran
> is.wholenumber <- function(x, tol = .Machine$double.eps^0.5) abs(x - round
> (x)) < tol
and I'm now trying to have it stop at the 1st decimal content of a column.
Someone advised me to use break and I scripted
> is_integer = TRUE for (current_row in seq_along(data$column)) { if (!
> is.wholenumber(data$column[current_row])) { is_integer = FALSE break; } }
but I'm wondering if there's something better to check if a column is
entirely made of integers.
Thank you kindly for your help
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