[R] Sanity check in loading large dataframe

[PIKAL Petr]

Hi Bert

Yes, in this case which is not necessary. But in case NAs are involved 
sometimes logical indexing is not a best choice as NA propagates to the 
result, which may be not wanted.

x <- 1:10
x[c(2,5)] <- NA
y<- letters[1:10]
y[x<5]
[1] "a" NA  "c" "d" NA
y[which(x<5)]
[1] "a" "c" "d"
dat <- data.frame(x,y)
dat[x<5,]
      x    y
1     1    a
NA   NA <NA>
3     3    c
4     4    d
NA.1 NA <NA>

> dat[which(x<5),]
  x y
1 1 a
3 3 c
4 4 d

Both results are OK, but one has to consider this NA value propagation.

Cheers
Petr

From: Bert Gunter <bgunter.4567 using gmail.com>
Sent: Friday, August 6, 2021 1:29 PM
To: PIKAL Petr <petr.pikal using precheza.cz>
Cc: Luigi Marongiu <marongiu.luigi using gmail.com>; r-help <r-help using r-project.org>
Subject: Re: [R] Sanity check in loading large dataframe

... but remove the which() and use logical indexing ...  ;-)


Bert Gunter

"The trouble with having an open mind is that people keep coming along and 
sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )


On Fri, Aug 6, 2021 at 12:57 AM PIKAL Petr <mailto:petr.pikal using precheza.cz> 
wrote:
Hi

You already got answer from Avi. I often use dim(data) to inspect how many
rows/columns I have.
After that I check if some columns contain all or many NA values.

colSums(http://is.na(data))
keep <- which(colSums(http://is.na(data))<nnn)
cleaned.data <- data[, keep]

Cheers
Petr


> -----Original Message-----
> From: R-help <mailto:r-help-bounces using r-project.org> On Behalf Of Luigi 
> Marongiu
> Sent: Friday, August 6, 2021 7:34 AM
> To: Duncan Murdoch <mailto:murdoch.duncan using gmail.com>
> Cc: r-help <mailto:r-help using r-project.org>
> Subject: Re: [R] Sanity check in loading large dataframe
>
> Ok, so nothing to worry about. Yet, are there other checks I can
implement?
> Thank you
>
> On Thu, 5 Aug 2021, 15:40 Duncan Murdoch, <mailto:murdoch.duncan using gmail.com>
> wrote:
>
> > On 05/08/2021 9:16 a.m., Luigi Marongiu wrote:
> >  > Hello,
> >  > I am using a large spreadsheet (over 600 variables).
> >  > I tried `str` to check the dimensions of the spreadsheet and I got
> > > ```  >> (str(df))  > 'data.frame': 302 obs. of  626 variables:
> >  >   $ record_id                 : int  1 1 1 1 1 1 1 1 1 1 ...
> >  > ....
> >  > $ v1_medicamento___aceta    : int  1 NA NA NA NA NA NA NA NA NA ...
> >  >    [list output truncated]
> >  > NULL
> >  > ```
> >  > I understand that `[list output truncated]` means that there are
> > more  > variables than those allowed by str to be displayed as rows.
> > Thus I  > increased the row's output with:
> >  > ```
> >  >
> >  >> (str(df, list.len=1000))
> >  > 'data.frame': 302 obs. of  626 variables:
> >  >   $ record_id                 : int  1 1 1 1 1 1 1 1 1 1 ...
> >  > ...
> >  > NULL
> >  > ```
> >  >
> >  > Does `NULL` mean that some of the variables are not closed?
> > (perhaps a  > missing comma somewhere)  > Is there a way to check the
> > sanity of the data and avoid that some  > separator is not in the
> > right place?
> >  > Thank you
> >
> > The NULL is the value returned by str().  Normally it is not printed,
> > but when you wrap str in parens as (str(df, list.len=1000)), that
> > forces the value to print.
> >
> > str() is unusual in R functions in that it prints to the console as it
> > runs and returns nothing.  Many other functions construct a value
> > which is only displayed if you print it, but something like
> >
> > x <- str(df, list.len=1000)
> >
> > will print the same as if there was no assignment, and then assign
> > NULL to x.
> >
> > Duncan Murdoch
> >
>
>       [[alternative HTML version deleted]]
>
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