Hi Martin and list:
First let me thank you for thinking of me. It is probably apparent that my
programming experience is limited, and the vector aspect of R has taken some
getting used to. A very very long time ago I did some programming in Fortran
and for loops and if statements were ordinary, useful, and used frequently.
Now if I see a for loop and if statement together then it is flatly apparent
that I need to rework that code to take advantage of R's strengths using
vectoized functions on whatever object I am working on.
It has also become apparent that one should read manuals, experiment with some
code to firmly cement the knowledge, and build a solid foundation. My natural
inclination is the opposite. I am anxious to produce some code to solve a
problem or satisfy curiosity or .... R is not something one can learn and use
productively in a few weeks or months. It is powerful, subtle, and takes some
thinking. I started this trek into R with Jared Lander's book "R for
Everyone", progressed to Prof Norman Matloff's "The Art of R Programming"
(which was way beyond my comprehension at the beginning) and Hadley Wickham's
"ggplot2" book, and several courses with Data Camp and Couresra. One day I
will be at the point where I do know what I don't know about R and at that time
I will almost be competent with the language.
I have taken the work from Bill Dunlap and Giorgio Garziano and have applied it
to my little project and am just amazed that so little code can do so much. I
have also followed Bert Gunter's advice and taken that code and dissected it
item by item to comprehend what each element is doing.
The knowledge and help on this list is just amazing and I do appreciate the
efforts of all involved. I read the digest daily .
Carl Sutton CPA
On Wednesday, May 4, 2016 12:06 AM, Martin Maechler
<maech...@stat.math.ethz.ch> wrote:
>>>>> <g.maub...@weinwolf.de>
>>>>> on Wed, 4 May 2016 08:30:50 +0200 writes:
> Hi All,
> Hi Carl,
>
> I am not sure if this is useful to you, but I followed your conversation
> and thought of you when I read this:
>
> for (i in 1:ncol(dataset)) {
> if(class(dataset) == "character|numeric|factor|or whatsoever") {
> dataset[, i] <- as.factor(dataset[, i])
> }
> }
Ouch -- so many problems in such a short piece of R code !!!
> Source: Zumel, Nina / Mount, John: Practical Data Science with R, Manning
> Publications: Shelter Island, 2014, Chapter 2: Loading data into R, p. 25
Sorry, but after reading the above, I'd strongly recommend getting
better books about R...
{{maybe do not take those containing "data science" ;-)}}
Compared to the nice and efficient solution of Bill Dunlap,
the above is really bad-bad-bad in at least four ways :
0) They way you write it above, you cannot use it,
<string> == "variant1|variant2|..."
is pseudocode and does not really work
1) Note the missing "[, i]" in the 2nd line: It should be
if(class(dataset[, i]) ...
2) A for loop changing each column at a time is really slow for
largish data sets
3) [last but not at all least!]
Please ... many of you readers, do learn:
Using checks such as
if ( class(x) == "numeric" )
are (almost) always wrong by design !!!
Instead you really should (almost) always use
if(inherits(x, "numeric"))
Why? Because classes in R (S3 or S4) can *extend* other classes.
Example: Many of you know that after fm <- glm(...)
class(fm) is c("glm", "lm") and so
> if(class(fm) == "lm")
+ "yes"
Warning message:
In if (class(fm) == "lm") "yes" :
the condition has length > 1 and only the first element will be used
Similarly, in your case
y <- 1:10
class(y) <- c("myNumber", "numeric")
when that 'y' is a column in your data frame,
the test for if(class(dataset[,i]) == "numeric") will *not*
work but actually produce the above warning.
However, one could als have had
Num <- setClass("Num", contains="numeric")
N <- Num(1:10)
> Num <- setClass("Num", contains="numeric")
> N <- Num(1:10)
> N
An object of class "Num"
[1] 1 2 3 4 5 6 7 8 9 10
> if(class(N) == "numeric") "yes" else "no"
[1] "no"
>
I hope that many of the readers --- including *MANY* authors of
R packages !! --- have understood the above and will fix their R
code -- and even more their books where applicable !!
Martin Maechler,
ETH Zurich & R Core Team
>
> This way you can select variables of a certain class only and do
> transformations. I found that this approach is not applicable if used with
> statistical functions like head(). Transformations worked fine for me.
>
> I found reading the above given source worthwile.
>
> Kind regards
>
> Georg
>
> PS: I am not related to the above given authors. I am just a reader
> reporting on - at least to me - a valuable ressource.
>
>
>
> Von: Carl Sutton via R-help <r-help@r-project.org>
> An: William Dunlap <wdun...@tibco.com>,
> Kopie: "r-help@r-project.org" <r-help@r-project.org>
> Datum: 29.04.2016 22:08
> Betreff: Re: [R] selecting columns from a data frame or data table
> by type, ie, numeric, integer
> Gesendet von: "R-help" <r-help-boun...@r-project.org>
>
>
>
> Thank you Bill Dunlap. So simple I never tried that approach. Tried
> dozens of others though, read manuals till I was getting headaches, and of
> course the answer was simple when one is competent. Learning, its a
> struggle, but slowly getting there.
> Thanks again
> Carl Sutton CPA
>
>
> On Friday, April 29, 2016 10:50 AM, William Dunlap <wdun...@tibco.com>
> wrote:
>
>
>
> > dt1[ vapply(dt1, FUN=is.numeric, FUN.VALUE=NA) ] a c1 1 1.12 2
> 1.0...10 10 0.2
>
>
> Bill Dunlap
> TIBCO Software
> wdunlap tibco.com
> On Fri, Apr 29, 2016 at 9:19 AM, Carl Sutton via R-help
> <r-help@r-project.org> wrote:
>
> Good morning RGuru's
> I have a data frame of 575 columns. I want to extract only those columns
> that are numeric(double) or integer to do some machine learning with. I
> have searched the web for a couple of days (off and on) and have not found
> anything that shows how to do this. Lots of ways to extract rows, but
> not columns. I have attempted to use "(x == y)" indices extraction method
> but that threw error that == was for atomic vectors and lists, and I was
> doing this on a data frame.
>
> My test code is below
>
> # a technique to get column classes
> library(data.table)
> a <- 1:10
> b <- c("a","b","c","d","e","f","g","h","i","j")
> c <- seq(1.1, .2, length = 10)
> dt1 <- data.table(a,b,c)
> str(dt1)
> col.classes <- sapply(dt1, class)
> head(col.classes)
> dt2 <- subset(dt1, typeof = "double" | "numeric")
> str(dt2)
> dt2 # not subset
> dt2 <- dt1[, list(typeof = "double")]
> str(dt2)
> class_data <- dt1[,sapply(dt1,is.integer) | sapply(dt1, is.numeric)]
> class_data
> sum(class_data)
> typeof(class_data)
> names(class_data)
> str(class_data)
> Any help is appreciated
> Carl Sutton CPA
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