Message-ID: <CAAxdm-4+mtgznadVne7q0dc8x0F7RwFDyuHSWokyH7WeXjCUbg@mail.gmail.com>
Date: 2019-03-29T21:29:29Z
From: jim holtman
Subject: Structuring data for Correspondence Analysis
In-Reply-To: <005201d4e634$3f537930$bdfa6b90$@fastwebnet.it>
I am not familiar with SAS, so what did you want your output to look like.
There is the 'table' function that might do the job and then there is
always 'dplyr' which can do the hard stuff. So we need more information on
what you want.
Jim Holtman
*Data Munger Guru*
*What is the problem that you are trying to solve?Tell me what you want to
do, not how you want to do it.*
On Fri, Mar 29, 2019 at 6:35 AM Alfredo <alfredo.roccato at fastwebnet.it>
wrote:
> Hi, I am very new to r and need help from you to do a correspondence
> analysis because I don't know how to structure the following data:
>
> Thank you.
>
> Alfredo
>
>
>
> library(ca,lib.loc=folder)
>
> table <- read.csv(file="C:\\Temp\\Survey_Data.csv", header=TRUE, sep=",")
>
> head (table, n=20)
>
> Preference Sex Age Time
>
> 1 News/Info/Talk M 25-30 06-09
>
> 2 Classical F >35 09-12
>
> 3 Rock and Top 40 F 21-25 12-13
>
> 4 Jazz M >35 13-16
>
> 5 News/Info/Talk F 25-30 16-18
>
> 6 Don't listen F 30-35 18-20
>
> ...
>
> 19 Rock and Top 40 M 25-30 16-18
>
> 20 Easy Listening F >35 18-20
>
>
>
> In SAS I would simply do this:
>
> proc corresp data=table dim=2 outc=_coord;
>
> table Preference, Sex Age Time;
>
> run;
>
>
>
> I don't know how convert in R a data frame to a frequency table to execute
> properly this function:
>
> ca <- ca(<frequency table>, graph=FALSE)
>
>
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>
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