[R-sig-teaching] data preprocessing resources

[Greg Snow]

I would start here:

http://www.burns-stat.com/pages/Tutor/spreadsheet_addiction.html

Then point the students to a basic tutorial about databases (or at least MSaccess).

Or you can have them enter the data directly into R without using outside products.  Some of the GUI front ends for R have spreadsheet like interfaces, or you can create an empty data frame with the correct column types, then use edit/fix to enter the data in a way that feels familiar to spreadsheet users, but prevents some of the major problems that come from abuse of spreadsheets.

-- 
Gregory (Greg) L. Snow Ph.D.
Statistical Data Center
Intermountain Healthcare
greg.snow at imail.org
801.408.8111


> -----Original Message-----
> From: r-sig-teaching-bounces at r-project.org [mailto:r-sig-teaching-
> bounces at r-project.org] On Behalf Of Tyler Smith
> Sent: Friday, October 01, 2010 2:09 PM
> To: r-sig-teaching at r-project.org
> Subject: [R-sig-teaching] data preprocessing resources
> 
>   Hi,
> 
> I'll be teaching a grad course in multivariate stats for ecologists
> (ordination et al.) for the first time this coming spring. One thing
> I've discovered in my interactions with grad students is that they each
> develop their own rather idiosyncratic approaches to data processing.
> I've seen some really odd and weirdly illogical use of spreadsheets
> since I started teaching last year.
> 
> Do any of you know of good online or in-print resources to either use
> in
> my teaching or direct students to? I've come across bits and pieces
> scattered in various online lecture slides and notes, but nothing
> really
> focused on this particular issue.
> 
> I know this is not really an R issue per se, but it's much easier to
> get
> data into R if it is originally entered into a spreadsheet in a
> sensible
> fashion.
> 
> Thanks for any suggestions!
> 
> Tyler
> 
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